2024_09_09_4483b288d6b88e850475g

RESEARCH ARTICLE SUMMARY
研究文章摘要

BICCN

Transcriptomic cytoarchitecture reveals principles of human neocortex organization
转录组细胞结构揭示人类新皮层组织的原则

Nikolas L. Jorstad, Jennie Close, Nelson Johansen, Anna Marie Yanny, Eliza R. Barkan,
尼科拉斯·L·约斯塔德，珍妮·克洛斯，尼尔森·约汉森，安娜·玛丽·扬尼，伊丽莎·R·巴尔坎，Kyle J. Travaglini, Darren Bertagnolli, Jazmin Campos, Tamara Casper, Kirsten Crichton, Nick Dee,
凯尔·J·特拉瓦格利尼，达伦·贝尔塔尼奥利，贾兹敏·坎波斯，塔玛拉·卡斯珀，基尔斯滕·克赖顿，尼克·迪Song-Lin Ding, Emily Gelfand, Jeff Goldy, Daniel Hirschstein, Katelyn Kiick, Matthew Kroll,
丁松林，艾米莉·盖尔芬德，杰夫·戈尔迪，丹尼尔·赫希斯坦，凯特琳·基克，马修·克罗尔，Michael Kunst, Kanan Lathia, Brian Long, Naomi Martin, Delissa McMillen, Trangthanh Pham,
迈克尔·库恩斯特，卡南·拉西亚，布赖恩·朗，娜奥米·马丁，德丽莎·麦克米伦，张清芳，Christine Rimorin, Augustin Ruiz, Nadiya Shapovalova, Soraya Shehata, Kimberly Siletti,
克里斯廷·里莫林，奥古斯丁·鲁伊斯，纳迪亚·沙波瓦洛娃，索拉雅·谢哈塔，金伯莉·西莱蒂，Saroja Somasundaram, Josef Sulc, Michael Tieu, Amy Torkelson, Herman Tung, Edward M. Callaway,
萨罗贾·索马苏达拉姆，约瑟夫·苏尔茨，迈克尔·蒂欧，艾米·托克尔森，赫尔曼·唐，爱德华·M·卡拉威，Patrick R. Hof, C. Dirk Keene, Boaz P. Levi, Sten Linnarsson, Partha P. Mitra, Kimberly Smith,
帕特里克·R·霍夫，C·迪克·基恩，博阿兹·P·莱维，斯滕·林纳尔松，帕尔塔·P·米特拉，金伯利·史密斯，Rebecca D. Hodge*, Trygve E. Bakken*, Ed S. Lein*
丽贝卡·D·霍奇*，特里格·E·巴肯*，埃德·S·莱因*

INTRODUCTION: The human neocortex is generally organized into six layers of neurons but the size and cellular composition of these layers varies across the cortex, and this variation is thought to underlie differences in connectivity that impart specific functional specialization to distinct cortical areas. However, the degree to which cortical areas have a canonical versus noncanonical organization has proved difficult to reliably quantify. Single-nucleus and spatial transcriptomic methods enable highresolution characterization of the cellular structure of the human neocortex providing a means to quantitatively compare the molecular and cellular structure and specialization of distinct cortical areas.
引言：人类新皮层通常分为六层神经元，但这些层的大小和细胞组成在皮层中有所不同，这种变化被认为是导致不同连接的基础，从而赋予不同皮层区域特定的功能特化。然而，皮层区域的典型组织与非典型组织的程度一直难以可靠量化。单核和空间转录组学方法能够高分辨率地表征人类新皮层的细胞结构，为定量比较不同皮层区域的分子和细胞结构及特化提供了一种手段。

RATIONALE: Eight cortical areas that are representative of major variation in cellular architecture and include primary sensory and
理由：八个皮层区域代表了细胞结构的主要变化，包括初级感觉和

A Cross-areal consensus taxonomy
跨区域共识分类法

B Excitatory: Inhibitory ratio
B 兴奋性：抑制性比率

Neuronal proportions 神经元比例

Neuronal expression 神经元表达

C V1-specific L4 IT types
C V1 特异性 L4 IT 类型

Regional specializations of human cortical cell types. (A) Single nucleus RNA-sequencing data from eight areas of the human neocortex were used to generate a cross-areal taxonomy with shared and area-specific types. (B) The relative number of excitatory and inhibitory neurons is similar across all areas except V1. Neuronal cell-type proportions and gene expression varied systematically from the rostral (R) to caudal (C) cortex with additional regional signatures. (C) Some excitatory neuron types that are exclusive to V1 are in the visual input layer 4 , which is expanded compared to other cortical areas. association cortices were sampled using si nucleus transcriptomics to generate a dat comprised of more than 1.1 million nuclei.
人类皮层细胞类型的区域特化。(A) 使用来自人类新皮层八个区域的单核 RNA 测序数据生成了一个具有共享和区域特定类型的跨区域分类法。(B) 除 V1 外，所有区域的兴奋性和抑制性神经元的相对数量相似。神经元细胞类型的比例和基因表达从前脑(R)到后脑(C)系统性变化，并具有额外的区域特征。(C) 一些仅存在于 V1 的兴奋性神经元类型位于视觉输入层 4，相较于其他皮层区域，该层有所扩展。使用单核转录组学对联想皮层进行采样，生成了一个包含超过 110 万个细胞核的数据。

RESULTS: Nuclei were grouped based on gene expression similarity into one of 24 cellular subclasses, which were found in all cortical areas. Layer 4 intratelencephalic excitatory neurons were present even in agranular areas that lacked a histologically distinct layer 4, suggesting a common subclass-level cellular blueprint across the cortex. However, gene expression and subclass proportions varied substantially between cortical areas, with more differences in excitatory projection neurons than inhibitory neurons. All non-neuronal subclasses were shared across cortical areas but their laminar distributions varied between areas, and astrocytes also expressed regional marker genes. Variation as a function of rostrocaudal location in the cortex was a clear organizational feature where neighboring cortical areas were most similar, in line with previous observations of gene expression similarity by topographic proximity in the cortex. At a finer cell-type level of analysis, area-enriched and area-specific cell types were apparent in multiple cortical areas, but most notably in the primary visual cortex (V1) that had many distinct excitatory neuron types and several distinct inhibitory neuron types that reflect the specialized cellular architecture of this area in humans and other primates. V1 specialized inhibitory cell types were mostly Somatostatin-expressing neurons likely originating from the medial ganglionic eminence during development. Layer 4 in V1, which is visibly enlarged and has multiple sublayers, was notably different from other areas with discrete sublaminar distributions of specialized excitatory and inhibitory neurons revealed by spatial transcriptomics.
结果：细胞核根据基因表达相似性被分组为 24 个细胞亚类，这些亚类在所有皮层区域中均有发现。即使在缺乏组织学上明显的 4 层的无颗粒区域，4 层的皮层内兴奋性神经元仍然存在，这表明皮层内存在一个共同的亚类级别细胞蓝图。然而，基因表达和亚类比例在皮层区域之间差异显著，兴奋性投射神经元的差异大于抑制性神经元。所有非神经元亚类在皮层区域之间共享，但它们的层分布在不同区域之间有所不同，星形胶质细胞也表达区域标记基因。皮层中前后位置的变化是一个明显的组织特征，邻近的皮层区域最为相似，这与之前关于皮层中基因表达相似性与地形接近性的观察一致。在更细的细胞类型分析层面，多个皮层区域中明显存在区域富集和区域特异性的细胞类型，但最显著的是在初级视觉皮层（V1），该区域有许多不同的兴奋性神经元类型和几种不同的抑制性神经元类型，反映了人类和其他灵长类动物该区域的特化细胞结构。V1 特化的抑制性细胞类型主要是表达生长抑素的神经元，可能在发育过程中起源于内侧神经节隆起。V1 的第 4 层明显增大并具有多个亚层，与其他区域的特定兴奋性和抑制性神经元的离散亚层分布显著不同，这一点通过空间转录组学得以揭示。

CONCLUSION: A common set of cell types are found across human cortical areas that have diverse functions. Excitatory projection neurons exhibit large spatial gradients and regional differences in proportions, laminar distributions, and gene expression that are less pronounced in inhibitory neurons or non-neuronal cells. V1 is molecularly distinct from other cortical areas and several excitatory and inhibitory neuronal types are found only in V1. The ratio of excitatory to inhibitory neurons in V1 is also more than double that of other cortical areas, reflecting specialization of the human cortex for processing visual information.
结论：在人类皮层区域中发现了一组共同的细胞类型，这些细胞具有多样的功能。兴奋性投射神经元在比例、层次分布和基因表达上表现出较大的空间梯度和区域差异，而抑制性神经元或非神经细胞则不那么明显。V1 在分子上与其他皮层区域不同，且有几种兴奋性和抑制性神经元类型仅在 V1 中发现。V1 中兴奋性神经元与抑制性神经元的比例也超过其他皮层区域的两倍，反映了人类皮层在处理视觉信息方面的专业化。
The list of author affiliations is available in the full article. *Corresponding author. Email: edI@alleninstitute.org (E.S.L.); trygveb@alleninstitute.org (T.E.B.); rebeccah@alleninstitute.org (R.D.H.)
作者所属机构的列表可在完整文章中找到。*通讯作者。电子邮件：edI@alleninstitute.org（E.S.L.）；trygveb@alleninstitute.org（T.E.B.）；rebeccah@alleninstitute.org（R.D.H.）

Cite this article as N. L. Jorstad et al., Science 382, eadf6812 (2023). DOI: 10.1126/science.adf6812
引用本文为 N. L. Jorstad 等，科学 382, eadf6812 (2023)。DOI: 10.1126/science.adf6812

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https://doi.org/10.1126/science.adf6812

RESEARCH ARTICLE 研究文章

BICCN

Transcriptomic cytoarchitecture reveals principles of human neocortex organization
转录组细胞结构揭示人类新皮层组织的原则

Nikolas L. Jorstad , Jennie Close , Nelson Johansen , Anna Marie Yanny , Eliza R. Barkan ,
尼科拉斯·L·约斯塔德 , 珍妮·克洛斯 , 纳尔逊·约汉森 , 安娜·玛丽·扬尼 , 伊丽莎·R·巴尔坎 ,Kyle J. Travaglini , Darren Bertagnolli , Jazmin Campos , Tamara Casper , Kirsten Crichton ,
凯尔·J·特拉瓦格利尼 , 达伦·贝尔塔尼奥利 , 贾兹敏·坎波斯 , 塔玛拉·卡斯珀 , 基尔斯滕·克赖顿 ,Nick Dee , Song-Lin Ding , Emily Gelfand , Jeff Goldy , Daniel Hirschstein , Katelyn Kiick ,
尼克·迪 , 宋林·丁 , 艾米莉·盖尔芬德 , 杰夫·戈尔迪 , 丹尼尔·赫希斯坦 , 凯特琳·基克 ,Matthew Kroll , Michael Kunst , Kanan Lathia , Brian Long , Naomi Martin , Delissa McMillen ,
马修·克罗尔 , 迈克尔·库恩斯 , 卡南·拉西亚 , 布赖恩·朗 , 娜奥米·马丁 , 德丽莎·麦克米伦 ,Trangthanh Pham , Christine Rimorin , Augustin Ruiz , Nadiya Shapovalova , Soraya Shehata ,Kimberly Siletti , Saroja Somasundaram , Josef Sulc , Michael Tieu , Amy Torkelson , Herman Tung ,
金伯莉·西莱蒂 , 萨罗贾·索马苏达拉姆 , 约瑟夫·苏尔茨 , 迈克尔·蒂欧 , 艾米·托克尔森 , 赫尔曼·唐 ,Edward M. Callaway , Patrick R. Hof , C. Dirk Keene , Boaz P. Levi , Sten Linnarsson ,
爱德华·M·卡拉威 , 帕特里克·R·霍夫 , C·迪克·基恩 , 博阿兹·P·莱维 , 斯滕·林纳尔松 ,Partha P. Mitra , Kimberly Smith , Rebecca D. Hodge , Trygve E. Bakken , Ed S. Lein
帕尔塔·P·米特拉 , 金伯莉·史密斯 , 丽贝卡·D·霍奇 , 特里格维·E·巴肯 , 爱德·S·莱因

Abstract 摘要

Variation in cytoarchitecture is the basis for the histological definition of cortical areas. We used single cell transcriptomics and performed cellular characterization of the human cortex to better understand cortical areal specialization. Single-nucleus RNA-sequencing of 8 areas spanning cortical structural variation showed a highly consistent cellular makeup for 24 cell subclasses. However, proportions of excitatory neuron subclasses varied substantially, likely reflecting differences in connectivity across primary sensorimotor and association cortices. Laminar organization of astrocytes and oligodendrocytes also differed across areas. Primary visual cortex showed characteristic organization with major changes in the excitatory to inhibitory neuron ratio, expansion of layer 4 excitatory neurons, and specialized inhibitory neurons. These results lay the groundwork for a refined cellular and molecular characterization of human cortical cytoarchitecture and areal specialization.
细胞结构的变化是皮层区域组织学定义的基础。我们使用单细胞转录组学，对人类皮层进行了细胞特征分析，以更好地理解皮层区域的专业化。对跨越皮层结构变化的 8 个区域进行的单核 RNA 测序显示，24 个细胞亚类的细胞组成高度一致。然而，兴奋性神经元亚类的比例差异显著，可能反映了初级感觉运动皮层和联结皮层之间的连接差异。星形胶质细胞和少突胶质细胞的层次组织在不同区域也有所不同。初级视觉皮层显示出特征性组织，兴奋性神经元与抑制性神经元的比例发生重大变化，层 4 兴奋性神经元扩展，以及专业化的抑制性神经元。这些结果为人类皮层细胞结构和区域专业化的精细细胞和分子特征化奠定了基础。

Areal parcellation of the neocortex is premised on the idea that structural variations in cellular architecture (1-3) and myeloarchitecture (4) underlie functional divisions [reviewed in (5)]. Neocortex has a 6-layered organization common across species and areas, apart from agranular areas such as the primary motor cortex (M1) that lack layer 4. Cortical layers contain projection neurons with generally stereotyped input and output properties hypothesized to represent a "canonical" circuitry (6, 7). However, cortical areas differ in positional topography, shape and size, laminar and columnar organization, and neuron proportions (

).
新皮层的区域划分基于这样一个理念：细胞结构（1-3）和髓鞘结构（4）的结构变化是功能划分的基础[在（5）中回顾]。新皮层具有在物种和区域间普遍存在的六层组织，除了缺乏第四层的无颗粒区，如初级运动皮层（M1）。皮层层次包含投射神经元，这些神经元通常具有刻板的输入和输出特性，假设代表了一种“典型”电路（6，7）。然而，皮层区域在位置拓扑、形状和大小、层状和柱状组织以及神经元比例上存在差异（

）。
Advances in single cell transcriptomics have revealed a complex, hierarchical cortical celltype architecture based on gene expression signatures that is conserved across species except at the finest cell-type distinctions (11-15). Prior work in M1 established a cellular hierarchy consisting of 24 neuronal and non-neuronal subclasses with distinct laminar patterning
单细胞转录组学的进展揭示了一种复杂的、分层的皮层细胞类型结构，基于基因表达特征，这种结构在物种之间是保守的，除了最细的细胞类型区分（11-15）。之前在 M1 的研究建立了一个细胞层次结构，由 24 个具有不同层状模式的神经和非神经亚类组成。

and correlated phenotypic properties (table S1) and revealed deeper cellular complexity in any given cortical area than previously appreciated (12, 13, 15-19). The current study aims to quantitatively define the cellular architecture of eight human neocortical areas representative of topographic, functional, and structural variation, using single nucleus RNA-seq (snRNA-seq) and spatial transcriptomics methods.
并且相关的表型特性（表 S1）显示出任何给定皮层区域的细胞复杂性比之前认识的更深（12, 13, 15-19）。本研究旨在定量定义八个人类新皮层区域的细胞结构，这些区域代表了地形、功能和结构的变化，使用单核 RNA 测序（snRNA-seq）和空间转录组学方法。

Within-area cell taxonomies demonstrate common subclass architecture
区域内细胞分类法展示了共同的亚类结构

To sample major axes of cortical variation, we analyzed eight neocortical areas that included M1 and primary somatosensory (S1), auditory (A1), visual (V1) and association areas [dorsolateral prefrontal cortex (DFC), anterior cingulate cortex (ACC), middle temporal gyrus (MTG) angular gyrus (AnG)], which spanned the rostral to caudal (anterior to posterior in many mammals) extent of the cortical sheet, and represented major variations in cortical cytoarchitecture (Fig. 1A) (20). Cortical areas were identified across tissue donors using a combination of surface anatomical landmarks and histological verification of cytoarchitecture (Methods). Human postmortem brain samples were collected from 5 individuals ( 3 males, 2 females, table S2). Tissue photographs taken at the time of autopsy and tissue dissection were used to manually align tissue samples to three-dimensional (3D) reference atlases [Allen Human Reference Atlas 3D https:// github.com/BICCN/cell-locator; Julich-Brain v2.9 parcellation, DOI:10.25493/VSMK-H94 (21)] and a 2D plate-based reference (Allen Human Reference Atlas http://atlas.brain-map. org/). The best matching structure in each reference atlas is reported (table S2, Methods) and secondary structures are reported when more than one cortical area is predicted according to the mapping results (table S2). Most tissue samples map to a single area in the Allen Human Reference Atlas but MTG samples included both the intermediate and caudal subdivisions of A21. Mapping to the probabilistic Julich Brain Atlas suggests that several areas may have been sampled for ACC (area 33 and area p24ab) and A1 (area TE 1.0 and area TE 1.1), and variation in the precise location of sampling might result in increased variability in the cellular compositions of these areas.
为了采样皮层变异的主要轴线，我们分析了八个新皮层区域，包括 M1 和初级躯体感觉区（S1）、听觉区（A1）、视觉区（V1）以及关联区域[背外侧前额叶皮层（DFC）、前扣带皮层（ACC）、中颞回（MTG）、角回（AnG）]，这些区域跨越了皮层片的前端到后端（在许多哺乳动物中为前到后），并代表了皮层细胞结构的主要变异（图 1A）（20）。通过结合表面解剖标志和细胞结构的组织学验证，识别了不同组织供体的皮层区域（方法）。人类尸检脑样本来自 5 名个体（3 名男性，2 名女性，表 S2）。在尸检和组织解剖时拍摄的组织照片被用来手动对齐组织样本到三维（3D）参考图谱[艾伦人类参考图谱 3D https://github.com/BICCN/cell-locator；朱利希大脑 v2.9 分区，DOI:10.25493/VSMK-H94（21）]和二维基于平板的参考（艾伦人类参考图谱 http://atlas.brain-map.org/）。在每个参考图谱中报告最佳匹配结构（表 S2，方法），当根据映射结果预测到多个皮层区域时，报告次级结构（表 S2）。大多数组织样本映射到艾伦人类参考图谱中的单一区域，但 MTG 样本包括 A21 的中间和尾部细分。映射到概率性朱利希脑图谱表明，ACC（区域 33 和区域 p24ab）和 A1（区域 TE 1.0 和区域 TE 1.1）可能采样了多个区域，采样的精确位置变化可能导致这些区域的细胞组成增加变异性。
Three snRNA-seq datasets were generated: a 10x Chromium v3 (Cv3) dataset with

nuclei sampled from all cortical layers, a Cv3 dataset of

nuclei captured by microdissection of layer 5 to enrich for rare layer 5 extratelencephalic projecting (L5 ET) neurons (for all areas except AnG and M1), and a SMART-seqv4 (SSv4) dataset of over 60,000 nuclei sampled from individual cortical layers to provide laminar selectivity for all clusters. For AnG, only a Cv3 dataset of all cortical layers was generated (Fig. 1B).
生成了三个 snRNA-seq 数据集：一个包含

个来自所有皮层层的细胞核的 10x Chromium v3 (Cv3) 数据集，一个通过微切割第 5 层捕获的

个细胞核的 Cv3 数据集，以富集稀有的第 5 层外脑投射（L5 ET）神经元（除了 AnG 和 M1 的所有区域），以及一个来自单个皮层层的超过 60,000 个细胞核的 SMART-seqv4 (SSv4) 数据集，以为所有簇提供层选择性。对于 AnG，仅生成了一个包含所有皮层层的 Cv3 数据集（图 1B）。
Nuclei were assigned to one of 24 cell subclasses based on transcriptomic similarity to a reference taxonomy for human M1

