Cell systems最新文献_第3页

Multiobjective learning and design of bacteriophage specificity. 噬菌体特异性的多目标学习与设计。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-09-01 DOI: 10.1016/j.cels.2026.101712

Naia Novy, Phil Huss, Sarah Evert, Philip A Romero, Srivatsan Raman

引用次数: 0

Mapping the combinatorial coding between olfactory receptors and perception with deep learning. 用深度学习映射嗅觉受体和感知之间的组合编码。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-09-02 DOI: 10.1016/j.cels.2026.101711

Seyone Chithrananda, Judith Amores, Kevin K Yang

引用次数: 0

Plasma proteins are integral to cross-tissue gene regulatory networks implicated in cardiometabolic disorders and coronary artery disease. 血浆蛋白是涉及心脏代谢紊乱和冠状动脉疾病的跨组织基因调控网络的组成部分。

IF 7.7

Cell systems Pub Date : 2026-09-16 DOI: 10.1016/j.cels.2026.101735

Sean Bankier, Husain Talukdar, Mariyam Khan, Giuseppe Mocci, Katyayani Sukhavasi, Ke Hao, Angela Ma, Arno Ruusalepp, Eric E Schadt, Jason C Kovacic, Tom Michoel, Johan L M Björkegren

{"title":"Plasma proteins are integral to cross-tissue gene regulatory networks implicated in cardiometabolic disorders and coronary artery disease.","authors":"Sean Bankier, Husain Talukdar, Mariyam Khan, Giuseppe Mocci, Katyayani Sukhavasi, Ke Hao, Angela Ma, Arno Ruusalepp, Eric E Schadt, Jason C Kovacic, Tom Michoel, Johan L M Björkegren","doi":"10.1016/j.cels.2026.101735","DOIUrl":"https://doi.org/10.1016/j.cels.2026.101735","url":null,"abstract":"The plasma proteome has demonstrated promise for identifying diagnostic markers for cardiometabolic disorders (CMDs) and coronary artery disease (CAD). However, identifying the organ of origin for these biomarkers is critical for establishing biological relevance. We performed a multi-omic integrative analysis across multiple tissues from the STARNET study by profiling 974 plasma proteins in 532 CAD patients, integrating RNA sequencing (RNA-seq) data from the arterial wall, major metabolic organs, and blood. We identified 144 cis-protein quantitative trait loci in plasma, colocalizing with tissue cis-expression quantitative trait loci. Additionally, by mapping tissue mRNA \"seed genes,\" we traced 262 plasma proteins to their source organs, primarily the liver. Crucially, we found that 851 plasma proteins are associated with the activity of cross-tissue gene regulatory networks (GRNs), including GRNs implicated in CMD and CAD development. Our findings demonstrate that plasma proteins are integral components of GRNs, with potential for developing reliable diagnostics and precise therapeutic targets. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101735"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148984550","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

Quantifying protein unfolding kinetics with a high-throughput microfluidic platform. 用高通量微流控平台定量蛋白质展开动力学。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-07-29 DOI: 10.1016/j.cels.2026.101681

Beatriz Atsavapranee, Fanny Sunden, Daniel Herschlag, Polly M Fordyce

{"title":"Quantifying protein unfolding kinetics with a high-throughput microfluidic platform.","authors":"Beatriz Atsavapranee, Fanny Sunden, Daniel Herschlag, Polly M Fordyce","doi":"10.1016/j.cels.2026.101681","DOIUrl":"10.1016/j.cels.2026.101681","url":null,"abstract":"Even after folding, proteins sample unfolded intermediates at risk of irreversible alteration (e.g., via proteolysis, aggregation, or posttranslational modification). Thus, kinetic stability impacts protein lifetime and abundance. However, we have very few measurements of unfolding rates, largely due to technical challenges. To address this, we developed SPARKfold (simultaneous proteolysis assay revealing kinetics of folding), a microfluidic platform to express, purify, and measure unfolding rate constants at high throughput via native proteolysis. We applied SPARKfold to determine unfolding rate constants for 1,104 protein samples comprising 31 dihydrofolate reductase orthologs with up to 78 chamber replicates each, providing statistical power to resolve subtle effects. SPARKfold rate constants for 5 constructs agreed with traditional measurements across a 150-fold range and provided information about the folding transition state via φ analysis. In future work, SPARKfold can reveal mutations that drive misfolding and aggregation and enable the rational design of kinetically hyperstable variants for industrial use. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101681"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13484538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148622996","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

An optogenetic smart microscopy platform reveals signaling dynamics-dependent control over collective cell migration. 光遗传学智能显微镜平台揭示了对集体细胞迁移的信号动力学依赖控制。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-07-16 DOI: 10.1016/j.cels.2026.101674

