Article Online

Articles Online (Volume 24, Issue 1)

Preface

Single-cell Spatial Multiomics: Technologies, Methods, and Biological Applications

Dong Xing, Rong Fan, Fangqing Zhao

no abstract

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Research Highlight

Layering Methylome and Transcriptome in the Same Tissue Slice

Yang Xiao, Sai Ma

no abstract

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Review Article

The Evolution of Spatial Omics Technologies Introduces A Novel Avenue for Lung Cancer Research

Yue He, Zifan Li, Wenxiang Wang, Xu Liu, Shanshan Lu, Jing Bai, Lin Weng, Qingna Zhang, Jun Wang, Kezhong Chen

Lung cancer is a highly malignant disease, posing a significant threat to global health. The presence of tumor heterogeneity results in substantial variations in prognosis and therapeutic response among patients. Advances in bulk RNA sequencing and single-cell RNA sequencing have facilitated the identification of driver gene mutations and the exploration of cellular diversity within tumors. However, tumors are complex ecosystems comprising both tumor cells and their microenvironment, where interactions among different cell types give rise to specific functional and structural units that collectively drive tumorigenesis and progression. The emergence of spatial omics technologies has allowed for the analysis of tumor ecosystems, providing unprecedented insights into tumor heterogeneity. This review presents updates on spatial omics technologies and data analysis algorithms, discusses current technical limitations, and explores potential future developments. Furthermore, we summarize the latest applications of spatial omics in elucidating lung cancer heterogeneity, investigating mechanisms of lung cancer progression and drug resistance, and identifying novel biomarkers. Drawing from these insights, we propose strategies for integrating spatial omics into lung cancer research, offering new perspectives for precision medicine.
研究问题： 肺癌作为全球癌症相关死亡的主要原因，其肿瘤异质性导致患者对治疗的敏感性和预后存在显著差异。虽然常规RNA测序和单细胞RNA测序已帮助识别驱动性基因突变和探索细胞多样性，但肿瘤作为复杂的生态系统，细胞间的空间分布和相互作用对肿瘤发生和进展至关重要。空间组学技术的出现使我们能够在保留空间位置信息的同时分析肿瘤生态系统，为理解肿瘤异质性提供了前所未有的视角。 研究方法： 本综述系统性地调研和整合了空间组学技术的发展、算法流程及其在肺癌研究中的应用。通过文献检索和分析，全面比较了四大类空间转录组学技术和四大类空间蛋白质组学技术的原理、优缺点和适用场景。同时，详细介绍了空间组学数据分析的上游和下游方法，并总结了空间组学在肺癌异质性研究、耐药机制探索和生物标志物发现等方面的最新应用。 主要结果： 1. 系统比较了空间转录组学技术（包括原位杂交、原位测序、激光捕获和批量测序技术）和空间蛋白质组学技术（包括质谱、免疫荧光和条形码技术）的特点与适用场景。 2. 介绍了新兴的空间代谢组学和空间表观基因组学技术，为多组学整合提供了新维度。 3. 详细阐述了人工智能（artificial intelligence，AI）和深度学习在空间组学数据分析中的应用，包括卷积神经网络（convolutional neural network，CNN）用于细胞分割、图神经网络（graph neural network，GNN）用于细胞间互作建模等。 4. 总结了空间组学在肺癌研究中的应用策略：空间结构划分、细胞组成分析、细胞相互作用、微生态位鉴定和临床转化。 5. 提出了空间组学发展的四大挑战与解决方案：提高通量分辨率、降低成本、整合异质性数据、建立标准化基准。

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Original Research

Spatial Transcriptomics Unveils the Blueprint of Mammalian Lung Development

Mingyue Chen, Junjie Lv, Qiao Zhang, Qian Gong, Ting Zhao, Zhenping Chen, Haishen Xu, Nan Zhou, Shan Jiang, Jian Du, Xuepeng Chen, Yuwen Ke