, and subclasses were grouped into five neighborhoods (Fig. 1, C and D). For each area and neighborhood, nuclei profiled with Cv3 and SSv4 were integrated based on shared coexpression and clustered to identify transcriptomically distinct cell types. Neighborhood clusters were aggregated and organized into within-area taxonomies ranging between 120 and 142 cell types (Fig. 1C and figs. S1 to S8) with distinct marker expression (table S3). Cellular variation within subclasses was quantified as the average entropy of variably expressed genes. Entropy was higher for all neuronal than non-neuronal subclasses and did not differ between excitatory and inhibitory subclasses or across areas based on a two-way analysis of variance (ANOVA) followed by post-hoc Tukey HSD tests (Fig. 1E). The number of distinct cell types within a subclass was similar across areas for inhibitory and non-neuronal subclasses but varied for excitatory neuron subclasses (Fig. 1F). This within-subclass variation was not driven by differential sampling of nuclei across areas (Fig. 1, G and H). In V1, there were more layer (L)4 intratelencephalic projecting (IT) types, consistent with expansion and specialization of the thalamorecipient L4 in V1, and more L5 IT types. L6 IT Car3 neurons were more diverse in MTG, A1, and AnG compared with other areas. L5 ET neurons were least diverse in the rostral area ACC and in the
细胞核根据与人类 M1 参考分类法的转录组相似性被分配到 24 个细胞子类中的一个，并且子类被分组为五个邻域（图 1，C 和 D）。对于每个区域和邻域，使用 Cv3 和 SSv4 分析的细胞核基于共享共表达进行整合，并聚类以识别转录组学上不同的细胞类型。邻域聚类被汇总并组织成区域内的分类法，细胞类型数量在 120 到 142 之间（图 1C 和图 S1 至 S8），具有不同的标记表达（表 S3）。子类内的细胞变异通过可变表达基因的平均熵进行量化。所有神经子类的熵均高于非神经子类，并且在兴奋性和抑制性子类之间或不同区域之间没有差异，基于双向方差分析（ANOVA）及后续的 Tukey HSD 检验（图 1E）。在抑制性和非神经子类中，子类内的不同细胞类型数量在各个区域相似，但兴奋性神经子类的数量则有所不同（图 1F）。这种子类内的变异并不是由于不同区域细胞核的采样差异所驱动的（图。 1，G 和 H）。在 V1 中，层（L）4 的皮层内投射（IT）类型更多，这与 V1 中丘脑接受的 L4 的扩展和专业化一致，并且 L5 的 IT 类型也更多。与其他区域相比，MTG、A1 和 AnG 中的 L6 IT Car3 神经元更加多样化。L5 ET 神经元在前额叶皮层的前部区域多样性最低

Fig. 1. Transcriptomic cell type diversity across human cortical areas.
图 1. 人类皮层区域的转录组细胞类型多样性。
(A) Eight areas of the neocortex were sampled from four lobes of the adult human brain. (B) snRNA-seq sampling across areas grouped by RNA-seq platform, layer dissection strategy, and number of male and female donors. (C) Schematic of snRNA-seq clustering to generate cell-type taxonomies for each area. (D) UMAPs of single nuclei from each area based on variable gene expression and colored by cell subclass as in (J). (E) Distributions of subclass transcriptomic entropy differ between neuronal (Exc and Inh) and non-neuronal
（A）从成年人的大脑四个叶中采样了八个新皮层区域。（B）根据 RNA-seq 平台、层解剖策略以及男性和女性供体的数量，对区域进行 snRNA-seq 采样分组。（C）snRNA-seq 聚类的示意图，用于生成每个区域的细胞类型分类。（D）基于可变基因表达的每个区域单核的 UMAP，并按细胞亚类着色，如（J）所示。（E）神经元（兴奋性和抑制性）与非神经元的亚类转录组熵分布存在差异。
(NN) classes and not between areas. (

, and

) Summary of within-area taxonomies showing the number of nuclei sampled from each subclass and the number of distinct clusters (cell types) identified for excitatory (F) and inhibitory (G) neurons and non-neuronal cells (H). (I) Number of subclass markers in each area (box plots) and shared across areas (blue points). Box plots show median, interquartile range (IQR), up to 1.5*IQR (whiskers), and outliers (points).
（NN）类别之间而不是区域之间。（

和

）区域内分类法的摘要，显示从每个亚类中采样的核数量以及为兴奋性（F）和抑制性（G）神经元及非神经细胞（H）识别的不同簇（细胞类型）数量。（I）每个区域的亚类标记数量（箱形图）和跨区域共享的标记数量（蓝点）。箱形图显示中位数、四分位数范围（IQR）、最多 1.5*IQR（须）和异常值（点）。
(J) Heatmaps of conserved marker expression for 50 random nuclei sampled from each area for chandelier interneurons and horizontally compressed for all subclasses.
（J）来自每个区域的 50 个随机细胞核的保守标记表达热图，针对吊灯中间神经元，并对所有亚类进行水平压缩。
most caudal area V1, whereas L6 corticothalamicprojecting (L6 CT) neurons were most diverse in M1, S1, MTG, and V1. Individual subclasses had hundreds of distinct markers in each area (table S4), and 20 to

of markers were conserved across areas (Fig. 1I). For example, Fig. 1J plots expression of a set of chandelier cell markers that were common across areas (left), and a set of common markers for all subclasses (right). Excitatory subclasses had the smallest fraction of conserved markers, pointing to more variable expression of excitatory neuron gene expression across the cortex as reported in mice (15).
最尾部区域 V1，而 L6 皮层-丘脑投射（L6 CT）神经元在 M1、S1、MTG 和 V1 中最为多样。每个区域的个别亚类有数百个不同的标记（表 S4），并且在各个区域之间保留了 20 到

个标记（图 1I）。例如，图 1J 绘制了一组在各个区域中共同存在的吊灯细胞标记的表达（左），以及所有亚类的共同标记的一组（右）。兴奋性亚类的保留标记比例最小，指向兴奋性神经元基因表达在皮层中的更大变异性，正如在小鼠中所报道的那样（15）。

Cross-areal abundance changes reveal areal specification
跨区域丰度变化揭示区域特异性

The areas analyzed have distinct cytoarchitecture based on Nissl staining that shows variation in cell size, shape, and laminar organization (Fig. 2A), and spanned the rostrocaudal axis of the cortical sheet (Fig. 2B). Relative proportions of transcriptionally defined neuronal subclasses varied across areas (Fig. 2C and table S5). Excitatory neuron subclasses had the greatest differences in proportions across areas and often reflected known differences in cellular architecture. Agranular M1 and ACC (Fig. 2A) had L4 IT neurons but at lower proportions than other areas (12), with the lowest proportion observed in ACC. By contrast, in V1 where L4 is visibly enlarged, L4 IT neuron proportion was increased. As described previously in mouse cortex (15) and between human M1 and MTG (12), inhibitory neuron subclasses were similar across areas except for a marked increase of medial ganglionic eminence (MGE)-derived PVALB neurons and fewer caudal ganglionic eminence (CGE)-derived interneurons (LAMP5 LHX6, LAMP5, SNCG, VIP, PAX6) in V1. These proportion differences in excitatory and inhibitory neurons were validated in situ by label- ing of neuronal subclasses in MTG and V1 using MERFISH spatial transcriptomics (Fig. 2C, right panels, and table S5), demonstrating that they were not an artifact of nuclear isolation or snRNA-seq processing.
分析的区域基于尼氏染色显示出细胞大小、形状和层次组织的差异，具有明显的细胞结构特征（图 2A），并跨越皮层片的头尾轴（图 2B）。转录定义的神经元亚类的相对比例在不同区域之间有所不同（图 2C 和表 S5）。兴奋性神经元亚类在不同区域的比例差异最大，通常反映已知的细胞结构差异。无颗粒 M1 和 ACC（图 2A）具有 L4 IT 神经元，但其比例低于其他区域（12），ACC 中观察到的比例最低。相比之下，在 V1 中，L4 明显增大，L4 IT 神经元的比例增加。如之前在小鼠皮层（15）以及人类 M1 和 MTG 之间（12）所描述，抑制性神经元亚类在不同区域之间相似，除了在 V1 中，来自内侧神经节隆起（MGE）的 PVALB 神经元显著增加，而来自尾侧神经节隆起（CGE）的中间神经元（LAMP5 LHX6, LAMP5, SNCG, VIP, PAX6）则较少。这些兴奋性和抑制性神经元的比例差异在原位通过在 MTG 和 V1 中使用 MERFISH 空间转录组学标记神经元亚类得到了验证（图 2C，右侧面板和表 S5），证明它们不是核分离或 snRNA-seq 处理的伪影。
Subclass proportions were highly consistent across donors despite variation in the precise location sampled for areas such as MTG (Fig. 2D). Examined from a subclass perspective, the most obvious proportion differences were seen in L4 IT (range 10-fold, from 3 to

of excitatory neurons), and in the much sparser L5 ET neurons (range 50 -fold, from 0.1 to

). Many of these proportion differences varied in a graded fashion generally along the rostrocaudal (R-C) axis. Pairwise correlations in excitatory neuron proportions revealed correlated R-C decreases in L5 ET, L6B, L6 IT, and L5/6 near-projecting neurons (L5/6 NP), with an anticorrelated R-C increase in L4 IT (Fig. 2E). Among inhibitory subclasses, the rarest types
亚类比例在捐赠者之间高度一致，尽管在采样位置的精确性上存在差异，例如 MTG（图 2D）。从亚类的角度来看，最明显的比例差异出现在 L4 IT（范围 10 倍，从 3 到

的兴奋性神经元）和稀疏得多的 L5 ET 神经元（范围 50 倍，从 0.1 到

）。这些比例差异在一般沿着头尾（R-C）轴呈现出分级变化。兴奋性神经元比例的成对相关性显示 L5 ET、L6B、L6 IT 和 L5/6 近投射神经元（L5/6 NP）在 R-C 方向上呈现相关的减少，而 L4 IT 则呈现出反相关的 R-C 增加（图 2E）。在抑制性亚类中，最稀有的类型
A

snRNA-seq

B

MERFISH

D IT-projecting D IT 项目管理

Deep layer (Non-IT) 深层（非 IT）

labeling using MERFISH. Arrowhead directions indicate subclasses that significantly increase (pointing up) or decrease (pointing down) across areas based on scCODA analysis (D) For each donor, subclass proportions were calculated as a fraction of all neurons in the same class (excitatory or inhibitory) and grouped by neighborhood (

nominal

; **Bonferroni-corrected

). (E) Spearman correlations of excitatory and inhibitory subclass proportions across areas. Scale bar on (A) is

.
使用 MERFISH 进行标记。箭头方向表示基于 scCODA 分析在不同区域中显著增加（向上指）或减少（向下指）的亚类（D）。对于每个供体，亚类比例作为同一类（兴奋性或抑制性）中所有神经元的比例进行计算，并按邻域分组（

名义

；**Bonferroni 校正

）。 (E) 不同区域兴奋性和抑制性亚类比例的 Spearman 相关性。 (A)上的比例尺为

。
Fig. 2. Cell subclass composition reflects cytoarchitecture and varies systematically along the R-C axis. (A) Images of Nissl-stained sections of cortical areas are labeled with approximate layer boundaries and show distinct cytoarchitecture. Areas are ordered by position along the R-C axis of the cortex. (B) Representative cortical gyral locations of sampled tissue. (C) Relative proportions of neuronal subclasses as a fraction of all excitatory or inhibitory neurons in each area and estimated based on snRNA-seq profiling or in situ
图 2. 细胞亚类组成反映细胞结构，并沿 R-C 轴系统性变化。(A) Nissl 染色的皮层区域切片图像标注了大致的层界限，显示出明显的细胞结构。区域按皮层 R-C 轴的位置排序。(B) 采样组织的代表性皮层回位点。(C) 每个区域中兴奋性或抑制性神经元的相对比例，作为所有神经元的一个分数，并基于 snRNA-seq 分析或原位估计。
(SNCG, PAX6, and SST CHODL) had the most correlated changes in proportions with a decreasing R-C gradient.
(SNCG, PAX6 和 SST CHODL) 的比例变化与 R-C 梯度降低最相关。
Smaller-scale areal specializations in proportions were overlaid on these broad trends of conservation or R-C gradients and many subclasses showed a particularly large difference in V1 (L5/6 NP, L6B, PAX6). Chandelier inhibitory neuron proportions were lowest in MTG and AnG and highest in S1 (Fig. 2, C and D). There were more L4 IT neurons and fewer L5 ET neurons in DFC, more L6 IT neurons in M1, and fewer L6 IT Car3 neurons in
较小规模的区域特化比例叠加在这些广泛的保护或 R-C 梯度趋势上，许多子类在 V1（L5/6 NP，L6B，PAX6）中表现出特别大的差异。吊灯抑制神经元的比例在 MTG 和 AnG 中最低，而在 S1 中最高（图 2，C 和 D）。DFC 中 L4 IT 神经元更多，L5 ET 神经元更少，M1 中 L6 IT 神经元更多，而 L6 IT Car3 神经元更少。
ACC than expected based on the broad trends. In summary, cell subclass proportions define a quantitative cytoarchitecture that is canonical in having all 24 subclasses in all areas, with varying proportions and gradient properties that likely reflect developmental gradients and specializations driven by the circuit requirements of functionally distinct cortical areas.
ACC 比预期的更高，基于广泛的趋势。总之，细胞亚类比例定义了一种定量细胞结构，其典型特征是在所有区域中都有 24 个亚类，具有不同的比例和梯度特性，这可能反映了由功能上不同的皮层区域的电路需求驱动的发展梯度和专业化。

Excitatory to inhibitory neuron ratio varies across cortical areas and layers
兴奋性神经元与抑制性神经元的比例在皮层区域和层次之间有所不同

In addition to areal specializations in neuronal subclass proportions, we found differences in relative proportions of excitatory and inhibitory neurons (E:I ratio) (table S5). As previously reported for M1 (12), the E:I ratio was 2:1 for most cortical areas, in contrast to the reported E:I ratio of 5:1 in mice. However, the E:I ratio in V1 (4.5:1) was much higher (Fig. 3A) and comparable to that of rodents. MERFISH analysis in MTG and V1 confirmed these values and areal differences (table S5).
除了神经元亚类比例的区域特化，我们发现兴奋性和抑制性神经元的相对比例（E:I 比率）存在差异（表 S5）。如之前对 M1 的报道（12），大多数皮层区域的 E:I 比率为 2:1，而小鼠的 E:I 比率报告为 5:1。然而，V1 的 E:I 比率（4.5:1）要高得多（图 3A），并且与啮齿动物相当。MTG 和 V1 的 MERFISH 分析确认了这些数值和区域差异（表 S5）。
Layer-specific dissections of nuclei from 7 regions (excluding AnG) allowed a deeper exploration of E:I ratio variation. E:I ratios varied by area and layer and were consistent across
来自 7 个区域（不包括 AnG）的层特异性核切割允许对 E:I 比率变化进行更深入的探索。E:I 比率因区域和层而异，并且在各层之间保持一致。
A

C MERFISH

E SNCG

LAMP5 LHX6

ACC	DFC	M1	S1	MTG	A1	V1
L1	L1	L1	L1	L1	L1	L1	Layer prop. 层属性
	L2	D		L2	L2	L2	Layer prop. 层属性
	L	L2	L3		L3	L3	0.5
L5A	L3	L3	L4		L4	L4AB	0.3
L5B			L5		L6A	L5
L6	L6	L6	16	L6

F LAMP5 LHX6

Fig. 3. E:I ratio variation across cortical areas and layers. (A) Relative number of excitatory neurons to inhibitory neurons (E:I ratio) in each area. Bar plots indicate average and standard deviation across donors. (B) E:I ratios estimated for a common set of layers dissected from each area. Box plots show median, interquartile range (IQR), up to

(whiskers), and outliers (points) across multiple donors. (C) Validation of increased E:I ratios in all cortical layers in V1 compared with MTG based on MERFISH experiments. Bar plots and whiskers indicate average and standard deviation of E:I ratios across donors, respectively. (D) E:I ratios estimated for all layers dissected from each area. (E) Laminar distributions of interneurons were conserved (SNCG) or divergent (LAMP5 LHX6) across areas based on counts of layer-dissected nuclei. Note that primary sensory areas (

, and V 1 ) have a distinct distribution of LAMP5 LHX6 neurons. (F) MERFISH in situ labeling of LAMP5 LHX6 cells shows a decreased proportion of cells in layer 6 of V1 compared with MTG.
图 3. 皮层区域和层的 E:I 比率变化。(A) 每个区域兴奋性神经元与抑制性神经元的相对数量（E:I 比率）。条形图表示捐赠者之间的平均值和标准差。(B) 从每个区域解剖出的共同层次估计的 E:I 比率。箱形图显示多个捐赠者的中位数、四分位范围（IQR）、最大值（须）和异常值（点）。(C) 基于 MERFISH 实验验证 V1 中所有皮层层次的 E:I 比率相较于 MTG 的增加。条形图和须分别表示捐赠者之间 E:I 比率的平均值和标准差。(D) 从每个区域解剖出的所有层次估计的 E:I 比率。(E) 根据层解剖核的计数，抑制性神经元的层分布在区域间保持一致（SNCG）或出现差异（LAMP5 LHX6）。注意，初级感觉区域（

和 V1）具有 LAMP5 LHX6 神经元的独特分布。(F) MERFISH 原位标记 LAMP5 LHX6 细胞显示 V1 的第 6 层细胞比例较 MTG 减少。
donors (Fig. 3B). However, increased variability was seen in MTG perhaps due to sampling of both the intermediate and caudal subdivisions of A21. V1 had the highest E:I ratio across all layers, not just in L4 (7:1) but also in L5 and L6, where the highest E:I ratio of 10:1 was seen. Moreover, there was a monotonic increase in the E:I ratio of other areas along a R-C gradient, which was most apparent in L2/3. E:I ratios were more variable in L4 and L5, masking the trend in overall E:I ratios (Fig. 3A). In situ cell counts in MTG and V1 using MERFISH confirmed a higher E:I ratio in all layers of V1 (Fig. 3C). From a within-area perspective, E:I ratios increased with cortical depth, with the highest ratios in L6 for all areas (Fig. 3D). Furthermore, the E:I ratio in L4 was distinctly elevated in V1 relative to L2, L3, and L5, highlighting specialization of visual processing compared with other sensory modalities. Finally, laminar distributions of excitatory and inhibitory neurons were relatively consistent across cortical areas (fig. S9), such as SNCG in L1 (Fig. 3 E ), but some areal and laminar variation was apparent, such as LAMP5 LHX6 proportions in L6 (Fig. 3, E and F). Taken together, E:I ratios vary extensively both by layer and area, with markedly different ratios in V1 and areal variation that is masked by averaging across cortical layers.
供体（图 3B）。然而，MTG 中观察到的变异性增加，可能是由于对 A21 的中间和尾部亚区的采样。V1 在所有层中具有最高的兴奋性与抑制性比率，不仅在 L4 中（7:1），在 L5 和 L6 中也观察到最高的兴奋性与抑制性比率为 10:1。此外，其他区域的兴奋性与抑制性比率沿 R-C 梯度单调增加，这在 L2/3 中最为明显。L4 和 L5 中的兴奋性与抑制性比率更具变异性，掩盖了整体兴奋性与抑制性比率的趋势（图 3A）。使用 MERFISH 在 MTG 和 V1 中的原位细胞计数确认了 V1 所有层中较高的兴奋性与抑制性比率（图 3C）。从区域内部的角度来看，兴奋性与抑制性比率随着皮层深度的增加而增加，所有区域中 L6 的比率最高（图 3D）。此外，V1 中 L4 的兴奋性与抑制性比率相对于 L2、L3 和 L5 明显升高，突显了视觉处理的专业化，与其他感觉模态相比。最后，兴奋性和抑制性神经元的层分布在皮层区域之间相对一致（图 S9），例如 L1 中的 SNCG（图 3E），但某些区域和层的变异是明显的，例如 L6 中的 LAMP5 和 LHX6 的比例（图 3E 和 F）。综合来看，E:I 比率在不同层和区域之间变化广泛，V1 中的比率明显不同，而通过平均皮层层次掩盖了区域变化。

