Adipose tissue retains an epigenetic memory of obesity after weight loss

Hinte, Laura C.; Castellano-Castillo, Daniel; Ghosh, Adhideb; Melrose, Kate; Gasser, Emanuel; Noé, Falko; Massier, Lucas; Dong, Hua; Sun, Wenfei; Hoffmann, Anne; Wolfrum, Christian; Rydén, Mikael; Mejhert, Niklas; Blüher, Matthias; von Meyenn, Ferdinand

doi:10.1038/s41586-024-08165-7

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Published: 18 November 2024

Adipose tissue retains an epigenetic memory of obesity after weight loss
脂肪组织在体重减轻后仍保留肥胖的表观遗传记忆

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Abstract 摘要

Reducing body weight to improve metabolic health and related comorbidities is a primary goal in treating obesity^1,2. However, maintaining weight loss is a considerable challenge, especially as the body seems to retain an obesogenic memory that defends against body weight changes^3,4. Overcoming this barrier for long-term treatment success is difficult because the molecular mechanisms underpinning this phenomenon remain largely unknown. Here, by using single-nucleus RNA sequencing, we show that both human and mouse adipose tissues retain cellular transcriptional changes after appreciable weight loss. Furthermore, we find persistent obesity-induced alterations in the epigenome of mouse adipocytes that negatively affect their function and response to metabolic stimuli. Mice carrying this obesogenic memory show accelerated rebound weight gain, and the epigenetic memory can explain future transcriptional deregulation in adipocytes in response to further high-fat diet feeding. In summary, our findings indicate the existence of an obesogenic memory, largely on the basis of stable epigenetic changes, in mouse adipocytes and probably other cell types. These changes seem to prime cells for pathological responses in an obesogenic environment, contributing to the problematic ‘yo-yo’ effect often seen with dieting. Targeting these changes in the future could improve long-term weight management and health outcomes.
减轻体重以改善代谢健康及相关合并症是治疗肥胖的主要目标 ^1,2 。然而，维持体重减轻是一个相当大的挑战，尤其是身体似乎保留了一种肥胖记忆，以抵御体重变化 ^3,4 。由于支撑这一现象的分子机制在很大程度上仍未被了解，因此克服这一障碍以实现长期治疗成功是困难的。在此，通过使用单核 RNA 测序，我们发现人类和小鼠的脂肪组织在显著体重减轻后仍保留了细胞转录变化。此外，我们发现小鼠脂肪细胞的表观基因组中存在持续的肥胖诱导改变，这些改变对其功能和代谢刺激的反应产生负面影响。携带这种肥胖记忆的小鼠表现出加速的体重反弹，而表观遗传记忆可以解释在高脂饮食进一步喂养后脂肪细胞中未来转录调控的失调。总之，我们的研究结果表明，小鼠脂肪细胞中存在一种基于稳定表观遗传变化的肥胖记忆，可能还包括其他细胞类型。这些变化似乎使细胞在肥胖环境中易于发生病理反应，从而导致节食时常出现的“溜溜球”效应。未来针对这些变化可能有助于改善长期体重管理和健康结果。

snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis
snRNA-seq 揭示了一种调节产热的小脂肪细胞亚群

Article 28 October 2020
文章 2020 年 10 月 28 日

Insulin resistance rewires the metabolic gene program and glucose utilization in human white adipocytes
胰岛素抵抗重编程了人类白色脂肪细胞的代谢基因程序和葡萄糖利用

Article 19 November 2021
文章 19 2021 年 11 月

Adipocyte p53 coordinates the response to intermittent fasting by regulating adipose tissue immune cell landscape
脂肪细胞 p53 通过调节脂肪组织免疫细胞景观协调间歇性禁食的反应

Article Open access 文章开放获取 15 February 2024 2024 年 2 月 15 日

Main 主要

Obesity and its related comorbidities represent substantial health risks¹. A primary clinical objective in managing obesity is to achieve appreciable weight loss (WL), typically through rigorous dietary and lifestyle interventions, pharmaceutical treatments or bariatric surgery (BaS)². Strategies relying on behavioural and dietary changes frequently only result in short-term WL and are susceptible to the ‘yo-yo’ effect, in which individuals regain weight over time^3,5,6. This recurrent pattern may be partially attributable to an (obesogenic) metabolic memory that persists even after notable WL^4,7,8,9,10 or metabolic improvements^11,12,13. Indeed, lasting phenotypic changes from previous metabolic states, that is, metabolic memory, have been reported in mouse adipose tissue (AT) or the stromal vascular fraction (SVF)^14,15,16, whereas in liver these were reversible^15,16,17. Persistent alterations after WL in the immune compartment¹⁸, and transcriptional and functional memory of obesity in endothelial cells of many organs^19,20,21,22, have also been reported.
肥胖及其相关并发症代表着重大的健康风险 ¹ 。在管理肥胖的主要临床目标中，实现显著的体重减轻（WL）是一个关键，通常通过严格的饮食和生活方式干预、药物治疗或减重手术（BaS）来实现 ² 。依赖于行为和饮食改变的策略往往只能导致短期体重减轻，并且容易受到“溜溜球”效应的影响，即个体随着时间的推移会重新增重 ^3,5,6 。这种反复的模式可能部分归因于一种（肥胖诱导的）代谢记忆，即使在显著的体重减轻 ^4,7,8,9,10 或代谢改善 ^11,12,13 后仍然持续存在。确实，从先前代谢状态产生的持久表型变化，即代谢记忆，已在小鼠脂肪组织（AT）或基质血管成分（SVF）中被报道 ^14,15,16 ，而在肝脏中这些变化是可逆的 ^15,16,17 。在体重减轻后，免疫成分的持续改变 ¹⁸ ，以及多个器官内皮细胞中肥胖的转录和功能记忆 ^19,20,21,22 ，也已被报道。

Epigenetic mechanisms and modifications are essential for development, differentiation and identity maintenance of adipocytes in vitro and in vivo^{23,24,25,26,27}, but are also expected to be crucial contributors to the cellular memory of obesity^4,7. For example, lasting chromatin accessibility changes have been associated with pathological memory of obesity in mouse myeloid cells²⁸ and, also, cold exposure studies have indicated the existence of (epigenetic) cellular memory^26,29. Hitherto, most human studies have focused on DNA methylation analysis in bulk tissues or whole blood to assess putative cellular memory^30,31,32,33. These reports might be confounded by variations in cell type composition, which are poorly characterized in the AT during WL, and therefore serve foremost as indicators of cellular epigenetic memory.
表观遗传机制和修饰对于脂肪细胞在体外和体内的发育、分化和身份维持至关重要 ^{23,24,25,26,27} ，但也预计是肥胖细胞记忆的关键贡献者 ^4,7 。例如，持久的染色质可及性变化与小鼠髓系细胞中肥胖的病理记忆相关 ²⁸ ，并且冷暴露研究也表明了（表观遗传）细胞记忆的存在 ^26,29 。迄今为止，大多数人类研究集中在通过分析大量组织或全血中的 DNA 甲基化来评估潜在的细胞记忆 ^30,31,32,33 。这些报告可能受到细胞类型组成变异的影响，这些变异在体重减轻（WL）期间的脂肪组织（AT）中未得到充分表征，因此主要作为细胞表观遗传记忆的指标。

In summary, it remains unresolved whether individual cells retain a metabolic memory and whether it is conferred through epigenetic mechanisms. Here, we set out to address this by first performing single-nucleus RNA sequencing (snRNA-seq) of AT from individuals living with obesity before and after significant WL, as well as lean, obese and formerly obese mice, confirming the presence of retained transcriptional changes, and, second, by characterizing the epigenome of mouse adipocytes, which revealed the long-term persistence of an epigenetic obesogenic memory.
总之，目前仍未解决的是单个细胞是否保留了代谢记忆，以及这种记忆是否通过表观遗传机制传递。为此，我们首先对肥胖个体在显著体重减轻前后的脂肪组织进行了单核 RNA 测序（snRNA-seq），并与瘦、肥胖和曾经肥胖的小鼠进行了比较，证实了保留的转录变化的存在；其次，通过对小鼠脂肪细胞的表观基因组进行特征分析，揭示了表观遗传肥胖记忆的长期持久性。

Transcriptional changes in human AT
人类 AT 的转录变化

To explore whether signatures of previous obesogenic states persist in humans after appreciable WL, we obtained subcutaneous AT (scAT) and omental AT (omAT) biopsies from individuals with healthy weight who have never had obesity (called healthy weight here) and people living with obesity (but without diabetes) before (T0) and 2 yr after (T1) BaS from multiple independent studies (Fig. 1a). The omAT samples were from the multicentre two-step surgery (MTSS) study (n = 5 lean individuals, 1 male, 4 females; n = 8 individuals with obesity, 2 males, 6 females) and Leipzig two-step surgery (LTSS) study (n = 5 lean individuals, 2 males, 3 females; n = 5 individuals with obesity, 2 males, 3 females). Only patients exhibiting a minimum of 25% body mass index (BMI) reduction were included into our study (Fig. 1a,b and Extended Data Table 1). We performed snRNA-seq on pooled omAT per group and could annotate, on the basis of published data^34,35, 18 cell clusters in the omAT samples (Fig. 1c and Extended Data Figs. 1a and 2a–d), including adipocytes, adipocyte progenitor cells (APCs), mesothelial cells, immune cells and endothelial cells. Although we did not observe consistent cellular composition differences between T0 and T1 in omAT, we observed inter-individual cellular composition variations after single nucleotide polymorphism (SNP)-based demultiplexing, possibly also affected by sampling during surgery (Extended Data Fig. 2e,f). Notably, cell type-specific gene expression analysis revealed that many differentially expressed genes (DEGs) at T0 (obese versus healthy weight) were also deregulated at T1 in both studies (Fig. 1d and Extended Data Fig. 1b,c). We next performed the same analysis with scAT biopsies from the LTSS study (n = 5 lean individuals, 2 males, 3 females; n = 5 individuals with obesity, 2 males, 3 females) and NEFA trial (ClinicalTrials.gov registration no. NCT01727245; n = 8 lean individuals, all female; n = 7 individuals with obesity, all female), including only patients exhibiting a minimum of 25% BMI reduction (Fig. 1a,b and Extended Data Table 1). We annotated 13 cell clusters for scAT (Fig. 1e and Extended Data Fig. 1d), including APCs, adipocytes, endothelial cells and immune cells, on the basis of published markers^34,35,36,37 (Extended Data Fig. 3a–d). We did not observe consistent cellular composition differences between T0 and T1 in scAT (Extended Data Fig. 3e,f). However, similar to omAT we found in both studies that many cell types retained transcriptional differences from T0 to T1 (Fig. 1f and Extended Data Fig. 1e,f). A further detailed analysis of cell type-specific gene expression changes in omAT and scAT showed that transcriptional deregulation during obesity was most pronounced in adipocytes, APCs and endothelial cells (Extended Data Fig. 1g–j). In line with this observation, the absolute number of retained DEGs from T0 to T1 was highest in these cell types as well (Extended Data Fig. 1k). Given that adipocytes showed strong retainment of transcriptional differences in each individual sample, we integrated the snRNA-seq data of all adipocytes from the omAT and scAT studies, respectively (Extended Data Fig. 1l,m), and performed differential gene expression analysis. Pooled omAT adipocytes displayed a strong retention of downregulated DEGs (Fig. 1g), including relevant metabolic genes^38,39,40,41 such as IGF1, LPIN1, IDH1 or PDE3A (Fig. 1h). Similarly, the retention of downregulated DEGs in scAT adipocytes was pronounced (Fig. 1i) and included relevant metabolic genes^38,42,43,44 such as IGF1, DUSP1, GPX3 and GLUL (Fig. 1j). Gene set enrichment analysis (GSEA) of retained DEGs in adipocytes of each study showed persistent downregulation of pathways linked to adipocyte metabolism and function (Extended Data Fig. 4a–d) and persistent upregulation of pathways linked to fibrosis (related to TGFβ signalling) and apoptosis (Extended Data Fig. 4e–h). These results indicate that obesity induces cellular and transcriptional (obesogenic) changes in the AT, which are not resolved following significant WL.
为了探究在显著体重减轻后，人类体内是否仍保留着先前肥胖状态的特征，我们从多个独立研究中获取了健康体重个体（此处称为健康体重）和肥胖个体（但无糖尿病）在基线（T0）和两年后（T1）进行胃旁路手术（BaS）前后的皮下脂肪组织（scAT）和网膜脂肪组织（omAT）活检样本（图 1a）。网膜脂肪样本来自多中心两步手术（MTSS）研究（n = 5 名健康体重个体，1 名男性，4 名女性；n = 8 名肥胖个体，2 名男性，6 名女性）和莱比锡两步手术（LTSS）研究（n = 5 名健康体重个体，2 名男性，3 名女性；n = 5 名肥胖个体，2 名男性，3 名女性）。只有体重指数（BMI）减少至少 25%的患者被纳入我们的研究（图 1a,b 和扩展数据表 1）。我们对每组混合的网膜脂肪组织进行了单细胞 RNA 测序（snRNA-seq），并根据已发表的数据，在网膜脂肪样本中注释了 18 个细胞簇（图 1c 和扩展数据图 1a 以及 2a–d），包括脂肪细胞、脂肪细胞前体细胞（APCs）、间皮细胞、免疫细胞和内皮细胞。尽管我们在网膜脂肪组织中未观察到 T0 和 T1 之间细胞组成的持续差异，但我们观察到基于单核苷酸多态性（SNP）解复用后的个体间细胞组成变异，这可能还受到手术采样的影响（扩展数据图 2e,f）。值得注意的是，细胞类型特异性基因表达分析显示，在 T0（肥胖与健康体重）时许多差异表达基因（DEGs）在两项研究中的 T1 阶段也发生了失调（图 1d 和扩展数据图 1b,c）。接下来，我们对 LTSS 研究（n = 5 名瘦个体，2 名男性，3 名女性；n = 5 名肥胖个体，2 名男性，3 名女性）和 NEFA 试验（ClinicalTrials.gov 注册号 NCT01727245；n = 8 名瘦个体，均为女性；n = 7 名肥胖个体，均为女性）的 scAT 活检进行了相同的分析，仅包括 BMI 减少至少 25%的患者（图 1a,b 和扩展数据表 1）。我们为 scAT 注释了 13 个细胞集群（图 1e 和扩展数据图 1d），包括 APCs、脂肪细胞、内皮细胞和免疫细胞，基于已发表的标记物 ^34,35,36,37 （扩展数据图 3a–d）。我们没有观察到 scAT 在 T0 和 T1 之间细胞组成的差异（扩展数据图 3e,f）。然而，与 omAT 类似，我们在两项研究中发现许多细胞类型在 T0 到 T1 之间保持了转录差异（图 1f 和扩展数据图 1e,f）。进一步详细分析 omAT 和 scAT 中细胞类型特异性基因表达变化显示，肥胖期间转录失调在脂肪细胞、APCs 和内皮细胞中最为显著（扩展数据图 1g–j）。根据这一观察，从 T0 到 T1 保留的差异表达基因（DEGs）的绝对数量在这些细胞类型中也是最高的（扩展数据图 1k）。鉴于脂肪细胞在每个个体样本中显示出强烈的转录差异保留，我们分别整合了 omAT 和 scAT 研究中所有脂肪细胞的单核 RNA 测序（snRNA-seq）数据（扩展数据图 1l,m），并进行了差异基因表达分析。合并的 omAT 脂肪细胞显示出强烈的下调 DEGs 保留（图 1g），包括相关的代谢基因 ^38,39,40,41 如 IGF1、LPIN1、IDH1 或 PDE3A（图 1h）。类似地，scAT 脂肪细胞中下调 DEGs 的保留也很显著（图 1i），并包括相关的代谢基因 ^38,42,43,44 如 IGF1、DUSP1、GPX3 和 GLUL（图 1j）。对每个研究中脂肪细胞保留的 DEGs 进行基因集富集分析（GSEA）显示，与脂肪细胞代谢和功能相关的通路持续下调（扩展数据图 4a–d），与纤维化（与 TGFβ信号传导相关）和凋亡相关的通路持续上调（扩展数据图 4e–h）。这些结果表明，肥胖诱导了 AT 中的细胞和转录（肥胖相关）变化，这些变化在显著体重减轻后并未得到解决。

Fig. 1: Human AT retains cellular transcriptional changes after BaS-induced WL.
图 1：人类 AT 在 BaS 诱导的 WL 后保留了细胞转录变化。

Pathophysiology mostly resolves after WL
病理生理学大多在体重减轻后得到解决

To investigate the molecular mechanisms and pathophysiological importance of this putative metabolic memory of obesity, we assessed WL in an experimental animal model (Fig. 2a). The 6-week-old male mice were fed a high-fat diet (HFD; 60% kcal from fat) or low-fat chow diet (10% kcal from fat) for 12 (H and C) or 25 weeks (HH and CC_l). Subsequently, we switched the diet to a standard chow diet (HC, CC_s, HHC, CCC), leading to weight normalization in 4–8 weeks (Fig. 2b,c). Glucose tolerance was impaired in H but not in HH mice (compared with age-matched controls), whereas insulin sensitivity was lower in HH but not in H mice (Extended Data Fig. 5a,b). Fasting blood glucose levels were greater in both groups (Extended Data Fig. 5c). WL restored insulin sensitivity in HHC mice, whereas HC mice still showed impaired glucose tolerance (Extended Data Fig. 2d,e). Fasting glucose levels were normalized by WL in both groups, matching those of control mice (Extended Data Fig. 2f). After WL, hyperinsulinemia was resolved in HC mice, but only diminished in HHC mice (Extended Data Fig. 5g–i). Leptin levels, which were elevated in obese mice, returned to control levels after WL (Extended Data Fig. 5j). Energy expenditure and food intake showed no differences between HC and CC_s mice after WL (Extended Data Fig. 5k,l). Liver triglyceride accumulation was normalized (to control levels) in HC, and most HHC, mice. (Extended Data Fig. 5m,n). Similarly, C and H mice, and CC_s and HC mice, did not differ in the amount of lean mass nor did HC mice lose lean mass (Extended Data Fig. 5o). Obese H mice had larger subcutaneous inguinal AT (ingAT), epididymal AT (epiAT) and brown AT (BAT) depots than corresponding control mice (Extended Data Fig. 5p,q). ingAT and BAT depot sizes normalized after WL. In line with a recent report, epiAT of HC mice was smaller than that of controls after WL¹⁸. Interestingly, the phenomenon of epiAT shrinkage was already observed during obesity in 25-week HFD-fed (HH) mice, as previously reported⁴⁵, and maintained after WL in HHC mice (Extended Data Fig. 5r–v). Adipocyte sizes varied between depots, and adipocytes were enlarged in ingAT of H and HH mice and normalized after WL in HC, but not in HHC, mice (Extended Data Fig. 5w,x). epiAT adipocytes were also enlarged and shrunk to normal sizes in H and HC mice, respectively, whereas in HH and HHC mice adipocytes were of equal size, probably owing to the tissue shrinkage (Extended Data Fig. 5v,y). The epiAT of obese mice (H and HH) showed immune cell infiltration and apical fibrosis, which partially improved after WL in HC, but not HHC, mice (Extended Data Fig. 5t–v). Masson’s trichrome staining showed more collagen deposition in epiAT after WL (Extended Data Fig. 5z). Overall, after WL, only a few mild metabolic impartments persisted, including glucose intolerance in HC mice, hyperinsulinemia and slight liver steatosis in HHC mice and a notable decrease in epiAT depot size after WL in both groups.
为了研究这种假定的肥胖代谢记忆的分子机制和病理生理重要性，我们在一个实验动物模型中评估了体重减轻（WL）（图 2a）。6 周龄的雄性小鼠被喂食高脂饮食（HFD；60%的卡路里来自脂肪）或低脂饲料饮食（10%的卡路里来自脂肪）12 周（H 和 C）或 25 周（HH 和 CC_l）。随后，我们将饮食切换为标准饲料饮食（HC，CC_s，HHC，CCC），导致体重在 4-8 周内恢复正常（图 2b，c）。葡萄糖耐量在 H 组中受损，而在 HH 组中未受损（与年龄匹配的对照组相比），而胰岛素敏感性在 HH 组中较低，但在 H 组中未受损（扩展数据图 5a，b）。空腹血糖水平在两组中均较高（扩展数据图 5c）。WL 恢复了 HHC 小鼠的胰岛素敏感性，而 HC 小鼠仍表现出葡萄糖耐量受损（扩展数据图 2d，e）。空腹血糖水平在两组中均通过 WL 恢复正常，与对照小鼠相匹配（扩展数据图 2f）。WL 后，HC 小鼠的高胰岛素血症得到解决，而 HHC 小鼠的高胰岛素血症仅有所减轻（扩展数据图 5g-i）。瘦素水平在肥胖小鼠中升高，WL 后恢复到对照水平（扩展数据图 5j）。WL 后，HC 和 CC_s 小鼠的能量消耗和食物摄入量没有差异（扩展数据图 5k，l）。肝脏甘油三酯积累在 HC 小鼠中恢复正常（达到对照水平），在大多数 HHC 小鼠中也恢复正常（扩展数据图 5m，n）。同样，C 和 H 小鼠，以及 CC_s 和 HC 小鼠，在瘦体重量上没有差异，HC 小鼠也没有失去瘦体重（扩展数据图 5o）。肥胖的 H 小鼠的腹股沟皮下脂肪组织（ingAT）、附睾脂肪组织（epiAT）和棕色脂肪组织（BAT）的储存量比相应的对照小鼠更大（扩展数据图 5p,q）。在体重减轻后，ingAT 和 BAT 的储存量恢复正常。与最近的一份报告一致，HC 小鼠在体重减轻后的 epiAT 比对照小鼠小 ¹⁸ 。有趣的是，如先前报道的 ⁴⁵ ，在 25 周高脂饮食喂养的（HH）小鼠中，肥胖期间已经观察到 epiAT 的缩小现象，并且在 HHC 小鼠体重减轻后仍然保持（扩展数据图 5r–v）。脂肪细胞的大小在不同储存部位有所不同，H 和 HH 小鼠的 ingAT 脂肪细胞增大，在 HC 小鼠体重减轻后恢复正常，但在 HHC 小鼠中没有恢复正常（扩展数据图 5w,x）。epiAT 脂肪细胞在 H 和 HC 小鼠中分别增大和缩小至正常大小，而在 HH 和 HHC 小鼠中脂肪细胞大小相同，可能是由于组织缩小（扩展数据图 5v,y）。肥胖小鼠（H 和 HH）的 epiAT 显示出免疫细胞浸润和顶端纤维化，这在 HC 小鼠体重减轻后部分改善，但在 HHC 小鼠中没有改善（扩展数据图 5t–v）。Masson 三色染色显示，体重减轻后 epiAT 中胶原蛋白沉积增多（扩展数据图 5z）。总体而言，WL 后仅持续存在少数轻微的代谢异常，包括 HC 小鼠的葡萄糖不耐受、HHC 小鼠的高胰岛素血症和轻度脂肪肝，以及两组小鼠在 WL 后显著减少的皮下脂肪库大小。

