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B-cell-specific checkpoint molecules that regulate anti-tumour immunity
调节抗肿瘤免疫的 B 细胞特异性检查点分子

Received: 2 November 2021
收稿日期: 2021-11-02
Accepted: 17 May 2023
录用日期: 2023-05-17
Published online: 21 June 2023
网络出版日期: 2023-06-21
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Abstract 抽象

Lloyd Bod , Yoon-Chul Kye , Jingwen Shi , Elena Torlai Triglia , Alexandra Schnell , Johannes Fessler , Stephen M. Ostrowski , Max Y. Von-Franque , Juhi R. Kuchroo , Rocky M. Barilla , Sarah Zaghouani', Elena Christian , Toni Marie Delorey , Kanishka Mohib , Sheng Xiao', Nadine Slingerland , Christopher J. Giuliano , Orr Ashenberg , Zhaorong Li7 , David M. Rothstein , David E. Fisher , Orit Rozenblatt-Rosen , Arlene H. Sharpe , Francisco J. Quintana , Lionel Apetoh , Aviv Regev Vijay K. Kuchroo
劳埃德·博德 , 桂允哲 , 史静雯 , 埃琳娜·托莱·特里利亚 , 亚历山德拉·施内尔 , 约翰内斯·费斯勒 , 斯蒂芬·奥斯特罗夫斯基 , 马克斯·冯·弗兰克 , Juhi R. Kuchroo , 洛基·巴里拉 , 莎拉·扎古阿尼', 埃琳娜·克里斯蒂安 , 托妮·玛丽·德洛里 , 卡尼什卡·莫希布 , 盛晓, 娜丁·斯林格兰 , 克里斯托弗·朱利亚诺 , 奥尔·阿森伯格 , 李兆荣7 , 大卫·罗斯坦 , 大卫·费舍尔 , 奥里特·罗森布拉特-罗森 , 阿琳·夏普 , 弗朗西斯科·金塔纳 , 莱昂内尔·阿佩托 , 阿维夫·雷格夫· 维杰·库克鲁

Abstract 抽象

The role of cells in anti-tumour immunity is still debated and, accordingly, immunotherapies have focused on targeting and natural killer cells to inhibit tumour growth . Here, using high-throughput flow cytometry as well as bulk and single-cell RNA-sequencing and B-cell-receptor-sequencing analysis of B cells temporally during B16F10 melanoma growth, we identified a subset of B cells that expands specifically in the draining lymph node over time in tumour-bearing mice. The expanding B cell subset expresses the cell surface molecule cell immunoglobulin and mucin domain 1(TIM-1, encoded by Havcr1) and a unique transcriptional signature, including multiple co-inhibitory molecules such as PD-1, TIM-3, TIGIT and LAG-3. Although conditional deletion of these co-inhibitory molecules on B cells had little or no effect on tumour burden, selective deletion of Havcr1 in B cells both substantially inhibited tumour growth and enhanced effector cell responses. Loss of TIM-1 enhanced the type 1 interferon response in B cells, which augmented B cell activation and increased antigen presentation and co-stimulation, resulting in increased expansion of tumour-specific effector T cells. Our results demonstrate that manipulation of TIM-1-expressing cells enables engagement of the second arm of adaptive immunity to promote anti-tumour immunity and inhibit tumour growth.
细胞在抗肿瘤免疫中的作用仍然存在争议,因此,免疫疗法的重点是靶向 和自然杀伤细胞以抑制肿瘤生长 。在这里,使用高通量流式细胞术以及 B16F10 黑色素瘤生长期间 B 细胞的批量和单细胞 RNA 测序以及 B 细胞受体测序分析,我们鉴定了一个 B 细胞亚群,该亚群随着时间的推移在荷瘤小鼠的引流淋巴结中特异性扩增。扩增的 B 细胞亚群表达细胞表面分子 细胞免疫球蛋白和粘蛋白结构域 1(TIM-1,由 Havcr1 编码)和独特的转录特征,包括多种共抑制分子,如 PD-1、TIM-3、TIGIT 和 LAG-3。尽管 B 细胞上这些共抑制分子的条件性缺失对肿瘤负荷几乎没有影响,但 B 细胞中 Havcr1 的选择性缺失既显着抑制了肿瘤生长,又增强了效应 细胞反应。TIM-1 的缺失增强了 B 细胞中的 1 型干扰素反应,从而增强了 B 细胞活化并增加了抗原呈递和共刺激,导致肿瘤特异性效应 T 细胞的扩增增加。我们的结果表明,操纵表达 TIM-1 的细胞可以参与适应性免疫的第二臂,以促进抗肿瘤免疫并抑制肿瘤生长。

B cells have key roles in both innate and adaptive immunity. Distinct specialized cell subsets engage a range of responses from antigen presentation to antibody production and cells are one of the most abundant cell types of tumour-infiltrating leukocytes (TILs) , especially in melanoma . However, the role of B cells in anti-tumour immunity remains unclear. Here we examine the cell repertoire at the single-cell resolution from tumour-infiltrating cells and tumour-draining lymph nodes (dLNs) and identify and characterize a subset of B cells expressing the checkpoint molecule TIM-1. We find that targeting TIM-1 enables engagement of this cell subset, with subsequent enhancement of anti-tumour and cell responses and inhibition of tumour cell growth, with implications for approaches to cancer therapy.
B细胞在先天免疫和适应性免疫中都起着关键作用。不同的特化 细胞亚群参与从抗原呈递到抗体产生的一系列反应, 细胞是肿瘤浸润性白细胞 (TIL) 最丰富的细胞类型之一,尤其是在黑色素瘤 中。然而,B细胞在抗肿瘤免疫中的作用仍不清楚。在这里, 我们以单细胞分辨率检查肿瘤浸润 细胞和肿瘤引流淋巴结 (dLN) 的细胞库,并鉴定和表征表达检查点分子 TIM-1 的 B 细胞亚群。我们发现靶向 TIM-1 能够使该 细胞亚群参与,从而增强抗肿瘤 细胞反应并抑制肿瘤细胞生长,从而对癌症治疗方法产生影响。

Distinct cell infiltrates in B16F10 TME
B16F10 TME中的不同 细胞浸润

To understand the role of B cell subsets in regulating immune responses to tumours, we characterized B cells from tumours, and non-draining LNs (ndLNs) in the B16F10 melanoma mouse model. We confirmed that B cells infiltrate the tumour and are increased in frequency within the dLN compared with in the ndLN (Extended Data Fig. 1a). Depletion of B cells globally using anti-CD20 monoclonal antibodies significantly enhanced melanoma tumour growth; however, abrogating plasma cell generation (using mice) did not affect the tumour burden (Extended Data Fig. 1b,c). Tumour-infiltrating B cells had distinct expression profiles on the basis of bulk RNA-sequencing (RNA-seq) analysis compared with B cells from lymphoid tissues, reflecting the induction of proliferative and migratory pathways associated with B cell activation (Extended Data Fig. 1d-g). Moreover, tumour-infiltrating B cells were predominantly follicular B cells of the B2 lineage with bimodal IgD expression (Extended Data Fig. 1h). Thus, although plasma cells seemed to be dispensable, total cells produced an anti-tumour effect and displayed a distinct phenotype after infiltration in B16F10 tumours, prompting a deeper analysis.
为了了解 B 细胞亚群在调节对肿瘤的免疫反应中的作用,我们在 B16F10 黑色素瘤小鼠模型中表征了来自肿瘤的 B 细胞和非 引流 LN (ndLN)。我们证实,与ndLN相比,B细胞浸润肿瘤,并且dLN内的频率增加(扩展数据图1a)。使用抗 CD20 单克隆抗体在全球范围内耗竭 B 细胞显着增强了黑色素瘤肿瘤的生长;然而,消除浆细胞生成(使用 小鼠)不会影响肿瘤负荷(扩展数据图1b,c)。与来自淋巴组织的B细胞相比,基于大量RNA测序(RNA-seq)分析,肿瘤浸润B细胞具有不同的表达谱,反映了与B细胞活化相关的增殖和迁移途径的诱导(扩展数据图1d-g)。此外,肿瘤浸润性 B 细胞主要是 B2 谱系的滤泡 B 细胞,具有双峰 IgD 表达(扩展数据图 1h)。因此,尽管浆细胞似乎是可有可无的,但总 细胞在浸润B16F10肿瘤后产生了抗肿瘤作用并显示出明显的表型,这促使了更深入的分析。
C d b
h j Ref. 24 . 7 Ref. 20 Melanoma Ref. 21 Colorectal cancer
h j 参考文献 24 .7 参考文献 20 黑色素瘤 参考文献 21 结直肠癌

Fig. Characterization of cells expressing TIM-1 and several checkpoint molecules in mouse melanoma and human tumours. a, Workflow for singlecell transcriptome profiling of 34,071 viable leukocytes from TME, and ndLN samples. mice per time point (days 7 (D7; early), 10 (intermediate) and 16 (late)). s.c., subcutaneous. b, Uniform manifold approximation and projection (UMAP) embedding of all cells sequenced with each colour representing tissues of origin (left), timepoint (centre) and expression of Cd19 (right). c, UMAP visualization of the immune cell types. , conventional CD4 cells; cDC1/2/3, type 1, 2 and 3 conventional dendritic cells; NK, natural killer. d,e, UMAP visualization of the 6,226 B cells (dots) collected from wild-type mice bearing B16F10 melanoma, depicting tissues of origin (d) or Leiden cell clusters (resolution 0.85 , The frequencies of cells from each cluster within the tissues of origin (f) or from cluster 3 over time and tissues of origin (g).h, The -transformed fold change (FC) in RNA levels between B cells derived from cluster 3 with the rest of the clusters and between the and ndLN. , Bulk RNA-seq analysis of TIM-1 and TIM-1- B cells derived from dLNs and ndLNs of B16F10-bearing wild-type mice. . i, Pathway enrichment analysis of dLN-derived TIM-1+ B cells. FACS, fluorescence-activated cell sorting; FDR, false-discovery rate.j, The expression pattern of a set of selected genes. k,1, UMAP plot of published scRNA-seq dat depicting 2,615 B cells (dots) isolated from human tumours, coloured by cell clusters (k, left), selected gene expression (k, right) and immune checkpoint signature score (1,top), and a stacked bar graph displaying the frequencies of B cells derived from responder and pre- and post-ICB samples among each Leiden cluster (1, bottom).
无花果。 小鼠黑色素瘤和人类肿瘤中表达 TIM-1 和几种检查点分子的 细胞表征。a,对来自 TME 和 ndLN 样品的 34,071 个活白细胞进行单细胞转录组分析的工作流程。 每个时间点的小鼠(第7天(D7;早期),第10天(中间)和第16天(晚期))。皮下皮下注射。b,所有细胞的均匀流形近似和投影 (UMAP) 嵌入,每种颜色代表 Cd19 的起源组织(左)、时间点(中)和表达(右)。c,免疫细胞类型的UMAP可视化。 ,常规CD4 细胞;cDC1/2/3,1、2 和 3 型常规树突状细胞;NK,自然杀手。d,e,从携带 B16F10 黑色素瘤的野生型小鼠中收集的 6,226 个 B 细胞(点)的 UMAP 可视化,描绘了起源组织 (d) 或 Leiden 细胞簇(分辨率 0.85 ,来自起源组织 (f) 或来自簇 3 的每个簇的细胞随时间推移的频率和起源组织 (g).h,来自簇 3 的 B 细胞与其他簇之间的 RNA 水平的 转化倍数变化 (FC) 和 在 和 ndLN 之间。 ,来自携带 B16F10 的野生型小鼠的 dLN 和 ndLN 的 TIM-1 和 TIM-1- B 细胞的批量 RNA-seq 分析。 。i, dLN来源的TIM-1+ B细胞的通路富集分析。FACS,荧光激活细胞分选;FDR, false-discovery rate.j, 一组选定基因的表达模式。 k,1,已发表的 scRNA-seq dat 的 UMAP 图描绘了从人类肿瘤中分离出的 2,615 个 B 细胞(点),按细胞簇(k,左)、选定的基因表达(k,右)和免疫检查点特征评分(1,top)着色,以及显示每个 Leiden 簇中来自应答者和 ICB 前后样本的 B 细胞频率的堆叠条形图 (1, 底部)。

B16F10 tumour growth induces a specific cell subset
B16F10 肿瘤生长诱导特定 细胞亚群

To further decipher B cell heterogeneity, we performed 5' single-cell RNA-seq (scRNA-seq) combined with VDJ/B cell receptor (BCR)-seq (scRNA/BCR-seq) analysis of cells in the tumour microenvironment (TME), dLN and ndLN at three different timepoints of B16F10 melanoma growth (Fig. 1a,b and Extended Data Fig. 2). The 34,071 high-quality cell profiles were grouped by respective lineages and tissue origin, and expressed known marker genes, which we used for their annotation (Fig. 1c and Extended Data Fig. 2c). We searched for B cell populations that were expanded over time or in the three compartments (tumour, dLN and ndLN) on the basis of either transcriptional states or BCR clones (Fig. 1d and Extended Data Fig. 2d-h). Although known cell subset expression signatures and markers did not identify discrete B cell groups (except for germinal-centre-like B cells;Extended Data Fig. 2g), unsupervised graph clustering partitioned them into five distinct clusters (Fig. 1e and Extended Data Fig. 2h). The main separation was by tissue origin (Fig. 1f), with clusters 1 and 2 comprising tumour-infiltrating cells with a highly activated or inflammatory phenotype (Cd69, Cd86 or Cxcr4 in cluster 1; Cd274,Apoe or Hspa1a in
为了进一步破译 B 细胞异质性,我们在 B16F10 黑色素瘤生长的三个不同时间点对 肿瘤微环境 (TME)、dLN 和 ndLN 中的细胞进行了 5' 单细胞 RNA-seq (scRNA-seq) 联合 VDJ/B 细胞受体 (BCR)-seq (scRNA/BCR-seq) 分析(图 1a、b 和扩展数据图 2)。34,071 个高质量细胞图谱按各自的谱系和组织来源分组,并表达已知的标记基因,我们将其用于注释(图 1c 和扩展数据图 2c)。我们根据转录状态或BCR克隆(图1d和扩展数据图2d-h)搜索了随时间推移或在三个区室(肿瘤、dLN和ndLN)中扩增的B细胞群。尽管已知 的细胞亚群表达特征和标记物无法识别离散的 B 细胞群(生发中心样 B 细胞除外;扩展数据图2g),无监督图聚类将它们划分为五个不同的聚类(图1e和扩展数据图2h)。主要分离是按组织来源进行的(图 1f),簇 1 和簇 2 包括具有高度活化或炎症表型的肿瘤浸润 细胞(簇 1 中的 Cd69、Cd86 或 Cxcr4;Cd274,Apoe 或 Hspa1a 在

cluster 2), clusters 4 and 5 consisting of both dLN and ndLN B cells with a naive-like profile (Cr2, Cxcr5, Tnfrsf13c in cluster 4; Fcer2a, Tnfrsf13b in cluster 5) and cluster 3 mainly comprising cells from the tumour dLN with proliferative and germinal-centre-like profiles (Mki67,Aicda). The frequency of dLN cells in cluster 3 B cells augmented over time as tumours increased in size, suggesting a specific induction of cluster 3 in response to melanoma growth (Fig.1g), consistent with the expression of activation and germinal centre cell signatures in these cells. Moreover, BCR-based clonal analysis (using Immcantation) identified only a small fraction of cells expressing immunoglobulin heavy chain gamma (IGHG), and those cells were predominantly members of cluster 3 and were moderately clonally expanded within the dLN compartment (Extended Data Fig. .
簇 2)、簇 4 和 5 由具有幼稚样特征的 dLN 和 ndLN B 细胞组成(簇 4 中的 Cr2、Cxcr5、Tnfrsf13c;簇 5) 和簇 3 中的 Fcer2a、Tnfrsf13b 主要由来自肿瘤 dLN 的细胞组成,具有增殖和生发中心样特征 (Mki67,Aicda)。随着肿瘤大小的增加,簇 3 B 细胞中 dLN 细胞的频率随着时间的推移而增加,表明簇 3 响应黑色素瘤生长的特异性诱导(图 1g),与这些细胞中激活和生发中心 细胞特征的表达一致。此外,基于 BCR 的克隆分析(使用 Immcantation)仅鉴定出一小部分表达免疫球蛋白重链 γ (IGHG) 的细胞,这些细胞主要是簇 3 的成员,并且在 dLN 区室内适度克隆扩增(扩展数据图 1)。

TIM-1 marks checkpoint-expressing cells
TIM-1 标记表达 检查点的细胞

We sought to isolate and purify the cell subset that increases with tumour growth by identifying cell surface markers that are expressed on this B cell population. The dLN-derived expanded cluster 3 B cells expressed genes encoding specific cell surface markers, especially Haucr1, encoding TIM-1 (using ; Fig. 1h and Extended Data Fig. 2f). In the B16F10 tumour model, TIM-1 B cells poorly infiltrated the tumour but were found in the lymphoid organs and increased preferentially within the dLN (Extended Data Fig. 3a), consistent with our RNA profiles. TIM-1 is a member of the TIM family, of which TIM-3 is the most characterized molecule in the context of autoimmunity and anti-tumour immunity . TIM- 1 is not well studied in the context of cancer but is expressed on a fraction (around ) of peripheral B cells and can promote tissue tolerance by binding to phosphatidylserine exposed on apoptotic cells .
我们试图通过鉴定在该 B 细胞群上表达的细胞表面标志物来分离和纯化随着肿瘤生长而增加的 细胞亚群。dLN 衍生的扩增簇 3 B 细胞表达编码特定细胞表面标记物的基因,尤其是编码 TIM-1 的 Haucr1(使用 ;图1h和扩展数据图2f)。在 B16F10 肿瘤模型中,TIM-1 B 细胞浸润不良,但在淋巴器官中发现,并在 dLN 内优先增加(扩展数据图 3a),与我们的 RNA 图谱一致。TIM-1 是 TIM 家族的成员,其中 TIM-3 是自身免疫和抗肿瘤免疫背景下最具特征的分子 。TIM-1在癌症的背景下没有得到很好的研究,但在外周B细胞的一部分(周围 )上表达,并且可以通过与暴露在凋亡细胞 上的磷脂酰丝氨酸结合来促进组织耐受性。
Sorted TIM-1 and B cells from the dLN and ndLN of B16F10-bearing mice showed distinct transcriptional profiles on the basis of bulk RNA-seq and flow cytometry analysis (Fig. 1i,j and Extended Data Fig. 3b,c), clustering by TIM-1 expression and not tissue origin, with TIM- cells from the dLN displaying a unique expression signature, enriched in B cell activation and proliferation genes (Fig. li and Extended Data Fig. 3 b,c). These features of TIM- B cells were confirmed functionally in vitro, as TIM- cells had increased proliferation and differentiation into plasma cells (Extended Data Fig. 3d).
从携带 B16F10 的小鼠的 dLN 和 ndLN 中分选的 TIM-1 B 细胞在大量 RNA-seq 和流式细胞术分析的基础上显示出不同的转录谱(图 1i,j 和扩展数据图 3b,c),按 TIM-1 表达而不是组织来源聚类,来自 dLN 的 TIM- 细胞显示出独特的表达特征,富含 B 细胞活化和增殖基因(图 li 和扩展数据图 3 b,TIM-B 细胞的这些特征在体外功能上得到证实,因为TIM- 细胞的增殖和分化为浆细胞的增加(扩展数据Fig. 3d)。
However, scRNA-seq analysis of sorted TIM- and TIM-1 B cells from the , ndLN and spleen showed that germinal-centre-like TIM-1 B cellsconsist of only around of allTIM-1-expressingB cells(Extended Data Fig. 3e-g), indicating that TIM-1 is not simply a marker of germinal centres, or a unique B cell lineage. Instead, our data suggest that TIM-1 may be expressed on all cells during cell activation. Consistent with this model, TIM-1 is transiently induced across cell divisions on the cell surface of TIM-1 B cells after cell activation in vitro with and/or CD40 but not lipopolysaccharide (LPS), supporting that TIM-1 could be induced on all cells after antigen-driven cell activation (Extended Data Fig. ).
然而,对来自、ndLN和脾脏的TIM- 和TIM-1 B细胞进行scRNA-seq分析表明,生发中心样TIM-1 B细胞仅由所有表达TIM-1的B细胞组成 (扩展数据图3e-g),表明TIM-1不仅仅是生发中心的标志物或独特的B细胞谱系。 相反,我们的数据表明,TIM-1可能在细胞活化过程中 在所有 细胞上表达。与该模型一致,在体外用 和/或CD40而不是脂多糖(LPS) 激活细胞后,TIM-1在TIM-1 B细胞表面的细胞分裂中被瞬时诱导,支持TIM-1可以在抗原驱动的 细胞活化后在所有 细胞上诱导(扩展数据图1)。 )。
Notably, TIM- B cells from the dLN of B16F10 tumour-bearing mice also express higher levels of various co-inhibitory and immunoregulatory molecules that are expressed on T cells, including PD-1, TIGIT, LAG3, TIM-3, CD39, CD73 and IL-10 (Fig. 1j and Extended Data Fig. 4a,b). These molecules were preferentially induced on TIM cells compared with on TIM-1 B cells after treatment with anti-IgM or anti-CD40 antibodies or LPS stimulation in vitro (Extended Data Fig. 4c).
值得注意的是,来自B16F10荷瘤小鼠dLN的TIM-B 细胞也表达更高水平的T细胞上表达的各种共抑制和免疫调节分子,包括PD-1、TIGIT、LAG3、TIM-3、CD39、CD73和IL-10(图1j和扩展数据图4a,b)。在体外用抗 IgM 或抗 CD40 抗体或 LPS 刺激处理后,与 TIM-1 B 细胞相比,这些分子优先诱导在 TIM 细胞上(扩展数据图 4c)。
To study the relevance of TIM- cells in human tumours, we reanalysed TILs from human tumours using publicly available datasets that we and others have previously generated with high sensitivity (Smart-seq2 protocol) . While focusing on tumour-infiltrating cells derived from immune checkpoint blockade (ICB)-naive samples, we identified a cluster of B cells (cluster 4) co-expressing TIM-1 and multiple co-inhibitory molecules (HAVCR2, TIGIT,PDCD1,LAG3) and IL10, comprising a distinct cell subset and a signature that overlaps with human exhausted T cells (Fig.1k-l and Extended Data Fig. 4d,e).Notably, cells in cluster 4 , which largely included TIM- B cells, were more frequent among cells derived from ICB-naive patients and were decreased in TILs after checkpoint blockade therapy in human tumours (Fig. 1 l and Extended Data Fig. 4f,g). We corroborated these findings by investigating additional human cancer datasets derived from breast, colorectal, ovarian and lung tumours in which we could identify a similar cluster of B cells expressing checkpoint receptors (IC ) enriched in ICB-naive patient samples (Extended Data Fig. 4h-j). Clinically, high expression of HAVCR1 correlated with poor overall survival in patients with lung, pancreatic and stomach adenocarcinomas, while being protective in the context of colorectal cancer (Extended Data Fig. 4k,I). Furthermore, except for a poor impact on survival for stomach cancer, a high score for the cell signature did not affect the clinical outcomes of the patients (Extended Data Fig. ). These data indicate that TIM-1 marks a subset of activated cells expressing co-inhibitory molecules and IL-10 in both mouse and human tumours and their presence in human tumours seems to be inhibited after checkpoint blockade therapy.
为了研究 TIM- 细胞在人类肿瘤中的相关性,我们使用我们和其他人之前以高灵敏度生成的公开数据集(Smart-seq2 协议) 重新分析了来自人类肿瘤的 TIL。在关注源自免疫检查点阻断 (ICB) 初治样本的肿瘤浸润 细胞的同时,我们鉴定了一簇共表达 TIM-1 的 B 细胞(簇 4)和多个共抑制分子(HAVCR2、TIGIT、PDCD1、LAG3)和 IL10,包括一个不同的 细胞亚群和一个与人耗尽的 T 细胞 重叠的特征(图 1k-l 和扩展数据图 4d,e)。值得注意的是,簇 4 中的细胞(主要包括 TIM-B 细胞)在源自 ICB 初治患者的细胞中 更常见,并且在人类肿瘤中检查点阻断治疗后 TIL 中减少(图 1 l 和扩展数据图 4f,g)。我们通过研究来自乳腺癌、结直肠癌、卵巢癌和肺肿瘤的其他人类癌症数据集来证实这些发现,在这些数据集中,我们可以识别出一个类似的 B 细胞簇,这些细胞表达富含 ICB 初治患者样本中的检查点受体 (IC )(扩展数据图 4h-j)。临床上,HAVCR1 的高表达与肺癌、胰腺癌和胃腺癌患者的总生存期差相关,同时在结直肠癌的背景下具有保护作用(扩展数据图 4k,I)。此外,除了对胃癌生存率的影响较差外, 细胞特征的高分不会影响患者的临床结果(扩展数据图1)。 )。 这些数据表明,TIM-1 在小鼠和人类肿瘤中都标记了表达共抑制分子和 IL-10 的活化 细胞子集,并且在检查点阻断治疗后,它们在人类肿瘤中的存在似乎受到抑制。