Transcriptomic cellular topography
转录组细胞地形学

To characterize the transcriptomic landscape of neuronal subclass cortical areas, neuronal nuclei were integrated by donor for each of four neighborhoods [IT-projecting excitatory, deep layer (non-IT) excitatory, MGE-derived GABAergic and CGE-derived GABAergic] and visualized as UMAPs colored by subclass (Fig. 4A) and area (Fig. 4B). Three organizational principles were apparent. First, excitatory neurons had strong areal signatures, visualized as clear banding by area, whereas inhibitory neurons were mostly intermixed across areas similar to reports in the mouse cortex (15). Second, there were visible V1 specializations including substantial expansion of L4 IT neurons and distinct islands in the UMAPs for most IT-projecting subclasses and L6 CT neurons. Distinct V1 islands were also seen for parts of the PVALB and SST subclasses (arrows in MGE-derived UMAPs). Third, the areal similarity of excitatory neurons appeared to vary in a R-C topographic order for many subclasses, similar to prior reports of gene expression similarity across the human cortex (22). Neighboring areas were similar and intermixed despite known functional distinctiveness; for example, nuclei from M1 and S1 intermingled despite their specificity for motor and somatosensory functions, respectively.
为了表征神经元亚类皮层区域的转录组景观，按捐赠者整合了四个邻域的神经元核 [IT 投射兴奋性、深层（非 IT）兴奋性、MGE 来源的 GABA 能和 CGE 来源的 GABA 能]，并以亚类（图 4A）和区域（图 4B）为颜色可视化为 UMAPs。显现出三种组织原则。首先，兴奋性神经元具有强烈的区域特征，在区域上可视化为清晰的带状分布，而抑制性神经元则大多在区域间混合，类似于小鼠皮层的报告（15）。其次，V1 区域的特化明显，包括 L4 IT 神经元的显著扩展，以及大多数 IT 投射亚类和 L6 CT 神经元在 UMAPs 中的独特岛屿。PVALB 和 SST 亚类的部分区域也观察到了独特的 V1 岛屿（MGE 来源的 UMAPs 中的箭头）。第三，兴奋性神经元的区域相似性在许多亚类中似乎以 R-C 拓扑顺序变化，类似于先前关于人类皮层基因表达相似性的报告（22）。邻近区域尽管已知功能上具有独特性，但仍然相似且交错；例如，M1 和 S1 的核尽管分别特异于运动和躯体感觉功能，但仍然交织在一起。
Areal variation in gene expression mirrored the UMAP trends. The number of differentially expressed genes (DEGs, table S6) across areas was largest for excitatory neurons (Fig. 4C), and highest for L4 IT and L5 ET subclasses (over 1000 DEGs). DEGs for inhibitory neuron subclasses varied widely, from over 100 DEGs for SST and PVALB interneurons to fewer than 10 DEGs for SNCG and SST CHODL and a single DEG (ADAMTS9-AS2) for PAX6. Nonneuronal cell subclasses similarly displayed few areal DEGs. We next used a previously defined tau score (23) to identify area-specific markers (table S7), which were much less common. Excitatory neurons expressed the most areal markers and ACC and V1 were the most distinct areas (Fig. 4D and fig. S10, A and B). IT-projecting neurons were specialized in both ACC and V1 whereas Non-IT L6 CT and L5 ET neurons were specialized mostly in V1.
基因表达的区域变化反映了 UMAP 趋势。不同区域的差异表达基因（DEGs，表 S6）数量在兴奋性神经元中最大（图 4C），而 L4 IT 和 L5 ET 亚类的 DEGs 数量最高（超过 1000 个 DEGs）。抑制性神经元亚类的 DEGs 变化幅度较大，从 SST 和 PVALB 中间神经元的超过 100 个 DEGs 到 SNCG 和 SST CHODL 的少于 10 个 DEGs，以及 PAX6 的单个 DEG（ADAMTS9-AS2）。非神经元细胞亚类同样显示出很少的区域 DEGs。接下来，我们使用先前定义的 tau 评分（23）来识别区域特异性标记（表 S7），这些标记要少得多。兴奋性神经元表达了最多的区域标记，ACC 和 V1 是最明显的区域（图 4D 和图 S10，A 和 B）。IT 投射神经元在 ACC 和 V1 中都有专门化，而非 IT 的 L6 CT 和 L5 ET 神经元主要在 V1 中专门化。

The topographic ordering of the excitatory neuron subclasses above suggested graded changes as a function of distance, similar to bulk tissue profiling studies reporting gradual changes in gene expression across the cortical sheet (22). We therefore calculated transcriptomic similarities of excitatory subclasses as a function of the approximate physical distance between pairs of areas on an unfolded cortical sheet (Fig. 4E and table S2). Because V1 was so distinct (Fig. 4A), we fit two linear models of subclass similarity versus areal distance, one that included pairwise comparisons to V1 and one that did not (Fig. 4E). All excitatory neuron subclasses showed the same monotonic decrease of similarity with distance but had different amounts of transcriptomic specialization in V1 (intercepts, but not slopes, are different in Fig. 4E). Interneuron similarity also decreased with distance at the same rate for all subclasses, albeit at about

the rate of excitatory neurons, and with much less specialization in V1 (fig. S10C). By contrast, non-neuronal expression did not change systematically with interareal distance and was not more specialized in V1 (fig. S10D).
兴奋性神经元亚类的地形排序表明，随着距离的变化，存在渐进的变化，这与大块组织分析研究报告的皮层片上基因表达的逐渐变化相似（22）。因此，我们计算了兴奋性亚类的转录组相似性，作为展开的皮层片上区域对之间的近似物理距离的函数（图 4E 和表 S2）。由于 V1 非常独特（图 4A），我们拟合了两个线性模型，分别是亚类相似性与区域距离的关系，一个包括与 V1 的成对比较，另一个不包括（图 4E）。所有兴奋性神经元亚类的相似性随着距离的增加而单调下降，但在 V1 的转录组专业化程度不同（截距不同，但斜率相同，见图 4E）。抑制性神经元的相似性也以相同的速率随着距离的增加而下降，尽管其速率约为兴奋性神经元的

，并且在 V1 的专业化程度要低得多（图 S10C）。相比之下，非神经元的表达并没有随着区域间距离的变化而系统性变化，并且在 V1 的专业化程度也没有更高（图 S10D）。

To determine how gene expression varied across the cortical sheet, we performed a variance partitioning analysis for each subclass (fig. S10E and table S8). Expression of hundreds of genes was explained by area identity and spatial gradients for excitatory neurons compared with a few genes for inhibitory neurons and non-neuronal cells. These genes had a similar proportion of expression variation explained by area or gradients (median 5 to 10%) in all subclasses (fig. S10F). Therefore, the observed differences in transcriptomic topography (Fig. 4E) were mainly due to the number of genes with areal variation and not to the strength of that variation. Among ITprojecting neurons, some genes showed distinct patterning in a single subclass whereas other genes were topographically patterned in all IT subclasses (fig. S10G). We calculated the expression variance explained by gradients along three axes: rostrocaudal (

), midlinesurface (M-S, anatomical left to right), and dorsoventral (D-V). The relative position of each cortical area along these axes was calculated using the voxel coordinates corresponding to the approximate center of each area in the Allen Human 3D Reference Atlas (table S2). For genes with at least

of expression variance explained by any gradient, we quantified the relative strength of gradients based on the relative proportion of expression variance that was explained (fig. S10H). For most subclasses, R -C gradients were dominant but non-IT subclasses also expressed many genes with M-S and D-V gradients (Fig. 4F and fig. S10H).
为了确定基因表达在皮层片上的变化，我们对每个亚类进行了方差分解分析（图 S10E 和表 S8）。与抑制性神经元和非神经元细胞相比，兴奋性神经元的数百个基因的表达受区域身份和空间梯度的影响。这些基因在所有亚类中由区域或梯度解释的表达变异比例相似（中位数 5% 到 10%）（图 S10F）。因此，观察到的转录组拓扑差异（图 4E）主要是由于具有区域变异的基因数量，而不是该变异的强度。在 IT 投射神经元中，一些基因在单一亚类中显示出明显的模式，而其他基因在所有 IT 亚类中呈现出拓扑模式（图 S10G）。我们计算了沿三个轴的梯度解释的表达方差：头尾轴（

）、中线表面（M-S，解剖左到右）和背腹轴（D-V）。每个皮层区域沿这些轴的相对位置是使用与艾伦人类 3D 参考图谱中每个区域的近似中心相对应的体素坐标计算的（表 S2）。对于至少有

的表达方差由任何梯度解释的基因，我们根据解释的表达方差的相对比例量化了梯度的相对强度（图 S10H）。对于大多数子类，R-C 梯度占主导地位，但非 IT 子类也表达了许多具有 M-S 和 D-V 梯度的基因（图 4F 和图 S10H）。
A set of R-C genes was defined for each subclass by requiring a Spearman correlation

between expression and areal position along the

axis and a correlation

after excluding V1 and ACC as these are transcriptionally distinct regions that might bias the identification of gradients. For the most varying L4 IT and L5 ET neurons, roughly equal numbers of genes increased and decreased expression rostrocaudally (Fig. 4G). For other subclasses, many more genes increased rather than decreased expression along the

axis. The correlations of R-C genes were greater than correlations to a randomly shuffled ordering of areas for most neuronal subclasses (fig. S10I). Genes with a R-C gradient in one subclass frequently had a gradient in the same direction in other subclasses that expressed the gene (fig. S10J), such as CBLN2 in L2/3 IT and L4 IT neurons (Fig. 4H), which is expressed in a similar gradient in maturing cortical neurons during human prenatal development (24). However, some genes such as

had opposing gradients in different subclasses (L5/6 NP and VIP), and some functionally related genes had opposing gradients in the same subclass, such as the cell adhesion molecules Contactin 5 and 6 (CNTN5 and CNTN6) in L5 IT neurons (Fig.

. Based on gene ontology (GO) analysis, genes with strong areal enrichment or R-C gradients included voltage-gated potassium and calcium channels (table S7). Notably, only

genes were associated with axon guidance pathways including SLIT/ROBO, ephrin, and semaphorin signaling molecules (table S7), likely reflecting developmental patterning of connectivity.
每个亚类定义了一组 R-C 基因，要求表达与沿

轴的面积位置之间的 Spearman 相关性

，并在排除 V1 和 ACC 后计算相关性

，因为这些是转录上不同的区域，可能会影响梯度的识别。对于变化最大的 L4 IT 和 L5 ET 神经元，前后表达增加和减少的基因数量大致相等（图 4G）。对于其他亚类，沿

轴增加表达的基因数量远多于减少表达的基因。对于大多数神经元亚类，R-C 基因的相关性大于与随机打乱的区域顺序的相关性（图 S10I）。在一个亚类中具有 R-C 梯度的基因，通常在表达该基因的其他亚类中也具有相同方向的梯度（图 S10J），例如在 L2/3 IT 和 L4 IT 神经元中的 CBLN2（图 4H），在人的产前发育过程中，在成熟的皮层神经元中以类似的梯度表达（24）。然而，一些基因如

在不同亚类（L5/6 NP 和 VIP）中具有相反的梯度，一些功能相关的基因在同一亚类中也具有相反的梯度，例如 L5 IT 神经元中的细胞粘附分子 Contactin 5 和 6（CNTN5 和 CNTN6）（图

）。基于基因本体（GO）分析，具有强区域富集或 R-C 梯度的基因包括电压门控钾和钙通道（表 S7）。值得注意的是，只有

个基因与轴突引导通路相关，包括 SLIT/ROBO、ephrin 和 semaphorin 信号分子（表 S7），这可能反映了连接的发育模式。

Cross-areal consensus taxonomy
跨区域共识分类法
Next, we sought to understand finer cell-type areal variation by clustering integrated cell neighborhoods (Fig. 4) to identify a set of cell types either common to or varying across cortical areas (Fig. 5A). We defined and organized 153 cell types by transcriptomic similarity into a consensus taxonomy (Fig. 5B). Consensus cell types had consistent markers across areas (table S9), were represented in all donors (Fig. 5 C ), and ranged from 0.01 to

of excitatory and inhibitory neurons and from 0.1 to

of non-neuronal cells (Fig. 5D). Most types were
接下来，我们试图通过聚类整合的细胞邻域（图 4）来理解更细致的细胞类型区域变异，以识别一组在皮层区域中共同存在或变化的细胞类型（图 5A）。我们根据转录组相似性定义并组织了 153 种细胞类型，形成共识分类法（图 5B）。共识细胞类型在各个区域具有一致的标记（表 S9），在所有供体中都有代表（图 5C），兴奋性和抑制性神经元的比例从 0.01 到

，非神经细胞的比例从 0.1 到

（图 5D）。大多数类型是

Deep layer (Non-IT) 深层（非 IT）

CGE-derived CGE 衍生的

E Gene expression similarity varies with distance between regions
E 基因表达相似性随区域之间的距离变化

Fig. 4. Transcriptional topography across cortical areas. (A and B) UMAPs showing transcriptomic similarities of single nuclei dissected from eight cortical areas and colored by neuronal subclass (A) and area (B) for excitatory and inhibitory neuron neighborhoods. Arrows indicate V1-specialized neurons. Curved arrows illustrate R-C ordering of areas on the cortical sheet. (C) The number of genes that are significantly differentially expressed across areas for each subclass grouped by neighborhood. Subclasses with 0 or 1 DEG are labeled. See table S6 for all DEGs. (D) The number of genes that have highly enriched expression in a single area for each subclass. (E) Spearman correlations of expression similarity between pairs of areas as a function of the approximate physical distance along an unfolded neocortical sheet. Pairwise comparisons that include V1 (blue points) or do not include V1 (red) are grouped separately because V1 is so transcriptomically distinct. (F) Ternary plot summarizing the relative proportion of variance explained by expression gradients across areas along R-C, M-S (anatomical left to right), and D-V axes for each subclass.
图 4. 皮层区域的转录拓扑图。(A 和 B) UMAP 显示从八个皮层区域解剖的单核的转录组相似性，并按神经元亚类(A)和区域(B)着色，针对兴奋性和抑制性神经元邻域。箭头指示 V1 特化神经元。弯曲箭头说明皮层片上的 R-C 区域排序。(C) 每个亚类按邻域分组的在各区域显著差异表达的基因数量。具有 0 或 1 个 DEG 的亚类被标记。所有 DEG 见表 S6。(D) 每个亚类在单一区域中高度富集表达的基因数量。(E) 表达相似性在区域对之间的 Spearman 相关性，作为沿展开的新皮层片的近似物理距离的函数。包括 V1（蓝点）或不包括 V1（红点）的成对比较被单独分组，因为 V1 在转录组上是如此独特。(F) 三元图总结了沿 R-C、M-S（解剖左到右）和 D-V 轴的表达梯度解释的方差相对比例。
Point size indicates the number of genes with

of variance explained by at least one gradient, and point location shows the weighted mean proportion across all genes (shown in fig. S1OH). Points are colored by cell neighborhood. (G) For each subclass, the number of genes with expression that increases

or decreases (

in areas ordered by their position along the

axis. (H) Examples of genes with R-C gradient expression that have been previously described in development (CBLN2) (32), have opposing gradients in different subclasses for the same gene (DCC), or for two related genes (CNTN5 and CNTN6) involved in neuronal connectivity for the same subclass. found in all areas, with generally uniform representation across areas for inhibitory neuron types and non-neuronal cells (Fig. 5E). However, we also identified area-enriched or area-specific cell types, particularly in V1 (dark blue). V1-enriched clusters were seen in most excitatory subclasses, particularly L4 IT, as well as SST and several PVALB and VIP types. There was also one ACC-selective VIP type. Similarity by proximity was evidenced by cross- areal excitatory cell types common to neighboring regions (M1 and S1, MTG and AnG).
点的大小表示至少有一个梯度解释的变异数为

的基因数量，点的位置显示所有基因的加权平均比例（见图 S1OH）。点的颜色由细胞邻域决定。(G) 对于每个亚类，基因表达增加

或减少

的基因数量按其在

轴上的位置排序。(H) 具有 R-C 梯度表达的基因示例，这些基因在发育中已被描述（CBLN2）（32），在不同亚类中同一基因（DCC）具有相反的梯度，或两个相关基因（CNTN5 和 CNTN6）在同一亚类中参与神经连接。所有区域均有发现，抑制性神经元类型和非神经元细胞在各区域的表现通常较为均匀（图 5E）。然而，我们还识别出区域富集或区域特异性的细胞类型，特别是在 V1（深蓝色）。在大多数兴奋性亚类中，尤其是 L4 IT 中，观察到 V1 富集的簇，以及 SST 和几种 PVALB 和 VIP 类型。还有一种 ACC 选择性 VIP 类型。相似性通过邻近区域的交叉区域兴奋性细胞类型得以证明，这些细胞类型在相邻区域（M1 和 S1，MTG 和 AnG）中是共同的。
To study changes in the relative abundances of cell types while accounting for the compositional nature of the snRNA-seq data, we applied
为了研究细胞类型相对丰度的变化，同时考虑 snRNA-seq 数据的组成特性，我们应用了

a Bayesian model (scCODA) (25). Nuclei were grouped by consensus types and iteratively tested for consistent differences using each type as the "unchanged" reference population. All subclasses included consensus types with both increased and decreased proportions (table S10), except for PAX6 inhibitory type abundances that were uniformly decreased in V1. V1 had the most consensus types with abundance changes ( 92 of

), including two types with the largest changes (L4 IT_5 and L2/3 IT_2). Excitatory neurons were the most specialized in V1, but several SST, PVALB, and VIP consensus types were also specific to V1. Specialized types were also found in other areas, including L2/3 IT and L5/6 NP excitatory types (L2/3 IT_3, L2/3 IT_4, L5/6 NP_3 and L5/6 NP_6) in M1 and S1, SST types (SST_4 and SST_10) in ACC, and distinct L5 ET types across the R-C axis. These substantial changes, along with more subtle abundance changes (median = 17 consensus types affected in each area), are likely important determinants of the functional role of each area.
贝叶斯模型（scCODA）（25）。细胞核按共识类型分组，并使用每种类型作为“未改变”参考群体进行迭代测试以寻找一致差异。所有子类均包括具有增加和减少比例的共识类型（表 S10），除了在 V1 中均匀减少的 PAX6 抑制型丰度。V1 拥有最多的丰度变化共识类型（92 个

），包括两个变化最大的类型（L4 IT_5 和 L2/3 IT_2）。兴奋性神经元在 V1 中最为专业，但几个 SST、PVALB 和 VIP 共识类型也特定于 V1。专业类型也在其他区域发现，包括 M1 和 S1 中的 L2/3 IT 和 L5/6 NP 兴奋性类型（L2/3 IT_3、L2/3 IT_4、L5/6 NP_3 和 L5/6 NP_6），ACC 中的 SST 类型（SST_4 和 SST_10），以及 R-C 轴上的不同 L5 ET 类型。这些显著变化，以及更微妙的丰度变化（中位数 = 每个区域受影响的 17 个共识类型），可能是每个区域功能角色的重要决定因素。

V1 specializations V1 专业化

The distinctiveness of V1 was reflected in the transcriptomic data for specific cell types. Con- sidering cell types with

membership in V1 compared with other areas to be V1-specialized, we identified specialized cell types in every excitatory subclass except L5/6 NP, with the greatest number of V1-specialized types in the L2/3 IT and L4 IT subclasses (Fig. 6A and table S11). Unexpectedly, given prior reports of common GABAergic neurons across the mouse neocortex (15, 17), V1 had a number of specialized CGE- and MGE-derived types.
V1 的独特性在特定细胞类型的转录组数据中得到了体现。考虑到与其他区域相比，具有

