Cell systems最新文献_第3页

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 : 2025-04-09 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":"https://doi.org/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":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058812","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

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 : 2025-04-09 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":"https://doi.org/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":"2025-04-09","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

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 : 2025-03-28 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":"https://doi.org/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":"2025-03-28","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

Geometric deep learning and multiple-instance learning for 3D cell-shape profiling. 三维细胞形状轮廓的几何深度学习和多实例学习。

Cell systems Pub Date : 2025-03-19 DOI: 10.1016/j.cels.2025.101229

Matt De Vries, Lucas G Dent, Nathan Curry, Leo Rowe-Brown, Vicky Bousgouni, Olga Fourkioti, Reed Naidoo, Hugh Sparks, Adam Tyson, Chris Dunsby, Chris Bakal

{"title":"Geometric deep learning and multiple-instance learning for 3D cell-shape profiling.","authors":"Matt De Vries, Lucas G Dent, Nathan Curry, Leo Rowe-Brown, Vicky Bousgouni, Olga Fourkioti, Reed Naidoo, Hugh Sparks, Adam Tyson, Chris Dunsby, Chris Bakal","doi":"10.1016/j.cels.2025.101229","DOIUrl":"10.1016/j.cels.2025.101229","url":null,"abstract":"The three-dimensional (3D) morphology of cells emerges from complex cellular and environmental interactions, serving as an indicator of cell state and function. In this study, we used deep learning to discover morphology representations and understand cell states. This study introduced MorphoMIL, a computational pipeline combining geometric deep learning and attention-based multiple-instance learning to profile 3D cell and nuclear shapes. We used 3D point-cloud input and captured morphological signatures at single-cell and population levels, accounting for phenotypic heterogeneity. We applied these methods to over 95,000 melanoma cells treated with clinically relevant and cytoskeleton-modulating chemical and genetic perturbations. The pipeline accurately predicted drug perturbations and cell states. Our framework revealed subtle morphological changes associated with perturbations, key shapes correlating with signaling activity, and interpretable insights into cell-state heterogeneity. MorphoMIL demonstrated superior performance and generalized across diverse datasets, paving the way for scalable, high-throughput morphological profiling in drug discovery. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"16 3","pages":"101229"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672010","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

Evaluation of De Vries et al.: Quantifying cellular shapes and how they correlate to cellular responses. De Vries等人的评价：量化细胞形状及其与细胞反应的关系。

Cell systems Pub Date : 2025-03-19 DOI: 10.1016/j.cels.2025.101242

Manuel D Leonetti

引用次数: 0

Virus targeting as a dominant driver of interfacial evolution in the structurally resolved human-virus protein-protein interaction network. 在结构上解决的人-病毒蛋白-蛋白相互作用网络中，病毒靶向是界面进化的主要驱动因素。

Cell systems Pub Date : 2025-03-19 Epub Date: 2025-02-28 DOI: 10.1016/j.cels.2025.101202

Wan-Chun Su, Yu Xia

{"title":"Virus targeting as a dominant driver of interfacial evolution in the structurally resolved human-virus protein-protein interaction network.","authors":"Wan-Chun Su, Yu Xia","doi":"10.1016/j.cels.2025.101202","DOIUrl":"10.1016/j.cels.2025.101202","url":null,"abstract":"Regions on a host protein that interact with virus proteins (exogenous interfaces) frequently overlap with those that interact with other host proteins (endogenous interfaces), resulting in competition between hosts and viruses for these shared interfaces (mimic-targeted interfaces). Yet, the evolutionary consequences of this competitive relationship on the host are not well understood. Here, we integrate experimentally determined structures and homology-based templates of protein complexes with protein-protein interaction networks to construct a high-resolution human-virus structural interaction network. We perform site-specific evolutionary rate analyses on this structural interaction network and find that exogenous-specific interfaces evolve faster than endogenous-specific interfaces. Mimic-targeted interfaces evolve as fast as exogenous-specific interfaces, despite being targeted by both human and virus proteins. Our findings suggest that virus targeting plays a dominant role in host interfacial evolution within the context of domain-domain interactions and that mimic-targeted interfaces on human proteins are the key battleground for a mammalian-specific host-virus evolutionary arms race.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101202"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538243","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