Genetic deletion of TIM-1 in B cells limits tumour growth
B 细胞中 TIM-1 的基因缺失限制了肿瘤的生长

As TIM- B cells expressed multiple known cell checkpoint molecules, some previously reported in B cells , we investigated their B-cell-intrinsic roles in regulating anti-tumour immunity. Conditional deletion of the checkpoint molecules Havcr2, Tigit, Pdcd1 (encoding PD-1) or Lag3 in B cells had a modest impact or no effect on tumour growth (Fig. 2a-e). Only loss of TIGIT on B cells led to a modest but significant decrease in tumour growth. Although IL-10 has previously been associated with regulatory cells and shown to be a critical driver of B cell regulatory function , loss of B-cell-specific IL-10 had no effect on B16F10 growth, arguing against a functional role of IL-10-producing B cells in this melanoma model (Fig. 2f).
由于 TIM-B 细胞表达了多种已知 的细胞检查点分子,其中一些先前在 B 细胞 中报道过,我们研究了它们在调节抗肿瘤免疫方面的 B 细胞内在作用。B细胞中检查点分子Havcr2、Tigit、Pdcd1(编码PD-1)或Lag3的条件性缺失对肿瘤生长有适度影响或没有影响(图2a-e)。只有 B 细胞上 TIGIT 的缺失导致肿瘤生长适度但显着减少。尽管 IL-10 以前与调节 细胞 相关并被证明是 B 细胞调节功能 的关键驱动因素,但 B 细胞特异性 IL-10 的缺失对 B16F10 的生长没有影响,反对产生 IL-10 的 B 细胞在该黑色素瘤模型中的功能作用(图 2f)。
Conversely, conditional deletion of Havcr1 on B cells substantially inhibited tumour growth in various B16F10 melanoma tumour models, as well as MC38 colon carcinoma or KP1.9 lung adenocarcinoma (Fig. 2g-i and Extended Data Fig.5a-e), indicating that TIM-1 is not only a marker of checkpoint-receptor-expressing B cells, but that TIM-1 has a functional role in regulating tumour growth in vivo. Notably, although TIM-1 was initially described to be expressed on T cells, Havcr1 conditional deletion using , which deleted TIM-1 on all T cells, had no effect on tumour growth in mice implanted with B16F10 melanoma (Extended Data Fig. 5f,g), supporting a cell-intrinsic role of TIM-1 in B cell function. Together, these data demonstrate an important role of TIM-1 specifically expressed on B cells in regulating anti-tumour immune responses and tumour growth in vivo.
相反,在各种 B16F10 黑色素瘤肿瘤模型以及 MC38 结肠癌或 KP1.9 肺腺癌中,B 细胞上 Havcr1 的条件性缺失显着抑制了肿瘤生长(图 2g-i 和扩展数据图 5a-e),表明 TIM-1 不仅是表达检查点受体的 B 细胞的标志物,而且 TIM-1 在体内调节肿瘤生长中具有功能作用。值得注意的是,尽管 TIM-1 最初被描述为在 T 细胞上表达,但使用 Havcr1 条件性缺失在所有 T 细胞上删除 TIM-1,对植入 B16F10 黑色素瘤的小鼠的肿瘤生长没有影响(扩展数据图 5f,g),支持 TIM-1 在 B 细胞功能中的细胞内在作用。总之,这些数据表明,在B细胞上特异性表达的TIM-1在调节体内抗肿瘤免疫反应和肿瘤生长方面具有重要作用。

Therapeutic targeting of TIM-1 reduces tumour growth
TIM-1 的治疗靶向可减少肿瘤生长

To examine whether acute deletion of Havcr1 also regulates tumour growth, we generated CD20.TamCre Havcr (hereafter, Havcri ) mice and treated the mice with tamoxifen to trigger acute Cre-mediated Havcr1 deletion and observed inhibition of tumour growth similar to that with constitutive deletion of Havcr1 in B cells (Extended Data Fig. 5h).Moreover, this indicates that deletion of TIM-1 on B cells using another Cre driver independent of induces similar control of tumour growth.
为了检查 Havcr1 的急性缺失是否也调节肿瘤生长,我们生成 了 CD20。TamCre Havcr (以下简称Havcri )小鼠并用他莫昔芬处理小鼠以触发急性Cre介导的Havcr1缺失,并观察到肿瘤生长的抑制类似于B细胞中Havcr1的组成型缺失(扩展数据图5h)。此外,这表明使用另一个独立于 Cre 的驱动因素在 B 细胞上缺失 TIM-1 会诱导类似的肿瘤生长控制。
Next, therapeutic administration of a commercially available high-affinity anti-TIM-1 antibody (3B3) also induced marked inhibition of B16F10 tumour growth (Extended Data Fig. 5i). This therapeutic effect required the presence of B cells, and of TIM-1 expression on B cells, such that the therapeutic effect of the anti-TIM-1 antibody was lost in MT (lacking B cells) or Havcr mice (Fig. 3a and Extended Data Fig. 5i,j). Notably, we found that anti-TIM-1 treatment had a therapeutic effect inhibiting tumour growth selectively in mice with intact MHCII
接下来,市售高亲和力抗 TIM-1 抗体 (3B3) 的治疗性给药也诱导了对 B16F10 肿瘤生长的显着抑制(扩展数据图 5i)。这种治疗效果需要B细胞的存在,以及B细胞上TIM-1的表达,使得抗TIM-1抗体的治疗效果在MT(缺乏B细胞)或Havcr 小鼠中 丧失(图3a和扩展数据图5i,j)。值得注意的是,我们发现抗 TIM-1 治疗具有治疗作用,可选择性地抑制完整 MHCII 小鼠的肿瘤生长

h
Fig. 2 | Screening of in vivo regulatory molecules reveals TIM-1 as a B cell immune checkpoint controlling tumour growth. a-f, Subcutaneous (s.c.) B16F10 melanoma growth in Cd19 , Tigit (c), and controls versus mice. a, Experimental schematic. g-i, Schematic (g), quantification (h) and imaging (i) of tumour growth in and mice implanted s.c. with ( control versus Havcr ) or intravenously (i.v.) injected with KP1.9 cells ( mice per group). Tumour burden was assessed by histological analysis of lung tissue collected 4 weeks after injection. Data are mean s.e.m. and pooled from two to three independent experiments. Statistical analysis was performed using repeated-measures two-way analysis of variance (ANOVA) (b-f and ) and two-tailed Student's -tests (i). Scale bar, (i). expression on the B cell surface (Extended Data Fig. ). Whereas 3B3 has previously been reported to be an agonistic antibody based on activating cell effector functions, in B cells, the effects of the 3B3 antibody are very similar to what we observed after the genetic loss of TIM-1 on B cells. Whether this is due to differential effects of TIM-1 on T cells versus cells needs to be further characterized; nonetheless, the therapeutic effects of anti-TIM-1 antibodies on tumour growth are unequivocal. As TIM-1 expression on T cells has no effect on tumour growth, in vivo effects of anti-TIM-1 antibodies appear to be entirely dependent on the expression of TIM-1 on B cells. Moreover, we performed anti-TIM-1 treatment experiments using the spontaneous melanoma
图2 |体内调节分子的筛选显示 TIM-1 是控制肿瘤生长的 B 细胞免疫检查点。a-f,皮下 (sc) B16F10 黑色素瘤在 Cd19 中的生长,Tigit (c) 和对 照组与 小鼠的对比。a, 实验示意图。g-i,示意图(g),定量(h)和成像(i)肿瘤生长 植入皮下注射 对照与 Havcr )或静脉注射(i.v.)注射KP1.9细胞(每组 小鼠)的小鼠。通过对注射后 4 周收集的肺组织进行组织学分析来评估肿瘤负荷。数据是平均 的,并汇集了两到三个独立的实验。使用重复测量双因素方差分析(ANOVA)(b-f和 )和双尾学生 检验(i)进行统计分析。比例尺, (i)。在B细胞表面的表达(扩展数据图。 )。虽然 3B3 之前被报道为一种基于激活 细胞效应功能的激动性抗体,但在 B 细胞中,3B3 抗体的作用与我们在 B 细胞上 TIM-1 遗传缺失后观察到的非常相似。这是否是由于 TIM-1 对 T 细胞与 细胞的不同影响需要进一步表征;尽管如此,抗 TIM-1 抗体对肿瘤生长的治疗作用是明确的。由于 TIM-1 在 T 细胞上的表达对肿瘤生长没有影响,因此抗 TIM-1 抗体的体内效应似乎完全依赖于 TIM-1 在 B 细胞上的表达。此外,我们使用自发性黑色素瘤进行了抗 TIM-1 治疗实验

ing a tamoxifen-inducible Cre-recombinase under the control of the tyrosinase promoter. This model enables melanocyte lineage-specific induction of a BRAF(V600E) mutation and deletion of Pten, inducing spontaneous formation of melanoma and replicating many of the features of human melanoma. Notably, treatment with anti-TIM-1 (clone 3B3) significantly reduced melanoma genesis and proximal metastatic dissemination (Fig. 3b,c). Finally, combined PD-1blockade (as a T-cell-relevant target) together with anti-TIM-1 antibody treatment had an additive effect, consistent with an impact on two different compartments, resulting in more rapid and consistent growth control and prolonged survival in B16F10-bearing mice compared with either treatment alone (Fig. 3d and Extended Data Fig.5I).Monotherapy with anti-TIM-1 antibodies or in combination with PD-1blockade was accompanied by an increased frequency of effector and cells infiltrating the tumours of antibody-treated animals, without affecting B cell or regulatory cell infiltration (Extended Data Fig. ) and with an induction of a larger fraction of granzyme cells and cells among both the CD4 and cell compartments (Fig. 3e and Extended Data Fig. 5n). Together, these data show that therapeutic antibody blockade of TIM-1 in vivo results in tumour growth control of both transplanted and spontaneous tumour models and requires TIM-1 expression on B cells, but not on other cell types, which is consistent with the phenotype observed in tumour-bearing mice with genetic deletion of Havcr1 in B cells.
在酪氨酸酶启动子的控制下,他莫昔芬诱导的Cre-重组酶。该模型能够对 BRAF(V600E) 突变和 Pten 缺失进行黑色素细胞谱系特异性诱导,诱导黑色素瘤的自发形成并复制人类黑色素瘤的许多特征。值得注意的是,抗 TIM-1(克隆 3B3)治疗显着减少了黑色素瘤的发生和近端转移播散(图 3b、c)。最后,PD-1阻断剂(作为T细胞相关靶标)与抗TIM-1抗体治疗联合使用具有累加效应,与对两个不同区室的影响一致,与单独治疗相比,在携带B16F10的小鼠中,生长控制更快,更一致,生存期更长(Fig. 3d和扩展数据图5I)。抗 TIM-1 抗体单药治疗或与 PD-1 阻断剂联合使用时,效应器 细胞浸润抗体治疗动物肿瘤的频率增加,而不影响 B 细胞或调节 细胞浸润(扩展数据图 1)。 ),并在CD4 细胞区室中诱导更大比例的颗粒酶 细胞和 细胞(图3e和扩展数据图5n)。总之,这些数据表明,体内 TIM-1 的治疗性抗体阻断导致移植和自发性肿瘤模型的肿瘤生长控制,并且需要 TIM-1 在 B 细胞上表达,但在其他细胞类型上不表达,这与在 B 细胞中 Havcr1 基因缺失的荷瘤小鼠中观察到的表型一致。

Loss of TIM- 1 in B cells enhances effector T cell responses
B 细胞中 TIM-1 的缺失增强了效应 T 细胞的反应

To investigate how TIM-1 loss in B cells affects tumour growth, we analysed the composition of cells in the TME, dLN and ndLN of control or Havcr mice using flow cytometry at 16 days after receiving subcutaneous B16F10 cells (Fig. 4a,b and Extended Data Fig. 6). There was an increased immune cell infiltration in Havcr tumours versus control tumours (Extended Data Fig. 6b), and a significant increase in
为了研究 B 细胞中 TIM-1 缺失如何影响肿瘤生长,我们在接受皮下 B16F10 细胞后 16 天使用流式细胞术分析了对照组或 Havcr 小鼠的 TME、dLN 和 ndLN 中的细胞组成(图 4a、b 和扩展数据图 6)。与对照组相比,Havcr 肿瘤中的免疫细胞浸润增加(扩展数据图6b),并且

d
Fig. 3 | Targeting of TIM-1 reduces B16F10 growth, is dependent on TIM-1 expression on B cells and augments PD-1 blockade therapy. a, B16F10 tumour growth in and mice ( mice per group) that were treated with anti-TIM-1 or isotype control antibodies.b,c,Braf-Pten mice were painted with 4-hydroxytamoxifen (tamox.) on one ear and treated with anti-TIM-1 antibodies beginning 27 days later when visible lesions were apparent. Representative photographs, and measurements of pigmentation (b) and the number of facial nodules (c) are shown for isotype-treated ( mice) or
图3 |靶向 TIM-1 可减少 B16F10 的生长,依赖于 B 细胞上 TIM-1 的表达并增强 PD-1 阻断治疗。a,B16F10肿瘤生长和 用抗TIM-1或同型对照抗体治疗的小鼠(每组 小鼠).b,c,Braf-Pten小鼠在一只耳朵上涂上4-羟基他莫昔芬(tamox.),并在27天后开始用抗TIM-1抗体治疗,当可见病变明显时。代表性照片,色素沉着(b)和面部结节(c)的测量值显示为同型处理( 小鼠)或
initiation/7 weeks after tumour induction. Data are mean s.e.m. pooled from anti-TIM-1-treated ( mice) ears at treatment and 3 weeks after treatment two to three independent experiments. d,e, Tumour growth (d) and flow cytometry immunophenotyping of TILs showing the frequencies of IFN cells among and TILs (e) of C57Bl/6J mice implanted with B16F10 melanoma and treated with anti-TIM-1, anti-PD-1, anti-TIM-1+ anti-PD-1 (combo) or isotype controls. mice per group for tumour growth analysis and mice per group for flow cytometry analysis. Statistical analysis was performed using repeated-measures two-way ANOVA ( and d) and one-way ANOVA with Tukey's multiple-comparison test (e).
开始/肿瘤诱导后 7 周。数据是在治疗时和治疗后 3 周的两到三个独立实验中从抗 TIM-1 治疗( 小鼠)耳朵汇总的平均 s.e.m. 合并的。d,e,TIL的肿瘤生长(d)和流式细胞术免疫表型,显示植入B16F10黑色素瘤的C57Bl/6J小鼠的IFN 细胞频率 TILs(e),并用抗TIM-1,抗PD-1,抗TIM-1+抗PD-1(组合)或同型对照治疗。 每组小鼠进行肿瘤生长分析, 每组小鼠进行流式细胞术分析。使用重复测量的双因素方差分析( 和d)和单因素方差分析以及Tukey的多重比较检验(e)进行统计分析。
the frequency of cells, and decreased frequency of cells ( cells) among cells, resulting in an approximately fourfold increase in the ratio of cells to cells (Extended Data Fig. . Moreover, there was a decreased proportion of cells within the dLN of Havcr mice (Extended Data Fig. ). Myeloid cell subsets and cells were unchanged in either the tumour or the LNs (Extended Data Fig. 6f). Moreover, among TILsfrom Havcr mice, a larger fraction of and cells secreted both TNF and IFN in tumours compared with the control mice, and cells displayed a stronger cytotoxic profile, with elevated expression of CD107a and an increased frequency of cells co-expressing granzyme and perforin or the transcription factors EOMES and TBET that regulate IFN production (Fig. 4a,b and Extended Data Fig. 6f,g). However, IL-2 production was not changed in or cells (Fig. 4 a), and there were no alterations in TCF1 expression levels or in the co-expression of the checkpoint molecules PD-1 and TIM-3 (Extended Data Fig. 6h,i). Similar results were obtained in mice that received MC38 colon adenocarcinoma (Extended Data Fig. 61).
细胞的 频率,以及细胞之间 细胞( 细胞)频率的降低,导致 细胞与 细胞的比例增加约四倍(扩展数据图1)。 。此外,Havcr 小鼠dLN内 的细胞比例降低(扩展数据图1)。 )。肿瘤或LN中的髓系细胞亚群和 细胞均未改变(扩展数据图6f)。此外,在来自Havcr 小鼠的TIL中,与对照小鼠相比,肿瘤中分泌TNF和IFN 细胞比例更大 ,并且 细胞表现出更强的细胞毒性特征,CD107a的表达升高,共表达颗粒酶 和穿孔素或调节IFN 的转录因子EOMES和TBET的 细胞频率增加生产(图4a,b和扩展数据图6f,g)。然而,IL-2 的产生在细胞 中没有 改变(图 4 a),并且 TCF1 表达水平或检查点分子 PD-1 和 TIM-3 的共表达没有改变(扩展数据图 6h,i)。在接受MC38结肠腺癌的小鼠中也获得了类似的结果(扩展数据图61)。
To further characterize these changes in the tumours of Havcr1 mice, we profiled cells infiltrating the tumours, and ndLN from these mice by combined single-cell RNA- and TCR-seq (scRNA/TCR-seq; Fig. 4c,d and Extended Data Fig. 7a,b). scRNA-seq confirmed an increase in cytotoxic cell infiltration in Havcr tumours versus the controls and showed a higher frequency of clonally expanded CD8 cells in Havcr1 tumours on the basis of TCR analysis ( versus of clones with more than 2 cells) (Fig. 4e,
为了进一步表征 Havcr1 小鼠肿瘤的这些变化,我们 通过联合单细胞 RNA- 和 TCR-seq (scRNA/TCR-seq;图4c,d和扩展数据图7a,b)。scRNA-seq证实,与对照组相比,Havcr 肿瘤中细胞毒性 细胞浸润增加,并且根据TCR分析( 与具有2个以上细胞的克隆相比 ),Havcr1 肿瘤中克隆扩增的CD8 细胞的频率更高(图4e,

Methods and Extended Data Fig. 7c). Notably, clonally expanded CD8 cells from tumours displayed a higher expression of genes associated with an effector/cytotoxic phenotype (that is, Gzmb, Gzma, Gzmc, Prf1, Ifng and Ccl4) (Fig. 4f,g and Extended Data Fig. 7d). Consistently, TILs from B16-OVA-bearing mice showed an increased frequency of proliferating OVA-specific cells in Havcr tumours versus the control as determined by dextramer staining and Ki-67 expression (Fig. 4h). Taken together, these data indicate that the deletion of Havcr1 in B cells resulted in decreased infiltration and increased clonally expanded antigen-specific TILS.
方法和扩展数据图7c)。值得注意的是,来自 肿瘤的克隆扩增的CD8 细胞显示出与效应/细胞毒性表型(即Gzmb,Gzma,Gzmc,Prf1,Ifng和Ccl4)相关的基因的更高表达(图4f,g和扩展数据图7d)。一致地,来自携带 B16-OVA 的小鼠的 TIL 显示 Havcr 肿瘤中 OVA 特异性 细胞增殖的频率增加,与通过 右聚糖染色和 Ki-67 表达确定的对照组相比(图 4h)。综上所述,这些数据表明,B细胞中Havcr1的缺失导致浸润减少 和克隆扩增抗原特异性 TILS增加。

TIM-1 restrains cell antigen presentation
TIM-1 抑制 细胞抗原呈递

To determine the mechanism by which Havcr1 deletion in B cells influenced T-cell-mediated anti-tumour responses, we analysed the B-cell-intrinsic effects of the genetic loss of Havcr1. Although there were no differences in the total frequency of B cells in Havcr tumours, dLNs and ndLNs relative to their respective controls (Extended Data Fig. 8a), scRNA-seq profiles of Havcr1 cells from dLNs and tumours (but not ndLNs) had a higher expression of signatures of the response to type I and type II interferons (Fig. 5a-c and Extended Data Figs. 9a and 10a; for example, Ifnar2, Irf1, Irf9,Stat1 and Stat2). Type I interferons are critical regulators of B cell homeostasis and responses and potentiate BCR-driven activation, co-stimulation and antigen presentation pathways in B cells . Consistently, we found significant enrichment for BCR signalling (not shown), B cell activation (Lyn, Tnfrsf13c, Btla,
为了确定 B 细胞中 Havcr1 缺失影响 T 细胞介导的抗肿瘤反应的机制,我们分析了 Havcr1 遗传缺失的 B 细胞内在效应。尽管 Havcr 肿瘤、dLN 和 ndLN 中 B 细胞的总频率相对于其各自的对照没有差异(扩展数据图 8a),但来自 dLN 和肿瘤(但不是 ndLN)的 Havcr1 细胞的 scRNA-seq 谱对 I 型和 II 型干扰素的反应特征表达更高(图 5a-c 和扩展数据图 9a 和 10a;例如, Ifnar2、Irf1、Irf9、Stat1 和 Stat2)。I 型干扰素是 B 细胞稳态和反应 的关键调节因子,可增强 B 细胞 中 BCR 驱动的激活、共刺激和抗原呈递途径。一致地,我们发现 BCR 信号传导(未显示)、B 细胞活化 (Lyn、Tnfrsf13c、Btla、

IFN -BUV737 干扰素 -BUV737
b

Percentage of clonally expanded cells among T cells
T 细胞中 克隆扩增细胞的百分比
h
Fig. 4 | Havcr1 deletion in B cells enhances anti-tumour T cell immunity. , Flow cytometry analysis of TILs derived from Cd1 and Havcr1 mice implanted s.c. with B16F10. a, Representative FACS plot and the percentage of IFN and TNF double-positive cells and IL-2 within tumour-infiltrating CD8 (top) and CD4 (bottom) T cells. mice per group.b, Representative FACS plot and the percentage of granzyme and perforin double-expressing T cells. control and mice. , scRNA/BCR-seq and TCR-seq analysis of the TME, dLNs and ndLNs from and mice bearing B16F10 melanoma.c,d,Schematic of the experimental design and UMAP analysis of 11,884 CD cells coloured by their tissue of origin (c) and immune cell types (d).ISG, IFN-stimulated gene; moDCs, monocyte-derived dendritic cells;PMN, polymorphonuclear leukocytes. e, UMAP projection of Cd19 (blue) and (red) cells delineated between conventional cells, cells and cells (left) and clonally expanded cells (middle). Right, the frequencies of clonally expanded CD8 cells in different compartments.f. plot of gene expression comparing versus TILs. Positive -transformed fold change corresponds to upregulation within Havcr1 TILs and vice versa.g, UMAP analysis of TILs coloured by cell types (top left), genotypes (top middle) and clonal expansion (top right). Bottom, expression of the indicated markers. , The frequencies of OVA-specific cells among CD8 TILs (top) and Ki-67-expressing OVA-specific CD8 TILs (bottom). mice per group. Data are mean s.e.m. pooled from at least two to three independent experiments. Statistical analysis was performed using two-tailed Student's -tests and .
图4 |B 细胞中的 Havcr1 缺失可增强抗肿瘤 T 细胞免疫力。 ,流式细胞术分析来自植入 B16F10 皮下注射的 Cd1 和 Havcr1 小鼠的 TIL。a,肿瘤浸润性 CD8 (上)和 CD4 (下)T 细胞中 IFN 和 TNF 双阳性细胞和 IL-2 的代表性 FACS 图和百分比。 每组小鼠.b,代表性FACS图以及颗粒酶 和穿孔素双表达 T细胞的百分比。 对照和 小鼠。 ,来自 携带 B16F10 黑色素瘤的 小鼠的 TME、dLN 和 ndLN 的 scRNA/BCR-seq 和 TCR-seq 分析.c,d,11,884 个 CD 细胞的实验设计和 UMAP 分析示意图,按其来源组织 (c) 和免疫细胞类型 (d) 着色。ISG, IFN刺激基因;moDCs,单核细胞来源的树突状细胞;PMN,多形核白细胞。e,Cd19 (蓝色)和 (红色) 细胞的UMAP投影,在常规 细胞、 细胞和 细胞(左)和克隆扩增 细胞(中)之间 划定。右图为不同区室中克隆扩增的CD8 细胞的频率。 基因表达与 TIL的比较 图。正 转化的倍数变化对应于 Havcr1 TIL 内的上调,反之亦然,按细胞类型(左上)、基因型(中上)和克隆扩增(右上)着色的 TIL 的 UMAP 分析。底部,指示标记的表达。 ,CD8 TILs(上图)和表达Ki-67的OVA特异性CD8 TILs(下图)中OVA特异性细胞的频率。 每组小鼠。数据是平均 s.e.m. 汇集的至少两到三个独立实验。使用双尾 Student's -检验 .