Fig. 2: Transcriptional changes persist WL induced (partial) remodelling of epiAT.
图 2：转录变化持续存在，WL 诱导（部分）重塑了 epiAT。

Transcriptional obesogenic memory in mice
小鼠中的转录性肥胖记忆

Considering our observations of persistent transcriptional changes in human AT, we examined mouse epiAT cellular changes throughout obesity and WL using snRNA-seq. We annotated 15 key cell populations using common marker genes^34,36,46, including APCs, immune cells, adipocytes, mesothelial cells, endothelial cells and epithelial cells (Fig. 2d and Extended Data Fig. 6a,b). Consistent with previous findings^16,18,46, macrophage cell number in epiAT was higher in obese conditions (H and HH), and was not fully normalized after WL, especially in HHC mice (Fig. 2e). Resident macrophages in control mice (C, CC and CCC) primarily consisted of perivascular macrophages and non-perivascular macrophages. Notably, during obesity mainly lipid-associated macrophage (LAM) and non-perivascular macrophage cell numbers increased in the epiAT, altering the macrophage population composition persistently (Fig. 2f and Extended Data Fig. 6c,d).
鉴于我们在人类 AT 中观察到的持续转录变化，我们利用 snRNA-seq 技术研究了肥胖和体重减轻（WL）过程中小鼠 epiAT 细胞的变化。我们使用常见标记基因 ^34,36,46 注释了 15 个关键细胞群体，包括抗原呈递细胞（APCs）、免疫细胞、脂肪细胞、间皮细胞、内皮细胞和上皮细胞（图 2d 和扩展数据图 6a,b）。与先前研究结果 ^16,18,46 一致，肥胖条件下（H 和 HH）epiAT 中的巨噬细胞数量较高，且在 WL 后未能完全恢复正常，尤其是在 HHC 小鼠中（图 2e）。对照组小鼠（C、CC 和 CCC）的驻留巨噬细胞主要由血管周围巨噬细胞和非血管周围巨噬细胞组成。值得注意的是，在肥胖过程中，主要脂质相关巨噬细胞（LAM）和非血管周围巨噬细胞的数量在 epiAT 中增加，持续改变巨噬细胞群体的组成（图 2f 和扩展数据图 6c,d）。

Motivated by our own observation of persistent transcriptional changes in human AT (Fig. 1 and Extended Data Fig. 1) and corresponding recent reports in endothelial and immune cells^18,19, we next investigated transcriptional retention (‘memory’) in the mouse epiAT. On the basis of the number of DEGs in each cell type, we found stronger transcriptional deregulation in obesity and after WL in adipocytes, APCs, endothelial cells, epithelial cells and macrophages than in other cell types (Extended Data Fig. 7a), corroborating the existence of persistent, cell-specific transcriptional changes in mouse epiAT. Indeed, across cell types many DEGs from the obesity time point remained deregulated after WL (Fig. 2g,h and Extended Data Fig. 7b,c). GSEA of retained DEGs in adipocytes, APCs, endothelial cells, LAMs, non-perivascular macrophages, perivascular macrophages and mesothelial cells showed persistent upregulation in HC and HHC mice of genes related to lysosome activity, apoptosis and other inflammatory pathways (Extended Data Fig. 7c,d), indicating endoplasmic reticulum and cellular stress. Persistently downregulated retained DEGs in HC and HHC mice were mainly related to metabolic AT pathways, such as fatty acid omega oxidation, fatty acid biosynthesis, adipogenesis or peroxisome proliferator-activated receptor signalling (Extended Data Fig. 7e,f), pointing to potential dysfunction in the AT after WL.
受我们自身对人类 AT 持续转录变化（图 1 和扩展数据图 1）以及近期在血管内皮细胞和免疫细胞中相关报道的观察启发 ^18,19 ，我们接下来研究了小鼠 epiAT 中的转录保留（“记忆”）。基于每种细胞类型的 DEG 数量，我们发现脂肪细胞、APCs、内皮细胞、上皮细胞和巨噬细胞在肥胖和 WL 后比其他细胞类型表现出更强的转录失调（扩展数据图 7a），证实了小鼠 epiAT 中存在持续的、细胞特异性的转录变化。实际上，跨细胞类型，许多来自肥胖时间点的 DEG 在 WL 后仍然失调（图 2g,h 和扩展数据图 7b,c）。对脂肪细胞、APCs、内皮细胞、LAMs、非血管周围巨噬细胞、血管周围巨噬细胞和中皮细胞中保留的 DEG 进行 GSEA 分析显示，HC 和 HHC 小鼠中与溶酶体活性、凋亡和其他炎症途径相关的基因持续上调（扩展数据图 7c,d），表明内质网和细胞应激。在 HC 和 HHC 小鼠中持续下调的保留 DEG 主要与代谢 AT 途径相关，如脂肪酸ω氧化、脂肪酸生物合成、脂肪生成或过氧化物酶体增殖物激活受体信号传导（扩展数据图 7e,f），提示 WL 后 AT 可能存在功能障碍。

Focusing specifically on adipocytes, we identified three distinct patterns of DEGs (Fig. 2i): a group that failed to restore normal expression after WL in HC or HHC (for example, Maob or Ctsd); another group that restored expression in HC but not in HHC (for example, Cyp2e1 or Runx2); and a third group that restored normal expression after WL in both HC and HHC mice (for example, Gpam or Tyrobp). Notably, we did not identify any DEGs that exclusively restored normal expression after WL in HHC mice but not in HC mice, suggesting that longer durations of obesity or relatively shorter WL periods exert a stronger influence on retainment of a transcriptional memory. In summary, after WL, adipocytes from mice maintained an upregulation of inflammatory- and extracellular matrix remodelling-related pathways, whereas adipocyte-specific metabolic pathways remained downregulated (Extended Data Fig. 7g,h), mirroring our findings from human adipocytes (Fig. 1h,j and Extended Data Fig. 4).
专注于脂肪细胞，我们识别出三种不同的差异表达基因（DEGs）模式（图 2i）：一组在 HC 或 HHC 中未能恢复正常表达（例如，Maob 或 Ctsd）；另一组在 HC 中恢复表达但在 HHC 中未恢复（例如，Cyp2e1 或 Runx2）；还有一组在 HC 和 HHC 小鼠中均在 WL 后恢复了正常表达（例如，Gpam 或 Tyrobp）。值得注意的是，我们未发现任何仅在 HHC 小鼠中 WL 后恢复正常表达而在 HC 小鼠中未恢复的 DEGs，这表明较长时间的肥胖或相对较短的 WL 周期对保持转录记忆有更强的影响。总之，WL 后，小鼠脂肪细胞维持了炎症和细胞外基质重塑相关通路的激活，而脂肪细胞特异性代谢通路仍处于下调状态（扩展数据图 7g,h），这与我们从人类脂肪细胞中获得的结果相呼应（图 1h,j 和扩展数据图 4）。

Epigenetic obesogenic memory in mice
小鼠中的表观遗传肥胖记忆

Having established the persistence of obesity-associated transcriptional changes after WL in human AT and mouse epiAT, our attention shifted towards exploring the underlying mechanisms conferring this putative memory. We decided to focus on adipocytes given their post-mitotic nature, immobility, long lifespan and central position in AT biology⁴⁷. We conducted an epigenetic analysis of adipocytes derived from mouse epiAT. Considering the inherent difficulties in studying epigenetic signatures in heterogenous cell populations, we crossed tamoxifen-inducible AdipoERCre mice with NuTRAP reporter mice, and thereby labelled adipocyte nuclei with biotin and GFP-tagged ribosomes before HFD feeding (Fig. 3a). We then developed a protocol to assay multiple modalities from labelled adipocytes of the same epiAT depot (Fig. 3b) and performed paired analysis of the translatome using targeted purification of polysomal messenger RNA (translating ribosome affinity purification followed by RNA sequencing technology (TRAP–seq)), chromatin accessibility using assay for transposase-accessible chromatin (ATAC) with sequencing (ATAC–seq) and four histone post-translational modifications (hPTMs) using cleavage under targets and tagmentation (CUT&Tag). In essence, we generated extensive epigenetic datasets from adipocytes of each epiAT sample (Extended Data Fig. 8a,b) encompassing H3K27me3 (a polycomb-mediated repressive hPTM), H3K4me3 (which marks active transcription start sites (TSS)), H3K4me1 (indicative of active or poised enhancers) and H3K27ac (which marks active enhancers and other candidate cis-regulatory elements)^48,49. We observed strong correlation between the transcriptional profiles of labelled adipocytes and the adipocyte clusters identified by snRNA-seq (Extended Data Fig. 8c). Consistent with our observation from the snRNA-seq, we also noted a restoration of the translational profile in adipocytes from HC and HHC mice (Fig. 3c).
在确定了人类脂肪组织（AT）和小鼠皮下脂肪组织（epiAT）在体重减轻（WL）后肥胖相关转录变化持续存在后，我们的注意力转向探索赋予这种潜在记忆的潜在机制。我们决定聚焦于脂肪细胞，因为它们具有非分裂性、不可移动性、长寿命以及在 AT 生物学中的核心位置 ⁴⁷ 。我们对从小鼠 epiAT 中提取的脂肪细胞进行了表观遗传学分析。考虑到在异质细胞群中研究表观遗传特征的固有困难，我们将他莫昔芬诱导的 AdipoERCre 小鼠与 NuTRAP 报告小鼠杂交，从而在 HFD 喂养前用生物素和 GFP 标记的核糖体标记脂肪细胞核（图 3a）。然后，我们开发了一种协议，从同一 epiAT 库的标记脂肪细胞中检测多种模式（图 3b），并进行了配对分析，使用靶向纯化多聚核糖体信使 RNA（翻译核糖体亲和纯化后进行 RNA 测序技术（TRAP–seq））、染色质可及性使用转座酶可及染色质检测（ATAC）与测序（ATAC–seq）以及四种组蛋白翻译后修饰（hPTMs）使用靶向切割和标记（CUT&Tag）。本质上，我们从每个 epiAT 样本的脂肪细胞中生成了广泛的表观遗传数据集（扩展数据图）。 8a,b) 包括 H3K27me3（一种由多梳蛋白介导的抑制性组蛋白修饰）、H3K4me3（标记活跃的转录起始位点（TSS））、H3K4me1（指示活跃或准备状态的增强子）和 H3K27ac（标记活跃的增强子及其他候选顺式调控元件） ^48,49 。我们观察到标记的脂肪细胞的转录谱与通过 snRNA-seq 识别的脂肪细胞簇之间存在强相关性（扩展数据图 8c）。与我们在 snRNA-seq 中的观察一致，我们还注意到来自 HC 和 HHC 小鼠的脂肪细胞中翻译谱的恢复（图 3c）。

Fig. 3: Adipocyte promoters retain an epigenetic memory.
图 3：脂肪细胞启动子保留表观遗传记忆。

Next, to identify sources of biological variability (factors) in our datasets on the basis of all modalities across all conditions we used multi-omics factor analysis (MOFA)⁵⁰. This enables unsupervised integration and clustering of our paired multi-omic (epigenetic) datasets to overcome potential limitations of modality-specific analyses. HC and HHC samples clustered closer to H and HH samples than to controls along Factor 1, indicating that WL did not induce complete normalization of the adipocyte epigenome (Fig. 3d). MOFA inferred Factor 1 as the main source of data variability between the conditions, which was predominantly influenced by active hPTMs (Fig. 3e).
接下来，为了基于所有条件下的所有模态识别我们数据集中的生物变异性来源（因素），我们使用了多组学因子分析（MOFA） ⁵⁰ 。这使得我们能够对配对的多组学（表观遗传学）数据集进行无监督的整合和聚类，以克服特定模态分析的潜在局限性。健康对照（HC）和健康超重对照（HHC）样本在因子 1 上比对照样本更接近超重（H）和超重超重（HH）样本，这表明 WL 并未完全诱导脂肪细胞表观基因组的正常化（图 3d）。MOFA 推断因子 1 是条件间数据变异性的主要来源，这主要受到活性组蛋白翻译后修饰（hPTMs）的影响（图 3e）。

Motivated by our MOFA findings, we investigated promoters marked by H3K4me3 or H3K27me3 to identify differentially marked promoters for these hPTMs (Extended Data Fig. 8d). We examined the dynamics of these modifications between adipocytes from obese and WL mice. More than 1,000 promoters showed differential enrichment of H3K4me3 in H and HC mice (H: 1,475; HC: 1,094), with a majority showing increased H3K4me3 levels (Fig. 3f). Similarly, 859 promoters were differentially marked in HH and HHC mice (Extended Data Fig. 8e). Overall, many promoters remained activated after WL that were less actively marked in controls, and vice versa. In contrast to H3K4me3, overall, more promoters lost than gained H3K27me3 in obese mice, and a substantial number of these promoters remained repressed or did not regain trimethylation at K27 after WL compared with controls (Fig. 3g and Extended Data Fig. 8e).
受 MOFA 研究结果的启发，我们研究了由 H3K4me3 或 H3K27me3 标记的启动子，以识别这些组蛋白修饰的不同标记启动子（扩展数据图 8d）。我们检查了这些修饰在肥胖和 WL 小鼠脂肪细胞之间的动态变化。超过 1,000 个启动子在 H 和 HC 小鼠中显示出 H3K4me3 的差异富集（H：1,475；HC：1,094），其中大多数显示出 H3K4me3 水平的增加（图 3f）。类似地，859 个启动子在 HH 和 HHC 小鼠中显示出差异标记（扩展数据图 8e）。总体而言，许多启动子在 WL 后仍然保持激活状态，而在对照组中这些启动子的标记活性较低，反之亦然。与 H3K4me3 相反，总体上，肥胖小鼠中失去 H3K27me3 标记的启动子多于获得标记的启动子，并且与对照组相比，大量这些启动子在 WL 后仍然保持抑制状态或未重新获得 K27 的三甲基化（图 3g 和扩展数据图 8e）。

We next performed a functional analysis of differentially marked promoters. The activity status of many promoters switched, transitioning from active (H3K4me3 and/or H3K27ac) to repressed (H3K27me3), or vice versa, in obese and WL conditions, compared with control samples. Many of these epigenetic changes were also reflected in the translatome (Fig. 3h) and nuclear transcriptome (Fig. 2f). Promoters that remained repressed (high H3K27me3 and low H3K4me3 and/or H3K27ac) were linked to adipocyte function-related genes (for example, Gpam, Cyp2e1 or Acacb), whereas promoters that remained active (that is, high H3K4me3 and/or H3K27ac and low H3K27me3) were related to genes involved in extracellular matrix remodelling and inflammatory signalling (for example, Icam1, Lyz2 or Tyrobp) (Fig. 3h,i). By GSEA, we confirmed that H3K4me3 persistence in adipocytes from H/HC and HH/HHC mice was associated with chemokine and inflammatory processes (Extended Data Fig. 8f,g). Persistent H3K4me3 loss in H/HC-affected genes included those involved in adipocyte functions (for example, adipogenesis, triacylglyceride synthesis, peroxisome proliferator-activated receptor signalling, leptin and adiponectin signalling) (Extended Data Fig. 8f), whereas adipogenesis-related genes were repressed by H3K27me3 gain and H3K4me3 loss in adipocytes from HHC/HH mice (Extended Data Fig. 8g), suggesting a persistently impaired adipocyte function. Notably, the expression of relevant epigenetic modifiers was not deregulated in HC or HHC adipocytes (Extended Data Fig. 8h).
接下来，我们对差异标记的启动子进行了功能分析。与对照样本相比，在肥胖和 WL 条件下，许多启动子的活性状态发生了变化，从活跃（H3K4me3 和/或 H3K27ac）转变为抑制（H3K27me3），或反之。这些表观遗传变化也在转录组（图 3h）和核转录组（图 2f）中得到了反映。保持抑制状态（高 H3K27me3 和低 H3K4me3 和/或 H3K27ac）的启动子与脂肪细胞功能相关基因（例如，Gpam、Cyp2e1 或 Acacb）相关联，而保持活跃状态（即高 H3K4me3 和/或 H3K27ac 和低 H3K27me3）的启动子则与细胞外基质重塑和炎症信号传导相关基因（例如，Icam1、Lyz2 或 Tyrobp）相关（图 3h,i）。通过 GSEA 分析，我们确认了 H/HC 和 HH/HHC 小鼠脂肪细胞中 H3K4me3 的持续存在与趋化因子和炎症过程相关（扩展数据图 8f,g）。在 H/HC 影响的基因中，持续丢失 H3K4me3 的基因包括参与脂肪细胞功能的基因（例如，脂肪生成、甘油三酯合成、过氧化物酶体增殖物激活受体信号传导、瘦素和脂联素信号传导）（扩展数据图 8f），而在 HHC/HH 小鼠的脂肪细胞中，脂肪生成相关基因通过 H3K27me3 的增加和 H3K4me3 的减少而被抑制（扩展数据图 8g），这表明脂肪细胞功能持续受损。值得注意的是，相关表观遗传修饰因子的表达在健康对照（HC）或高血压对照（HHC）脂肪细胞中并未失调（扩展数据图 8h）。

Enhancers are key drivers of cellular identity and cell fate^51,52. MOFA (Fig. 3d) indicated that active (H3K27ac) and enhancer (H3K4me1) hPTMs, together with chromatin accessibility (ATAC), were the modalities mostly explaining data variability across all conditions. An analysis of the correlation coefficients of hPTM signatures in each condition against an aggregated control (composed of averaged healthy young controls) revealed large deviations between H3K27ac and H3K4me1 in obese or WL conditions from control mice (Fig. 4a and Extended Data Fig. 9a). We generated adipocyte-specific enhancer annotations for each condition on the basis of our data (Extended Data Fig. 9b–e) and analysed enhancer dynamics in obese and WL mice. Next, we performed differential enrichment analysis of H3K4me1, H3K27ac and ATAC–seq in enhancers. By principal component analysis (PCA), we found that HC and HHC samples clustered closer to H and HH than to controls for H3K4me1, ATAC and H3K27ac (Fig. 4b and Extended Data Fig. 9f–h). H3K4me1 separated H/HH and HC/HHC from controls, indicating that not only active but also poised enhancers could drive persistent epigenetic alterations. We then analysed the dynamic behaviour of enhancers between the obese and WL adipocytes. Several thousand enhancers were differentially marked by H3K4me1 during obesity (H: 4,255, HH: 3,237) and/or after WL (HC: 3,439, HHC: 6,589), and remained altered from H to HC (n = 848) and from HH to HHC (n = 857) (Fig. 4c).
增强子是细胞身份和细胞命运的关键驱动因素 ^51,52 。MOFA（图 3d）表明，活性（H3K27ac）和增强子（H3K4me1）组蛋白修饰，以及染色质可及性（ATAC），是解释所有条件下数据变异性的主要模式。对每种条件下组蛋白修饰特征的相关系数与聚合对照（由健康年轻对照的平均值组成）的分析显示，肥胖或 WL 条件下的小鼠与对照组相比，H3K27ac 和 H3K4me1 之间存在较大偏差（图 4a 和扩展数据图 9a）。我们基于数据生成了每种条件下的脂肪细胞特异性增强子注释（扩展数据图 9b–e），并分析了肥胖和 WL 小鼠中的增强子动态。接下来，我们对增强子中的 H3K4me1、H3K27ac 和 ATAC-seq 进行了差异富集分析。通过主成分分析（PCA），我们发现 HC 和 HHC 样本在 H3K4me1、ATAC 和 H3K27ac 方面比对照组更接近 H 和 HH（图 4b 和扩展数据图 9f–h）。H3K4me1 将 H/HH 和 HC/HHC 与对照组分开，表明不仅活性增强子，而且准备状态的增强子也可能驱动持久的表观遗传改变。然后，我们分析了肥胖和 WL 脂肪细胞之间增强子的动态行为。在肥胖（H：4,255，HH：3,237）和/或 WL 后（HC：3,439，HHC：6,589），数千个增强子在 H3K4me1 标记上表现出差异，并且在从 H 到 HC（n = 848）和从 HH 到 HHC（n = 857）的过程中保持改变（图 4c）。

Fig. 4: Adipocyte enhancers retain an epigenetic memory.
图 4：脂肪细胞增强子保留表观遗传记忆。

We termed enhancers that gained (and maintained) H3K4me1 in obesity and WL ‘new enhancers’. Most of these ‘new enhancers’ were also active (that is, marked by H3K27ac) during obesity and/or WL (Fig. 4d). We then annotated the enhancers to their closest gene and performed a GSEA. In agreement with the promoter GSEA above, we found that the ‘new active enhancers’ were related to inflammatory signalling, lysosome activity and extracellular matrix remodelling (Fig. 4e and Extended Data Fig. 9i), indicating a persistent shift of adipocytes towards a more inflammatory and less adipogenic identity. Corroborating these results, Roh et al. had analysed H3K27ac in adipocytes of obese mice and reported impaired identity maintenance during obesity²⁵.
我们将那些在肥胖和体重减轻（WL）过程中获得（并维持）H3K4me1 标记的增强子称为“新增强子”。大多数这些“新增强子”在肥胖和/或体重减轻期间也是活跃的（即，被 H3K27ac 标记）（图 4d）。随后，我们将这些增强子注释到其最近的基因，并进行了基因集富集分析（GSEA）。与上述启动子 GSEA 一致，我们发现“新活跃增强子”与炎症信号传导、溶酶体活性和细胞外基质重塑相关（图 4e 和扩展数据图 9i），表明脂肪细胞向更具炎症性和较少脂肪生成性的身份持续转变。这些结果得到了 Roh 等人的支持，他们分析了肥胖小鼠脂肪细胞中的 H3K27ac，并报告了肥胖期间身份维持受损 ²⁵ 。

