Cell systems最新文献_第4页

What is the main bottleneck in deriving biological understanding from spatial transcriptomic profiling?

Cell systems Pub Date : 2025-02-19 DOI: 10.1016/j.cels.2025.101200

Jeffrey R Moffitt, Mingyao Li, Qing Nie, Kazumasa Kanemaru, Sarah A Teichmann, Daniel Dar, Luciane T Kagohara, Shalev Itzkovitz, Roser Vento-Tormo, Itai Yanai, Elana J Fertig, Fabian J Theis

引用次数: 0

Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome. 多组扰动序列解锁可扩展的发现对转录组和表观基因组的综合扰动效应。

Cell systems Pub Date : 2025-01-15 Epub Date: 2024-12-16 DOI: 10.1016/j.cels.2024.12.002

Eli Metzner, Kaden M Southard, Thomas M Norman

{"title":"Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.","authors":"Eli Metzner, Kaden M Southard, Thomas M Norman","doi":"10.1016/j.cels.2024.12.002","DOIUrl":"10.1016/j.cels.2024.12.002","url":null,"abstract":"Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here, we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Modeling of perturbation-induced heterogeneity connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101161"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11738662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848688","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

CDK2 activity crosstalk on the ERK kinase translocation reporter can be resolved computationally. ERK激酶易位报告基因上的CDK2活性串扰可以通过计算解决。

Cell systems Pub Date : 2025-01-15 DOI: 10.1016/j.cels.2024.12.003

Timothy E Hoffman, Chengzhe Tian, Varuna Nangia, Chen Yang, Sergi Regot, Luca Gerosa, Sabrina L Spencer

引用次数: 0

Modeling non-genetic adaptation in tumor cells. 模拟肿瘤细胞的非遗传适应。

Cell systems Pub Date : 2025-01-15 DOI: 10.1016/j.cels.2024.12.007

Edmund C Lattime, Subhajyoti De

引用次数: 0

Contrastive learning of T cell receptor representations. T细胞受体表征的对比学习。

Cell systems Pub Date : 2025-01-15 Epub Date: 2025-01-07 DOI: 10.1016/j.cels.2024.12.006

Yuta Nagano, Andrew G T Pyo, Martina Milighetti, James Henderson, John Shawe-Taylor, Benny Chain, Andreas Tiffeau-Mayer

{"title":"Contrastive learning of T cell receptor representations.","authors":"Yuta Nagano, Andrew G T Pyo, Martina Milighetti, James Henderson, John Shawe-Taylor, Benny Chain, Andreas Tiffeau-Mayer","doi":"10.1016/j.cels.2024.12.006","DOIUrl":"10.1016/j.cels.2024.12.006","url":null,"abstract":"Computational prediction of the interaction of T cell receptors (TCRs) and their ligands is a grand challenge in immunology. Despite advances in high-throughput assays, specificity-labeled TCR data remain sparse. In other domains, the pre-training of language models on unlabeled data has been successfully used to address data bottlenecks. However, it is unclear how to best pre-train protein language models for TCR specificity prediction. Here, we introduce a TCR language model called SCEPTR (simple contrastive embedding of the primary sequence of T cell receptors), which is capable of data-efficient transfer learning. Through our model, we introduce a pre-training strategy combining autocontrastive learning and masked-language modeling, which enables SCEPTR to achieve its state-of-the-art performance. In contrast, existing protein language models and a variant of SCEPTR pre-trained without autocontrastive learning are outperformed by sequence alignment-based methods. We anticipate that contrastive learning will be a useful paradigm to decode the rules of TCR specificity. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101165"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960222","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

Inferring metabolic objectives and trade-offs in single cells during embryogenesis. 推断胚胎发生过程中单细胞的代谢目标和权衡。

Cell systems Pub Date : 2025-01-15 Epub Date: 2025-01-07 DOI: 10.1016/j.cels.2024.12.005

