如果扫描失败，无法进群，可以添加微信 ： 15623525389 扫码进群领取CNS文章生信分析课程 ►►► 空转班内容介绍 第1节：Python编程语言入门 1.Spyder和Anaconda安装 2.环境配置；基本语法、数据类型等 3.Python控制流与函数（条件语句、循环语句、函数定义、返回值） 第2节：Python数据结构进阶 1. 列表、元组、字典、集合高级用法 2.Python包（numpy、pandas、matplotlib等） 3.了解Shell，基础命令学习（文件操作、权限管理） 第3节：空间转录组在CNS文章应用 1. 空间转录组CNS文章思路解析 2.空间转录组技术在科研领域的应用 3.空间转录组技术在不同科学等领域的研究内容及思路及常见图形解读 第4节：以 Nature Genetics 文章源代码为例，系统学习squidpy和scanpy的系统分析 1.常规空间转录组数据读取 2.数据质控-- scrublet 去除双细胞 3. 归一化处理、PCA降维、聚类、UMAP 第7节： Stereo-seq 空转数据分析 【Cell】源代码 1.Tissue Segmentation、Ce ...

Core Insights - The collaboration between the University of California, San Francisco, and the Allen Institute has led to the development of an AI model named CellTransformer, which has created the most detailed mouse brain map to date, encompassing 1,300 brain regions and subregions [1][3] Group 1: AI Model and Technology - CellTransformer utilizes a Transformer architecture similar to that used in models like ChatGPT, which excels in understanding contextual relationships [3] - The model analyzes the relationships between adjacent cells in spatial contexts, predicting molecular characteristics based on a cell's "neighborhood structure" [3] Group 2: Brain Mapping Advancements - Unlike previous brain maps that primarily categorized based on cell types, this new model focuses on the brain's structural regions, automatically defining boundaries based on cellular and molecular features rather than human judgment [3][4] - The resulting brain map is one of the most precise and complex data-driven maps of an animal brain to date, accurately representing known regions like the hippocampus and discovering new subregions in less understood areas like the midbrain reticular formation [3][4] Group 3: Implications and Applications - The new brain region delineation is entirely data-driven, revealing numerous unknown areas that may correspond to unexplored brain functions [4] - The potential applications of the CellTransformer model extend beyond neuroscience, with the algorithm being applicable to other organ systems and cancer tissues, utilizing spatial transcriptomics data to uncover biological mechanisms in health and disease, thus providing new tools for drug development and disease treatment [4]

撰文丨王聪 编辑丨王多鱼 排版丨水成文 食管鳞状细胞癌 （ESCC） 是一种发生在食管内壁黏膜的恶性肿瘤，起源于食管内壁的鳞状上皮细胞。它是全球范围内 最常见的食管癌类型 ，但其地理分布极 不均衡，在高发地区 （例如中国太行山周边区域、伊朗北部、南非等） 尤为常见。 2025 年 10 月 10 日， 广州医科大学附属第五医院 李斌 教授、中国医学科学院肿瘤医院/国家癌症中心 刘芝华 教授、广州医科大学基础医学院 许雯雯 教授、 上海交通大学医学院附属胸科医院李志刚教授、中国医科大学附属第一医院王振宁教授等 ， 在 Nature 子刊 Nature Genetics 上发表了题为： Single-cell multi-omic and spatial profiling of esophageal squamous cell carcinoma reveals the immunosuppressive role of GPR116⁺ pericytes in cancer metastasis 的研究论文。 该研究通过 单细胞多组学和空间转录组学分析，发现了 一种由 PRRX1 驱动的 GPR116⁺ 周细 ...

Core Insights - The collaboration between the University of California, San Francisco, and the Allen Institute has led to the development of an AI model named CellTransformer, which has created the most detailed mouse brain map to date, encompassing 1,300 brain regions and subregions [1] - This new model utilizes a Transformer architecture similar to that used in large models like ChatGPT, allowing for the analysis of spatial relationships between adjacent cells to predict molecular characteristics and construct a detailed brain organization atlas [1] - Unlike previous brain maps that were primarily based on cell types, this new approach focuses on the brain's structural organization, automatically defining boundaries based on cellular and molecular features rather than relying on human judgment [1] Summary by Sections AI Model Development - CellTransformer accurately reproduces known brain regions such as the hippocampus and identifies new, more subdivided subregions in less understood areas like the midbrain reticular formation [2] - The model's data-driven approach reveals numerous unknown regions, which are likely associated with unexplored brain functions, akin to transforming a map that only shows continents and countries into one that includes cities [2] Broader Applications - The potential of the CellTransformer model extends beyond neuroscience, with its algorithms being applicable to other organ systems and even cancer tissues, utilizing spatial transcriptomics data to uncover biological mechanisms in health and disease [2]