成员资格的细胞类型被视为 V1 特化，我们在每个兴奋性亚类中识别出了特化的细胞类型，除了 L5/6 NP，在 L2/3 IT 和 L4 IT 亚类中发现了最多的 V1 特化类型（图 6A 和表 S11）。出乎意料的是，考虑到之前关于小鼠新皮层中常见 GABA 能神经元的报道（15, 17），V1 中有许多特化的 CGE 和 MGE 来源的类型。
MERFISH analysis of V1 demonstrated the spatial organization of all cell types (fig. S11, A and B). L2/3 IT types had distinct markers (table S12), sublaminar distributions, and relative proportions (Fig. 6B). L2/3 IT5 and L2/3 IT2 clearly delineated L2 and L3 from one another, respectively. Other L2/3 IT types were more sparsely distributed in L2 (L2/3 IT4), L3 (L2/3 IT3), or both (L2/3 IT1 and 6). L2/3 IT types were also found in layer 4 A (L2/3 IT2) and the superficial part of layer 4B (L2/3 IT3), and these types were V1-specialized. Conversely, several L4 IT types were found in L4A and L4B and into the deep part of L3 (L4 IT1 and 3, Fig. 6 D ). Thus, the specialized L4A and L4B contain not only L4 IT-type neurons, but also L2/3 IT-type neurons. This finding may help resolve ongoing questions about primate V1 L4A and
MERFISH 分析显示 V1 中所有细胞类型的空间组织（图 S11，A 和 B）。L2/3 IT 类型具有不同的标记（表 S12）、亚层分布和相对比例（图 6B）。L2/3 IT5 和 L2/3 IT2 分别清晰地区分了 L2 和 L3。其他 L2/3 IT 类型在 L2（L2/3 IT4）、L3（L2/3 IT3）或两者（L2/3 IT1 和 6）中分布较稀疏。L2/3 IT 类型也在 4A 层（L2/3 IT2）和 4B 层的表层部分（L2/3 IT3）中被发现，这些类型是 V1 特化的。相反，几个 L4 IT 类型在 L4A 和 L4B 以及 L3 的深部（L4 IT1 和 3，图 6 D）中被发现。因此，特化的 L4A 和 L4B 不仅包含 L4 IT 类型神经元，还包含 L2/3 IT 类型神经元。这个发现可能有助于解决关于灵长类动物 V1 L4A 的持续问题。
L4B, which contain both stellate (L4 IT-like) and pyramidal corticocortical projection neurons (L2/3 IT-like) (26).
L4B，包含星形（L4 IT 样）和锥体皮层皮层投射神经元（L2/3 IT 样）（26）。
L4 in V1 is highly distinctive even in unlabeled tissues as a result of the band of myelinated thalamocortical axons entering L4 that form the stria of Gennari. This distinctiveness was also seen at the level of L4 IT neuron types, all but one of which were V1-specialized (Fig. 6, C and D). L4 IT types had specific markers (table S12) and sublaminar distributions, from dense pan-L4 (L4 IT3) to sublayer-specific distributions. Layers

and

receive selective inputs from magnocellular and parvocellular layers of the thalamic lateral geniculate nucleus, respectively (27), and MERFISH revealed localization of specific types to each sublayer. L4 IT5 was selectively localized in 4C

, whereas L4 IT2 was enriched in

but extended into L4B, consistent with the fuzzy boundary between

and 4 B described in other human studies (28). Sparser L4 IT types were scattered across layers. Together, these results illustrate the cellular specialization of the distinctive input layer of V1 and reveal a complexity of putative thalamorecipient stellate neurons that offers many avenues for future exploration.
L4 在 V1 中即使在未标记的组织中也具有高度特征性，这是由于进入 L4 的髓鞘化丘脑皮层轴突形成了 Gennari 带。这种特征性在 L4 IT 神经元类型的水平上也得到了体现，除了一个外，所有类型都是 V1 特化的（图 6，C 和 D）。L4 IT 类型具有特定的标记（表 S12）和亚层分布，从密集的全 L4（L4 IT3）到特定亚层的分布。层

和

分别接收来自丘脑外侧膝状体的巨细胞层和小细胞层的选择性输入（27），而 MERFISH 揭示了特定类型在每个亚层的定位。L4 IT5 在 4C

中选择性定位，而 L4 IT2 在

中富集，但延伸到 L4B，与其他人类研究中描述的

和 4B 之间的模糊边界一致（28）。稀疏的 L4 IT 类型分散在各层中。总的来说，这些结果展示了 V1 特征性输入层的细胞特化，并揭示了假定的丘脑接受星形神经元的复杂性，为未来的探索提供了许多途径。

D 4 IT types
D 4 IT 类型

SST types SST 类型
F SST types F SST 类型

Fig. 6. V1 cell-type specialization. (A) Transcriptional distinctiveness of cell types in the V1 taxonomy. Cell types with specificity

are considered V1specialized and are highlighted in blue (see table S11). (B) Laminar distributions of specialized (blue text) and common (gray) L2/3 IT types based on MERFISH in situ labeling experiments. (C and

) Scaled expression of marker genes of V1 specialized (blue labels) and common (black) L4 IT (C) and SST (E) types across areas. Dendrograms were pruned from the V1 taxonomy in (A). (D and F) Laminar distributions of specialized and common L4 IT (D) and SST (F) types based on MERFISH experiments.
图 6. V1 细胞类型特化。(A) V1 分类中细胞类型的转录特异性。具有特异性

的细胞类型被视为 V1 特化，并用蓝色突出显示（见表 S11）。(B) 基于 MERFISH 原位标记实验的特化（蓝色文本）和常见（灰色）L2/3 IT 类型的层分布。(C 和

) V1 特化（蓝色标签）和常见（黑色）L4 IT（C）和 SST（E）类型在各区域的标记基因的缩放表达。树状图从(A)中的 V1 分类中修剪。(D 和 F) 基于 MERFISH 实验的特化和常见 L4 IT（D）和 SST（F）类型的层分布。
L6 CT neurons that send reciprocal projections to the LGN were also highly specialized in V1 (Fig. 6A), with two distinct types that expressed many V1-enriched genes (fig. S11C). Gene set enrichment analysis showed enrichment for calcium signaling, axon guidance, and axonal and synaptic compartments, including axon guidance molecules CDH7, EPHA6, and SEMA6A (fig. S11D). Various ion channels (KCNT2 and

) and synaptic genes (SYT6), as well as calcium and calmodulin signaling-associated genes (PCP4,

), were similarly enriched, and several of these have conserved V1 enrichment in monkeys (29). Myelin basic protein (MBP), normally described in oligodendrocytes but known to function in certain neurons as part of a Golli-MBP complex (30), was also enriched in V1 L6 CT neurons.
L6 CT 神经元向 LGN 发送互惠投射，在 V1 中也高度专业化（图 6A），具有两种不同类型，表达许多 V1 富集基因（图 S11C）。基因集富集分析显示钙信号、轴突引导以及轴突和突触区室的富集，包括轴突引导分子 CDH7、EPHA6 和 SEMA6A（图 S11D）。各种离子通道（KCNT2 和

）和突触基因（SYT6），以及与钙和钙调蛋白信号相关的基因（PCP4，

）也同样富集，其中一些在猴子中具有保守的 V1 富集（29）。髓鞘基础蛋白（MBP），通常在少突胶质细胞中描述，但已知在某些神经元中作为 Golli-MBP 复合体的一部分发挥作用（30），在 V1 L6 CT 神经元中也富集。
V1 also contained specialized GABAergic interneuron types. Most were SST types (Fig. 6, E and F , and table S12), as well as one PVALB and two VIP types (fig. S11A). The SST types common across V1 and other cortical areas were concentrated in L2 with sparser representation in other layers. By contrast, the V1specialized SST types were concentrated in L4 near the V1-specialized L4 IT types, suggestive of a relationship between these specialized excitatory and inhibitory types.
V1 还包含了专门的 GABA 能性中间神经元类型。大多数是 SST 类型（图 6，E 和 F，以及表 S12），还有一种 PVALB 和两种 VIP 类型（图 S11A）。在 V1 和其他皮层区域中常见的 SST 类型主要集中在 L2，而在其他层中的表现较稀疏。相比之下，V1 专门的 SST 类型集中在 L4，靠近 V1 专门的 L4 IT 类型，暗示这些专门的兴奋性和抑制性类型之间存在关系。

L5 ET neuron diversity
L5 ET 神经元多样性

Neocortical L5 ET neurons are sparse and capturing them required additional L5-specific sampling. L5 ET neurons were most abundant in ACC and their abundance generally decreased along the R-C axis (Fig. 2D). V1 had the lowest proportion of L5 ET neurons (

of excitatory neurons), consistent with data from macaque monkeys demonstrating projections to subcortical targets such as the superior colliculus from large, very sparse neurons (Meynert cells) localized to deep layers in V1 (fig. S12A) (31-33).
新皮层 L5 ET 神经元稀少，捕捉它们需要额外的 L5 特异性采样。L5 ET 神经元在前扣带皮层（ACC）最为丰富，其丰度通常沿 R-C 轴减少（图 2D）。V1 的 L5 ET 神经元比例最低（

的兴奋性神经元），与来自猕猴的数据一致，显示出从位于 V1 深层的大型、非常稀疏的神经元（Meynert 细胞）向上丘等皮层下靶点的投射（图 S12A）（31-33）。

We identified 4 consensus L5 ET types (Fig. 5 and Fig. 7A), several of which were dominated by nuclei derived from cortical areas near each other. M1 and S1 predominantly contributed to L5 ET 1, whereas L5 ET 3 was largely composed of nuclei from MTG and A1 (and to a lesser extent AnG), again suggesting similarity based on topographic position. V1 specialization was also apparent in L5 ET types, with only a single type (L5 ET 4) consisting of nuclei predominantly from V1 (Fig. 7, A and B). L5 ET neurons could be divided into at least two transcriptomically distinct subtypes in most regions (Fig. 7B). M1 had 3 distinct subtypes and we showed previously (12) that at least two L5 ET M1 subtypes included Betz cells.
我们识别了 4 种共识 L5 ET 类型（图 5 和图 7A），其中几种主要由来自相邻皮层区域的核组成。M1 和 S1 主要贡献于 L5 ET 1，而 L5 ET 3 则主要由 MTG 和 A1 的核组成（在较小程度上还有 AnG），这再次表明基于拓扑位置的相似性。V1 的特化在 L5 ET 类型中也很明显，只有一种类型（L5 ET 4）主要由来自 V1 的核组成（图 7，A 和 B）。在大多数区域，L5 ET 神经元可以分为至少两种转录组学上不同的亚型（图 7B）。M1 有 3 种不同的亚型，我们之前已经显示（12）至少有两种 L5 ET M1 亚型包括 Betz 细胞。
Notably, despite having the highest proportion of L5 ET neurons of all areas, only one subtype was identified in ACC, implying that VENs in ACC likely do not represent a distinct transcriptomic cluster, consistent with our previous findings in frontoinsula (34).
值得注意的是，尽管 ACC 区域的 L5 ET 神经元比例最高，但仅识别出一种亚型，这意味着 ACC 中的 VEN 可能并不代表一个独特的转录组簇，这与我们在前岛叶的先前发现一致（34）。
L5 ET neurons had more genes with variable expression across areas than any other cell type (fig. S10F). Up to

of variation in gene expression across areas was explained by gradients along the M-S (anatomical left to right), D-V, and R-C axes (Fig. 7C). Top gradient genes included a glutamate receptor subunit (GRID2), a semaphorin (SEMA3D), and a neuropilin (NRPI) that are involved in trans-synaptic signaling and connectivity (Fig. 7C). Some gene expression varied across areas but not as a gradient, such as

, which was selectively down-regulated in primary sensory areas (A1, S1, and V1). L5 ET neurons also expressed distinct areal markers (Fig. 7D and table S13), including the voltage-gated potassium channel KCNG2 in ACC, glutamate receptor subunit GRIK1 in MTG and AnG, and ANK1 in V1, a gene that encodes for the scaffolding protein Ankyrin 1 (35). Applying GO enrichment analysis to L5 ET areal markers identified enriched pathways associated with synaptic signaling, connectivity, and intrinsic neuronal firing
L5 ET 神经元在不同区域的基因表达变异性超过了任何其他细胞类型（图 S10F）。在不同区域的基因表达变异中，

的变异由 M-S（解剖左到右）、D-V 和 R-C 轴的梯度解释（图 7C）。主要梯度基因包括一个谷氨酸受体亚单位（GRID2）、一个趋化因子（SEMA3D）和一个神经元细胞粘附蛋白（NRPI），它们参与突触间信号传递和连接（图 7C）。一些基因表达在不同区域有所变化，但并非呈梯度分布，例如

，该基因在初级感觉区域（A1、S1 和 V1）中选择性下调。L5 ET 神经元还表达了不同的区域标记（图 7D 和表 S13），包括 ACC 中的电压门控钾通道 KCNG2、MTG 和 AnG 中的谷氨酸受体亚单位 GRIK1，以及 V1 中的 ANK1，该基因编码支架蛋白 Ankyrin 1（35）。对 L5 ET 区域标记进行 GO 富集分析，识别出与突触信号传递、连接性和内在神经元放电相关的富集通路。

Fig. 7. L5 ET-projecting neuronal diversity. (A) UMAPs of L5 ET neurons labeled by area and cross-area consensus type. (B) Within-area L5 ET subtypes for each area shown in the same UMAP space as (A). (C) Bar plots summarizing the expression variance explained by human donor, L5 ET subtype, and four types of variation across areas: R-C, M-S (anatomical left to right), and D-V gradients and more complex patterns or in a single area (area).
图 7. L5 ET 投射神经元的多样性。(A) 按区域和跨区域共识类型标记的 L5 ET 神经元的 UMAP 图。(B) 在同一 UMAP 空间中显示的每个区域的区域内 L5 ET 亚型。(C) 条形图总结了由人类供体、L5 ET 亚型和四种区域间变异类型（R-C、M-S（解剖左到右）、D-V 梯度及更复杂模式或单一区域（区域））解释的表达方差。

For the four types of areal variation, the distribution of expression across areas is shown for one of the top five genes. (D) Examples of genes with L5 ET neuron expression restricted to one or a few areas. (E) Number of genes in the top 10 significantly enriched terms from gene ontology (GO) analyses (biological process, BP; cellular component, CC; molecular function, MF) of L5 ET areal markers (table S13).
对于四种区域变异类型，显示了前五个基因之一在各个区域的表达分布。(D) 表达限制在一个或几个区域的 L5 ET 神经元基因示例。(E) 来自 L5 ET 区域标记的基因本体（GO）分析中前 10 个显著富集术语的基因数量（生物过程，BP；细胞成分，CC；分子功能，MF）（表 S13）。
properties (Fig. 7E), consistent with known areal variation in firing properties.
特性（图 7E），与已知的放电特性区域变化一致。

Glial specialization 胶质细胞特化

Non-neuronal cells comprised at least 40 to

of cortical cells across areas based on flow cytometric analysis of dissociated nuclei labeled with the neuronal marker NeuN and gated based on NeuN fluorescence intensity (fig. S12A). However, these proportions under- estimate the total non-neuronal population because vascular cells, including endothelial cells and VLMCs, are difficult to dissociate (36) and are under sampled in the snRNA-seq dataset based on in situ labeling with MERFISH (fig. S12B) (37). M1 and S1 had a higher proportion of non-neuronal (NeuN-negative) cells than other areas, and snRNA-seq data showed that this was driven by an expansion of oligodendrocytes relative to OPCs, astro- cytes, and microglia (fig. S12, B, C, and D), consistent with neuroimaging studies showing that these areas are the most heavily myelinated in the cortex

. By contrast, areas described to be among the most lightly myelinated in the cortex (ACC and DFC) (38) had the lowest proportion of oligodendrocytes (fig. S12, B and C).
非神经元细胞在基于流式细胞术分析的皮层细胞中占至少 40%至

，该分析使用神经元标记物 NeuN 标记的解离核，并根据 NeuN 荧光强度进行分选（图 S12A）。然而，这些比例低估了总的非神经元群体，因为血管细胞，包括内皮细胞和 VLMCs，难以解离（36），并且在基于 MERFISH 原位标记的 snRNA-seq 数据集中样本不足（图 S12B）（37）。M1 和 S1 的非神经元（NeuN 阴性）细胞比例高于其他区域，snRNA-seq 数据表明，这一现象是由于相对于 OPCs、星形胶质细胞和小胶质细胞，少突胶质细胞的扩展所驱动的（图 S12，B、C 和 D），与神经影像学研究一致，显示这些区域是皮层中髓鞘化最重的区域

。相比之下，被描述为皮层中髓鞘化最轻的区域（ACC 和 DFC）（38）则具有最低比例的少突胶质细胞（图 S12，B 和 C）。
Non-neuronal cells were grouped into major subclasses based on conserved marker
非神经细胞根据保守标记被分为主要亚类
expression (fig. S12F), and many subclasses could be further divided into distinct subtypes. Astrocytes were subdivided into previously described protoplasmic, interlaminar (ILM), and fibrous types, which also had robust markers across areas (fig. S12G). Consistent with previous reports of shared non-neuronal types across the cortex

, there was little areal expression signature for most non-neuronal cell types (Fig. 8A and fig. S12E). However, areal variation in protoplasmic-but not ILM or fibrous astrocytes-was apparent, consistent with previous descriptions of brain-wide astrocyte heterogeneity

and variation in astrocytes across cortical and hippocampal areas in mice (42). Protoplasmic astrocytes from ACC showed clear banding on the UMAP (Fig. 8A) and distinct areal marker gene expression (Fig. 8B).
表达（图 S12F），许多亚类可以进一步细分为不同的亚型。星形胶质细胞被细分为先前描述的原质型、层间型（ILM）和纤维型，这些类型在各个区域也有强烈的标记（图 S12G）。与之前关于皮层中共享非神经元类型的报告一致

，大多数非神经元细胞类型的区域表达特征很少（图 8A 和图 S12E）。然而，原质型星形胶质细胞的区域变化明显，但 ILM 或纤维型星形胶质细胞则没有，这与之前对全脑星形胶质细胞异质性的描述一致

，以及小鼠皮层和海马区域星形胶质细胞的变化（42）。来自 ACC 的原质型星形胶质细胞在 UMAP 上显示出明显的带状（图 8A）和独特的区域标记基因表达（图 8B）。
Laminar distributions varied across areas for all glial subclasses (Fig. 8C and fig. S12H). There was a notable depletion of astrocytes in L4A and L4B of V1 but not in L4 of other sensory or granular cortices (Fig. 8C). To validate this finding, we compared in situ expression of the astrocyte marker GFAP in V1 and DFC. GFAP protein and gene expression was reduced in L4B of V1 (Fig. 8D), and only protein expression was reduced in L4 of DFC (fig. S12I) based on immunofluorescence (IF) and in situ hybridization (ISH) labeling of adult human tissue. In V1, a band of dense GFAP labeling was apparent in L6A and L6B, which tapered off in the underlying white matter. GFAP IF in V1 revealed a population of astrocytes that extended long processes away from the white matter and into L5, similar to descriptions of varicose projection astrocytes (VPA) that are distinctive to humans and great apes and not found in the cortex of other anthropoid primates

(Fig. 8D and S12I). Deep-layer astrocytes in DFC did not extend long processes and had morphologies typical of protoplasmic and fibrous astrocytes (fig. S12I).
层状分布在所有胶质亚类的不同区域有所不同（图 8C 和图 S12H）。在 V1 的 L4A 和 L4B 中，星形胶质细胞显著减少，但在其他感觉或颗粒皮层的 L4 中并未观察到这种现象（图 8C）。为了验证这一发现，我们比较了 V1 和 DFC 中星形胶质细胞标记物 GFAP 的原位表达。V1 的 L4B 中 GFAP 蛋白和基因表达减少（图 8D），而在 DFC 的 L4 中仅 GFAP 蛋白表达减少（图 S12I），基于对成人人体组织的免疫荧光（IF）和原位杂交（ISH）标记。在 V1 中，L6A 和 L6B 中明显可见一条密集的 GFAP 标记带，向下逐渐减弱至下方的白质。V1 中的 GFAP IF 显示出一群星形胶质细胞，它们从白质延伸出长突起进入 L5，类似于人类和大猩猩特有的静脉曲张投射星形胶质细胞（VPA）的描述，而在其他类人猿的皮层中并未发现

（图 8D 和 S12I）。DFC 中的深层星形胶质细胞没有延伸长突起，形态典型为原质和纤维星形胶质细胞（图 S12I）。

The spatial organization of astrocytes in V1 was further investigated using MERFISH (fig. S8). Based on laminar distributions (Fig. 8E) and marker gene expression (Fig. 8F), there were two subtypes of protoplasmic (Astro_1 and Astro_3) and ILM (Astro_2 and Astro_5) astrocytes and one fibrous subtype (Astro_4). In contrast to prior descriptions of protoplasmic astrocytes as relatively homogenous cells, protoplasmic subtypes in V1 displayed distinct laminar patterns with Astro_1 localized to the sublayers of L4 and Astro

spread across L2-6 but absent in L1, L6B, and white matter. Astro_1 markers were related to en- ergy metabolism, including mitochondrial genes COXI (Fig. 8F), COX2, and COX3, and Astro_1 cells may represent highly active protoplasmic astrocytes (an Astro 3 cell state) rather than a developmentally distinct type. Astro_5 cells were mostly restricted to the L1-pial border, whereas Astro_2 cells were enriched in the deeper part of L1. These subtypes likely represent pial and subpial ILMs (45, 46), respectively. The putative subpial ILM type (Astro_2) included a small number of cells localized to deep L6. Because ILMs and VPAs have previously been shown to express shared marker genes (AQP4 and CRYAB, Fig. 8F) and have similar morphologies

, these deep-layer Astro_2 cells may represent a type of VPA. However, further work will be needed to fully characterize the diversity of astrocyte morphologies across the cortex and their relationships to transcriptomic astrocyte types.
在 V1 中，使用 MERFISH 进一步研究了星形胶质细胞的空间组织（图 S8）。根据层状分布（图 8E）和标记基因表达（图 8F），存在两种原质型（Astro_1 和 Astro_3）和 ILM 型（Astro_2 和 Astro_5）星形胶质细胞，以及一种纤维型（Astro_4）。与之前对原质型星形胶质细胞作为相对均质细胞的描述相反，V1 中的原质型亚型显示出明显的层状模式，Astro_1 定位于 L4 的亚层，而 Astro_