Harrison R Oatman, Beena C Lad, Long Nguyen, Justin H Gubbens, Jared E Toettcher

{"title":"An optogenetic smart microscopy platform reveals signaling dynamics-dependent control over collective cell migration.","authors":"Harrison R Oatman, Beena C Lad, Long Nguyen, Justin H Gubbens, Jared E Toettcher","doi":"10.1016/j.cels.2026.101674","DOIUrl":"10.1016/j.cels.2026.101674","url":null,"abstract":"In cell biology, optical techniques can measure cells' internal states (biosensors) and stimulate cellular responses (optogenetics). Yet the design of all-optical experiments is often manual: a predetermined stimulus pattern is applied to cells, biosensors are measured over time, and data are processed offline. Here, we develop PyCLM, a Python-based suite enabling closed-loop measurement, image segmentation, and optogenetic control of thousands of cells per experiment. We showcase PyCLM on diverse applications, including performing feedback control on single cells and delivering developmental signaling patterns to Drosophila embryos. We compare single-cell versus tissue-scale optogenetic control of epithelial migration, revealing that fast and slow waves of receptor tyrosine kinase activity determine the direction of tissue movement, matching prior in vivo observations in zebrafish and mouse. PyCLM enables simple setup of dynamic experiments to probe cell and tissue properties and provides a first step toward real-time control of single-cell states at the tissue scale.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101674"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13479152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474579","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

Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models. 利用机器学习和生物物理模型预测TCR-pMHC相互作用的特异性。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-08-13 DOI: 10.1016/j.cels.2026.101700

Martin Culka, Nicolas W Lounsbury, William Thrift, Santrupti Nerli, Andrew Wallace, Gergő Nikolényi, Darya Orlova, Kiran Mukhyala, Mohammed AlQuraishi

{"title":"Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models.","authors":"Martin Culka, Nicolas W Lounsbury, William Thrift, Santrupti Nerli, Andrew Wallace, Gergő Nikolényi, Darya Orlova, Kiran Mukhyala, Mohammed AlQuraishi","doi":"10.1016/j.cels.2026.101700","DOIUrl":"10.1016/j.cels.2026.101700","url":null,"abstract":"Understanding T cell receptor (TCR) discrimination of MHC-presented epitope peptides (pMHCs) remains challenging. While machine-learning (ML)-based predictions of TCR specificity have gained attention, their capacity to generalize to unseen peptides is often misinterpreted. Using a proprietary cancer patient dataset, we show that ML methods succeed in predicting TCR specificity for known peptides but fail to generalize to novel peptides. Conversely, physics-based methods outperform ML methods on novel peptides but underperform on known peptides. In light of these observations, we develop a new ML method that leverages protein foundation models to achieve better or comparable performance than existing ML and biophysical methods on both in- and out-of-distribution TCR-pMHC specificity prediction. We furthermore characterize method performance as a function of distance of TCR sequence specificity between training and test sets. Our analysis elucidates the current limitations of modeling TCR-pMHC interactions and outlines new avenues for method development and data acquisition.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101700"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13502210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148762880","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

Multimolecular proofreading overcomes the activity-fidelity trade-off. 多分子校对克服了活度与保真度的权衡。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-07-13 DOI: 10.1016/j.cels.2026.101658

Zhuo Mao, Yuanqi Jia, Yuxuan Yan, Bingze Wu, Fangzhou Xiao, Zibo Chen

{"title":"Multimolecular proofreading overcomes the activity-fidelity trade-off.","authors":"Zhuo Mao, Yuanqi Jia, Yuxuan Yan, Bingze Wu, Fangzhou Xiao, Zibo Chen","doi":"10.1016/j.cels.2026.101658","DOIUrl":"10.1016/j.cels.2026.101658","url":null,"abstract":"Accurate signal processing is essential for proper cell functions and can be achieved through kinetic proofreading (KPR), where an enzyme undergoes sequential state transition and irreversible deactivation to enable high fidelity. However, synthetically constructing a biological proofreading system has been hindered by the difficulty in engineering single-molecular state transitions. Here, we designed a protein circuit that combines diffusion and endocytosis to enable kinetic proofreading at the multimolecular level without the conservation of total enzymes implicitly assumed in classic kinetic proofreading. Simulations revealed an experimentally crucial trade-off between circuit activity and fidelity, and theoretical analysis confirmed it to be fundamental in all kinetic proofreading systems. By integrating self-activation and mutual inhibition mechanisms, the circuit overcomes this activity-fidelity trade-off within biologically plausible parameter regimes. Our results extend proofreading schemes from single enzymes to a multimolecular context and represent a practical and generalizable strategy for constructing high-fidelity synthetic biological circuits. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101658"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148440081","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

A diversified consortium of engineered biosensors enables multi-target quantification. 多样化的工程生物传感器联盟使多目标量化成为可能。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-08-18 DOI: 10.1016/j.cels.2026.101702

Alyssa Jasmine Chiang, Nicholas Csicsery, Richard O'Laughlin, Leo Baumgart, Elizabeth Stasiowski, Phuc Nguyen, Austin Doughty, Myra Ashraf, Raegan Mink, Elina C Olson, Michael Ferry, Adam M Feist, Nan Hao, Karsten Zengler, Jeff Hasty