Cd81 and Cd22) and antigen processing and T cell antigen presentation and co-stimulation (Icosl, Cd4O and Ciita) gene signatures (Fig. 5a-c and Extended Data Fig. 9b). Supporting these RNA expression patterns, there was increased surface expression of CD86, MHC II and ICOSL on Havcr cells infiltrating the tumours (Extended Data
Cd81 和 Cd22) 和抗原加工以及 T 细胞抗原呈递和共刺激(Icosl、Cd4O 和 Ciita)基因特征(图 5a-c 和扩展数据图 9b)。支持这些 RNA 表达模式的是,浸润肿瘤的 Havcr 细胞上 CD86、MHC II 和 ICOSL 的表面表达增加(扩展数据

Fig. 9c). Although Havcr1 deletion increases the response to type-1 interferons and cell activation, humoral immunity was largely unaffected by its deletion in the tumour setting. Flow cytometry analysis showed similar frequencies of plasmablasts , plasma cells , germinal centre cells or
图9c)。尽管 Havcr1 缺失增加了对 1 型干扰素和 细胞活化的反应,但在肿瘤环境中,体液免疫在很大程度上不受其缺失的影响。流式细胞术分析显示浆母细胞 、浆细胞 、生发中心 细胞
C
Up in Havcr Up in Cd19 .
在 Havcr 中 在 CD19 中。
Fig. 5 | TIM-1 deficiency in cells results in cell activation, antigen presentation and co-stimulatory function. a-c, scRNA-seq analysis of B cells derived from TILs, dLNs and ndLNs of and mice bearing B16F10 melanoma. plot of gene expression comparing tumour-derived Cd1 and Haucr1 cells (a), gene set enrichment analysis (GSEA) analysis (b) and dot plots depicting selected genes (c) between tumour-infiltrating Havcr and B cells. Selected genes are annotated. NES, normalized enrichment score. d, peptide-pulsed and B cells were co-cultured with CellTrace Violet (CTV)-labelled OVA-restricted CD4 T cells (OTII) at different ratios for 4 days. T cell proliferation was determined by dilution of CTV. Representative histograms and quantitative analysis of the proliferation indices are shown. mice per group.e, cells were analysed for expression of IFN , ICOS and FOXP3. Representative and quantitative data are shown. The circles denote data points from individual mice. , Naive
图5 |细胞中 TIM-1 缺乏会导致 细胞活化、抗原呈递和共刺激功能。a-c,scRNA-seq 分析来自携带 B16F10 黑色素瘤的 小鼠的 TIL、dLN 和 ndLN 的 B 细胞。 比较肿瘤来源的 Cd1 和 Haucr1 细胞的基因表达图 (a)、基因集富集分析 (GSEA) 分析 (b) 和描述肿瘤浸润性 Havcr B 细胞之间选定基因的点图 (c)。对选定的基因进行注释。NES,归一化富集评分。d、 肽脉冲 细胞和 B 细胞与 CellTrace Violet (CTV) 标记的 OVA 限制性 CD4 T 细胞 (OTII) 以不同比例共培养 4 天。T细胞增殖通过CTV稀释决定。图中显示了具有代表性的直方图和扩散指数的定量分析。 每组小鼠,分析 细胞IFN 、ICOS和FOXP3的表达。显示了代表性和定量数据。圆圈表示来自单个小鼠的数据点。 朴素

CD45.1 OVA-restricted CD4 cells were transferred i.v. 1 day before B16-OVA melanoma cell s.c. implantation into CD45.2 and К mice. mice per group. Tumour-infiltrating OTII cells were examined for expression of IFN and FOXP3. A schematic of the experimental and quantitative results is shown. g, Quantification and representative histogram of IFNAR1 surface expression of B cells derived from TILs and dLNs of and Havcr mice implanted s.c. with B16F10. mice per group. , Tumour growth in the indicated mice implanted with B16F10 melanoma and treated with isotype control ( mice per group) or neutralizing anti-IFNAR1 ( mice per group) antibodies. Data are mean s.e.m. pooled or representative of at least two to three independent experiments. Statistical analysis was performed using repeated-measures two-way ANOVA (d and ) and two-tailed Student's -tests .
在 B16-OVA 黑色素瘤细胞皮下植入 CD45.2 К 小鼠前 1 天,将 CD45.1 OVA 限制性 CD4 细胞转移。 每组小鼠。检查肿瘤浸润 OTII 细胞的 IFN 和 FOXP3 表达。图中显示了实验和定量结果的示意图。g,来自植入 B16F10 的 TIL 和 dLN 的 B 细胞以及 Havcr 小鼠的 IFNAR1 表面表达的定量和代表性直方图。 每组小鼠。 ,植入 B16F10 黑色素瘤并用同型对照(每组 小鼠)或中和抗 IFNAR1(每组 小鼠)抗体治疗的指示小鼠的肿瘤生长。数据是平均 s.e.m. 汇总或代表至少两到三个独立实验。使用重复测量双因素方差分析(d 和 )和双尾学生 检验 进行统计分析。

T follicular helper cells within the and spleen from and control mice (Extended Data Fig. 8b-d,k-m). Furthermore, we did not observe significant differences in circulating immune complexes or in the total amount of or in the serum of either naive or B16F10-bearing Havcr and control mice (Extended Data Fig. 8e-h). Importantly, the levels of B16F10-reactive IgGs and IgM were also unaltered in Havcr sera (Extended Data Fig. 8i). Finally, we did not detect a significant increase in class-switched or clonally expanded B cells across the compartments, and there was no difference in major B cell subsets in Havcr mice or mice treated with anti-TIM-1 monoclonal antibodies (Extended Data Fig. ). Thus, Havcr1 deletion had little to no effect on humoral immunity in tumours and lymphoid organs.
T滤泡辅助细胞 和脾脏内的T滤泡辅助细胞来自 对照小鼠(扩展数据图8b-d,k-m)。此外,我们没有观察到幼稚或携带B16F10的Havcr 和对照小鼠的循环免疫复合物 或血清总量 血清中的显着差异(扩展数据图8e-h)。重要的是,Havcr 血清中 B16F10 反应性 IgG 和 IgM 的水平也没有改变(扩展数据图 8i)。最后,我们没有检测到跨区室的类别转换或克隆扩增的B细胞的显着增加,并且在Havcr 小鼠或用抗TIM-1单克隆抗体治疗的小鼠中,主要B细胞亚群没有差异(扩展数据图1)。 )。因此,Havcr1缺失对肿瘤和淋巴器官的体液免疫几乎没有影响。
On the other hand, Havcr1 deletion enhanced B cell antigen presentation to cells, expanded helper cells and reduced
另一方面,Havcr1 缺失增强了 B 细胞抗原向细胞的 呈递,扩大了 辅助 细胞并减少了

FOXP3 cell expansion. Indeed, in vitro, Havcr1 cells induced greater cell proliferation in a manner dependent on MHC II presentation (Fig. 5d and Extended Data Fig. 9d,e). Moreover, in vivo MHC II blockade abolished the enhanced anticancer efficacy in Havcr mice, suggesting a critical role for antigen presentation through MHC II in mediating tumour control in mice lacking TIM-1 in B cells (Extended Data Fig. 9f). Notably, Havcr1 cells also influenced cell expansion and function as cells induced a greater fraction of IFN cells, including a substantial increase of ICOS expression, while inhibiting FOXP3 expression in CD4 cells (Fig.5e). This effect on cell polarization was recapitulated in vivo by adoptively transferring naive cells from CD45.1 OT-II donors into congenic CD45.2 Havcr or control mice (Fig. 5 f and Extended Data Fig. 9g). Tumour-derived CD45.1 cells in hosts exhibited increased expression
FOXP3 细胞扩增。事实上,在体外,Havcr1 细胞以依赖于MHC II呈递的方式诱导更大的 细胞增殖(图5d和扩展数据图9d,e)。此外,体内MHC II阻断消除了Havcr 小鼠增强的抗癌功效,表明通过MHC II呈递抗原在介导B细胞中缺乏TIM-1的小鼠的肿瘤控制中起关键作用(扩展数据图9f)。值得注意的是,Havcr1 细胞还影响了 细胞的扩增和功能,因为 细胞诱导了更大比例的 IFN 细胞,包括 ICOS 表达的显着增加,同时抑制了 CD4 细胞中 FOXP3 的表达(图 5e)。通过将来自CD45.1 OT-II供体的 幼稚细胞过继地转移到同源的CD45.2 Havcr 或对照小鼠中,在体内概括了这种对 细胞极化的影响(图5 f和扩展数据图9g)。 宿主中肿瘤来源的 CD45.1 细胞表达增加

of IFN and reduced FOXP3 expression (Fig. 5f). Moreover, whereas FOXP3 OT II cells exhibited similar proliferative ability in Havcr1 or control tumours, FOXP3 OT II cell proliferation was reduced in Havcr1 tumours (Extended Data Fig. 9g), indicating that cell proliferation is impaired in the TME of Havcr1 tumours. Moreover, Havcr1 cells expressed higher levels of the costimulatory ligand ICOSL both in vitro and ex vivo (Extended Data Fig. 9c,d), a recently described marker of anti-tumour B cells, potentiating -cell-mediated anticancer immunity .
IFN 和 FOXP3 表达降低(图 5f)。此外,虽然 FOXP3 OT II 细胞在 Havcr1 或对照肿瘤中表现出相似的增殖能力,但 FOXP3 OT II 细胞在 Havcr1 肿瘤中增殖减少(扩展数据图 9g),表明 Havcr1 肿瘤的 TME 中的细胞增殖受损。此外,Havcr1 细胞在体外和体外都表达了更高水平的共刺激配体ICOSL(扩展数据图9c,d),这是最近描述的抗肿瘤B细胞的标志物,增强 了细胞介导的抗癌免疫

Enhanced IFN type I and II sensing in TIM-1-deficient B cells
增强 TIM-1 缺陷 B 细胞中的 IFN I 型和 II 型感知

During cell activation, antigen presentation and expression of co-stimulatory molecules such as ICOSL are tightly regulated by the type I and type IIIFN signalling cascade, influencing B cell-T cell cooperation and effector T cell responses. In tumours, Havcr1 cells exhibit a marked enrichment for a type IIFN gene signature, enhanced IFN- responsiveness and substantially increased expression of IFN receptor (IFNAR), comprising the IFNAR1 and IFNAR2 chains, ex vivo (Fig.5c,g). We hypothesized that TIM-1 expression on B cells during activation suppresses the type I interferon response and, as a result, limits B cell activation and antigen presentation ability. Indeed, activation of wild-type B cells (Cd19 ) with anti-IgM and anti-CD40 increases the expression of TIM-1 on B cells (Extended Data Fig. 9h), but IFN limits TIM-1 upregulation with a significantly increased surface expression of CD86 and MHC II in Havcr1 B cells after anti-IgM and anti-CD40 stimulation (Extended Data Fig. 9h). These data suggest an interplay between the TIM-1 and type I interferon pathways in that increased TIM-1 expression limits the response to type 1 interferons and, conversely, type 1 interferons limit TIM-1 expression on B cells and increase B cell activation, supporting antagonism between the two pathways.
在细胞活化过程中 ,抗原呈递和共刺激分子(如ICOSL)的表达受到I型和IIIFN型信号级联反应的严格调控,影响B细胞-T细胞合作和效应T细胞反应。在肿瘤中,Havcr1 细胞表现出显着富集的 IIFN 型基因特征、增强的 IFN- 反应性和显着增加的 IFN 受体 (IFNAR) 表达,包括 IFNAR1 和 IFNAR2 链,离体(图 5c,g)。我们假设 B 细胞在激活过程中的 TIM-1 表达抑制了 I 型干扰素反应,从而限制了 B 细胞活化和抗原呈递能力。事实上,用抗 IgM 和抗 CD40 激活野生型 B 细胞 (Cd19 ) 会增加 B 细胞上 TIM-1 的表达(扩展数据图 9h),但 IFN 限制了 TIM-1 的上调,在抗 IgM 和抗 CD40 刺激后,Havcr1 B 细胞中 CD86 和 MHC II 的表面表达显着增加(扩展数据图 9h)。这些数据表明 TIM-1 和 I 型干扰素通路之间存在相互作用,因为 TIM-1 表达的增加限制了对 1 型干扰素的反应,相反,1 型干扰素限制了 B 细胞上 TIM-1 的表达并增加 B 细胞活化,支持两种通路之间的拮抗作用。
We postulated that enhanced IFNAR signalling could regulate the anti-tumour immune response of Havcr1 cells, and treated B16F10-tumour-engrafted control and Havcr mice with either anti-IFNAR1 or isotype control antibodies. IFNAR1 blockade completely abrogated tumour growth control observed in Havcr1 mice (Fig.5h), and inhibited the increased cell abundance normally observed in the TILs of Havcr mice, but did not affect or IFN cell proportions in Havcr1 mice (Extended Data Fig. 9i). Furthermore, tumour-derived leukocytes from anti-IFNAR1-treated mice displayed decreased B cell infiltration and lower expression of MHCI, MHC II and CD86 on the B cell surface (Extended Data Fig. 9j). Finally, projection of the intratumoural cell signature onto the single-cell profiles of human melanoma-infiltrating B cells obtained from ICB responder versus non-responder samples marked a distinct cluster of cells overlapping with cells derived from the patients who responded but not in the B cells from patients who did not respond to anti-PD-1 therapy (Extended Data Fig. 9k). Furthermore, the type I interferon response or antigen processing and presentation signatures were increased in B cell clusters (particularly cluster 4) from responders of ICB therapy and particularly the ones enriched for the Havcr1 cell signature, supporting a potential role of these pathways in promoting anti-tumour immunity in humans (Extended Data Fig. 9l-n). As downstream signalling from interferons converges onto similar pathways, and Havcr1 cells from tumours present a high signature score for the response to IFN (Extended Data Fig.10a), we tested whether other interferons could inhibit TIM-1 induction in B cells in vitro.Notably, although IFN had no effect on TIM-1 expression, both IFN and IFN significantly inhibited TIM-1 induction, with a more potent role for IFN in both mouse and human cells (Extended Data Fig. 10b). Moreover, blockade of the IFN pathway using anti-IFNGR monoclonal antibodies partially abrogated the protective effects and restored the B16F10 growth in Havcr mice (Extended Data Fig.10d). Finally, we examined the cellular source of IFN in the TME that acts on
我们假设增强的 IFNAR 信号可以调节 Havcr1 细胞的抗肿瘤免疫反应,并用抗 IFNAR1 或同型对照抗体治疗 B16F10 肿瘤移植的对照和 Havcr 小鼠。IFNAR1阻断完全消除了在Havcr1 小鼠中观察到的肿瘤生长控制(图5h),并抑制了通常在Havcr 小鼠的TIL中观察到的细胞丰度增加 ,但不影响 Havcr1 小鼠的IFN 细胞比例(扩展数据图9i)。此外,来自抗IFNAR1治疗 小鼠的肿瘤来源的白细胞在B细胞表面显示出B细胞浸润减少和MHCI、MHC II和CD86的表达降低(扩展数据图9j)。最后,将肿瘤内 细胞特征投射到从 ICB 应答者与非应答者样本 中获得的人黑色素瘤浸润性 B 细胞的单细胞谱上,标记出一个明显的 细胞簇,这些细胞簇与 来自有反应的患者的细胞重叠,但在对抗 PD-1 治疗无反应的患者的 B 细胞中没有重叠(扩展数据图 9k)。此外,来自 ICB 治疗反应者的 B 细胞簇(特别是第 4 簇)的 I 型干扰素反应或抗原加工和呈递特征增加,尤其是富集 Havcr1 细胞特征的细胞簇,支持这些途径在促进人类抗肿瘤免疫方面的潜在作用(扩展数据图 9l-n)。随着来自干扰素的下游信号汇聚到相似的通路上,来自肿瘤的 Havcr1 细胞对 IFN 的反应呈现出高特征评分(扩展数据图 1)。10a),我们测试了其他干扰素是否可以在体外抑制 B 细胞中 TIM-1 的诱导。值得注意的是,尽管 IFN 对 TIM-1 表达没有影响,但 IFN 和 IFN 都显着抑制了 TIM-1 的诱导,IFN 在小鼠和人类 细胞中的作用更有效(扩展数据图 10b)。此外,使用抗IFNGR单克隆抗体阻断IFN 通路部分消除了保护作用,并恢复了Havcr 小鼠的B16F10生长(扩展数据图10d)。最后,我们检查了 TME 中 IFN 的细胞来源

Havcr1 cells and leads to tumour control. IFN was found at a high abundance in the TME, consistent with previous reports , but was not changed in Havcr mice, and plasmacytoid dendritic cells (pDCs) were the highest IFN -expressing cell type in the TME (Extended Data Fig.10e,f).Moreover, pDC depletion using anti-PDCA1 antibodies abrogated the tumour control observed in Havcr mice, consistent with the anti-IFNAR1 blockade and highlighting the contribution of pDCs as the major source of IFN within the TME (Extended Data Fig. ). Overall, these results suggest that TIM-1surface expression is regulated by type I and type II interferons. Moreover, TIM-1 expression limited B cell responses in the TME by regulating type I interferon receptor expression/signalling, consequently dampening their ability to present antigen and co-stimulate anti-tumour effector cells.
Havcr1 细胞并导致肿瘤控制。在TME中发现IFN 的丰度很高,与之前的报道 一致,但在Havcr 小鼠中没有改变,浆细胞样树突状细胞(pDCs)是TME中表达IFN 最高的细胞类型(扩展数据图10e,f)。此外,使用抗 PDCA1 抗体的 pDC 耗竭消除了在 Havcr 小鼠中观察到的肿瘤控制,这与抗 IFNAR1 阻断一致,并突出了 pDC 作为 TME 中 IFN 主要来源的贡献(扩展数据图 1)。 )。总体而言,这些结果表明 TIM-1surface 表达受 I 型和 II 型干扰素的调节。此外,TIM-1 表达通过调节 I 型干扰素受体表达/信号传导来限制 TME 中的 B 细胞反应,从而抑制它们呈递抗原和共同刺激抗肿瘤效应 细胞的能力。

Discussion 讨论

Whereas the role of cells in anti-tumour immunity has been exhaustively studied, the role of B cells in anti-tumour immunity remains less well understood, hampering efforts to harness the cell response for cancer immunotherapy. Here we identified a subset of B cells that co-expressed TIM-1 among several other checkpoint molecules, and the proportion increased with tumour progression in the tumour dLN. Although various checkpoint molecules expressed on B cells have an important intrinsic role in B cell homeostasis and responses , only the selective deletion of Havcr1 in B cells profoundly limited tumour growth. In patients with cancer, cells also co-expressed multiple checkpoint molecules, suggesting that this co-expression cluster identifies a B cell programme or activation state that is conserved between mice and humans. Importantly, this subset was strongly decreased in the patients with cancer who had received checkpoint blockade therapy. However, our observed association of high TIM-1 expression or immune checkpoint expressing cells with poor clinical outcome in human cancers requires further study owing to the lack of sufficient B cells captured in human tumour single-cell atlases. Our data also suggest that cells may have an important role during and T cell priming within the , before acting locally within the tumour. Analysis of TIM- B cells co-expressing checkpoint molecules within the sentinel LNs of patients with cancer would provide additional insights into the emergence of this cell subset in human tumours.
虽然 细胞在抗肿瘤免疫中的作用已经得到了详尽的研究,但 B 细胞在抗肿瘤免疫中的作用仍然不太清楚,阻碍了利用 细胞反应进行癌症免疫治疗的努力。在这里,我们鉴定了在其他几种检查点分子中共表达 TIM-1 的 B 细胞子集,并且该比例随着肿瘤 dLN 中的肿瘤进展而增加。虽然在B细胞上表达的各种检查点分子在B细胞稳态和反应 中具有重要的内在作用,但只有B细胞中Havcr1的选择性缺失才极大地限制了肿瘤的生长。在癌症患者中, 细胞还共表达多个检查点分子,这表明该共表达簇可识别小鼠和人类之间保守的B细胞程序或激活状态。重要的是,在接受检查点阻断治疗的癌症患者中,这一亚群显着减少。然而,由于在人类肿瘤单细胞图谱中缺乏足够的 B 细胞,我们观察到的高 TIM-1 表达或免疫检查点表达 细胞与人类癌症中不良临床结果的关联需要进一步研究。我们的数据还表明, 细胞可能在 肿瘤内部局部起作用之前,在T细胞启动期间 起重要作用。分析癌症患者前哨 LN 中共表达检查点分子的 TIM-B 细胞将为人类肿瘤中该 细胞亚群的出现提供额外的见解。
Our analysis reveals a critical role for TIM-1 expression by B cells in promoting tumour growth, strengthening our initial finding . The induction of TIM-1 after BCR-driven activation suggests that TIM-1 does not define a separate B cell lineage (Extended Data Fig. 3h). While TIM-1 marks B cells that express IL-10, a key mediator of B cell regulatory function , loss of IL-10 from B cells had no effect on tumour growth control. Although TIM-1 is also expressed on other cell types , including T cells as we previously described , we did not observe changes in tumour burden in mice with conditional deletion of Havcr1 in T cells, suggesting a B-cell-specific role for TIM-1 in anti-tumour immunity.
我们的分析揭示了 B 细胞表达 TIM-1 在促进肿瘤生长中的关键作用,加强了我们的初步发现 。BCR 驱动激活后 TIM-1 的诱导表明 TIM-1 不定义单独的 B 细胞谱系(扩展数据图 3h)。虽然 TIM-1 标记表达 IL-10 的 B 细胞,IL-10 是 B 细胞调节功能 的关键介质,但 B 细胞中 IL-10 的缺失对肿瘤生长控制没有影响。虽然TIM-1也在其他细胞类型 上表达,包括我们之前描述 的T细胞,但我们没有观察到T细胞中Havcr1条件缺失的小鼠肿瘤负荷的变化,这表明TIM-1在抗肿瘤免疫中具有B细胞特异性作用。
Our comprehensive scRNA-seq profiling and functional analysis of the TME reveals two interconnected roles of TIM B cells: (1) inhibition of anti-tumour and cells, limiting the expansion of tumour-specific effector cells; and (2) promotion of regulatory FOXP3 cell induction. Notably, the enhanced effector and cytotoxic profiles of T cells from tumour-bearing mice were not accompanied by an increase in the fraction of stem-like TCF1 progenitors or a reduction in checkpoint receptor expression on cells, suggesting a selective promotion of T cell effector function by TIM-1-deficient B cells. TIM-1 expressed on B cells may curtail multiple B cell functions, including antigen presentation, expression of co-stimulatory ligands, inflammatory cytokine production and cytokine responsiveness, which all coordinately promote effector anti-tumour cell responses.
我们对TME的全面scRNA-seq分析和功能分析揭示了TIM B细胞的两个相互关联的作用:(1)抑制抗肿瘤 细胞,限制肿瘤特异性效应 细胞的扩增;(2)促进调节性FOXP3 细胞诱导。值得注意的是,来自 荷瘤小鼠的 T 细胞的效应子和细胞毒性特征增强并未伴随干细胞样 TCF1 祖细胞分数的增加或 细胞上检查点受体表达的减少,这表明 TIM-1 缺陷的 B 细胞选择性地促进 T 细胞效应功能。在 B 细胞上表达的 TIM-1 可能会削弱多种 B 细胞功能,包括抗原呈递、共刺激配体的表达、炎症细胞因子的产生和细胞因子反应性,这些都协调促进效应抗肿瘤 细胞反应。
Our results highlighted a role for TIM-1 in regulating intrinsic B cell activation and function. The humoral response to B16F10 melanoma, which has been shown to either promote tumour growth or clearance
我们的研究结果强调了 TIM-1 在调节内在 B 细胞活化和功能中的作用。对 B16F10 黑色素瘤的体液反应,已被证明可以促进肿瘤生长或清除

