Cell systems最新文献_第3页

Flexible and robust cell-type annotation for highly multiplexed tissue images. 灵活和鲁棒的细胞类型注释高度复用的组织图像。

IF 7.7

Cell systems Pub Date : 2025-09-17 Epub Date: 2025-09-08 DOI: 10.1016/j.cels.2025.101374

Huangqingbo Sun, Shiqiu Yu, Anna Martinez Casals, Anna Bäckström, Yuxin Lu, Cecilia Lindskog, Matthew Ruffalo, Emma Lundberg, Robert F Murphy

引用次数: 0

TissueMosaic: Self-supervised learning of tissue representations enables differential spatial transcriptomics across samples. 组织嵌合：组织表征的自我监督学习使跨样本的差异空间转录组学成为可能。

IF 7.7

Cell systems Pub Date : 2025-09-17 Epub Date: 2025-09-08 DOI: 10.1016/j.cels.2025.101394

Sandeep Kambhampati, Luca D'Alessio, Fedor Grab, Stephen Fleming, Sophia Liu, Ruth Raichur, Fei Chen, Mehrtash Babadi

{"title":"TissueMosaic: Self-supervised learning of tissue representations enables differential spatial transcriptomics across samples.","authors":"Sandeep Kambhampati, Luca D'Alessio, Fedor Grab, Stephen Fleming, Sophia Liu, Ruth Raichur, Fei Chen, Mehrtash Babadi","doi":"10.1016/j.cels.2025.101394","DOIUrl":"10.1016/j.cels.2025.101394","url":null,"abstract":"Spatial transcriptomics allows for the measurement of gene expression within the native tissue context. However, despite technological advancements, computational methods to link cell states with their microenvironment and compare these relationships across samples and conditions remain limited. To address this, we introduce Tissue Motif-Based Spatial Inference across Conditions (TissueMosaic), a self-supervised convolutional neural network designed to discover and represent tissue architectural motifs from multi-sample spatial transcriptomic datasets. TissueMosaic further links these motifs to gene expression, enabling the study of how changes in tissue structure impact cell-intrinsic function. TissueMosaic increases the signal-to-noise ratio of spatial differential expression analysis through a motif enrichment strategy, resulting in more reliable detection of genes that covary with tissue structure changes. Here, we demonstrate that TissueMosaic learns representations that outperform neighborhood cell-type composition baselines and existing methods on downstream tasks. These findings underscore the potential of self-supervised learning to advance spatial transcriptomics discovery.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101394"},"PeriodicalIF":7.7,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030783","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-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models. 单序列蛋白质- rna复合物结构预测的几何注意激活配对的生物语言模型。

IF 7.7

Cell systems Pub Date : 2025-09-16 DOI: 10.1016/j.cels.2025.101400

Rahmatullah Roche, Sumit Tarafder, Debswapna Bhattacharya

{"title":"Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.","authors":"Rahmatullah Roche, Sumit Tarafder, Debswapna Bhattacharya","doi":"10.1016/j.cels.2025.101400","DOIUrl":"10.1016/j.cels.2025.101400","url":null,"abstract":"Groundbreaking progress has been made in structure prediction of biomolecular assemblies, including the recent breakthrough of AlphaFold 3. However, it remains challenging for AlphaFold 3 and other state-of-the-art deep-learning-based methods to accurately predict protein-RNA complex structures, in part due to the limited availability of evolutionary information related to protein-RNA interactions that are used as inputs to the existing approaches. Here, we introduce ProRNA3D-single, a deep-learning framework for protein-RNA complex structure prediction. Using a geometric attention-enabled pairing of biological language models of protein and RNA, a previously unexplored avenue, ProRNA3D-single predicts interatomic protein-RNA interaction maps, which are then transformed into multi-scale geometric restraints for modeling 3D structures of protein-RNA complexes via geometry optimization. Benchmark tests show that ProRNA3D-single outperforms state-of-the-art methods, including AlphaFold 3, particularly when evolutionary information is limited, and exhibits robustness and performance resilience by attaining state-of-the-art accuracy with only single-sequence input.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101400"},"PeriodicalIF":7.7,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082673","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

Microbial bellwether: Community-scale metabolic modeling to predict infection. 微生物领头羊：社区规模代谢模型预测感染。