分布在 L2-6，但在 L1、L6B 和白质中缺失。Astro_1 标记与能量代谢相关，包括线粒体基因 COXI（图 8F）、COX2 和 COX3，Astro_1 细胞可能代表高度活跃的原质型星形胶质细胞（Astro 3 细胞状态），而不是发育上不同的类型。Astro_5 细胞主要限制在 L1-软膜边界，而 Astro_2 细胞则富集在 L1 的深层部分。这些亚型可能分别代表软膜和亚软膜 ILM（45, 46）。假定的亚软膜 ILM 类型（Astro_2）包括少量定位于深层 L6 的细胞。由于 ILMs 和 VPAs 之前已被证明表达共享标记基因（AQP4 和 CRYAB，图 8F）并具有相似的形态

，这些深层 Astro_2 细胞可能代表一种 VPA。然而，仍需进一步研究以全面表征皮层中星形胶质细胞形态的多样性及其与转录组星形胶质细胞类型的关系。

Discussion 讨论

The cellular complexity of the cortex has challenged generations of neuroscientists aiming to understand the structural basis of cognitive function. The BRAIN Initiative Cell Census Network established a paradigm for mapping cortical cellular diversity, developed methods to work across species, and established the
大脑皮层的细胞复杂性挑战了几代神经科学家，旨在理解认知功能的结构基础。BRAIN 计划细胞普查网络建立了一种映射皮层细胞多样性的范式，开发了跨物种工作的方法，并建立了
A

Astro 天文
ACC	DFC	M1	S1	MTG	A1	V1
L1	L1		L1		L1	L2	Layer prop. 层属性
L2/3			L3	L2	L3		0.3
L5A	14			L3	4	L4AB	0.2
	L4	L5	Li: 李：	-4	6a	L5	0.1
	L5	5		Lo 洛	L6B	L6A
ᄂ6	L6	L6	L6B	L6	WM	L6B

Fig. 8. Areal specialization of astrocytes. (A) UMAP of non-neuronal cells labeled by cortical area. (B) UMAPs of astrocyte expression for genes with areal enrichment. Arrows in (

and

) shows grouping of nuclei from ACC on the UMAP. (C) Laminar distributions of astrocytes vary across areas and are depleted in V1 L4A and L4B. (D) GFAP IF and ISH illustrate variable laminar distributions and morphologies of astrocytes in V1 and validates depletion in L4A and L4B. Single channel IF images were inverted to increase visibility of GFAP IF. Scale bars: IF columns

, GFAP tracing images

, ISH

. (E) Laminar distributions of astrocyte subtypes in V1 based on MERFISH in situ labeling experiments. (F). Pan-astrocyte and subtype marker expression.
图 8. 星形胶质细胞的区域特化。(A) 按皮层区域标记的非神经元细胞的 UMAP。(B) 星形胶质细胞在区域富集基因表达的 UMAP。箭头在(

和

)中显示 ACC 的细胞核在 UMAP 上的分组。(C) 星形胶质细胞的层分布在不同区域之间变化，并在 V1 L4A 和 L4B 中减少。(D) GFAP 免疫荧光和原位杂交显示 V1 中星形胶质细胞的可变层分布和形态，并验证 L4A 和 L4B 的减少。单通道免疫荧光图像被反转以增加 GFAP 免疫荧光的可见性。比例尺：免疫荧光列

，GFAP 追踪图像

，原位杂交

。(E) 基于 MERFISH 原位标记实验的 V1 中星形胶质细胞亚型的层分布。(F) 全星形胶质细胞和亚型标记的表达。
concordance of a transcriptomic cellular classification with other cellular properties in a way that integrates prior literature while identifying greater cellular diversity than previously appreciated (

). We used these principles to analyze a series of human cortical areas, building on our highly annotated M1 taxonomy (12). Because the cortex has a common organization as well as graded changes and areal specializations, we applied two complementary strategies to define cell types. First, each area was analyzed independently, transferring labels from the M1 taxonomy to other areas, which provides the highest resolution clustering in each area and identifies a common cell subclass organization. Next, data from all areas was analyzed jointly, identifying a set of consensus clusters present in multiple areas while also capturing specialized cell types distinct to a single area. Similar joint analysis strategies have been used on mouse cortex with comparable results (17).
转录组细胞分类与其他细胞特性的一致性，以整合先前文献的方式，同时识别出比以前更大的细胞多样性（

）。我们利用这些原则分析了一系列人类皮层区域，基于我们高度注释的 M1 分类法（12）。由于皮层具有共同的组织结构以及渐变变化和区域特化，我们应用了两种互补策略来定义细胞类型。首先，独立分析每个区域，将 M1 分类法的标签转移到其他区域，这提供了每个区域中最高分辨率的聚类，并识别出共同的细胞亚类组织。接下来，联合分析所有区域的数据，识别出在多个区域中存在的一组共识聚类，同时捕捉到特定于单个区域的专门细胞类型。类似的联合分析策略已在小鼠皮层中使用，得到了可比的结果（17）。

A key finding of this study is that all 24 subclasses first identified in M1 are found in all cortical areas analyzed here, substantiating the idea that there is a common cellular organization across the cortex. This was true even for L4 IT neurons, which were present in agranular ACC and M1 (12, 48-50). Each cortical area analyzed could be defined as a distinct proportional makeup of cell subclasses. Proportional differences were mostly due to variation in excitatory neuron subclasses, which could be large ( 10 - to 50 -fold). Finer cell-type analysis demonstrated substantial areal variation where distant areas had distinct gene expression and some cell types clustered separately. Thus, both a canonical and a noncanonical architecture were apparent, depending on the granularity of cellular detail analyzed.
本研究的一个关键发现是，在这里分析的所有皮层区域中，首次在 M1 中识别的 24 个亚类均被发现，证实了皮层存在共同细胞组织的观点。即使对于 L4 IT 神经元，这一点也是成立的，它们出现在无颗粒 ACC 和 M1（12, 48-50）。每个分析的皮层区域都可以定义为细胞亚类的不同比例组成。比例差异主要是由于兴奋性神经元亚类的变化，这种变化可能很大（10 到 50 倍）。更细致的细胞类型分析显示出显著的区域差异，远离的区域具有不同的基因表达，并且某些细胞类型聚集在一起。因此，依赖于分析的细胞细节的粒度，既可以观察到经典的结构，也可以观察到非经典的结构。
Topographic variation as a function of R-C position was a clear organizational feature. Prior microarray-based analysis of human (22) and macaque (29) cortex showed that molecular similarity varies as a function of distance on the cortical sheet that likely mirrors early developmental gradients

. Here we see comparable variation by R-C position and similarity as a function of distance, predominantly in select cell types. As in the mouse cortex (15), areal variation was mostly in excitatory and not inhibitory neurons (except in V1). These results are consistent with the fact that most inhibitory neurons migrate from the ganglionic eminences and are relatively homogeneous across the cortex, whereas excitatory neurons are generated from progenitor cells with developmental gradients that are maintained in postmitotic neurons. R-C variation was seen not just in gene expression but also in excitatory neuron proportions. L4 IT neuron proportions increased from rostral to caudal, whereas L5 ET neurons proportions showed negative correlation, suggesting that develop- mental gradients likely sculpt the cortical cellular makeup. Similar R-C variation in cellular morphology in macaque monkeys supports this idea (53).
作为 R-C 位置的函数的地形变化是一个明显的组织特征。先前对人类（22）和猕猴（29）皮层的微阵列分析显示，分子相似性随着皮层片上距离的变化而变化，这可能反映了早期发育梯度

。在这里，我们看到 R-C 位置和相似性作为距离的函数的可比变化，主要在特定的细胞类型中。与小鼠皮层（15）一样，区域变化主要发生在兴奋性神经元中，而不是抑制性神经元（V1 除外）。这些结果与大多数抑制性神经元从神经节隆起迁移并在皮层中相对均匀的事实一致，而兴奋性神经元则是从具有维持在后有丝分裂神经元中的发育梯度的祖细胞生成的。R-C 变化不仅在基因表达中被观察到，还在兴奋性神经元的比例中。L4 IT 神经元的比例从头侧到尾侧增加，而 L5 ET 神经元的比例则显示出负相关，表明发育梯度可能在塑造皮层细胞组成方面发挥作用。猕猴细胞形态的类似 R-C 变异支持了这一观点（53）。

The molecular and cell-type distinctiveness of V1 in the present study mirrors the specialized cytoarchitecture of V1 in humans, primates, and other binocular mammals, unlike in mice in which V1 is similar to other cortical areas (17). V1 was more molecularly distinct than expected by topographic position, consistent with previous bulk microarray analysis (22) and expansion of thalamorecipient L4 was reflected in increased L4 IT proportions. There has been a long-standing debate about the cellular makeup of L4A and L4B in V1, which have alternatively been called 3 BP and 3 C , respectively

. Our results show that L4A and L4B contain both L2/3 IT and L4 IT neuron types, providing a potential explanation for this confusion. Additional cellular diversity that does not strictly obey laminar boundaries complicates this organization, similar to previous work showing lack of strict laminar organization in human MTG (11).
在本研究中，V1 的分子和细胞类型特异性反映了人类、灵长类动物和其他双眼哺乳动物中 V1 的特殊细胞结构，这与小鼠中 V1 与其他皮层区域相似的情况不同（17）。V1 在分子上比预期的地形位置更具特异性，这与之前的整体微阵列分析一致（22），而丘脑接受区 L4 的扩展反映在 L4 IT 比例的增加上。关于 V1 中 L4A 和 L4B 的细胞组成一直存在长期争论，它们分别被称为 3 BP 和 3 C

。我们的结果表明，L4A 和 L4B 同时包含 L2/3 IT 和 L4 IT 神经元类型，这为这种混淆提供了潜在的解释。额外的细胞多样性并不严格遵循层状边界，使这种组织更加复杂，类似于之前的研究显示人类 MTG 缺乏严格的层状组织（11）。
The balance of excitation and inhibition is thought to be critical to proper balance of neuronal circuitry (55). E:I ratios of

in the human frontal cortex (56) and 4:1 in monkey

have been reported based on GABA immunohistochemistry. Transcriptomic quantification of cell proportions indicates a 5:1 E:I ratio in mouse cortex (12) versus a 2:1 ratio in human MTG and M1, which was confirmed by MERFISH here and in (37), and by electron microscopy analysis of mouse and human L2 (59). We find that the human E:I ratio of

is consistent across all areas except V1 in which the ratio is 4.5:1, likely as a result of increased L4 IT neurons in V1. However, the E:I ratio varies substantially by layer and is as high as 10:1 in L6 of V1. Whether this variation can be compensated by homeostatic processes remains to be studied, but these results indicate that the E:I ratio can vary substantially in the human cortex.
兴奋与抑制的平衡被认为对神经回路的适当平衡至关重要（55）。根据 GABA 免疫组化，已报告人类额叶皮层的 E:I 比率为

，猴子的 E:I 比率为 4:1

。细胞比例的转录组定量表明，小鼠皮层的 E:I 比率为 5:1（12），而人类 MTG 和 M1 的比率为 2:1，这在此处和（37）通过 MERFISH 得到了确认，并通过小鼠和人类 L2 的电子显微镜分析（59）得到了验证。我们发现人类的 E:I 比率

在所有区域中是一致的，除了 V1，其比率为 4.5:1，这可能是由于 V1 中 L4 IT 神经元的增加。然而，E:I 比率在不同层之间变化显著，在 V1 的 L6 中高达 10:1。是否这种变化可以通过稳态过程进行补偿仍需研究，但这些结果表明人类皮层中的 E:I 比率可以有显著变化。
The current results illustrate the potential of single cell transcriptomics to provide a comprehensive cellular map of the cortex that can be thought of as a form of quantitative cytoarchitectonics based on the genes that give the cell types their properties. These analyses place a cellular lens on thinking about cortical areal variation as variation in the proportions and properties of the component cell types that define the input-output properties of those areas. Recent studies have shown that morphological and anatomical characteristics are correlated with transcriptomic identity (

), indicating that transcriptomic maps can also be highly predictive of cell phenotype variation. The present study sampled a small number of human tissue donors and further work will be required to understand variation of cortical gene expression and cell types across diverse individuals. Another future challenge will be creation of a multimodal map inclusive of the entire human neocortex where areal sampling is guided by detailed anatomical and functional parcellations that will reveal graded features versus discrete boundaries and enable direct linkage between the cellular and functional architecture of the cortex.
当前的结果展示了单细胞转录组学在提供皮层全面细胞图谱方面的潜力，这可以被视为一种基于赋予细胞类型其特性的基因的定量细胞结构学。这些分析将细胞视角应用于思考皮层区域变异，作为定义这些区域输入-输出特性的组成细胞类型的比例和特性的变异。最近的研究表明，形态学和解剖学特征与转录组身份相关（

），这表明转录组图谱也可以高度预测细胞表型的变异。本研究对少量人类组织捐赠者进行了取样，未来的工作将需要理解不同个体之间皮层基因表达和细胞类型的变异。另一个未来的挑战将是创建一个包含整个大脑新皮层的多模态地图，其中区域采样由详细的解剖和功能分区指导，这将揭示渐变特征与离散边界，并使皮层的细胞和功能结构之间能够直接关联。

Materials and Methods 材料与方法

Postmortem tissue donors 尸体捐献者
Males and females 18 to 68 years of age with no known history of neuropsychiatric or neurological conditions, evidence of head trauma, intubation, or neuropathology were considered for inclusion in this study. De-identified postmortem human brain tissue was collected after obtaining permission from the decedent's legal next-of-kin. Tissue collection was performed in accordance with the provisions of the US Uniform Anatomical Gift Act of 2006 described in the California Health and Safety Code section 7150 (effective

) and other applicable state and federal laws and regulations. The Western Institutional Review Board (WIRB) reviewed the use of de-identified postmortem brain tissue for research purposes and determined that, in accordance with federal regulation 45 CFR 46 and associated guidance, the use of de-identified specimens from deceased individuals did not constitute human subjects research requiring IRB review. Routine serological screening for infectious disease (HIV, Hepatitis B, and Hepatitis C) was conducted where possible using donor blood samples and donors negative for these infectious diseases were considered for inclusion in the study. Tissue RNA quality was assessed using samples of total RNA derived from the frontal and occipital poles of each donor brain which were processed on an Agilent 2100 Bioanalyzer using the RNA 6000 Nano kit to generate RNA Integrity Number (RIN) scores for each sample. Specimens with average RIN values

were considered for inclusion in the study. Tissue samples from five individuals ( 3 males, 2 females, mean postmortem interval 12.8 hours, mean age 47 years, table S2) were used for snRNA-seq data generation. Tissue samples from 3 individuals (3 males, table S2) were used for MERFISH data generation.
年龄在 18 至 68 岁之间的男性和女性，且没有已知的神经精神或神经系统疾病史、头部创伤、插管或神经病理证据，均被考虑纳入本研究。在获得死者法定近亲的许可后，收集了去标识化的尸检人脑组织。组织收集遵循 2006 年美国统一解剖捐赠法的规定，该法在加利福尼亚州健康与安全法典第 7150 条中描述（生效于

）以及其他适用的州和联邦法律法规。西方机构审查委员会（WIRB）审查了去标识化尸检脑组织的研究用途，并确定根据联邦法规 45 CFR 46 及相关指导，使用已故个体的去标识化标本不构成需要 IRB 审查的人类受试者研究。在可能的情况下，使用供体血样进行了常规传染病（HIV、乙型肝炎和丙型肝炎）的血清学筛查，且对这些传染病呈阴性的供体被考虑纳入研究。组织 RNA 质量通过来自每位供体大脑额极和枕极的总 RNA 样本进行评估，这些样本在 Agilent 2100 生物分析仪上使用 RNA 6000 Nano 试剂盒处理，以生成每个样本的 RNA 完整性编号（RIN）分数。平均 RIN 值

的标本被考虑纳入研究。来自五名个体（3 名男性，2 名女性，平均死后间隔 12.8 小时，平均年龄 47 岁，表 S2）的组织样本用于 snRNA-seq 数据生成。来自 3 名个体（3 名男性，表 S2）的组织样本用于 MERFISH 数据生成。

Processing of postmortem brain specimens
尸检脑标本的处理

Postmortem brain specimens were transported to the Allen Institute or the University of Washington on ice and processed as previously described (https://dx.doi.org/10.17504/ protocols.io.bf4ajqse). Briefly, brain specimens were bisected through the midline and individual hemispheres were embedded in Cavex Impressional Alginate for slabbing. Coronal brain slabs were cut at 1 cm intervals for all donors except H20.30.002 (table S2), which
尸检脑标本在冰上运输至艾伦研究所或华盛顿大学，并按照之前描述的方式进行处理（https://dx.doi.org/10.17504/protocols.io.bf4ajqse）。简而言之，脑标本沿中线切分，单个半球嵌入 Cavex Impressional Alginate 以便切片。除了 H20.30.002（表 S2）外，所有供体的冠状脑切片以 1 厘米间隔切割。
was processed at a slab interval of 4 mm . Tissue photographs were acquired for all slabs prior to freezing. Individual slabs were frozen in a slurry of dry ice and isopentane. Frozen slabs were vacuum sealed and stored at

until the time of use.
在 4 毫米的切片间隔下进行处理。所有切片在冷冻前拍摄了组织照片。单个切片在干冰和异戊烷的混合物中冷冻。冷冻切片真空密封并存储在

，直到使用时。

Tissue mapping and dissection
组织映射和解剖

Cortical areas of interest were identified on tissue slab photographs taken at the time of autopsy and at the time of dissection. Tissue samples used for Cv3 and SSv4 data generation were on average 3 mm wide, encompassed the full height of the cortical depth from pia to white matter of the sampled area (

), and were 1 cm in thickness. Tissue photographs were used to map the tissue blocks sampled for Cv3 data generation across donors and areas to several reference atlases (table S2). First, samples were pinned to the Allen Human Reference Atlas 3D in MNI volume space, which includes labeling of 141 brain structures drawn on the ICBM 152 2009b Nonlinear Symmetric reference volume, using the publicly available BICCN Cell Locator tool (https://github.com/ BICCN/cell-locator). Table S2 lists the coordinates and structure name corresponding to the approximate center of each cortical area pinned using the Cell Locator tool. As the Allen Human 3D Reference makes use of a gyral structural ontology the best matching structure in the Allen Human Reference platebased 2D and associated Modified Brodmann structural ontology (see documentation at http://atlas.brain-map.org/) was also determined (table S2). Additionally, samples were mapped to the Julich Brain Maximum Probability Maps in MNI ICBM 152 2009c Nonlinear Asymmetric reference space (DOI: 10.25493/TAKY-64D) using Connectome Workbench (https://www. humanconnectome.org/software/connectomeworkbench) for file viewing and annotation and mapped structures were cross-referenced to the publicly available Julich-Brain v2.9 parcellation (DOI: 10.25493/VSMK-H94) in the same 3D reference volume using the EBRAINS Siibra Explorer (https://atlases.ebrains.eu/ viewer/#/). Table S2 lists the best matching (primary) brain structure from the Julich-Brain v2.9 parcellation and, for cases where the Julich Maximum Probability Maps predict more than one cortical area at the reference coordinates corresponding to a mapped sample, a secondary structure term is listed. In the Allen Institute Modified Brodmann ontology, dissections of DFC mapped to the superior frontal gyrus corresponding to the lateral subdivision of Brodmann Area (A) 9 (A91). Dissections of ACC corresponded to A24 in the rostral (anterior) cingulate gyrus. A1 was localized in the transverse temporal gyrus (Heschl's gyrus) corresponding to A41. MTG dissections were mostly targeted to the caudal subdivision of A21 (A21c), but some dissections mapped to the interme- diate subdivision of A21 (A21i). M1, S1, and V1 dissections mapped to the primary sensory regions M1C, S1C, and V1C, respectively. Localization of V1 was also confirmed by identification of the Stria of Gennari on tissue slab photographs. For SSv4 data generation, M1 and S 1 dissections targeted the putative hand and trunk-lower limb sub-regions of each cortical area. Confirmation of the localization of tissue blocks in M1 and S1 was also carried out by processing one block from each donor for cryosectioning and fluorescent Nissl staining (Neurotrace 500/525, ThermoFisher Scientific). Nissl-stained sections were screened for histological hallmarks of each cortical area (such as the presence of Betz cells in L5 of M1) to verify that dissected regions were appropriately localized to either M1 or S1. AnG dissections targeted the caudal subdivision of A39 (A39c). All tissue dissections from parent tissue slabs were carried out using a custom cold table maintained at

for the duration of dissection.
在尸检和解剖时拍摄的组织切片照片中识别出感兴趣的皮层区域。用于 Cv3 和 SSv4 数据生成的组织样本平均宽度为 3 毫米，涵盖了从软膜到白质的采样区域的皮层深度的全部高度（

