Cell systems最新文献

Dango: Predicting higher-order genetic interactions. 预测高阶遗传相互作用。

IF 7.7

Cell systems Pub Date : 2026-04-24 DOI: 10.1016/j.cels.2026.101593

Ruochi Zhang, Mihir Bafna, Jianzhu Ma, Jian Ma

引用次数: 0

Modeling chimeric antigen receptor response at the single-cell level with conditional optimal transport. 模拟嵌合抗原受体反应在单细胞水平与条件最佳运输。

IF 7.7

Cell systems Pub Date : 2026-04-22 DOI: 10.1016/j.cels.2026.101591

Alice Driessen, Jannis Born, Rocio Castellanos Rueda, Sai T Reddy, Marianna Rapsomaniki

{"title":"Modeling chimeric antigen receptor response at the single-cell level with conditional optimal transport.","authors":"Alice Driessen, Jannis Born, Rocio Castellanos Rueda, Sai T Reddy, Marianna Rapsomaniki","doi":"10.1016/j.cels.2026.101591","DOIUrl":"https://doi.org/10.1016/j.cels.2026.101591","url":null,"abstract":"Chimeric antigen receptor (CAR) T cell therapy is a promising cancer immunotherapy; however, several challenges hamper its clinical efficacy. Although new computational models are attempting to explore the vast combinatorial design space of CAR components and suggest novel designs, challenges remain in capturing cellular heterogeneity as well as generalizing to unseen CAR variants. Here, we introduce an optimal transport (OT)-based framework designed to predict responses to CAR expression at the single-cell level, including variants that have not been experimentally tested. Our model accurately captures gene expression changes across diverse CAR variants, outperforming the baseline for in-distribution CARs while reflecting biological characteristics. By embedding CARs using protein language models, we extend our framework to a conditional OT-based model that successfully generalizes our predictions to out-of-distribution CAR designs. Our findings highlight the utility of OT-based modeling in elucidating CAR design-function relationships, enabling the rational design of novel CARs with therapeutic potential. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101591"},"PeriodicalIF":7.7,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147792172","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

Comprehensive RNA-binding protein analyses and deep learning uncover genetic constraints and disease associations in protein-RNA interfaces. 全面的rna结合蛋白分析和深度学习揭示了蛋白质- rna界面的遗传限制和疾病关联。

IF 7.7

Cell systems Pub Date : 2026-04-22 DOI: 10.1016/j.cels.2026.101588

Hsuan-Lin Her, Brian A Yee, Shuhao Xu, Evan A Boyle, Katherine L Rothamel, Zia Z Zhao, Steven M Blue, Jasmine R Mueller, Samuel S Park, Grady G Nguyen, Jack T Naritomi, Adam Klie, Xintao Wei, Sara Olson, Lijun Zhan, Stefan Aigner, Brenton R Graveley, Gene W Yeo

{"title":"Comprehensive RNA-binding protein analyses and deep learning uncover genetic constraints and disease associations in protein-RNA interfaces.","authors":"Hsuan-Lin Her, Brian A Yee, Shuhao Xu, Evan A Boyle, Katherine L Rothamel, Zia Z Zhao, Steven M Blue, Jasmine R Mueller, Samuel S Park, Grady G Nguyen, Jack T Naritomi, Adam Klie, Xintao Wei, Sara Olson, Lijun Zhan, Stefan Aigner, Brenton R Graveley, Gene W Yeo","doi":"10.1016/j.cels.2026.101588","DOIUrl":"10.1016/j.cels.2026.101588","url":null,"abstract":"RNA-binding proteins (RBPs) orchestrate post-transcriptional processes, including splicing, cleavage and polyadenylation, and translation. Our updated RBP resource integrates data from 92 additional RBPs (286 in total) profiled by enhanced CLIP (eCLIP), enabling comprehensive characterization of RNA elements within human K562 and HepG2 cells. To interrogate RBP-binding syntax, we trained deep-learning models on eCLIP profiles, allowing us to score genetic variants and quantify constraints on RBP-binding sites. We observed opposing selective-constraint profiles at splicing enhancers versus silencers, including an unexpected enrichment of strengthening mutations in ELAVL1- and HNRNPC-binding sites. Finally, our model prioritizes disease variants, exposing unexpected RBP-related mechanisms of pathogenesis, exemplified by the enrichment of weakening mutations in spliceosomal protein-binding sites among retinal disease variants. The complete eCLIP resource offers an integrated platform for exploring RBP-RNA interactomes.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101588"},"PeriodicalIF":7.7,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147792223","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

Recurrent neural chemical reaction networks that approximate arbitrary dynamics. 近似任意动态的循环神经化学反应网络。

