单细胞测序技术

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华大发表最新Science论文:立体单细胞技术,开启百亿细胞大数据新纪元,推动虚拟细胞构建
生物世界· 2025-08-22 10:30
Core Viewpoint - The article discusses the groundbreaking Stereo-cell technology in single-cell sequencing, which overcomes the limitations of traditional methods by enabling multi-modal integration, real-time monitoring, and high-throughput capabilities, thus providing a comprehensive understanding of cellular characteristics and dynamics [4][20]. Group 1: Technology Breakthroughs - Stereo-cell technology achieves multi-modal integration, allowing for the simultaneous capture of cellular morphology, transcriptomics, and protein characteristics, akin to taking a "3D photo" of cells [12][13]. - The technology utilizes high-density DNA nanoball arrays, enabling unbiased capture of hundreds to millions of cells without the physical limitations of traditional methods, thus ensuring accurate identification of rare cell populations [11][9]. - Stereo-cell supports in-situ dynamic monitoring of cells, capturing gene transcription activity changes and spatial-temporal information, which expands the boundaries of single-cell research [15][19]. Group 2: Collaborative Initiatives - The establishment of the "10 Billion Cells Alliance" aims to create a comprehensive cell atlas and decode the underlying principles of life, driving innovation in life sciences from data accumulation to intelligent technology applications [4][20]. - The technology is expected to significantly impact clinical molecular medicine, providing new avenues for patient care and treatment through enhanced cellular analysis [20][21]. Group 3: Future Prospects - Stereo-cell is positioned as a next-generation life data engine, with plans to develop a "three major cell universe database" encompassing life maps, disease maps, and disturbance response maps, inviting global research teams to collaborate [20][22]. - The technology is anticipated to facilitate a transition from billions to trillions of cells in analysis, enabling a comprehensive understanding of cellular fates and functions throughout life [20].
一次“看清”百万细胞!科学家突破单细胞测序局限
Xin Lang Cai Jing· 2025-08-22 03:48
Core Insights - The article discusses the breakthrough of the Stereo-cell technology in single-cell sequencing, which overcomes the limitations of traditional methods by enabling multi-modal integration and high-throughput analysis of cellular information [1][2][3] Group 1: Technology Advancements - Stereo-cell technology allows for the capture and sequencing of millions of cells simultaneously, providing comprehensive insights into cellular morphology, transcriptomics, and protein signals without the need for specialized equipment [2][3] - The technology utilizes a high-density DNA nanosphere array chip, which captures cells through electrostatic adsorption, thus preventing loss or deformation of cells that often occurs with traditional methods [2][3] - Stereo-cell can identify rare cell subpopulations, even those constituting as little as 0.05% of the total sample, significantly enhancing the ability to detect and analyze rare cellular events [3] Group 2: Research Applications - The integration of fluorescence staining and antibody labeling in Stereo-cell allows for simultaneous capture of various cellular characteristics, enabling a more profound analysis of cellular pathology in a single experiment [4][5] - The technology supports in situ dynamic sequencing, capturing changes in gene transcription activity and cellular interactions over time, which is crucial for understanding cellular communication [5][6] - Stereo-cell has shown potential in studying complex biological systems, such as muscle fibers and oocytes, by accurately mapping gene expression changes and spatial heterogeneity [5][6] Group 3: Future Implications - The establishment of the "10 Billion Cells Alliance" aims to create comprehensive cellular maps and virtual cell models, driving innovations in life sciences and enhancing the understanding of fundamental biological principles [6] - The integration of AI with the Stereo-cell platform is expected to facilitate breakthroughs in life science theories by analyzing vast amounts of single-cell data, potentially transforming disease understanding and treatment [6][7] - The article emphasizes the need for a more integrated approach to single-cell data, which could lead to significant advancements in the study of complex and chronic diseases [6][7]
重庆医科大学新任校长张泽民院士最新Nature论文:跨组织细胞模块新概念,揭开人体细胞的协同模式及其在衰老和肿瘤中的重塑
生物世界· 2025-05-29 04:14
Core Viewpoint - The article discusses a groundbreaking study published in Nature by a team led by Academician Zhang Zemin, focusing on the concept of "cross-tissue cellular modules" and their role in multicellular coordination within human tissues, particularly in the context of cancer progression [2][16]. Group 1: Research Background - The study integrates single-cell transcriptomic data from 706 healthy samples across 35 human tissues, creating the most comprehensive cross-tissue single-cell atlas to date, covering 2.29 million cells [8][16]. - The research identifies significant differences in cellular composition across various healthy tissues, revealing 12 distinct cross-tissue cellular modules (CMs) with unique cellular compositions and distributions [9][16]. Group 2: Cellular Modules and Their Functions - The identified cellular modules include CM04, CM05, CM06, and CM09, which are abundant in primary and secondary immune organs, indicating their roles in immune cell production and maturation [10][13]. - Other modules, such as CM02 and CM03, are primarily found in the urinary system and gastrointestinal tract, while CM08 is enriched in barrier tissues like skin and mucosal surfaces, suggesting their specific functional roles [10][11]. Group 3: Spatial Dynamics and Aging - The study employs spatial transcriptomics to illustrate how these cellular modules are spatially organized within tissues, highlighting their functional roles in maintaining tissue homeostasis [14][16]. - Notably, the immune cell modules CM05 and CM06 exhibit contrasting temporal dynamics with aging, where CM05 increases while CM06 decreases, indicating their potential as biomarkers for age-related changes [14][16]. Group 4: Implications for Cancer Research - The research extends to the tumor microenvironment (TME), analyzing single-cell transcriptomic data from 1,062 clinical samples across 29 cancer types, identifying 91 cell subpopulations [15][16]. - It reveals a dual remodeling of cellular modules during tumor progression, where healthy tissue-specific modules are lost, and cancer-associated modules emerge, providing insights into the fundamental organizational principles of multicellular ecosystems in health and disease [15][16].