To combine our findings regarding retained translational changes and epigenetic memory, we investigated whether epigenetic mechanisms, such as differentially marked promoters or enhancers, could explain the persistent translational obesity-associated changes after WL. Notably, 57–62% of downregulated and 68–75% of upregulated persistent translational DEGs after WL could be accounted for by one or more of the analysed epigenetic modalities (Fig. 4f). Overall, these results strongly suggest the presence of stable cellular, epigenetic and transcriptional memory in mouse adipocytes that persists after WL.
为了结合我们在保留的转录变化和表观遗传记忆方面的发现，我们研究了表观遗传机制，如差异标记的启动子或增强子，是否可以解释 WL 后持续存在的与肥胖相关的转录变化。值得注意的是，WL 后下调的持久性转录差异基因（DEGs）的 57-62%和上调的持久性转录 DEGs 的 68-75%可以通过分析的一种或多种表观遗传模式来解释（图 4f）。总体而言，这些结果强烈表明，在小鼠脂肪细胞中存在稳定的细胞、表观遗传和转录记忆，这种记忆在 WL 后仍然持续存在。

Metabolic memory primes adipocytes
代谢记忆激活脂肪细胞

We then asked whether this persistent memory primed mature adipocytes to respond differently to nutritional stimuli than non-primed controls. We collected mature epiAT and ingAT adipocytes from WL and control mice, cultured them for 48 h and then assessed glucose and palmitate uptake. Adipocytes from WL epiAT showed increased glucose and palmitate uptake compared with controls (Fig. 5a,b). ingAT adipocytes from HHC mice displayed significantly increased glucose uptake compared with controls, and for HC adipocytes we observed a trend towards an increased uptake (Extended Data Fig. 10a). Assessing adipogenesis capacity, we found that the SVF from epiAT of HC and HHC mice accumulated lipids in response to insulin but failed to differentiate, unlike controls (Extended Data Fig. 10b). Adipogenesis was slightly impaired in the SVF from ingAT of WL mice compared with controls (Extended Data Fig. 10c). These findings indicate that persistent cellular memory confers phenotypic consequence ex vivo.
我们随后询问这种持续的记忆是否使成熟的脂肪细胞对营养刺激的反应与未被激活的对照组不同。我们从 WL 和对照组小鼠中收集了成熟的皮下脂肪细胞（epiAT）和内脏脂肪细胞（ingAT），培养 48 小时后评估葡萄糖和棕榈酸的摄取。来自 WL 小鼠皮下脂肪细胞的葡萄糖和棕榈酸摄取量较对照组增加（图 5a,b）。来自 HHC 小鼠的内脏脂肪细胞葡萄糖摄取量显著高于对照组，而对于 HC 小鼠的脂肪细胞，我们观察到摄取量有增加的趋势（扩展数据图 10a）。评估脂肪生成能力时，我们发现 HC 和 HHC 小鼠皮下脂肪细胞的 SVF 在胰岛素作用下积累了脂质，但未能分化，与对照组不同（扩展数据图 10b）。与对照组相比，WL 小鼠内脏脂肪细胞的 SVF 脂肪生成能力略有受损（扩展数据图 10c）。这些发现表明，持续的细胞记忆在体外赋予了表型后果。

Fig. 5: Memory primes adipocytes and mice for an accelerated response to obesogenic stimuli.
图 5：记忆促使脂肪细胞和小鼠对致肥胖刺激产生加速反应。

Next, we investigated the response of WL and control mice to 4 weeks of HFD feeding. HC mice gained weight faster than CC_s mice (called HCH and CCH, respectively, here) (Fig. 5c). Fasting blood glucose levels and postprandial insulin levels were elevated in HCH mice (Fig. 5d,e), but neither glucose tolerance nor insulin sensitivity was impaired when compared with CCH mice (Extended Data Fig. 10d–g). Leptin levels in HCH mice returned to H mice levels, whereas CCH mice did not show a significant increase (Fig. 5f). Adipocytes in epiAT from HCH mice were larger on average, resembling the adipocyte size distribution of H mice, whereas epiAT adipocytes from CCH mice were similar to those in CC_s mice (Extended Data Fig. 10h). HCH mice exhibited larger ingAT, BAT and epiAT depots compared with CCH mice (Fig. 5g,h) and showed increased triglyceride accumulation and hepatic steatosis (Extended Data Fig. 10i–k).
接下来，我们研究了 WL 和对照小鼠对 4 周高脂饮食（HFD）喂养的反应。HC 小鼠的体重增加速度比 CC_s 小鼠（分别称为 HCH 和 CCH）更快（图 5c）。HCH 小鼠的空腹血糖水平和餐后胰岛素水平升高（图 5d,e），但与 CCH 小鼠相比，葡萄糖耐量和胰岛素敏感性并未受损（扩展数据图 10d–g）。HCH 小鼠的瘦素水平恢复到 H 小鼠的水平，而 CCH 小鼠则未见显著增加（图 5f）。HCH 小鼠的 epiAT 脂肪细胞平均较大，类似于 H 小鼠的脂肪细胞大小分布，而 CCH 小鼠的 epiAT 脂肪细胞与 CC_s 小鼠相似（扩展数据图 10h）。与 CCH 小鼠相比，HCH 小鼠的 ingAT、BAT 和 epiAT 脂肪库更大（图 5g,h），并表现出甘油三酯积累增加和肝脂肪变性（扩展数据图 10i–k）。

We performed snRNA-seq of epiAT from HCH and CCH mice and observed higher macrophage infiltration in both HCH and CCH epiAT compared with the WL time point, with a greater infiltration in HCH epiAT (Fig. 5i and Extended Data Fig. 10l). The proportion of LAMs was greater in HCH epiAT, similar to that of H and HC mice, whereas CCH epiAT showed a greater proportion of LAMs compared with CC epiAT, indicating LAM infiltration occurred early during HFD feeding (Extended Data Fig. 10m).
我们对 HCH 和 CCH 小鼠的 epiAT 进行了 snRNA-seq，观察到与 WL 时间点相比，HCH 和 CCH 的 epiAT 中巨噬细胞浸润均增加，且 HCH epiAT 中的浸润更为显著（图 5i 和扩展数据图 10l）。HCH epiAT 中 LAM 的比例较高，与 H 和 HC 小鼠相似，而 CCH epiAT 中 LAM 的比例高于 CC epiAT，表明在高脂饮食喂养早期即发生了 LAM 的浸润（扩展数据图 10m）。

We assessed whether HCH and CCH adipocytes exhibited transcriptional differences. Neither the previous transcriptional status nor the transcriptional memory at the HC time point explained the transcriptional deregulation observed in adipocytes from HCH mice (Fig. 5j). Further analysis revealed that several DEGs in the HCH group were altered during obesity but recovered after WL in HC mice. Interestingly, these overlapped with promoters and enhancers carrying epigenetic memory (Figs. 2f, 3h and 5k,l). A more detailed analysis showed that epigenetic signatures could explain the 3–6 times more DEGs in the HCH group than the transcriptional memory or previous transcriptional status during HC (Fig. 5m). Specifically, the four hPTMs and ATAC–seq could predict or explain 31% of upregulated DEGs, which were related to inflammation, and 60% of downregulated DEGs, many of which were related to adipocyte function and identity, in the HCH group (Extended Data Fig. 10n,o).
我们评估了 HCH 和 CCH 脂肪细胞是否表现出转录差异。先前的转录状态或 HC 时间点的转录记忆均无法解释 HCH 小鼠脂肪细胞中观察到的转录失调（图 5j）。进一步分析显示，HCH 组中的多个差异表达基因（DEGs）在肥胖期间发生改变，但在 HC 小鼠中经过体重减轻（WL）后恢复。有趣的是，这些基因与携带表观遗传记忆的启动子和增强子重叠（图 2f、3h 和 5k,l）。更详细的分析表明，表观遗传特征可以解释 HCH 组中比转录记忆或 HC 期间先前转录状态多 3-6 倍的 DEGs（图 5m）。具体而言，四种组蛋白修饰（hPTMs）和 ATAC-seq 可以预测或解释 HCH 组中 31%的上调 DEGs（与炎症相关）和 60%的下调 DEGs（其中许多与脂肪细胞功能和身份相关）（扩展数据图 10n,o）。

Together, these findings suggest that a persistent epigenetic memory, including local changes of hPTM deposition, contributes to the altered transcriptional response in adipocytes in the ‘yo-yo’ model of dieting and primes adipocytes for pathological responses to further HFD feeding, thus contributing to the pathophysiology of rebound obesity in mice. It is possible that other epigenetic modifications, such as other hPTMs, DNA methylation or non-coding RNAs, also contribute to the observed phenomena.
这些发现共同表明，持续的表观遗传记忆，包括局部 hPTM 沉积的变化，有助于在节食“悠悠球”模型中脂肪细胞转录反应的改变，并使脂肪细胞对进一步高脂饮食喂养的病理反应做好准备，从而在啮齿动物中促成反弹性肥胖的病理生理学。可能还有其他表观遗传修饰，如其他 hPTMs、DNA 甲基化或非编码 RNAs，也对观察到的现象有所贡献。

Although we performed well-controlled dietary intervention experiments in mice, the human AT samples were obtained from different BaS studies and AT depots, and reflect an overall heterogenous group of participants. Indeed, BaS is a successful but invasive method for achieving long-term WL⁵³, yet sleeve gastrectomy and Roux-en-Y gastric bypass (RYGB) also affect the gut microbiome, micronutrient absorption, bile acid metabolism and incretin signalling^54,55,56,57. Nonetheless, we consistently observed retained transcriptional differences after significant WL in AT cells after sleeve gastrectomy (MTSS and LTSS studies), which induced significant WL, as well as after RYGB (NEFA study), which resulted in a complete return to a non-obese or lean state. The aforementioned alterations and the degree of WL achieved between individuals and studies are confounders that limit the direct comparability of our mouse and human data. The rapid WL achieved by BaS may even reduce or modify putative cellular memory in the human AT. Owing to the current lack of methods to isolate pure adipocyte nuclei from frozen human tissue, we could not perform the corresponding epigenetic analyses in human samples. Nonetheless, it stands to reason that obesity-induced transcriptional (and cellular) changes in humans are also mediated through epigenetic mechanisms that can persist after WL in the AT and contribute to human (patho)physiology.
尽管我们在小鼠中进行了严格控制的饮食干预实验，但人类脂肪组织（AT）样本来自不同的减重手术（BaS）研究和不同的 AT 库，反映了参与者整体的异质性。确实，BaS 是一种成功但具有侵入性的长期体重减轻（WL）方法 ⁵³ ，然而袖状胃切除术和 Roux-en-Y 胃旁路术（RYGB）也会影响肠道微生物群、微量营养素吸收、胆汁酸代谢和肠促胰岛素信号传导 ^54,55,56,57 。尽管如此，我们在袖状胃切除术后（MTSS 和 LTSS 研究）以及 RYGB 后（NEFA 研究），在显著 WL 后的 AT 细胞中持续观察到保留的转录差异，这些差异导致了显著的 WL，并使体重完全恢复到非肥胖或瘦的状态。上述变化以及个体和研究之间 WL 的程度是混杂因素，限制了我们小鼠和人类数据之间的直接可比性。BaS 实现的快速 WL 甚至可能减少或改变人类 AT 中潜在的细胞记忆。由于目前缺乏从冷冻人体组织中分离纯脂肪细胞核的方法，我们无法在人类样本中进行相应的表观遗传学分析。尽管如此，有理由认为，肥胖引起的转录（和细胞）变化在人类中也是通过表观遗传机制介导的，这些机制在 WL 后可以在 AT 中持续存在，并有助于人类（病理）生理学。

Although our results do not provide final proof of a causal relationship between AT memory and the systemic yo-yo effect of accelerated weight gain, Hata et al. have shown that transplantation of WL epiAT into control mice enhances macular degeneration by impacting immune cells and angiogenesis and that epiAT macrophages retain an altered chromatin accessibility after WL²⁸. Our epigenetic analysis of adipocytes of the same tissue could serve as an explanation of how these alterations in chromatin accessibility can be retained. Further investigations are required to determine whether—in addition to adipocytes and macrophages²⁸—other post-mitotic or cycling cells, such as myofibers, neurons or APCs, also establish an epigenetic memory of obesity and contribute to the observed systemic weight regain effect.
尽管我们的结果并未提供加速体重增加的系统性“悠悠球”效应与 AT 记忆之间因果关系的最终证据，但 Hata 等人已证明，将 WL epiAT 移植到对照小鼠中会通过影响免疫细胞和血管生成来加剧黄斑变性，并且 WL 后的 epiAT 巨噬细胞保留了改变的染色质可及性。我们对同种组织脂肪细胞的表观遗传分析可以解释这些染色质可及性改变如何得以保留。进一步的研究需要确定，除了脂肪细胞和巨噬细胞外，其他如肌纤维、神经元或 APC 等非分裂或分裂细胞是否也建立了肥胖的表观遗传记忆，并参与了观察到的系统性体重恢复效应。

Although our results are on the basis of BaS studies, the susceptibility to weight regain in human subjects undergoing WL using strict dietary regimens might be related to a transcriptional and/or epigenetic memory as well. At present, the use of incretin receptor agonists such as semaglutide or tirzepatide^6,58,59 has emerged as a promising non-invasive strategy for significant WL. However, the extent to which these agonists induce long-lasting WL and physiological changes in humans beyond withdrawal remains poorly studied. Studies on semaglutide and on tirzepatide have shown that substantial weight regain occurs after their withdrawal^6,59, indicating that at least these treatments do not induce stable, persistent changes. Whether this is also the case for other agonists remains to be investigated. Further studies are needed to elucidate whether these treatments could erase or diminish an obesogenic memory better than other non-surgery-based WL strategies.
尽管我们的结果基于 BaS 研究，但接受严格饮食方案进行体重减轻（WL）的人类受试者对体重恢复的易感性可能也与转录和/或表观遗传记忆有关。目前，使用如司美格鲁肽或替西帕肽等肠促胰岛素受体激动剂已成为一种有前景的非侵入性显著 WL 策略。然而，这些激动剂在停药后在人类中诱导持久 WL 和生理变化的程度上仍研究不足。关于司美格鲁肽和替西帕肽的研究表明，停药后会出现显著的体重恢复，这表明至少这些治疗并未诱导稳定、持久的改变。其他激动剂是否也是如此仍有待研究。需要进一步的研究来阐明这些治疗是否能比其他非手术 WL 策略更好地消除或减弱肥胖记忆。

The presence of a putative obesogenic epigenetic memory in adipocytes and potentially other cells suggests new potential therapeutic avenues to improve WL maintenance in humans. Although our experiments focused on obesity, it is plausible that epigenetic memory could also play a role in many other contexts, including addictive diseases. Recent advancements in targeted epigenetic editing⁶⁰ and global remodelling of the epigenome^61,62 provide promising new approaches.
脂肪细胞中存在一种假定的致肥胖表观遗传记忆，并可能存在于其他细胞中，这为改善人类体重维持提供了新的潜在治疗途径。尽管我们的实验集中在肥胖上，但表观遗传记忆也可能在许多其他情况下发挥作用，包括成瘾性疾病。最近在靶向表观遗传编辑 ⁶⁰ 和表观基因组的全局重塑 ^61,62 方面的进展提供了有前景的新方法。

Methods 方法

Data reporting 数据报告

No statistical methods were used to predetermine sample size. The experiments were not randomized, and the investigators were not blinded to allocation during experiments and outcome assessment.
未使用统计方法预先确定样本量。实验未进行随机化，且研究者在实验和结果评估过程中未对分配进行盲法处理。

Clinical sample acquisition
临床样本采集

Human AT biopsies were obtained from three independent studies: MTSS, LTSS and NEFA.
人类 AT 活检样本来自三个独立研究：MTSS、LTSS 和 NEFA。

MTSS

The MTSS samples comprised samples from omental visceral AT biopsies obtained in the context of a two-step BaS treatment, which included a sleeve gastrectomy as the first step (T0) and laparoscopic RYGB as the second step (T1)¹⁶. Individuals with syndromal, monogenic, early-onset obesity or individuals with other known concurrent diseases, including acute infections or malignant diseases, were not included in the study. Individuals were not required to adhere to any specific diet before or after surgery but received individual dietary recommendations during regular visits in the obesity management centre. Insulin resistance was determined using a hyperinsulinaemic–euglycaemic clamp technique or the homeostatic model assessment for insulin resistance (HOMA-IR). Only biopsies from individuals that (1) lost 25% or more of BMI between T0 and T1 (Extended Data Table 1), (2) had undergone surgery at the Municipal Hospital Karlsruhe or Municipal Hospital Dresden-Neustadt, (3) were not diagnosed with diabetes, and (4) did not receive any glucose-lowering medication were used for snRNA-seq in this study. AT samples were collected during elective laparoscopic abdominal surgery as previously described⁶³, snap-frozen in liquid nitrogen and stored at −80 °C. Body composition and metabolic parameters were measured as previously described⁶⁴. Samples of healthy individuals who were not obese were collected during routine elective surgeries such as herniotomies, explorative laparoscopies and cholecystectomies at the same hospitals. The study was approved by the Ethics Committee of the University of Leipzig under approval number 159-12–21052012 and was performed in agreement with the Declaration of Helsinki.
MTSS 样本包括从网膜内脏脂肪组织（AT）活检中获得的样本，这些活检是在两步 BaS 治疗过程中进行的，第一步为袖状胃切除术（T0），第二步为腹腔镜下 RYGB 手术（T1） ¹⁶ 。研究未包括患有综合征性、单基因性、早发性肥胖或患有其他已知并发疾病的个体，包括急性感染或恶性疾病。个体在手术前后无需遵循任何特定饮食，但在肥胖管理中心定期就诊时会接受个体化的饮食建议。胰岛素抵抗通过高胰岛素正糖钳技术或胰岛素抵抗稳态模型评估（HOMA-IR）来确定。本研究仅使用以下个体的活检样本进行单细胞 RNA 测序：(1) T0 至 T1 期间 BMI 减少 25%或以上（扩展数据表 1），(2) 在卡尔斯鲁厄市立医院或德累斯顿-新城市立医院接受手术，(3) 未被诊断为糖尿病，以及(4) 未接受任何降糖药物。AT 样本在择期腹腔镜腹部手术中采集，如前所述 ⁶³ ，迅速冷冻于液氮中并储存于-80°C。身体成分和代谢参数的测量如前所述 ⁶⁴ 。在同一医院进行的常规择期手术（如疝修补术、探查性腹腔镜检查和胆囊切除术）中，收集了非肥胖健康个体的样本。该研究经莱比锡大学伦理委员会批准，批准号为 159-12–21052012，并按照赫尔辛基宣言进行。

LTSS

The human study samples comprised samples from omental visceral and subcutaneous abdominal AT, collected in the context of a two-step BaS treatment. Following an initial sleeve gastrectomy (T0), a laparoscopic RYGB was made in the second step (T1)¹⁶. Individuals with syndromal, early-onset obesity or individuals with other known concurrent diseases, including acute infections or malignant diseases, were not included in the study. Individuals did not adhere to any specific diet before or after surgery but received individual healthy diet recommendations during regular visits in the obesity management centre. Insulin resistance was determined using HOMA-IR. Only individuals that (1) lost 25% or more of BMI between T0 and T1 (Extended Data Table 1), (2) had undergone surgery at the Leipzig University Hospital, (3) were not diagnosed with diabetes and (4) did not receive any glucose-lowering medication were included. AT samples were collected during elective laparoscopic abdominal surgery as previously described⁶³, snap-frozen in liquid nitrogen and stored at −80 °C. Body composition and metabolic parameters were measured as previously described⁶⁴. Samples from healthy donors that were not obese were collected during routine elective surgeries (herniotomies, explorative laparoscopies, cholecystectomies) at the same hospital. The study was approved by the Ethics Committee of the University of Leipzig under approval number 159-12–21052012 and performed in agreement with the Declaration of Helsinki.
人类研究样本包括从网膜内脏和皮下腹部脂肪组织（AT）中收集的样本，这些样本是在两步 BaS 治疗过程中收集的。在初始袖状胃切除术（T0）后，第二步进行腹腔镜下 RYGB 手术（T1）。研究未包括患有综合征性、早发性肥胖或患有其他已知并发疾病（包括急性感染或恶性疾病）的个体。个体在手术前后未遵循任何特定饮食，但在肥胖管理中心定期就诊期间接受了个性化的健康饮食建议。胰岛素抵抗通过 HOMA-IR 确定。仅包括以下个体：（1）在 T0 和 T1 之间体重指数（BMI）减少 25%或以上（扩展数据表 1），（2）在莱比锡大学医院接受手术，（3）未被诊断为糖尿病，以及（4）未接受任何降糖药物。AT 样本在择期腹腔镜腹部手术中收集，如前所述，迅速冷冻在液氮中并储存于-80°C。身体成分和代谢参数的测量如前所述。从非肥胖的健康供体中收集的样本在同一家医院的常规择期手术（疝修补术、探查性腹腔镜检查、胆囊切除术）中收集。该研究已获得莱比锡大学伦理委员会的批准，批准编号为 159-12–21052012，并按照赫尔辛基宣言进行。

NEFA study NEFA 研究

The NEFA study (NCT01727245) comprises samples from subcutaneous abdominal AT from individuals before and after RYGB surgery, as well as healthy controls who had never been obese^8,65. For this, biopsies were obtained under local anaesthesia before (T0) and 2 yr post-surgery (T1). Only samples from individuals that (1) lost more than 25% BMI between T0 and T1, (2) were not diagnosed with diabetes at T0 and T1 and (3) did not take glucose-lowering medication were included in the present study (Extended Data Table 1). Samples from control subjects were obtained from individuals that were BMI- and age-matched to RYGB patients at T1 as reported previously⁸. AT samples were handled as reported before⁶⁵, snap-frozen in liquid nitrogen and stored at −80 °C. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Karolinska Institute, Stockholm (approval number 2011/1002-31/1).
NEFA 研究（NCT01727245）包括来自接受 RYGB 手术前后个体的腹部皮下脂肪组织样本，以及从未肥胖的健康对照组样本 ^8,65 。为此，在局部麻醉下分别于手术前（T0）和手术后 2 年（T1）获取活检样本。本研究仅纳入以下个体样本：(1) T0 至 T1 期间 BMI 减少超过 25%，(2) T0 和 T1 时未诊断为糖尿病，以及(3)未服用降糖药物（扩展数据表 1）。对照组样本来自与 RYGB 患者在 T1 时 BMI 和年龄匹配的个体，如先前报道 ⁸ 。脂肪组织样本的处理方式如前所述 ⁶⁵ ，迅速冷冻于液氮中并储存于-80°C。该研究遵循赫尔辛基宣言，并获得斯德哥尔摩卡罗林斯卡学院伦理委员会的批准（批准号 2011/1002-31/1）。

