Cell systems最新文献_第2页

What is the current bottleneck in mapping molecular interaction networks? 绘制分子相互作用网络的当前瓶颈是什么？

Cell systems Pub Date : 2025-05-21 DOI: 10.1016/j.cels.2025.101295

Michael A Skinnider, Katja Luck, M Shahid Mukhtar, Martin Garrido-Rodriguez, Julio Saez-Rodriguez, Jolanda van Leeuwen, Pedro Beltrao, Anne-Ruxandra Carvunis, Mikko Taipale, Andrew Emili, Martha L Bulyk, Nevan J Krogan

引用次数: 0

Library-based single-cell analysis of CAR signaling reveals drivers of in vivo persistence. 基于文库的CAR信号单细胞分析揭示了体内持久性的驱动因素。

Cell systems Pub Date : 2025-05-21 Epub Date: 2025-04-10 DOI: 10.1016/j.cels.2025.101260

Caleb R Perez, Andrea Garmilla, Avlant Nilsson, Hratch M Baghdassarian, Khloe S Gordon, Louise G Lima, Blake E Smith, Marcela V Maus, Douglas A Lauffenburger, Michael E Birnbaum

{"title":"Library-based single-cell analysis of CAR signaling reveals drivers of in vivo persistence.","authors":"Caleb R Perez, Andrea Garmilla, Avlant Nilsson, Hratch M Baghdassarian, Khloe S Gordon, Louise G Lima, Blake E Smith, Marcela V Maus, Douglas A Lauffenburger, Michael E Birnbaum","doi":"10.1016/j.cels.2025.101260","DOIUrl":"10.1016/j.cels.2025.101260","url":null,"abstract":"The anti-tumor function of engineered T cells expressing chimeric antigen receptors (CARs) is dependent on signals transduced through intracellular signaling domains (ICDs). Different ICDs are known to drive distinct phenotypes, but systematic investigations into how ICD architectures direct T cell function-particularly at the molecular level-are lacking. Here, we use single-cell sequencing to map diverse signaling inputs to transcriptional outputs, focusing on a defined library of clinically relevant ICD architectures. Informed by these observations, we functionally characterize transcriptionally distinct ICD variants across various contexts to build comprehensive maps from ICD composition to phenotypic output. We identify a unique tonic signaling signature associated with a subset of ICD architectures that drives durable in vivo persistence and efficacy in liquid, but not solid, tumors. Our findings work toward decoding CAR signaling design principles, with implications for the rational design of next-generation ICD architectures optimized for in vivo function.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101260"},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12097926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining spatial transcriptomics and ECM imaging in 3D for mapping cellular interactions in the tumor microenvironment. 结合空间转录组学和ECM成像在三维绘制肿瘤微环境中的细胞相互作用。

Cell systems Pub Date : 2025-05-21 Epub Date: 2025-04-11 DOI: 10.1016/j.cels.2025.101261

Tancredi Massimo Pentimalli, Simon Schallenberg, Daniel León-Periñán, Ivano Legnini, Ilan Theurillat, Gwendolin Thomas, Anastasiya Boltengagen, Sonja Fritzsche, Jose Nimo, Lukas Ruff, Gabriel Dernbach, Philipp Jurmeister, Sarah Murphy, Mark T Gregory, Yan Liang, Michelangelo Cordenonsi, Stefano Piccolo, Fabian Coscia, Andrew Woehler, Nikos Karaiskos, Frederick Klauschen, Nikolaus Rajewsky

