Cell systems最新文献_第5页

Subspecies phylogeny in the human gut revealed by co-evolutionary constraints across the bacterial kingdom. 人类肠道的亚种系统发育揭示了跨细菌王国的共同进化约束。

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-01-17 DOI: 10.1016/j.cels.2024.12.008

Benjamin A Doran, Robert Y Chen, Hannah Giba, Vivek Behera, Bidisha Barat, Anitha Sundararajan, Huaiying Lin, Ashley Sidebottom, Eric G Pamer, Arjun S Raman

{"title":"Subspecies phylogeny in the human gut revealed by co-evolutionary constraints across the bacterial kingdom.","authors":"Benjamin A Doran, Robert Y Chen, Hannah Giba, Vivek Behera, Bidisha Barat, Anitha Sundararajan, Huaiying Lin, Ashley Sidebottom, Eric G Pamer, Arjun S Raman","doi":"10.1016/j.cels.2024.12.008","DOIUrl":"10.1016/j.cels.2024.12.008","url":null,"abstract":"The human gut microbiome contains many bacterial strains of the same species (\"strain-level variants\") that shape microbiome function. The tremendous scale and molecular resolution at which microbial communities are being interrogated motivates addressing how to describe strain-level variants. We introduce the \"Spectral Tree\"-an inferred tree of relatedness built from patterns of co-evolutionary constraint between greater than 7,000 diverse bacteria. Using the Spectral Tree to describe over 600 diverse gut commensal strains that we isolated, whole-genome sequenced, and metabolically profiled revealed (1) widespread phylogenetic structure among strain-level variants, (2) the origins of subspecies phylogeny as a shared history of phage infections across humans, and (3) the key role of inter-human strain variation in predicting strain-level metabolic qualities. Overall, our work demonstrates the existence and metabolic importance of structured phylogeny below the level of species for commensal gut bacteria, motivating a redefinition of individual strains according to their evolutionary context. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101167"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018343","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

Concentration buffering and noise reduction in non-equilibrium phase-separating systems. 非平衡相分离系统的浓度缓冲与降噪。

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-02-07 DOI: 10.1016/j.cels.2025.101168

Christoph Zechner, Frank Jülicher

引用次数: 0

Inferring cell trajectories of spatial transcriptomics via optimal transport analysis. 通过最优转运分析推断空间转录组学的细胞轨迹。

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-02-03 DOI: 10.1016/j.cels.2025.101194

Xunan Shen, Lulu Zuo, Zhongfei Ye, Zhongyang Yuan, Ke Huang, Zeyu Li, Qichao Yu, Xuanxuan Zou, Xiaoyu Wei, Ping Xu, Yaqi Deng, Xin Jin, Xun Xu, Liang Wu, Hongmei Zhu, Pengfei Qin

{"title":"Inferring cell trajectories of spatial transcriptomics via optimal transport analysis.","authors":"Xunan Shen, Lulu Zuo, Zhongfei Ye, Zhongyang Yuan, Ke Huang, Zeyu Li, Qichao Yu, Xuanxuan Zou, Xiaoyu Wei, Ping Xu, Yaqi Deng, Xin Jin, Xun Xu, Liang Wu, Hongmei Zhu, Pengfei Qin","doi":"10.1016/j.cels.2025.101194","DOIUrl":"10.1016/j.cels.2025.101194","url":null,"abstract":"The integration of cell transcriptomics and spatial position to organize differentiation trajectories remains a challenge. Here, we introduce SpaTrack, which leverages optimal transport to reconcile both gene expression and spatial position from spatial transcriptomics into the transition costs, thereby reconstructing cell differentiation. SpaTrack can construct detailed spatial trajectories that reflect the differentiation topology and trace cell dynamics across multiple samples over temporal intervals. To capture the dynamic drivers of differentiation, SpaTrack models cell fate as a function of expression profiles influenced by transcription factors over time. By applying SpaTrack, we successfully disentangle spatiotemporal trajectories of axolotl telencephalon regeneration and mouse midbrain development. Diverse malignant lineages expanding within a primary tumor are uncovered. One lineage, characterized by upregulated epithelial mesenchymal transition, implants at the metastatic site and subsequently colonizes to form a secondary tumor. Overall, SpaTrack efficiently advances trajectory inference from spatial transcriptomics, providing valuable insights into differentiation processes.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101194"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191538","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

Synthetically programmed antioxidant delivery by a domesticated skin commensal. 合成程序抗氧化输送由驯化的皮肤共生。

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-02-06 DOI: 10.1016/j.cels.2025.101169

Guillermo Nevot, Javier Santos-Moreno, Nil Campamà-Sanz, Lorena Toloza, Cristóbal Parra-Cid, Patrick A M Jansen, Içvara Barbier, Rodrigo Ledesma-Amaro, Ellen H van den Bogaard, Marc Güell

引用次数: 0

What is the main bottleneck in deriving biological understanding from spatial transcriptomic profiling? 从空间转录组分析中获得生物学理解的主要瓶颈是什么？

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