Mice 小鼠

All mice were kept on a 12-h/12-h light/dark cycle at 20–60% (23 °C) humidity in individually ventilated cages, in groups of between two and five mice, in a pathogen-free animal facility in the SLA building at ETH Zurich. The health of mice was monitored closely, and any mouse exhibiting persistent clinical signs of ill health or distress was excluded from this study. The 16- and 29-week-old male C57BL/6J diet-induced obesity mice (catalogue no. 380050) and diet-induced obesity control mice (catalogue no. 380056) were obtained from The Jackson Laboratory and were kept on the respective diets for another 2 weeks until tissue harvest or diet switch. Different mice were used for insulin tolerance tests and glucose tolerance tests. AdipoERCre⁶⁶ and NuTRAP⁶⁷ mice were maintained on a C57BL/N background. Homozygous NuTRAP and AdipoERCre mice were bred to generate AdipoERCre x NuTRAP mice. AdipoERCre x NuTRAP mice were kept on HFD or chow diet for 12 or 25 weeks before tissue harvest or diet switch. The HFD used contained 60% (kcal%) fat (diet no. 2127, Provimi Kliba); the low-fat chow diet used contained 10% (kcal%) fat (diet no. 2125, Provimi Kliba). During the WL period both experimental groups received chow diet (diet no. 3437, Provimi Kliba). All animal experiments were approved by the Cantonal Veterinary Office, Zurich.
所有小鼠在 12 小时光照/12 小时黑暗周期下，以 20-60%（23°C）的湿度，在单独通风的笼子中，每笼 2 至 5 只小鼠，在苏黎世联邦理工学院 SLA 大楼的无病原体动物设施中饲养。密切监测小鼠的健康状况，任何表现出持续健康问题或不适临床症状的小鼠均被排除在本研究之外。16 周和 29 周龄的雄性 C57BL/6J 饮食诱导肥胖小鼠（目录号 380050）和饮食诱导肥胖对照小鼠（目录号 380056）从杰克逊实验室获得，并在组织采集或饮食转换前继续分别饮食 2 周。不同的小鼠用于胰岛素耐量测试和葡萄糖耐量测试。AdipoERCre ⁶⁶ 和 NuTRAP ⁶⁷ 小鼠在 C57BL/N 背景下饲养。纯合 NuTRAP 和 AdipoERCre 小鼠交配以产生 AdipoERCre x NuTRAP 小鼠。AdipoERCre x NuTRAP 小鼠在组织采集或饮食转换前分别在高脂饮食或普通饮食下饲养 12 或 25 周。使用的高脂饮食含有 60%（kcal%）脂肪（饮食编号 2127，Provimi Kliba）；使用的低脂普通饮食含有 10%（kcal%）脂肪（饮食编号 2125，Provimi Kliba）。在体重减轻期间，两组实验小鼠均接受普通饮食（饮食编号 3437，Provimi Kliba）。所有动物实验均经苏黎世州兽医办公室批准。

Tamoxifen application 他莫昔芬应用

The 4–5-week-old AdipoERCre x NuTRAP mice were gavaged two times with 1 mg of tamoxifen dissolved in corn oil. Tamoxifen was washed out for 2 weeks before starting HFD.
4-5 周龄的 AdipoERCre x NuTRAP 小鼠通过灌胃两次给予 1 毫克溶解于玉米油中的他莫昔芬。在开始高脂饮食前，他莫昔芬被冲洗 2 周。

Physiological measurements
生理测量

Glucose tolerance test 葡萄糖耐量试验

Mice were fasted for 6 h during dark phase before administration of 1 g of glucose per kg body weight by intraperitoneal injection. Blood was collected from the tail vein at 0, 15, 30, 60, 90 and 120 min and blood glucose concentrations were measured using an Accu-Check Aviva glucometer.
小鼠在暗期禁食 6 小时后，通过腹腔注射给予每公斤体重 1 克葡萄糖。在 0、15、30、60、90 和 120 分钟时从尾静脉采集血液，并使用 Accu-Check Aviva 血糖仪测量血糖浓度。

Insulin tolerance test 胰岛素耐量试验

Mice were fasted for 6 h during dark phase before administration of 1 U per kg body weight of human insulin (insulin Actrapid HM, Novo Nordisk) by intraperitoneal injection. Blood was collected from the tail vein at 0, 15, 30, 60, 90 and 120 min and blood glucose concentrations were measured using a Accu-Check Aviva glucometer.
在小鼠给予每公斤体重 1 单位的人胰岛素（胰岛素 Actrapid HM，诺和诺德）之前，在暗期禁食 6 小时。通过腹腔注射给药后，分别在 0、15、30、60、90 和 120 分钟从尾静脉采集血液，并使用 Accu-Check Aviva 血糖仪测量血糖浓度。

In vivo indirect calorimetry
体内间接测热法

Measurements were obtained from one 8-cage and one 16-cage Promethion Core Behavioral System that were in the same room. Mice were habituated to the system for 36 h before measurements were started.
从同一房间内的一个 8 笼和一个 16 笼的 Promethion 核心行为系统中获取了测量数据。在开始测量前，小鼠已适应该系统 36 小时。

Live body composition 活体成分分析

Mice were fasted for 6 h during dark phase. Live mouse body composition was measured with a magnetic resonance imaging technique (EchoMRI 130, Echo Medical Systems). Fat and lean mass were analysed using EchoMRI 14 software.
小鼠在暗期禁食 6 小时。使用磁共振成像技术（EchoMRI 130，Echo Medical Systems）测量活体小鼠的身体成分。通过 EchoMRI 14 软件分析脂肪和瘦肉质量。

Fasting insulin 空腹胰岛素

EDTA plasma was isolated from fasted blood samples (fasting 6 h). Insulin was measured with Ultra Sensitive Mouse Insulin ELISA Kit (Crystal Chem, catalogue no. 90080).
EDTA 血浆从禁食血液样本中分离（禁食 6 小时）。胰岛素使用超敏感小鼠胰岛素 ELISA 试剂盒（Crystal Chem，目录号 90080）进行测量。

Postprandial insulin 餐后胰岛素

EDTA plasma (50 µl) was thawed on ice and used in a custom U-PLEX assay (Meso Scale Discovery) according to the manufacturer’s instructions. A Mesoscale SI 2400 was used to read the plate.
EDTA 血浆（50 µl）在冰上解冻后，按照制造商的说明用于定制的 U-PLEX 检测（Meso Scale Discovery）。使用 Meso Scale SI 2400 读取板。

Postprandial leptin 餐后瘦素

EDTA plasma (50 µl) was thawed on ice and used in a custom U-PLEX assay (Meso Scale Discovery) according to the manufacturer’s instructions. A Mesoscale SI 2400 was used to read the plate.
EDTA 血浆（50 µl）在冰上解冻，并根据制造商的说明用于定制的 U-PLEX 检测（Meso Scale Discovery）。使用 Meso Scale SI 2400 读取板。

Liver triglycerides 肝脏甘油三酯

First, 50 mg of frozen liver was homogenized in 1 ml of isopropanol, lysed for 1 h at 4 °C and centrifuged for 10 min at 2,000g at 4 °C. The supernatant was transferred into a new tube and stored at −80 °C until use. Triglyceride levels were measured by mixing 200 µl of reagent R (Monlab, catalogue no. SR-41031) and 5 µl of sample or Cfas calibrator dilutions (Roche, catalogue no. 10759350; lot no. 41009301), then incubating for 10 min while shaking at room temperature and measuring optical density at 505 nm (OD₅₀₅) with a plate reader (BioTek Gen5 Microplate Reader).
首先，将 50 mg 冷冻肝脏在 1 ml 异丙醇中均质化，在 4 °C 下裂解 1 小时，然后在 4 °C 下以 2,000g 离心 10 分钟。将上清液转移至新管中，并在−80 °C 下储存直至使用。甘油三酯水平通过混合 200 µl 试剂 R（Monlab，目录号 SR-41031）和 5 µl 样品或 Cfas 校准品稀释液（Roche，目录号 10759350；批号 41009301）来测量，然后在室温下振荡孵育 10 分钟，并使用酶标仪（BioTek Gen5 微孔板读数仪）在 505 nm 处测量光密度（OD ₅₀₅ ）。

Cell culture experiments 细胞培养实验

AT digestion AT 消化

AT was minced and digested at 37 °C while shaking in collagenase buffer (25 mM NaHCO₃, 12 mM KH₂PO₄, 1.3 mM MgSO₄, 4.8 mM KCl, 120 mM NaCl, 1.2 mM CaCl₂, 5 mM glucose, 2.5% BSA; pH 7.4) using 2 mg of collagenase type II (Sigma-Aldrich, catalogue no. C6885-1G) per 0.25 g of tissue. After 30 min tissues were resuspended, and for ingAT digestion continued for 15 min whereas epiAT was processed immediately. An equal volume of growth medium (DMEM (Gibco, catalogue no. 31966021), 10% FBS (Gibco, catalogue no. 10500-064, Lot no. 2378399H), 1% penicillin-streptomycin (Gibco, catalogue no. 15140-122)) was added and digested tissue was centrifuged for 4 min at 300g, and the floating fraction was transferred into a new Falcon tube and kept at 37 °C. The SVF was resuspended in 5 ml of erythrocyte lysis buffer (154 mM NH₄Cl, 10 mM NaHCO₃, 0.1 mM EDTA, 1% penicillin-streptomycin), incubated at room temperature for 5 min, filtered through a 40 µM mesh filter and centrifuged for 5 min, 300g. The SVF was resuspended in growth medium and counted.
AT 被切碎并在 37°C 下在胶原酶缓冲液（25 mM NaHCO ₃ ，12 mM KH ₂ PO ₄ ，1.3 mM MgSO ₄ ，4.8 mM KCl，120 mM NaCl，1.2 mM CaCl ₂ ，5 mM 葡萄糖，2.5% BSA；pH 7.4）中振荡消化，每 0.25 g 组织使用 2 mg 胶原酶 II 型（Sigma-Aldrich，目录号 C6885-1G）。30 分钟后，组织被重新悬浮，ingAT 消化继续进行 15 分钟，而 epiAT 则立即处理。加入等体积的生长培养基（DMEM（Gibco，目录号 31966021），10% FBS（Gibco，目录号 10500-064，批号 2378399H），1% 青霉素-链霉素（Gibco，目录号 15140-122）），消化后的组织在 300g 下离心 4 分钟，浮在上层的部分被转移到新的 Falcon 管中并保持在 37°C。SVF 在 5 ml 红细胞裂解缓冲液（154 mM NH ₄ Cl，10 mM NaHCO ₃ ，0.1 mM EDTA，1% 青霉素-链霉素）中重新悬浮，在室温下孵育 5 分钟，通过 40 µM 孔径的滤网过滤并在 300g 下离心 5 分钟。SVF 在生长培养基中重新悬浮并计数。

SVF differentiation SVF 分化

A total of 10,000 cells were plated into one well of a collagen-coated (Sigma-Aldrich, catalogue no. C3867) 96-well plate and kept in culture until they reached confluency, with media change every 48 h. At 2 d post-confluence, medium was changed to induction medium (DMEM, 10% FBS, 1% penicillin-streptomycin, 10 nM insulin (Sigma-Aldrich, catalogue no. I9278), 0.5 mM 3-isobutyl-1-methylxanthin (Sigma-Aldrich, catalogue no. I7018-1G), 1 µM dexamethasone (Sigma-Aldrich, catalogue no. D4902), 1 µM rosiglitazone (Adipogen, catalogue no. AG-CR1-3570-M010)). After 48 h medium was changed to maintenance medium (DMEM, 10% FBS, 1% penicillin-streptomycin, 10 nM insulin). Medium was changed every 48 h for 8 d.
将 10,000 个细胞接种到一块胶原包被的（Sigma-Aldrich，目录号 C3867）96 孔板的一个孔中，并在培养至汇合前每 48 小时更换培养基。汇合后 2 天，将培养基更换为诱导培养基（DMEM，10% FBS，1%青霉素-链霉素，10 nM 胰岛素（Sigma-Aldrich，目录号 I9278），0.5 mM 3-异丁基-1-甲基黄嘌呤（Sigma-Aldrich，目录号 I7018-1G），1 µM 地塞米松（Sigma-Aldrich，目录号 D4902），1 µM 罗格列酮（Adipogen，目录号 AG-CR1-3570-M010））。48 小时后，将培养基更换为维持培养基（DMEM，10% FBS，1%青霉素-链霉素，10 nM 胰岛素）。每 48 小时更换一次培养基，持续 8 天。

AdipoRed assay AdipoRed 检测

The SVF was cultured as described and controls were either kept in growth medium or only maintenance medium without induction. On day 8 after induction, cells were washed twice in PBS, and AdipoRed (Lonza, catalogue no. LZ-PT-7009) reagent was used according to the manufacturer’s instructions and read with a plate reader (BioTek Gen5 Microplate Reader).
SVF 按照所述方法进行培养，对照组要么保持在生长培养基中，要么仅在无诱导的维持培养基中。诱导后第 8 天，细胞用 PBS 洗涤两次，并根据制造商的说明使用 AdipoRed（Lonza，目录号 LZ-PT-7009）试剂，通过酶标仪（BioTek Gen5 微孔板读数仪）读取结果。

Primary adipocyte culture
原代脂肪细胞培养

Primary floating adipocytes were cultured under membranes according to Harms et al.⁶⁸. Packed adipocytes (30 µl) were seeded onto one membrane and kept in inverted culture for 48 h in maintenance medium (DMEM-F12 (Gibco, catalogue no. 31330095), 10% FBS, 1% penicillin-streptomycin, 10 nM insulin). After 48 h of maintenance, adipocytes were washed and serum and glucose starved overnight in KREBBS-Ringer buffer (120 mM NaCl, 4.7 mM KCl, 1.2 mM KH₂PO₄, 1.2 mM MgSO₄, 2.5 mM CaCl₂, 25 mM HEPES (Lonza, catalogue no. BEBP17-737E), pH 7.4) and 2.5% fat-free BSA (Sigma-Aldrich, catalogue no. A6003).
根据 Harms 等人的方法，初级浮游脂肪细胞在膜下培养。将脂肪细胞（30 µl）接种到一张膜上，并在维持培养基（DMEM-F12（Gibco，目录号 31330095），10% FBS，1% 青霉素-链霉素，10 nM 胰岛素）中倒置培养 48 小时。维持 48 小时后，脂肪细胞用 KREBBS-Ringer 缓冲液（120 mM NaCl，4.7 mM KCl，1.2 mM KH ₂ PO ₄ ，1.2 mM MgSO ₄ ，2.5 mM CaCl ₂ ，25 mM HEPES（Lonza，目录号 BEBP17-737E），pH 7.4）和 2.5% 无脂 BSA（Sigma-Aldrich，目录号 A6003）洗涤并过夜无血清和无葡萄糖饥饿。

Glucose uptake 葡萄糖摄取

Glucose uptake from primary adipocytes was measured using the Glucose Uptake-Glo Assay Kit (Promega, catalogue no. J1341) according to the manufacturer’s instructions. Adipocytes were preincubated with 5 nM insulin for 15 min before 2-deoxy-d-glucose was added at 1 mM final concentration. Protein concentration was measured using a Pierce 660 nm Protein Assay Kit (Thermo Fisher, catalogue no. 22662) and the Ionic Detergent Compatibility Reagent (Thermo Fisher, catalogue no. 22663). Both assays were read with a plate reader (BioTek Gen5 Microplate Reader).
使用 Glucose Uptake-Glo Assay Kit（Promega，目录号 J1341）按照制造商的说明测量了原代脂肪细胞的葡萄糖摄取。在加入 1 mM 最终浓度的 2-脱氧-D-葡萄糖之前，脂肪细胞先用 5 nM 胰岛素预孵育 15 分钟。使用 Pierce 660 nm 蛋白检测试剂盒（Thermo Fisher，目录号 22662）和离子洗涤剂兼容试剂（Thermo Fisher，目录号 22663）测量蛋白浓度。两种检测均使用酶标仪（BioTek Gen5 微孔板读数仪）读取。

C16 uptake C16 摄取

Starved adipocytes were incubated with 5 nM BODIPY-palmitate (Thermo Fisher, catalogue no. D3821) in the presence of 10 nM insulin for 1 h. Subsequently, adipocytes were washed twice and lysed in 200 µl of RIPA buffer. Then, 100 µl of lysate was used to measure BODIPY signal. Diluted lysate was used to measure protein concentration using a DC Protein Assay Kit II (Bio-Rad Laboratories, catalogue no. 5000112) for normalization. Both assays were read with a plate reader (BioTek Gen5 Microplate Reader).
饥饿的脂肪细胞在含有 10 nM 胰岛素的情况下与 5 nM BODIPY-棕榈酸（Thermo Fisher，目录号 D3821）孵育 1 小时。随后，脂肪细胞被洗涤两次并在 200 µl RIPA 缓冲液中裂解。然后，使用 100 µl 裂解液测量 BODIPY 信号。稀释的裂解液用于使用 DC 蛋白测定试剂盒 II（Bio-Rad Laboratories，目录号 5000112）测量蛋白质浓度以进行归一化。两种测定均使用酶标仪（BioTek Gen5 微孔板读数仪）读取。

Histology 组织学

Tissues were collected, fixed in 4% PBS-buffered formalin for 72 h at 4 °C and stored in PBS at 4 °C. Following paraffin embedding, tissues were sent to the pathology service centre at Instituto Murciano de Investigación Biosanitaria Virgen de la Arrixaca for sectioning, trichrome staining, haematoxylin and eosin staining, and imaging. Tissues from two independent experiments were sent for sectioning.
组织被收集，固定在 4% PBS 缓冲的福尔马林中，于 4°C 下保存 72 小时，然后保存在 4°C 的 PBS 中。经过石蜡包埋后，组织被送往 Virgen de la Arrixaca 生物卫生研究所病理服务中心进行切片、三色染色、苏木精和伊红染色以及成像。来自两个独立实验的组织被送去做切片。

Adipocyte size quantification
脂肪细胞大小定量

Images of ingAT and epiAT were taken with 3DHISTECH Slide Viewer 2 and then analysed with Adiposoft⁶⁹ using Fiji ImageJ⁷⁰. Five to ten images were taken of each section belonging to a biological replicate (n = 4).
使用 3DHISTECH Slide Viewer 2 获取 ingAT 和 epiAT 的图像，然后使用 Fiji ImageJ ⁷⁰ 通过 Adiposoft ⁶⁹ 进行分析。每个生物学重复（n = 4）的切片取 5 到 10 张图像。

Sample processing and library preparation
样本处理与文库制备

Isolation of nuclei from mouse tissue
从小鼠组织中分离细胞核

Nuclei were isolated from snap-frozen epiAT in ice-cold Nuclei Extraction Buffer (Miltenyi, catalogue no. 130-128-024) supplemented with 0.2 U µl⁻¹ recombinant RNase Inhibitor (Takara, catalogue no. 2313) and 1× cOmplete EDTA-free Protease Inhibitor (Roche, catalogue no. 5056489001) using the gentleMACS Octo Dissociator (Miltenyi, catalogue no. 130-096-427), using C-tubes (Miltenyi, catalogue no. 130-093-237). Nuclei were subsequently filtered through a 50 µm cell strainer (Sysmex, catalogue no. 04-0042-2317) and washed two times in PBS-BSA (1% w/v) containing 0.2 U µl⁻¹ RNase inhibitor. For snRNA-seq, five mice were pooled per condition.
从快速冷冻的 epiAT 中分离出细胞核，使用冰冷的细胞核提取缓冲液（Miltenyi，目录号 130-128-024），其中添加了 0.2 U µl ⁻¹ 重组 RNase 抑制剂（Takara，目录号 2313）和 1×不含 EDTA 的 cOmplete 蛋白酶抑制剂（Roche，目录号 5056489001），通过 gentleMACS Octo 解离器（Miltenyi，目录号 130-096-427），使用 C 管（Miltenyi，目录号 130-093-237）进行操作。随后，细胞核通过 50 µm 细胞筛（Sysmex，目录号 04-0042-2317）过滤，并在含有 0.2 U µl ⁻¹ RNase 抑制剂的 PBS-BSA（1% w/v）中洗涤两次。对于 snRNA-seq，每种条件下合并五只小鼠的样本。

Isolation of nuclei from human tissue
从人体组织中分离细胞核

Nuclei were isolated from snap-frozen human AT (10–50 mg) in ice-cold Nuclei Extraction Buffer (Miltenyi, catalogue no. 130-128-024) supplemented with 1 U µl⁻¹ recombinant RNase Inhibitor (Takara, catalogue no. 2313), 1× cOmplete EDTA-free Protease Inhibitor (Roche, catalogue no. 5056489001) and 10 mM sodium butyrate using the gentleMACS Octo Dissociator (Miltenyi, catalogue no. 130-096-427), using C-tubes (Miltenyi, catalogue no. 130-093-237).
从速冻的人类 AT（10–50 mg）中分离出细胞核，使用冰冷的细胞核提取缓冲液（Miltenyi，目录号 130-128-024），其中添加了 1 U µl ⁻¹ 重组 RNase 抑制剂（Takara，目录号 2313）、1×不含 EDTA 的 cOmplete 蛋白酶抑制剂（Roche，目录号 5056489001）和 10 mM 丁酸钠，通过 gentleMACS Octo 解离器（Miltenyi，目录号 130-096-427），使用 C 管（Miltenyi，目录号 130-093-237）进行操作。

