Cell systems最新文献_第6页

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

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

What limits our ability to study the effects of microbial diversity on health and the environment? 是什么限制了我们研究微生物多样性对健康和环境影响的能力？

Cell systems Pub Date : 2025-06-18 DOI: 10.1016/j.cels.2025.101322

Karoline Faust, Ju-Sheng Zheng, Ana Djukovic, Joao Xavier, Yang Bai, Katie Pollard, Amy Willis, Rebecca Vega Thurber, Thomas Sharpton

引用次数: 0

FlowDesign: Improved design of antibody CDRs through flow matching and better prior distributions. FlowDesign：通过流程匹配和更好的先验分布，改进抗体cdr的设计。

Cell systems Pub Date : 2025-06-18 Epub Date: 2025-04-29 DOI: 10.1016/j.cels.2025.101270

Jun Wu, Xiangzhe Kong, Ningguan Sun, Jing Wei, Sisi Shan, Fuli Feng, Feng Wu, Jian Peng, Linqi Zhang, Yang Liu, Jianzhu Ma

{"title":"FlowDesign: Improved design of antibody CDRs through flow matching and better prior distributions.","authors":"Jun Wu, Xiangzhe Kong, Ningguan Sun, Jing Wei, Sisi Shan, Fuli Feng, Feng Wu, Jian Peng, Linqi Zhang, Yang Liu, Jianzhu Ma","doi":"10.1016/j.cels.2025.101270","DOIUrl":"10.1016/j.cels.2025.101270","url":null,"abstract":"Designing antibodies with desired binding specificity and affinity is essential for pharmaceutical research. While diffusion-based models have advanced the co-design of the complementarity-determining region (CDR) sequences and structures, challenges remain, including non-informative priors, incompatibility with discrete amino acid types, and impractical computational costs in large-scale sampling. To address these, we propose FlowDesign, a sequence-structure co-design approach via flow matching, offering (1) flexible prior selection, (2) direct matching of discrete distributions, and (3) enhanced efficiency for large-scale sampling. By leveraging various priors, data-driven structural models proved the most informative. FlowDesign outperformed baselines in amino acid recovery (AAR), root-mean-square deviation (RMSD), and Rosetta energy. We also applied FlowDesign to design antibodies targeting the HIV-1 receptor CD4. FlowDesign yielded antibodies with improved binding affinity and neutralizing potency compared with the antibody ibalizumab across multiple HIV mutants, validated by biolayer interferometry (BLI) and pseudovirus neutralization. This highlights FlowDesign's potential in antibody and protein design. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101270"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060427","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

Scalable image-based visualization and alignment of spatial transcriptomics datasets. 可扩展的基于图像的可视化和空间转录组学数据集的对齐。

Cell systems Pub Date : 2025-05-21 Epub Date: 2025-04-22 DOI: 10.1016/j.cels.2025.101264

Stephan Preibisch, Michael Innerberger, Daniel León-Periñán, Nikos Karaiskos, Nikolaus Rajewsky

引用次数: 0