Self-resistance-gene-guided, high-throughput automated genome mining of bioactive natural products from Streptomyces. 链霉菌生物活性天然产物的自抗性基因引导、高通量自动基因组挖掘。

Cell systems Pub Date : 2025-03-19 Epub Date: 2025-03-11 DOI: 10.1016/j.cels.2025.101237

Yujie Yuan, Chunshuai Huang, Nilmani Singh, Guanhua Xun, Huimin Zhao

{"title":"Self-resistance-gene-guided, high-throughput automated genome mining of bioactive natural products from Streptomyces.","authors":"Yujie Yuan, Chunshuai Huang, Nilmani Singh, Guanhua Xun, Huimin Zhao","doi":"10.1016/j.cels.2025.101237","DOIUrl":"10.1016/j.cels.2025.101237","url":null,"abstract":"Natural products (NPs) from bacteria, fungi, and plants are a vital source of drug leads, with Streptomyces species being particularly significant due to their capability of producing diverse bioactive compounds. Here, we present a fully automated, scalable, high-throughput platform for discovering bioactive NPs in Streptomyces (FAST-NPS). This platform integrates computational biosynthetic gene cluster (BGC) prediction and prioritization guided by self-resistance genes, automated cloning and heterologous expression, high-throughput fermentation, and product extraction. As a proof of concept, we cloned 105 BGCs (10-100 kb) from 11 Streptomyces strains with a 95% success rate. Heterologous expression in Streptomyces lividans TK24 led to the detection of 23 NPs, including 8 with antibacterial or antitumor bioactivities from 5 BGCs. This work highlights the potential of FAST-NPS to accelerate bioactive NP discovery for biomedical and biotechnological applications. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101237"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617374","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

Engineering highly active nuclease enzymes with machine learning and high-throughput screening. 利用机器学习和高通量筛选技术设计高活性核酸酶。

Cell systems Pub Date : 2025-03-19 Epub Date: 2025-03-12 DOI: 10.1016/j.cels.2025.101236

Neil Thomas, David Belanger, Chenling Xu, Hanson Lee, Kathleen Hirano, Kosuke Iwai, Vanja Polic, Kendra D Nyberg, Kevin G Hoff, Lucas Frenz, Charlie A Emrich, Jun W Kim, Mariya Chavarha, Abi Ramanan, Jeremy J Agresti, Lucy J Colwell

{"title":"Engineering highly active nuclease enzymes with machine learning and high-throughput screening.","authors":"Neil Thomas, David Belanger, Chenling Xu, Hanson Lee, Kathleen Hirano, Kosuke Iwai, Vanja Polic, Kendra D Nyberg, Kevin G Hoff, Lucas Frenz, Charlie A Emrich, Jun W Kim, Mariya Chavarha, Abi Ramanan, Jeremy J Agresti, Lucy J Colwell","doi":"10.1016/j.cels.2025.101236","DOIUrl":"10.1016/j.cels.2025.101236","url":null,"abstract":"Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged fitness landscape and costly experiments. In this work, we present TeleProt, a machine learning (ML) framework that blends evolutionary and experimental data to design diverse protein libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds. After multiple rounds of high-throughput experiments, TeleProt found a significantly better top-performing enzyme than directed evolution (DE), had a better hit rate at finding diverse, high-activity variants, and was even able to design a high-performance initial library using no prior experimental data. We have released a dataset of 55,000 nuclease variants, one of the most extensive genotype-phenotype enzyme activity landscapes to date, to drive further progress in ML-guided 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":"101236"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626689","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

Multiplexed dynamic control of temperature to probe and observe mammalian cells. 对温度进行多重动态控制，以探测和观察哺乳动物细胞。

Cell systems Pub Date : 2025-03-19 Epub Date: 2025-03-12 DOI: 10.1016/j.cels.2025.101234

William Benman, Pavan Iyengar, Thomas R Mumford, Zikang Huang, Manya Kapoor, Grace Liu, Lukasz J Bugaj

引用次数: 0

Tissue sculpting with light. 组织雕刻与光。

Cell systems Pub Date : 2025-03-19 DOI: 10.1016/j.cels.2025.101241

Romane Petit, Léo Valon

引用次数: 0