Cell systems最新文献

{"title":"The metabolome of an E. coli CRISPRi library identifies benefits of minimal metabolite levels and targets for engineering.","authors":"Johanna Rapp, Andreas Verhülsdonk, Anton Garcke, Amelie Stadelmann, Niklas Farke, Felicia Troßmann, Thales Kronenberger, Alejandra Alvarado, Daniel Petras, Hannes Link","doi":"10.1016/j.cels.2025.101518","DOIUrl":"10.1016/j.cels.2025.101518","url":null,"abstract":"<p><p>Metabolite concentration changes can have broad consequences on the function and robustness of metabolic networks. Here, we measured the metabolome response of 1,515 CRISPR interference (CRISPRi) E. coli strains targeting all genes in the iML1515 metabolic model. Metabolites that are hardly measurable in wild-type E. coli accumulated in specific CRISPRi strains, indicating that they are normally maintained at low levels. We confirmed metabolite accumulation using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and generated putative reference spectra for 102 metabolites for which no MS² data had previously been available. We show that minimal metabolite levels are beneficial because they (1) enable substrate level regulation of enzyme activity, (2) prevent competitive inhibition, and (3) suppress side reactions. However, minimal metabolite pools also limit flux through engineered pathways. For example, low levels of farnesyl diphosphate (frdp) constrained a synthetic carotenoid pathway, and we show that the knockdown of octaprenyl diphosphate synthase (IspB) increased frdp levels and carotenoid production. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101518"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147370919","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}

{"title":"Constructing a holistic map of cell fate decision by hyper solution landscape.","authors":"Xiaoyi Zhang, Zhiyuan Li, Lei Zhang","doi":"10.1016/j.cels.2026.101562","DOIUrl":"10.1016/j.cels.2026.101562","url":null,"abstract":"<p><p>The Waddington landscape metaphor has inspired extensive quantitative studies of cell fate decisions using dynamical systems. While these approaches provide valuable insights, the intrinsic nonlinear complexity and the parameter dependence limit systematic analysis of fate transitions. Here, we introduce the hyper solution landscape (HSL), a minimally parameter-dependent methodology showing a comprehensive structure of all possible landscape configurations for gene regulatory networks. HSL connects different solution landscapes to reflect dynamic changes in the landscapes associated with bifurcations. Applied to the cross-inhibition with self-activation motif, HSL analysis identifies key hyperparameters driving distinct directional changes in cell-fate propensity. Different routes through the HSL between the same initial and final states can produce markedly different fate distributions. This enables the rational design of transition strategies. We validate the HSL's utility in the seesaw model of cellular reprogramming, establishing a powerful framework for understanding and engineering cell-fate decisions. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101562"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147577021","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}

{"title":"The impact of package selection and versioning on single-cell RNA-seq analysis.","authors":"Joseph M Rich, Lambda Moses, Pétur Helgi Einarsson, Kayla Jackson, Laura Luebbert, A Sina Booeshaghi, Sindri Antonsson, Delaney K Sullivan, Nicolas Bray, Páll Melsted, Lior Pachter","doi":"10.1016/j.cels.2026.101560","DOIUrl":"10.1016/j.cels.2026.101560","url":null,"abstract":"<p><p>Standard single-cell RNA sequencing (scRNA-seq) workflows convert raw reads into cell-gene count matrices and then perform filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely used implementations of these workflows and are generally assumed to yield similar results. Here, we examine the underlying algorithms of both packages and show that their outputs differ substantially across multiple analytic steps. The magnitude of Seurat-Scanpy differences is comparable to the variability introduced by sequencing fewer than 5% of reads or analyzing fewer than 20% of cells. We further find that software version changes can alter results, particularly in differential expression. Our findings underscore the need for users to critically evaluate analysis tools and for developers to prioritize transparency and reproducibility.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101560"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576977","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}