Article 

of tumour cells , was unaffected by Havcr1 deletion. However, B cells lacking TIM-1 exhibit an enhanced type I interferon response gene signature that has been described to lower the BCR activation threshold, and to promote cell antigen presentation and costimulatory function . Our data suggest that TIM-1 limits excessive cell activation, antigen presentation and cell activation-B cell responses that are associated with a positive outcome in multiple cancers-by fostering intratumoural B cell-T cell cooperation . Gene expression of lymphotoxin and cosl, associated with formation of tertiary lymphoid structures, was increased in B cells derived from Havcr tumours (data not shown), suggesting that the enhanced interferon response may promote the development of ectopic lymphoid follicles (tertiary lymphoid structures). This suggests a mechanism whereby the loss of TIM-1 on B cells affects T cell activation and expansion and is reminiscent of recent studies highlighting the cooperation of and cells in anticancer immunity, and the formation of tertiary lymphoid structures in effective checkpoint blockade immunotherapy in tumours . Particularly, our results set the stage for future investigations regarding the spatial organization of TIM cells in tissues and evaluating how this affects tumour growth or the response to ICB in human tumour samples.
的肿瘤细胞 ,不受 Havcr1 缺失的影响。然而,缺乏 TIM-1 的 B 细胞表现出增强的 I 型干扰素反应基因特征,该特征已被描述为降低 BCR 激活阈值,并促进 细胞抗原呈递和共刺激功能 。我们的数据表明,TIM-1 通过促进肿瘤内 B 细胞-T 细胞合作 ,限制了与多种癌症的阳性结果相关的过度 细胞活化、抗原呈递和 细胞活化-B 细胞反应。在源自 Havcr 肿瘤的 B 细胞中,与三级淋巴结构形成相关的淋巴毒素 cosl 的基因表达增加(数据未显示),表明增强的干扰素反应可能促进异位淋巴滤泡(三级淋巴结构)的发育。这表明 B 细胞上 TIM-1 的缺失会影响 T 细胞活化和扩增的机制,并让人想起最近的研究,这些研究强调了细胞在抗癌免疫中的合作 ,以及肿瘤中有效检查点阻断免疫治疗中三级淋巴结构的形成 。特别是,我们的研究结果为未来研究组织中TIM 细胞的空间组织奠定了基础,并评估了这如何影响肿瘤生长或对人类肿瘤样本中ICB的反应。
In summary, our study identifies TIM-1 as a critical checkpoint of B cell activation. TIM-1 impacts type 1 interferon responsiveness in B cells, limiting B cell activation, antigen-presentation and co-stimulation, thereby highlighting TIM-1 as a potential target by which B cell responses can be unleashed in promoting anti-tumour immunity. Identifying specific checkpoint molecules on B cells, such as TIM-1, may enable the harnessing of this second arm of the adaptive immune system, thereby improving therapeutic efficacy and broadening the application of immune checkpoint blockade in cancer immunotherapy.
总之,我们的研究将 TIM-1 确定为 B 细胞活化的关键检查点。TIM-1 影响 B 细胞中 1 型干扰素的反应性,限制 B 细胞活化、抗原呈递和共刺激,从而突出 TIM-1 作为 B 细胞反应的潜在靶点,通过该靶点可以释放 B 细胞反应以促进抗肿瘤免疫。鉴定B细胞上的特异性检查点分子,如TIM-1,可能能够利用适应性免疫系统的第二臂,从而提高治疗效果并扩大免疫检查点阻断在癌症免疫治疗中的应用。

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Methods 方法

Mice 小 鼠

(Prdm1 andB6.129P2(C)-Cd19tm1(cre)Cgn/J (Cd19 ), B6.Cg-Tg(TcraTcrb)425Cbn/J (OT II), B6.SJL-Ptprca Pepcb/ BoyJ (CD45.1) and B6.129S2-H2 /J (MHC II KO) mice were purchased from Jackson Laboratory and bred in our facility or used for experiments after at least 1 week of housing in our facility. CD45.1 and OT II mice were crossed to generate CD45.1-OT II mice. Havcr , Tigit , Havcr , and mice generated on the C57BL/6 background and described previously mice were provided by M. Shlomchik. Floxed mice were crossed to or mice in our facility. Havcr or Havcr and TIM- ) mice were gavaged with Tamoxifen in corn oil on the days indicated in the figure. While TIM-1 and animals had a similar tumour growth profile (not shown), we preferentially used the mice as controls as this strain has been generated as 'knock-in/knock-out', which partially impairs CD19 expression. Braf-Pten mice (B6.Cg-Braf (Tyr-cre/ERT2)13Bos/ BosJ) and ZsG mice (B6.Cg-Gt(ROSA)26Sortm6(CAG-ZsGreen1) Hze/J) were purchased from The Jackson Laboratory. Mice used in the inducible cancer model (Braf-Pten-ZsG) were crosses of Braf-Pten and ZsG bred in-house carrying the following genotype: , Pten (Tyr-cre/ERT2)13Bos and B6.Cg-Gt(ROSA)26Sortm 6(CAG-ZsGreen1)Hze/J or B6.Cg-Gt(ROSA)26Sortm6(CAG-ZsGreen1) , where the plus (+) indicates presence of the mutant/transgenic allele and a minus (-) indicates allele absence. and mice were included in equal proportions in each treatment group. Mice aged 4-10 weeks were used for experiments. All of the experiments were conducted in accordance with animal protocols approved by the Harvard Medical Area Standing Committee on Animals or BWH and MGHIACUC.
(Prdm1 和 B6.129P2(C)-Cd19tm1(cre)Cgn/J (Cd19) , B6.Cg-Tg(TcraTcrb)425Cbn/J (OT II), B6.SJL-Ptprca Pepcb/BoyJ (CD45.1) 和 B6.129S2-H2 /J (MHC II KO) 小鼠购自杰克逊实验室,并在我们的设施中繁殖或在我们的设施中饲养至少 1 周后用于实验。CD45.1 和 OT II 小鼠杂交产生 CD45.1-OT II 小鼠。Havcr 、Tigit 、Havcr 在C57BL/6背景上产生的小鼠和前面 描述的 小鼠由M. Shlomchik提供。Floxed小鼠与我们 设施中的小鼠杂交或 小鼠。Havcr 或Havcr 和TIM- )小鼠在图中所示的日期用 他莫昔芬在玉米油中 灌胃。虽然 TIM-1 动物具有相似的肿瘤生长特征(未显示),但我们优先使用 小鼠作为对照,因为该菌株是以“敲入/敲除”的形式产生的,这部分损害了 CD19 的表达。Braf-Pten小鼠(B6.Cg-Braf (Tyr-cre/ERT2)13Bos/ BosJ)和ZsG小鼠(B6.Cg-Gt(ROSA)26Sortm6(CAG-ZsGreen1) Hze/J)购自杰克逊实验室。用于诱导性癌症模型(Braf-Pten-ZsG)的小鼠是内部培育的Braf-Pten和ZsG的杂交,携带以下基因型: ,Pten (Tyr-cre/ERT2)13Bos 和B6。Cg-Gt(ROSA)26Sortm 6(CAG-ZsGreen1)Hze/J 或 B6。Cg-Gt(ROSA)26Sortm6(CAG-ZsGreen1) ,其中正 (+) 表示突变/转基因等位基因的存在,减 (-) 表示等位基因不存在。 小鼠以相等的比例纳入每个治疗组。 使用4-10周龄的小鼠进行实验。所有实验均按照哈佛医学区动物常务委员会或BWH和MGHIACUC批准的动物方案进行。

Cell lines 细胞系

B16F10 mouse melanoma and MC38 mouse colon adenocarcinoma cell lines were obtained from ATCC. B16-OVA cells (B16-F10 cells engineered to express OVA) were provided by K. Wucherpfennig. KP1.9 was derived from lung tumours of C57BL/6 KP mice and was provided by . Zippelius. All cells were cultured in a humidified, incubator at , and grown in RPMI or DMEM with fetal bovine serum (FBS) and penicillin-streptomycin (Life Technologies). All cell lines were tested and were negative for mycoplasma contamination.
B16F10小鼠黑色素瘤和MC38小鼠结肠腺癌细胞系均取自ATCC。B16-OVA 细胞(B16-F10 细胞经过工程改造以表达 OVA)由 K. Wucherpfennig 提供。KP1.9来源于C57BL/6 KP小鼠的肺肿瘤,由. 齐佩留斯。将所有细胞在加 湿 的培养箱中培养,并在 RPMI 或 DMEM 中用 胎牛血清 (FBS) 和 青霉素-链霉素 (Life Technologies) 生长。所有细胞系均经过检测,支原体污染呈阴性。

Tumour models 肿瘤模型

For primary tumour growth experiments, MC38 , B16F10 and B16-OVA cells were s.c. or intradermally injected into the right flank at a final volume of . Tumour growth was measured using digital callipers, and tumour sizes were recorded. For primary tumour cell dissemination experiments, B16F10 cells were injected i.v. into the tail vein, lungs were collected on day 14 and B16F10 colonies were counted using a dissecting microscope. For lung tumours (KP1.9, containing Kras and Trp53 mutations) cells were injected i.v. in I PBS to develop orthotopic tumours. Then, 4 weeks after injection, lungs were collected, embedded in paraffin, sectioned ( thickness) and stained with haematoxylin and eosin. Quantification of tumour area was calculated as the percentage of area occupied by the tumour among total lung tissue surface.
对于原发性肿瘤生长实验,将MC38 、B16F10 B16-OVA细胞皮下注射或皮内注射到右腹,最终体积为 。使用数字卡尺测量肿瘤生长,并记录肿瘤大小。对于原发性肿瘤细胞播散实验, 将 B16F10 细胞静脉注射到尾静脉中,在第 14 天收集肺,并使用解剖显微镜计数 B16F10 菌落。对于肺肿瘤(KP1.9,含有 Kras 和 Trp53 突变), 将细胞静脉注射到 I PBS 中以 发展原位肿瘤。然后,在注射后 4 周,收集肺,包埋在石蜡中,切片( 厚度)并用苏木精和伊红染色。肿瘤面积的量化计算为肿瘤占总肺组织表面所占面积的百分比。
Autochthonous mouse melanoma experiments. Tamoxifen induction was initiated when Braf-Pten mice were 4 weeks old. To induce tumours, l of -hydroxytamoxifen (Sigma-Aldrich, H6278) dissolved in ethanol was administered to the left ear on three consecutive days. Tumours were allowed to develop for 24-27 days, at which time visible pigmentation was present. The anti-TIM-1 (clone3B3) treatment schedule is indicated in the figure schematic. Mice were euthanized 3 weeks after initiation of treatment with anti-TIM-1 antibodies. Differences in darkening of the skin were measured by reflective colorimetry (Commission Internationale del'Eclairage [CIE] white-black colour axis) using the CR-400 Colorimeter (Minolta) calibrated to a white standard background calibration plate before each set of measurements. Photos were taken using a Nikon D750 DSLR camera with a Nikon Nikkor AF-S Micro lens. Photos were taken on manual with settings of shutter speed , aperture , ISO 320 . Ott-Lite Model L139AB lamps were used to create uniform lighting for photos. Facial tumour diameters were measured, and the number of tumour nodules was counted manually.
本土小鼠黑色素瘤实验。他莫昔芬诱导在Braf-Pten小鼠4周龄时开始。为了诱导肿瘤, 连续三天将溶解在乙醇中的 l -羟基他莫昔芬(Sigma-Aldrich,H6278)给予左耳。允许肿瘤发展 24-27 天,此时存在可见的色素沉着。抗TIM-1(clone3B3)治疗方案如图示意图所示。小鼠在开始抗TIM-1抗体治疗3周后被安乐死。在每组测量之前,使用CR-400色度计(美能达)通过反射比色法(Commission Internationale del'Eclairage [CIE] 白黑色轴)测量皮肤变黑的差异。照片是使用尼康 D750 数码单反相机和尼康尼克尔 AF-S 微 镜头拍摄的。照片是手动拍摄的,设置了快门速度 、光圈 、ISO 320。Ott-Lite L139AB 型灯用于为照片创造均匀的照明。测量面部肿瘤直径,手动计算肿瘤结节的数量。

In vivo treatments 体内治疗

In some experiments, mice were treated with of anti-TIM-1(3B3) and/or of anti-PD-1(RMP1-14), anti-MHC II (M5/114), anti-IFNAR1 (MAR1-5A3) or anti-IFNGR (GR-20) antibodies or of control immunoglobulin (rat IgG2a) intraperitoneally (i.p.) on days 7,9 and 11 after tumour implant. For in vivo B cell or pDC depletion, some groups of mice were injected i.v. or i.p. with of anti-CD20 (SA271G2) or anti-PDCA1 (927), respectively, or their isotype control (rat IgG2b) with schedules as indicated on the figures or figure legends.
在一些实验中,小鼠在肿瘤植入后第 7、9 和 11 天腹膜内 (ip) 腹膜内 (ip) 用抗 TIM-1(3B3) 和/或 抗 PD-1(RMP1-14)、抗 MHC II (M5/114)、抗 IFNAR1 (MAR1-5A3) 或抗 IFNGR (GR-20) 抗体 或对照免疫球蛋白(大鼠 IgG2a)腹膜内 (ip) 治疗 。对于体内B细胞或pDC耗竭,分别向一些小鼠组静脉注射或腹腔 注射抗CD20(SA271G2)或抗PDCA1(927)或其同型对照(大鼠IgG2b),其时间表如图或图例所示。

Preparation of cell suspensions
细胞悬液的制备

Single-cell suspensions were prepared from mouse , spleens or tumours as previously described . In brief, tumours were dissociated mechanically and digested with collagenase and DNase I for at . LNs and spleens were mechanically dissociated, digested with collagenase and DNase I for at , and passed through a cell strainer and lysed of red blood cells (using ACK buffer) then washed with cold PBS and centrifuged.
如前所述 ,从小鼠、脾脏或肿瘤中制备单细胞悬浮液 。简而言之,肿瘤被机械解离并用 胶原酶 DNase I 消化 at 。LN和脾脏机械解离,用 胶原酶 DNase I消化 并通过 细胞过滤器裂解红细胞(使用ACK缓冲液),然后用冷PBS洗涤并离心。

Multiplexing and droplet-based scRNA-seq, scBCR-seq and scTCR-seq
多重和基于液滴的scRNA-seq、scBCR-seq和scTCR-seq

For the B cell atlas analysis or the examination of and mice, viable leukocytes were sorted by FACS from tumours CD3e and CD19 cells, total CD45 cells), dLN and ndLN ( CD45 cells) at three different timepoints as shown in Extended Date Fig. 2a. For the analysis of TIM-1-expressing B cells, viable and cells derived from the , ndLN and spleen from C57Bl6/J mice were sorted by FACS. Cells were resuspended in PBS containing FCS and stained with oligo-tagged TotalSeq antibodies (BioLegend) for on ice. Cells were washed and pooled accordingly, centrifuged at for at and resuspended in PBS FCS. For the B cell temporal profiling, nine samples were combined into each channel of the Chromium system (10x Genomics): tumour, dLN and ndLN from three different timepoints (days 7,10 and 16) of one replicate. For the examination of and Haucr1 , six samples were combined into each channel: tumour, derived from one biological replicate of each genotype. For the analysis of TIM cells, cells derived from the LN were loaded in separate channels and the TIM and TIM-1 splenic cells were combined. For samples that did not include scBCR-seq and/or scTCR-seq and 5 ' feature barcoding, sorted cells were separated into droplet emulsions using the Chromium Single Cell3'Solution (v2) according to manufacturer's instructions (10x Genomics). Samples that included scBCR-seq and/ or scTCR-seq and 5 ' feature barcoding were separated into droplet emulsions using the Chromium Single Cell 5' V2 Solution, according to manufacturer's instructions (10x Genomics). scBCR-seq, scTCR-seq and 5 ' feature barcoding libraries were prepared according to the manufacturer's instructions (10x Genomics). scRNA-seq libraries ( 5 ' and ) and 5 ' feature barcoding libraries were sequenced on the Illumina NextSeq 550 using the 75 -cycle kit to a depth of 100 million reads per library.
对于B细胞图谱分析或 小鼠的检查 ,在三个不同的时间点,通过FACS从肿瘤 CD3e 和CD19 细胞, 总CD45 细胞),dLN和ndLN( CD45 细胞)中分选活白细胞,如扩展日期图2a所示。为了分析表达TIM-1的B细胞,通过FACS对C57Bl6 / J小鼠的活 细胞和 来源于ndLN和脾脏的细胞进行分 选。将细胞重悬于含有 FCS的PBS中,并用寡核苷酸标记的TotalSeq抗体(BioLegend) 在冰上染色。洗涤细胞并相应地汇集,在 at 离心并重悬于 PBS FCS 中。对于 B 细胞时间分析,将 9 个样本合并到 Chromium 系统 (10x Genomics) 的每个通道中:来自三个不同时间点(第 7、10 和 16 天)的肿瘤、dLN 和 ndLN。为了检查 和Haucr1 ,将六个样本合并到每个通道中:肿瘤, 源自每个基因型的一个生物学重复。对于 TIM 细胞的分析,将来源于 LN 的细胞加载到单独的通道中,并将 TIM 和 TIM-1 脾细胞组合在一起。对于不包括scBCR-seq和/或scTCR-seq以及5'特征条形码的样品,根据制造商的说明(10x Genomics),使用Chromium Single Cell3'Solution(v2)将分选的细胞分离成液滴乳液。 根据制造商的说明(10x Genomics),使用铬单细胞5' V2溶液将包含scBCR-seq和/或scTCR-seq和5'特征条形码的样品分离成液滴乳液。根据制造商的说明制备了scBCR-seq、scTCR-seq和5'特征条形码文库(10x Genomics)。使用75个循环试剂盒在Illumina NextSeq 550上对scRNA-seq文库(5'和 )和5'特征条形码文库进行测序,每个文库的深度为1亿次。

Pre-processing of the droplet-based scRNA-seq data and VDJ-seq time-course dataset
基于液滴的scRNA-seq数据和VDJ-seq时程数据集的预处理