Mammalian lung development is a complex, highly orchestrated process involving the precise coordination of diverse cell types. Despite significant advances, the spatial gene expression patterns and regulatory mechanisms within the developmental niches of different lung structures remain incompletely understood. In this study, we present a comprehensive spatial transcriptomic atlas of mouse lung development, spanning from the early pseudoglandular to the alveolar stage. We further uncovered transcription factor (TF) regulatory landscapes by integrating the spatial epigenome, including novel TF-enhancer-driven regulons (eRegulons) critical for epithelial progenitors during early lung development. Our analysis also identified hundreds of spatiotemporally dynamic cell–cell communications, such as Bmp8b-mediated ligand–receptor signaling enriched in airway branching. Notably, we delineated the distinct developmental trajectories of alveolar AT1 and AT2 cells and revealed that collagen pathways facilitated their spatial convergence, forming primary alveoli during the canalicular–saccular transition. Together, this spatial transcriptomic atlas provides a foundational resource for understanding the complex cellular and molecular orchestration underlying mammalian lung development.
研究问题： 肺作为哺乳动物重要的呼吸器官，承担着气体交换的关键功能。然而，肺发育过程中不同生态位内细胞的空间分布与基因表达的精准调控尚不明确，这一关键科学问题的阐明对理解正常肺发育及相关呼吸系统疾病的发病机制至关重要。 研究方法： 本研究整合多组学技术体系，通过高分辨率空间转录组（DBiT-seq，10-20 μm；Stereo-seq，500 nm）和表观组（MISAR-seq）分析，结合RNA原位杂交、免疫荧光共定位等实验验证，通过单细胞联合分析以及基因表达调控网络分析等，系统解析了小鼠肺发育的时空调控网络。 主要结果： 1) 构建了覆盖肺发育关键阶段的高分辨率空间转录图谱。 2) 空间多组学解析了早期肺发育中上皮和间质等祖细胞的转录调控网络。 3) 阐明了肺形态发生中不同区位细胞间通讯的动态变化。 4) 解析了IV型胶原信号通路介导的AT1和AT2上皮细胞在肺泡生态位建立过程中空间汇聚的调控作用。 5) 揭示了在小鼠肺发育中的特异性高表达的转座元件。 数据及代码链接： https://ngdc.cncb.ac.cn/gsa（GSA: CRA014010） https://github.com/EddieLv/Temporal-and-spatial-development-of-mouse-lung https://ngdc.cncb.ac.cn/biocode/tool/BT007890

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Original Research

Spatial Chromatin Accessibility Analysis of Intratumor Heterogeneity in Breast Cancer

Yingying Qian, Miao Zhu, Chongyang Ren, Yeyong Zhou, Jian Xu, Liang Dong, Guangyu Zhang, Cheukfai Li, Jiaoyi lv, Qiaorui Xing, Guochun Zhang, Guangdun Peng, Ning Liao