{"title":"The modifiers that cause changes in gene essentiality.","authors":"Amandine Batté, Núria Bosch-Guiteras, Carles Pons, Marina Ota, Maykel Lopes, Sushma Sharma, Nicolò Tellini, Claire Paltenghi, Michelle Conti, Kwan Ting Kan, Uyen Linh Ho, Michaël Wiederkehr, Jonas Barraud, Mark Ashe, Patrick Aloy, Gianni Liti, Andrei Chabes, Leopold Parts, Jolanda van Leeuwen","doi":"10.1016/j.cels.2025.101515","DOIUrl":"10.1016/j.cels.2025.101515","url":null,"abstract":"<p><p>Mutant phenotypes often vary across genetically distinct individuals. To identify the causes of such genetic background effects, we studied differences in gene essentiality across 18 genetically diverse natural yeast strains. We identified 39 genes that were essential in the laboratory reference strain but not in at least one other genetic background, and we mapped and validated the genetic variants that were responsible for the differences in gene essentiality. These variants typically occurred in single modifier genes that tended to differ between genetic backgrounds. The affected genes often indirectly compensated for the loss of the essential gene and identified naturally occurring evolutionary trajectories. Overall, our results highlight the prevalence of changes in gene essentiality in natural populations, as well as the underlying mechanisms. A thorough understanding of the causes of genetic background effects is crucial for the interpretation of genotype-to-phenotype relationships, including those associated with human disease.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101515"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350023","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}

{"title":"NOT-gated chimeric antigen receptor circuits in T and NK cells.","authors":"Seunghee Lee, Jingyao Chen, Menna Y Siddiqui, Jaehoon Choi, Joey Huang, Derrick Lee, Chen-Ting Lee, Niran Almudhfar, Mengxi Tian, Andrew Banicki, Marcela Guzmán Ayala, Russell Gordley, Tim K Lu, Nicholas W Frankel, Wilson W Wong","doi":"10.1016/j.cels.2026.101561","DOIUrl":"10.1016/j.cels.2026.101561","url":null,"abstract":"<p><p>Chimeric antigen receptor (CAR) T cells are powerful cancer immunotherapies, but the lack of tumor-specific single antigens for most cancers limits the therapeutic window. A NOT-gated CAR circuit, consisting of an activating CAR (aCAR) and an inhibitory CAR (iCAR), is particularly powerful because it enables new sets of antigens to be used in tumor targeting. We systematically evaluated more than 60 pairs of NOT-gated CAR circuits. Different inhibitory dose-response characteristics were observed depending on the CD3ζ signaling domain on the aCAR. Furthermore, LIR1-iCAR-expressing T cells display fewer exhaustion phenotypes. Mechanistic studies have shown that SHP-1 is the major intracellular regulator of LIR1-iCAR and the iCAR function in cis. Several of the NOT-gated circuits developed in T cells were functionally transferred into primary natural killer (NK) cells. Our NOT-gated CAR circuits provide a precise targeting strategy for safer and more effective cancer immunotherapies.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101561"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576980","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}

{"title":"Context-informed subgraph foundation models enable interpretable protein-function prediction.","authors":"Zhuomin Zhou, Jiahua Rao, Zhongyue Zhang, Runze Ma, Jiancheng Yang, Shuangjia Zheng","doi":"10.1016/j.cels.2026.101535","DOIUrl":"10.1016/j.cels.2026.101535","url":null,"abstract":"<p><p>Protein-function prediction is crucial for elucidating molecular mechanisms driving biological processes and therapeutics development. Despite numerous computational tools demonstrating promising performance, they fall short when predicting rare, uncharacterized functions or indirect activities. Here, we present COSMOS, a context-aware Gene Ontology (GO) subgraph mining system for protein-function prediction. By leveraging inductive subgraph foundation models and an enriched knowledge graph of protein-GO relationships, COSMOS performs zero-shot, few-shot, and low-homology protein-function prediction. Built on 7,923,952 functional semantic relationships, COSMOS demonstrates robust capabilities to (1) generate state-of-the-art predictions for GO classes with sparse or no experimental annotations, (2) provide interpretable functional subgraphs for transparent rationale analysis, and (3) deliver complementary benefits when integrated with existing embedding-based prediction methods. We anticipate that COSMOS will serve as a complementary approach to conventional protein annotation methods and an interpretable tool for predicting protein functions within underexplored GO classes, thereby advancing genomics and therapeutic research.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101535"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492274","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}