Three sample sets were loaded, each sample set on two separate 10x channels. Sample sets included samples from tumours, dLNs and ndLNs from day 7, day 10 and day 16 after injection. Cells from a separate location and timepoint were hashed separately to be distinguishable in the analysis. Hashed scRNA-seq expression profiles were processed in Terra (https://app.terra.bio/) using the 'demultiplexing' workflow in scCloud/ Cumulus (v.0.8.0) , a wrapper for cellranger_mkfastq, cellranger_count (v.3.0.2) and cumulus_adt. The profiles were mapped to the prebuilt mouse reference mm10, CellRanger reference v.1.2.0 (Ensembl v84 gene annotation), specifying that the profiles were obtained with the chemistry. After mapping, cell profiles were processed to remove ambient RNA with CellBender through the Terra workflow 'run_cellbender_remove_background_gpu', with Docker image 'us.gcr.io/broad-dsde-methods/cellbender:latest' (as of 30 January 2020) with epochs , low-count-threshold = Null, expected-cells: 15000 (Timecourse_1, repl1&2), 3000 (Timecourse2_repl1&2) or 7000 (Timecourse3_repl1&2). Next, cell profiles were matched with antibody-derived tag counts to assign their identity, as samples from different timepoints or locations had been associated with unique combinations of two hashing antibodies. Cells with incorrect combinations of hashing antibodies were discarded from the analysis. Separately, reads from the VDJ libraries (BCR and TCR) were processed with Cumulus, using the prebuilt reference GRCm38_vdj_v3.1.0, part of CellRanger reference v.3.1.0, annotation built from Ensembl Mus_musculus.GRCm38.94.gtf. Filtered_contig annotations and filtered_contig. fasta from the two separate channels of each sample set (technical replicates) were merged before further processing.
加载三个样品组,每个样品组位于两个独立的 10x 通道上。样本组包括注射后第 7 天、第 10 天和第 16 天的肿瘤、dLN 和 ndLN 样本。对来自不同位置和时间点的细胞进行单独哈希处理,以便在分析中加以区分。在 Terra (https://app.terra.bio/) 中使用 scCloud/Cumulus (v.0.8.0) 中的“解复用”工作流程处理散列的 scRNA-seq 表达谱,scCloud/Cumulus 是 cellranger_mkfastq、cellranger_count (v.3.0.2) 和 cumulus_adt 的包装器。这些图谱被映射到预先构建的小鼠参考 mm10,CellRanger 参考 v.1.2.0(Ensembl v84 基因注释),指定图谱是用 化学方法获得的。定位后,通过 Terra 工作流程“run_cellbender_remove_background_gpu”处理细胞图谱以使用 CellBender 去除环境 RNA,Docker 映像“us.gcr.io/broad-dsde-methods/cellbender:latest”(截至 2020 年 1 月 30 日),具有 epochs , low-count-threshold = Null,预期细胞:15000 (Timecourse_1, repl1&2)、3000 (Timecourse2_repl1&2) 或 7000 (Timecourse3_repl1&2)。接下来,将细胞图谱与抗体衍生的标签计数相匹配,以分配其身份,因为来自不同时间点或位置的样本与两种散列抗体的独特组合相关联。具有不正确的散列抗体组合的细胞被丢弃在分析中。另外,使用预构建的参考 GRCm38_vdj_v3.1.0(CellRanger 参考 v.3.1.0 的一部分,从 Ensembl Mus_musculus.GRCm38.94.gtf 构建的注释,使用 Cumulus 处理来自 VDJ 库(BCR 和 TCR)的读取。 Filtered_contig注释和filtered_contig。 在进一步处理之前,将来自每个样品组的两个独立通道(技术重复)的 FASTA 合并。
RNA profiles were then processed with Scanpy (v.1.7.2). Cells were filtered out if their fraction of mitochondrial genes was or if they had counts or or genes. Genes detected in cell were also filtered out. Each cell transcriptome was scaled to sum to 10,000 , and expression values were further normalized with log1p, finally obtaining TP10K +1 values for each gene. Scrublet was run to detect doublets and only cells with a doublet score of were retained for the analysis. Highly variable genes were selected using the highly_variable _genes function in scanpy, with min_mean , max_mean , min_disp . Normalized values were then scaled to unit variance with a max_value for standard deviation equal to 10 . Dimensionality reduction with UMAP, using a -nearest neighbour graph ( ), was performed after batch correction using Harmony (using the harmony-pytorch wrapper) on biological replicates. Cells from the and ndLN at day 16 , in the third biological replicate, clustered separately from cells from the other two biological replicates even after batch correction and displayed higher expression of ribosomal genes and genes associated with oxidative stress. We removed these samples from the analysis. Furthermore, around 300 cells were identified as potential doublets from the expression of markers from different cell types (that is, Cd19/Lyz2, Cd19/Cd3e or Cd4/Cd8) and were excluded from the analysis. Pre-processing described above was repeated after removing these cells from the dataset. Finally, the dataset included 34,071 cells, 17,763 genes with 1,658 genes identified as highly variable genes.
然后用 Scanpy (v.1.7.2) 处理 RNA 图谱。如果细胞的线粒体基因比例是 ,或者它们有 计数或 基因,则细胞被过滤掉。在细胞中 检测到的基因也被过滤掉。将每个细胞转录组缩放至10,000,并用log1p进一步归一化表达值,最终得到 每个基因的TP10K+1 值。运行Scrublet 检测双峰,仅保留双峰评分为的 细胞进行分析。在scanpy中使用highly_variable _genes函数选择高度可变的基因, min_mean,max_mean,min_disp 。然后将归一化值缩放为单位方差,标准差的max_value等于 10。使用 最近邻图( )使用UMAP进行降维,使用Harmony (使用harmony-pytorch包装器)对生物重复进行批量校正后进行。在第16天,在第三次生物重复中,来自 ndLN的细胞与其他两个生物重复的细胞分开聚集,即使在批量校正后,也显示出更高的核糖体基因和与氧化应激相关的基因的表达。我们从分析中移除了这些样本。此外,从不同细胞类型(即 Cd19/Lyz2、Cd19/Cd3e 或 Cd4/Cd8)的标记物的表达中,将大约 300 个细胞鉴定为潜在的双峰,并被排除在分析之外。从数据集中删除这些单元后,重复上述预处理。最后,该数据集包括 34,071 个细胞、17,763 个基因,其中 1,658 个基因被确定为高度可变基因。
A B-cell-only embedding was obtained repeating the same processing described above starting from only single cells annotated as B cells in the full time-course dataset, with the exception of in sc.pp. neighbours.
获得仅 B 细胞嵌入,重复上述相同处理,从全时程数据集中仅注释为 B 细胞的单个细胞开始,sc.pp 除外。邻居。
Pre-processing of the droplet-based scRNA-seq data and VDJ-seq Havcr dataset
基于液滴的scRNA-seq数据和VDJ-seq Havcr 数据集的预处理
Hashed transcriptional profiles from three sample sets of and Havcr samples (each sample set including cells from tumour, dLN and ndLN from a Cd1 and Havcr replicate, each loaded onto a single channel) were processed in Terra with scCloud/Cumulus (v.0.8.0) as described for the time-course dataset above. After mapping, cell profiles were processed to remove ambient RNA with CellBender (latest version as of 30 January 2020) as described above, with expected-cells at 5,000 (replicate 1 ), 10,000 (replicate 2 ) and 1,000 (replicate 3). Cells with incorrect combinations of hashing antibodies were discarded from the analysis. Reads from the VDJ libraries (BCR and TCR) were processed using Cumulus, as described for the time-course dataset. RNA profiles were processed using Scanpy (v.1.7.2). Cells were filtered out if their fraction of mitochondrial genes was or if they had or counts, or or genes. Genes detected in less than three cells were also filtered out. Each cell profile was scaled to sum to 10,000 and gene expression values were further normalized with , finally obtaining values for each gene. Scruble was run to detect doublets and only cells with a doublet score of were retained for the analysis. Highly variable genes were selected using the highly_variable_genes function in scanpy, with min_mean , max_mean , min_disp run in each individual replicate. Only genes identified as variable in at least two batches were retained. Normalized values were then scaled to unit variance with a max_value for standard deviation equal to 10 . Dimensionality reduction with UMAP, using a -nearest neighbours graph ( ) was performed after batch correction with Harmony (using the harmony-pytorch wrapper) on biological replicates. Finally, the dataset included 11,884 cells, 15,337 genes with 1,668 genes identified as highly variable genes.
来自三 组 Havcr 样品(每组样本包括来自肿瘤的细胞、来自 Cd1 和 Havcr 重复的 dLN 和 ndLN,每个样本都加载到单个 通道上)的哈希转录谱在 Terra 中使用 scCloud/Cumulus (v.0.8.0) 进行处理,如上所述时程数据集。如上所述,图谱后,使用CellBender (截至2020年1月30日的最新版本)处理细胞图谱以去除环境RNA,预期细胞为5,000(重复1),10,000(重复2)和1,000(重复3)。具有不正确的散列抗体组合的细胞被丢弃在分析中。从 VDJ 库(BCR 和 TCR)读取的数据使用 Cumulus 进行处理,如时程数据集所述。使用 Scanpy (v.1.7.2) 处理 RNA 图谱。如果细胞的线粒体基因比例是 ,或者它们有 计数,或 或基因 ,则细胞被过滤掉。在不到三个细胞中检测到的基因也被过滤掉。将每个细胞谱缩放到总和为 10,000,并进一步归 一化基因表达值,最终获得 每个基因的值。运行 Scruble 以检测双峰,并且仅保留双峰评分为 的 细胞进行分析。在scanpy中使用highly_variable_genes函数选择高度可变的基因,每个重复中都有min_mean 、max_mean 、min_disp 。仅保留了至少两批中被鉴定为可变基因的基因。然后将归一化值缩放为单位方差,标准差的max_value等于 10。 在使用 Harmony 对生物重复进行批量校正后,使用 -最近邻图 ( ) 使用 UMAP 进行降维。最后,该数据集包括 11,884 个细胞、15,337 个基因,其中 1,668 个基因被确定为高度可变基因。
A T-cell-only embedding was obtained repeating the same process described above starting only from single-cell profiles annotated as T cells in the full Cd19 and Havcr dataset with the exception of the harmonization.
获得仅 T 细胞的包埋,重复上述相同过程,仅从完整 Cd19 和 Havcr 数据集中注释为 T 细胞的单细胞图谱开始,但协调除外。

Pre-processing of the droplet-based scRNA-seq TIM1 TIM1
基于液滴的scRNA-seq TIM1 TIM1 的预处理

dataset
scRNA-seq profiles from B cells from dLNs and ndLNs, sorted for TIM-1 surface presence and processed in four separate channels were processed in Terra scCloud/Cumulus (v.0.10.0) as described above, specifying chemistry. After mapping, cell profiles were processed to remove ambient RNA using CellBender (latest version as of 12 February 2020) as described above, with expected cells at 2,500 (dLN_T1p), 2,500 (nLN_T1n) and 700 (nLN/nLN_T1p).
如上所述,在Terra scCloud/Cumulus(v.0.10.0)中处理来自dLN和ndLN的B细胞的scRNA-seq图谱,分选TIM-1表面存在并在四个单独的 通道中处理,并指定 化学成分。如上所述,图谱绘制后,使用CellBender (截至2020年2月12日的最新版本)处理细胞图谱以去除环境RNA,预期细胞数为2,500(dLN_T1p)、2,500(nLN_T1n)和700(nLN/nLN_T1p)。
scRNA-Seq profiles from B cells from the spleens of tumour-bearing mice, sorted for TIM-1 surface presence and hashed together, were also processed in Terra with scCloud/Cumulus(v.0.8.0) as described above, specifying that the profiles were obtained with the chemistry. After mapping, cell profiles were processed to remove ambient RNA with CellBender (latest version as of ) as described above, with expected cells: 12000 . scRNA-seq profiles were then processed with Scanpy (v.1.7.2). Cells were filtered out if their fraction of mitochondrial genes was or if they had or counts, or or genes. Genes detected in cell were also filtered out. Each cell profile was scaled to sum to 10,000 and gene expression values were further normalized with , finally obtaining values for each gene. Scrublet was run to detect doublets and only cells with a doublet score of were retained for the analysis. Highly variable genes were selected using the highly_variable_genes function in Scanpy, with min_mean , max_mean , min_disp . Normalized values were then scaled to unit variance with a max_value for a standard deviation equal to 10 . Dimensionality reduction with UMAP, using a -nearest neighbours graph ( ), was performed after regressing out with Harmony (using the harmony-pytorch wrapper), the tissue of origin (dLN, ndLN, spleen) and differences in sample processing (hashed versus non-hashed samples).
如上所述,在Terra中,使用scCloud/Cumulus(v.0.8.0)处理来自荷瘤小鼠脾脏的B细胞的scRNA-Seq图谱,分选TIM-1表面存在并散列在一起,并指定图谱是用 化学方法获得的。作图后,如上所述,使用CellBender (最新版本 )处理细胞图谱以去除环境RNA,预期细胞数:12000。然后用 Scanpy (v.1.7.2) 处理 scRNA-seq 图谱。如果细胞的线粒体基因比例是 ,或者它们有 计数,或 或基因 ,则细胞被过滤掉。在细胞中 检测到的基因也被过滤掉。将每个细胞谱缩放到总和为 10,000,并进一步归 一化基因表达值,最终获得 每个基因的值。运行Scrublet 检测双峰,仅保留双峰评分为的 细胞进行分析。在Scanpy中使用highly_variable_genes函数选择高度可变的基因,其中min_mean 、max_mean 、min_disp 。然后将归一化值缩放为单位方差,标准差等于 10 的max_value。使用 最近邻图( )使用UMAP进行降维,使用Harmony (使用harmony-pytorch包装器),起源组织(dLN,ndLN,脾脏)和样品处理差异(散列与非散列样品)进行回归。
A small number ( ) of possible contaminant cells expressing Lyz2 and Timd 4 were excluded from the analysis, and the dataset was reprocessed as described above.
从分析中排除了少量 表达 Lyz2 和 Timd 4 的可能污染细胞,并如上所述对数据集进行了重新处理。
Finally, the dataset included 13,067 cells, 15,284 genes with 2,215 genes identified as highly variable genes.
最后,该数据集包括 13,067 个细胞、15,284 个基因,其中 2,215 个基因被确定为高度可变基因。

Scoring cells using signature gene sets
使用特征基因集对细胞进行评分

To calculate a score for a specific set of genes in a given cell, cell lineage signatures in Supplementary Table 1, signatures obtained from MSigDB or other sources as indicated in the figures, we computed scores using scanpy (tl.score genes). The signature score for each cell was then defined as the average expression of a set of genes subtracted with the average expression of a reference set of genes randomly sampled from the gene pool for each binned expression value.
为了计算给定细胞中一组特定基因的分数、 补充表 1 中的细胞谱系特征、从 MSigDB 获得的特征或图中所示的其他来源,我们使用 scanpy (tl.score genes) 计算分数。然后将每个细胞的特征分数定义为一组基因的平均表达减去从基因库中随机抽样的参考基因集的平均表达,以获得每个分箱表达值。

Differentially expressed genes in scRNA-seq
scRNA-seq中差异表达的基因

Differential expression analysis was performed using two-sided -tests or Wilcoxon rank-sum tests as indicated using scanpy's rank_genes_ groups function. Subsequently, genes were retained if the fraction of expressing cells within the considered group was , the fraction of expressing cells in the other group was and the fold change between groups was at least 2 (Extended Data Fig. 3h) or 1 (Figs. 4 f and 5a). We considered genes with a Benjamini-Hochberg FDR of as significant in Extended Data Fig. 3h. The ranked gene lists for cluster 3 cells from the time-course dataset and cells derived from tumour, dLN and ndLN are shown in Supplementary Tables 2 and 5.
使用双侧 检验或 Wilcoxon 秩和检验进行差异表达分析,如使用 scanpy 的rank_genes_组函数所示。随后,如果所考虑组内表达细胞的比例为 ,另一组中表达细胞的比例为 ,并且组间的倍数变化至少为 2 (扩展数据图 3h) 或 1 (图 4 f 和 5a),则保留基因。我们考虑了 Benjamini-Hochberg FDR 在扩展数据图 3h 中同样重要的基因。来自时程数据集的簇 3 细胞和 源自肿瘤、dLN 和 ndLN 的细胞的排名基因列表显示在补充表 2 和 5 中。

Surface marker prediction using COMET
使用COMET进行表面标记预测

was applied to predict cell surface markers for clusters of interest. The mouse surfaceome gene list was used, and other parameters were set to default.
用于预测目标簇的细胞表面标记物。使用小鼠表面组 基因列表,并将其他参数设置为默认值。

Analysis of scTCR-seq data
scTCR-seq数据分析

TCR sequences for each single T cell were assembled using the CellRanger vdj pipeline (v.3.1.0) as described above, leading to the identification of CDR3 sequences and the rearranged TCR gene. TCR repertoire analysis was performed using Scirpy (v.4.2). TCR diversity and TCR clonal size were estimated using scirpy.tl.alpha_diversity and scirpy. pl.clonal_expansion (performing the normalization), respectively. V(D) J gene usage was estimated with scirpy.pl.vdj_usage.
如上所述,使用CellRanger vdj管道(v.3.1.0)组装每个T细胞的TCR序列,从而鉴定出CDR3序列和重排的TCR基因。使用 Scirpy (v.4.2) 进行 TCR 库分析。使用scirpy.tl.alpha_diversity和scirpy估计TCR多样性和TCR克隆大小。pl.clonal_expansion(执行规范化)。V(D) J 基因使用量用 scirpy.pl.vdj_usage 估计。

Analysis of scBCR-seq data
scBCR-seq数据分析

sequences for each single cell were assembled using the CellRanger vdj pipeline (v.3.1.0) as described above. V, D,J chain assignment and clonal group definition was performed using Immcantation , run using the provided Docker container image (v.4.1.0), according to the recommendations for datasets from the tutorial, specifying species «mouse» and a conservative distance threshold « 0.1 ».
如上所述,使用CellRanger vdj管道(v.3.1.0)组装每个单个 细胞的序列。V、D、J 链分配和克隆组定义使用 Immcantation 执行,使用提供的 Docker 容器映像 (v.4.1.0) 运行,根据教程中对 数据集的建议,指定物种“小鼠”和保守距离阈值“0.1”。

Analysis of published scRNA-seq studies of human cancer
分析已发表的人类癌症scRNA-seq研究

Processed scRNA-seq data were obtained from previously published, publicly available datasets and are shown in Supplementary Table 4. These datasets included tumour-derived leukocytes isolated before and/or after ICB, from both responding and non-responding patients. We preferentially included count data that had been generated using plate-based platform Smart-seq2, for a higher sequencing depth and better capture of HAVCR1 transcripts. However, owing to the limited availability of Smart-seq2-generated datasets with a design relevant to the current study, we also selected datasets that had been generated using droplet-based platforms (e.g. 10x Genomics Chromium). For downstream analysis, datasets from these respective protocols were analysed separately. All datasets were used without any change to processing, using the same expression values and cell annotations as originally reported. Moreover, we obtained published and processed scRNA-seq data from ICB responders or non-responders from the Gene Expression Omnibus (GEO:GSE120575). B cells and plasma cells were identified on the basis of the expression of , SDC1,JCHAIN and PRDM1, then subclustered and processed as described above. For some analysis, the human orthologues of selected genes or Havcr1 cell signature gene were determined with the Ensembl project's Biomart database (Ensembl v.101). The signature score was defined as the relative average expression of the orthologue genes of the signature of tumour-infiltrating cells, response to type I IFN (GO: 0034340) and GO antigen processing and presentation of peptide antigen (GO: 0048002) as computed using scanpy (tl.score_genes). The cell density of the depicted categories was shown by sc.tl.embedding_density (Extended Data Figs. 4j and 9k).
处理后的scRNA-seq数据来自先前发表的公开数据集,如补充表4所示。这些数据集包括在 ICB 之前和/或之后从有反应和无反应的患者中分离出的肿瘤来源的白细胞。我们优先纳入使用基于孔板的平台Smart-seq2生成的计数数据,以获得更高的测序深度和更好的HAVCR1转录本捕获。然而,由于 Smart-seq2 生成的数据集的可用性有限,其设计与当前研究相关,我们还选择了使用基于液滴的平台(例如 10x Genomics Chromium)生成的数据集。对于下游分析,分别分析了这些协议的数据集。所有数据集均未对处理进行任何更改,使用与最初报告的相同的表达式值和单元格注释。此外,我们从基因表达综合数据库(GEO:GSE120575)的ICB应答者或非应答者 那里获得了已发表和处理的scRNA-seq数据。根据 、SDC1、JCHAIN和PRDM1的表达鉴定B细胞和浆细胞,然后按上述方式进行亚簇和处理。对于一些分析,使用Ensembl项目的Biomart数据库(Ensembl v.101)确定所选基因或Havcr1 细胞特征基因的人类直系同源物。特征评分定义为肿瘤浸润 细胞特征的直系同源基因的相对平均表达、 对 I 型 IFN (GO: 0034340) 和 GO 抗原加工的反应以及肽抗原 (GO: 0048002) 的呈递,使用 scanpy (tl.score_genes) 计算。所描绘类别的细胞密度由 sc.tl 表示。embedding_density(扩展数据图4j和9k)。

Merging, integrating and clustering of Smart-seq2 datasets
Smart-seq2 数据集的合并、集成和聚类

For eachSmart-seq 2 scRNA-seq dataset, transcripts per million (TPM) count tables and metadata (including quality control metrics, cell type assignment, ICB treatment status) were obtained directly from the original publications or through the Single Cell Portal from the Broad Institute (https://singlecell.broadinstitute.org/single_cell). B cells were selected from each dataset, with selection based on the original annotation as provided by the authors. Although we did not change the pre-processing of the cells, we did remove genes that were expressed in less than two cells to exclude artifacts and redundantly expressed genes. Similarly, mitochondrial and ribosomal protein transcripts marked with the prefix 'MT-' and 'RP-' were discarded.
对于每个 Smart-seq 2 scRNA-seq 数据集,每百万转录本 (TPM) 计数表和元数据(包括质量控制指标、细胞类型分配、ICB 处理状态)直接从原始出版物或通过 Broad Institute 的单细胞门户 (https://singlecell.broadinstitute.org/single_cell) 获得。从每个数据集中选择B细胞,并根据作者提供的原始注释进行选择。虽然我们没有改变细胞的预处理,但我们确实去除了在少于两个细胞中表达的基因,以排除伪影和冗余表达的基因。同样,标有前缀“MT-”和“RP-”的线粒体和核糖体蛋白转录本被丢弃。
The individual datasets were merged using 'AnnData.concatenate()' and the normalized counts were subsequently log1ptransformed. Highly variable genes among the concatenated dataset were identified using scanpy's highly_variable_genes() function, with the mean-normalized expression set between 0.5 and 3 , and a quantile-normalized variance of . Normalized values were scaled to unit variance with a maximum standard deviation set to 10 . We ran principal component analysis of the highly variable genes and subsequently used harmony_integrate() from Harmony to correct for batch effects between the different datasets. We next computed a -nearest neighbour graph, with the number of neighbours set to 20 , followed by dimensionality reduction using UMAP. Cells were clustered using the Leiden algorithm, an improved version of the Louvain algorithm, with a clustering resolution of 1.2. The default values were used for the remaining parameters. The resulting dataset included 2,615 cells, 10,687 genes with 1,618 genes identified as highly variable genes, divided among six clusters.
使用“AnnData.concatenate()”合并各个数据集,随后对归一化计数进行log1ptransformed。使用 scanpy 的 highly_variable_genes() 函数鉴定串联数据集中的高变异基因,均值归一化表达式设置在 0.5 和 3 之间,分位数归一化方差为 。归一化值按比例缩放为单位方差,最大标准差设置为 10。我们对高度可变的基因进行了主成分分析,随后使用Harmony的harmony_integrate()来校正不同数据集之间的批次效应。接下来,我们计算了一个 最近邻图,将邻域数设置为 20,然后使用 UMAP 进行降维。使用 Leiden 算法对细胞进行聚类,Leiden 算法是 Louvain 算法的改进版本,聚类分辨率为 1.2。其余参数使用默认值。由此产生的数据集包括 2,615 个细胞、10,687 个基因和 1,618 个基因被确定为高度可变基因,分为六个簇。

Merging, integrating and clustering of data from droplet-based platforms (10x Genomics Chromium)
合并、集成和聚类来自基于 droplet 的平台的数据 (10x Genomics Chromium)

For each 10x scRNA-seq dataset, gene transcript count tables and metadata (including quality control metrics, cell type assignment, ICB treatment status) were obtained directly from the original publications or through the Single Cell Portal from the Broad Institute (https:// singlecell.broadinstitute.org/single_cell). B cells were selected from each dataset, with selection based on the original annotation as provided by the authors. Although we did not change the pre-processing of the cells, we did remove genes that were expressed in less than two cells to exclude artifacts and redundantly expressed genes. Similarly, mitochondrial and ribosomal protein transcripts marked with the prefix 'MT-' and 'RP-' were discarded.
对于每个 10x scRNA-seq 数据集,基因转录本计数表和元数据(包括质量控制指标、细胞类型分配、ICB 治疗状态)直接从原始出版物或通过 Broad Institute 的单细胞门户获得 (https:// singlecell.broadinstitute.org/single_cell)。从每个数据集中选择B细胞,并根据作者提供的原始注释进行选择。虽然我们没有改变细胞的预处理,但我们确实去除了在少于两个细胞中表达的基因,以排除伪影和冗余表达的基因。同样,标有前缀“MT-”和“RP-”的线粒体和核糖体蛋白转录本被丢弃。
The individual datasets were merged using 'AnnData.concatenate()'. Expression values were normalized to sum 10,000 reads per cell and the normalized countswere subsequently log1p-transformed. Highlyvariable genes among the concatenated dataset were identified using scanpy's highly_variable genes() function, with the mean-normalized expression set between 0.00125 and 3 , and a quantile-normalized variance of . Normalized values werescaled to unit variance with a maximum standard deviation set to 10 . We next ran principal component analysis of the highly variable genes and used harmony_integrate() from Harmony to correct for batch effects between the different datasets. We next computed a -nearest neighbour graph, with the number of neighbours set to 25 , followed by dimensionality reduction using UMAP. Cells were clustered
使用“AnnData.concatenate()”合并各个数据集。将表达值归一化为每个细胞 10,000 个读段的总和,随后对归一化计数进行 log1p 转换。使用 scanpy 的 highly_variable genes() 函数鉴定了级联数据集中的高变异基因,均值归一化表达式设置在 0.00125 和 3 之间,分位数归一化方差为 。将归一化值缩放为单位方差,最大标准差设置为 10。接下来,我们对高度可变的基因进行了主成分分析,并使用 Harmony 的 harmony_integrate() 来校正不同数据集之间的批次效应。接下来,我们计算了一个 -最近邻图,将邻域数设置为 25,然后使用 UMAP 进行降维。细胞被聚类

using the Leiden algorithm, an improved version of the Louvain algorithm, with the resolution of clustering of 1.2. The default values were used for the remaining parameters. The resulting dataset included 110,064 cells, 16,313 genes with 2,008 genes identified as highly variable genes.
使用 Leiden 算法,这是 Louvain 算法的改进版本,聚类分辨率为 1.2。其余参数使用默认值。由此产生的数据集包括 110,064 个细胞、16,313 个基因和 2,008 个基因被确定为高度可变基因。