以下文章来源于肥胖世界ObesityWorld ，作者肥胖世界 肥胖世界ObesityWorld . 《肥胖世界》Obesity World - 同步传真肥胖及代谢国际新学术进展，为医学减重临床、教研人员搭建一座与国际接轨的桥梁，「每医健」旗下内容平台。 研究团队构建了迄今最全面的脂肪组织单细胞图谱，涵盖171,247个细胞，样本来自25名极度肥胖者（术前及减重后）和24名健康瘦者，并结 合空间转录组数据（每组4人），聚焦与代谢异常紧密关联的腹部皮下脂肪。分析显示，肥胖脂肪组织中免疫细胞（尤其是巨噬细胞和淋巴细 胞）大量涌入，成熟脂肪细胞比例明显降低（暗示细胞死亡或更新不足）；而减重能有效缓解这些病理变化。 二、巨噬细胞的"记忆效应" 全球已有超10亿人被肥胖困扰。许多人以为"胖"仅是外表问题，殊不知腹部脂肪的异常实际上是代谢疾病的"隐形杀手"——它悄然引发胰岛素 抵抗、糖尿病、心血管疾病，甚至提高癌症风险。令人惊奇的是，减重能迅速扭转这些健康危机：血糖平稳了、血压下降了、血管恢复弹性 了。但科学界一直未能破解这一谜团：脂肪组织内部到底经历了什么变化？是细胞数量减少了？还是基因表达重新"洗牌"了？ 为揭开这一 ...

Core Insights - The article highlights the significant contributions of Chinese scholars in top international academic journals, with 8 out of 18 papers published in Nature on October 1, 2025, authored by Chinese researchers [2][5][6][7][8][10][12][14]. - A notable paper from Yale University discusses a new method in spatial transcriptomics, RAEFISH, which achieves whole-genome coverage and single-molecule resolution, marking a significant advancement in the field [16][19]. - A study from the National Laboratory of Yacheng Bay reveals the genetic selection trajectories in soybean domestication, providing new insights into breeding strategies and genetic resources [20][23][24]. Group 1: Contributions to Nature - On October 1, 2025, multiple papers authored by Chinese scholars were published in Nature, including significant studies on T cell exhaustion, dietary impacts on intestinal stemness, and new paradigms in protein biogenesis [2][5][6][7][8][10]. - The research from Ohio State University on T cell exhaustion highlights the role of proteotoxic stress in immune evasion [2]. - The study from MIT explores how dietary cysteine enhances intestinal stemness through CD8 T cell-derived IL-22 [5]. Group 2: Innovations in Spatial Transcriptomics - The Yale University team developed RAEFISH, a new spatial transcriptomics method that allows for whole-genome coverage and single-molecule resolution, addressing previous limitations in the field [16][19]. - This advancement provides a powerful tool for various biological research areas, including developmental biology and drug discovery [19]. Group 3: Soybean Genetic Research - The research team at Yacheng Bay National Laboratory studied 8,105 soybean accessions, revealing key genetic selections during domestication and improvement processes [20][23]. - The findings indicate the existence of two independent centers of soybean domestication and highlight the importance of black soybean in this process [23]. - The study also provides insights into the changing breeding priorities in China, emphasizing high protein content in the early years and more recently focusing on yield, oil content, and stress resistance [23][24].

来源：科技日报 图片由AI生成 ◎本报记者 张梦然 人们所知道的绝大多数关于植物的基本原理知识，都是在一种你可能从未听说过的植物——拟南芥中首 次发现的。 绘制植物的基因表达图谱 在作为模式植物的几十年间，拟南芥经历了无数实验。科学家们持续致力于解码其基因组，并绘制出不 同组织和器官中各类细胞的基因表达图谱。借助这些局部图谱，人们得以逐步揭示控制植物各部位身份 与功能的关键基因。 其中，单细胞RNA测序成为构建细胞图谱的核心工具。该技术不直接分析DNA，而是检测基因组的表 达产物——RNA分子，从而精准识别哪些基因在特定细胞中被激活，以及其表达水平的高低。由于生 物体所有细胞共享同一套遗传密码，细胞类型的区分依赖于其独特的基因表达模式，单细胞RNA测序 因此成为识别和分类细胞类型的有力手段。 然而，传统方法存在明显局限：科学家必须将组织解离为单个细胞，导致原本的空间结构被破坏。这意 味着虽然能获知"有哪些细胞"，却难以回答"它们在哪儿"以及"如何组织"。 为突破这一瓶颈，索尔克生物研究所团队将单细胞RNA测序与空间转录组学相结合，实现了从"碎片化 图谱"向"全景式地图"的跨越。 更先进技术带来更完整图谱 空间 ...