），厚度为 1 厘米。组织照片用于将用于 Cv3 数据生成的组织块在不同供体和区域之间映射到多个参考图谱（表 S2）。首先，样本被固定在 MNI 体积空间中的艾伦人类参考图谱 3D 上，该图谱包括在 ICBM 152 2009b 非线性对称参考体积上绘制的 141 个脑结构的标记，使用公开可用的 BICCN 细胞定位工具（https://github.com/BICCN/cell-locator）。表 S2 列出了使用细胞定位工具固定的每个皮层区域的近似中心对应的坐标和结构名称。由于艾伦人类 3D 参考利用了回旋结构本体，因此在艾伦人类参考基于平面的 2D 和相关的修改布罗德曼结构本体中，最佳匹配结构（请参见文档：http://atlas.brain-map）。org/) 也被确定（表 S2）。此外，样本被映射到 MNI ICBM 152 2009c 非线性不对称参考空间中的 Julich 大脑最大概率图（DOI: 10.25493/TAKY-64D），使用 Connectome Workbench（https://www.humanconnectome.org/software/connectomeworkbench）进行文件查看和注释，映射的结构与公开可用的 Julich-Brain v2.9 分区（DOI: 10.25493/VSMK-H94）在同一 3D 参考体积中进行了交叉引用，使用 EBRAINS Siibra Explorer（https://atlases.ebrains.eu/viewer/#/）。表 S2 列出了来自 Julich-Brain v2.9 分区的最佳匹配（主要）大脑结构，对于 Julich 最大概率图在与映射样本对应的参考坐标预测多个皮层区域的情况，列出了次要结构术语。在艾伦研究所修改的 Brodmann 本体中，DFC 的解剖映射到对应于 Brodmann 区域（A）9（A91）的上额回。ACC 的解剖对应于前扣带回的 A24。A1 被定位在横向颞回（Heschl 回），对应于 A41。 MTG 解剖主要针对 A21 的尾部亚区 (A21c)，但一些解剖映射到 A21 的中间亚区 (A21i)。M1、S1 和 V1 解剖分别映射到主要感觉区域 M1C、S1C 和 V1C。通过在组织切片照片中识别 Gennari 纹理也确认了 V1 的定位。为了生成 SSv4 数据，M1 和 S1 解剖针对每个皮层区域的假定手部和躯干-下肢亚区。通过处理每个供体的一个块进行冷冻切片和荧光 Nissl 染色 (Neurotrace 500/525, ThermoFisher Scientific) 也确认了 M1 和 S1 中组织块的定位。Nissl 染色切片被筛选以寻找每个皮层区域的组织学特征（例如 M1 的 L5 中 Betz 细胞的存在），以验证解剖区域是否适当地定位于 M1 或 S1。AnG 解剖针对 A39 的尾部亚区 (A39c)。所有来自母体组织切片的组织解剖均在维持在

的定制冷台上进行。

Nuclear isolation and capture
核隔离与捕获

For SMART-seqv4 (SSv4) and Cv3 with layer 5 microdissection, tissue blocks were placed in ice-cold IX PBS supplemented with 10 mM DLDithiothreitol (DTT, Sigma Aldrich) and mounted on a vibratome (Leica) for sectioning at

in the coronal plane. Sections were placed in fluorescent Nissl staining solution (Neurotrace 500/525, ThermoFisher Scientific) prepared in 1X PBS with 10 mM DTT and

RNasin Plus RNase inhibitor (Promega) and stained for 5 min on ice. After staining, sections were visualized on a fluorescence dissecting microscope (Leica) and cortical layers were individually microdissected using a needle blade micro-knife (Fine Science Tools) as previously described (https:// dx.doi.org/10.17504/protocols.io.bq6ymzfw). Nuclear suspensions were prepared from microdissected tissue pieces as described (https:// dx.doi.org/10.17504/protocols.io.ewov149p7vr2/ v2). Dissected L5 tissue pieces for Cv3 processing were pooled across multiple sections per tissue block to ensure adequate sample for Cv3 chip loading. For Cv3 processing of tissue blocks encompassing all cortical layers, samples were placed directly into a Dounce homogenizer after removal from the

freezer and processed as described (https://dx.doi.org/10.17504/ protocols.io.bq64mzgw).
对于 SMART-seqv4 (SSv4)和 Cv3 的第 5 层微切割，组织块被放置在冰冷的 1X PBS 中，添加了 10 mM DLDithiothreitol (DTT, Sigma Aldrich)，并安装在振动切割机（Leica）上，以在冠状面进行切割。切片被放置在荧光 Nissl 染色溶液（Neurotrace 500/525, ThermoFisher Scientific）中，该溶液在 1X PBS 中准备，含有 10 mM DTT 和

RNasin Plus RNase 抑制剂（Promega），并在冰上染色 5 分钟。染色后，切片在荧光解剖显微镜（Leica）上进行观察，皮层层次使用针刀微刀（Fine Science Tools）进行单独微切割，如前所述（https://dx.doi.org/10.17504/protocols.io.bq6ymzfw）。从微切割的组织块中准备核悬液，如所述（https://dx.doi.org/10.17504/protocols.io.ewov149p7vr2/v2）。为 Cv3 处理而切割的 L5 组织块在每个组织块的多个切片中汇总，以确保 Cv3 芯片加载的样本充足。对于包含所有皮层层的组织块的 Cv3 处理，样本在从

冰箱取出后直接放入 Dounce 匀浆器中，并按照描述进行处理（https://dx.doi.org/10.17504/protocols.io.bq64mzgw）。
All samples were immunostained for fluorescence activated nuclear sorting (FANS) with mouse anti-NeuN conjugated to PE (EMD Millipore, FCMAB317PE) at a dilution of 1:500 with incubation for 30 min at

. Control samples were incubated with mouse IgG1,k-PE Isotype control (BD Pharmingen). A subset of SSv4 samples was immunostained with rabbit anti-SATB2 conjugated to Alexa Fluor 647 (Abcam, ab196536) at a dilution of 1:500 to discriminate excitatory (SATB2+/NeuN+) and inhibitory (SATB2-/NeuN+) nuclei. After immunostaining, samples were centrifuged to concentrate nuclei and were resuspended in 1X PBS, 1% BSA, and 0.5% RNasin Plus for FACS. DAPI ( 4 ', 6-diamidino-2-phenylindole, ThermoFisher Scientific) was applied to samples at a concentration of

. Single nucleus sorting was carried out on either a BD FACSAria II SORP or BD FACSAria Fusion instrument (BD Biosciences) using a

nozzle. A standard gating strategy was applied to all samples as previously described (11). Briefly, nuclei were gated on their size and scatter properties and then on DAPI signal. Doublet discrimination gates were applied to exclude multiplets. Lastly, samples were gated on NeuN signal (PE) and SATB2 (Alexa Fluor 647) signal where applicable. For Cv3 experiments, NeuN+ and NeuN- nuclei were sorted into separate tubes and combined at a defined ratio of neurons and non-neurons (

NeuN+,

NeuN-), except for L5 dissected samples where only neuronal (NeuN+) nuclei were captured. Samples were then centrifuged and resuspended in 1XPBS, 1% BSA,

RNasin Plus, and 5 to

DMSO and frozen at

until the time of chip loading. Samples were processed according to the following protocol for chip loading (https://dx.doi.org/10.17504/protocols. io. 774 hrqw ). For SSv4, single nuclei were sorted into 8 -well strip tubes containing

of SMART-seq v4 collection buffer (Takara) supplemented with ERCC MIX1 spike-in synthetic RNAs at a final dilution of

(Ambion). Strip tubes containing sorted nuclei were briefly centrifuged and stored at

until the time of further processing.
所有样本均使用小鼠抗 NeuN 抗体（与 PE 结合，EMD Millipore，FCMAB317PE）进行荧光激活核分选（FANS）免疫染色，稀释比例为 1:500，孵育 30 分钟于

。对照样本使用小鼠 IgG1,k-PE 同型对照（BD Pharmingen）进行孵育。部分 SSv4 样本使用兔抗 SATB2 抗体（与 Alexa Fluor 647 结合，Abcam，ab196536）进行免疫染色，稀释比例为 1:500，以区分兴奋性（SATB2+/NeuN+）和抑制性（SATB2-/NeuN+）细胞核。免疫染色后，样本被离心以浓缩细胞核，并重悬于 1X PBS、1% BSA 和 0.5% RNasin Plus 中以进行 FACS。DAPI（4',6-二氨基-2-苯基吲哚，ThermoFisher Scientific）以浓度

应用于样本。单核分选在 BD FACSAria II SORP 或 BD FACSAria Fusion 仪器（BD Biosciences）上进行，使用

喷嘴。对所有样本应用了标准分选策略，如前所述（11）。简而言之，细胞核根据其大小和散射特性进行分选，然后根据 DAPI 信号进行分选。应用双重体排除门以排除多重体。最后，样本根据 NeuN 信号（PE）和 SATB2（Alexa Fluor 647）信号进行分选（如适用）。对于 Cv3 实验，NeuN+和 NeuN-细胞核被分选到不同的管中，并以定义的神经元和非神经元比例（

NeuN+，

NeuN-）结合，除了 L5 解剖样本，仅捕获神经元（NeuN+）细胞核。样本随后被离心并重悬于 1XPBS、1% BSA、

RNasin Plus 和 5 至

DMSO 中，并在

处冷冻，直到芯片加载时。样本根据以下协议进行芯片加载（https://dx.doi.org/10.17504/protocols.io.774hrqw）。对于 SSv4，单个细胞核被分选到含有

SMART-seq v4 收集缓冲液（Takara）的 8 孔条形管中，补充有 ERCC MIX1 尖峰合成 RNA，最终稀释度为

（Ambion）。含有分选细胞核的条形管被短暂离心并存储在

，直到进一步处理的时间。

SMART-seqv4 RNA-sequencing
SMART-seqv4 RNA 测序

We used the SMART-Seq v4 Ultra Low Input RNA Kit for Sequencing (Takara #634894) per the manufacturer's instructions for reverse transcription of RNA and subsequent cDNA amplification as described (https://dx.doi.org/ 10.17504/protocols.io.8epv517xdl1b/v2). Standard controls were processed alongside each batch of experimental samples. Control strips included: 2 wells without cells, 2 wells without cells or ERCCs (i.e., no template controls), and either 4 wells of 10 pg of Human Universal Reference Total RNA (Takara 636538) or 2 wells of 10 pg of Human Universal Reference and 2 wells of 10 pg Control RNA provided in the Clontech kit. cDNA was amplified with 21 PCR cycles after the reverse transcription step. cDNA libraries were examined on either an Agilent Bioanalyzer 2100 using High Sensitivity DNA chips or an Advanced Analytics Fragment Analyzer (96) using the High Sensitivity NGS Fragment Analysis Kit (1bp to 6000bp). Purified cDNA was stored in 96 -well plates at

until library preparation.
我们使用 SMART-Seq v4 超低输入 RNA 测序试剂盒（Takara #634894），按照制造商的说明进行 RNA 的逆转录和后续 cDNA 扩增，如所述（https://dx.doi.org/10.17504/protocols.io.8epv517xdl1b/v2）。标准对照与每批实验样本一起处理。对照条带包括：2 个无细胞的孔，2 个无细胞或 ERCC 的孔（即无模板对照），以及 4 个含有 10 pg 人类通用参考总 RNA（Takara 636538）或 2 个含有 10 pg 人类通用参考和 2 个含有 10 pg 对照 RNA 的孔，这些 RNA 由 Clontech 试剂盒提供。cDNA 在逆转录步骤后经过 21 个 PCR 循环进行扩增。cDNA 文库在 Agilent Bioanalyzer 2100 上使用高灵敏度 DNA 芯片或在 Advanced Analytics Fragment Analyzer (96)上使用高灵敏度 NGS 片段分析试剂盒（1bp 到 6000bp）进行检查。纯化的 cDNA 存储在 96 孔板中，温度为

，直到文库准备。
The NexteraXT DNA Library Preparation (Illumina FC-131-1096) kit with NexteraXT
NexteraXT DNA 文库制备（Illumina FC-131-1096）试剂盒与 NexteraXT

Index Kit V2 Sets A to D (FC-131-2001, 2002, 2003, or 2004) was used for sequencing library preparation as described (11). NexteraXT DNA Library prep was done at either 0.5 x volume manually or 0.4 x volume on the Mantis instrument (Formulatrix, https://dx.doi.org/ 10.17504/protocols.io.brdjm24n). Samples were quantitated using PicoGreen on a Molecular Bynamics M2 SpectraMax instrument. Sequencing libraries were assessed using either an Agilent Bioanalyzer 2100 with High Sensitivity DNA chips or an Advanced Analytics Fragment Analyzer with the High Sensitivity NGS Fragment Analysis Kit for sizing. Molarity was calculated for each sample using average size as reported by Bioanalyzer or Fragment Analyzer and

l concentration as determined by PicoGreen. Samples were normalized to 2 to 10 nM with Nuclease-free Water (Ambion). Libraries were multiplexed at 96 samples/lane and sequenced on an Illumina HiSeq 2500 instrument using Illumina High Output V4 chemistry.
索引工具包 V2 A 到 D（FC-131-2001、2002、2003 或 2004）用于测序文库准备，如所述（11）。NexteraXT DNA 文库准备在手动 0.5 x 体积或在 Mantis 仪器（Formulatrix，https://dx.doi.org/ 10.17504/protocols.io.brdjm24n）上以 0.4 x 体积进行。样品使用 PicoGreen 在 Molecular Bynamics M2 SpectraMax 仪器上进行定量。测序文库使用 Agilent Bioanalyzer 2100 高灵敏度 DNA 芯片或 Advanced Analytics Fragment Analyzer 高灵敏度 NGS 片段分析试剂盒进行评估以进行大小测定。每个样品的摩尔浓度是根据 Bioanalyzer 或 Fragment Analyzer 报告的平均大小和 PicoGreen 确定的

l 浓度计算的。样品用无核酸酶水（Ambion）标准化至 2 到 10 nM。文库在 96 个样品/通道中进行多重化，并在 Illumina HiSeq 2500 仪器上使用 Illumina High Output V4 化学进行测序。

SMART-seqv4 RNA-seq gene expression quantification
SMART-seqv4 RNA-seq 基因表达定量

Raw read (fastq) files were aligned to the GRCh38 human genome sequence (Genome Reference Consortium, 2011) with the RefSeq transcriptome version GRCh38.p2 (current as of 4/13/ 2015) and updated by removing duplicate Entrez gene entries from the gtf reference file for STAR processing. For alignment, Illumina sequencing adapters were clipped from the reads using the fastqMCF program (61). After clipping, the paired-end reads were mapped using Spliced Transcripts Alignment to a Reference (STAR) (62) using default settings. Reads that did not map to the genome were then aligned to synthetic constructs (External RNA Controls Consortium, ERCC) and the E. coli genome (version ASM584v2). The results files included quantification of the mapped reads (raw exon and intron counts for the transcriptomemapped reads), and percentages of reads mapped to the RefSeq transcriptome, to ERCC spikein controls, and to E. coli. Quantification was performed using summerizeOverlaps from the R package GenomicAlignments (63).
原始读取（fastq）文件与 GRCh38 人类基因组序列（基因组参考联盟，2011 年）对齐，使用 RefSeq 转录组版本 GRCh38.p2（截至 2015 年 4 月 13 日）并通过从 gtf 参考文件中删除重复的 Entrez 基因条目进行 STAR 处理。对齐时，使用 fastqMCF 程序（61）从读取中剪切 Illumina 测序接头。剪切后，使用 Spliced Transcripts Alignment to a Reference（STAR）（62）在默认设置下对配对末端读取进行映射。未映射到基因组的读取随后对齐到合成构建（外部 RNA 对照联盟，ERCC）和大肠杆菌基因组（版本 ASM584v2）。结果文件包括映射读取的定量（转录组映射读取的原始外显子和内含子计数），以及映射到 RefSeq 转录组、ERCC 插入对照和大肠杆菌的读取百分比。定量使用 R 包 GenomicAlignments 中的 summerizeOverlaps 进行（63）。
Expression was calculated as counts per million (CPM) of exonic plus intronic reads, and

transformed values were used for a subset of analyses as described below. Gene detection was calculated as the number of genes expressed in each sample with

values reflected absolute transcript number and gene length. Short and abundant transcripts may have the same apparent expression as long but rarer transcripts. Intron retention varied across genes, so no reliable estimates of effective gene lengths were available for expression normalization. Instead, absolute expression was estimated as fragments per kilobase per million (FPKM) using only exonic reads so that annotated transcript lengths could be used.
表达量计算为外显子加内含子读取的每百万计数（CPM），并且

转换值用于以下描述的子集分析。基因检测计算为每个样本中表达的基因数量，

值反映绝对转录本数量和基因长度。短且丰度高的转录本可能与长但较稀有的转录本具有相同的表观表达。内含子保留在基因间变化，因此没有可靠的有效基因长度估计可用于表达标准化。相反，绝对表达量估计为每千碱基每百万片段（FPKM），仅使用外显子读取，以便可以使用注释的转录本长度。

10x Chromium RNA-sequencing and expression quantification
10 倍铬 RNA 测序和表达定量

Samples were processed using the 10x Chromium Single-Cell 3' Reagent Kit v3 following the manufacturer's protocol as described (https:// dx.doi.org/10.17504/protocols.io.bq7cmziw). Gene expression was quantified using the default 10x Cell Ranger v3 (Cell Ranger, RRID:SCR_017344) pipeline. The human reference genome used included the modified genome annotation described above for SMART-seq v4 quantification. Introns were annotated as "mRNA" and intronic reads were included in expression quantification.
样本使用 10x Chromium 单细胞 3'试剂盒 v3 按照制造商的协议进行处理，如所述（https://dx.doi.org/10.17504/protocols.io.bq7cmziw）。基因表达使用默认的 10x Cell Ranger v3（Cell Ranger，RRID:SCR_017344）管道进行定量。所使用的人类参考基因组包括上述用于 SMART-seq v4 定量的修改基因组注释。内含子被注释为“mRNA”，内含子读取被纳入表达定量中。

RNA-sequencing processing and clustering Cell-type label transfer
RNA 测序处理和聚类细胞类型标签转移

Human M1 reference taxonomy subclass labels (12) were transferred to nuclei in the current MTG dataset using Seurat's label transfer (3000 high variance genes using the 'vst' method then filtered through exclusion list). This was carried out for each RNA-seq modality dataset; for example, human-Cv3 and human-SSv4 were labeled independently. Each dataset was subdivided into 5 neighborhoods-IT and Non-IT excitatory neurons, CGE- and MGE-derived interneurons, and non-neuronal cells-based on marker genes and transferred subclass labels from published studies of human and mouse cortical cell types and cluster grouping relationships in a reduced dimensional gene expression space.
人类 M1 参考分类法子类标签（12）通过 Seurat 的标签转移方法转移到当前 MTG 数据集中（使用“vst”方法的 3000 个高方差基因，然后通过排除列表进行过滤）。这对于每个 RNA-seq 模态数据集进行了处理；例如，human-Cv3 和 human-SSv4 是独立标记的。每个数据集根据标记基因和来自已发表的人类和小鼠皮层细胞类型及聚类关系的转移子类标签，细分为 5 个邻域——IT 和非 IT 兴奋性神经元、CGE 和 MGE 来源的中间神经元，以及非神经细胞。

