Cell systems最新文献

Explainable modeling of single-cell perturbation data using attention and sparse dictionary learning. 利用注意力和稀疏字典学习对单细胞扰动数据进行可解释建模

Cell systems Pub Date : 2025-03-31 DOI: 10.1016/j.cels.2025.101245

Yang Xu, Stephen Fleming, Matthew Tegtmeyer, Steven A McCarroll, Mehrtash Babadi

{"title":"Explainable modeling of single-cell perturbation data using attention and sparse dictionary learning.","authors":"Yang Xu, Stephen Fleming, Matthew Tegtmeyer, Steven A McCarroll, Mehrtash Babadi","doi":"10.1016/j.cels.2025.101245","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101245","url":null,"abstract":"Single-cell transcriptomics, in conjunction with genetic and compound perturbations, offers a robust approach for exploring cellular behaviors in diverse contexts. Such experiments allow uncovering cell-state-specific responses to perturbations and unraveling the intricate molecular mechanisms governing cellular behavior. However, prevailing computational methods predominantly focus on predicting average cellular responses, disregarding inherent response heterogeneity associated with cell state diversity and model explainability. In this study, we present CellCap, a deep generative model designed for the end-to-end analysis of single-cell perturbation experiments. CellCap employs sparse dictionary learning in a latent space to deconstruct cell-state-specific perturbation responses into a set of transcriptional response programs and utilizes an attention mechanism to capture correspondence between cell state and perturbation response. We thoroughly evaluate CellCap's interpretability using multiple simulated scenarios as well as two real single-cell perturbation datasets. Our results demonstrate that CellCap successfully uncovers the relationship between cell state and perturbation response, unveiling insights overlooked in previous analyses.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101245"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789413","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

SpaGRN: Investigating spatially informed regulatory paths for spatially resolved transcriptomics data.

Cell systems Pub Date : 2025-03-27 DOI: 10.1016/j.cels.2025.101243

Yao Li, Xiaobin Liu, Lidong Guo, Kai Han, Shuangsang Fang, Xinjiang Wan, Dantong Wang, Xun Xu, Ling Jiang, Guangyi Fan, Mengyang Xu

{"title":"SpaGRN: Investigating spatially informed regulatory paths for spatially resolved transcriptomics data.","authors":"Yao Li, Xiaobin Liu, Lidong Guo, Kai Han, Shuangsang Fang, Xinjiang Wan, Dantong Wang, Xun Xu, Ling Jiang, Guangyi Fan, Mengyang Xu","doi":"10.1016/j.cels.2025.101243","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101243","url":null,"abstract":"Cells spatially organize into distinct cell types or functional domains through localized gene regulatory networks. However, current spatially resolved transcriptomics analyses fail to integrate spatial constraints and proximal cell influences, limiting the mechanistic understanding of tissue organization. Here, we introduce SpaGRN, a statistical framework that reconstructs cell-type- or functional-domain-specific, dynamic, and spatial regulons by coupling intracellular spatial regulatory causality with extracellular signaling path information. Benchmarking across synthetic and real datasets demonstrates SpaGRN's superior precision over state-of-the-art tools in identifying context-dependent regulons. Applied to diverse spatially resolved transcriptomics platforms (Stereo-seq, STARmap, MERFISH, CosMx, Slide-seq, and 10x Visium), complex cancerous samples, and 3D datasets of developing Drosophila embryos and larvae, SpaGRN not only provides a versatile toolkit for decoding receptor-mediated spatial regulons but also reveals spatiotemporal regulatory mechanisms underlying organogenesis and inflammation.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101243"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782281","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

Assembly of stool-derived bacterial communities follows "early-bird" resource utilization dynamics.

Cell systems Pub Date : 2025-03-25 DOI: 10.1016/j.cels.2025.101240

Andrés Aranda-Díaz, Lisa Willis, Taylor H Nguyen, Po-Yi Ho, Jean Vila, Tani Thomsen, Taylor Chavez, Rose Yan, Feiqiao Brian Yu, Norma Neff, Brian C DeFelice, Alvaro Sanchez, Sylvie Estrela, Kerwyn Casey Huang

{"title":"Assembly of stool-derived bacterial communities follows \"early-bird\" resource utilization dynamics.","authors":"Andrés Aranda-Díaz, Lisa Willis, Taylor H Nguyen, Po-Yi Ho, Jean Vila, Tani Thomsen, Taylor Chavez, Rose Yan, Feiqiao Brian Yu, Norma Neff, Brian C DeFelice, Alvaro Sanchez, Sylvie Estrela, Kerwyn Casey Huang","doi":"10.1016/j.cels.2025.101240","DOIUrl":"10.1016/j.cels.2025.101240","url":null,"abstract":"Diet can impact host health through changes to the gut microbiota, yet we lack mechanistic understanding linking nutrient availability and microbiota composition. Here, we use thousands of microbial communities cultured in vitro from human stool to develop a predictive model of community composition upon addition of single nutrients from central carbon metabolism to a complex medium. Among these communities, membership was largely determined by the donor stool, whereas relative abundances were determined by the supplemental carbon source. The absolute abundance of most taxa was independent of the supplementing nutrient due to the ability of a few organisms to quickly exhaust their niche in the complex medium and then exploit and monopolize the supplemental carbon source. Relative abundances of dominant taxa could be predicted from the nutritional preferences and growth dynamics of species in isolation, and exceptions were consistent with strain-level variation in growth capabilities. Our study reveals that assembly of this community of gut commensals can be explained by nutrient utilization dynamics that provide a predictive framework for manipulating community composition through nutritional perturbations.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101240"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744676","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

Virus targeting as a dominant driver of interfacial evolution in the structurally resolved human-virus protein-protein interaction network.

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

Evaluation of De Vries et al.: Quantifying cellular shapes and how they correlate to cellular responses.

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

Manuel D Leonetti

引用次数: 0

Geometric deep learning and multiple-instance learning for 3D cell-shape profiling.

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":"https://doi.org/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

Engineering highly active nuclease enzymes with machine learning and high-throughput screening.

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

Self-resistance-gene-guided, high-throughput automated genome mining of bioactive natural products from Streptomyces.

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

Tissue sculpting with light.

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

Romane Petit, Léo Valon

引用次数: 0