Da-Wei Lin, Ling Zhang, Jin Zhang, Sriram Chandrasekaran

{"title":"Inferring metabolic objectives and trade-offs in single cells during embryogenesis.","authors":"Da-Wei Lin, Ling Zhang, Jin Zhang, Sriram Chandrasekaran","doi":"10.1016/j.cels.2024.12.005","DOIUrl":"10.1016/j.cels.2024.12.005","url":null,"abstract":"While proliferating cells optimize their metabolism to produce biomass, the metabolic objectives of cells that perform non-proliferative tasks are unclear. The opposing requirements for optimizing each objective result in a trade-off that forces single cells to prioritize their metabolic needs and optimally allocate limited resources. Here, we present single-cell optimization objective and trade-off inference (SCOOTI), which infers metabolic objectives and trade-offs in biological systems by integrating bulk and single-cell omics data, using metabolic modeling and machine learning. We validated SCOOTI by identifying essential genes from CRISPR-Cas9 screens in embryonic stem cells, and by inferring the metabolic objectives of quiescent cells, during different cell-cycle phases. Applying this to embryonic cell states, we observed a decrease in metabolic entropy upon development. We further uncovered a trade-off between glutathione and biosynthetic precursors in one-cell zygote, two-cell embryo, and blastocyst cells, potentially representing a trade-off between pluripotency and proliferation. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101164"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11738665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960238","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

Stochastic modeling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones. 单细胞基因表达适应性的随机建模揭示了肿瘤亚克隆进化的非基因组因素。

Cell systems Pub Date : 2025-01-15 Epub Date: 2024-12-18 DOI: 10.1016/j.cels.2024.11.013

M G Hirsch, Soumitra Pal, Farid Rashidi Mehrabadi, Salem Malikic, Charli Gruen, Antonella Sassano, Eva Pérez-Guijarro, Glenn Merlino, S Cenk Sahinalp, Erin K Molloy, Chi-Ping Day, Teresa M Przytycka

{"title":"Stochastic modeling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones.","authors":"M G Hirsch, Soumitra Pal, Farid Rashidi Mehrabadi, Salem Malikic, Charli Gruen, Antonella Sassano, Eva Pérez-Guijarro, Glenn Merlino, S Cenk Sahinalp, Erin K Molloy, Chi-Ping Day, Teresa M Przytycka","doi":"10.1016/j.cels.2024.11.013","DOIUrl":"10.1016/j.cels.2024.11.013","url":null,"abstract":"Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present a formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Sublines previously observed to be resistant to anti-CTLA4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101156"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11867576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866641","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

Active learning of enhancers and silencers in the developing neural retina. 在发育中的神经视网膜中增强和沉默的主动学习。

Cell systems Pub Date : 2025-01-15 Epub Date: 2025-01-07 DOI: 10.1016/j.cels.2024.12.004

Ryan Z Friedman, Avinash Ramu, Sara Lichtarge, Yawei Wu, Lloyd Tripp, Daniel Lyon, Connie A Myers, David M Granas, Maria Gause, Joseph C Corbo, Barak A Cohen, Michael A White

{"title":"Active learning of enhancers and silencers in the developing neural retina.","authors":"Ryan Z Friedman, Avinash Ramu, Sara Lichtarge, Yawei Wu, Lloyd Tripp, Daniel Lyon, Connie A Myers, David M Granas, Maria Gause, Joseph C Corbo, Barak A Cohen, Michael A White","doi":"10.1016/j.cels.2024.12.004","DOIUrl":"10.1016/j.cels.2024.12.004","url":null,"abstract":"Deep learning is a promising strategy for modeling cis-regulatory elements. However, models trained on genomic sequences often fail to explain why the same transcription factor can activate or repress transcription in different contexts. To address this limitation, we developed an active learning approach to train models that distinguish between enhancers and silencers composed of binding sites for the photoreceptor transcription factor cone-rod homeobox (CRX). After training the model on nearly all bound CRX sites from the genome, we coupled synthetic biology with uncertainty sampling to generate additional rounds of informative training data. This allowed us to iteratively train models on data from multiple rounds of massively parallel reporter assays. The ability of the resulting models to discriminate between CRX sites with identical sequence but opposite functions establishes active learning as an effective strategy to train models of regulatory DNA. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101163"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11827711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960219","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