Filtering low-quality nuclei
过滤低质量细胞核

SSv4 nuclei were included for analysis if they passed all QC criteria:
SSv4 核心被纳入分析，如果它们通过了所有 QC 标准：

cDNA longer than 400 base pairs

cDNA 长于 400 个碱基对

reads aligned to exonic or intronic sequence

读取与外显子或内含子序列对齐

of total reads aligned
总读取数对齐的

unique reads

独特读取

TA nucleotide ratio

TA 核苷酸比率
QC was then performed at the neighborhood level. Neighborhoods were integrated together across all areas and modality; for example, deep excitatory neurons from human-Cv3, humanCv3-Layer5 and human-SSv4 datasets were integrated using Seurat integration functions with 2000 high variance genes. Integrated neighborhoods were Louvain clustered into over 100 meta cells, and Low-quality meta cells were removed from the dataset based on relatively low UMI or gene counts (included glia and neurons with greater than 500 and 1000 genes detected, respectively), predicted doublets (include nuclei with doublet scores under 0.3 ), and/or subclass label prediction metrics within the neighborhood (excitatory labeled nuclei that clustered with majority inhibitory or non-neuronal nuclei).
QC 随后在邻域层面进行。邻域在所有区域和模式中整合在一起；例如，来自 human-Cv3、humanCv3-Layer5 和 human-SSv4 数据集的深层兴奋性神经元使用 Seurat 整合函数与 2000 个高变基因进行整合。整合后的邻域被 Louvain 聚类成超过 100 个元细胞，低质量的元细胞根据相对较低的 UMI 或基因计数（包括检测到超过 500 和 1000 个基因的胶质细胞和神经元）从数据集中移除，预测的双重细胞（包括双重分数低于 0.3 的细胞核），和/或邻域内的亚类标签预测指标（与大多数抑制性或非神经元细胞核聚类的兴奋性标记细胞核）。

RNA-seq clustering RNA-seq 聚类

Nuclei were normalized using SCTransform (64), and neighborhoods were integrated together within an area and across individuals and modalities by identifying mutual nearest neighbor anchors and applying canonical correlation analysis as implemented in Seurat (65). For example, deep excitatory neurons from human-Cv3 were split by individuals and integrated with the human-SSv4 deep excitatory neurons. Integrated neighborhoods were Louvain clustered into over 100 meta cells. Meta cells were then merged with their nearest neighboring meta cell until merging criteria were sufficed, a split and merge approach that has been previously described (12). The remaining clusters underwent further QC to exclude Low-quality and outlier populations. These exclusion criteria were based on irregular groupings of metadata features that resided within a cluster.
细胞核使用 SCTransform 进行标准化（64），并通过识别相互最近邻锚点并应用 Seurat 中实现的典型相关分析，将邻域在一个区域内以及跨个体和模态进行整合（65）。例如，来自 human-Cv3 的深层兴奋性神经元按个体分开，并与 human-SSv4 深层兴奋性神经元整合。整合后的邻域被 Louvain 聚类成超过 100 个元细胞。元细胞随后与其最近邻的元细胞合并，直到满足合并标准，这是一种先前描述的分裂和合并方法（12）。剩余的簇经过进一步的质量控制，以排除低质量和异常群体。这些排除标准基于位于簇内的元数据特征的不规则分组。

Defining cross-area consensus cell types
定义跨区域共识细胞类型

For each neighborhood, Cv3 nuclei were integrated together across individuals. The integrated latent space was Louvain clustered into over 100 meta cells. Meta cells were then merged with their nearest neighboring meta cell until merging criteria were sufficed, a split and merge approach that has been previously described (12) and was also used to define the withinarea cluster identities. The process was repeated for each neighborhood, with an example diagram of the workflow shown in Fig. 5A.
对于每个邻域，Cv3 核心在个体之间整合在一起。整合的潜在空间被 Louvain 聚类成超过 100 个元细胞。元细胞随后与其最近的邻近元细胞合并，直到满足合并标准，这是一种之前已描述的分裂与合并方法（12），也用于定义区域内的聚类身份。该过程对每个邻域重复进行，工作流程的示例图示见图 5A。

Cell-type taxonomy generation
细胞类型分类生成

For each area, a taxonomy was built using the final set of clusters and was annotated using subclass mapping scores, dendrogram relationships, marker gene expression, and inferred laminar distributions. Within-area taxonomy dendrograms were generated using build_dend function from scrattch_hicat R package. A matrix of cluster median

expression across the 3000 High-variance genes for Cv3 nuclei from a given area were used as input. The cross-area dendrogram was generated with a similar workflow but was downsampled to a maximum of 100 nuclei per crossarea cluster per area. The 3000 High-variance genes used for dendrogram construction were identified from the downsampled matrix containing Cv3 nuclei from all eight areas.
对于每个区域，使用最终的聚类集构建了一个分类法，并通过子类映射分数、树状图关系、标记基因表达和推断的层状分布进行了注释。区域内分类法树状图是使用 scrattch_hicat R 包中的 build_dend 函数生成的。来自特定区域的 Cv3 细胞核的 3000 个高方差基因的聚类中位数

表达矩阵被用作输入。跨区域树状图是使用类似的工作流程生成的，但每个区域的跨区域聚类的最大细胞核数量被下采样到 100 个。用于树状图构建的 3000 个高方差基因是从包含所有八个区域的 Cv3 细胞核的下采样矩阵中识别的。

Cell-type comparisons across cortical areas
皮层区域之间的细胞类型比较

Differential gene expression
差异基因表达
To identify subclass marker genes within an area, Cv3 datasets from each area were downsampled to a maximum of 100 nuclei per cluster per individual. Differentially expressed marker genes were then identified using the FindAllMarkers function from Seurat, using the Wilcoxon sum rank test on log-normalized matrices with a maximum of 500 nuclei per
为了在一个区域内识别亚类标记基因，来自每个区域的 Cv3 数据集被下采样到每个个体每个簇最多 100 个细胞核。然后使用 Seurat 中的 FindAllMarkers 函数识别差异表达的标记基因，采用 Wilcoxon 和秩和检验对最大 500 个细胞核的对数标准化矩阵进行分析。
group (subclass versus. all other nuclei as background). Statistical thresholds for markers are indicated in their respective figures. To identify area marker genes across subclasses, Cv3 datasets from each area were downsampled to a maximum of 50 nuclei per cluster per individual. Downsampled counts matrices were then grouped into pseudo-bulk replicates (area, individual, subclass) and the counts were summed per replicate. DESeq2 functionality was then used to perform a differential expression analysis between area pairs (or comparisons of interest) for each subclass using the Wald test statistic.
组（亚类与其他所有细胞核作为背景）。标记的统计阈值在各自的图中指示。为了识别亚类中的区域标记基因，来自每个区域的 Cv3 数据集被下采样到每个个体每个簇最多 50 个细胞核。下采样的计数矩阵随后被分组为伪批次重复（区域、个体、亚类），并对每个重复的计数进行求和。然后使用 DESeq2 功能对每个亚类之间的区域对（或感兴趣的比较）进行差异表达分析，采用 Wald 检验统计量。

Transcriptomic entropy across areas
转录组熵在不同区域的变化

To quantify intercell transcriptomic heterogeneity across areas for each subclass we calculated the transcriptomic entropy in the observed data (structured) and compared against entropy in permuted data (unstructured). Transcriptomic heterogeneity is defined as the difference between the structured and unstructured entropy. To compute transcriptomic entropy, we followed these steps: (1) Randomly down-sample the cells within each subclass by taking 250 cells from each crossarea cell type. (2) Identify the highly variable genes in each area and take the union of genes as our set of interest. (3) Then, by following a recently reported computational approach to quantify transcriptomic heterogeneity (66), we computed the per-area transcriptomic entropy for each subclass.
为了量化每个子类在不同区域的细胞间转录组异质性，我们计算了观察数据（结构化）的转录组熵，并与置换数据（非结构化）的熵进行了比较。转录组异质性定义为结构化熵与非结构化熵之间的差异。为了计算转录组熵，我们遵循以下步骤：（1）随机下采样每个子类中的细胞，从每个交叉区域细胞类型中取 250 个细胞。（2）识别每个区域中的高变基因，并将基因的并集作为我们关注的集合。（3）然后，按照最近报道的计算方法量化转录组异质性（66），我们计算了每个子类的每个区域的转录组熵。

Identifying changes in cell-type proportions across areas
识别不同区域细胞类型比例的变化
Cell-type proportions are compositional, where the gain or loss of one population necessarily affects the proportions of the others, so we used scCODA (25) to determine which changes in cell class, subclass, and cell-type proportions across areas were statistically significant. We analyzed neuronal and non-neuronal populations separately because nuclei were sorted based on NeuN immunostaining to enrich for neurons. The proportion of each cell type was estimated using a Bayesian approach where proportion differences across individuals were used to estimate the posterior. All compositional and categorical analyses require a reference population to describe differences with respect to and, because we were uncertain which populations should be unchanged, we iteratively used each cell type and each area as a reference when computing abundance changes. To account for sex differences, we included it as a covariate when testing for abundance changes. Separately for neuronal and non-neuronal populations, we reported the effect size of each area for each cell type (table S10) and used a mean inclusion probability cutoff of 0.7 for calling a population consistently different.
细胞类型的比例是组成性的，一个群体的增加或减少必然会影响其他群体的比例，因此我们使用了 scCODA（25）来确定不同区域细胞类别、亚类和细胞类型比例的变化是否具有统计学意义。我们分别分析了神经元和非神经元群体，因为细胞核是基于 NeuN 免疫染色进行分选，以富集神经元。每种细胞类型的比例是通过贝叶斯方法估计的，其中个体之间的比例差异用于估计后验分布。所有组成和分类分析都需要一个参考群体来描述差异，由于我们不确定哪些群体应该保持不变，因此在计算丰度变化时，我们迭代地使用每种细胞类型和每个区域作为参考。为了考虑性别差异，我们在测试丰度变化时将其作为协变量。对于神经元和非神经元群体，我们报告了每个区域对每种细胞类型的效应大小（表 S10），并使用 0.7 的平均包含概率阈值来判断一个群体是否一致不同。
Partitioning variation in gene expression across areas
基因表达在不同区域的分区变异
Variation partitioning analysis was performed to prioritize the drivers of variation across areas within each subclass. Using linear mixedeffect models implemented in the variancePartitioning bioconductor package: http://bioconductor.org/packages/variancePartition (66) we identify genes whose variance is best explained along the M-S (anatomical left to right), R-C, and D-V axes as well as by cortical area and donor. The order of areas along these axes was defined based on the approximate

, and z coordinates of tissue samples based on a common coordinate framework of the adult human brain (20) (table S2). Genes were removed from the analysis based on the following criteria: (1) expressed in less than 10 cells, (2) greater than

dropout rate, (3) zero variance in expression, and (4) expression less than 1 CPM on average. The variance partitioning linear mixed-effect model was then defined as:
变异分解分析被执行以优先考虑每个子类内区域变异的驱动因素。使用在 variancePartitioning 生物导体包中实现的线性混合效应模型：http://bioconductor.org/packages/variancePartition (66)，我们识别出其变异在 M-S（解剖左到右）、R-C 和 D-V 轴以及皮层区域和供体方面得到最佳解释的基因。这些轴上区域的顺序是基于组织样本的近似

和 z 坐标，基于成人大脑的共同坐标框架(20)（表 S2）。根据以下标准从分析中移除基因：(1) 在少于 10 个细胞中表达，(2) 高于

的掉落率，(3) 表达的方差为零，以及(4) 平均表达低于 1 CPM。然后将方差分解线性混合效应模型定义为：

Gene medial_lateral + rostral_caudal + dorsal_ventral + (1|area) + (1|(donor)
基因 medial_lateral + rostral_caudal + dorsal_ventral + (1|area) + (1|(donor)
and passed into the variancePartition function fitVarPartModel(). We determined the amount of variation explained per covariate for each gene from the extractVarPart() function.
并传递到 variancePartition 函数 fitVarPartModel() 。我们从 extractVarPart() 函数中确定了每个基因每个协变量解释的变异量。

In situ profiling of gene expression
原位基因表达谱分析

Human postmortem frozen brain tissue was embedded in Optimum Cutting Temperature medium (VWR,25608-930) and sectioned on a Leica cryostat at

onto Vizgen MERSCOPE coverslips (VIZGEN 2040003). These sections were then processed for MERSCOPE imaging according to the manufacturer's instructions. Briefly: sections were allowed to adhere to these coverslips at room temperature for 10 min prior to a 1 min wash in nucleasefree phosphate buffered saline (PBS) and fixation for 15 min in

paraformaldehyde in PBS. Fixation was followed by

washes in PBS prior to a 1 min wash in

ethanol. Fixed sections were then stored in

ethanol at 4 C prior to use and for up to one month. Human sections were photobleached using a 150W LED array for 72 hours at

prior to hybridization then washed in 5 ml Sample Prep Wash Buffer (VIZGEN 20300001) in a 5 cm petri dish. Sections were then incubated in 5 ml Formamide Wash Buffer (VIZGEN 20300002) at 37 C for 30 min . Sections were hybridized by placing

of VIZGEN-supplied Gene Panel Mix onto the section, covering with parafilm and incubating at

for 36 to 48 hours in a humidified hybridization oven.
人类尸检后冷冻脑组织嵌入最佳切割温度介质（VWR,25608-930），并在 Leica 冷冻切片机上以

的温度切片到 Vizgen MERSCOPE 载玻片（VIZGEN 2040003）。这些切片随后按照制造商的说明进行 MERSCOPE 成像处理。简而言之：切片在室温下粘附在这些载玻片上 10 分钟，然后在无核酸酶的磷酸盐缓冲盐水（PBS）中洗涤 1 分钟，并在

的 PBS 中固定 15 分钟。固定后在 PBS 中进行

次洗涤，然后在

的乙醇中洗涤 1 分钟。固定的切片在使用前存放在

的乙醇中，温度为 4°C，最长可存放一个月。人类切片在杂交前使用 150W LED 阵列光漂白 72 小时，光照强度为

，然后在 5 cm 培养皿中用 5 ml 样品准备洗涤缓冲液（VIZGEN 20300001）洗涤。切片随后在 37°C 下用 5 ml 甲酰胺洗涤缓冲液（VIZGEN 20300002）孵育 30 分钟。通过将

的 VIZGEN 提供的基因面板混合物放置在切片上，覆盖上聚乙烯薄膜，并在湿润的杂交烘箱中以

的温度孵育 36 到 48 小时进行杂交。

Following hybridization, sections were washed twice in 5 ml Formamide Wash Buffer for 30 min at

. Sections were then embedded in acrylamide by polymerizing VIZGEN Embedding Premix (VIZGEN 20300004) according to the manufacturer's instructions. Sections were embedded by inverting sections onto

of Embedding Premix and 10% Ammonium Persulfate (Sigma A3678) and TEMED (BioRad 161-0800) solution applied to a Gel Slick (Lonza 50640) treated 2x3 glass slide. The coverslips were pressed gently onto the acrylamide solution and allowed to polymerize for 1.5 hours. Following embedding, sections were cleared for 24 to 48 hours with a mixture of VIZGEN Clearing Solution (VIZGEN 20300003) and Proteinase K (New England Biolabs P8107S) according to the Manufacturer's instructions. Following clearing, sections were washed twice for 5 min in Sample Prep Wash Buffer (PN 20300001). VIZGEN DAPI and PolyT Stain (PN 20300021) was applied to each section for 15 min followed by a 10 min wash in Formamide Wash Buffer. Formamide Wash Buffer was removed and replaced with Sample Prep Wash Buffer during MERSCOPE set up.

of RNAse Inhibitor (New England BioLabs M0314L) was added to

of Imaging Buffer Activator (PN 203000015) and this mixture was added through the cartridge activation port to a pre-thawed and mixed MERSCOPE Imaging cartridge (VIZGEN PN1040004). 15 ml mineral oil (Millipore-Sigma m5904-6X500ML) was added to the activation port and the MERSCOPE fluidics system was primed according to VIZGEN instructions. The flow chamber was assembled with the hybridized and cleared section coverslip according to VIZGEN specifications and the imaging session was initiated after collection of a 10X mosaic DAPI image and selection of the imaging area. For specimens that passed the minimum count threshold, imaging was initiated, and processing completed according to VIZGEN's proprietary protocol. Following processing and segmentation through MERSCOPE software, cells with fewer than 50 counts, or with an area outside the 100 to

range were eliminated from the mapping process.
在杂交后，切片在

的 5 ml 甲酰胺洗涤缓冲液中洗涤两次，每次 30 分钟。然后按照制造商的说明，将切片嵌入丙烯酰胺中，使用 VIZGEN 嵌入预混合物（VIZGEN 20300004）聚合。通过将切片倒置在

的嵌入预混合物和 10%过硫酸铵（Sigma A3678）及 TEMED（BioRad 161-0800）溶液上，嵌入到经过处理的 2x3 玻璃载玻片（Lonza 50640）上。将盖玻片轻轻压在丙烯酰胺溶液上，并允许其聚合 1.5 小时。嵌入后，切片用 VIZGEN 清洗溶液（VIZGEN 20300003）和蛋白酶 K（New England Biolabs P8107S）的混合物清洗 24 到 48 小时，按照制造商的说明进行。清洗后，切片在样品准备洗涤缓冲液（PN 20300001）中洗涤两次，每次 5 分钟。将 VIZGEN DAPI 和 PolyT 染色剂（PN 20300021）应用于每个切片 15 分钟，然后在甲酰胺洗涤缓冲液中洗涤 10 分钟。在 MERSCOPE 设置期间，去除甲酰胺洗涤缓冲液并用样品准备洗涤缓冲液替换。

的 RNA 酶抑制剂（新英格兰生物实验室 M0314L）被添加到

的成像缓冲激活剂（PN 203000015）中，并通过卡 cartridge 激活端口添加到预先解冻和混合的 MERSCOPE 成像 cartridge（VIZGEN PN1040004）中。15 毫升矿物油（Millipore-Sigma m5904-6X500ML）被添加到激活端口，并根据 VIZGEN 的说明对 MERSCOPE 流体系统进行了预充。流动室按照 VIZGEN 的规格与杂交和清除的部分盖玻片组装，并在收集 10X 马赛克 DAPI 图像和选择成像区域后启动成像会话。对于通过最低计数阈值的样本，成像被启动，并根据 VIZGEN 的专有协议完成处理。经过 MERSCOPE 软件的处理和分割后，计数少于 50 的细胞或面积在 100 到

范围之外的细胞被排除在映射过程之外。

The 140 gene human cortical panel was selected using a combination of manual and algorithmic based strategies requiring a reference single cell/nucleus RNA-seq data set from the same tissue, in this case the human MTG snRNAseq dataset and resulting taxonomy (II). First, an initial set of high-confidence marker genes are selected through a combination of literature search and analysis of the reference data. These genes are used as input for a greedy algorithm (detailed below). Second, the reference RNA-seq data set is filtered to only include genes compatible with mFISH. Retained genes need to 1) be long enough to allow probe design (> 960 base pairs); 2) be expressed highly enough to be detected (FPKM

), but not so high as to overcrowd the signal of other genes in a cell (FPKM < 500); 3) have low expression in off-target cells (FPKM < 50 in nonneuronal cells); and 4) be differentially expressed
选择了 140 个基因的人类皮层面板，采用手动和基于算法的策略相结合，要求来自同一组织的参考单细胞/细胞核 RNA-seq 数据集，在这种情况下为人类 MTG snRNAseq 数据集及其结果分类（II）。首先，通过文献检索和参考数据分析相结合，选择一组高置信度的标记基因。这些基因作为贪婪算法的输入（详见下文）。其次，参考 RNA-seq 数据集被过滤，仅包括与 mFISH 兼容的基因。保留的基因需要满足以下条件：1）足够长以便设计探针（> 960 个碱基对）；2）表达水平足够高以便被检测到（FPKM

），但不能过高以至于淹没细胞中其他基因的信号（FPKM < 500）；3）在非靶细胞中表达水平低（非神经元细胞中 FPKM < 50）；4）具有差异表达。
between cell types (top 500 remaining genes by marker score20). To sample each cell type more evenly, the reference data set is also filtered to include a maximum of 50 cells per cluster.
在细胞类型之间（按标记分数排名前 500 的剩余基因）。为了更均匀地采样每种细胞类型，参考数据集也被过滤，以每个簇最多包含 50 个细胞。

The main step of gene selection uses a greedy algorithm to iteratively add genes to the initial set. To do this, each cell in the filtered reference data set is mapped to a cell type by taking the Pearson correlation of its expression levels with each cluster median using the initial gene set of size n, and the cluster corresponding to the maximum value is defined as the "mapped cluster". The "mapping distance" is then defined as the average cluster distance between the mapped cluster and the originally assigned cluster for each cell. In this case a weighted cluster distance, defined as one minus the Pearson correlation between cluster medians calculated across all filtered genes, is used to penalize cases where cells are mapped to very different types, but an unweighted distance, defined as the fraction of cells that do not map to their assigned cluster, could also be used. This mapping step is repeated for every possible

gene set in the filtered reference data set, and the set with minimum cluster distance is retained as the new gene set. These steps are repeated using the new get set (of size