{"title":"Combining spatial transcriptomics and ECM imaging in 3D for mapping cellular interactions in the tumor microenvironment.","authors":"Tancredi Massimo Pentimalli, Simon Schallenberg, Daniel León-Periñán, Ivano Legnini, Ilan Theurillat, Gwendolin Thomas, Anastasiya Boltengagen, Sonja Fritzsche, Jose Nimo, Lukas Ruff, Gabriel Dernbach, Philipp Jurmeister, Sarah Murphy, Mark T Gregory, Yan Liang, Michelangelo Cordenonsi, Stefano Piccolo, Fabian Coscia, Andrew Woehler, Nikos Karaiskos, Frederick Klauschen, Nikolaus Rajewsky","doi":"10.1016/j.cels.2025.101261","DOIUrl":"10.1016/j.cels.2025.101261","url":null,"abstract":"Tumors are complex ecosystems composed of malignant and non-malignant cells embedded in a dynamic extracellular matrix (ECM). In the tumor microenvironment, molecular phenotypes are controlled by cell-cell and ECM interactions in 3D cellular neighborhoods (CNs). While their inhibition can impede tumor progression, routine molecular tumor profiling fails to capture cellular interactions. Single-cell spatial transcriptomics (ST) maps receptor-ligand interactions but usually remains limited to 2D tissue sections and lacks ECM readouts. Here, we integrate 3D ST with ECM imaging in serial sections from one clinical lung carcinoma to systematically quantify molecular states, cell-cell interactions, and ECM remodeling in CN. Our integrative analysis pinpointed known immune escape and tumor invasion mechanisms, revealing several druggable drivers of tumor progression in the patient under study. This proof-of-principle study highlights the potential of in-depth CN profiling in routine clinical samples to inform microenvironment-directed therapies. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101261"},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144031039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint analysis of chromatin accessibility and gene expression in the same single cells reveals cancer-specific regulatory programs. 对同一细胞中染色质可及性和基因表达的联合分析揭示了癌症特异性调控程序。

Cell systems Pub Date : 2025-05-21 Epub Date: 2025-04-21 DOI: 10.1016/j.cels.2025.101266

Lei Tang, Jinsong Zhang, Yanqiu Shao, Yifan Wei, Yuzhe Li, Kang Tian, Xiang Yan, Changjiang Feng, Qiangfeng Cliff Zhang

{"title":"Joint analysis of chromatin accessibility and gene expression in the same single cells reveals cancer-specific regulatory programs.","authors":"Lei Tang, Jinsong Zhang, Yanqiu Shao, Yifan Wei, Yuzhe Li, Kang Tian, Xiang Yan, Changjiang Feng, Qiangfeng Cliff Zhang","doi":"10.1016/j.cels.2025.101266","DOIUrl":"10.1016/j.cels.2025.101266","url":null,"abstract":"Biological analyses conducted at the single-cell scale have revealed profound impacts of heterogeneity and plasticity of chromatin states and gene expression on physiology and cancer. Here, we developed Parallel-seq, a technology for simultaneously measuring chromatin accessibility and gene expression in the same single cells. By combining combinatorial cell indexing and droplet overloading, Parallel-seq generates high-quality data in an ultra-high-throughput fashion and at a cost two orders of magnitude lower than alternative technologies (10× Multiome and ISSAAC-seq). We applied Parallel-seq to 40 lung tumor and tumor-adjacent clinical samples and obtained over 200,000 high-quality joint scATAC-and-scRNA profiles. Leveraging this large dataset, we characterized copy-number variations (CNVs) and extrachromosomal circular DNA (eccDNA) heterogeneity in tumor cells, predicted hundreds of thousands of cell-type-specific regulatory events, and identified enhancer mutations affecting tumor progression. Our analyses highlight Parallel-seq's power in investigating epigenetic and genetic factors driving cancer development at the cell-type-specific level and its utility for revealing vulnerable therapeutic targets.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101266"},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-cell phylodynamic inference of stem cell differentiation and tumor evolution. 干细胞分化和肿瘤进化的单细胞系统动力学推断。

Cell systems Pub Date : 2025-05-21 Epub Date: 2025-04-01 DOI: 10.1016/j.cels.2025.101244