Intratumoral heterogeneity (ITH) is a major driver of mortality in breast cancer (BC) patients and a critical factor in the variable therapeutic outcomes observed in BC treatment. Understanding the mechanisms underlying ITH is essential for advancing both clinical and basic BC research. Chromatin accessibility is critical for the regulation of gene expression and cellular identity and plays a central role in shaping ITH and tumor evolution. However, studying chromatin accessibility in situ has been challenging due to the limited availability of technical platforms. Here, we leveraged the spatial assay for transposase-accessible chromatin sequencing (ATAC-seq) platform to profile the chromatin accessibility landscape of tumors from six BC patients. Our analyses revealed prominent heterogeneity within tumor regulatory modules and spatial variations in immune cell composition and stromal structures, offering a framework for the investigation of the molecular architecture underlying ITH. Moreover, we identified two tumor subclones with a potential common origin but distinct immune infiltration conferred by regulatory cascades, suggesting that epigenetic regulation may further contribute to the divergent tumor microenvironments and phenotypic diversity of these subclones. Our study provides novel insights into the molecular mechanisms driving ITH and opens up potential avenues for therapeutic intervention.
研究问题： 乳腺癌的高度异质性是导致临床治疗差异的核心因素，这种复杂性受遗传、微环境及表观调控的多重驱动。目前，空间转录组学已成为解析肿瘤空间异质性的主流工具，但其分析多局限于基因表达的“结果”层面。 本研究旨在回答一个关键问题：作为基因表达的上游环节，染色质可及性的空间分布差异是否决定了肿瘤内部不同细胞群响应微环境信号的“潜能”？即在转录组解释之外，空间表观组学能否提供理解肿瘤演化与表型多样性的底层调控蓝图？ 研究方法： 本文利用空间ATAC-seq平台，对来自6名乳腺癌患者的肿瘤组织进行染色质可及性图谱绘制。通过整合空间聚类、细胞反卷积与病理注释，在样本中识别出三个共有的保守空间功能模块：免疫浸润区（CM1）、肿瘤核心区（CM2）与基质反应区（CM3）。其次，本研究利用了基于空间ATAC-seq数据的CNV (Copy Number Variation)推断方法，在CM2区域内识别出具有不同拷贝数变异特征的空间亚克隆，并利用全外显子组测序（Whole Exome Sequencing，WES）进行验证。 主要结果： 1.本研究构建了六例乳腺癌样本的染色质可及性图谱； 2.本研究通过整合空间聚类、细胞反卷积与病理注释，在样本中识别出三个共有的保守空间功能模块：免疫浸润区（CM1）、肿瘤核心区（CM2）与基质反应区（CM3）。各模块在细胞组成、染色质开放模式及功能通路上均呈现显著差异，并形成相互区隔且功能互补的微环境单元； 3.本研究利用了基于空间ATAC-seq数据的CNV推断方法，在CM2区域内识别出具有不同拷贝数变异特征的空间亚克隆，CM2中的两个亚克隆在基因组层面呈现进化相关性但功能上呈现差异，亚克隆1富集FOXA1介导的雌激素信号通路，而亚克隆2则呈现SP1更活跃的状态。亚克隆在空间位置上的分布差异，可以解释肿瘤内部的功能异质性及其潜在的治疗响应差异。 数据和代码链接： 数据连接：https://ngdc.cncb.ac.cn/gsa-human 代码链接：https://ngdc.cncb.ac.cn/biocode/tool/BT007984

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Original Research

A Single-cell and Spatially Resolved Cell Atlas of Human Esophageal Squamous Cell Carcinoma

Yong Shi, Ke An, Yu Qi, Xinhan Zhang, Yueqin Wang, Xuran Zhang, Shaoxuan Zhou, Ouwen Li, Yanan Song, Jiayi Zhou, Yue Du, Mingyang Hou, Yun-Gui Yang, Xin Tian

Tumor heterogeneity and the suppressive microenvironment are key challenges that limit the effectiveness of cancer treatment. In this study, we systematically elucidated the molecular characteristics and mechanisms underlying the suppressive immune microenvironment using a combination of single-cell RNA sequencing, spatial transcriptomics, and metabolomics for a series of human esophageal squamous cell carcinoma and matched non-tumor tissues. We found that COL17A1+ epithelial cells exhibited greater malignancy, characterized by the accumulation of triglycerides and phosphocholine. We also identified a tumor-specific POSTN+ fibroblast subgroup. We identified a unique epithelial-fibroblast niche with low infiltration of effector immune cells and substantial lipid enrichment, composed of POSTN+ fibroblasts and COL17A1+ epithelial cells, in which their crosstalk contributed to tumor progression. We confirmed that the INHBA/TP63 axis played a key role in mediating the regulation of COL17A1+ tumor cells by POSTN+ fibroblasts. Our findings provide new insights into the characteristics of the tumor microenvironment and the crosstalk between tumor cells and fibroblasts, offering valuable multi-omics data for elucidating tumor progression mechanisms.

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Original Research

Spatial Transcriptomics of Human Decidua Identifies Molecular Signatures in Recurrent Pregnancy Loss

Qing Sha, Qiaoni Yu, Kaixing Chen, Junyu Wang, Feiyang Wang, Chen Jiang, Yuanzhe Li, Meifang Tang, Yanbing Hou, Ke Liu, Kun Chen, Zongcheng Yang, Shouzhen Li, Jingwen Fang, Sihui Luo, Xueying Zheng, Jianping Weng, Kun Qu, Chuang Guo