The nuclei suspension was filtered through a 50 µm strainer, supplemented with PBS-BSA (1% w/v) containing 1× protease inhibitor and RNase inhibitor and centrifuged at 4 °C, at 500g for 10 min. The nuclei pellet was resuspended in 1 ml of PBS-BSA (1%, w/v) supplemented with RNase inhibitor (0.5 U µl⁻¹) and 1× protease inhibitor and was transferred into a new 1.5 ml tube.
核悬液通过 50 µm 筛网过滤，补充含有 1×蛋白酶抑制剂和 RNase 抑制剂的 PBS-BSA（1% w/v），并在 4 °C、500g 条件下离心 10 分钟。随后，将得到的核沉淀物重新悬浮于 1 ml 含有 RNase 抑制剂（0.5 U µl ⁻¹ ）和 1×蛋白酶抑制剂的 PBS-BSA（1% w/v）中，并转移至新的 1.5 ml 管中。

snRNA-seq of AT AT 的 snRNA-seq

Nuclei were counted using a haemocytometer and Trypan blue, concentration was adjusted to approximately 1,000 nuclei per µl and they were loaded onto a G-chip (10x Genomics, catalogue no. PN-1000127). Single-cell gene expression libraries were prepared using the Chromium Next GEM Single Cell 3′ v3.1 kit (10x Genomics) according to the manufacturer’s instructions. To accommodate for low RNA content, two cycles were added to the complementary DNA amplification PCR. Libraries were pooled equimolecularly and sequenced in PE150 (paired-end 150) mode on a NovaSeq 6000 with about 40,000 reads per nucleus at Novogene or using a NovaSeqX at the Functional Genomics Center, Zurich.
使用血细胞计数器和台盼蓝计数细胞核，调整浓度至每微升约 1,000 个细胞核，并将其加载到 G-chip（10x Genomics，目录号 PN-1000127）上。根据制造商的说明，使用 Chromium Next GEM 单细胞 3′ v3.1 试剂盒（10x Genomics）制备单细胞基因表达文库。为了适应低 RNA 含量，在互补 DNA 扩增 PCR 中增加了两个循环。文库等摩尔混合，并在 NovaSeq 6000 上以 PE150（双端 150）模式进行测序，每个细胞核约 40,000 条读取，或在苏黎世功能基因组学中心使用 NovaSeqX 进行测序。

Paired TRAP–seq, CUT&Tag and ATAC–seq
配对 TRAP-seq、CUT&Tag 和 ATAC-seq

Paired TRAP–seq, CUT&Tag and ATAC–seq protocols were developed on the basis of published protocols^{67,71,72,73,74}.
基于已发表的协议，开发了配对的 TRAP-seq、CUT&Tag 和 ATAC-seq 协议 ^{67,71,72,73,74} 。

Ribosome and nuclei isolation
核糖体与细胞核的分离

Nuclei and ribosomes were isolated from snap-frozen epiAT from AdipoERCre x NuTRAP mice in ice-cold Nuclei Extraction Buffer (Miltenyi, catalogue no. 130-128-024) supplemented with 0.2 U µl⁻¹ recombinant RNase Inhibitor (Takara, catalogue no. 2313), 1× cOmplete EDTA-free Protease Inhibitor (Roche, catalogue no. 5056489001) and 10 mM sodium butyrate using the gentleMACS Octo Dissociator (Miltenyi, catalogue no. 130-096-427), using C-tubes (Miltenyi, catalogue no. 130-093-237). The nuclei suspension was filtered through a 50 µm strainer and centrifuged at 4 °C, 500g for 5 min. The supernatant was transferred into a new tube and supplemented with 2 mM dithiothreitol, 100 µg ml⁻¹ cycloheximide (Sigma-Aldrich, catalogue no. 01810) and 1 mg ml⁻¹ sodium heparin (Sigma-Aldrich, catalogue no. H3149-10KU) and kept on ice. The nuclei pellet was resuspended in 1 ml of PBS-BSA (1%, w/v) supplemented with 0.2 U µl⁻¹ RNase inhibitor, 1× cOmplete EDTA-free Protease Inhibitor and 10 mM sodium butyrate and transferred into a new 1.5 ml tube. Nuclei were centrifuged and subsequently bound to Dynabeads MyOne Streptavidin C1 beads (Thermo Fisher, catalogue no. 65002) for 30 min at 4 °C followed by three washes with PBS-BSA (1% w/v).
从 AdipoERCre x NuTRAP 小鼠的快速冷冻 epiAT 中分离出细胞核和核糖体，使用冰冷的 Nuclei Extraction Buffer（Miltenyi，目录号 130-128-024），补充 0.2 U µl ⁻¹ 重组 RNase 抑制剂（Takara，目录号 2313），1× cOmplete 无 EDTA 蛋白酶抑制剂（Roche，目录号 5056489001）和 10 mM 丁酸钠，使用 gentleMACS Octo 解离器（Miltenyi，目录号 130-096-427），采用 C 管（Miltenyi，目录号 130-093-237）。细胞核悬液通过 50 µm 筛网过滤并在 4 °C、500g 下离心 5 分钟。上清液转移至新管中，补充 2 mM 二硫苏糖醇、100 µg ml ⁻¹ 环己酰亚胺（Sigma-Aldrich，目录号 01810）和 1 mg ml ⁻¹ 肝素钠（Sigma-Aldrich，目录号 H3149-10KU），并置于冰上。细胞核沉淀物在 1 ml PBS-BSA（1%，w/v）中重新悬浮，补充 0.2 U µl ⁻¹ RNase 抑制剂、1× cOmplete 无 EDTA 蛋白酶抑制剂和 10 mM 丁酸钠，并转移至新的 1.5 ml 管中。细胞核离心后，与 Dynabeads MyOne 链霉亲和素 C1 珠（Thermo Fisher，目录号 65002）在 4 °C 下结合 30 分钟，随后用 PBS-BSA（1%，w/v）洗涤三次。

TRAP–seq

Per sample, 25 µl of GFP-Trap Magnetic Agarose Beads (ChromoTEK, catalogue no. gtma-20) were washed in 2 ml of polysome lysis buffer (50 mM TRIS-HCl pH 7.5, 100 mM NaCl, 12 mM MgCl₂, 1% Igepal CA-630 (Sigma-Aldrich, catalogue no. I8896), 1× protease inhibitor). The supernatant was mixed with the beads and incubated at 4 °C on a rotator for 1–2 h. Subsequently, tubes were put on a magnetic stand and the supernatant was removed. The beads were washed three times with polysome lysis buffer supplemented with 2 mM dithiothreitol (Sigma-Aldrich, catalogue no. D0632-10G), 100 µg ml⁻¹ cycloheximide (Sigma, catalogue no. D0632-10G) and 1 mg ml⁻¹ sodium heparin (VWR, catalogue no. ACRO411210010) and resuspended in 1 ml Trizol (Thermo Fisher, catalogue no. 15596). Trizol preserved samples were kept at −80 °C until RNA isolation. RNA was isolated by adding 200 µl of chloroform (Sigma-Aldrich, catalogue no. 288306) to samples, followed by shaking and centrifugation at 4 °C, 12,000g for 15 min. The aqueous phase was transferred into a new tube and RNA was isolated and DNase treated with the RNA Clean and Concentrator-5 kit (Zymo Research, catalogue no. R1016), following the manufacturer’s instructions.
每份样品中，使用 25 µl 的 GFP-Trap 磁性琼脂糖珠（ChromoTEK，目录号 gtma-20）在 2 ml 的多核糖体裂解缓冲液（50 mM TRIS-HCl pH 7.5，100 mM NaCl，12 mM MgCl ₂ ，1% Igepal CA-630（Sigma-Aldrich，目录号 I8896），1×蛋白酶抑制剂）中洗涤。将上清液与珠子混合，并在 4 °C 下在旋转器上孵育 1-2 小时。随后，将试管置于磁力架上，移除上清液。珠子用添加了 2 mM 二硫苏糖醇（Sigma-Aldrich，目录号 D0632-10G）、100 µg/ml 环己酰胺（Sigma，目录号 D0632-10G）和 1 mg/ml 肝素钠（VWR，目录号 ACRO411210010）的多核糖体裂解缓冲液洗涤三次，并在 1 ml Trizol（Thermo Fisher，目录号 15596）中重新悬浮。Trizol 保存的样品在-80 °C 下保存，直至 RNA 提取。通过向样品中加入 200 µl 氯仿（Sigma-Aldrich，目录号 288306），随后在 4 °C、12,000g 下离心 15 分钟，进行 RNA 提取。将水相转移至新试管中，并按照制造商的说明，使用 RNA Clean and Concentrator-5 试剂盒（Zymo Research，目录号 R1016）进行 RNA 提取和 DNase 处理。

RNA libraries were prepared by performing reverse transcription and template switching using Maxima H Minus reverse transcriptase (Thermo Fisher, catalogue no. EP0753), a template switch oligo and an oligodT primer to generate full-length cDNA. cDNA was amplified using the KAPA Hotstart 2x ReadyMix (Roche Diagnostics, catalogue no. 7958935001). Then, 1–3 ng of cDNA was tagmentated using 1.3 µg of Tn5 and amplified using KAPA HiFi plus dNTPs (Roche Diagnostics, catalogue no. 07958846001) and the following PCR settings: 72 °C 5 min, 98 °C 30 s, 10 cycles of 98 °C for 10 s, 63 °C for 30 s, 72 °C for 1 min, hold at 4 °C. Libraries were quantified using the KAPA library quantification kit (Roche Diagnostics, catalogue no. 079602), and sequenced in PE150 mode on a NovaSeq 6000 at Novogene.
RNA 文库通过使用 Maxima H Minus 反转录酶（Thermo Fisher，目录号 EP0753）进行反转录和模板切换，结合模板切换寡核苷酸和 oligo(dT)引物生成全长 cDNA。cDNA 通过 KAPA Hotstart 2x ReadyMix（Roche Diagnostics，目录号 7958935001）进行扩增。然后，使用 1.3 µg 的 Tn5 对 1–3 ng 的 cDNA 进行标签化，并使用 KAPA HiFi plus dNTPs（Roche Diagnostics，目录号 07958846001）进行扩增，PCR 设置如下：72°C 5 分钟，98°C 30 秒，10 个循环的 98°C 10 秒，63°C 30 秒，72°C 1 分钟，保持在 4°C。文库通过 KAPA 文库定量试剂盒（Roche Diagnostics，目录号 079602）进行定量，并在 Novogene 的 NovaSeq 6000 上以 PE150 模式进行测序。

CUT&Tag

CUT&Tag was performed as previously described with minor adjustments^74,75. All buffers were supplemented with 1 x cOmplete EDTA-free Protease Inhibitor and 10 mM sodium butyrate. Briefly, nuclei bound to beads were aliquoted into 96-well LoBind plates (Eppendorf, catalogue no. 0030129547) and incubated with primary antibodies—anti-H3K4me3 (abcam, catalogue no. ab8580), anti-H3K27me3 (Cell Signaling Technology, catalogue no. C36B11), anti-H3K27ac (abcam, catalogue no. ab4729), anti-H3K4me1 (abcam, catalogue no. ab8895)—overnight at 4 °C. With the plate on a magnet, the primary antibody solution was removed, and the beads were resuspended in secondary antibody solution (guinea pig anti-rabbit IgG (antibodies-online, catalogue no. ABIN101961)) and incubated at room temperature. pA-Tn5 was bound to antibodies, and transposition was performed at 37 °C and stopped using TAPS-Wash solution. Nuclei were lysed and pA-Tn5 decrosslinked using SDS-release solution. PCR was performed using KAPA HiFi plus dNTPs (Roche Diagnostics, catalogue no. 07958846001) with the following PCR settings: 72 °C 5 min, 98 °C 30 s, 15 cycles of 98 °C 10 s, 63 °C 30 s, and 72 °C final extension for 1 min, hold at 4 °C.
CUT&Tag 按照先前描述的方法进行，略有调整 ^74,75 。所有缓冲液均补充了 1 倍不含 EDTA 的 cOmplete 蛋白酶抑制剂和 10 mM 丁酸钠。简言之，结合在磁珠上的细胞核被分装到 96 孔 LoBind 板（Eppendorf，目录号 0030129547）中，并与一抗——抗 H3K4me3（abcam，目录号 ab8580）、抗 H3K27me3（Cell Signaling Technology，目录号 C36B11）、抗 H3K27ac（abcam，目录号 ab4729）、抗 H3K4me1（abcam，目录号 ab8895）——在 4 °C 下过夜孵育。将板置于磁铁上，移除一抗溶液，磁珠在二抗溶液（豚鼠抗兔 IgG（antibodies-online，目录号 ABIN101961））中重悬，并在室温下孵育。pA-Tn5 与抗体结合，在 37 °C 下进行转座反应，并使用 TAPS-Wash 溶液终止。细胞核被裂解，pA-Tn5 通过 SDS-release 溶液进行去交联。PCR 使用 KAPA HiFi plus dNTPs（Roche Diagnostics，目录号 07958846001）进行，PCR 设置如下：72 °C 5 分钟，98 °C 30 秒，15 个循环的 98 °C 10 秒，63 °C 30 秒，以及 72 °C 最终延伸 1 分钟，保持在 4 °C。

ATAC–seq ATAC-seq

Beads with nuclei were resuspended in ATAC–seq solution (10 mM TAPS pH 8.5, 5 mM MgCl₂, 10% DMF (Sigma-Aldrich, catalogue no. D4551), 0.2 µg µl⁻¹ transposase (Tn5)) and incubated at 37 °C for 30 min. Thereafter, 100 µl of DNA binding buffer (Zymo Research, catalogue no. D4003-1) was added and samples were stored at −20 °C. Then, DNA was extracted using Zymo DNA Clean and Concentrator-5 (Zymo Research, catalogue no. D4004). Library amplification was performed using KAPA HiFi plus dNTPs (Roche Diagnostics, catalogue no. 07958846001) and the following PCR settings: 72 °C 5 min, 98 °C 30 s, 10 cycles of 98 °C 10 s, 63 °C 30 s, 72 °C 1 min, hold at 4 °C.
含有细胞核的珠子在 ATAC-seq 溶液（10 mM TAPS pH 8.5，5 mM MgCl ₂ ，10% DMF（Sigma-Aldrich，目录号 D4551），0.2 µg µl ⁻¹ 转座酶（Tn5））中重新悬浮，并在 37 °C 下孵育 30 分钟。随后，加入 100 µl 的 DNA 结合缓冲液（Zymo Research，目录号 D4003-1），样品在-20 °C 下储存。然后，使用 Zymo DNA Clean and Concentrator-5（Zymo Research，目录号 D4004）提取 DNA。使用 KAPA HiFi plus dNTPs（Roche Diagnostics，目录号 07958846001）进行文库扩增，PCR 设置如下：72 °C 5 分钟，98 °C 30 秒，10 个循环的 98 °C 10 秒，63 °C 30 秒，72 °C 1 分钟，保持在 4 °C。

Both ATAC–seq and CUT&Tag libraries were cleaned using SPRI beads, eluted in nuclease-free water and pooled equimolecularly after library quantification using the KAPA library quantification kit (Roche Diagnostics, catalogue no. 079602). Libraries were sequenced in PE150 mode on a NovaSeq 6000 at Novogene.
ATAC-seq 和 CUT&Tag 文库均使用 SPRI 珠进行清洗，在无核酸酶水中洗脱，并在使用 KAPA 文库定量试剂盒（Roche Diagnostics，目录号：079602）进行文库定量后等摩尔混合。文库在 Novogene 的 NovaSeq 6000 上以 PE150 模式进行测序。

Sequencing data processing
测序数据处理

snRNA-seq data processing and analysis
snRNA-seq 数据处理与分析

Data integration and differential expression analysis for mouse snRNA-seq
数据整合与小鼠 snRNA-seq 的差异表达分析

The 10x Genomics Cell Ranger v.6.1.2 pipeline was used for demultiplexing, read alignment to reference genome mm10-2020A (10x Genomics), barcode processing and unique molecular identifier (UMI) counting with Include introns argument set to ‘True’. The R package Seurat v.4.1.0 (ref. ⁷⁶) was used to process, integrate and analyse datasets. scDblFinder⁷⁷ was used to identify and remove doublets. Nuclei with unique feature counts less than 500 or greater than 3,000 and UMI counts greater than 40,000 were discarded during quality control (Extended Data Fig. 11a). Highly expressed genes such as mitochondrial genes, pseudogenes and Malat1 were excluded from the count matrix before normalization. SoupX⁷⁸ was used to estimate potential ambient RNA contamination in all samples, but no sample required any correction. Samples were normalized using sctransform and integrated using the CCA (canonical correlation analysis) method built into Seurat. Filtered, normalized and integrated nuclei data were clustered by using the Louvain algorithm with a resolution of 0.4 using the first 30 principal components. Cluster markers were identified on the basis of differential gene expression analysis (Wilcoxon rank-sum test with |log₂FC| > 0.25 and adjusted P < 0.05). Clusters were then annotated on the basis of known markers from literature^{34,36,37,46,79,80}. Additionally, our manual cluster annotation was confirmed by reference mapping against a reference male mouse epiAT³⁴ dataset (Extended Data Fig. 11b,c). Differential expression analysis (Wilcoxon rank-sum test with |log₂FC| > 0.5 and adjusted P < 0.01) per cell type between different conditions was done using the FindMarkers function from Seurat. Differential expression analysis hits were intersected with a list of epigenetic modifier genes (see the Source Data to Extended Data Fig. 8) to investigate their expression dynamics. For visualization of snRNA-seq data we used the R package SCpubr v.1 (ref. ⁸¹).
使用 10x Genomics Cell Ranger v.6.1.2 管道进行解复用、读取对参考基因组 mm10-2020A（10x Genomics）的比对、条形码处理和唯一分子标识符（UMI）计数，其中 Include introns 参数设置为‘True’。使用 R 包 Seurat v.4.1.0（参考文献 ⁷⁶ ）处理、整合和分析数据集。使用 scDblFinder ⁷⁷ 识别并去除双峰。在质量控制过程中，丢弃独特特征计数少于 500 或多于 3,000 以及 UMI 计数超过 40,000 的细胞核（扩展数据图 11a）。在归一化之前，从计数矩阵中排除高表达基因如线粒体基因、假基因和 Malat1。使用 SoupX ⁷⁸ 估计所有样本中潜在的背景 RNA 污染，但没有任何样本需要修正。样本使用 sctransform 进行归一化，并使用 Seurat 内置的 CCA（典型相关分析）方法进行整合。过滤、归一化和整合的细胞核数据通过使用 Louvain 算法以 0.4 的分辨率进行聚类，使用前 30 个主成分。基于差异基因表达分析（Wilcoxon 秩和检验，|log ₂ FC| > 0.25 且调整 P < 0.05）识别聚类标记。然后根据文献 ^{34,36,37,46,79,80} 中的已知标记对聚类进行注释。此外，我们的手动聚类注释通过与参考雄性小鼠 epiAT ³⁴ 数据集的参考映射得到了确认（扩展数据图 11b,c）。使用 Seurat 的 FindMarkers 函数，在不同条件下对每种细胞类型进行了差异表达分析（Wilcoxon 秩和检验，|log ₂ FC| > 0.5 且校正 P < 0.01）。将差异表达分析结果与表观遗传修饰基因列表（见扩展数据图 8 的源数据）相交，以研究其表达动态。为了可视化 snRNA-seq 数据，我们使用了 R 包 SCpubr v.1（参考文献 ⁸¹ ）。

Data integration and differential expression analysis for human snRNA-seq
人类 snRNA-seq 数据整合与差异表达分析

The 10x Genomics Cell Ranger v.7.2.0 pipeline was used for demultiplexing, read alignment to reference genome GRCh38-2020-A (10x Genomics), barcode processing and UMI counting, with force cells set to 10,000. The R package Seurat v.4.1.0 (ref. ⁷⁶) was used to process, integrate and analyse datasets. scDblFinder⁷⁷ was used to identify and remove doublets. Nuclei with unique feature counts <300 or >4,000 (LTSS) / 6,000 (NEFA), UMI counts >15,000 (LTSS) / 25,000 (NEFA) and mitochondrial gene counts greater than 5% were discarded during quality control (Extended Data Fig. 12). SoupX⁷⁸ was used to estimate and correct for potential ambient RNA contamination in all samples. Samples were normalized using sctransform and integrated using the CCA method built into Seurat. Filtered, normalized and integrated nuclei data were clustered by using Louvain algorithm using the first 30 principal components. For each study, the cluster resolution was determined using the R package clustree⁸². Cluster markers were identified on the basis of differential gene expression analysis (Wilcoxon rank-sum test with |log₂FC| > 0.25 and adjusted P < 0.01). Clusters were then annotated on the basis of known markers from literature^{34,35,36,37,83}. Additionally, our manual cluster annotation was confirmed by reference mapping against reference human white AT atlas³⁴ (Extended Data Figs. 2 and 3). For each AT depot, adipocytes from two studies were integrated together using the first 20 principal components following the steps as mentioned above. Differential expression analysis (Wilcoxon rank-sum test with |log₂FC| > 0.5 and adjusted P < 0.01) per cell type between different conditions was done using the FindMarkers function from Seurat. Differential expression analysis hits were validated using MAST and likelihood-ratio tests using the FindMarkers function from Seurat. For visualization of snRNA-seq data, we used the R package SCpubr v.1 (ref. ⁸¹).
使用 10x Genomics Cell Ranger v.7.2.0 管道进行解复用、读取对参考基因组 GRCh38-2020-A（10x Genomics）的比对、条形码处理和 UMI 计数，设置强制细胞数为 10,000。使用 R 包 Seurat v.4.1.0（参考文献 ⁷⁶ ）处理、整合和分析数据集。使用 scDblFinder ⁷⁷ 识别并去除双细胞。在质量控制过程中，丢弃唯一特征计数<300 或>4,000（LTSS）/ 6,000（NEFA）、UMI 计数>15,000（LTSS）/ 25,000（NEFA）以及线粒体基因计数大于 5%的细胞核（扩展数据图 12）。使用 SoupX ⁷⁸ 估计并校正所有样本中潜在的背景 RNA 污染。样本使用 sctransform 进行归一化，并使用 Seurat 内置的 CCA 方法进行整合。过滤、归一化和整合后的细胞核数据通过使用前 30 个主成分的 Louvain 算法进行聚类。对于每个研究，使用 R 包 clustree ⁸² 确定聚类分辨率。基于差异基因表达分析（Wilcoxon 秩和检验，|log ₂ FC| > 0.25 且调整 P < 0.01）识别聚类标记。然后根据文献 ^{34,35,36,37,83} 中的已知标记对聚类进行注释。此外，我们的手动聚类注释通过与参考人类白色 AT 图谱 ³⁴ 的参考映射得到确认（扩展数据图 2 和 3）。对于每个 AT 库，来自两个研究的脂肪细胞通过上述步骤使用前 20 个主成分进行整合。使用 Seurat 的 FindMarkers 函数，在不同条件下对每种细胞类型进行差异表达分析（Wilcoxon 秩和检验，|log ₂ FC| > 0.5 且调整 P < 0.01）。差异表达分析结果通过 MAST 和 Seurat 的 FindMarkers 函数进行似然比检验验证。对于 snRNA-seq 数据的可视化，我们使用了 R 包 SCpubr v.1（参考文献 ⁸¹ ）。