Differential abundance analysis
差异丰度分析

To explore the differential abundance of each cluster between the treatment-naive cohort and post-treatment group, the MiloR R package was used.Specifically, we used a predesigned pipeline that allowed interoperability between the version of Milo with Python-compatible anndata objects according to the following code depicting by the authors of an algorithm available at GitHub (https://github.com/ MarioniLab/milo_analysis_2020/blob/main/notebooks/milo_in_python. ipynb). Before running the pipeline, we selected only cells derived from patients with cells from both the before- and after-treatment conditions. One dataset did not contain both timepoints and was excluded from further differential abundance analysis. Likewise, cells of which the timing of acquisition was unclear were discarded. The remaining cells were used to recompute a -nearest neighbour graph, with the number of nearest neighbours set to 10 , and the number of reduced dimensions set to 40 . Subsequently, cell neighbourhoods were computed using miloR's makeNhoods() function, with of the cells, the value of set to 5 and a number of reduced dimensions of 30. For each neighbourhood, the fraction of cells derived from the pre-treatment and post-treatment was established. We then used 'calcNhoodDistance()' to calculate the distance between neighbourhoods, followed by differential abundance testing within each neighbourhood using the testNhoods() function. Differentially abundant neighbourhoods (classified as having an FDR-corrected value of lower than 0.05 ) were assigned one of the previously established cell subtypes when of the cells in the neighbourhood belonged to this specific subset. Neighbourhoods where of the cells belonged to a single cell subset were annotated as mixed.
为了探索初治队列和治疗后组之间每个集群的差异丰度,使用了 MiloR R 包。具体来说,我们使用了一个预先设计的管道,该管道允许 Milo 版本与 Python 兼容的 anndata 对象之间的互操作性,该代码由 GitHub 上可用的算法(https://github.com/ MarioniLab/milo_analysis_2020/blob/main/notebooks/milo_in_python. ipynb)的作者描述。在运行管道之前,我们仅选择了来自患者的细胞,以及来自治疗前和治疗后条件的细胞。一个数据集 不包含两个时间点,因此被排除在进一步的差异丰度分析之外。同样,采集时间不明确的细胞也被丢弃。剩余的像元用于重新计算 最近邻图,最近邻数设置为 10,缩减维数设置为 40。随后,使用 miloR 的 makeNhoods() 函数计算细胞邻域, 其中细胞 的 set 值为 5,一些简化维数为 30。对于每个邻域,确定来自处理前和处理后的细胞比例。然后,我们使用“calcNhoodDistance()”来计算邻域之间的距离,然后使用testNhoods()函数在每个邻域内进行差异丰度测试。当邻域中的细胞属于该特定子集时 ,差异丰度邻域(分类为FDR校正 值低于0.05)被分配为先前建立 的细胞亚型之一。 细胞属于单个 细胞子集的邻域 被注释为混合。

Bulk RNA-seq 批量RNA-seq

A total of 1,000 live PAN-B cells (CD45 CD19 cells) or TIM-1 versus TIM-1 B cells were double-sorted by FACS and immediately lysed in TCL buffer (QIAGEN) supplemented with -mercaptoethanol (Sigma-Aldrich). Full-length RNA-seq libraries were prepared according to a modified Smart-seq 2 protocol as previously described . cDNA concentration was measured using the Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific) and normalized to . cDNA libraries were prepared using the NexteraXT DNA Library Preparation kit (Illumina). The final libraries were confirmed to have a size of using a Bioanalyzer (Agilent). Before sequencing, the uniquely barcoded libraries were pooled, normalized to and denatured using . Flow cell cluster amplification and sequencing were performed according to the manufacturer's protocols by the paired-end Illumina sequencing ( ) using the 75 cycle NextSeq 500 high output V2 kit (Illumina).
共 1,000 个活 PAN-B 细胞(CD45、 CD19 细胞)或 TIM-1 与 TIM-1 B 细胞通过 FACS 进行双重分选,并立即在补充有 巯基乙醇 (Sigma-Aldrich) 的 TCL 缓冲液 (QIAGEN) 中裂解。如前所述,根据改进的 Smart-seq 2 方案 制备全长 RNA-seq 文库 。使用 Quant-iT PicoGreen dsDNA 检测试剂盒 (Thermo Fisher Scientific) 测量 cDNA 浓度,并将其归一化为 .使用NexteraXT DNA文库制备试剂盒(Illumina)制备cDNA文库。最终文库的大小被确认 为使用生物分析仪(安捷伦)。在测序之前,将唯一条形码文库合并、归一化为 并使用 .使用75个周期的NextSeq 500高输出V2试剂盒(Illumina ),通过双端Illumina测序( )根据制造商的方案进行流通池簇扩增和测序。

Bulk RNA-seq data analysis
批量RNA-seq数据分析

Reads were extracted with Illumina's Bcl2Fastq, run through the KCO (https://usegalaxy.org/) Galaxy server . Reads were mapped and expression of genes was quantified using rsem-1.2.8 , run from the KCO Galaxy server as above using as annotation 'mm10_ucsc _genomestudio genes'.Expression was quantified as gene-level TPMs (transcripts per kilobase million). Differential expression analysis and pathway enrichment analysis (Fig. 11 and Extended Data Fig. 1d-g) were performed using iDEP (v.0.92) and DESeq2 (v.1.28.1), respectively. The list of differentially expressed genes between TIM- and TIM- B cells derived from the dLN is provided in Supplementary Table 3.
使用Illumina的Bcl2Fastq提取读数,通过KCO(https://usegalaxy.org/)Galaxy服务器 运行。使用rsem-1.2.8对reads进行定位并量化基因的表达 ,如上所述,使用注释“mm10_ucsc _genomestudio基因”从KCO银河服务器运行。表达量化为基因水平 TPM(每千碱基百万转录本)。分别使用 iDEP (v.0.92) 和 DESeq2 (v.1.28.1) 进行差异表达分析和通路富集分析(图 11 和扩展数据图 1d-g)。补充表 3 中提供了源自 dLN 的 TIM 和 TIM-B 细胞之间差异表达的基因列表。

GSEA GSEA公司

GSEA was performed for each cell subset based on scores in pre-ranked list mode with 1,000 permutations (nominal value cut-off of ).
根据具有 1,000 个排列的预排名列表模式下的分数对每个单元格子集执行 GSEA (标称 值截止值为 )。

Flow cytometry and FACS
流式细胞术和流式细胞仪

Single-cell suspensions were prepared from mouse LNs, spleens or tumours as described above. Live/dead cell discrimination was performed using Live/Dead Fixable viability dye e506 (eBioscience). Surface antibodies used in this study were as follows: CD45 (30-F11), TCRb (H57-597), CD3e (17A2), TCRү8, CD8a (53-6.7), CD4 (RM4-5), CD19 (6D5), B220, CD138 (281-2), GL-7 (GL-7), FAS (Jo2), IgD (11-26c.2a), IgM (RMM-1), CD21 (CR2/CR1), CD43 (S7), CD93 (AA4.1), CD23(B3B4), TIM-1(RMT1-4), Ly6C (HK1.4), Ly6G (1A8), CD11c (N418), CD11b (M1/70), CD64 (X54-5/7.1), CD11c (N418), PD-1(RMP1-30), TIGIT (1G9), LAG3 (C9B7W), TIM-3 (5D12), CD39 (5F2), CD73 (TY/11.8), CD107a (1D4B), NK1.1 (PK136), MHC I (H-2K (I-A/E, M5/114.15.2), CD80 (16-10A1), CD86 (A17199A), ICOSL (HK5.3), CD40 (3/23), CD25 (3C7), IFNAR1 (MAR1-5A3). The following cell populations were identified on the basis of cell marker expression: cells cells ( ), cells (CD45 , natural killer ( ) cells , NKT cells (CD45 NK1.1 TCR ), PMN (CD45 CD11b Ly6G ), DCs CD11c , macrophages CD11b cells .
如上所述,从小鼠LN、脾脏或肿瘤制备单细胞悬液。使用活/死可固定活性染料 e506 (eBioscience) 进行活/死细胞鉴别。本研究中使用的表面抗体如下:CD45 (30-F11)、TCRb (H57-597)、CD3e (17A2)、TCRү8、CD8a (53-6.7)、CD4 (RM4-5)、CD19 (6D5)、B220、CD138 (281-2)、GL-7 (GL-7)、FAS (Jo2)、IgD (11-26c.2a)、IgM (RMM-1)、CD21 (CR2/CR1)、CD43 (S7)、CD93 (AA4.1)、CD23(B3B4)、TIM-1(RMT1-4)、Ly6C (HK1.4)、Ly6G (1A8)、CD11c (N418)、CD11b (M1/70)、CD64 (X54-5/7.1)、 CD11c (N418)、PD-1(RMP1-30)、TIGIT (1G9)、LAG3 (C9B7W)、TIM-3 (5D12)、CD39 (5F2)、CD73 (TY/11.8)、CD107a (1D4B)、NK1.1 (PK136)、MHC I (H-2K (I-A/E, M5/114.15.2)、CD80 (16-10A1)、CD86 (A17199A)、ICOSL (HK5.3)、CD40 (3/23)、CD25 (3C7)、IFNAR1 (MAR1-5A3)。根据细胞标志物表达鉴定了以下细胞群:细胞( )、细胞 (CD45 、自然杀伤 ( ) 细胞 、NKT 细胞 (CD45 NK1.1 TCR )、PMN (CD45 CD11b Ly6G )、DC CD11c 、巨噬细胞 CD11b 细胞
For intracytoplasmic cytokine staining, cells were stimulated with phorbol myristate acetate and ionomycin . Permeabilized cells were then stained with antibodies against IL-2 (JES6-5H4), TNF (MP6-XT22) and IFN (XMG1.2). For FOXP3, EOMES (W17001A), TBET (4B10), HELIOS (22F6), Ki-67 (16A8), granzyme B (2C5/F5) and perforin (S16009A) staining were performed using the FoxP3/Transcription Factor Staining Buffer Set (eBioscience). To assess OVA-specific CD8 cells, TILs were stained with dextramers (Immudex) and then stained with surface antibodies. To determine TCF1 protein levels, TILs were stained with surface antibodies then fixed and permeabilized with eBioscience Transcription Factor Staining Buffer Set. Cells were then stained with anti-TCF1 antibodies (C63D9) followed by fluorescently tagged anti-rabbit IgG (Cell Signaling). All data were collected on the BD Symphony A5 (BD Biosciences) system and analysed using FlowJo (Tree Star).
对于胞浆内细胞因子染色,用佛波肉豆蔻酸酯 乙酸酯和离子霉素 刺激细胞。然后用针对 IL-2 (JES6-5H4)、TNF (MP6-XT22) 和 IFN (XMG1.2) 的抗体对透化细胞进行染色。对于 FOXP3,使用 FoxP3/转录因子染色缓冲液组 (eBioscience) 进行 EOMES (W17001A)、TBET (4B10)、HELIOS (22F6)、Ki-67 (16A8)、颗粒酶 B (2C5/F5) 和穿孔素 (S16009A) 染色。为了评估 OVA 特异性 CD8 细胞,TIL 用 右聚糖 (Immudex) 染色,然后用表面抗体染色。为了测定 TCF1 蛋白水平,用表面抗体染色 TIL,然后用 eBioscience 转录因子染色缓冲液组固定和透化。然后用抗 TCF1 抗体 (C63D9) 染色细胞,然后用荧光标记的抗兔 IgG(细胞信号传导)染色。所有数据均在BD Symphony A5(BD Biosciences)系统上收集,并使用FlowJo(Tree Star)进行分析。

In vitro cell cultures
体外 细胞培养

FACS-sorted total B cells from Cd19 , Havcr mice or TIM-1 and TIM-1 B cells from C57BI/6J mice were labelled with CTV and plated in 96-well U-bottom plates in the presence or absence of LPS ( , InvivoGen), fragment donkey anti-mouse (anti-IgM) ( ,Jackson ImmunoResearch) and/or anti-CD40 antibodies ( , BioLegend) for in complete medium with or without addition of IFN , IFN or IFN , R&D systems). Cells were then analysed by flow cytometry.
将来自 Cd19 、Havcr 小鼠的 FACS 分选的总 B 细胞或来自 C57BI/6J 小鼠的 TIM-1 和 TIM-1 B 细胞用 CTV 标记,并在存在或不存在 LPS ( 、InvivoGen)、 片段驴抗小鼠 (抗 IgM)( Jackson ImmunoResearch)和/或抗 CD40 抗体( ,BioLegend) 的情况下接种在 96 孔 U 底板中,用于在有或没有添加 IFN 的完全培养基中, IFN 或IFN ,研发系统)。然后通过流式细胞术分析细胞。

Antibodies and humoral response analysis
抗体和体液反应分析

Serum immunoglobulin levels were measured using the LEGENDplex Mouse Immunoglobulin Isotyping Panel according to the manufacturer's protocol (BioLegend). For the B16F10-specific antibody assay, sera from naive or B16F10-bearing mice were obtained after intracardiac blood collection. B16F10 and MC38 cell lines were incubated with purified anti-CD16/32 antibodies. Cells were incubated with or without sera and then stained with Alexa Fluor 647 -conjugated goat anti-mouse к (GAM) from Invitrogen to reveal B16F10-specific antibodies. Data are expressed using the mean fluorescent intensity ratio between serum + GAM and GAM alone. Circulating immune complexes were analysed using the circulating immune complex Ig's (total ELISA kit (Alpha Diagnostic International) according to the manufacturer's instructions.
根据制造商的方案 (BioLegend),使用 LEGENDplex 小鼠免疫球蛋白分型组合测量血清免疫球蛋白水平。对于 B16F10 特异性抗体测定,在心内采血后获得来自幼稚或携带 B16F10 的小鼠的血清。B16F10 和 MC38 细胞系与纯化的抗 CD16/32 抗体一起孵育。将细胞与或不与血清一起孵育,然后用来自 Invitrogen 的 Alexa Fluor 647 偶联山羊抗小鼠 к (GAM) 染色,以揭示 B16F10 特异性抗体。使用血清 + GAM 和单独 GAM 之间的平均荧光强度比来表示数据。根据制造商的说明,使用循环免疫复合物 Ig(总 ELISA 试剂盒 (Alpha Diagnostic International))分析循环免疫复合物。

In vitro cell-T cell co-culture assays
体外 细胞-T细胞共培养测定

For antigen presentation assays, LNs and spleens from Cd1 cre/t or Havcr mice were dissociated into single-cell suspensions, as
对于抗原呈递测定,将来自 Cd1 cre/t 或 Havcr 小鼠的 LN 和脾脏解离成单细胞悬浮液,如

described above, pulsed with and sorted by FACS for B cells, and then co-cultured with CTV-labelled OT-II T cells at different ratios in a 96 -well -bottom plate. After 4 days, cells were analysed by flow cytometry.
如上所述,用 FACS 脉冲并对 B 细胞进行分选,然后在 96 孔 底板中以不同比例与 CTV 标记的 OT-II T 细胞共培养。4天后,通过流式细胞术分析细胞。

In vivo OT II transfer
体内OT II转移

CD45.1 OT II cells were isolated from LNs and spleens of CD45.1 OT II
从CD45.1 OT II的LN和脾脏中分离CD45.1 OT II细胞

before s.c. injection of -OVA cells. Tumour growth was monitored and on day 16 , OT II cells isolated from TILs and dLNs were analysed by flow cytometry.
在皮下注射 -OVA细胞之前。监测肿瘤生长,并在第16天,通过流式细胞术分析从TIL和dLN中分离的OT II细胞。

Human B cell cultures and analysis
人B细胞培养和分析

Human peripheral blood mononuclear cells (PBMCs) were isolated using density-gradient centrifugation from whole blood drawn from healthy volunteers. PBMCs were labelled with CellTrace Violet (CTV) and plated in 96-well U-bottom plates in the presence of fragment donkey anti-human IgM (anti-IgM) ,Jackson ImmunoResearch) with anti-CD40 antibodies ( , Peprotech) for 7 days in X-vivo medium. For some experiments, PBMCs were stimulated in the presence of recombinant IFN , IFN or IFN (all , Peprotech) as indicated. Cells were then analysed by flow cytometry. In brief, Human PBMCs were analysed using the following reagents. Live/ dead cell discrimination was performed using the Live/Dead Fixable viability dye 455UV (Thermo Fisher Scientific). For surface staining, the following antibodies were used: CD19 (SJ25C1), CD27 (M-T271), CD38 (HB7), CD86 (IT2.2), IgD (IA6-2) and Tim-1 (1D12) were used. All data were collected on the BD Symphony A5 (BD Biosciences) system and analysed using FlowJo (Tree Star).
使用密度梯度离心从健康志愿者的全血中分离人外周血单核细胞 (PBMC)。用 CellTrace Violet (CTV) 标记 PBMC,并在 96 孔 U 底板中 接种在 96 孔 U 底板中,在片段驴抗人 IgM(抗 IgM,Jackson ImmunoResearch)存在下,在 X-体内培养基中 7 天。对于一些实验,PBMC在重组IFN 、IFN 或IFN (所有 ,Peprotech)存在下被刺激。然后通过流式细胞术分析细胞。简而言之,使用以下试剂分析人PBMC。使用活/死可固定活性染料 455UV (Thermo Fisher Scientific) 进行活/死细胞鉴别。对于表面染色,使用以下抗体:CD19 (SJ25C1)、CD27 (M-T271)、CD38 (HB7)、CD86 (IT2.2)、IgD (IA6-2) 和 Tim-1 (1D12)。所有数据均在BD Symphony A5(BD Biosciences)系统上收集,并使用FlowJo(Tree Star)进行分析。

Statistics and reproducibility
统计和再现性

Unless otherwise specified, each experiment was repeated independently at least twice and all statistical analyses were performed using two-tailed Student's -tests, Mann-Whitney U-tests or one-way ANOVA followed by Tukey's multiple-comparison test, using GraphPad Prism (v.8.0). was considered to be significant; , , unless otherwise indicated.
除非另有说明,否则每个实验至少独立重复两次,所有统计分析均使用双尾学生 检验、Mann-Whitney U 检验或单因素方差分析进行,然后使用 GraphPad Prism (v.8.0) 进行 Tukey 多重比较检验。 被认为具有重要意义; ,除非另有说明。

Reporting summary 报告摘要

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
有关研究设计的更多信息,请参阅本文链接的《自然投资组合报告摘要》。

Data availability 数据可用性

All genomics data produced for this study have been deposited at the GEO under accession number GSE225717. All other data needed to evaluate the conclusions in this paper are available in the Article and its Supplementary Information. Source data are provided with this paper.
为本研究生成的所有基因组学数据均已存放在GEO的登录号GSE225717。评估本文结论所需的所有其他数据均可在本文及其补充信息中找到。本文提供了源数据。
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Acknowledgements We thank all of the members of the Kuchroo laboratory; A. C. Anderson, S. H. Krovi, A. Kohl and M. Collins for discussions; J. Xia, H. Stroh, D. Kozoriz and R. Kumar for laboratory support; and C. Lambden for computational advice. The work was supported by the grants Melanoma Research Alliance (MRA; 926682); P01Al129880, P01AIO39671, P01AI073748, P01AIO56299 and R01Al144166 from the National Institutes of Health (to V.K.K.); by the Klarman Cell Observatory and HHMI (to A.R.). Y.-C.K. was supported by the NMSS FG-2007-36929; L.B. by the Philippe Foundation; and L.A. by the LabEx MAbImprove (ANR-10-LABX-53-01).
致谢 我们感谢 Kuchroo 实验室的所有成员;A. C. Anderson、S. H. Krovi、A. Kohl 和 M. Collins 进行讨论;J. Xia、H. Stroh、D. Kozoriz 和 R. Kumar 提供实验室支持;和 C. Lambden 提供计算建议。这项工作得到了黑色素瘤研究联盟(MRA; 926682)的资助;P01Al129880、P01AIO39671、P01AI073748、P01AIO56299 和 R01Al144166 来自美国国立卫生研究院(至 V.K.K.);由Klarman细胞天文台和HHMI(到A.R.)。Y.-C.K. 由 NMSS FG-2007-36929 支持;菲利普基金会(Philippe Foundation)的L.B.;和洛杉矶的LabEx MAbImprove (ANR-10-LABX-53-01)。
Author contributions L.B. and V.K.K. conceived the study. L.B., with assistance from Y.-C.K., J.S., A.S., S.M.O., M.Y.V.-F., D.E.F., J.F., R.M.B., S.Z. and S.X., designed, performed and analysed the biological experiments. L.B., with assistance from Y.-C.K., J.S., A.S., E.C. and T.M.D., performed the sequencing experiments with guidance from A.R. and O.R.-R. L.B., N.S., C.J.G., Z.L., F.J.Q., O.A. and E.T.T. designed and performed the computational analysis with guidance from A.R L.B., J.S., E.T.T., L.A., V.K.K. and A.R. interpreted the results. J.R.K. and A.H.S. generated and provided the Pdcdt mice. K.M. and D.M.R. generated and performed the experiments using the mice. The manuscript was written by L.B. with assistance from E.T.T. and was edited by L.A., A.R. and V.K.K. with input from all of the authors.
作者贡献 L.B. 和 V.K.K. 构思了这项研究。L.B.在Y.-C.K.、J.S.、A.S.、S.M.O.、M.Y.V.-F.、D.E.F.、J.F.、R.M.B.、S.Z.和S.X.的协助下,设计、执行和分析了生物实验。L.B.在Y.-C.K.、J.S.、A.S.、E.C.和T.M.D.的协助下,在A.R.和O.R.-R的指导下进行了测序实验。L.B.、N.S.、C.J.G.、Z.L.、F.J.Q.、O.A. 和 E.T.T. 在 A.R L.B.、J.S.、E.T.T.、L.A.、V.K.K. 的指导下设计并执行了计算分析,A.R. 解释了结果。J.R.K. 和 A.H.S. 生成并提供了 Pdcdt 小鼠。K.M.和D.M.R.使用 小鼠生成并进行了实验。该手稿由 L.B. 在 E.T.T. 的协助下撰写,并由 L.A.、A.R. 和 V.K.K. 编辑,并听取了所有作者的意见。
Competing interests V.K.K. has an ownership interest in and is a member of the scientific advisory board for Tizona Therapeutics, Bicara Therapeutics, Compass Therapeutics, Larkspur Biosciences and Trishula Therapeutics. L.B., S.X. and V.K.K. are named as inventors on a provisional patent that has been filed including work from this study. L.A. performed consultancy work for Roche, Merck, Bristol-Myers Squibb and Orega Biotech, and was a recipient of a research grant from Sanofi. A.R. and V.K.K. are co-founders of and have an ownership interest in Celsius Therapeutics. A.R. is also a co-founder and equity holder in Immunitas Therapeutics and was a scientific advisory board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Asimov and Neogene Therapeutics until 31 July 2020. A.R. and O.R.-R. are listed as co-inventors on patent applications filed by the Broad Institute to inventions relating to single-cell genomics. The interests of V.K.K. were reviewed and managed by the Brigham and Women's Hospital and Partners Healthcare in accordance with their conflict-of-interest policies. The interests of A.R. were reviewed and managed by the Broad Institute and HHMI in accordance with their conflict-of-interest policies. Since 1 August 2020, A.R. has been an employee of Genentech, a member of the Roche group. O.R.-R. is currently an employee of Genentech. The other authors declare no competing interests.
竞争利益 V.K.K.拥有Tizona Therapeutics、Bicara Therapeutics、Compass Therapeutics、Larkspur Biosciences和Trishula Therapeutics的所有权权益,并且是其科学顾问委员会的成员。L.B.、S.X. 和 V.K.K. 被指定为已提交的临时专利的发明人,其中包括本研究的工作。L.A.曾为罗氏、默克、百时美施贵宝和欧瑞加生物技术公司提供咨询工作,并获得了赛诺菲的研究资助。A.R.和V.K.K.是Celsius Therapeutics的联合创始人,并拥有Celsius Therapeutics的所有权权益。A.R.还是Immunitas Therapeutics的联合创始人和股东,并在2020年7月31日之前担任赛默飞世尔科技、Syros Pharmaceuticals、Asimov和Neogene Therapeutics的科学顾问委员会成员。A.R. 和 O.R.-R.在博德研究所提交的单细胞基因组学相关发明专利申请中被列为共同发明人。布莱根妇女医院(Brigham and Women's Hospital)和Partners Healthcare根据其利益冲突政策对V.K.K.的利益进行了审查和管理。A.R.的利益由博德研究所和HHMI根据其利益冲突政策进行审查和管理。自 2020 年 8 月 1 日起,A.R. 一直是罗氏集团成员基因泰克的员工。手术室目前是基因泰克的一名员工。其他作者声明没有竞争利益。
Additional information 其他信息:
Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41586-023-06231-0.
补充资料 在线版本包含 https://doi.org/10.1038/s41586-023-06231-0 提供的补充材料。
Correspondence and requests for materials should be addressed to Aviv Regev or Vijay K. Kuchroo.
信件和材料请求应寄给 Aviv Regev 或 Vijay K. Kuchroo。
Peer review information Nature thanks Menna Clatworthy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
同行评审信息 《自然》杂志感谢Menna Clatworthy和其他匿名审稿人对这项工作的同行评审的贡献。
Reprints and permissions information is available at http://www.nature.com/reprints.
重印本和权限信息可在 http://www.nature.com/reprints 上找到。
Article 
a
b
e
Days after tumor cell injection
肿瘤细胞注射后几天
f
h
Extended Data Fig. 1 | Total B cells but not plasma cells limit tumour growth and B16F10-infiltrating B cells have a distinct phenotype. a, Frequencies of B cells among CD45 cells derived from tumour, , ndLN from C57Bl6/J mice 16 days post tumour implantation.b,c, B16F10 tumour growth in C57Bl/6J treated with anti-CD20 (48h prior to tumour injections) or isotype control antibodies ( mice per group) (b) or CD19 and CD19 ( mice per group). d-g, Bulk RNAseq analysis of B cells derived from tumour, , ndLN and spleen of B16F10-bearing wild-type mice ). Experimental design and PCA plot (d), Heatmap of global gene expression (e), Pathway enrichment analysis of genes up-regulated in tumour-derived B cells (f) and heatmap of a selected set of genes , Flow cytometry analysis of B cells derived from tumour, dLN, ndLN and spleen of C57Bl6/J mice implanted with B16F10 s.c. Representative FACS plot and percentage of B cell subsets. Heatmap depicting the MFI of various cell markers in cells derived from tumours or dLN from C57Bl6/J mice (h). Data are mean s.e.m and pooled or representative of at least two to three independent experiments. , . Repeated measures two-way ANOVA test in and . two-tailed Student's t-test in a. two-way ANOVA with Tukey's multiple comparisons test in .
扩展数据 图 1 |总 B 细胞而非浆细胞限制肿瘤生长,B16F10 浸润 B 细胞具有独特的表型。a,肿瘤植入后 16 天来自肿瘤的 CD45 细胞中 B 细胞的频率, 来自 C57Bl6/J 小鼠的 ndLN.b,c,B16F10 肿瘤在用抗 CD20(肿瘤注射前 48 小时)或同型对照抗体(每组 小鼠)处理的 C57Bl/6J 中的肿瘤生长(b) 或 CD19 和 CD19 (每组 小鼠)。d-g, B16F10野生型小鼠肿瘤 、ndLN和脾脏来源的B细胞的Bulk RNAseq分析 )。实验设计和 PCA 图 (d)、全局基因表达热图 (e)、肿瘤来源的 B 细胞中上调基因的通路富集分析 (f) 和选定一组基因的热图 、植入 B16F10 s.c 的 C57Bl6/J 小鼠的肿瘤、dLN、ndLN 和脾脏来源的 B 细胞的流式细胞术分析。代表性的 FACS 图和 B 细胞亚群的百分比。热图描绘了来自肿瘤或来自 C57Bl6/J 小鼠的 dLN 细胞中 各种 细胞标志物的 MFI (h)。数据是平均 值,并且是汇总的或代表至少两到三个独立实验。 .重复测量双因素方差分析检验 in 。双因素方差分析中的双尾学生 t 检验与 Tukey 的多重比较检验
TLLs : B dLN and ndLN: A
TLL: B dLN 和 ndLN: A
Led  发光二极管
f
Cd69 CD69型
Mki67 Mki67型
Extended Data Fig. 2 |scRNAseq and BCRseq of TILs, dLN and ndLN derived from B16F10 melanoma bearing mice. a, Gating strategy for the sorting of singlet viable cells prior to scRNAseq. b, Flow cytometry analysis depicting proportions of cell types infiltrating tumours across time. Data are mean s.e.m from two experiments. mice per group.c,UMAP of expression of different lineage marker transcripts. d,e, UMAPs and quantification of immunoglobulin class-switch (d) and clonal expansion (e) in B cells. f and , UMAPs of B cells coloured by time points or relative expression of the indicated genes (f) and ,
扩展数据 图 2 |来自 B16F10 黑色素瘤小鼠的 TIL、dLN 和 ndLN 的 scRNAseq 和 BCRseq。a,在scRNAseq之前对单线态活细胞进行分选的门控策略。b,流式细胞术分析,描述随时间浸润肿瘤的细胞类型比例。数据是来自两个实验的平均 值。 每组小鼠.c,不同谱系标记转录本表达的UMAP。d,e,UMAPs和B细胞中免疫球蛋白类别转换(d)和克隆扩增(e)的定量。f 和 , 按时间点或指示基因的相对表达着色的 B 细胞的 UMAP (f) 和