Optimized reporters for multiplexed detection of transcription factor activity. 优化的转录因子活性多重检测报告。

Cell systems Pub Date : 2024-12-18 Epub Date: 2024-12-06 DOI: 10.1016/j.cels.2024.11.003

Max Trauernicht, Teodora Filipovska, Chaitanya Rastogi, Bas van Steensel

{"title":"Optimized reporters for multiplexed detection of transcription factor activity.","authors":"Max Trauernicht, Teodora Filipovska, Chaitanya Rastogi, Bas van Steensel","doi":"10.1016/j.cels.2024.11.003","DOIUrl":"10.1016/j.cels.2024.11.003","url":null,"abstract":"In any given cell type, dozens of transcription factors (TFs) act in concert to control the activity of the genome by binding to specific DNA sequences in regulatory elements. Despite their considerable importance, we currently lack simple tools to directly measure the activity of many TFs in parallel. Massively parallel reporter assays (MPRAs) allow the detection of TF activities in a multiplexed fashion; however, we lack basic understanding to rationally design sensitive reporters for many TFs. Here, we use an MPRA to systematically optimize transcriptional reporters for 86 TFs and evaluate the specificity of all reporters across a wide array of TF perturbation conditions. We thus identified critical TF reporter design features and obtained highly sensitive and specific reporters for 62 TFs, many of which outperform available reporters. The resulting collection of \"prime\" TF reporters can be used to uncover TF regulatory networks and to illuminate signaling pathways. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"1107-1122.e7"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667439/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792465","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

Tracking the gene expression programs and clonal relationships that underlie mast, myeloid, and T lineage specification from stem cells. 跟踪基因表达程序和克隆关系的基础上，肥大，髓系，和T谱系规范的干细胞。

Cell systems Pub Date : 2024-12-18 Epub Date: 2024-11-29 DOI: 10.1016/j.cels.2024.11.001

Yale S Michaels, Matthew C Major, Becca Bonham-Carter, Jingqi Zhang, Tiam Heydari, John M Edgar, Mona M Siu, Laura Greenstreet, Roser Vilarrasa-Blasi, Seungjoon Kim, Elizabeth L Castle, Aden Forrow, M Iliana Ibanez-Rios, Carla Zimmerman, Yvonne Chung, Tara Stach, Nico Werschler, David J H F Knapp, Roser Vento-Tormo, Geoffrey Schiebinger, Peter W Zandstra

{"title":"Tracking the gene expression programs and clonal relationships that underlie mast, myeloid, and T lineage specification from stem cells.","authors":"Yale S Michaels, Matthew C Major, Becca Bonham-Carter, Jingqi Zhang, Tiam Heydari, John M Edgar, Mona M Siu, Laura Greenstreet, Roser Vilarrasa-Blasi, Seungjoon Kim, Elizabeth L Castle, Aden Forrow, M Iliana Ibanez-Rios, Carla Zimmerman, Yvonne Chung, Tara Stach, Nico Werschler, David J H F Knapp, Roser Vento-Tormo, Geoffrey Schiebinger, Peter W Zandstra","doi":"10.1016/j.cels.2024.11.001","DOIUrl":"10.1016/j.cels.2024.11.001","url":null,"abstract":"T cells develop from hematopoietic progenitors in the thymus and protect against pathogens and cancer. However, the emergence of human T cell-competent blood progenitors and their subsequent specification to the T lineage have been challenging to capture in real time. Here, we leveraged a pluripotent stem cell differentiation system to understand the transcriptional dynamics and cell fate restriction events that underlie this critical developmental process. Time-resolved single-cell RNA sequencing revealed that downregulation of the multipotent hematopoietic program, upregulation of >90 lineage-associated transcription factors, and cell-cycle exit all occur within a highly coordinated developmental window. Gene-regulatory network inference uncovered a role for YBX1 in T lineage specification. We mapped the differentiation cell fate hierarchy using transcribed lineage barcoding and discovered that mast and myeloid potential bifurcate from each other early in hematopoiesis, upstream of T lineage restriction. Our systems-level analyses provide a quantitative, time-resolved model of human T cell fate specification. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"1245-1263.e10"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775404","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