SNP-based demultiplexing of human snRNA-seq datasets
基于 SNP 的单细胞 RNA 测序数据集解复用

To perform SNP calling and demultiplexing on the pooled samples, cellsnp-lite⁸⁴ was first used to call SNPs on a cell level using the 1000 Genomes-based reference variant call file for hg38 at a resolution of 7.4 million SNPs. SNPs with less than 20 counts and a minor allele frequency of less than 10% were filtered out, as per the developer recommendations. Finally, the tool vireo⁸⁵ was used to demultiplex the pooled data using the cellsnp-lite-derived genotype information.
为了对混合样本进行 SNP 分型和解复用，首先使用 cellsnp-lite ⁸⁴ 基于 hg38 的 1000 Genomes 参考变异调用文件，在 740 万个 SNP 的分辨率下进行单细胞水平的 SNP 分型。根据开发者建议，过滤掉计数少于 20 且次要等位基因频率低于 10%的 SNP。最后，使用 vireo ⁸⁵ 工具，基于 cellsnp-lite 生成的基因型信息对混合数据进行解复用。

For each donor, we analysed tissue composition and removed nuclei belonging to donors in the case in which no nuclei were assigned as adipocytes (one case in NEFA) or more than 50% or nuclei were assigned as B cells (one case in MTSS; lean donor) after correspondence with surgeons.
对于每位供体，我们分析了组织成分，并在以下情况下移除了属于供体的细胞核：如果没有细胞核被分配为脂肪细胞（NEFA 中的一例），或者超过 50%的细胞核被分配为 B 细胞（MTSS 中的一例；瘦供体），这些都是在与外科医生沟通后进行的。

Transcriptional retention
转录保留

DEGs from obese and WL cells from mouse and human were overlayed, respectively. A DEG was considered restored if it was no longer deregulated in WL cells when compared with controls. If not restored, we considered a DEG part of a transcriptional memory. Clusters identified as similar cell types (for example, three clusters of endothelial cells) were merged for DEG quantification but not differential expression analysis itself. For human snRNA-seq, only cell types for which we obtained at least 30 cells per donor were considered for the retention analysis. T cells were not included in differential expression analysis or transcriptional retention analysis. For integrated human adipocyte differential expression analysis quantification, non-coding transcripts were excluded.
来自肥胖和体重减轻（WL）的小鼠和人类细胞的差异表达基因（DEGs）分别进行了叠加。如果一个 DEG 在 WL 细胞中不再失调，与对照组相比，则被认为是恢复的。如果未恢复，我们将其视为转录记忆的一部分。被识别为相似细胞类型的集群（例如，三个内皮细胞集群）被合并用于 DEG 定量，但不用于差异表达分析本身。对于人类单细胞 RNA 测序（snRNA-seq），只有我们从每个供体获得至少 30 个细胞的细胞类型才被考虑用于保留分析。T 细胞不包括在差异表达分析或转录保留分析中。对于整合的人类脂肪细胞差异表达分析定量，非编码转录本被排除在外。

TRAP–seq

Quality control of the raw reads was performed using FastQC v.0.11.9. Raw reads were trimmed using TrimGalore v.0.6.6 (https://github.com/FelixKrueger/TrimGalore). Filtered reads were aligned against the reference mouse genome assembly mm10 using HISAT2 v.2.2.1. Raw gene counts were quantified using the featureCounts⁸⁶ program of subread v.2.0.1. Differential expression analysis was performed using the R package EdgeR⁸⁷, with |log₂FC| ≥ 1 and nominal P < 0.01 as cut-offs.
使用 FastQC v.0.11.9 对原始读取数据进行了质量控制。原始读取数据通过 TrimGalore v.0.6.6（https://github.com/FelixKrueger/TrimGalore）进行修剪。过滤后的读取数据使用 HISAT2 v.2.2.1 对参考小鼠基因组组装 mm10 进行比对。使用 subread v.2.0.1 的 featureCounts ⁸⁶ 程序对原始基因计数进行了量化。使用 R 包 EdgeR ⁸⁷ 进行差异表达分析，采用|log ₂ FC| ≥ 1 和名义 P < 0.01 作为阈值。

CUT&Tag and ATAC–seq data processing and analysis
CUT&Tag 和 ATAC-seq 数据的处理与分析

Quality control of CUT&Tag and ATAC–seq data and generation of bedgraph files was performed as described previously⁷⁵. Peaks were called from CUT&Tag sequencing and ATAC–seq libraries on individual bedgraph files using SEACR⁸⁸ v.1.3 in stringent mode with a peak calling threshold of 0.01. Peaks overlapping with mouse blacklist regions⁸⁹ were filtered out. Called peaks were annotated using the R package ChIPSeeker⁹⁰. Peak fold enrichment against genomic features was calculated using the formula: Σ(base pair (bp) overlap) × genome_size/[Σ(bp hPTM peak) × Σ(bp genomic feature)]. Genomic features tracks were downloaded from ENCODE using the R package annotatr⁹¹. Visual quality control of bam files was performed with Seqmonk⁹². Called peaks were combined to generate a union peak list and quantified using the R package chromVAR⁹³ v.1.16, generating a raw peak count matrix.
CUT&Tag 和 ATAC-seq 数据的质量控制以及 bedgraph 文件的生成按照先前描述的方法进行 ⁷⁵ 。使用 SEACR ⁸⁸ v.1.3 在严格模式下，以 0.01 的峰值调用阈值，从 CUT&Tag 测序和 ATAC-seq 文库的单个 bedgraph 文件中调用峰值。与小鼠黑名单区域 ⁸⁹ 重叠的峰值被过滤掉。使用 R 包 ChIPSeeker ⁹⁰ 对调用的峰值进行注释。通过以下公式计算相对于基因组特征的峰值倍数富集：Σ(碱基对(bp)重叠) × 基因组大小/[Σ(bp hPTM 峰值) × Σ(bp 基因组特征)]。基因组特征轨迹通过 R 包 annotatr ⁹¹ 从 ENCODE 下载。使用 Seqmonk ⁹² 对 bam 文件进行可视化质量控制。调用的峰值被合并以生成联合峰值列表，并使用 R 包 chromVAR ⁹³ v.1.16 进行量化，生成原始峰值计数矩阵。

MOFA

MOFA^50,94 was run to identify the driving variation source across all conditions using all data modalities. For each modality, the top 3,000 variable features (genes or peaks) between all samples were selected using the R package DESeq2 (ref. ⁹⁵) and used as input to train the MOFA model. The trained MOFA model represented data variability in terms of five latent factors, which were further explored and visualized.
MOFA ^50,94 被运行以识别所有条件下使用所有数据模式的主要变异来源。对于每种模式，使用 R 包 DESeq2（参考文献 ⁹⁵ ）在所有样本之间选择了前 3,000 个可变特征（基因或峰），并将其作为输入来训练 MOFA 模型。训练后的 MOFA 模型以五个潜在因子表示数据变异性，这些因子进一步被探索和可视化。

Generation of enhancer tracks of adipocytes
脂肪细胞增强子轨迹的生成

Adipocyte chromatin states were identified using ChromHMM v.1.22 (ref. ⁹⁶) in concatenated mode with binned bam files (200-bp bins) from each condition combining all hPTMs and ATAC–seq. After final model selection⁷⁵ with eight chromatin states and emission parameter calculation of hPTMs and ATAC–seq, chromatin state fold enrichment was performed against genomic features and ENCODE candidate cis-regulatory elements. Enhancer states were selected on the basis of genomic localization and hPTM enrichment. Subsequently, an enhancer track was generated per condition and merged for differential analysis.
使用 ChromHMM v.1.22（参考文献 ⁹⁶ ）在拼接模式下，结合所有 hPTMs 和 ATAC-seq 的 200-bp 分箱 bam 文件，识别了脂肪细胞的染色质状态。在最终模型选择 ⁷⁵ 中，使用八种染色质状态和 hPTMs 及 ATAC-seq 的发射参数计算后，进行了染色质状态相对于基因组特征和 ENCODE 候选顺式调控元件的折叠富集分析。根据基因组定位和 hPTM 富集情况选择增强子状态。随后，为每种条件生成增强子轨迹并合并进行差异分析。

Differential analysis of hPTMs and ATAC–seq
hPTMs 和 ATAC-seq 的差异分析

Promoters 启动子

Promoters were defined using the getPromoters function from ChIPSeeker with TxDb.Mmusculus.UCSC.mm10.knownGene as input and setting the TSSRegion to c(-2000, 2000). Peaks overlapping with promoters were extracted using the annotatePeak function from ChIPseeker⁹⁰ by selecting peaks annotated as promoters. For differential analysis, our raw peak count matrix was filtered for these promoter regions and counts were aggregated at gene level. Differential analysis of the same hPTM between two conditions was performed using the R package EdgeR⁸⁷ with nominal P < 0.01 and |log₂FC| > 1 as cut-offs.
使用 ChIPSeeker 中的 getPromoters 函数，以 TxDb.Mmusculus.UCSC.mm10.knownGene 作为输入，并将 TSSRegion 设置为 c(-2000, 2000)来定义启动子。通过 ChIPseeker 的 annotatePeak 函数 ⁹⁰ 选择注释为启动子的峰，提取与启动子重叠的峰。对于差异分析，我们的原始峰计数矩阵经过过滤，仅保留这些启动子区域，并在基因水平上汇总计数。使用 R 包 EdgeR ⁸⁷ 进行相同 hPTM 在两种条件下的差异分析，采用名义 P < 0.01 和|log ₂ FC| > 1 作为阈值。

Enhancers 增强子

ChromHMM was used to identify regions in the genome that were marked by H3K4me1, H3K27ac and open (ATAC–seq) but not enriched for H3K4me3 and that were not promoters (Extended Data Fig. 9b–e). States 6 and 5 were selected as enhancer regions on the basis of their genomic locations (distal enhancer elements) (Extended Data Fig. 9b–e).
ChromHMM 被用于识别基因组中被 H3K4me1、H3K27ac 和开放（ATAC-seq）标记，但未富集 H3K4me3 且非启动子的区域（扩展数据图 9b-e）。基于其基因组位置（远端增强子元件），状态 6 和 5 被选为增强子区域（扩展数据图 9b-e）。

Our raw peak count matrix was filtered for enhancer regions defined by chromHMM, and peaks around the TSS (±2,000 bp) were discarded. Linkage of putative enhancers to genes was done using the R package ChIPSeeker by selecting the closest gene (TSS or gene body) within 20,000 bp distance. Putative enhancers farther away than 20,000 from a TSS or gene body were not linked to any gene and were discarded from downstream GSEA.
我们对原始的峰计数矩阵进行了过滤，保留了由 chromHMM 定义的增强子区域，并排除了 TSS（±2,000 bp）周围的峰。通过使用 R 包 ChIPSeeker，我们将潜在的增强子与基因进行了关联，选择在 20,000 bp 距离内最接近的基因（TSS 或基因体）。距离 TSS 或基因体超过 20,000 bp 的潜在增强子未与任何基因关联，并从下游 GSEA 中剔除。

For each hPTM, the raw filtered peak matrices were log-normalized using the R package EdgeR and Pearson’s correlation coefficient was computed using the cor function from the R package stats v.3.6.2.
对于每个 hPTM，原始过滤后的峰值矩阵使用 R 包 EdgeR 进行对数归一化，并使用 R 包 stats v.3.6.2 中的 cor 函数计算皮尔逊相关系数。

Differential analysis of the same hPTM between two conditions was performed using the R package EdgeR with nominal FDR < 0.05 and |log₂FC| > 1 as cut-offs.
使用 R 包 EdgeR 对两种条件下相同的 hPTM 进行了差异分析，采用名义 FDR < 0.05 和|log ₂ FC| > 1 作为截断值。

PCA

Raw gene and promoter/enhancer-specific peak count matrices were log-normalized using the R package EdgeR. PCA of the normalized count matrices was performed using the prcomp function of R package stats v.3.6.2.
原始基因和启动子/增强子特异性峰计数矩阵使用 R 包 EdgeR 进行对数归一化。归一化计数矩阵的主成分分析（PCA）使用 R 包 stats v.3.6.2 的 prcomp 函数进行。

GSEA 基因集富集分析

GSEA was performed using the R package enrichR^97,98,99. For generation of heatmaps summarizing GSEA across cell types, significantly enriched terms were selected using the adjusted P value (<0.01) and the combined.score (enrichment score) was scaled and visualized.
使用 R 包 enrichR 进行 GSEA 分析 ^97,98,99 。为了生成总结不同细胞类型 GSEA 的热图，我们选择了调整后的 P 值（<0.01）显著富集的术语，并将 combined.score（富集分数）进行缩放并可视化。

Visualization 可视化

R v.4.2, GraphPad Prism v.9.5.1 and Seqmonk v.1.48.1 were used to generate plots and Affinity Designer and Publisher were used to adjust plots for clarity (for example, colour schemes).
R v.4.2、GraphPad Prism v.9.5.1 和 Seqmonk v.1.48.1 用于生成图表，Affinity Designer 和 Publisher 用于调整图表以提高清晰度（例如，颜色方案）。

Statistical analysis of physiological parameters from mice
小鼠生理参数的统计分析

GraphPad Prism v.9.5.1 was used to analyse physiological data from mice. Each dataset of physiological parameters was tested for normality using the Shapiro–Wilk test. On the basis of the results, parametric or non-parametric tests were used to compare experimental with age-matched control groups. Tests are indicated in figure legends and the Source Data.
使用 GraphPad Prism v.9.5.1 分析了小鼠的生理数据。每个生理参数数据集均通过 Shapiro–Wilk 检验进行正态性检验。根据结果，采用参数或非参数检验方法比较实验组与同龄对照组。检验方法在图例和源数据中注明。

Reporting summary 报告摘要

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
有关研究设计的更多信息，请参阅本文所附的《自然》系列报告摘要。

Data availability 数据可用性

All mouse sequencing data that support the findings of this study have been deposited on GEO, with the accession code GSE236580. Human snRNA-seq data from the MTSS and LTSS cohorts are available upon request from C.W. and M.B. Human snRNA-seq data from the NEFA cohort are available upon request from F.v.M., N.K. and M.R. Analysis code for human and mouse data is available on GitHub and Zenodo¹⁰⁰. An interactive snRNA-seq data browser link and links to interactive tables with results of differential gene expression and epigenetic analysis are available on GitHub (https://github.com/vonMeyennLab/AT_memory). Source data are provided with this paper.
本研究支持的所有小鼠测序数据已存放在 GEO 上，访问代码为 GSE236580。来自 MTSS 和 LTSS 队列的人类 snRNA-seq 数据可向 C.W.和 M.B.申请获取。来自 NEFA 队列的人类 snRNA-seq 数据可向 F.v.M.、N.K.和 M.R.申请获取。人类和小鼠数据的分析代码可在 GitHub 和 Zenodo 上获取 ¹⁰⁰ 。交互式 snRNA-seq 数据浏览器链接以及包含差异基因表达和表观遗传分析结果的交互式表格链接可在 GitHub 上获取（https://github.com/vonMeyennLab/AT_memory）。源数据随本文提供。

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Acknowledgements 致谢

We thank all members of the von Meyenn group and the Wolfrum group for helpful discussions and support. We also thank M. Sütö, C. Sert, M. Klug, C. Leuzinger, C. Kellenberger, L. Maak and R. von Wartburg for help with animal experiments and handling; J. P. A. de Sousa for help with computational pipelines; E. Masschelein and T. Dahlby for help with the metabolic cage measurements; E. Seelig for help with acquisition of human samples and scientific support; J. Bohacek and R. Waag for NuTRAP mice; and the Protein Production and Structure Core Facility at EPFL for the production and purification of pA-Tn5 and Tn5, especially K. Lau, F. Pojer and M. Francois. This work was supported by ETH Zurich core funding (F.v.M.), a European Research Council Starting Grant (no. 803491, BRITE to F.v.M.), the Basel Research Centre for Child Health (Multi-Investigator Project 2020 to F.v.M.), the Deutsche Forschungsgemeinschaft (Project no. 209933838–SFB 1052 (project B1) to M.B.), the Margareta af Ugglas foundation (M.R.), the Swedish Research Council (M.R., N.M., including an establishing grant to L.M.), a European Research Council Synergy Grant (no. 856404, SPHERES to M.R.), the Novo Nordisk Foundation (including the MeRIAD consortium grant no. 0064142 to M.R., and no. NNF20OC0061149 to N.M.), the Knut and Alice Wallenberg’s Foundation (Wallenberg Clinical Scholar to M.R.), the Center for Innovative Medicine (M.R.), the Swedish Diabetes Foundation (M.R.), the Stockholm County Council (M.R.), the Strategic Research Program in Diabetes at Karolinska Institutet (M.R.) and the European Foundation for the Study of Diabetes (Future Leaders award to N.M.). L.M. was funded by a postdoctoral grant from the Swedish Society for Medical Research.
我们感谢 von Meyenn 小组和 Wolfrum 小组的所有成员提供的宝贵讨论和支持。同时，感谢 M. Sütö、C. Sert、M. Klug、C. Leuzinger、C. Kellenberger、L. Maak 和 R. von Wartburg 在动物实验和处理方面的帮助；J. P. A. de Sousa 在计算流程方面的帮助；E. Masschelein 和 T. Dahlby 在代谢笼测量方面的帮助；E. Seelig 在获取人类样本和科学支持方面的帮助；J. Bohacek 和 R. Waag 提供的 NuTRAP 小鼠；以及 EPFL 的蛋白质生产和结构核心设施在 pA-Tn5 和 Tn5 的生产和纯化方面的帮助，特别是 K. Lau、F. Pojer 和 M. Francois。这项工作得到了苏黎世联邦理工学院核心资金（F.v.M.）、欧洲研究委员会启动资助（编号 803491，BRITE 至 F.v.M.）、巴塞尔儿童健康研究中心（多调查员项目 2020 至 F.v.M.）、德国研究基金会（项目编号 209933838–SFB 1052（项目 B1）至 M.B.）、Margareta af Ugglas 基金会（M.R.）、瑞典研究委员会（M.R.、N.M.，包括 L.M.的建立资助）、欧洲研究委员会协同资助（编号 856404，SPHERES 至 M.R.）、诺和诺德基金会（包括 MeRIAD 联盟资助编号 0064142 至 M.R.，以及编号 NNF20OC0061149 至 N.M.）、Knut 和 Alice Wallenberg 基金会（Wallenberg 临床学者至 M.R.）、创新医学中心（M.R.）、瑞典糖尿病基金会（M.R.）、斯德哥尔摩县议会（M.R.）的支持。), 卡罗林斯卡学院糖尿病战略研究项目（M.R.）和欧洲糖尿病研究基金会（N.M.的未来领袖奖）。L.M. 获得了瑞典医学研究会提供的博士后研究资助。

Funding 资助

Open access funding provided by Swiss Federal Institute of Technology Zurich.
瑞士联邦理工学院苏黎世分校提供的开放获取资助。

Author information 作者信息

Daniel Castellano-Castillo
Present address: Medical Oncology Department, Virgen de la Victoria University Hospital, Málaga Biomedical Research Institute (IBIMA)-CIMES-UMA, Málaga, Spain
Lucas Massier
Present address: Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), Helmholtz Zentrum München, University of Leipzig and University Hospital Leipzig, Leipzig, Germany
Hua Dong
Present address: Stem Cell Bio Regenerative Med Institute, Stanford University, Stanford, CA, USA
Wenfei Sun
Present address: Department of Bioengineering, Stanford University, Stanford, CA, USA
These authors contributed equally: Laura C. Hinte, Daniel Castellano-Castillo

Authors and Affiliations

Laboratory of Nutrition and Metabolic Epigenetics, Institute of Food, Nutrition and Health, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
Laura C. Hinte, Daniel Castellano-Castillo, Adhideb Ghosh, Kate Melrose, Emanuel Gasser, Falko Noé & Ferdinand von Meyenn
Biomedicine Programme, Life Science Zurich Graduate School, Zurich, Switzerland
Laura C. Hinte & Kate Melrose
Functional Genomics Center Zurich, ETH Zurich and University Zurich, Zurich, Switzerland
Adhideb Ghosh & Falko Noé
Laboratory of Translational Nutrition Biology, Institute of Food, Nutrition and Health, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
Adhideb Ghosh, Falko Noé, Hua Dong, Wenfei Sun & Christian Wolfrum
Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden
Lucas Massier, Mikael Rydén & Niklas Mejhert
Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), Helmholtz Zentrum München, University of Leipzig and University Hospital Leipzig, Leipzig, Germany
Anne Hoffmann & Matthias Blüher
Medical Department III – Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
Matthias Blüher

Authors

Laura C. Hinte
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Daniel Castellano-Castillo
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Adhideb Ghosh
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Kate Melrose
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Emanuel Gasser
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Falko Noé
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Lucas Massier
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Hua Dong
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Wenfei Sun
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Anne Hoffmann
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Christian Wolfrum
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Mikael Rydén
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Niklas Mejhert
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Matthias Blüher
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Ferdinand von Meyenn
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Contributions

D.C.-C. and L.C.H. performed mouse experiments and made libraries from mouse tissues. L.C.H. performed experiments with human samples and cell culture experiments. L.C.H., A.G. and D.C.-C. analysed data. K.M. assisted with dissection, cell culture experiments and artwork. E.G. assisted with dissection. M.B., C.W. and M.R. supervised the clinical studies and M.B., A.H., L.M., M.R. and N.M. provided human AT samples and clinical data. F.N. SNP-demultiplexed snRNA-seq data. H.D. and W.S. collected additional metadata of human samples. M.B., C.W., M.R. and N.M. supervised the human studies and human AT sample collections. L.C.H., D.C.-C. and F.v.M. conceptualized the study, interpreted the data and wrote the manuscript. All authors contributed to editing and reviewing the manuscript and approved its publication.