Panels I-VIII, cells are coloured by Cd19 expression (I) or by their signature score that reflects the relative average expression of the genes overlapping with the signatures for several indicated B cell subsets (II-VIII). Follicular (FO_B), Marginal zone (MZ_B), Immature (Imm_B), Antibody secreting cells (ASC), germinal centre (GC) B cells derived from the dark zone (DZ_B) or light zone (LZ_B).h, Heatmap depicting the log fold change of the top 100 genes uniquely up-regulated in each Leiden cluster (t-test; fold change ). Selected genes are shown.
图I-VIII,细胞通过Cd19表达(I)或其特征评分着色,该评分反映了与几个指示的B细胞亚群(II-VIII)的特征重叠的基因的相对平均表达。滤泡 (FO_B)、边缘区 (MZ_B)、未成熟 (Imm_B)、抗体分泌细胞 (ASC)、生发中心 (GC) B 细胞源自暗区 (DZ_B) 或浅色区 (LZ_B).h,热图描绘了每个 Leiden 簇中唯一上调的前 100 个基因的对数倍数变化(t 检验;倍数变化 )。显示选定的基因。

Extended Data Fig. 3 | See next page for caption.
扩展数据 图 3 |有关标题,请参阅下一页。

Extended Data Fig. 3 | TIM-1 expressing cells characterization.
扩展数据 图 3 |表达 TIM-1 的细胞表征。

a, Proportions of TIM- cells among CD19 cells derived from tumour, , ndLN, and spleen from B16F10 bearing C57B16/J mice 16 days post tumour injection together with inguinal LN (iLN) and spleen from tumour-free WT mice ( for pLN, for spleens, for Tumour, dLN and ndLN).b, TIM- B cells derived from dLN and ndLN were sorted and analysed by bulk RNAseq . Experimental design, PCA plot and heatmap of selected genes are shown. c, Flow cytometry analysis of subsets and marker expression of TIM-1 B cells derived from dLN vs ndLN from B16F10-bearing WT mice . d) FACSsorted TIM- and TIM- B cells were stained with CTV and stimulated in vitro with anti-IgM, anti-CD40 or LPS for 72h. Cell proliferation and plasma cell differentiation was analysed by flow cytometry. Representative FACS plot (left) and quantification (right) are shown ( for medium, for stimulation for TIM-1- and for TIM-1+).e,f, scRNAseq analysis depicting the experimental design, UMAPs coloured by tissue of origin (I), TIM-1 sorting (II), expression of havcr1 (III) and gene signature score of cell cycle S-phase (IV), germinal centre cells (V) and antibody secreting cells (VI). Dotplot of Havcr1 expression (III. right). f, UMAP coloured by B cell clusters annotated according to TIM-1 expression. Pie chart depicting the frequency of the two main TIM-1-expressing subsets and foldchange of cell numbers between and for each subset . g, Top 5 differentially expressed genes (FDR and LFC ) (x axis) by cluster (y axis). Dot size represents the fraction of cells in the cluster that express the gene; colour indicates the mean expression (logTP10K (see Methods)) in all cells, relative to other clusters. h, FACS-sorted TIM-1 B cells were stained with CTV and stimulated in vitro with LPS, anti-IgM, anti-CD40 or both anti anti-CD40 for . TIM-1 surface expression across cell divisions was analysed by flow cytometry. Representative FACS plot (left) and TIM-1 MFI quantification (right). Flow cytometry data are mean s.e.m and pooled or representative of at least two to three independent experiments. , , two-way ANOVA test in h. two-tailed Student's t-test in and .
a,肿瘤注射后16天来自B16F10携带C57B16 / J小鼠的CD19细胞中 TIM- 细胞的比例,以及来自无肿瘤WT小鼠的腹股沟LN(iLN)和脾脏( pLN, 脾脏, 肿瘤,dLN和ndLN).b,通过大量RNAseq 对来自dLN和ndLN的TIM-B 细胞进行分类和分析 .显示了所选基因的实验设计、PCA图和热图。c,流式细胞术分析来自 dLN 的 TIM-1 B 细胞的亚群和标志物表达与来自 B16F10 的 WT 小鼠的 ndLN 。d) 用CTV染色FACS分选的TIM- 和TIM-B 细胞,并在体外用抗IgM、抗CD40或LPS刺激72h。通过流式细胞术分析细胞增殖和浆细胞分化。e,f ,描述实验设计的scRNAseq分析,按来源组织着色的UMAP(I),TIM-1分选(II),havcr1(III)的表达和细胞周期S期(IV),生发中心细胞(V)和抗体分泌细胞(VI)的基因特征评分。Havcr1 表达的点图(III.右)。f,根据TIM-1表达注释的B细胞簇着色的UMAP。饼图描述了两个主要表达 TIM-1 的亚群的频率以及每个亚集 之间 细胞 数的倍数变化。g, 按簇(y轴)划分的前5个差异表达基因(FDR 和LFC )(x轴)。 点大小表示簇中表达基因的细胞比例;颜色表示所有细胞中相对于其他簇的平均表达 (logTP10K(参见方法))。h,FACS分选的TIM-1 B细胞用CTV染色,体外用LPS、抗IgM、抗CD40 或两种抗 CD40 刺激 。通过流式细胞术分析细胞分裂中 TIM-1 的表面表达。代表性的 FACS 图(左)和 TIM-1 MFI 定量(右)。流式细胞术数据是平均 值,并且是合并或代表至少两到三个独立实验的数据。 , , 双向方差分析检验 h. 双尾学生 t 检验 in .

Extended Data Fig. 4 |See next page for caption.
扩展数据 图 4 |有关标题,请参阅下一页。
Extended Data Fig. 4 | TIM-1 expressing B cells express higher levels of checkpoint molecules and IL-10. a-b, TIM- and TIM-1 B cells derived from and ndLN from B16F10 bearing C57Bl6/J mice were analysed ex vivo. MFI of various checkpoint molecules ( mice per group) (a), IL-10 secretion post anti-IgM stimulation as determined by LegendPlex ( mice per group) (b). c, FACS-sorted TIM-1 and TIM-1 B cells were stimulated in vitro with antiIgM, anti-CD40 or LPS for . MFI of checkpoint molecules was analysed by flow cytometry. d-f, UMAP plot of published scRNAseq data depicting 2615 B cells(dots) isolated from human tumours, coloured by their signature score that reflects the relative average expression of the genes overlapping with the signature of human melanoma exhausted T cells from Tirosh et al. 2016 (d), known B-cell subsets or Leiden clusters (f). , Beeswarm plots of the distribution of log fold change across Pre and Post ICB treatment from the Merge SS2 datasets using miloR UMAPs depicting each single cell dots coloured by Leiden clusters (h), Immune checkpoint signature score (i) or density plot for treatment-naive samples (j). Flow cytometry data are mean s.e.m and pooled or representative of at least two to three independent experiments. , Survival map depicting the association of HAVCR1 high expression and clinical outcome in 32 cancer types. High log10 Hasard ratio (HR) (Reds) indicates a negative correlation with survival which would be outlined if . I and , Kaplan Meier disease free (top row) or Overall (bottom row) survival curves for TIM-1 expression (I) or IC B cells signature (m) in Lung (LUAD), pancreatic (PAAD), stomach (STAD) and colon (COAD) adenocarcinomas. For each signature gene set, the cohorts were divided into high and low expression groups by median value ( cutoff).Analyses were performed with log-rank Mantel-Cox test using web server GEPIA2 , based on TCGA and GTEx databases. , , paired two-tailed -test in , and .
扩展数据 图 4 |表达 TIM-1 的 B 细胞表达更高水平的检查点分子和 IL-10。体外分析来自 携带 C57Bl6/J 的 B16F10 小鼠的 a-b、TIM 和 TIM-1 B 细胞和 ndLN。各种检查点分子(每组 小鼠)的MFI(a),由LegendPlex(每组 小鼠)测定的抗IgM刺激 后IL-10分泌(b)。c,用抗IgM、抗CD40或LPS在体外刺激FACS分选的TIM-1 和TIM-1 B细胞 。通过流式细胞术分析检查点分子的MFI。d-f,已发表的 scRNAseq 数据的 UMAP 图,描绘了从人类肿瘤中分离出的 2615 个 B 细胞(点),按其特征分数着色,反映了与 Tirosh 等人 2016 (d)、已知 B 细胞亚群 或 Leiden 簇的人黑色素瘤耗尽 T 细胞的特征重叠的基因的相对平均表达 (f)。 ,使用 miloR UMAP 从合并 SS2 数据集中绘制 ICB 处理前和处理后 ICB 处理前后对数倍数变化分布的蜂群图,描绘了由 Leiden 簇着色的每个单细胞点 (h)、免疫检查点特征评分 (i) 或未处理样本的密度图 (j)。流式细胞术数据是平均 值,并且是合并或代表至少两到三个独立实验的数据。 ,生存图描绘了 HAVCR1 高表达与 32 种癌症类型临床结果的关联。高 log10 Hasard 比值 (HR) (Reds) 表示与生存率呈负相关,如果 . 肺癌 (LUAD)、胰腺癌 (PAAD)、胃癌 (STAD) 和结肠癌 (COAD) 中 TIM-1 表达 (I) 或 IC B 细胞特征 (m) 的 I 和 Kaplan Meier 无病(上行)或总体(下行)生存曲线。对于每个特征基因集,队列按中值( 临界值)分为高表达组和低表达组。使用基于TCGA和GTEx数据库的Web服务器GEPIA2 进行对数秩Mantel-Cox测试进行分析。 、 和 成对的双尾 检验。

Extended Data Fig. 5 |See next page for caption.
扩展数据 图 5 |有关标题,请参阅下一页。
Extended Data Fig. 5 | TIM-1 loss in B cells but not T cells limits tumor growth and anti-TIM-1 treatment requires MHC II expression on B cells. a-e, Tumour growth in CD19 and TIM- mice implanted with B16-OVA ( control vs 5 TIM , intravenously control vs 5 TIM , intradermally control vs 5 TIM d) or subcutaneous MC38 colon adenocarcinoma control vs 6 TIM ) (e).f, Tumour growth curve of B16F10 implanted into TIM-1 and CD TIM mice , Subcutaneous B16F10 melanoma were subcutaneously implanted into CD19 TIM- and CD4 xTIM-1 mice. On day were harvested followed by flow cytometric analysis of TIM-1 expression of or CD3e cells. mice per group. h, B16F10 melanoma growth in TIM- and mice treated with tamoxifen on days indicated prior to tumour inoculation ( mice per group). , B16F10 tumour growth with anti-isotype control or anti-TIM-1 treatment in C57BI/6J ( treated with isotype control vs treated with anti-TIM-1), MT ( per group) (j) or MT mice were reconstituted with WT or
扩展数据 图 5 |B 细胞中的 TIM-1 缺失而非 T 细胞限制了肿瘤的生长,抗 TIM-1 治疗需要 B 细胞上 MHC II 的表达。a-e,植入 B16-OVA( 对照 vs 5 TIM ,静脉 注射对照 vs 5 TIM,皮内 对照 vs 5 TIM d)或皮下 MC38 结肠腺癌 对照 vs 6 TIM 的 CD19 和 TIM- 小鼠的肿瘤生长曲线 (e).f,植入 TIM-1 和 CD TIM 小鼠 的 B16F10 的肿瘤生长曲线,皮下注射B16F10黑色素瘤,皮下植入CD19 TIM- 和CD4 xTIM-1 小鼠体内。当天 收获,然后对 TIM-1 或 CD3e 细胞的表达进行流式细胞术分析。 每组小鼠。h,在肿瘤接种前指示的天数(每组 小鼠)用他莫昔芬治疗的 TIM 小鼠中 B16F10 黑色素瘤的生长。 ,B16F10 肿瘤生长在 C57BI/6J 中采用抗同种型对照或抗 TIM-1 治疗( 同种型对照与 抗 TIM-1 治疗)、 MT( 每组)(j)或 MT 小鼠用 WT 或

MHCII KO B cells and treated with anti-TIM-1 antibody ( mice per group) (k). Experimental design (k, left), tumour growth curves (k, right). 1-n, Survival curves (I) and flow cytometry immunophenotyping of TILs depicting frequencies of B cells, CD4+ and CD8+ TILs among living CD45 FOXP3+ cells among CD4+TILs (m, right) and granzyme cells among CD8+ TILs (n) of implanted with B16F10 melanoma and treated with either antiTIM-1, anti-PD-1, anti-TIM-1+ anti-PD-1 (combo), or isotype controls ( mice per group for tumour growth analysis and 5 mice per group for flow cytometry analysis). Data are mean s.e.m and pooled from two to three independent experiments. . Repeated measures two-way ANOVA test in and . unpaired two-tailed -test in c and . Differences between survival curves were analysed by log-rank (Mantel-Cox) test (I). One or two-way ANOVA with Tukey's multiple comparisons test in and .
MHCII KO B细胞并用抗TIM-1抗体(每组 小鼠)(k)处理。实验设计(k,左),肿瘤生长曲线(k,右)。1-n,TILs的生存曲线(I)和流式细胞术免疫表型,描述了CD4 + TILs(m,右)和CD8 + TILs中的活CD45 FOXP3+细胞和CD8+ TILs中的颗粒酶 细胞的频率(n) 植入B16F10黑色素瘤并接受抗TIM-1,抗PD-1,抗TIM-1+抗PD-1(组合)或同型对照(每组 小鼠进行肿瘤生长分析,每组5只小鼠进行流式细胞术分析)。数据是平均 值,并汇总了两到三个独立实验。 。重复测量双因素方差分析检验 in 。C 和 . 中的未配对双尾 检验。通过log-rank(Mantel-Cox)检验(I)分析生存曲线之间的差异。带有 Tukey 多重比较检验的单因素或双因素方差分析在 中。
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Extended Data Fig. 6 | Immunophenotyping of tumour-bearing CD19 and Havcr1/TIM-1 mice. a-k, Flow cytometry analysis of TILs, dLN and ndLN derived from CD19 and TIM-1 mice implanted with B16F10 s.c. Absolute number of live cells per gram of tumour ( controls and TIM mice) (b), Macs, DCs ( controls and TIM- mice), mono, PMN ( controls and TIM-1 mice), cells ( controls and TIM-1 mice), CD4+ and CD8+ T cells frequencies among CD45 cells ( controls and TIM-1 mice) (c), Frequency of Tregs among CD4+T cells ( mice per group) (d), CD8+T cells us Tregs ratio (e). CD107a-expressing CD4+( controls and TIM- mice) and CD8 + T cells ( controls and TIM- mice) (f), Eomes and/or Tbet fraction ( mice per group) (g), MFI of TCF1
扩展数据 图 6 |荷瘤 CD19 和 Havcr1/TIM-1 小鼠的免疫表型分析。a-k,来自植入 B16F10 s.c. 的 CD19 和 TIM-1 小鼠的 TIL、dLN 和 ndLN 的流式细胞术分析。每克肿瘤( 对照和 TIM 小鼠)活 细胞的绝对数量 (b)、Mac、DC( 对照和 TIM- 小鼠)、单细胞、PMN( 对照和 TIM-1 小鼠)、细胞( 对照和 TIM-1 小鼠)、 CD4+ 和 CD8+ T 细胞频率 CD45 细胞( 对照和 TIM-1 小鼠) (c), CD4+T 细胞中 Tregs 的频率(每组 小鼠) (d), CD8+T 细胞与 Tregs 比率 (e)。表达CD107a的CD4+( 对照和 TIM- 小鼠)和CD8 + T细胞( 对照和 TIM- 小鼠)(f),Eomes和/或Tbet分数(每组 小鼠)(g),TCF1的MFI

( controls and TIM-1 mice) (h) and Frequency PD-1 TIM-3 among CD8+ T cells (d).j, pie charts depicting the proportions of various immune cell populations with dLN and ndLN.k, frequencies of cells among T cells ( mice per group). 1, Flow cytometry analysis of TILs from derived from CD19 and TIM- mice implanted with MC38 colon adenocarcinoma s.c. Experimental design, pie chart of immune population and frequencies of cells and of IFN or TNF expressing CD8+ and CD4+ T cells ( mice per group). Data are mean s.e.m and pooled from two to three independent experiments. , two-tailed Student's t-test in and .
对照和 TIM-1 小鼠)(h) 和 CD8+ T 细胞 中的频率 PD-1 TIM-3 (d).j,描绘具有 dLN 和 ndLN.k 的各种免疫细胞群比例的饼图,T 细胞间 细胞的频率(每组 小鼠)。1、流式细胞术分析来自CD19 和植入MC38结肠腺癌s的TIM 小鼠的TILs。实验设计,免疫群体和 细胞频率以及表达 CD8+ 和 CD4+ T 细胞的 IFN 或 TNF 的饼图(每组 小鼠)。数据是平均 值,并汇总了两到三个独立实验。 ,双尾学生的 t 检验在

Extended Data Fig. 7 | scRNAseq of TILs, dLN and ndLN derived from B16F10 bearing CD19 and Havcr1/TIM-1 mice. a,b, scRNA/TCR-seq of TILs, dLN and ndLN from CD19 and TIM-1 mice bearing B16F10 melanoma. UMAPs coloured by genotype (a, top), biological replicates (a, bottom) or the relative expression of the indicated genes (b). c, UMAPs of T cells coloured by tissue,
扩展数据 图 7 |来自携带 CD19 和 Havcr1/TIM-1 小鼠的 B16F10 的 TIL、dLN 和 ndLN 的 scRNAseq。a,b,来自携带 B16F10 黑色素瘤的 CD19 和 TIM-1 小鼠的 TIL、dLN 和 ndLN 的 scRNA/TCR-seq。按基因型(a,顶部)、生物复制(a,底部)或指示基因的相对表达(b)着色的UMAP。c, 组织着色的T细胞的UMAP,