The human decidua establishes immune tolerance at the maternal–fetal interface and is essential for successful embryo implantation and development. Here, we conducted a spatial transcriptomic analysis of human decidua from early pregnancies in both healthy donors and patients with recurrent pregnancy loss (RPL). Our analysis revealed two distinct spatial domains, named implantation zone (IZ) and glandular-secretory zone (GZ), corresponding to the layers of decidua compacta and spongiosa, respectively. The decidual natural killer cell subset (dNK1) and the decidual macrophage subset (dM2), both associated with growth promotion and immune regulation, were predominantly localized in the healthy IZ but were significantly reduced in RPL patients. In contrast, cytotoxic CD8+ T cells, sparsely distributed in the healthy decidual IZ and GZ domains, were elevated in both domains under RPL conditions. Spatial cell–cell interaction analysis indicated a broad exhibition but a marked downregulation of immunoregulatory interactions in the IZ of RPL patients. Through integrated single-cell chromatin accessibility and transcription factor occupancy analyses, we identified FOSL2 as a pivotal regulator orchestrating the spatial transformation of dNK1 cells. Decreased FOSL2 expression correlated with compromised IL-15-induced dNK1 cell transformation and diminished immunoregulatory capabilities. Our findings delineate the intricate spatial and regulatory architecture of immune tolerance within the human decidua, providing new insights into immune tolerance dysregulation in RPL.
研究问题： 人类蜕膜在母胎界面建立免疫耐受，对于胚胎成功着床和发育至关重要。复发性流产（Recurrent Pregnancy Loss, RPL）是指连续两次及以上的妊娠丢失，影响全球1%~5%的育龄女性。近年来研究表明，RPL患者的蜕膜免疫细胞在转录表达和细胞组成上普遍发生改变，揭示母胎界面蜕膜免疫微环境紊乱与RPL的发病机制密切相关。值得注意的是，蜕膜组织的生理结构复杂，由靠近胚胎植入位点的致密层和靠近母体肌层的海绵层组成。这种分区特征意味着蜕膜免疫细胞的空间分布及其与微环境的相互作用会显著影响组织局部免疫状态。然而，蜕膜免疫细胞是否在不同的组织区域形成差异性的免疫微环境以适应胚胎植入的过程，以及RPL患者蜕膜组织环境变化如何影响局部的免疫微环境稳态尚未完全阐明。 研究方法： 本研究对妊娠早期健康女性和RPL患者的完整蜕膜组织进行了空间转录组测序，并整合单细胞RNA-seq数据，系统解析了蜕膜组织的空间功能区域，揭示了健康和疾病状态下不同细胞类型的空间分布及相互作用的差异。进一步通过单细胞ATAC-seq、CUT&Tag及体外功能实验，探究并验证了关键免疫细胞亚群的转化调控机制。 主要结果： 1.绘制了健康女性和RPL患者早期妊娠蜕膜的高分辨率空间转录组图谱，揭示了蜕膜致密层和海绵层的差异基因表达特征，系统性阐明了RPL患者蜕膜细胞的空间分布和细胞互作网络变化。 2.揭示了关键dNK细胞亚群空间发育轨迹及其微环境调控信号，并通过空间多组学联合分析解析了dNK细胞亚群转化的关键转录因子和调控机制。 数据及代码链接： https://ngdc.cncb.ac.cn/biocode/tool/BT007938 https://ngdc.cncb.ac.cn/gsa-human/browse/HRA003056

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Original Research

Function and Development of Deep-sea Mussel Bacteriocytes Revealed by snRNA-seq and Spatial Transcriptomics

Hao Chen, Mengna Li , Zhaoshan Zhong, Inge Seim, Minxiao Wang, Chao Lian, Lianhong Zhuo, Xinjiang Wan, Hao Wang, Guanghui Han, Li Zhou, Huan Zhang, Lei Cao, Chaolun Li