{"title":"Systemic rhythmicity of host and bacterial bile acid amidates in the mouse.","authors":"Erin R Reilly, Vincent Charron-Lamoureux, Helena Mannochio-Russo, Ethan W Morgan, Nina R Boyle, Imhoi Koo, Emma Coudriet, Fuhua Hao, Ipsita Mohanty, Gary H Perdew, Pieter C Dorrestein, Andrew D Patterson","doi":"10.1016/j.cels.2026.101541","DOIUrl":"10.1016/j.cels.2026.101541","url":null,"abstract":"<p><p>Bacterial bile acid amidates (BBAAs) represent an emerging class of host-microbe co-metabolites formed when gut bacteria conjugate bile acids with amino acids beyond taurine and glycine. Although these conjugates have been shown to modulate immune signaling and epithelial integrity, their spatial and temporal distribution across host tissues remains poorly understood. Here, we profiled 690 samples from time-restricted-fed mice, spanning 14 organs, digestive tract contents, and biofluids, to map the temporal dynamics of BBAAs. We identified widespread and tissue-specific oscillations of BBAAs, with the greatest temporal synchronization observed in the ileal contents and progressively diminished rhythmicity in the periphery. Amidates with hydrophilic or hydrophobic side chains remained relatively constant, while aromatic conjugates exhibited the most substantial variation. These findings reveal that BBAAs are temporally and spatially distributed throughout the body, supporting a model in which microbes encode systemic signals through the timing and structure of their metabolites.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101541"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147535082","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}

{"title":"Deep coverage and extended sequence reads obtained with a single archaeal protease expedite de novo protein sequencing by mass spectrometry.","authors":"Laura Pérez Pañeda, Tereza Kadavá, Tatiana M Shamorkina, Douwe Schulte, Patrick Pribil, Sibylle Heidelberger, Allison Michele Narlock-Brand, Steven M Yannone, Joost Snijder, Albert J R Heck","doi":"10.1016/j.cels.2026.101536","DOIUrl":"10.1016/j.cels.2026.101536","url":null,"abstract":"<p><p>The ability to sequence proteins without reliance on a genomic template defines a critical frontier in proteomics. This approach, known as de novo protein sequencing, is essential for applications in antibody sequencing, microbiome proteomics, and antigen discovery, which require accurate reconstruction of target sequences. To advance this field, we here explore two hyperthermoacidic archaeal (HTA) proteases for de novo antibody sequencing, benchmarking them against trypsin and chymotrypsin. Each HTA-protease generated about five times more unique peptide reads than trypsin or chymotrypsin, providing high redundancy across all complementarity-determining regions. Combined with EAciD fragmentation on a ZenoTOF, this methodology enabled complete, unambiguous antibody sequencing. De novo analysis showed much higher alignment scores and reduced the sequence errors by using the HTA-generated data. With short digestion times, minimal sample cleanup, and analysis in just a single liquid chromatography-mass spectrometry (LC-MS/MS) run, this streamlined single-protease approach delivers a scalable and efficient strategy for de novo protein sequencing across diverse applications. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101536"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13083267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147446451","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}

