Cell systems最新文献

Designing quantitative gene therapy on ComMAND. 按指令设计定量基因治疗。

Cell systems Pub Date : 2025-06-18 DOI: 10.1016/j.cels.2025.101323

Connie An, Noa Katz, Xiaojing J Gao

引用次数: 0

A systems view of cellular heterogeneity: Unlocking the "wheel of fate". 细胞异质性的系统观点：解开“命运之轮”。

Cell systems Pub Date : 2025-06-18 Epub Date: 2025-06-04 DOI: 10.1016/j.cels.2025.101300

Hourieh Movasat, Enzo Giacopino, Ali Shahdoost, Yeganeh Dorri Nokoorani, Ali Houshyar Abrbekouh, Yaser Tahamtani, Nika Shakiba

{"title":"A systems view of cellular heterogeneity: Unlocking the \"wheel of fate\".","authors":"Hourieh Movasat, Enzo Giacopino, Ali Shahdoost, Yeganeh Dorri Nokoorani, Ali Houshyar Abrbekouh, Yaser Tahamtani, Nika Shakiba","doi":"10.1016/j.cels.2025.101300","DOIUrl":"10.1016/j.cels.2025.101300","url":null,"abstract":"Systems biology offers a view of the cell as an input-output device: a biochemical network (or cellular \"processor\") that interprets cues from the microenvironment to drive cell fate. Advancements in single-cell technologies are unlocking the cellular black box, revealing heterogeneity in seemingly homogeneous cell populations. But are these differences technical variability or biology? In this review, we explore this question through a systems biology lens, offering a framework for conceptualizing heterogeneity from the cell's perspective and summarizing systems and synthetic biology tools for capturing heterogeneity. While cellular inputs shape the probability of attaining particular fates, each cell spins a stochastic \"wheel of fate.\" Applying this framework, we explore heterogeneity in two case studies: human pluripotent stem cell (hPSC) culture and beta cell differentiation. Looking forward, we discuss how a systems approach to heterogeneity may enable more predictable outcomes in stem cell research, with broad implications for developmental biology and regenerative medicine.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101300"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236142","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

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 : 2025-06-18 Epub Date: 2025-05-15 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":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144087033","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

Uncovering the principles coordinating systems-level organelle biogenesis with cellular growth. 揭示系统级细胞器生物发生与细胞生长的协调原理。

Cell systems Pub Date : 2025-06-18 Epub Date: 2025-06-06 DOI: 10.1016/j.cels.2025.101267

Shixing Wang, Deepthi Kailash, Shankar Mukherji

{"title":"Uncovering the principles coordinating systems-level organelle biogenesis with cellular growth.","authors":"Shixing Wang, Deepthi Kailash, Shankar Mukherji","doi":"10.1016/j.cels.2025.101267","DOIUrl":"10.1016/j.cels.2025.101267","url":null,"abstract":"A complete framework of eukaryotic cellular growth control must include the growth of its defining hallmarks: organelles. Organelle coordination with cellular growth is opaque without measuring multiple organelles in the same cell with adequate statistics to test theoretical frameworks. Here, we map out the correlation structure of systems-level organelle biogenesis with cellular growth using \"rainbow yeast,\" simultaneously visualizing 6 major metabolically active organelles. Hyperspectral imaging of thousands of rainbow yeast cells revealed that systems-level organelle biogenesis is organized into collective organelle modes activated by changes in nutrient availability. Chemical biological dissection suggests that sensed growth rate and cell size specifically activate these organelle modes. Mathematical modeling and synthetic control of cytoplasmic availability suggest that the organelle mode structure allows growth homeostasis in constant environments and responsiveness to environmental change. This regulatory architecture may underlie how compartmentalization allows cell size and growth rate flexibility to satisfy otherwise incompatible environmental and developmental constraints.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101267"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251317","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

Integrase enables synthetic intercellular logic via bacterial conjugation. 整合酶使合成细胞间逻辑通过细菌偶联。

Cell systems Pub Date : 2025-06-18 Epub Date: 2025-04-28 DOI: 10.1016/j.cels.2025.101268

Fang Ba, Yufei Zhang, Luyao Wang, Xiangyang Ji, Wan-Qiu Liu, Shengjie Ling, Jian Li

引用次数: 0

Reframing the role of the objective function in its proper context for metabolic network modeling. 在代谢网络建模的适当背景下重构目标函数的作用。

Cell systems Pub Date : 2025-06-18 DOI: 10.1016/j.cels.2025.101298

Mohammad Mazharul Islam, William Shao, Mariska Batavia, Roseanne M Ford, Bernhard O Palsson, Jens Nielsen, Costas D Maranas, Sang Yup Lee, Jason A Papin

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 : 2025-06-18 Epub Date: 2025-05-08 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":"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":"2025-06-18","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

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 : 2025-06-18 Epub Date: 2025-05-12 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":"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":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055145","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

Model-guided design of microRNA-based gene circuits supports precise dosage of transgenic cargoes into diverse primary cells. 基于microrna的基因电路的模型引导设计支持转基因货物进入不同原代细胞的精确剂量。

Cell systems Pub Date : 2025-06-18 Epub Date: 2025-04-28 DOI: 10.1016/j.cels.2025.101269

Kasey S Love, Christopher P Johnstone, Emma L Peterman, Stephanie Gaglione, Michael E Birnbaum, Kate E Galloway

{"title":"Model-guided design of microRNA-based gene circuits supports precise dosage of transgenic cargoes into diverse primary cells.","authors":"Kasey S Love, Christopher P Johnstone, Emma L Peterman, Stephanie Gaglione, Michael E Birnbaum, Kate E Galloway","doi":"10.1016/j.cels.2025.101269","DOIUrl":"10.1016/j.cels.2025.101269","url":null,"abstract":"In a therapeutic context, supraphysiological expression of transgenes can compromise engineered phenotypes and lead to toxicity. To ensure a narrow range of transgene expression, we developed a single-transcript, microRNA-based incoherent feedforward loop called compact microRNA-mediated attenuator of noise and dosage (ComMAND). We experimentally tuned the ComMAND output profile, and we modeled the system to explore additional tuning strategies. By comparing ComMAND to two-gene implementations, we demonstrate the precise control afforded by the single-transcript architecture, particularly at low copy numbers. We show that ComMAND tightly regulates transgene expression from lentiviruses and precisely controls expression in primary human T cells, primary rat neurons, primary mouse embryonic fibroblasts, and human induced pluripotent stem cells. Finally, ComMAND effectively sets levels of the clinically relevant transgenes frataxin (FXN) and fragile X messenger ribonucleoprotein 1 (Fmr1) within a narrow window. Overall, ComMAND is a compact tool well suited to precisely specify the expression of therapeutic cargoes. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101269"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144063516","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

Statistical modeling and analysis of cell counts from multiplexed imaging data. 统计建模和分析细胞计数从多路复用成像数据。

Cell systems Pub Date : 2025-06-18 Epub Date: 2025-05-29 DOI: 10.1016/j.cels.2025.101296

Pierre Bost, Ruben Casanova, Uria Mor, Martina Haberecker, Chantal Pauli, Susanne Dettwiler, Bernd Bodenmiller

引用次数: 0