IF 7.7

Cell systems Pub Date : 2026-04-22 DOI: 10.1016/j.cels.2026.101572

Alexander Dack, Benjamin Qureshi, Thomas E Ouldridge, Tomislav Plesa

{"title":"Recurrent neural chemical reaction networks that approximate arbitrary dynamics.","authors":"Alexander Dack, Benjamin Qureshi, Thomas E Ouldridge, Tomislav Plesa","doi":"10.1016/j.cels.2026.101572","DOIUrl":"https://doi.org/10.1016/j.cels.2026.101572","url":null,"abstract":"Many important phenomena in biochemistry and biology exploit dynamical features such as multi-stability, oscillations, and chaos. The construction of novel chemical systems with such rich dynamics is a challenging problem central to the fields of synthetic biology and molecular nanotechnology. In this paper, we address this problem by putting forward a molecular version of a recurrent artificial neural network, which we call a \"recurrent neural chemical reaction network\" (RNCRN). The RNCRN uses a modular architecture-a network of chemical neurons-to approximate arbitrary dynamics. We first prove that, with sufficiently many chemical neurons and suitably fast reactions, the RNCRN can be systematically trained to achieve any dynamics. RNCRNs with a relatively small number of chemical neurons and a moderate range of reaction rates are then trained to display a variety of biologically important dynamical features. We also demonstrate that such RNCRNs are experimentally implementable with DNA-strand-displacement technologies. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101572"},"PeriodicalIF":7.7,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147792219","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

Fusing imaging and metabolic modeling via multimodal deep learning in ovarian cancer. 基于多模态深度学习的卵巢癌影像与代谢模型融合研究。

IF 7.7

Cell systems Pub Date : 2026-04-22 DOI: 10.1016/j.cels.2026.101594

Noushin Eftekhari, Suraj Verma, Aninda Saha, Guido Zampieri, Saladin Sawan, Annalisa Occhipinti, Claudio Angione

{"title":"Fusing imaging and metabolic modeling via multimodal deep learning in ovarian cancer.","authors":"Noushin Eftekhari, Suraj Verma, Aninda Saha, Guido Zampieri, Saladin Sawan, Annalisa Occhipinti, Claudio Angione","doi":"10.1016/j.cels.2026.101594","DOIUrl":"https://doi.org/10.1016/j.cels.2026.101594","url":null,"abstract":"Integrating genotype (e.g., transcriptomics), phenotype (e.g., imaging), and tumor microenvironment (e.g., metabolomics) is crucial to elucidating the molecular basis of ovarian cancer. However, there is a lack of robust multimodal integration methods when only a limited number of common samples is available. Here, we generate patient-specific metabolic models starting from transcriptomics data and integrate them with imaging data. We show that this multimodal integration-never attempted before-improves survival estimation and enables a mechanistic interpretation of the predictions. We assess the robustness of our approach with different combinations of transcriptomics, fluxomics, and 3D computerized tomography (CT) imaging data, correctly stratifying patients based on risk. Fusing metabolic modeling with imaging and transcriptomics significantly improves model accuracy compared with widely used transcriptomics-imaging approaches and elucidates critical metabolic reactions. Our approach is general and can be applied to other cancer types where coupled imaging-transcriptomics data are available. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101594"},"PeriodicalIF":7.7,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147792228","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

Mechanistic modeling of recessive disease through allelic integration of variant effects. 通过变异效应的等位基因整合建立隐性疾病的机制模型。

IF 7.7

Cell systems Pub Date : 2026-04-21 DOI: 10.1016/j.cels.2026.101595

Hasan Çubuk, Marcin Plech, Vahid Aslanzadeh, Marie Zikanova, Vaclava Skopova, Stanislav Kmoch, Yuxin Shen, Joseph A Marsh, Grzegorz Kudla

{"title":"Mechanistic modeling of recessive disease through allelic integration of variant effects.","authors":"Hasan Çubuk, Marcin Plech, Vahid Aslanzadeh, Marie Zikanova, Vaclava Skopova, Stanislav Kmoch, Yuxin Shen, Joseph A Marsh, Grzegorz Kudla","doi":"10.1016/j.cels.2026.101595","DOIUrl":"https://doi.org/10.1016/j.cels.2026.101595","url":null,"abstract":"Interpreting variants in recessive diseases is difficult because clinical severity depends on the combined function of both alleles. Deep mutational scanning (DMS) experiments can provide functional measurements at scale, but their scores often relate nonlinearly to true biochemical activity. Here, we describe a method for inferring enzymatic activities for thousands of variants by running two fitness assays at different expression levels and modeling the nonlinear activity-fitness relationship. These inferred activities allow the computation of a bi-allelic pathogenicity score that captures the joint effect of two alleles. We applied this approach to adenylosuccinate lyase (ADSL), quantifying the effects of >8,000 coding variants in a yeast-based DMS assay. The inferred activities separated pathogenic from benign alleles, and the bi-allelic scores correlated strongly with biochemical measurements and clinical outcomes, outperforming existing predictors. This framework provides a broadly applicable strategy for the mechanistic interpretation of variants in recessive enzymes.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101595"},"PeriodicalIF":7.7,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147792196","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