T cell types, relative expression or clonal expansion as indicated. d, Gene expression for functional marker genes in T cells. For each gene (columns) in each group (rows), the proportion of cells in the group expressing the gene (dot size) and the relative mean expression of expressing cells (colour) is plotted.
T细胞类型、 相对表达或克隆扩增如图所示。d, T细胞中功能性标记基因的基因表达。对于每组(行)中的每个基因(列),绘制表达基因的组中细胞的比例(点大小)和表达细胞的相对平均表达(颜色)。
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Extended Data Fig. 8 |See next page for caption.
扩展数据 图 8 |有关标题,请参阅下一页。
Extended Data Fig. Analysis of the humoral immunity and -cell subsets in B16F10 bearing CD19 and Havcr1/TIM-1 mice. a, Frequencies of B cells among TILs derived from and mice implanted with B16F10 s.c.b,c, Representative FACS plot (b) and percentage (c) of plasma cells (B220 CD138 ) or plasmablasts (B220 CD138 high or TFh cells (d) from CD19 and TIM-1 mice implanted with B16F10 s.c. e-h, serum immunoglobulins or CICs from naive and TIM- ) or B16F10-bearing CD19 and TIM-1 mice ( per group) and measured by LegendPlex ( ) or ELISA (h).i, Flow-cytometric analysis of the presence of antitumor antibodies in the sera of CD19 and mice implanted with B16F10 s.c. Representative histograms (light grey, staining with the secondary antibody alone; blue,
扩展数据图。 分析携带 CD19 和 Havcr1/TIM-1 的 B16F10 小鼠的体液免疫和 细胞亚群。a,来自 和植入 B16F10 s.c.b,c 的 TIL 和 B16F10 s.c.b,c 的浆细胞 (b ) 或浆母细胞(B220 CD138 高 或 TFh 细胞 (d) 来自 CD19 和 TIM-1 小鼠的代表性 FACS 图 (b) 和百分比 (c) 来自植入 B16F10 s.c. e-h、血清免疫球蛋白或来自幼稚 TIM- 的 CIC 的 CIC 或携带 B16F10 的 CD19 和 TIM-1 小鼠( 每组)并通过 LegendPlex ( ) 或 ELISA (h).i 测量,流式细胞术分析 CD19 血清中 是否存在抗肿瘤抗体和植入 B16F10 s.c. 的小鼠。代表性直方图(浅灰色,仅用二抗染色;蓝色,

CD19 mice serum ; red, TIM- mice serum ), and MFI ratios were calculated by dividing the MFI obtained with a given serum by the MFI obtained with the secondary antibody.j, Quantification of Immunoglobulin class-switch (left) and BCR clonality (right) in CD19 and TIM- cells. , Flow cytometry analysis of B cell subsets in Tumour, dLN, ndLN and spleen from isotype vs anti-TIM-1 (3B3) treatment mice or in control vs TIM- mice ( mice per group). , Gating strategy used. I and , Bar plots depicting the frequencies of major B-cell subsets ( ) or subsets within B2 cells ( mice per group) (m). Data are mean s.e.m and pooled from two to three independent experiments. Two-tailed Student'st-test in a,c,d,h and i.two-way ANOVA with Tukey's multiple comparisons test in .
CD19 小鼠血清 ;red,TIM- 小鼠血清 )和MFI比率的计算方法是将用给定血清获得的MFI除以使用二抗获得的MFI.j,CD19 和TIM- 细胞中免疫球蛋白类别转换的定量(左)和BCR克隆性(右)。 ,来自同种型与抗 TIM-1 (3B3) 治疗小鼠或对照组与 TIM- 小鼠(每组 小鼠)的肿瘤、dLN、ndLN 和脾脏中 B 细胞亚群的流式细胞术分析。 ,使用门控策略。I 和 ,条形图描绘了主要 B 细胞亚群 ( ) 或 B2 细胞内亚群(每组 小鼠)的频率 (m)。数据是平均 值,并汇总了两到三个独立实验。a,c,d,h 和 i.双因素方差分析中的双尾学生检验,以及 Tukey 的 多重比较检验。
Extended Data Fig. See next page for caption.
扩展数据图。 有关标题,请参阅下一页。
Extended Data Fig. Havcr1/TIM-1 B cells exhibit enhanced antigen presentation and co-stimulation capacity. and , Violin plots displaying the distribution of the type I interferon response signature score (a) or the antigen processing and presentation of peptide antigen (APC) signature score (b) between TIM-1 and CD19 cells derived from ndLN, dLN and TILs. c and d, MFI and histograms of MHC I and II as well as co-stimulation molecules exvivo ( mice per group) (c) or in vitro co-cultured with OT II CD4+T cells and peptide-pulsed TIM-1 and CD19 cells were co-cultured with CTV-labelled OVA-restricted CD4+T cells for 4 days with or without anti-MHC II antibody. T cell proliferation was determined by dilution of CTV. Quantitative analysis of proliferation indices is shown (e).f, B16F10 melanoma growth in CD19 and TIM-1 mice treated with anti-MHC II or isotype control antibodies ( mice per group). g, Naive CD45.1 OVA-restricted CD4+T cells were transferred i.v.1 day prior to B16-OVA melanoma cells s.c implantation into CD45.2 and TIM- mice ( mice per group). Tumour-infiltrating OT II cells were examined for expression of KI67 as proportions of expressing cells or MFI of FOXP3, CD25 and Helios ( CD19 and TIM- mice). Schematic of the experimental and quantitative results are depicted. , TIM- and cells cultured with anti-IgM/anti-CD40 for in the absence (medium) or with of IFN- . Representative histograms (left) and quantitative analysis of the MFI of TIM-1, CD86 and MHC II ( mice per group). and , Flow cytometry analysis of TILs of indicated mice implanted with B16F10 melanoma and treated with isotype control ( mice per group) or neutralizing anti-IFNAR-1 antibody ( mice per group). Frequencies of CD8+ T cells (i, left), FOXP3+ and IFN cells among CD4+ T cells (i, middle and right), B cells ( , left) and MFI of MHC I, MHC II and CD86 among B cells ( , right) are depicted. , Analysis of published RNAseq data depicting 1462 B cells (dots) isolated from human melanoma tumours, projected onto UMAPs coloured by treatment group (top left), density of cells associated with responder, non-responder lesions (top middle and right) or signature scores of tumour-derived TIM-1 cells, GO type I interferon response and GO antigen processing and presentation gene signatures as detailed in Methods. 1,UMAP coloured by Leiden cell clusters (resolution 1). m, stacked bar graph displaying the frequencies of B cells derived from Responder and Non-responder samples among each Leiden cluster and , violin plots displaying the signature scores of the indicated signatures across clusters. Data are mean s.e.m and pooled from two to three independent experiments. , . Kruskal-Wallis test in a and b. Two-tailed Student's -test in c,d,e,g,h,i and j. Repeated measures two-way ANOVA test in .
扩展数据图。 Havcr1/TIM-1 B 细胞表现出增强的抗原呈递和共刺激能力。 ,小提琴图显示了 I 型干扰素反应特征评分 (a) 或肽抗原 (APC) 特征评分 (b) 在源自 ndLN、dLN 和 TIL 的 TIM-1 和 CD19 细胞之间的抗原加工和呈递。c 和 d、MHC I 和 II 的 MFI 和直方图以及共刺激分子体外(每组 小鼠)(c) 或与 OT II 共培养的体外 CD4+T 细胞 肽脉冲 TIM-1 和 CD19 细胞与 CTV 标记的 OVA 限制性 CD4+T 细胞共培养 4 天,有或没有抗 MHC II 抗体。T细胞增殖通过CTV稀释决定。增殖指数的定量分析显示(e).f,用抗MHC II或同型对照抗体(每组 小鼠)治疗的CD19 和TIM-1 小鼠中的B16F10黑色素瘤生长。g,在B16-OVA黑色素瘤细胞植入CD45.2 和TIM- 小鼠(每组 小鼠)之前,将幼稚CD45.1 OVA限制性CD4 + T细胞转移IVv.1天。检查肿瘤浸润 OT II 细胞的 KI67 表达比例或 FOXP3、CD25 和 Helios( CD19 TIM- 小鼠)的 MFI。描绘了实验和定量结果的示意图。 、TIM- 用抗 IgM/抗 CD40 培养的细胞, 在没有(培养基)或用 IFN- 的情况下培养。 TIM-1、CD86 和 MHC II(每组 小鼠)的 MFI 的代表性直方图(左)和定量分析。 以及 ,对植入 B16F10 黑色素瘤并用同型对照(每组 小鼠)或中和抗 IFNAR-1 抗体(每组 小鼠)治疗的指示小鼠的 TIL 进行流式细胞术分析。CD8+ T 细胞(i,左)、FOXP3+ 和 IFN 细胞在 CD4+ T 细胞(i,中、右)、B 细胞( , 左)和 MHC I、MHC II 和 CD86 在 B 细胞中的频率( , 右)被描述。 ,分析已发表的 RNAseq 数据,这些数据描绘了从人类黑色素瘤肿瘤中分离出的 1462 个 B 细胞(点),投影到 UMAP 上,按治疗组(左上)、与应答者相关的细胞密度、无应答者病变(上、中和右)或肿瘤衍生的 TIM-1 细胞的特征评分、GO I 型干扰素反应和 GO 抗原加工和呈递基因特征,详见方法。1,由莱顿细胞簇着色的 UMAP(分辨率 1)。m,堆叠条形图,显示每个莱顿簇中从响应者和非响应者样本中得出的 B 细胞的频率 ,以及显示跨簇中指示特征的特征分数的小提琴图。数据是平均 值,并汇总了两到三个独立实验。 .a 和 b 中的 Kruskal-Wallis 检验。 c、d、e、g、h、i 和 j 中的双尾学生 检验。重复测量双因素方差分析检验。
Article 

Extended Data Fig. Source of Interferons in B16F10 tumours and impact on TIM-1-mediated anti-tumour immunity. a) GSEA analysis for the "Response to type II IFN pathway" of tumour-infiltrating TIM-1 and CD19 cells. b and c) Murine (b) or human (c) B cells were stimulated with IgM/CD40 for 3 and 7 days respectively in the presence or not of IFN , IFN or IFN . TIM-1 expression (MFI) was analysed by flow cytometry. d) Tumour growth in indicated mice implanted with B16F10 melanoma and treated with isotype control or neutralizing anti-IFNGR-1 antibody ( mice per group). e and ) B16F10 tumour and dLN supernatants derived from CD19 and TIM- mice were collected, and levels of IFN were determined by ELISA CD19 TIM-1 mice in e).f) Matrixplot depicting IFNb1 mRNA expression profile across immune populations in B16F10 tumours by scRNAseq.g) Tumour growth in indicated mice implanted with B16F10 melanoma and treated with isotype control or depleting anti-PDCA1 antibody (two i.p injections 48 and prior to tumour injection, mice per group).h) Flow cytometry analysis of pDC frequencies in B16F10 CD19 and TIM-1 tumours ( istoype treated and anti-PDCA1 treated mice). Data are mean s.e.m and pooled from two to three independent experiments. Two-tailed Student's t-test in b,c,e,h and i. , , repeated measures two-way ANOVA test in d and .
扩展数据图。 B16F10 肿瘤中干扰素的来源及其对 TIM-1 介导的抗肿瘤免疫的影响。a) 肿瘤浸润性 TIM-1 和 CD19 细胞的“对 II 型 IFN 通路的反应”的 GSEA 分析。b 和 c) 小鼠 (b) 或人 (c) B 细胞分别在存在或不存在 IFN 、IFN 或 IFN 的情况下用 IgM/CD40 刺激 3 天和 7 天。通过流式细胞术分析 TIM-1 表达 (MFI)。d) 植入 B16F10 黑色素瘤并用同型对照或中和抗 IFNGR-1 抗体治疗的指示小鼠的肿瘤生长(每组 小鼠)。e和 )收集来自CD19 和TIM- 小鼠的B16F10肿瘤和dLN上清液, 并通过ELISA CD19 TIM-1 小鼠在e).f).f)的矩阵图描绘了B16F10肿瘤中免疫群体中IFNb1 mRNA表达谱的基质图.g)植入B16F10黑色素瘤并用同种型对照或消耗抗PDCA1抗体治疗的指示小鼠的肿瘤生长(两个ip注射48和 肿瘤注射前, 每组小鼠).h)B16F10 CD19 和TIM-1 肿瘤( istoype处理和 抗PDCA1处理小鼠)pDC 频率的流式细胞术分析。数据是平均 值,并汇总了两到三个独立实验。b,c,e,h 和 i 中的双尾学生 t 检验 ,重复测量 d 和 的双向方差分析检验。

nature portfolio 自然投资组合

Reporting Summary 报告摘要

Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our Editorial Policies and the Editorial Policy Checklist.
Nature Portfolio希望提高我们发表作品的可重复性。此表格为报告的一致性和透明度提供了结构。有关Nature Portfolio政策的更多信息,请参阅我们的编辑政策和编辑政策清单。

Statistics 统计学

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
对于所有统计分析,请确认图例、表格图例、正文或方法部分中存在以下项目。
n/a Confirmed n/a 已确认
The exact sample size ( ) for each experimental group/condition, given as a discrete number and unit of measurement
每个实验组/条件的确切样本量 ( ),以离散数字和测量单位的形式给出
A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly
关于测量是从不同的样品中进行的,还是重复测量同一样品的声明
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使用的统计检验以及它们是单侧的还是双侧的
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Our web collection on statistics for biologists contains articles on many of the points above.
我们为生物学家提供的统计学网络合集包含有关上述许多要点的文章。

Software and code 软件和代码

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Data analysis GraphPad Prism (v9), Microsoft Excel(v15), FlowJo(v10), Cell Ranger (v3.1.0), Scanpy (v1.7.2), COMET, Scirpy (v4.2), Cumulus (v0.8),
Cellbender (v0.1), Immcantation(v4.1),MiloR(v0.1), iDEP(v0.92), DESeq2 (v1.28.1)
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\section*{Data}

Policy information about availability of data

All manuscripts must include a data availability statement. This statement should provide the following information, where applicable:
- Accession codes, unique identifiers, or web links for publicly available datasets
- A description of any restrictions on data availability
- For clinical datasets or third party data, please ensure that the statement adheres to our policy

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. \(\boxtimes\) Life sciences \(\quad \square\) Behavioural \& social sciences \(\square\) Ecological, evolutionary \& environmental sciences For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

\section*{Life sciences study design}

\begin{tabular}{|c|c|}
\hline Sample size & \begin{tabular}{l} 
Sample sizes were determined based on technical feasibility of the experimental workflow being mindful to provide a reasonable number of \\
replicates to be confident in obtained results.
\end{tabular} \\
\hline Data exclusions & \begin{tabular}{l} 
As described in methods, doublets, cells with incorrect combinations of hashing antibodies or low quality (if their fraction of mitochondrial \\
genes was \(\geq 4.5 \%\) or if they had \(<1,000\) counts or \(<300\) or \(>6,000\) genes) were discarded from the scRNAseq analysis.
\end{tabular} \\
\hline Replication & \begin{tabular}{l} 
Replicates were used in all experiments as noted in text, figure legends and methods. All in vivo experiments were repeated at least twice \\
with consonant results.
\end{tabular} \\
\hline Randomization & \begin{tabular}{l} 
Mice were age and sex-matched and randomized where appropriate (e.g. prior to initiating treatment for matched conditions). For antibody \\
treatments, tumors were measured and fairly distributed into groups based on size so that each group had the same approx mean growth \\
before experimental perturbation.
\end{tabular} \\
\hline Blinding & \begin{tabular}{l} 
Investigators were not blinded to treatment groups or genotypes for in vivo or in vitro studies, as knowledge of this information was essential \\
to conduct the studies. For animal studies, no blinding was performed due to requirements for cage labeling and staffing needs with the \\
exception of replicating some tumor measurements (Fig.2, Fig.3, Fig.5) the researcher was blinded to the groupings.
\end{tabular} \\
\hline
\end{tabular}

\section*{Reporting for specific materials, systems and methods}

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

\begin{tabular}{|c|c|c|c|}
\hline \multicolumn{2}{|r|}{ Materials \& experimental systems } & \multicolumn{2}{|c|}{ Methods } \\
\hline n/a & Involved in the study & n/a & Involved in the study \\
\hline ᄂ & \(\triangle\) Antibodies & \(\boxtimes\) & \(\square\) ChIP-seq \\
\hline\(\square\) & \(\boxtimes\) Eukaryotic cell lines & \(\square\) & \(\boxtimes\) Flow cytometry \\
\hline Х & \(\square\) Palaeontology and archaeology & Х & \(\square\) MRI-based neuroimaging \\
\hline\(\square\) & \(\triangle\) Animals and other organisms & & \\
\hline\(\square\) & \(\triangle\) Human research participants & & \\
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\hline\(\bigotimes\) & \(\square\) Dual use research of concern & & \\
\hline
\end{tabular}
\footnotetext{
Antibodies

Antibodies used

Surface antibodies (from Biolegend,eBioscience and BD) used in this study were diluted 1:100 and against: CD45 (30-F11,564279), TCRb (H57-597,612821), CD3e (17A2,100310), TCRbeta, CD8a (53-6.7,612898), CD4 (RM4-5,100540), CD19 (6D5,115555), B220 (RA3-6B2,564662), CD138((281-2,142506), GL-7 (GL-7,144608), Fas (Jo2, 562633), IgD (11-26c.2a, 405721), IgM (RMM-1,406506), CD21 (CR2/CR1,123422), CD43 (S7,143204), CD93 (AA4.1,136505), CD23 (B3B4,101614),TIM-1 (RMT1-4,119506), Ly6C (HK1.4,128041), Ly6G (1A8,127606), CD11c (N418,565591), CD11b (M1/70,101212), CD64 (X54-5/7.1,139316), PD-1 (RMP1-30,109112), TIGIT (1G9,142106), LAG3 (C9B7W,741350), TIM-3 (5D12,747626), CD39 (5F2,135704), CD73 (TY/11.8,127210), CD107a (1D4B,565533), NK1.1 (PK136,564144), MHC I (H-2Kb/H-2Db, 28-8-6,114605) , MHC II (I-A/E, M5/114.15.2,107645), CD80 (16-10A1,104741), CD86 (A17199A,105029), ICOSL (HK5.3,107405), CD40 (3/23,124622), CD25 (3C7,101904), IFNAR-1 (MAR1-5A3,127326). For intracellular staining: against IL-2 (JES6-5H4,503810), TNF-a (MP6-XT22,563944) and IFN-g (XMG1.2, 612769). For FOXP3 (FJK-16s,11-5773-82), Eomes (W17001A,48-4875-80), tBet (4B10,644824), Helios (22F6,137222), KI67 (16A8,652404), Granzyme B (2C5/F5,515408), Perforin (S16009A, 154310) and Tcf1 (Cell signaling C63D9,14456S). For in vivo all the antibodies were from BioXcell: anti-TIM-1 (3B3, BE0289), anti-PD-1 (RMP1-14, BE0146), anti-IFNAR-1 (MAR1-5A3, BE0241), antiIFNGR (GR-20,BE0029), anti-PDCA1(927, BE0311) used where appropriate. FC block was added to all stainings to reduce non-specific staining.Validation for flow antibodies was shown previously (Dixon et al. Nature 2021) Further validation is present on the manufacturer's website as noted in the Methods section.
}

Policy information about cell lines

\begin{tabular}{l|l} 
Cell line source(s) & \begin{tabular}{l} 
We obtained cell line; B16F10 from ATCC (CRL-6475), B16F10-Ova from Kai Wucherpfennig (Dana-Farber Cancer Institute, \\
Boston, MA) and MC38 from Mark Smyth (QIMR Berghofer, Queensland Institute of Medical Research, Brisbane Australia). \\
KP1.9 was derived from lung tumors of C57BL/6 KP mice and was kindly provided by Dr. A. Zippelius.
\end{tabular} \\
Authentication & \begin{tabular}{l} 
Morphology check by microscope and growth curve analysis were performed \\
periodically.
\end{tabular} \\
Mycoplasma contamination & All cell lines tested negative for mycoplasma contamination. \\
Commonly misidentified lines & None of the cell lines used are listed in the ICLAC database \\
(See ICLAC register) &
\end{tabular}

\section*{Animals and other organisms}

Policy information about studies involving animals; ARRIVE guidelines recommended for reporting animal research

\begin{tabular}{|c|c|}
\hline Laboratory animals & ![](https://cdn.mathpix.com/cropped/2024_05_26_609c2df07acbd8c3c2e0g-32.jpg?height=485\&width=1495\&top_left_y=850\&top_left_x=403) \\
\hline Wild animals & No Wild animals were used in this study \\
\hline Field-collected samples & no field collected samples were used in the study. \\
\hline Ethics oversight & \begin{tabular}{l} 
All experiments were conducted in accordance with animal protocols approved by the Harvard Medical Area Standing Committee on \\
Animals or BWH and MGH IACUC.
\end{tabular} \\
\hline
\end{tabular}

Note that full information on the approval of the study protocol must also be provided in the manuscript.

\section*{Human research participants}

Policy information about studies involving human research participants

Population characteristics

4 healthy individuals, age \(24-35,3 / 1(f / m)\)

Recruitment

from lab staff

Ethics oversight

Ethics approval from the local ethics committee of the Medical University of Graz (29-586ex16-17)

Note that full information on the approval of the study protocol must also be provided in the manuscript.

\section*{Flow Cytometry}

\section*{Plots}

Confirm that:

\(\triangle\) The axis labels state the marker and fluorochrome used (e.g. CD4-FITC).

\(\boxtimes\) The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis of identical markers).

\(\triangle\) All plots are contour plots with outliers or pseudocolor plots.

\(\triangle\) A numerical value for number of cells or percentage (with statistics) is provided.

\section*{Methodology}
\footnotetext{
For single cell suspension: lymph nodes and spleens were mechanically dissociated, homogenized, and passed through a 40-
}

Instrument

Software

Cell population abundance

Gating strategy um cell strainer and lysed of red blood cells (RBCs; using ACK buffer) then washed with cold PBS and spun down. Tumors were dissociated mechanically and digested with \(1 \mathrm{mg} / \mathrm{mL}\) collagenase \(A\) and \(0.1 \mathrm{mg} / \mathrm{mL} \mathrm{DNase1}\) for \(20 \mathrm{~min}\) at \(37^{\circ} \mathrm{C}\). Live/ dead cell discrimination was performed using Live/Dead Fixable viability dye e506 (eBioscience).

BD Symphony A5 (BD Biosciences) and BD FACSAria™ for cell sorting

FlowJo (Tree Star)

The frequency of the purity for the populations of interest are specified in the appropriate figures/methods.

Cells were first gated for FSC/SSC, doublets were excluded based on FSC and SSC W/H parameters, then onlive CD45+ followed by population specific markers.

Xick this box to confirm that a figure exemplifying the gating strategy is provided in the Supplementary Information.

  1. 'Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA. Klarman Cell Observatory, Broad Institute of MIT and Harvard Cambridge, MA, USA. Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA. Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA. Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. INSERM, Tours, France. Faculté de Médecine, Université de Tours, Tours, France. "Present address: Massachusetts General Hospital Cancer Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Present address: BeiGene, Beijing, China. Present address: Division of Immunology and Pathophysiology, Medical University of Graz, Graz, Austria. " Present address: Genentech, San Francisco, CA, USA. -mail: aviv.regev.sc@gmail.com; vkuchroo@rics.bwh.harvard.edu
    哈佛医学院恒大免疫疾病中心和美国马萨诸塞州波士顿布莱根妇女医院。 美国麻省理工学院布罗德研究所和哈佛大学剑桥分校克拉曼细胞天文台。 美国马萨诸塞州波士顿布莱根妇女医院、布莱根妇女医院和哈佛医学院的 Gene Lay 免疫学和炎症研究所。 美国马萨诸塞州波士顿马萨诸塞州总医院皮肤科。 美国马萨诸塞州波士顿哈佛医学院微生物学和免疫生物学系。 美国宾夕法尼亚州匹兹堡匹兹堡大学医学院Thomas E. Starzl移植研究所。 美国马萨诸塞州波士顿哈佛医学院布莱根妇女医院安·罗姆尼神经系统疾病中心。 美国马萨诸塞州剑桥市麻省理工学院霍华德休斯医学研究所生物学系和科赫综合癌症研究所。 INSERM,图尔,法国。 法国图尔图尔大学医学院。“现地址:美国马萨诸塞州波士顿哈佛医学院马萨诸塞州总医院医学系马萨诸塞州总医院癌症中心。 现地址:百济神州,北京,中国。 现住址:奥地利格拉茨格拉茨医科大学免疫学和病理生理学系。“ 现地址:美国加利福尼亚州旧金山基因泰克。 -邮件:aviv.regev.sc@gmail.com;vkuchroo@rics.bwh.harvard.edu