Corresponding author

Correspondence to Ferdinand von Meyenn.

Ethics declarations 伦理声明

Competing interests 利益冲突

M.B. received honoraria as a consultant and speaker from Amgen, AstraZeneca, Bayer, Boehringer-Ingelhiem, Lilly, Novo Nordisk and Sanofi. M.R. received honoraria as a consultant and speaker from AstraZeneca, Boehringer-Ingelheim, Lilly, Novo Nordisk and Sanofi. The other authors declare no competing interests.
M.B. 作为顾问和演讲者从安进、阿斯利康、拜耳、勃林格殷格翰、礼来、诺和诺德和赛诺菲获得酬金。M.R. 作为顾问和演讲者从阿斯利康、勃林格殷格翰、礼来、诺和诺德和赛诺菲获得酬金。其他作者声明无利益冲突。

Peer review 同行评审

Peer review information 同行评审信息

Nature thanks Katherine Gallagher, Naomi Habib, Alyssa Hasty and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
自然感谢 Katherine Gallagher、Naomi Habib、Alyssa Hasty 以及其他的匿名审稿人，感谢他们对本工作的同行评审所做出的贡献。审稿报告可供查阅。

Additional information 附加信息

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
出版商声明 Springer Nature 对出版地图和机构隶属关系中的司法管辖权主张保持中立。

Extended data figures and tables
扩展数据图表

Extended Data Fig. 1 Human AT retains cellular transcriptional changes after bariatric surgery induced WL.
扩展数据图 1 人类 AT 在减肥手术诱导的体重减轻后保留了细胞转录变化。

a, UMAP of 19,494 nuclei representing omAT pools from lean subjects (n = 5; 1 male, 4 females) and paired omAT from T0 and T1 (n = 8 each; 2 males, 6 females) from the MTSS study. b,c Proportion of retained transcriptional changes in highly abundant cell types of MTSS omAT. d, UMAP of 31,721 nuclei representing scAT pools from lean subjects (n = 8; 8 females) and paired scAT from T0 and T1 (n = 7 each; 7 females) from the NEFA study. e,f Proportion of retained transcriptional changes in highly abundant cell types of NEFA scAT. g-j Number of upregulated and downregulated DEGs per cell type obese donor scaled by column at T0 for omAT (left) and scAT (right) from MTSS, LTSS and NEFA studies. k, Number of persistently deregulated genes from T0 to T1 per cell type across AT pools from all studies. l, UMAP of 4,958 nuclei representing adipocytes from MTSS omAT and LTSS omAT (total lean n = 10; total T0/T1 n = 13). m, UMAP of 13,231 nuclei representing adipocytes from NEFA scAT and LTSS scAT (total lean n = 13; total T0/T1 n = 12). Wilcoxon Rank Sum test, with adjusted p-value < 0.01 by the Bonferroni correction method and FC > ±0.5 was used for DEG identification in b, c, e-k. APCs, adipocyte progenitor cells; ASDCs, AXL+ dendritic cells; DCs, dendritic cells; EndoCs, endothelial cells; EndoACs, arteriolar EndoCs; EndoSCs, stalk EndoCs; EndoVCs, venular EndoCs; LECs, lymphatic endothelial cells; FAPs, fibro-adipogenic progenitors; Macro, macrophages; MastCs, mast cells; MesoCs, mesothelial cells; NeurCs, neuronal like cells; SMCs, (vascular) smooth muscle cells.
a. 来自 MTSS 研究的 19,494 个细胞核的 UMAP 图，代表瘦受试者（n = 5；1 名男性，4 名女性）的 omAT 池以及 T0 和 T1 配对的 omAT（各 n = 8；2 名男性，6 名女性）。b,c. MTSS omAT 中高丰度细胞类型中保留的转录变化比例。d. 来自 NEFA 研究的 31,721 个细胞核的 UMAP 图，代表瘦受试者（n = 8；8 名女性）的 scAT 池以及 T0 和 T1 配对的 scAT（各 n = 7；7 名女性）。e,f. NEFA scAT 中高丰度细胞类型中保留的转录变化比例。g-j. 来自 MTSS、LTSS 和 NEFA 研究的 omAT（左侧）和 scAT（右侧）在 T0 时每种细胞类型的上调和下调 DEG 数量，按列缩放。k. 所有研究中 AT 池中每种细胞类型从 T0 到 T1 持续失调的基因数量。l. 来自 MTSS omAT 和 LTSS omAT 的 4,958 个脂肪细胞核的 UMAP 图（总瘦 n = 10；总 T0/T1 n = 13）。m. 来自 NEFA scAT 和 LTSS scAT 的 13,231 个脂肪细胞核的 UMAP 图（总瘦 n = 13；总 T0/T1 n = 12）。Wilcoxon 秩和检验，采用 Bonferroni 校正方法调整 p 值<0.01 且 FC > ±0.5 用于 b, c, e-k 中的 DEG 鉴定。 APCs，脂肪细胞前体细胞；ASDCs，AXL+ 树突状细胞；DCs，树突状细胞；EndoCs，内皮细胞；EndoACs，小动脉内皮细胞；EndoSCs，茎内皮细胞；EndoVCs，小静脉内皮细胞；LECs，淋巴内皮细胞；FAPs，纤维脂肪前体细胞；Macro，巨噬细胞；MastCs，肥大细胞；MesoCs，间皮细胞；NeurCs，神经样细胞；SMCs，（血管）平滑肌细胞。

Source Data 源数据

Extended Data Fig. 2 Characterization of omAT composition.
扩展数据图 2 omAT 成分的表征。

a,b, Cluster markers used for annotating cell clusters in human omAT of the MTSS (left) and LTSS (right) study. c,d, UMAP visualization representing omAT pools from the MTSS study (c) and LTSS study (d) coloured by predicted cell subtypes from the Emont et al. visceral AT dataset from Caucasian individuals. Feature plots showing reference mapping scores illustrating how well omAT dataset maps to the Emont et al. dataset. e,f, Relative cell type abundance in omAT per condition and tissue donor of the LTSS (e) and MTSS (f) study. Lines connecting dots indicate paired samples. Significance between T0 and T1 for e-f was calculated using paired multiple Wilcoxon tests with Benjamini, Krieger and Yekutieli post hoc test for multiple comparisons. Error bars represent s.d.
a,b, 用于注释 MTSS（左）和 LTSS（右）研究中人类 omAT 细胞集群的集群标记。c,d, UMAP 可视化表示来自 MTSS 研究（c）和 LTSS 研究（d）的 omAT 池，按 Emont 等人来自高加索个体的内脏 AT 数据集预测的细胞亚型着色。特征图显示参考映射分数，说明 omAT 数据集与 Emont 等人数据集的映射程度。e,f, LTSS（e）和 MTSS（f）研究中每种条件和组织供体的 omAT 中细胞类型的相对丰度。连接点的线条表示配对样本。e-f 中 T0 和 T1 之间的显著性使用配对多重 Wilcoxon 检验计算，并使用 Benjamini, Krieger 和 Yekutieli 事后检验进行多重比较。误差条表示标准差。

Source Data 源数据

Extended Data Fig. 3 Characterization of scAT composition.
扩展数据图 3 scAT 成分的表征。

a,b, Cluster markers used for annotating cell clusters in human scAT of the LTSS (left) and NEFA (right) study. c,d, UMAP visualization representing scAT pools from the LTSS study (c) and NEFA study (d) coloured by predicted cell subtypes from the Emont et al. subcutaneous AT dataset from Caucasian individuals. Feature plots showing reference mapping scores illustrating how well scAT dataset maps to the Emont et al. dataset. e,f, Relative cell type abundance in scAT per condition and tissue donor of the LTSS (e) and NEFA (f) study. Lines connecting dots indicate paired samples. Significance between T0 and T1 for e-f was calculated using paired multiple Wilcoxon tests with Benjamini, Krieger and Yekutieli post hoc test for multiple comparisons. Error bars represent s.d.
a,b, 用于标注 LTSS（左）和 NEFA（右）研究中人类 scAT 细胞集群的集群标记。c,d, UMAP 可视化展示了来自 LTSS 研究（c）和 NEFA 研究（d）的 scAT 池，颜色代表 Emont 等人亚皮下 AT 数据集中预测的细胞亚型，该数据集来自高加索个体。特征图显示了参考映射分数，说明了 scAT 数据集与 Emont 等人数据集的映射程度。e,f, LTSS（e）和 NEFA（f）研究中每种条件和组织供体的 scAT 中细胞类型的相对丰度。连接点的线条表示配对样本。e-f 中 T0 和 T1 之间的显著性通过配对多重 Wilcoxon 检验计算，并使用 Benjamini, Krieger 和 Yekutieli 事后多重比较检验。误差棒表示标准差。

Source Data 源数据

Extended Data Fig. 4 GSEA of retained DEGs in adipocytes.
扩展数据图 4 脂肪细胞中保留的差异表达基因的基因集富集分析。

a,b, Top (significant) persistently downregulated (memory) pathway terms in omental adipocytes of the MTSS (a) and LTSS (b) study based on Wikipathways database. c,d, Top (significant) persistently downregulated (memory) pathway terms in subcutaneous adipocytes of the LTSS (c) and NEFA (d) study based on Wikipathways database. e,f, Top (significant) persistently upregulated (memory) pathway terms in omental adipocytes of the MTSS (e) and LTSS (f) study based on Wikipathways database. g,h, Top (significant) persistently downregulated (memory) pathway terms in subcutaneous adipocytes of the LTSS (g) and NEFA (d) study based on Wikipathways database. In g enrichment is not significant. Significance was calculated using Fisher’s exact test, with adjusted P-value < 0.05 by the Benjamini-Hochberg method for correction.
a,b, 基于 Wikipathways 数据库，MTSS（a）和 LTSS（b）研究中网膜脂肪细胞中持续下调（记忆）通路术语的前几位（显著）。c,d, 基于 Wikipathways 数据库，LTSS（c）和 NEFA（d）研究中皮下脂肪细胞中持续下调（记忆）通路术语的前几位（显著）。e,f, 基于 Wikipathways 数据库，MTSS（e）和 LTSS（f）研究中网膜脂肪细胞中持续上调（记忆）通路术语的前几位（显著）。g,h, 基于 Wikipathways 数据库，LTSS（g）和 NEFA（d）研究中皮下脂肪细胞中持续下调（记忆）通路术语的前几位（显著）。在 g 中，富集不显著。显著性通过 Fisher 精确检验计算，使用 Benjamini-Hochberg 方法调整 P 值<0.05 进行校正。

Source Data 源数据

Extended Data Fig. 5 Weight loss largely resolves obesity induced physiological changes in mice.
扩展数据图 5 体重减轻很大程度上解决了小鼠肥胖引起的生理变化。

Data from mouse experiments. For n<11, each dot represents an biological replicate. Data from 2-3 independent experiments. a, Glucose tolerance tests (GTTs) and area of the curve (AOC) for GTTs; (n = 10 each). b, Insulin tolerance tests (ITTs) and AOC for ITTs (n = 10 each). c, Fasting blood glucose (n = 10 each). d, GTTs and AOCs for GTTs; (n = 10 each from 2 independent experiments). e, ITTs and AOC for ITTs (n = 10 each from 2 independent experiments). f, Fasting blood glucose (CC_s&HC: n = 10 each, CCC&HHC: n = 20 each; from 2 independent experiments). g,h, Fasting insulin levels (n = 6). i,j Postprandial insulin and leptin levels. C, H, CC_s, HC, HH, HHC: n = 6 each; CC_l, CCC: n = 5 each. Boxplot represents minimum, maximum and median. k, Cumulative food intake from HC and CC_s mice in the last 3 days of WL chow diet feeding. (n = 10 mice each). l, Energy expenditure of HC and CC_s mice in the last 3 days of WL chow diet feeding. (n = 10 mice each). m, Liver triglycerides (tg) per μg liver tissue (C&H: n = 6, CC_s: n = 10, HC, CC_l, HHC: n = 9, HH&CCC: n = 8). Boxplot represents minimum, maximum and median. n, Haematoxylin and eosin (HE) staining liver sections, 20x magnification. Scale bar, 200 μm. o, Lean mass of HC and CC_s mice relative to lean mass measured at C and H timepoints of the same mice (right) (n = 19 each). p, weights of ingAT, epiAT and BAT, normalized to body weight (C&H: n = 6, HC&CC_s: n = 10, from 2 experiments). q, Representative photos of epiAT depots. Ruler is in cm. r, Weights of ingAT, epiAT and BAT, normalized to body weight (HH&CC_l: n = 6, CCC&HHC: n = 10). s, Representative photos of epiAT depots. Ruler is in cm. t, Representative photo of a HHC mouse. u, Representative image of a histological and HE stained section of a whole epiAT depot from a HHC mouse. Scale bar 2000 μm. v, Haematoxylin and eosin (HE) staining of epiAT, 20x magnification. Scale bar, 100 μm. Representative pictures. w, ingAT adipocyte area across conditions. (n = 4 mice each, 5-8 pictures each). x, HE staining of scAT, 20x magnification. Scale bar, 200 μm. y, epiAT adipocyte area across conditions. (n = 4 mice each, 5-8 pictures each). z, Quantification of collagen content from Maison’s Trichome staining. (n = 4 mice each, 20 pictures each). Significance was calculated between age matched controls and experimental groups. Significance a, b, d, e, i, j, z was calculated using two-tailed Mann-Whitney tests. Significance for c, f-h, m was calculated using unpaired, two-tailed t-tests with Welch’s correction. Error bars represent s.d. Significance for p and r was calculated using unpaired, multiple t-tests with Benjamini, Krieger and Yekutieli post hoc test for multiple comparisons. ns = FDR > 0.01, **FDR < 0.01, ***FDR < 0.001, ****FDR < 0.0001.
来自小鼠实验的数据。对于 n<11，每个点代表一个生物学重复。数据来自 2-3 个独立实验。a, 葡萄糖耐量测试（GTTs）及 GTTs 的曲线下面积（AOC）；（n = 10）。b, 胰岛素耐量测试（ITTs）及 ITTs 的 AOC（n = 10）。c, 空腹血糖（n = 10）。d, GTTs 及 GTTs 的 AOCs；（n = 10，来自 2 个独立实验）。e, ITTs 及 ITTs 的 AOC（n = 10，来自 2 个独立实验）。f, 空腹血糖（CC_s&HC: n = 10，CCC&HHC: n = 20；来自 2 个独立实验）。g,h, 空腹胰岛素水平（n = 6）。i,j, 餐后胰岛素和瘦素水平。C, H, CC_s, HC, HH, HHC: n = 6；CC_l, CCC: n = 5。箱线图表示最小值、最大值和中位数。k, HC 和 CC_s 小鼠在 WL 饲料喂养最后 3 天的累积食物摄入量。（n = 10 只小鼠）。l, HC 和 CC_s 小鼠在 WL 饲料喂养最后 3 天的能量消耗。（n = 10 只小鼠）。m, 每微克肝组织中的甘油三酯（tg）（C&H: n = 6，CC_s: n = 10，HC, CC_l, HHC: n = 9，HH&CCC: n = 8）。箱线图表示最小值、最大值和中位数。n, 苏木精和伊红（HE）染色的肝切片，20 倍放大。标尺，200 μm。o, HC 和 CC_s 小鼠相对于 C 和 H 时间点测量的瘦体重的瘦体重（右侧）（n = 19）。p, ingAT、epiAT 和 BAT 的重量，按体重归一化（C&H: n = 6，HC&CC_s: n = 10，来自 2 个实验）。 q, 代表性照片显示了 epiAT 脂肪库。标尺单位为厘米。r, ingAT、epiAT 和 BAT 的重量，归一化为体重（HH&CC_l: n = 6, CCC&HHC: n = 10）。s, 代表性照片显示了 epiAT 脂肪库。标尺单位为厘米。t, HHC 小鼠的代表性照片。u, HHC 小鼠整个 epiAT 脂肪库的组织学和 HE 染色切片代表性图像。比例尺 2000 μm。v, epiAT 的苏木精和伊红（HE）染色，20 倍放大。比例尺，100 μm。代表性图片。w, 不同条件下 ingAT 脂肪细胞面积。（每组 n = 4 只小鼠，每只 5-8 张图片）。x, scAT 的 HE 染色，20 倍放大。比例尺，200 μm。y, 不同条件下 epiAT 脂肪细胞面积。（每组 n = 4 只小鼠，每只 5-8 张图片）。z, 通过 Maison’s Trichome 染色定量胶原蛋白含量。（每组 n = 4 只小鼠，每只 20 张图片）。显著性计算在年龄匹配的对照组和实验组之间进行。显著性 a, b, d, e, i, j, z 使用双尾 Mann-Whitney 检验计算。显著性 c, f-h, m 使用 Welch 校正的非配对双尾 t 检验计算。误差棒表示标准差。显著性 p 和 r 使用 Benjamini, Krieger 和 Yekutieli 事后多重比较检验的非配对多重 t 检验计算。ns = FDR > 0.01, **FDR < 0.01, ***FDR < 0.001, ****FDR < 0.0001。

Source Data 源数据

Extended Data Fig. 6 Annotation of mouse epiAT.
扩展数据图 6 小鼠 epiAT 的注释

a, Cluster markers used to annotate cell clusters of mouse epiAT. b, UMAP visualization representing epiAT samples coloured by predicted cell subtypes from the Emont et al. mouse epididymal AT dataset. Feature plots showing reference mapping scores illustrating how well this dataset maps to the Emont et al. dataset. c, Macrophage subcluster markers. d, UMAP of 16,567 nuclei representing macrophage subclusters. APCs, adipocyte progenitor cells; DCs, dendritic cells; EpiCs, epithelial cells; EndoCs, endothelial cells; FIPs, fibro-inflammatory progenitors; LECs, lymphatic endothelial cells; MastCs, mast cells; MesoCs, mesothelial cells; SMCs, (vascular) smooth muscle cells; NPVMs, non-perivascular macrophages; LAMs, lipid-associated macrophages; PVMs, perivascular macrophages; P-LAMs, proliferating LAMs.
a, 用于注释小鼠 epiAT 细胞群的集群标记。b, UMAP 可视化表示 epiAT 样本，颜色代表从 Emont 等人小鼠附睾 AT 数据集中预测的细胞亚型。特征图显示参考映射分数，说明该数据集与 Emont 等人数据集的映射程度。c, 巨噬细胞亚群标记。d, 代表巨噬细胞亚群的 16,567 个核的 UMAP 图。APCs, 脂肪细胞前体细胞; DCs, 树突状细胞; EpiCs, 上皮细胞; EndoCs, 内皮细胞; FIPs, 成纤维炎症前体细胞; LECs, 淋巴内皮细胞; MastCs, 肥大细胞; MesoCs, 间皮细胞; SMCs, (血管)平滑肌细胞; NPVMs, 非血管周围巨噬细胞; LAMs, 脂质相关巨噬细胞; PVMs, 血管周围巨噬细胞; P-LAMs, 增殖性 LAMs。

Source Data 源数据

Extended Data Fig. 7 Transcriptional changes persist weight loss in epiAT.
扩展数据图 7 转录变化在 epiAT 体重减轻后持续存在。

a, Number of upregulated (left) and downregulated (right) DEGs per cell type per comparison (H vs C, HC vs CC, HH vs CC, HHC vs CCC) scaled by column. b, Proportion of retained transcriptional changes in different cell types. (Wilcoxon Rank Sum test, adjusted p-value < 0.05 by the Bonferroni correction method; FC > ±0.5). c,d, Top (significant) persistently upregulated (memory) (c) and downregulated (d). pathway terms in HC adipocytes based on Wikipathways database. e,f, Significant Wikipathways term enrichment scores related to persistently upregulated genes in HHC (f) and HC (g) per cell type. g,h, Significant Wikipathways term enrichment scores related to persistently downregulated genes in HHC (f) and HC (g) per cell type. Significance for c-h was calculated using Fisher’s exact test, with adjusted P-value < 0.05 by the Benjamini-Hochberg method for correction. APCs, adipocyte progenitor cells; DCs, dendritic cells; EpiCs, epithelial cells; EndoCs, endothelial cells; FIPs, fibro-inflammatory progenitors; LECs, lymphatic endothelial cells; MastCs, mast cells; MesoCs, mesothelial cells; SMCs, (vascular) smooth muscle cells; NPVMs, non-perivascular macrophages; LAMs, lipid-associated macrophages; PVMs, perivascular macrophages; P-LAMs, proliferating LAMs.
a, 每种细胞类型在每次比较（H vs C, HC vs CC, HH vs CC, HHC vs CCC）中上调（左）和下调（右）的差异表达基因（DEGs）数量，按列缩放。b, 不同细胞类型中保留的转录变化比例。（Wilcoxon 秩和检验，Bonferroni 校正后 p 值<0.05；FC > ±0.5）。c,d, 基于 Wikipathways 数据库，HC 脂肪细胞中持续上调（记忆）（c）和下调（d）的顶级（显著）通路术语。e,f, 与 HHC（f）和 HC（g）中持续上调基因相关的显著 Wikipathways 术语富集分数，按细胞类型划分。g,h, 与 HHC（f）和 HC（g）中持续下调基因相关的显著 Wikipathways 术语富集分数，按细胞类型划分。c-h 的显著性通过 Fisher 精确检验计算，Benjamini-Hochberg 方法校正后 p 值<0.05。APCs, 脂肪细胞前体细胞；DCs, 树突状细胞；EpiCs, 上皮细胞；EndoCs, 内皮细胞；FIPs, 纤维炎症前体细胞；LECs, 淋巴内皮细胞；MastCs, 肥大细胞；MesoCs, 间皮细胞；SMCs, （血管）平滑肌细胞；NPVMs, 非血管周围巨噬细胞；LAMs, 脂质相关巨噬细胞；PVMs, 血管周围巨噬细胞；P-LAMs, 增殖性 LAMs。