Kun Wang, Zhaolian Lu, Zeqi Yao, Xionglei He, Zheng Hu, Da Zhou

{"title":"Single-cell phylodynamic inference of stem cell differentiation and tumor evolution.","authors":"Kun Wang, Zhaolian Lu, Zeqi Yao, Xionglei He, Zheng Hu, Da Zhou","doi":"10.1016/j.cels.2025.101244","DOIUrl":"10.1016/j.cels.2025.101244","url":null,"abstract":"Phylodynamic inference (PI) quantifies population dynamics and evolutionary trajectories using phylogenetic trees. Single-cell lineage tracing enables phylogenetic tree reconstruction for thousands of cells in multicellular organisms, facilitating PI at the cellular level. However, cell differentiation and somatic evolution challenge the direct application of existing PI frameworks to somatic tissues. We introduce scPhyloX, a computational framework modeling structured cell populations by leveraging single-cell phylogenetic trees to infer tissue development and tumor evolution dynamics. A key advancement is its ability to infer time-varying parameters, capturing dynamic biological processes. Simulations demonstrate scPhyloX's accuracy in scenarios including tissue development, disease treatment, and tumor growth. Application to three real datasets reveals insights into somatic dynamics: cycling stem cell overshoot in fly organ development, clonal expansion of multipotent hematopoietic progenitors during human aging, and pronounced subclonal selection in early colorectal tumorigenesis. scPhyloX thus provides a computational approach for investigating somatic tissue development and evolution.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101244"},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories. 群体环境和大小依赖的补偿机制引起了核生长轨迹的变化。

Cell systems Pub Date : 2025-05-21 Epub Date: 2025-05-01 DOI: 10.1016/j.cels.2025.101265

Julie C Dixon, Christopher L Frick, Chantelle L Leveille, Philip Garrison, Peyton A Lee, Saurabh S Mogre, Benjamin Morris, Nivedita Nivedita, Ritvik Vasan, Jianxu Chen, Cameron L Fraser, Clare R Gamlin, Leigh K Harris, Melissa C Hendershott, Graham T Johnson, Kyle N Klein, Sandra A Oluoch, Derek J Thirstrup, M Filip Sluzewski, Lyndsay Wilhelm, Ruian Yang, Daniel M Toloudis, Matheus P Viana, Julie A Theriot, Susanne M Rafelski

{"title":"Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.","authors":"Julie C Dixon, Christopher L Frick, Chantelle L Leveille, Philip Garrison, Peyton A Lee, Saurabh S Mogre, Benjamin Morris, Nivedita Nivedita, Ritvik Vasan, Jianxu Chen, Cameron L Fraser, Clare R Gamlin, Leigh K Harris, Melissa C Hendershott, Graham T Johnson, Kyle N Klein, Sandra A Oluoch, Derek J Thirstrup, M Filip Sluzewski, Lyndsay Wilhelm, Ruian Yang, Daniel M Toloudis, Matheus P Viana, Julie A Theriot, Susanne M Rafelski","doi":"10.1016/j.cels.2025.101265","DOIUrl":"10.1016/j.cels.2025.101265","url":null,"abstract":"To investigate how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we performed a data-driven analysis of nuclear growth variations in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days and developed open-source tools for image and data analysis and feature-based timelapse data exploration. Together, these data, tools, and workflows comprise a framework for systematic quantitative analysis of dynamics at individual and population levels, and the analysis further highlights important aspects to consider when interpreting timelapse data. We found that individual nuclear volume growth trajectories arise from short-timescale variations attributable to their spatiotemporal context within the colony. We identified a time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101265"},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolutionary paths that link orthogonal pairs of binding proteins. 连接正交结合蛋白对的进化路径。

Cell systems Pub Date : 2025-05-21 Epub Date: 2025-04-10 DOI: 10.1016/j.cels.2025.101262

Ziv Avizemer, Carlos Martí-Gómez, Shlomo Yakir Hoch, David M McCandlish, Sarel J Fleishman

引用次数: 0

Integrative, high-resolution analysis of single-cell gene expression across experimental conditions with PARAFAC2-RISE. 利用PARAFAC2-RISE对不同实验条件下单细胞基因表达进行综合、高分辨率分析。

Cell systems Pub Date : 2025-05-12 DOI: 10.1016/j.cels.2025.101294

Andrew Ramirez, Brian T Orcutt-Jahns, Sean Pascoe, Armaan Abraham, Breanna Remigio, Nathaniel Thomas, Aaron S Meyer