IF 7.7

Cell systems Pub Date : 2025-08-20 DOI: 10.1016/j.cels.2025.101370

Connor Tiffany, Joseph P Zackular

引用次数: 0

Assessing generative model coverage of protein structures with SHAPES. 用SHAPES评估蛋白质结构的生成模型覆盖率。

IF 7.7

Cell systems Pub Date : 2025-08-20 Epub Date: 2025-07-29 DOI: 10.1016/j.cels.2025.101347

Tianyu Lu, Melissa Liu, Yilin Chen, Jinho Kim, Po-Ssu Huang

{"title":"Assessing generative model coverage of protein structures with SHAPES.","authors":"Tianyu Lu, Melissa Liu, Yilin Chen, Jinho Kim, Po-Ssu Huang","doi":"10.1016/j.cels.2025.101347","DOIUrl":"10.1016/j.cels.2025.101347","url":null,"abstract":"Recent advances in generative modeling enable efficient sampling of protein structures, but their tendency to optimize for designability imposes a bias toward idealized structures at the expense of loops and other complex structural motifs that are critical for function. We introduce SHAPES (structural and hierarchical assessment of proteins with embedding similarity) to evaluate five state-of-the-art generative models of protein structures. Using structural embeddings across multiple structural hierarchies, ranging from local geometries to global protein architectures, we reveal substantial undersampling of the observed protein structure space by these models. We use Fréchet protein distance (FPD) to quantify distributional coverage. Different models are distinct in their coverage behavior across different sampling noise scales and temperatures. The frequency of tertiary motifs (TERMs) further supports the observations. More robust sequence design and structure prediction methods are likely crucial in guiding the development of models with improved coverage of the designable protein space. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101347"},"PeriodicalIF":7.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12321228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755440","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

T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database. 综合突变扫描数据库改进T细胞受体交叉反应性预测。

IF 7.7

Cell systems Pub Date : 2025-08-20 Epub Date: 2025-07-25 DOI: 10.1016/j.cels.2025.101345

Amitava Banerjee, David J Pattinson, Cornelia L Wincek, Paul Bunk, Armend Axhemi, Sarah R Chapin, Saket Navlakha, Hannah V Meyer

{"title":"T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database.","authors":"Amitava Banerjee, David J Pattinson, Cornelia L Wincek, Paul Bunk, Armend Axhemi, Sarah R Chapin, Saket Navlakha, Hannah V Meyer","doi":"10.1016/j.cels.2025.101345","DOIUrl":"10.1016/j.cels.2025.101345","url":null,"abstract":"Comprehensively mapping all targets of a T cell receptor (TCR) is important for predicting pathogenic escape and off-target effects of TCR therapies. However, this mapping has been challenging due to lack of unbiased benchmarking datasets and computational methods sensitive to small-peptide mutations. To address this, we curated the benchmark for activation of T cells with cross-reactive avidity for epitopes (BATCAVE) database, encompassing near-complete single-amino-acid mutational assays, centered around 25 immunogenic epitopes, across both major histocompatibility complex classes, against 151 human and mouse TCRs, containing 22,000+ TCR-peptide pairs in total. We then introduce Bayesian inference of activation of TCR by mutant antigens (BATMAN), an interpretable Bayesian model, trained on BATCAVE, for predicting the peptides that activate a TCR, and an active learning extension, which efficiently maps targets of a novel TCR by selecting a few peptides to assay. We show that BATMAN outperforms existing methods, reveals structural and biochemical predictors of TCR-peptide interactions, and can predict polyclonal T cell responses and TCR targets with high sequence dissimilarity. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101345"},"PeriodicalIF":7.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719264","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

Tumor-adipose assembloids reveal cell-fate-transition-triggered multistage collective invasions. 肿瘤脂肪组合揭示了细胞命运转变引发的多阶段集体侵袭。

IF 7.7

Cell systems Pub Date : 2025-08-20 Epub Date: 2025-08-08 DOI: 10.1016/j.cels.2025.101353

Mengcheng Lei, Jiashuo Li, Xueqing Ren, Han Xie, Pengjie Li, Fukang Qi, Jinyun Shi, Xiaolu Cai, Yuanyuan Liu, Peng Chen, Xiaojun Feng, Limin Xia, Fubing Wang, Hui Li, Ming Guo, Yang Zhang, Bi-Feng Liu, Yiwei Li