Deep-sea chemosynthetic ecosystems are among the most unusual ecosystems on Earth, where most megafauna form close symbiotic associations with chemosynthetic microbes to obtain nutrition and shelter from the toxic environment. Despite the diverse forms of symbiotic organs in these deep-sea holobionts, the function and development of bacteriocytes, the host cells harboring symbionts, are still largely uncharacterized. Here, we conducted an in situ decolonization assay and state-of-the-art single-nucleus and spatial transcriptomic analyses to reveal the function and development of deep-sea mussel bacteriocytes. Bacteriocytes appear to optimize immune processes to facilitate the recognition, engulfment, and elimination of endosymbionts. They also interact directly with endosymbionts in carbohydrate and ammonia metabolism by exchanging metabolic intermediates via transporters such as SLC37A2 and RHBG-A. Bacteriocytes arise from three different proliferation cell types, and their successive development trajectories were delineated using multi-omics data and 3D reconstruction analyses. The molecular functions and developmental processes of bacteriocytes are guided by the same set of molluscan-conserved transcription factors and may be influenced by endosymbionts through sterol metabolism. The coordination in the functions and development of bacteriocytes, and between the host and symbionts, highlights the phenotypic plasticity of symbiotic cells, and underpins host–symbiont interdependence in adaptation to the deep sea.
研究问题： 深海具有黑暗、低温、高静水压和寡营养的特征，曾被认为是生命的“荒芜之地”。与此同时，以深海贻贝为代表的冷泉热液大型生物，被发现能与甲烷氧化菌、硫氧化菌等形成独特的化能共生关系，从而获得生存所需的大部分物质能量，适应深海极端环境。不仅如此，深海贻贝鳃组织还特化形成了一类独特的含菌细胞，以容纳共生甲烷氧化菌或硫氧化菌。这种密切的内共生关系适应力强且十分高效，使得深海贻贝成为深海化能生态系统中的广布种和优势种之一。尽管深海贻贝已成为认知深海极端环境适应性和化能共生的模式生物，但其含菌细胞共生相关功能与发育过程仍有待阐明。 研究方法： 为了深入研究深海贻贝含菌细胞的独特功能与分化过程，研究团队利用建立的深海原位实验装置，在南海冷泉区开展了Bathymodiolinae属深海贻贝（Gigantidas platifrons）移位去共生培养和新生细胞标记实验，获得了处于不同共生状态的原位保真固定样品。研究团队进而开展了空间转录组与单细胞核转录组测序，构建了深海贻贝鳃组织单细胞图谱，并利用原代细胞培养与电镜三维重建等技术，系统解析了含菌细胞共生相关免疫、代谢功能及其增殖分化过程。 主要结果： 1. 深海贻贝含菌细胞能响应环境扰动导致的共生菌丰度变化，并利用模式识别受体基因、吞噬相关基因和溶酶体酶等介导对共生菌的免疫识别、内吞与消化，从而实现内共生关系的建立与动态维持。 2. 深海贻贝含菌细胞与共生菌间存在紧密的代谢互作过程。除溶酶体消化途径外，宿主还能通过果糖磷酸转运体直接获得糖类物质，并通过铵转运体为共生菌直接提供氮源，保障共生菌化能合成作用高效运行。 3. 深海贻贝含菌细胞由三种不同类型增殖细胞分化形成，并受到由多个保守转录因子组成的核心调控网络控制。该网络通过基因选配还参与了含菌细胞对环境变化的响应，动态调控了含菌细胞共生相关功能与发育过程。 数据链接或代码连接或其他： https://academic.oup.com/gpb/advance-article/doi/10.1093/gpbjnl/qzaf109/8342444

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Method

Histo-LCM-Hi-C Reveals 3D Chromatin Conformations of Spatially Localized Rare Cells in Tissues at High Resolution

Yixin Liu, Min Chen, Xin Liu, Zeqian Xu, Xinhui Li, Yan Guo, Daniel M Czajkowsky, Zhifeng Shao

Understanding chromatin structure is essential to delineate the mechanisms underlying genomic processes. However, while methods to obtain such information from cells in vitro are widely available, there is presently a significant lack of techniques that can acquire these data from cells in tissue. Such a capability is critical to determine the dependence of the local tissue environment on cell function. Further, this ability is particularly necessary for cells that constitute a significant minority of the total tissue population, as they are often obscured in data dominated by more abundant cell types. Here, we developed a high-throughput chromosome conformation capture (Hi-C)-based method, Histological laser capture microdissection Hi-C (Histo-LCM-Hi-C), to enable the characterization of chromatin architecture of phenotype-defined, spatially localized cells within intact tissue sections from as few as ∼ 300 cells. We demonstrated the effectiveness of this approach by generating the first 3D Hi-C map of liver-resident macrophages, the Kupffer cells (KCs), a minor cell population in the normal liver. As expected, owing to their relative rarity, these KC maps were significantly different from those obtained from the whole liver, revealing distant contacts between putative enhancers and genes involved in key KC functions, as well as significant differences from those of in vitro induced bone marrow-derived macrophages. We anticipate that this method will prove to be an indispensable technique in the growing repertoire of methodologies for characterizing the genomic properties of cells within their native environment.