{"title":"Integrative, high-resolution analysis of single-cell gene expression across experimental conditions with PARAFAC2-RISE.","authors":"Andrew Ramirez, Brian T Orcutt-Jahns, Sean Pascoe, Armaan Abraham, Breanna Remigio, Nathaniel Thomas, Aaron S Meyer","doi":"10.1016/j.cels.2025.101294","DOIUrl":"10.1016/j.cels.2025.101294","url":null,"abstract":"Effective exploration and analysis tools are vital for the extraction of insights from single-cell data. However, current techniques for modeling single-cell studies performed across experimental conditions (e.g., samples) require restrictive assumptions or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that reduction and insight in single-cell exploration (RISE), an adaptation of the tensor decomposition method PARAFAC2, enables the dimensionality reduction and analysis of single-cell data across conditions. We demonstrate the benefits of RISE across distinct examples of single-cell RNA-sequencing experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus patient samples. RISE enables associations of gene variation patterns with patients or perturbations while connecting each coordinated change to single cells without requiring cell-type annotations. The theoretical grounding of RISE suggests a unified framework for many single-cell data modeling tasks while providing an intuitive dimensionality reduction approach for multi-sample single-cell studies across biological contexts. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101294"},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144087033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens. Image2Reg：利用遗传和化学干扰屏幕将染色质图像与基因调控联系起来。

Cell systems Pub Date : 2025-05-09 DOI: 10.1016/j.cels.2025.101293

Daniel Paysan, Adityanarayanan Radhakrishnan, Xinyi Zhang, G V Shivashankar, Caroline Uhler

{"title":"Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens.","authors":"Daniel Paysan, Adityanarayanan Radhakrishnan, Xinyi Zhang, G V Shivashankar, Caroline Uhler","doi":"10.1016/j.cels.2025.101293","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101293","url":null,"abstract":"Representation learning provides an opportunity to uncover the link between 3D genome organization and gene regulatory networks, thereby connecting the physical and the biochemical space of a cell. Our method, Image2Reg, uses chromatin images obtained in large-scale genetic and chemical perturbation screens. Through convolutional neural networks, Image2Reg generates gene embedding that represents the effect of gene perturbation on chromatin organization. In addition, combining protein-protein interaction data with cell-type-specific transcriptomic data through a graph convolutional network, we obtain a gene embedding that represents the regulatory effect of genes. Finally, Image2Reg learns a map between the resulting physical and biochemical representation of cells, allowing us to predict the perturbed gene modules based on chromatin images. Our results confirm the deep link between chromatin organization and gene regulation and demonstrate that it can be harnessed to identify drug targets and genes upstream of perturbed phenotypes from a simple and inexpensive chromatin staining.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101293"},"PeriodicalIF":0.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

All-at-once spatial proteome profiling of complex tissue context with single-cell-type resolution by proximity proteomics. 所有的空间蛋白质组分析复杂的组织背景与单细胞型分辨率接近蛋白质组学。

Cell systems Pub Date : 2025-05-05 DOI: 10.1016/j.cels.2025.101291

Yiheng Mao, Yuan Li, Zhendong Zheng, Yanfen Xu, Mi Ke, An He, Fuchao Liang, Keren Zhang, Xi Wang, Weina Gao, Ruijun Tian

{"title":"All-at-once spatial proteome profiling of complex tissue context with single-cell-type resolution by proximity proteomics.","authors":"Yiheng Mao, Yuan Li, Zhendong Zheng, Yanfen Xu, Mi Ke, An He, Fuchao Liang, Keren Zhang, Xi Wang, Weina Gao, Ruijun Tian","doi":"10.1016/j.cels.2025.101291","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101291","url":null,"abstract":"Spatial proteomics enables in-depth mapping of tissue architectures, mostly achieved by laser microdissection-mass spectrometry (LMD-MS) and antibody-based imaging. However, trade-offs among sampling precision, throughput, and proteome coverage still limit the applicability of these strategies. Here, we propose proximity labeling for spatial proteomics (PSPro) by combining precise antibody-targeted biotinylation and efficient affinity purification for all-at-once cell-type proteome capture with sub-micrometer resolution from single tissue slice. With fine-tuned labeling parameters, PSPro shows reliable performance in benchmarking against flow cytometry- and LMD-based proteomic workflows. We apply PSPro to tumor and spleen slices, enriching thousands of proteins containing known markers from ten cell types. We further incorporate LMD into PSPro to facilitate comparison of cell subpopulations from the same tissue slice, revealing spatial proteome heterogeneity of cancer cells and immune cells in pancreatic tumor. Collectively, PSPro converts the traditional \"antibody-epitope\" paradigm to an \"antibody-cell-type proteome\" for spatial biology in a user-friendly manner. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101291"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0