) until a gene panel of the desired size is attained. Code for reproducing this gene selection strategy is available as part of the mfishtools R library (https://github.com/ AllenInstitute/mfishtools).
基因选择的主要步骤使用贪心算法迭代地将基因添加到初始集合中。为此，过滤后的参考数据集中每个细胞通过将其表达水平与每个簇中位数的皮尔逊相关性进行比较，使用大小为 n 的初始基因集映射到一个细胞类型，最大值对应的簇被定义为“映射簇”。然后，“映射距离”被定义为每个细胞的映射簇与原始分配簇之间的平均簇距离。在这种情况下，使用加权簇距离，定义为 1 减去在所有过滤基因中计算的簇中位数之间的皮尔逊相关性，以惩罚细胞映射到非常不同类型的情况，但也可以使用未加权距离，定义为未映射到其分配簇的细胞的比例。这个映射步骤对过滤后的参考数据集中每个可能的

基因集重复进行，保留具有最小簇距离的集合作为新的基因集。这些步骤使用新的基因集（大小为

）重复进行，直到获得所需大小的基因面板。用于重现此基因选择策略的代码可作为 mfishtools R 库的一部分获得（https://github.com/AllenInstitute/mfishtools）。

Cell-type mapping of MERSCOPE data
MERSCOPE 数据的细胞类型映射

Any genes not matched across both the MERSCOPE gene panel and the snRNASeq mapping taxonomy were filtered from the snRNASeq dataset. We calculated the mean gene expression for each gene in each snRNAseq cluster. We assigned MERSCOPE cells to snRNAseq clusters by finding the nearest cluster to the mean expression vectors of the snRNASeq clusters using the cosine distance. All scripts and data used are available at: https://github. com/AllenInstitute/human_cross_areal.
任何在 MERSCOPE 基因面板和 snRNASeq 映射分类法中未匹配的基因都从 snRNASeq 数据集中过滤掉。我们计算了每个 snRNAseq 簇中每个基因的平均基因表达。我们通过使用余弦距离找到与 snRNASeq 簇的平均表达向量最近的簇，将 MERSCOPE 细胞分配到 snRNAseq 簇中。所有使用的脚本和数据可在以下网址获取：https://github.com/AllenInstitute/human_cross_areal。

GFAP Immunofluorescence GFAP 免疫荧光

Tissue blocks from cortical areas of interest were removed from fresh-frozen tissue slabs as described above. Immediately after dissection, tissue blocks were drop-fixed in cold

paraformaldehyde overnight in a

fridge. Tissue blocks were then rinsed in multiple washes of 1X PBS, cryoprotected in sequential

and

sucrose solutions, and embedded in OCT. Sections were cut free floating at

in the coronal plane on a Leica cryostat into 1 X PBS and were stored at

or at

in cryoprotectant solution until the time of use. Sections were processed for immunofluorescence using a rabbit polyclonal anti-GFAP antibody (Agilent, Z0334) at a dilution of 1:1000 and mouse monoclonal anti-NeuN antibody (Millipore Sigma, MAB377) at a dilution of 1:1000. Primary antibodies were incubated overnight at

, followed by incubation in Alexa Fluor conjugated secondary speciesspecific antibodies for 2 hours at room temperature. Sections were counterstained with DAPI and Neurotrace 500 fluorescent Nissl stain and were mounted in ProLong Gold Antifade Mountant (ThermoFisher Scientific). Sections were imaged on a Nikon TiE fluorescence microscope equipped with NIS-Elements Advanced Research imaging software ( 44.20 ). GFAP processes were traced using the SNT plugin in the Fiji distribution of ImageJ.
从感兴趣的皮层区域切取的组织块从新鲜冷冻组织块中移除，如上所述。解剖后，组织块立即在冷

多聚甲醛中固定过夜，放置在

冰箱中。然后，组织块在多次 1X PBS 冲洗中清洗，依次在

和

蔗糖溶液中进行冷冻保护，并嵌入 OCT。切片在 Leica 冷冻切片机上以

的方式在冠状面上自由漂浮切割，放入 1 X PBS 中，并在

或

的冷冻保护溶液中储存，直到使用时。切片使用兔多克隆抗 GFAP 抗体（Agilent, Z0334）以 1:1000 的稀释度和小鼠单克隆抗 NeuN 抗体（Millipore Sigma, MAB377）以 1:1000 的稀释度进行免疫荧光处理。初级抗体在

孵育过夜，随后在室温下用 Alexa Fluor 标记的二级特异性抗体孵育 2 小时。切片用 DAPI 和 Neurotrace 500 荧光 Nissl 染色进行复染，并用 ProLong Gold 抗褪色封片剂（ThermoFisher Scientific）封片。在配备 NIS-Elements 高级研究成像软件（44.20）的尼康 TiE 荧光显微镜上对切片进行了成像。使用 ImageJ 的 Fiji 版本中的 SNT 插件追踪 GFAP 过程。

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ACKNOWLEDGMENTS 致谢

We thank the tissue procurement, tissue processing and facilities teams at the Allen Institute for Brain Science for assistance with the transport and processing of postmortem and neurosurgical brain specimens; the technology team at the Allen Institute for assistance with data management; M. Vawter, J. Davis and the San Diego Medical Examiner's Office for assistance with postmortem tissue donations. This project made use of Connectome DB and Connectome Workbench, developed under the auspices of the Human Connectome Project at Washington University in St. Louis and associated consortium institutions (https://www.humanconnectome.org/). This publication was supported by and coordinated through the BICCN. This publication is part of the Human Cell Atlas- www.humancellatlas.org/ publications/. Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Numbers U01MH114812 and U19MH114830. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank the founder of the Allen Institute, P. G. Allen, for his vision, encouragement, and support. Funding: This work was supported by the following: Knut and Alice Wallenberg Foundation 2018.0220 (to S.L.); Nancy and Buster Alvord Endowment (to C.D.K.); National Institutes of Health grant U01MH114812 (to A.M.Y., A.T., C.R., D.B., D.M., E.S.L., H.T., J.G., J.S., K.Si., K.Sm., M.T., N.D., N.L.J., R.D.H., S.D., S.L., T.C., T.E.B., and T.P.); National Institutes of Health grant U19MH117023 (to P.R.H.); National Institutes of Health grant U19MH114830 (to A.T., D.B., D.M., H.T., J.G., J.S., K.C., K.Sm., M.Kr., M.T., N.D., T.C., and T.P.) Author contributions: RNA data generation: A.M.Y., A.T., B.P.L., C.D.K., C.R., D.B., D.H., D.M., E.R.B., E.S.L., H.T., J.G., J.S., K.C., K.L., K.Si., K.Sm., K.K., M.K.,, M.T., N.D., N.S., R.D.H., S.D., S.L., S.Sh., T.C., T.E.B., and T.P. Spatial transcriptomic data generation: A.R., B.L., D.M., E.G., J.Ca., J.Cl., M.Ku., and N.M. Data archive / Infrastructure: J.G., S.So. Data analysis: B.L., E.G., E.S.L., J.CI., J.G., K.J.T., K.Sm., N.J., N.L.J., R.D.H., and T.E.B. Data interpretation: E.M.C., E.S.L., J.CI., J.G., K.J.T., N.J., N.L.J., P.P.M., P.R.H., R.D.H., S.D., T.E.B.
我们感谢艾伦脑科学研究所的组织采购、组织处理和设施团队在尸检和神经外科脑标本的运输和处理方面的帮助；感谢艾伦研究所的技术团队在数据管理方面的支持；感谢 M. Vawter、J. Davis 和圣地亚哥医学检查办公室在尸检组织捐赠方面的帮助。该项目使用了 Connectome DB 和 Connectome Workbench，这些工具是在圣路易斯华盛顿大学及其相关联盟机构的人类连接组项目的支持下开发的（https://www.humanconnectome.org/）。本出版物得到了 BICCN 的支持和协调。本出版物是人类细胞图谱的一部分 - www.humancellatlas.org/publications/。本出版物中报告的研究得到了美国国立卫生研究院精神卫生研究所的支持，奖号为 U01MH114812 和 U19MH114830。内容仅代表作者的观点，并不一定代表美国国立卫生研究院的官方观点。作者感谢艾伦研究所的创始人 P. G. 艾伦，感谢他的远见、鼓励和支持。资金：本研究得到了以下机构的支持：克努特和爱丽丝·瓦伦贝里基金会 2018.0220（资助 S.L.）；南希和巴斯特·阿尔沃德基金（资助 C.D.K.）；美国国立卫生研究院 U01MH114812 资助（资助 A.M.Y., A.T., C.R., D.B., D.M., E.S.L., H.T., J.G., J.S., K.Si., K.Sm., M.T., N.D., N.L.J., R.D.H., S.D., S.L., T.C., T.E.B., 和 T.P.）；美国国立卫生研究院 U19MH117023 资助（资助 P.R.H.）；美国国立卫生研究院 U19MH114830 资助（资助 A.T., D.B., D.M., H.T., J.G., J.S., K.C., K.Sm., M.Kr., M.T., N.D., T.C., 和 T.P.）作者贡献：RNA 数据生成：A.M.Y., A.T., B.P.L., C.D.K., C.R., D.B., D.H., D.M., E.R.B., E.S.L., H.T., J.G., J.S., K.C., K.L., K.Si., K.Sm., K.K., M.K., M.T., N.D., N.S., R.D.H., S.D., S.L., S.Sh., T.C., T.E.B., 和 T.P. 空间转录组数据生成：A.R., B.L., D.M., E.G., J.Ca., J.Cl., M.Ku., 和 N.M. 数据存档/基础设施：J.G., S.So. 数据分析：B.L., E.G., E.S.L., J.CI., J.G., K.J.T., K.Sm., N.J., N.L.J., R.D.H., 和 T.E.B. 数据解释：E.M.C., E.S.L., J.CI., J.G., K.J.T., N.J., N.L.J., P.P.M., P.R.H., R.D.H., S.D., T.E.B.

Writing manuscript: E.S.L., J.CI., K.J.T., N.L.J., P.R.H., R.D.H., T.E.B.
撰写手稿：E.S.L.，J.CI.，K.J.T.，N.L.J.，P.R.H.，R.D.H.，T.E.B.
Competing interests: From April 11, 2022, N.L.J. has been an employee of Genentech. Data and materials availability: Raw sequence data were produced as part of the BRAIN Initiative Cell Census Network (BICCN: RRID:SCR_015820) are available for download from the Neuroscience Multi-omics Archive (RRID:SCR_016152; https://assets.nemoarchive.org/dat-rg2rc5m) and the Brain Cell Data Center (RRID:SCR_017266; https://biccn. org/data). Code for analysis and generation of figures is available for download from https://github.com/AllenInstitute/ human_cross_areal. MTG human SMARTseq v4 data (https:// portal.brain-map.org/atlases-and-data/rnaseq/human-mtg-smartseq, https://assets.nemoarchive.org/dat-swzf4kc). License
竞争利益：自 2022 年 4 月 11 日起，N.L.J.已成为基因泰克的员工。数据和材料可用性：原始序列数据是作为 BRAIN 倡议细胞普查网络（BICCN: RRID:SCR_015820）的一部分生成的，可从神经科学多组学档案（RRID:SCR_016152; https://assets.nemoarchive.org/dat-rg2rc5m）和脑细胞数据中心（RRID:SCR_017266; https://biccn.org/data）下载。分析和生成图形的代码可从 https://github.com/AllenInstitute/human_cross_areal 下载。MTG 人类 SMARTseq v4 数据（https://portal.brain-map.org/atlases-and-data/rnaseq/human-mtg-smartseq，https://assets.nemoarchive.org/dat-swzf4kc）。许可证
information: Copyright (c) 2023 the authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original US government works. https:// www.sciencemag.org/about/science-licenses-journal-article-reuse
信息：版权 (c) 2023 作者，保留部分权利；独家许可方美国科学促进会。对原美国政府作品不作任何声明。https:// www.sciencemag.org/about/science-licenses-journal-article-reuse

SUPPLEMENTARY MATERIALS 补充材料

science.org/doi/10.1126/science.adf6812
科学.org/doi/10.1126/science.adf6812
Figs. S1 to S12
图 S1 至 S12
Tables S1 to S13
表 S1 至 S13
Legends for tables S1 to S13
表 S1 至 S13 的图例
Submitted 6 November 2022; accepted 8 September 2023 10.1126/science.adf6812
提交于 2022 年 11 月 6 日；接受于 2023 年 9 月 8 日 10.1126/science.adf6812

Transcriptomic cytoarchitecture reveals principles of human neocortex organization
转录组细胞结构揭示人类新皮层组织的原则

Nikolas L. Jorstad, Jennie Close, Nelson Johansen, Anna Marie Yanny, Eliza R. Barkan, Kyle J. Travaglini, Darren Bertagnolli, Jazmin Campos, Tamara Casper, Kirsten Crichton, Nick Dee, Song-Lin Ding, Emily Gelfand, Jeff Goldy, Daniel Hirschstein, Katelyn Kiick, Matthew Kroll, Michael Kunst, Kanan Lathia, Brian Long, Naomi Martin, Delissa McMillen, Trangthanh Pham, Christine Rimorin, Augustin Ruiz, Nadiya Shapovalova, Soraya Shehata, Kimberly Siletti, Saroja Somasundaram, Josef Sulc, Michael Tieu, Amy Torkelson, Herman Tung, Edward M. Callaway, Patrick R. Hof, C. Dirk Keene, Boaz P. Levi, Sten Linnarsson, Partha P. Mitra, Kimberly Smith, Rebecca D. Hodge, Trygve E. Bakken, and Ed S Lein
尼科拉斯·L·乔斯塔德，珍妮·克洛斯，尼尔森·约汉森，安娜·玛丽·扬尼，伊丽莎·R·巴尔坎，凯尔·J·特拉瓦格利尼，达伦·贝尔塔尼奥利，贾兹敏·坎波斯，塔玛拉·卡斯珀，基尔斯滕·克赖顿，尼克·迪，宋林·丁，艾米莉·盖尔芬德，杰夫·戈尔迪，丹尼尔·赫希斯坦，凯特琳·基克，马修·克罗尔，迈克尔·库恩斯特，卡南·拉西亚，布莱恩·朗，娜奥米·马丁，德丽莎·麦克米伦，张清，克里斯汀·里莫林，奥古斯丁·鲁伊斯，纳迪亚·沙波瓦洛娃，索拉雅·谢哈塔，金伯利·西莱蒂，萨罗贾·索马苏达拉姆，约瑟夫·苏尔茨，迈克尔·蒂尤，艾米·托克尔森，赫尔曼·唐，爱德华·M·卡拉威，帕特里克·R·霍夫，C·迪克·基恩，博阿兹·P·利维，斯滕·林纳尔松，帕尔塔·P·米特拉，金伯利·史密斯，丽贝卡·D·霍奇，特里格维·E·巴肯，和埃德·S·莱因

Science 382 (6667), eadf6812. DOI: 10.1126/science.adf6812
科学 382 (6667), eadf6812. DOI: 10.1126/science.adf6812

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https://www.science.org/doi/10.1126/science.adf6812
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Copyright (c) 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works
版权 (c) 2023 作者，保留部分权利；独家许可方美国科学促进会。对原美国政府作品不作任何声明。

Allen Institute for Brain Science, Seattle, WA 98109, USA.
艾伦脑科学研究所，华盛顿州西雅图，邮政编码 98109，美国。
Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 17177 Stockholm, Sweden. Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY 11724, USA. *Corresponding author. Email: edI@alleninstitute.org (E.S.L.); trygveb@alleninstitute.org (T.E.B.); rebeccah@alleninstitute.org (R.D.H.)
卡罗林斯卡学院医学生物化学与生物物理学系，瑞典斯德哥尔摩 17177。萨克生物研究所系统神经生物学实验室，美国加利福尼亚州拉荷亚 92037。纳什家族神经科学系与弗里德曼脑研究所，西奈山伊坎医学院，美国纽约 10029。华盛顿大学实验医学与病理学系，美国西雅图 98195。冷泉港实验室，美国纽约冷泉港 11724。 *通讯作者。电子邮件：edI@alleninstitute.org (E.S.L.); trygveb@alleninstitute.org (T.E.B.); rebeccah@alleninstitute.org (R.D.H.)

RESEARCH ARTICLE SUMMARY 研究文章摘要

BICCN

Transcriptomic cytoarchitecture reveals principles of human neocortex organization转录组细胞结构揭示人类新皮层组织的原则

A Cross-areal consensus taxonomy跨区域共识分类法

READ THE FULL ARTICLE AT阅读全文请访问

RESEARCH ARTICLE 研究文章

BICCN

Transcriptomic cytoarchitecture reveals principles of human neocortex organization转录组细胞结构揭示人类新皮层组织的原则

Abstract 摘要

Within-area cell taxonomies demonstrate common subclass architecture区域内细胞分类法展示了共同的亚类结构

Cross-areal abundance changes reveal areal specification跨区域丰度变化揭示区域特异性

B

Excitatory to inhibitory neuron ratio varies across cortical areas and layers兴奋性神经元与抑制性神经元的比例在皮层区域和层次之间有所不同

LAMP5 LHX6

Transcriptomic cellular topography转录组细胞地形学

V1 specializations V1 专业化

L5 ET neuron diversityL5 ET 神经元多样性

Glial specialization 胶质细胞特化

Discussion 讨论

Materials and Methods 材料与方法

Processing of postmortem brain specimens尸检脑标本的处理

Tissue mapping and dissection组织映射和解剖

Nuclear isolation and capture核隔离与捕获

SMART-seqv4 RNA-sequencingSMART-seqv4 RNA 测序

SMART-seqv4 RNA-seq gene expression quantificationSMART-seqv4 RNA-seq 基因表达定量

10x Chromium RNA-sequencing and expression quantification10 倍铬 RNA 测序和表达定量

RNA-sequencing processing and clustering Cell-type label transferRNA 测序处理和聚类 细胞类型标签转移

Filtering low-quality nuclei过滤低质量细胞核

RNA-seq clustering RNA-seq 聚类

Defining cross-area consensus cell types定义跨区域共识细胞类型

Cell-type taxonomy generation细胞类型分类生成

Cell-type comparisons across cortical areas皮层区域之间的细胞类型比较

Transcriptomic entropy across areas转录组熵在不同区域的变化

In situ profiling of gene expression原位基因表达谱分析

Cell-type mapping of MERSCOPE dataMERSCOPE 数据的细胞类型映射

GFAP Immunofluorescence GFAP 免疫荧光

REFERENCES AND NOTES 参考文献和注释

ACKNOWLEDGMENTS 致谢

SUPPLEMENTARY MATERIALS 补充材料

Transcriptomic cytoarchitecture reveals principles of human neocortex organization转录组细胞结构揭示人类新皮层组织的原则

RESEARCH ARTICLE SUMMARY
研究文章摘要

Transcriptomic cytoarchitecture reveals principles of human neocortex organization
转录组细胞结构揭示人类新皮层组织的原则

A Cross-areal consensus taxonomy
跨区域共识分类法

READ THE FULL ARTICLE AT
阅读全文请访问

Transcriptomic cytoarchitecture reveals principles of human neocortex organization
转录组细胞结构揭示人类新皮层组织的原则

Within-area cell taxonomies demonstrate common subclass architecture
区域内细胞分类法展示了共同的亚类结构

Cross-areal abundance changes reveal areal specification
跨区域丰度变化揭示区域特异性

Excitatory to inhibitory neuron ratio varies across cortical areas and layers
兴奋性神经元与抑制性神经元的比例在皮层区域和层次之间有所不同

Transcriptomic cellular topography
转录组细胞地形学

L5 ET neuron diversity
L5 ET 神经元多样性

Processing of postmortem brain specimens
尸检脑标本的处理

Tissue mapping and dissection
组织映射和解剖

Nuclear isolation and capture
核隔离与捕获

SMART-seqv4 RNA-sequencing
SMART-seqv4 RNA 测序

SMART-seqv4 RNA-seq gene expression quantification
SMART-seqv4 RNA-seq 基因表达定量

10x Chromium RNA-sequencing and expression quantification
10 倍铬 RNA 测序和表达定量

RNA-sequencing processing and clustering Cell-type label transfer
RNA 测序处理和聚类细胞类型标签转移

Filtering low-quality nuclei
过滤低质量细胞核

Defining cross-area consensus cell types
定义跨区域共识细胞类型

Cell-type taxonomy generation
细胞类型分类生成

Cell-type comparisons across cortical areas
皮层区域之间的细胞类型比较

Transcriptomic entropy across areas
转录组熵在不同区域的变化

In situ profiling of gene expression
原位基因表达谱分析

Cell-type mapping of MERSCOPE data
MERSCOPE 数据的细胞类型映射

Transcriptomic cytoarchitecture reveals principles of human neocortex organization
转录组细胞结构揭示人类新皮层组织的原则