Core Insights - The article emphasizes the importance of understanding obesity through advanced scientific techniques, particularly single-nucleus RNA sequencing and spatial transcriptomics, which provide a detailed view of cellular changes in adipose tissue [6][7][12] Group 1: Research Findings - The study included three groups: 24 healthy individuals, and 25 obese individuals before and after weight loss surgery, revealing that weight loss surgery reduced the average BMI from 45.2 to 35.2, significantly improving fasting insulin and insulin resistance [7] - Analysis of over 170,000 cells identified more than 20 different cell states, showing a clear distinction in cellular organization between healthy and obese individuals, with a notable increase in macrophages in obese tissue [7][8] - In obese individuals, macrophages constituted 31% of adipose tissue, compared to 14% in healthy individuals, indicating a shift in immune cell dynamics [8] Group 2: Cellular Dynamics - The study identified two subtypes of lipid-associated macrophages (LAMs) in obese tissue: adaptive LAMs, which efficiently process lipids, and inflammatory LAMs, which are associated with insulin resistance [8][9] - The proportion of "stress-type" adipocytes in obese tissue was found to be 55%, which dropped to 14% post-weight loss, indicating a significant reduction in unhealthy adipocyte types [9][10] - The research linked obesity to cellular senescence, revealing that "stress-type" adipocytes express high levels of the senescence marker p21, which were largely eliminated after weight loss [10] Group 3: Implications for Treatment - The findings suggest that weight loss is not only about reducing fat but also involves a systemic cleansing of senescent cells, enhancing overall tissue health [12] - The persistence of inflammatory macrophages post-weight loss raises concerns about potential metabolic rebound, highlighting the need for preventive strategies [12] - The research provides insights into potential future treatments for obesity, focusing on targeting dysfunctional cells and signaling pathways rather than solely addressing energy balance [12]

Core Insights - The article emphasizes the importance of understanding obesity through advanced scientific techniques, particularly single-nucleus RNA sequencing and spatial transcriptomics, which provide detailed insights into cellular changes in adipose tissue [7][12]. Group 1: Research Methodology - The study involved three groups: 24 healthy individuals (LN group) and 25 obese individuals before and after weight loss surgery (OB and WL groups), allowing for both cross-sectional and longitudinal comparisons [8]. - The innovative "fat map" created through the research analyzed over 170,000 cells from 70 individuals, identifying more than 20 different cell states [8]. Group 2: Findings on Cellular Changes - Weight loss surgery significantly reduced the average Body Mass Index (BMI) from 45.2 to 35.2, with notable improvements in fasting insulin and insulin resistance [8]. - In healthy individuals, adipose tissue showed a well-organized community of cells, while in obese individuals, this balance was disrupted, particularly with an increase in macrophages and a decrease in mature adipocytes [8][9]. Group 3: Macrophage Dynamics - Macrophages in lean individuals constituted 14% of adipose tissue, while in obese individuals, this figure rose to 31%, with a notable presence of lipid-associated macrophages (LAMs) [9]. - LAMs were categorized into two subtypes: adaptive LAMs, which efficiently process lipids, and inflammatory LAMs, which are associated with insulin resistance [9]. Group 4: Adipocyte Changes - Analysis of over 44,000 mature adipocytes revealed a surge in unhealthy subtypes in obese tissue, including stress-type and fibrotic-type adipocytes, indicating functional failure of adipose tissue [10]. - Post-weight loss, the proportion of stress-type adipocytes dropped from 55% to 14%, indicating a significant reduction in stress and a potential for regeneration [10]. Group 5: Cellular Senescence - The study linked obesity to cellular senescence, identifying stress-type adipocytes as senescent cells expressing high levels of p21 [11]. - Weight loss effectively removed p21-positive senescent cells, leading to a decrease in harmful inflammatory factors, thus enhancing overall adipose tissue health [11]. Group 6: Implications for Future Treatments - The research highlights that weight loss is not just about reducing fat but also involves a systemic cleansing of senescent cells and restoration of tissue health [13]. - The findings suggest that future obesity interventions could focus on eliminating senescent cells or "re-educating" immune cells, moving beyond traditional energy balance models [13].

Core Viewpoint - The article discusses a significant advancement in understanding early organogenesis in mouse embryos through the creation of a 3D "digital embryo" using single-cell resolution techniques, which provides insights into organ formation and potential mechanisms of congenital malformations [2][10]. Group 1: Early Organogenesis - Early organogenesis is a critical phase in embryonic development characterized by extensive cell fate determination to initiate organ formation, while also being highly susceptible to developmental defects [4]. - At approximately day 7.5 of embryonic development (E7.5), mouse embryos undergo significant morphological changes, marked by the emergence of key structures such as the heart tube and primitive gut [4]. - The complex process of organ formation relies on precise cell migration, localization, and differentiation, regulated by spatiotemporal gene expression patterns and intricate signaling pathways [4][5]. Group 2: Research Methodology - The research team combined spatial transcriptomics methods (Stereo-seq) with cell segmentation techniques to analyze 285 continuous slices from six embryos at early organogenesis stages (E7.5-E8.0), generating a spatial transcriptomic map at single-cell resolution [6]. - A visualization platform named SEU-3D was developed to reconstruct the 3D "digital embryo," accurately reflecting gene expression patterns and cell states in the native embryonic environment [7]. Group 3: Findings and Implications - The research delineated spatial cell maps of endoderm and mesoderm derivatives, revealing complex signaling networks across germ layers and cell types [8]. - A region known as the progenitor determination zone (PDZ) was identified at the anterior interface of the embryo-extrembryonic region at E7.75, indicating coordinated signaling during heart progenitor formation [8]. - The results collectively establish a comprehensive spatiotemporal embryonic atlas at single-cell resolution, accompanied by a network-based exploration tool for navigating spatial gene expression and signaling networks, paving the way for deeper studies into embryonic development and diseases [10].