{"title":"A diversified consortium of engineered biosensors enables multi-target quantification.","authors":"Alyssa Jasmine Chiang, Nicholas Csicsery, Richard O'Laughlin, Leo Baumgart, Elizabeth Stasiowski, Phuc Nguyen, Austin Doughty, Myra Ashraf, Raegan Mink, Elina C Olson, Michael Ferry, Adam M Feist, Nan Hao, Karsten Zengler, Jeff Hasty","doi":"10.1016/j.cels.2026.101702","DOIUrl":"10.1016/j.cels.2026.101702","url":null,"abstract":"Whole-cell biosensors (WCBs) offer rapid, cost-effective monitoring of environmental contamination. Efforts to optimize detection of isolated target analytes under laboratory conditions have achieved vastly improved performance and set the stage for WCB deployment in complex environments. We propose a framework that leverages cross-reactivity of single-target WCBs to quantify multiple targets using supervised machine learning. Specifically, we engineer six sensors for heavy metal contaminants in laboratory E. coli. We then evolve the strain to generate five chassis with improved growth in seawater conditions and transform them with the sensors to create a set of 30 variants. The variant responses are characterized with microfluidics, revealing significant diversity. Leveraging this diversity, we combinatorially quantify multiple analytes with a machine learning model that takes an in silico consortium of response inputs and outperforms single-target WCBs in over 90% of test samples. These results form a generalizable framework that facilitates WCB translation toward settings beyond the laboratory. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101702"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803189","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

Synthetic tissue ecology: Ecological control of developmental self-organization across scales. 合成组织生态学：跨尺度发育自组织的生态控制。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-08-19 DOI: 10.1016/j.cels.2026.101699

Kamyar Keshavarz, Mo R Ebrahimkhani

{"title":"Synthetic tissue ecology: Ecological control of developmental self-organization across scales.","authors":"Kamyar Keshavarz, Mo R Ebrahimkhani","doi":"10.1016/j.cels.2026.101699","DOIUrl":"10.1016/j.cels.2026.101699","url":null,"abstract":"Embryonic development builds tissues and organs through the self-organization of heterogeneous cells across spatial and temporal scales, analogous to an ecosystem. Although ecological systems are often stochastic and multistable, embryogenesis is remarkably reproducible. We argue that this contrast is not a contradiction. Developmental robustness arises from evolutionarily filtered ecological interactions embedded within hierarchically organized, multiscale architectures that restrict accessible collective states. Competition, cooperation, niche construction, and density-dependent coupling are not metaphors but core interaction dynamics that shape tissues in vivo. In vitro systems frequently lack these multiscale constraints, allowing more permissive ecological dynamics and increased variability. We propose \"synthetic tissue ecology\" as a conceptual and engineering framework that treats development as a stabilized regime of ecological organization. By shifting focus to interaction architectures and constraints that direct cross-scale self-organization, this framework enables the decoding of mesoscale modules and tissue-coupling principles while supporting the engineering of robust organoids, embryoids, and regenerative systems.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101699"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803156","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

Pooled combinatorial screening identifies transcription factor sets that drive hematopoietic progenitor-like cell fate. 集合组合筛选确定驱动造血祖样细胞命运的转录因子集。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-06-18 DOI: 10.1016/j.cels.2026.101644

James A Briggs, Janghwan Cho, Daniel Strebinger, Julia Joung, Tatum W Braun, Rhiannon K Macrae, Hojun Li, Feng Zhang

{"title":"Pooled combinatorial screening identifies transcription factor sets that drive hematopoietic progenitor-like cell fate.","authors":"James A Briggs, Janghwan Cho, Daniel Strebinger, Julia Joung, Tatum W Braun, Rhiannon K Macrae, Hojun Li, Feng Zhang","doi":"10.1016/j.cels.2026.101644","DOIUrl":"10.1016/j.cels.2026.101644","url":null,"abstract":"Mammalian cells can be directed toward specific fates by overexpression of transcription factors (TFs). However, discovering and optimizing which TFs in combination produce a state of interest remains challenging. Here, we develop a scalable screening platform that addresses this challenge by combining high multiplicity of infection (MOI), pooled delivery of barcoded TF open reading frames (ORFs), data augmentation, targeted cell enrichments, and single-cell transcriptomic readouts. As proof of principle, we apply the platform to optimize the generation of hematopoietic stem and progenitor-like cells (HSPCs) from human embryonic stem cells. Our data demonstrate technical performance across a range of key metrics and reveal a richly structured reprogramming fitness landscape over millions of TF combinations. In silico optimization of HSPC-similarity metrics over this landscape revealed two TF combinations that demonstrate superior potency in generating naive multipotent hematopoietic progenitors relative to gold-standard controls. This study demonstrates a powerful approach for data-driven cell fate engineering using complex combinatorial perturbations.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101644"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280520","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