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Method

HyperSTAR: Unveiling Tissue Structure and Tumor Microenvironment from Spatial Omics by Hypergraph Learning

Yi Liao, Chong Zhang, Zhikang Wang, Fei Qi, Weitian Huang, Shangyan Cai, Junyu Li, Jiazhou Chen, Robin B Gasser, Zhiyuan Yuan, Jiangning Song, Hongmin Cai

Spatial omics technologies have revolutionized life sciences by enabling the simultaneous acquisition of biomolecular and spatial information. Identifying spatial patterns is crucial for understanding organ development and tumor microenvironments. However, the emergence of diverse spatial omics resolutions in these technologies has made it challenging to accurately characterize spatial domains at finer resolutions. To address this, we propose HyperSTAR, a hypergraph-based method designed to precisely identify spatial domains across varying resolutions by leveraging higher-order relationships among spatially adjacent tissue programs. Specifically, a gene expression-guided hyperedge decomposition module is introduced to refine the hypergraph structure to accurately delineate spatial domain boundaries. Additionally, a hypergraph attention convolutional neural network is designed to adaptively learn the importance of each hyperedge, enhancing the model’s ability to capture complex higher-order relationships within spatially neighboring multi-spots and/or single cells. HyperSTAR outperforms existing graph neural network models in tasks such as uncovering tissue substructures, inferring spatiotemporal patterns, and denoising spatially resolved gene expression. It effectively handles diverse spatial omics data types and scales seamlessly to large datasets. The method successfully reveals spatial heterogeneity in breast cancer sections, with findings validated through functional and survival analyses of independent clinical data. HyperSTAR represents a significant advancement in spatial omics analysis, representing a robust tool for exploring complex spatial patterns across varying resolutions and data types. Its ability to capture intricate higher-order relationships among spatially neighboring spots/cells makes it an invaluable tool for advancing research in life sciences, particularly in cancer and developmental biology. The toolbox is available at https://github.com/Ringoio/HyperSTAR.

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Method

DisConST: Distribution-aware Contrastive Learning for Spatial Domain Identification

Peimeng Zhen, Xiaofeng Wang, Han Shu, Jialu Hu, Yongtian Wang, Jiajie Peng, Xuequn Shang, Jing Chen, Tao Wang

Spatial transcriptomics (ST) is a cutting-edge technology that provides comprehensive insights into gene expression (GE) patterns from a spatial perspective. A key research focus within this field is spatial domain identification, which is essential for exploring tissue organization, biological development, and disease mechanisms. Although methods have been developed, they still face challenges in modeling GE information together with spatial locations, resulting in suboptimal accuracy. Here, we introduce Distribution-aware Contrastive Learning for Spatial Transcriptomics (DisConST), a novel deep learning method designed to improve spatial domain detection within ST datasets. DisConST addresses key challenges, such as the high dropout rates and the complex integration of spatial and GE data, by incorporating contrastive learning strategies that are aware of the underlying data distributions. It employs the zero-inflated negative binomial distribution, along with graph contrastive learning, to generate more informative latent representations. These representations efficiently integrate spatial positions, transcriptomic profiles, and cell-type proportions within spots. We validated DisConST across diverse ST datasets of tissues, organs, and embryos from various sequencing platforms in both normal and disease states. Our results consistently demonstrate that DisConST achieves superior spatial domain recognition accuracy compared to existing state-of-the-art methods. Furthermore, our experiments highlight the utility of DisConST in advancing research on tissue organization, embryonic development, and tumor immune microenvironment dissection. The source code for DisConST is freely available at https://github.com/Zhenpm/DisConST/.

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