{"title":"A community reconstruction of Chinese hamster metabolism and structural systems biology elucidate metabolic rewiring in lactate-free CHO cells.","authors":"Pablo Di Giusto, Dong-Hyuk Choi, Athanasios Antonakoudis, Vikash Gokul Duraikannan, Pierrick Craveur, Nicholas Luke Cowie, Tejaswini Ganapathy, Kannan Ramesh, Santiago Benavidez-López, Camila A Orellana, Natalia E Jiménez, Leo Alexander Dworkin, James Morrissey, Igor Marin de Mas, Benjamin Strain, Norma A Valdez-Cruz, Mauricio A Trujillo-Roldán, Jannis Marzluf, Verónica S Martínez, Leopold Zehetner, Claudia Altamirano, Ana Maria Vega-Letter, Bradley Priem, Haoyu Chris Cao, Martin Hold, Junyu Ma, Yi Fan Hong, Saratram Gopalakrishnan, Blaise Manga Enuh, Chaimaa Tarzi, Kuin Tian Pang, Claudio Angione, Jürgen Zanghellini, Cleo Kontoravdi, Hooman Hefzi, Michael J Betenbaugh, Lars K Nielsen, Meiyappan Lakshmanan, Dong-Yup Lee, Anne Richelle, Nathan E Lewis","doi":"10.1016/j.cels.2026.101574","DOIUrl":"10.1016/j.cels.2026.101574","url":null,"abstract":"<p><p>Genome-scale metabolic models (GEMs) are indispensable for studying cellular metabolism. We present iCHO3K, a community-consensus, manually curated reconstruction of the Chinese hamster metabolic network. Spanning 11,004 reactions linked to 3,597 genes, iCHO3K augments the network with 3,489 protein structures and physicochemical descriptors for >70% of 7,377 metabolites, enabling structure-aware analyses. We applied iCHO3K to contextualize transcriptomics and metabolomics from a fed-batch Chinese hamster ovary (CHO) cell line engineered to abolish lactate secretion. The model indicated reduced glycolytic flux with enhanced tricarboxylic acid (TCA) activity and elevated intracellular NADH and phosphoenolpyruvate (PEP), consistent with experimental measurements. Leveraging iCHO3K's structural annotations, we evaluated potential off-target binding of NADH and PEP across early glycolytic enzymes and identified a putative allosteric PEP interaction with phosphofructokinase, suggesting a structural mechanism underlying reduced glucose uptake and glycolytic flux. Overall, iCHO3K provides a framework for systematic multi-omics integration, improved flux prediction, and structure-guided mechanistic insight, advancing CHO cell engineering and biomanufacturing. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101574"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147701097","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}

{"title":"Single-cell epigenetic landscape, microenvironment interactions, and gene regulatory modules of non-functioning pituitary adenomas.","authors":"Zidong Zhang, Wan Sze Cheng, Keiko Taniguchi-Ponciano, Daniel Marrero-Rodríguez, Gregory R Smith, Hanna Pincas, Tyler J Sagendorf, Natalia Mendelev, Galia Strupinsky, Yongchao Ge, Michel Zamojski, Xi Chen, Mary Anne S Amper, Christopher Y Park, Venugopalan D Nair, Cynthia L Andoniadou, Judith L Turgeon, Elena Zaslavsky, Olga G Troyanskaya, Moises Mercado, Stuart C Sealfon, Frederique Ruf-Zamojski","doi":"10.1016/j.cels.2026.101532","DOIUrl":"10.1016/j.cels.2026.101532","url":null,"abstract":"<p><p>The epigenetic landscape and tumor microenvironment (TME) interactions of non-functioning pituitary adenomas (NFPAs), benign tumors with high morbidity and recurrence rates, are not well characterized. We completed single-nucleus (sn) multiomics assays on 4 gonadotrope NFPAs (34,819 cells) and 11 non-diseased postmortem control pituitaries (51,535 cells), finding decreased proportions of tumor-associated endothelial cells and pericytes and increased proportions of macrophages. We identified bidirectional tumor-macrophage crosstalk comprising nine ligand-receptor interactions and experimentally validated the macrophage-initiated SFRP1-FZD6 interaction, whose predicted target genes CCND1, CDK6, SGK1, and TGFBR2 were linked to tumorigenesis. We uncovered coordinated gene expression and chromatin accessibility programs, which distinguished adenoma cells from gonadotropes. Integrated transcriptome-chromatin modeling revealed gene regulatory circuits (GRCs) that showed altered activity in adenoma cells and were regulated by transcription factors (TFs), including PBX3 and MEF2C. Our study provides insight into the altered epigenetic gene control landscape and TME processes of the NFPA tumor phenotype. Our data are freely available at https://rstudio-connect.hpc.mssm.edu/nfpa_browser/.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101532"},"PeriodicalIF":7.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147476574","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}