Cell systems最新文献_第4页

Probing enzyme-dependent pseudouridylation using direct RNA sequencing to assess epitranscriptome plasticity in a neuronal cell line. 利用直接RNA测序探测酶依赖性假尿嘧啶化以评估神经细胞系的表转录组可塑性。

Cell systems Pub Date : 2025-04-16 Epub Date: 2025-03-20 DOI: 10.1016/j.cels.2025.101238

Oleksandra Fanari, Sepideh Tavakoli, Yuchen Qiu, Amr Makhamreh, Keqing Nian, Stuart Akeson, Michele Meseonznik, Caroline A McCormick, Dylan Bloch, Howard Gamper, Miten Jain, Ya-Ming Hou, Meni Wanunu, Sara H Rouhanifard

{"title":"Probing enzyme-dependent pseudouridylation using direct RNA sequencing to assess epitranscriptome plasticity in a neuronal cell line.","authors":"Oleksandra Fanari, Sepideh Tavakoli, Yuchen Qiu, Amr Makhamreh, Keqing Nian, Stuart Akeson, Michele Meseonznik, Caroline A McCormick, Dylan Bloch, Howard Gamper, Miten Jain, Ya-Ming Hou, Meni Wanunu, Sara H Rouhanifard","doi":"10.1016/j.cels.2025.101238","DOIUrl":"10.1016/j.cels.2025.101238","url":null,"abstract":"Chemical modifications in mRNAs, such as pseudouridine (psi), can control gene expression. Yet, we know little about how they are regulated, especially in neurons. We applied nanopore direct RNA sequencing to investigate psi dynamics in SH-SY5Y cells in response to two perturbations that model a natural and unnatural cellular state: retinoic-acid-mediated differentiation (healthy) and exposure to the neurotoxicant lead (unhealthy). We discovered that the expression of some psi writers changes significantly in response to physiological conditions. We also found that globally, lead-treated cells have more psi sites but lower relative occupancy than untreated cells and differentiated cells. Examples of highly plastic sites were accompanied by constant expression for psi writers, suggesting trans-regulation. Many positions were static throughout all three cellular states, suggestive of a \"housekeeping\" function. This study enables investigations into mechanisms that control psi modifications in neurons and their possible protective effects in response to cellular stress.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101238"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12006983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674910","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

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 : 2025-04-16 Epub Date: 2025-04-04 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":"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":"2025-04-16","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

Evaluation of De Vries et al.: Quantifying cellular shapes and how they correlate to cellular responses. De Vries等人的评价：量化细胞形状及其与细胞反应的关系。

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

Manuel D Leonetti

引用次数: 0

Virus targeting as a dominant driver of interfacial evolution in the structurally resolved human-virus protein-protein interaction network. 在结构上解决的人-病毒蛋白-蛋白相互作用网络中，病毒靶向是界面进化的主要驱动因素。

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

Geometric deep learning and multiple-instance learning for 3D cell-shape profiling. 三维细胞形状轮廓的几何深度学习和多实例学习。

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":"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

Self-resistance-gene-guided, high-throughput automated genome mining of bioactive natural products from Streptomyces. 链霉菌生物活性天然产物的自抗性基因引导、高通量自动基因组挖掘。

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

Engineering highly active nuclease enzymes with machine learning and high-throughput screening. 利用机器学习和高通量筛选技术设计高活性核酸酶。

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

Multiplexed dynamic control of temperature to probe and observe mammalian cells. 对温度进行多重动态控制，以探测和观察哺乳动物细胞。

Cell systems Pub Date : 2025-03-19 Epub Date: 2025-03-12 DOI: 10.1016/j.cels.2025.101234

William Benman, Pavan Iyengar, Thomas R Mumford, Zikang Huang, Manya Kapoor, Grace Liu, Lukasz J Bugaj

引用次数: 0

Tissue sculpting with light. 组织雕刻与光。

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

Romane Petit, Léo Valon

引用次数: 0

Large-scale control over collective cell migration using light-activated epidermal growth factor receptors. 利用光激活表皮生长因子受体对集体细胞迁移的大规模控制。

Cell systems Pub Date : 2025-03-19 Epub Date: 2025-03-03 DOI: 10.1016/j.cels.2025.101203

Kevin Suh, Richard H Thornton, Long Nguyen, Payam E Farahani, Daniel J Cohen, Jared E Toettcher

{"title":"Large-scale control over collective cell migration using light-activated epidermal growth factor receptors.","authors":"Kevin Suh, Richard H Thornton, Long Nguyen, Payam E Farahani, Daniel J Cohen, Jared E Toettcher","doi":"10.1016/j.cels.2025.101203","DOIUrl":"10.1016/j.cels.2025.101203","url":null,"abstract":"Receptor tyrosine kinases (RTKs) play key roles in coordinating cell movement at both single-cell and tissue scales. The recent development of optogenetic tools for controlling RTKs and their downstream signaling pathways suggests that these responses may be amenable to engineering-based control for sculpting tissue shape and function. Here, we report that a light-controlled epidermal growth factor (EGF) receptor (OptoEGFR) can be deployed in epithelial cells for precise, programmable control of long-range tissue movements. We show that in OptoEGFR-expressing tissues, light can drive millimeter-scale cell rearrangements to densify interior regions or produce rapid outgrowth at tissue edges. Light-controlled tissue movements are driven primarily by phosphoinositide 3-kinase (PI3K) signaling, rather than diffusible ligands, tissue contractility, or ERK kinase signaling as seen in other RTK-driven migration contexts. Our study suggests that synthetic, light-controlled RTKs could serve as a powerful platform for controlling cell positions and densities for diverse applications, including wound healing and tissue morphogenesis.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101203"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560400","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