Cell systems最新文献_第7页

BulkFormer: A large-scale foundation model for bulk transcriptomes. BulkFormer：批量转录组的大规模基础模型。

IF 7.7

Cell systems Pub Date : 2026-07-15 Epub Date: 2026-07-01 DOI: 10.1016/j.cels.2026.101657

Boming Kang, Rui Fan, Meizheng Yi, Chunmei Cui, Qinghua Cui

{"title":"BulkFormer: A large-scale foundation model for bulk transcriptomes.","authors":"Boming Kang, Rui Fan, Meizheng Yi, Chunmei Cui, Qinghua Cui","doi":"10.1016/j.cels.2026.101657","DOIUrl":"10.1016/j.cels.2026.101657","url":null,"abstract":"Foundation models are transforming transcriptome analysis, yet most RNA sequencing (RNA-seq) foundation models are pretrained on sparse single-cell RNA-seq data, which typically captures only ∼3,000 genes per cell. This creates a need for models tailored to bulk transcriptomes, a modality that profiles ∼16,000 genes per sample and supports clinical and tissue-level analyses. Here, we present BulkFormer, a foundation model for bulk transcriptome analysis. BulkFormer contains ∼150 million parameters, covers 20,010 protein-coding genes, and is pretrained on 581,503 human bulk RNA-seq profiles. Its hybrid encoder combines a graph neural network to model explicit gene-gene relationships with a Performer module to capture global expression dependencies. Across five downstream tasks, BulkFormer outperformed existing single-cell foundation models while requiring substantially lower training cost. These results identify pretraining data modality as a key determinant of foundation model performance and establish BulkFormer as a framework for bulk transcriptome modeling. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101657"},"PeriodicalIF":7.7,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371451","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

RIG-Ing the system to balance immune activation and inflammatory tissue damage in the airways. RIG-Ing系统平衡免疫激活和气道炎症组织损伤。

IF 7.7

Cell systems Pub Date : 2026-07-15 DOI: 10.1016/j.cels.2026.101679

Alec P Pankow, Margaret A Scull, Brad R Rosenberg

引用次数: 0

Active RNA synthesis patterns nuclear condensates. 活性RNA合成模式核凝聚。

IF 7.7

Cell systems Pub Date : 2026-07-15 Epub Date: 2026-05-26 DOI: 10.1016/j.cels.2026.101613

Andriy Goychuk, Salman F Banani, Pradeep Natarajan, Ming M Zheng, Haoran Wang, Giuseppe Dall'Agnese, Richard A Young, Mehran Kardar, Jonathan E Henninger, Arup K Chakraborty

{"title":"Active RNA synthesis patterns nuclear condensates.","authors":"Andriy Goychuk, Salman F Banani, Pradeep Natarajan, Ming M Zheng, Haoran Wang, Giuseppe Dall'Agnese, Richard A Young, Mehran Kardar, Jonathan E Henninger, Arup K Chakraborty","doi":"10.1016/j.cels.2026.101613","DOIUrl":"10.1016/j.cels.2026.101613","url":null,"abstract":"Biomolecular condensates are membraneless compartments that organize biochemical processes in cells. In contrast to well-understood mechanisms describing how condensates form and dissolve, the principles underlying condensate patterning-including their size, number, and spacing in the cell-remain largely unknown. We hypothesized that RNA, a key regulator of condensate formation and dissolution, influences condensate patterning. Using nucleolar fibrillar centers (FCs) as a model condensate, we found that inhibiting ribosomal RNA synthesis significantly alters the patterning of FCs. Physical theory and experimental observations support a model whereby active RNA synthesis generates a non-equilibrium state that arrests condensate coarsening and thus contributes to condensate patterning. Altering FC condensate patterning by expression of the FC component treacle ribosome biogenesis factor 1 (TCOF1) impairs ribosomal RNA processing, linking condensate patterning to biological function. These results reveal how non-equilibrium states driven by active chemical processes regulate condensate patterning, which is important for cellular biochemistry and function.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101613"},"PeriodicalIF":7.7,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148038547","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

A tissue-scale strategy for sensing threats in barrier organs. 在屏障器官中感知威胁的组织尺度策略。

IF 7.7

Cell systems Pub Date : 2026-07-15 Epub Date: 2026-05-14 DOI: 10.1016/j.cels.2026.101611

Diep H Nguyen, Jiakun Tian, Sean-Luc Shanahan, Connie Kangni Wang, Tyler Jacks, Xiao Wang, Pulin Li

{"title":"A tissue-scale strategy for sensing threats in barrier organs.","authors":"Diep H Nguyen, Jiakun Tian, Sean-Luc Shanahan, Connie Kangni Wang, Tyler Jacks, Xiao Wang, Pulin Li","doi":"10.1016/j.cels.2026.101611","DOIUrl":"10.1016/j.cels.2026.101611","url":null,"abstract":"Barrier organs rely on a limited set of pattern recognition receptors (PRRs) to detect diverse immunogenic challenges. How these organs assess threats and adjust immune responses to balance host protection with collateral damage remains unclear. Here, by characterizing the probability of detecting influenza infection across different cell types in the lung using single-molecule imaging and spatial transcriptomics, we discovered cell-type-specific probabilities of threat sensing: lowest in the epithelia and highest in the stroma. Such differential probabilities emerge from spatially graded expression of nucleic acid-sensing PRRs across lung tissue compartments, a strategy widely adopted by barrier organs. Selectively increasing the level of retinoic acid-inducible gene I (RIG-I) in lung epithelia in vivo exacerbates tissue damage upon non-infectious challenge, revealing the importance of dampening the sensitivity of barrier epithelia. Together, these results highlight a spatially tiered strategy to tolerate epithelia-restricted threats and yet enable progressively potent immune responses as threats invade deeper into the tissue.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101611"},"PeriodicalIF":7.7,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147950233","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

Phased variability in cell fate determination orchestrates antibody evolution. 细胞命运决定的阶段性变异性协调了抗体的进化。

IF 7.7

Cell systems Pub Date : 2026-06-17 DOI: 10.1016/j.cels.2026.101649

Zhaolin Hua

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 : 2026-06-17 Epub 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":"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":"2026-06-17","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

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 : 2026-06-17 Epub 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":"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":"2026-06-17","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 : 2026-06-17 Epub 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":"2026-06-17","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

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 : 2026-06-17 Epub 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

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 : 2026-06-17 Epub 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":"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":"2026-06-17","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