{"title":"Tumor-adipose assembloids reveal cell-fate-transition-triggered multistage collective invasions.","authors":"Mengcheng Lei, Jiashuo Li, Xueqing Ren, Han Xie, Pengjie Li, Fukang Qi, Jinyun Shi, Xiaolu Cai, Yuanyuan Liu, Peng Chen, Xiaojun Feng, Limin Xia, Fubing Wang, Hui Li, Ming Guo, Yang Zhang, Bi-Feng Liu, Yiwei Li","doi":"10.1016/j.cels.2025.101353","DOIUrl":"10.1016/j.cels.2025.101353","url":null,"abstract":"Tumor invasion constitutes a multifaceted process encompassing collective cellular migration and dynamic cell-fate transitions. Although these aspects have been studied separately by physicists and biologists, their spatiotemporal coupling remains unclear. To bridge this gap, we introduce a tumor-adipose assembloid model that facilitates live tracking and temporal analysis of cancer cells and adipocytes. The tumor assembloids manifest two discrete phases of morphogenic behavior, delineated by the reprogramming of adipocytes. In the initial phase, the biophysical interactions between cancer cells and adipocytes can be modeled as contact between viscoelastic drops. This interaction precedes the adipocytes' dedifferentiation and subsequent myofibrogenic reprogramming. The emergence of adipocyte-derived myofibroblasts instigates assembloid invasion through the mechanical remodeling of surrounding collagen networks. Our findings unveil a paradigm shift in understanding the evolutionary dynamics of heterotypic multicellular systems, wherein cell-fate transitions act as catalytic events that initiate serial patterns of collective morphogenesis via alterations in extracellular biophysical interactions.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101353"},"PeriodicalIF":7.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812790","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

Challenges in interpreting functional redundancy and quantifying functional selection in microbial communities. 在解释功能冗余和量化功能选择微生物群落的挑战。

IF 7.7

Cell systems Pub Date : 2025-08-20 Epub Date: 2025-08-04 DOI: 10.1016/j.cels.2025.101350

Po-Yi Ho, Kerwyn Casey Huang

引用次数: 0

Personalized Clostridioides difficile colonization risk prediction and probiotic therapy assessment in the human gut. 难辨梭菌个体化定殖风险预测及益生菌治疗评估。

IF 7.7

Cell systems Pub Date : 2025-08-20 Epub Date: 2025-08-06 DOI: 10.1016/j.cels.2025.101367

Alex V Carr, Nitin S Baliga, Christian Diener, Sean M Gibbons

{"title":"Personalized Clostridioides difficile colonization risk prediction and probiotic therapy assessment in the human gut.","authors":"Alex V Carr, Nitin S Baliga, Christian Diener, Sean M Gibbons","doi":"10.1016/j.cels.2025.101367","DOIUrl":"10.1016/j.cels.2025.101367","url":null,"abstract":"Clostridioides difficile (C. difficile) colonizes up to 40% of community-dwelling adults without causing disease but can eventually lead to infection (C. difficile infection [CDI]). There has been a lack of focus on how to prevent colonization and facilitate the successful clearance of C. difficile prior to the emergence of CDI. We show that microbial community-scale metabolic models (MCMMs) accurately predict C. difficile colonization susceptibility in vitro and in vivo, offering mechanistic insights into microbiota-specific interactions involving metabolites like succinate, trehalose, and ornithine. MCMMs reveal distinct C. difficile metabolic niches-two growth-associated and one non-growth-associated-observed across 15,204 individuals from five cohorts. We further demonstrate that MCMMs can predict personalized C. difficile growth suppression by a probiotic cocktail designed to replace fecal microbiota transplants (FMTs) for the treatment of recurrent CDI, and we identify new probiotic targets for future validation. MCMMs represent a powerful framework for predicting pathogen colonization and assessing probiotic efficacy across diverse microbiota 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":"101367"},"PeriodicalIF":7.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12497417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144801231","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

Shaping the uncharted: Revealing the protein structure space from the perspective of generative models. 塑造未知：从生成模型的视角揭示蛋白质结构空间。

IF 7.7

Cell systems Pub Date : 2025-08-20 DOI: 10.1016/j.cels.2025.101369

Luiz Felipe Piochi, Hamed Khakzad

引用次数: 0