Source Data 源数据

Extended Data Fig. 8 Epigenetic memory persists after weight loss.
扩展数据图 8 体重减轻后表观遗传记忆持续存在。

a, Peak fold enrichment of called peaks from each CUT&Tag library for genomic features, scaled from −2 to 2. b, Peak fold enrichment of called peaks from each CUT&Tag library for ENCODE cCREs, scaled from −2 to 2. c, Scatterplots of pairwise correlation of average normalized expression of pseudo bulk adipocytes from snRNA-seq (log2 cpm) and average normalized expression of translating RNA (TRAPseq) from labelled adipocytes (log2 cpm) per condition. Spearman’s correlation coefficient R is indicated. d, PCA plots of H3K4me3, H3K27me3 and ATAC-seq across all conditions quantified over peaks overlapping promoters with reads summed up at gene level; each dot represents one biological replicate. e, Dynamics of differentially H3K4me3-marked (left) and H3K27me3-marked promoters (y-axis) from HH to HHC. f, Significant Wikipathways term enrichment scores related to genes associated with persistently differentially marked promoters by H3K27me3 (from Fig. 4f) or H3K4me3 (from Fig. 4g) in HC adipocytes. g, Significant Wikipathways term enrichment scores related to genes associated with persistently differentially marked promoters by H3K27me3 (from e) or H3K4me3 (from e) in HHC adipocytes. h, Expression of genes encoding for epigenetic modifiers significantly deregulated either in H (*) or HH (#) adipocytes. (Wilcoxon Rank Sum test, adjusted p-value < 0.05 by the Bonferroni correction method; fold change (FC) > ±0.5). None of the epigenetic modifiers are deregulated in HC or HHC adipocytes. Significance for f-g was calculated using Fisher’s exact test, with adjusted P-value < 0.05 by the Benjamini-Hochberg method for correction. cCREs, candidate cis-regulatory elements as defined by ENCODE. CTCF, not TSS-overlapping and with high DNase and CTCF signals only; DNase–H3K4me3, not TSS-overlapping and with high DNase and H3K4me3 signals only; dELS, TSS-distal with enhancer-like signatures; PLS, TSS-overlapping with promoter-like signatures; pELS, TSS-proximal with enhancer-like signatures.
a, 每个 CUT&Tag 文库中调用峰的峰富集倍数，针对基因组特征，缩放范围为−2 到 2。b, 每个 CUT&Tag 文库中调用峰的峰富集倍数，针对 ENCODE cCREs，缩放范围为−2 到 2。c, 成对相关散点图，显示来自 snRNA-seq 的伪批量脂肪细胞的平均标准化表达（log2 cpm）与标记脂肪细胞的翻译 RNA（TRAPseq）的平均标准化表达（log2 cpm）在每种条件下的相关性。斯皮尔曼相关系数 R 已标示。d, H3K4me3、H3K27me3 和 ATAC-seq 在所有条件下基于重叠启动子峰的读数总和在基因水平上的 PCA 图；每个点代表一个生物学重复。e, 从 HH 到 HHC，不同 H3K4me3 标记（左侧）和 H3K27me3 标记启动子（y 轴）的动态变化。f, 与 HC 脂肪细胞中持续差异标记启动子相关的基因的显著 Wikipathways 术语富集分数，这些启动子由 H3K27me3（来自图 4f）或 H3K4me3（来自图 4g）标记。g, 与 HHC 脂肪细胞中持续差异标记启动子相关的基因的显著 Wikipathways 术语富集分数，这些启动子由 H3K27me3（来自 e）或 H3K4me3（来自 e）标记。h, 编码表观遗传修饰因子的基因在 H（*）或 HH（#）脂肪细胞中显著失调的表达情况。（Wilcoxon 秩和检验，通过 Bonferroni 校正方法调整的 p 值<0.05；倍数变化（FC）> ±0.5）。在 HC 或 HHC 脂肪细胞中，没有任何表观遗传修饰因子失调。使用 Fisher 精确检验计算 f-g 的显著性，并通过 Benjamini-Hochberg 方法调整 P 值<0.05 进行校正。cCREs，由 ENCODE 定义的候选顺式调控元件。CTCF，仅限于非 TSS 重叠且具有高 DNase 和 CTCF 信号的区域；DNase–H3K4me3，仅限于非 TSS 重叠且具有高 DNase 和 H3K4me3 信号的区域；dELS，TSS 远端具有增强子样特征的区域；PLS，TSS 重叠且具有启动子样特征的区域；pELS，TSS 近端具有增强子样特征的区域。

Source Data 源数据

Extended Data Fig. 9 Adipocyte specific enhancers retain an epigenetic memory.
扩展数据图 9 脂肪细胞特异性增强子保留表观遗传记忆。

a, Correlation coefficient R (Pearson) of quantified peaks of H3K27ac against a hypothetical healthy control (n = 2-3 each) with s.d. Each dot represents an individual biological replicate. b,c, ChromHMM analysis of the adipocyte hPTM profiles for conditions C, CC_s, H and HC (b) and CC_l, CCC, HH and HHC (c). The colour scale corresponds to the emission parameter of each hPTM for each state. d,e, Fold enrichment of ChromHMM states from b and c for total genomic fraction coverage, ENCODE cCREs, and genomic features scaled from −2 to 2. State 5, 6 and 7 are identified as enhancers. f, PCA plot of quantified adipocyte specific enhancers from all conditions as marked by H3K4me1. Each dot represents an individual biological replicate. g, PCA plot of quantified adipocyte specific enhancers as marked by H3K27ac. Each dot represents an individual biological replicate. h, PCA plot of quantified adipocyte specific enhancers as marked by H3K27ac. Each dot represents an individual biological replicate. i, Top (significant) GO Cellular Component terms for genes linked to newly emerged and acetylated enhancers for H and HC (left) and HH and HHC (right). (Fisher’s exact test, adjusted p-value < 0.05 by the Benjamini-Hochberg method for correction). cCREs, candidate cis-regulatory elements as defined by ENCODE. CTCF, not TSS-overlapping and with high DNase and CTCF signals only; DNase–H3K4me3, not TSS-overlapping and with high DNase and H3K4me3 signals only; dELS, TSS-distal with enhancer-like signatures; PLS, TSS-overlapping with promoter-like signatures; pELS, TSS-proximal with enhancer-like signatures.
a, H3K27ac 定量峰与假设健康对照（每组 n = 2-3）的相关系数 R（Pearson）及其标准差。每个点代表一个单独的生物学重复。b,c, 脂肪细胞 hPTM 谱在条件 C, CC_s, H 和 HC（b）以及 CC_l, CCC, HH 和 HHC（c）下的 ChromHMM 分析。颜色刻度对应于每个状态下 hPTM 的发射参数。d,e, 从 b 和 c 中 ChromHMM 状态的总基因组分数覆盖率、ENCODE cCREs 和基因组特征的倍数富集，范围从−2 到 2。状态 5、6 和 7 被识别为增强子。f, 所有条件下由 H3K4me1 标记的定量脂肪细胞特异性增强子的 PCA 图。每个点代表一个单独的生物学重复。g, 由 H3K27ac 标记的定量脂肪细胞特异性增强子的 PCA 图。每个点代表一个单独的生物学重复。h, 由 H3K27ac 标记的定量脂肪细胞特异性增强子的 PCA 图。每个点代表一个单独的生物学重复。i, 与 H 和 HC（左）以及 HH 和 HHC（右）中新出现的和乙酰化的增强子相关的基因的顶级（显著）GO 细胞组分术语。（Fisher 精确检验，通过 Benjamini-Hochberg 方法校正的调整 p 值< 0.05）。cCREs，由 ENCODE 定义的候选顺式调控元件。 CTCF，仅限于非 TSS 重叠且具有高 DNase 和 CTCF 信号的区域；DNase–H3K4me3，仅限于非 TSS 重叠且具有高 DNase 和 H3K4me3 信号的区域；dELS，TSS 远端具有增强子样特征的区域；PLS，TSS 重叠且具有启动子样特征的区域；pELS，TSS 近端具有增强子样特征的区域。

Source Data 源数据

Extended Data Fig. 10 Other responses of primed mice and cells to obesogenic stimuli.
扩展数据图 10 预处理小鼠和细胞对致肥胖刺激的其他反应。

a, Glucose uptake of isolated, cultured primary adipocytes from ingAT from CC_s and HC (left) and CCC and HHC (right) mice. Each dot represents an individual biological replicate of a pool of 3 mice. b,c, AdipoRed signal of dividing SVF (MM), SVF stimulated with 10 nm insulin only (MM + Ins) and induced SVF with 10 nm insulin (IMM + Ins) 10 days after induction/no induction of differentiation from epiAT (b) and ingAT (c) SVF from CC_s, HC, CCC and HHC mice. Each dot represents an individual biological replicate of a pool of 3 mice. Every SVF pool was tested in all three conditions. d, GTT of HCH and CCH mice; blood glucose levels (n = 5 each). e, AOC of GTTs from d. f, ITT of CCH and HCH mice; blood glucose levels (n = 5 each). g, AOC from ITTs from e. h, Distribution of epiAT adipocyte area. (n = 4 mice each, 10 pictures each). i, Representative images of liver HE stained sections from CCH and HCH, 20x magnification, scale bar 200 μm. j, Liver tg per μg liver tissue (C&H:n = 6 each, CC_s: n = 10, HC: n = 9, CCH: n = 11, HCH: n = 12, from 2-3 experiments). Boxplot represents minimum, maximum and median. k, Pathological scoring of liver sections per group (n = 4 each). l, UMAP of 15,665 nuclei representing epiAT pools (n = 5 pooled mice each) from CCH and HCH split by condition. m, Relative abundance of macrophage subclusters. n,o, Top significant pathway terms from upregulated (n) and downregulated (o) HCH DEGs that are explained by the epigenetic state in HC adipocytes based on Reactome database. (Fisher’s exact test, adjusted p-value < 0.05 by the Benjamini-Hochberg method for correction). Significance was calculated between age matched controls and experimental groups. Significance for a, e, g, was calculated using two-tailed Mann-Whitney tests. Significance for b, c was calculated using unpaired, two-tailed Student’s t-tests with Welch’s correction and Benjamini, Krieger, and Yekutieli correction for multiple testing. Significance for j was calculated using unpaired two-tailed Student’s t-tests with Welch’s correction. Error bars represent s.d. APCs, adipocyte progenitor cells; DCs, dendritic cells; EpiCs, epithelial cells; EndoCs, endothelial cells; FIPs, fibro-inflammatory progenitors; LECs, lymphatic endothelial cells; MastCs, mast cells; MesoCs, mesothelial cells; SMCs, (vascular) smooth muscle cells; NPVMs, non-perivascular macrophages; LAMs, lipid-associated macrophages; PVMs, perivascular macrophages; P-LAMs, proliferating LAMs.
a, 从 CC_s 和 HC（左）以及 CCC 和 HHC（右）小鼠中分离培养的原代脂肪细胞的葡萄糖摄取。每个点代表来自 3 只小鼠的个体生物学重复。b,c, 在 CC_s、HC、CCC 和 HHC 小鼠的 epiAT（b）和 ingAT（c）SVF 中，分化诱导/未诱导 10 天后，SVF（MM）、仅用 10 nm 胰岛素刺激的 SVF（MM + Ins）和诱导的 SVF（IMM + Ins）的 AdipoRed 信号。每个点代表来自 3 只小鼠的个体生物学重复。每个 SVF 池在所有三种条件下都进行了测试。d, HCH 和 CCH 小鼠的葡萄糖耐量试验；血糖水平（n = 5 每组）。e, 来自 d 的 GTT 的 AOC。f, CCH 和 HCH 小鼠的胰岛素耐量试验；血糖水平（n = 5 每组）。g, 来自 e 的 ITT 的 AOC。h, epiAT 脂肪细胞面积的分布。（n = 4 只小鼠每组，每组 10 张图片）。i, CCH 和 HCH 小鼠肝脏 HE 染色切片的代表性图像，20 倍放大，标尺 200 μm。j, 每μg 肝脏组织的肝脏 tg（C&H: n = 6 每组，CC_s: n = 10，HC: n = 9，CCH: n = 11，HCH: n = 12，来自 2-3 次实验）。箱线图表示最小值、最大值和中位数。k, 每组肝脏切片的病理评分（n = 4 每组）。l, 代表 epiAT 池（n = 5 只小鼠每池）的 15,665 个核的 UMAP，按条件分为 CCH 和 HCH。m, 巨噬细胞亚群的相对丰度。 n,o, 基于 Reactome 数据库，从上调（n）和下调（o）的 HCH 差异表达基因（DEGs）中提取的与 HC 脂肪细胞表观遗传状态相关的顶级显著通路术语。（Fisher 精确检验，通过 Benjamini-Hochberg 方法调整 p 值<0.05 进行校正）。显著性计算在年龄匹配的对照组和实验组之间进行。a, e, g 的显著性使用双尾 Mann-Whitney 检验计算。b, c 的显著性使用 Welch 校正和 Benjamini, Krieger, Yekutieli 多重检验校正的非配对双尾 Student's t 检验计算。j 的显著性使用 Welch 校正的非配对双尾 Student's t 检验计算。误差棒表示标准差。APCs, 脂肪细胞前体细胞; DCs, 树突状细胞; EpiCs, 上皮细胞; EndoCs, 内皮细胞; FIPs, 纤维炎症前体细胞; LECs, 淋巴内皮细胞; MastCs, 肥大细胞; MesoCs, 间皮细胞; SMCs, （血管）平滑肌细胞; NPVMs, 非血管周围巨噬细胞; LAMs, 脂质相关巨噬细胞; PVMs, 血管周围巨噬细胞; P-LAMs, 增殖性 LAMs。

Source Data 源数据

Extended Data Fig. 11 Quality metrics of mouse snRNAseq data.
扩展数据图 11 小鼠 snRNAseq 数据的质量指标。

a, Gene counts and the number of unique molecular identifiers (UMIs) per condition of mouse epiAT samples. b, UMAP visualization representing integrated epiAT samples from the weight loss study (C, CC, CCC, H, HH, HC, HHC) and from the “yoyo” study (CCH, HCH) coloured by predicted cell subtypes from the Emont et al. mouse epididymal AT dataset. Feature plots showing reference mapping scores illustrating how well these datasets maps to the Emont et al. dataset. c, gene counts and the number of UMIs per cell type from mouse epiAT samples.
a, 小鼠 epiAT 样本在不同条件下的基因计数和唯一分子标识符（UMIs）数量。b, UMAP 可视化展示了体重减轻研究（C, CC, CCC, H, HH, HC, HHC）和“yoyo”研究（CCH, HCH）中的整合 epiAT 样本，颜色代表 Emont 等人小鼠附睾 AT 数据集预测的细胞亚型。特征图显示了参考映射分数，说明了这些数据集与 Emont 等人数据集的映射程度。c, 小鼠 epiAT 样本中每种细胞类型的基因计数和 UMIs 数量。

Extended Data Fig. 12 Quality metrics of human snRNAseq data.
扩展数据图 12 人类 snRNAseq 数据的质量指标。

a, Gene counts and the number of UMIs per condition in the omAT samples from the MTSS (left), LTSS (second left) and in scAT samples from the LTSS (second from right) and NEFA (right) study. b,c, Gene counts and the number of UMIs per donor in the omAT samples from the MTSS (b) and LTSS (c) study. d,e, Gene counts and the number of UMIs per donor in scAT samples from the LTSS (d) and NEFA (e) study. f, Gene counts and the number of UMIs per assigned cell type in the omAT samples from the MTSS (left) and LTSS (right) study. g, Gene counts and the number of UMIs per assigned cell type in the scAT samples from the LTSS (left) and NEFA (right) study.
a, 从 MTSS（左）、LTSS（第二左）的 omAT 样本以及 LTSS（第二右）和 NEFA（右）研究的 scAT 样本中，每种条件下基因计数和 UMI 数量。b,c, 从 MTSS（b）和 LTSS（c）研究的 omAT 样本中，每位供体的基因计数和 UMI 数量。d,e, 从 LTSS（d）和 NEFA（e）研究的 scAT 样本中，每位供体的基因计数和 UMI 数量。f, 从 MTSS（左）和 LTSS（右）研究的 omAT 样本中，每种指定细胞类型的基因计数和 UMI 数量。g, 从 LTSS（左）和 NEFA（右）研究的 scAT 样本中，每种指定细胞类型的基因计数和 UMI 数量。

Extended Data Table 1 Clinical parameters
扩展数据表 1 临床参数

Full size table

Supplementary information
补充信息

Reporting Summary 报告摘要

Peer Review file 同行评审文件

Source data 源数据

Source Data Figs. 1–5 and Source Data Extended Data Figs. 1–10.
来源数据图 1-5 和扩展来源数据图 1-10。

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Hinte, L.C., Castellano-Castillo, D., Ghosh, A. et al. Adipose tissue retains an epigenetic memory of obesity after weight loss. Nature (2024). https://doi.org/10.1038/s41586-024-08165-7

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Received: 20 January 2023
Accepted: 07 October 2024
Published: 18 November 2024
DOI: https://doi.org/10.1038/s41586-024-08165-7

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Fat cells have a ‘memory’ of obesity — hinting at why it’s hard to keep weight off
脂肪细胞对肥胖有“记忆”——暗示了为何难以保持体重
- Traci Watson 特拉西·沃森
Nature (2024) 自然 (2024)

Abstract 摘要

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Main 主要

Transcriptional changes in human AT人类 AT 的转录变化

Pathophysiology mostly resolves after WL病理生理学大多在体重减轻后得到解决

Transcriptional obesogenic memory in mice小鼠中的转录性肥胖记忆

Epigenetic obesogenic memory in mice小鼠中的表观遗传肥胖记忆

Metabolic memory primes adipocytes代谢记忆激活脂肪细胞

Methods 方法

Data reporting 数据报告

Clinical sample acquisition临床样本采集

MTSS

LTSS

NEFA study NEFA 研究

Mice 小鼠

Tamoxifen application 他莫昔芬应用

Physiological measurements生理测量

Glucose tolerance test 葡萄糖耐量试验

Insulin tolerance test 胰岛素耐量试验

In vivo indirect calorimetry体内间接测热法

Live body composition 活体成分分析

Fasting insulin 空腹胰岛素

Postprandial insulin 餐后胰岛素

Postprandial leptin 餐后瘦素

Liver triglycerides 肝脏甘油三酯

Cell culture experiments 细胞培养实验

AT digestion AT 消化

SVF differentiation SVF 分化

AdipoRed assay AdipoRed 检测

Primary adipocyte culture原代脂肪细胞培养

Glucose uptake 葡萄糖摄取

C16 uptake C16 摄取

Histology 组织学

Adipocyte size quantification脂肪细胞大小定量

Sample processing and library preparation样本处理与文库制备

Isolation of nuclei from mouse tissue从小鼠组织中分离细胞核

Isolation of nuclei from human tissue从人体组织中分离细胞核

snRNA-seq of AT AT 的 snRNA-seq

Paired TRAP–seq, CUT&Tag and ATAC–seq配对 TRAP-seq、CUT&Tag 和 ATAC-seq

Ribosome and nuclei isolation核糖体与细胞核的分离

TRAP–seq

CUT&Tag

ATAC–seq ATAC-seq

Sequencing data processing测序数据处理

snRNA-seq data processing and analysissnRNA-seq 数据处理与分析

Data integration and differential expression analysis for mouse snRNA-seq数据整合与小鼠 snRNA-seq 的差异表达分析

Data integration and differential expression analysis for human snRNA-seq人类 snRNA-seq 数据整合与差异表达分析

SNP-based demultiplexing of human snRNA-seq datasets基于 SNP 的单细胞 RNA 测序数据集解复用

Transcriptional retention转录保留

TRAP–seq

CUT&Tag and ATAC–seq data processing and analysisCUT&Tag 和 ATAC-seq 数据的处理与分析

MOFA

Generation of enhancer tracks of adipocytes脂肪细胞增强子轨迹的生成

Differential analysis of hPTMs and ATAC–seqhPTMs 和 ATAC-seq 的差异分析

Promoters 启动子

Enhancers 增强子

PCA

GSEA 基因集富集分析

Visualization 可视化

Statistical analysis of physiological parameters from mice小鼠生理参数的统计分析

Reporting summary 报告摘要

Data availability 数据可用性

References 参考文献

Acknowledgements 致谢

Funding 资助

Author information 作者信息

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations 伦理声明

Competing interests 利益冲突

Peer review 同行评审

Peer review information 同行评审信息

Additional information 附加信息

Extended data figures and tables扩展数据图表

Supplementary information补充信息

Source data 源数据

Rights and permissions 版权与权限

About this article

Cite this article

Similar content being viewed by others
其他用户也在查看类似内容

Transcriptional changes in human AT
人类 AT 的转录变化

Pathophysiology mostly resolves after WL
病理生理学大多在体重减轻后得到解决

Transcriptional obesogenic memory in mice
小鼠中的转录性肥胖记忆

Epigenetic obesogenic memory in mice
小鼠中的表观遗传肥胖记忆

Metabolic memory primes adipocytes
代谢记忆激活脂肪细胞

Clinical sample acquisition
临床样本采集

Physiological measurements
生理测量

In vivo indirect calorimetry
体内间接测热法

Primary adipocyte culture
原代脂肪细胞培养

Adipocyte size quantification
脂肪细胞大小定量

Sample processing and library preparation
样本处理与文库制备

Isolation of nuclei from mouse tissue
从小鼠组织中分离细胞核

Isolation of nuclei from human tissue
从人体组织中分离细胞核

Paired TRAP–seq, CUT&Tag and ATAC–seq
配对 TRAP-seq、CUT&Tag 和 ATAC-seq

Ribosome and nuclei isolation
核糖体与细胞核的分离

Sequencing data processing
测序数据处理

snRNA-seq data processing and analysis
snRNA-seq 数据处理与分析

Data integration and differential expression analysis for mouse snRNA-seq
数据整合与小鼠 snRNA-seq 的差异表达分析

Data integration and differential expression analysis for human snRNA-seq
人类 snRNA-seq 数据整合与差异表达分析

SNP-based demultiplexing of human snRNA-seq datasets
基于 SNP 的单细胞 RNA 测序数据集解复用

Transcriptional retention
转录保留

CUT&Tag and ATAC–seq data processing and analysis
CUT&Tag 和 ATAC-seq 数据的处理与分析

Generation of enhancer tracks of adipocytes
脂肪细胞增强子轨迹的生成

Differential analysis of hPTMs and ATAC–seq
hPTMs 和 ATAC-seq 的差异分析

Statistical analysis of physiological parameters from mice
小鼠生理参数的统计分析

Extended data figures and tables
扩展数据图表

Supplementary information
补充信息

This article is cited by
本文被引用