Cell shapes decode molecular phenotypes in image-based spatial proteomics. 细胞形状解码分子表型在基于图像的空间蛋白质组学。

IF 7.7

Cell systems Pub Date : 2026-04-20 DOI: 10.1016/j.cels.2026.101589

Trang Le, William D Leineweber, Matheus P Viana, Anthony Cesnik, Jan N Hansen, Wei Ouyang, Susanne M Rafelski, Emma Lundberg

引用次数: 0

Phased fragility and stability of non-genetic B cell states in the germinal center accelerate the genetic evolution of antibodies. 生发中心非遗传B细胞状态的阶段性脆弱性和稳定性加速了抗体的遗传进化。

IF 7.7

Cell systems Pub Date : 2026-04-20 DOI: 10.1016/j.cels.2026.101590

Mark Y Xiang, Haripriya Vaidehi Narayanan, Vaibhava Kesarwani, Rohan Vanheusden, Tiffany Wang, Alexander Hoffmann

{"title":"Phased fragility and stability of non-genetic B cell states in the germinal center accelerate the genetic evolution of antibodies.","authors":"Mark Y Xiang, Haripriya Vaidehi Narayanan, Vaibhava Kesarwani, Rohan Vanheusden, Tiffany Wang, Alexander Hoffmann","doi":"10.1016/j.cels.2026.101590","DOIUrl":"https://doi.org/10.1016/j.cels.2026.101590","url":null,"abstract":"Vaccine responses depend on the Darwinian genetic evolution of B cells to generate high-affinity antibodies. However, B cells gain non-genetic heterogeneity while searching for antigen and T helper cells, but then their non-genetic cell states remain stable within proliferative clonal bursts. We explored the functional consequence of this dynamic control of non-genetic variability by developing a mathematical model, integrating a wealth of immunological knowledge. We discovered that variability in B cell fate decisions does not impair but instead accelerates affinity maturation by allowing high-affinity outliers to escape plasma cell differentiation and seed further rounds of Darwinian evolution. During clonal bursts, non-genetic cell state stability further promotes their amplification. The resulting model correctly predicts emergent vaccine response properties in mouse strains with altered B cell fate decision profiles. Our work reconciles classical B cell clonal selection theory with the experimentally observed non-genetic variability, and it provides an interpretable knowledge-based modeling framework to support personalized vaccination strategies.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101590"},"PeriodicalIF":7.7,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147792175","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

Pancreatic islet oscillation rhythmicity arises from δ and α cell interactions. 胰岛振荡节律性是由δ和α细胞相互作用引起的。

IF 7.7

Cell systems Pub Date : 2026-04-16 DOI: 10.1016/j.cels.2026.101587

Huixia Ren, Yanjun Li, Beichen Xie, Zhenchao Fu, Xiaohong Peng, Weiran Qian, Yi Yu, Tianyi Chang, Xiaojing Yang, Kim Sneppen, Liangyi Chen, Chao Tang

{"title":"Pancreatic islet oscillation rhythmicity arises from δ and α cell interactions.","authors":"Huixia Ren, Yanjun Li, Beichen Xie, Zhenchao Fu, Xiaohong Peng, Weiran Qian, Yi Yu, Tianyi Chang, Xiaojing Yang, Kim Sneppen, Liangyi Chen, Chao Tang","doi":"10.1016/j.cels.2026.101587","DOIUrl":"https://doi.org/10.1016/j.cels.2026.101587","url":null,"abstract":"Glucose-stimulated hormone secretion in pancreatic islets is closely linked to oscillations in cytoplasmic Ca2+, which arise from complex intra- and intercellular signaling. δ cells, intermingled with peripheral α cells, are important paracrine regulators, but their role in shaping Ca2+ oscillations remains unclear. Here, we show that δ-α cell interactions contribute to the variability of glucose-induced Ca2+ oscillation patterns. Somatostatin released by δ cells prolonged the oscillation period in an α cell-mass-dependent manner. Pharmacological and optogenetic perturbations of δ-α interactions prompted an oscillation transition. Continuous adjustment of δ-α coupling strength caused the fast-oscillating islets to transition to mixed and slow oscillations. Mathematical modeling indicated that this fast-mixed-slow transition is a Hopf bifurcation. In vivo, blood glucose correlated with oscillation mode: hyperglycemia with slow oscillations and euglycemia with fast oscillations. These findings explain how δ and α cells shape islet Ca2+ dynamics, a phenomenon dictated by the diverse cytoarchitecture of the islet. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101587"},"PeriodicalIF":7.7,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147719260","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

Engineering sensor-based antithetic integral controllers for enhanced dynamic performance and noise attenuation. 基于工程传感器的反相积分控制器，增强动态性能和噪声衰减。

IF 7.7

Cell systems Pub Date : 2026-04-15 Epub Date: 2026-04-01 DOI: 10.1016/j.cels.2026.101592

Maurice Filo, Stephanie K Aoki, Mucun Hou, Stanislav Anastassov, Mustafa Khammash

引用次数: 0