Cell genomicsPub Date : 2025-05-14DOI: 10.1016/j.xgen.2025.100883
Ali H Eid
{"title":"Readdressing the role of ADRA2A in Raynaud's phenomenon: Methodological concerns and implications.","authors":"Ali H Eid","doi":"10.1016/j.xgen.2025.100883","DOIUrl":"10.1016/j.xgen.2025.100883","url":null,"abstract":"","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"5 5","pages":"100883"},"PeriodicalIF":11.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081887","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}
Cell genomicsPub Date : 2025-05-14Epub Date: 2025-04-04DOI: 10.1016/j.xgen.2025.100842
Ofir Shorer, Asaf Pinhasi, Keren Yizhak
{"title":"Single-cell meta-analysis of T cells reveals clonal dynamics of response to checkpoint immunotherapy.","authors":"Ofir Shorer, Asaf Pinhasi, Keren Yizhak","doi":"10.1016/j.xgen.2025.100842","DOIUrl":"10.1016/j.xgen.2025.100842","url":null,"abstract":"<p><p>Despite the crucial role of T cell clones in anti-tumor activity, their characterization and association with clinical outcomes following immune checkpoint inhibitors are lacking. Here, we analyzed paired single-cell RNA sequencing/T cell receptor sequencing of 767,606 T cells across 460 samples spanning 6 cancer types. We found a robust signature of response based on expanded CD8<sup>+</sup> clones that differentiates responders from non-responders. Analysis of persistent clones showed transcriptional changes that are differentially induced by therapy in the different response groups, suggesting an improved reinvigoration capacity in responding patients. Moreover, a gene trajectory analysis revealed changes in the pseudo-temporal state of de novo clones that are associated with clinical outcomes. Lastly, we found that clones shared between tumor and blood are more abundant in non-responders and execute distinct transcriptional programs. Overall, our results highlight differences in clonal transcriptional states that are linked to patient response, offering valuable insights into the mechanisms driving effective anti-tumor immunity.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100842"},"PeriodicalIF":11.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789474","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}
Cell genomicsPub Date : 2025-05-14DOI: 10.1016/j.xgen.2025.100885
Anniina Tervi, Markus Ramste, Hanna M Ollila
{"title":"Response to Dr. Nicholas A. Flavahan's and Dr. Ali H. Eid's letters regarding our recent publication in Cell Genomics.","authors":"Anniina Tervi, Markus Ramste, Hanna M Ollila","doi":"10.1016/j.xgen.2025.100885","DOIUrl":"10.1016/j.xgen.2025.100885","url":null,"abstract":"","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"5 5","pages":"100885"},"PeriodicalIF":11.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082120","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}
Cell genomicsPub Date : 2025-05-14DOI: 10.1016/j.xgen.2025.100880
Hae Kyung Im
{"title":"Meet the author: Hae Kyung Im.","authors":"Hae Kyung Im","doi":"10.1016/j.xgen.2025.100880","DOIUrl":"10.1016/j.xgen.2025.100880","url":null,"abstract":"<p><p>Hae Kyung Im's research group focuses on quantitative computational and statistical methods to tackle genomic data analysis and provides methods to translate the vast amount of genomic data for health research. In collaboration with Mengjie Chen's group, also based at the University of Chicago, Im et al. have published their article \"scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework\" in Cell Genomics. This is a powerful deep learning approach to improve transcriptome-wide association study analysis, and researchers can apply this method to better understand complex disease genomics.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"5 5","pages":"100880"},"PeriodicalIF":11.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082635","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}
Cell genomicsPub Date : 2025-05-14Epub Date: 2025-04-14DOI: 10.1016/j.xgen.2025.100848
Tingting Xia, Jiahe Sun, Yongjiang Luo, Hailong Guo, Yudi Mao, Ling Xu, Fang Lu, Yi Wang
{"title":"Empowering integrative and collaborative exploration of single-cell and spatial multimodal data with SGS genome browser.","authors":"Tingting Xia, Jiahe Sun, Yongjiang Luo, Hailong Guo, Yudi Mao, Ling Xu, Fang Lu, Yi Wang","doi":"10.1016/j.xgen.2025.100848","DOIUrl":"10.1016/j.xgen.2025.100848","url":null,"abstract":"<p><p>Recent advancements in single-cell and spatial omics technologies have generated a large amount of complex, high-dimensional data, which poses significant challenges to visualization tools. We introduce SGS (single-cell and spatial genomics system), a user-friendly, collaborative, and versatile browser designed for visualizing single-cell and spatial multimodal data. SGS excels in the integrative visualization of complex multimodal data, offering an innovative genome browser, flexible visualization modes, and 3D spatially resolved transcriptomics (SRT) data visualization capabilities. Notably, SGS empowers users with advanced capabilities for comparative visualization through features like scCompare, scMultiView, and the dual-chromosome mode. It supports a variety of data formats and is compatible with established analysis tools, enabling collaborative data exploration and visualization without programming. Overall, SGS is a comprehensive browser that enables researchers to unlock novel insights from multimodal data.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100848"},"PeriodicalIF":11.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042225","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}
Cell genomicsPub Date : 2025-05-14Epub Date: 2025-04-10DOI: 10.1016/j.xgen.2025.100845
Yu-Ning Huang, Viorel Munteanu, Michael I Love, Cynthia Flaire Ronkowski, Dhrithi Deshpande, Annie Wong-Beringer, Russell Corbett-Detig, Mihai Dimian, Jason H Moore, Lana X Garmire, T B K Reddy, Atul J Butte, Mark D Robinson, Eleazar Eskin, Malak S Abedalthagafi, Serghei Mangul
{"title":"Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.","authors":"Yu-Ning Huang, Viorel Munteanu, Michael I Love, Cynthia Flaire Ronkowski, Dhrithi Deshpande, Annie Wong-Beringer, Russell Corbett-Detig, Mihai Dimian, Jason H Moore, Lana X Garmire, T B K Reddy, Atul J Butte, Mark D Robinson, Eleazar Eskin, Malak S Abedalthagafi, Serghei Mangul","doi":"10.1016/j.xgen.2025.100845","DOIUrl":"10.1016/j.xgen.2025.100845","url":null,"abstract":"<p><p>Metadata, or \"data about data,\" is essential for organizing, understanding, and managing large-scale omics datasets. It enhances data discovery, integration, and interpretation, enabling reproducibility, reusability, and secondary analysis. However, metadata sharing remains hindered by perceptual and technical barriers, including the lack of uniform standards, privacy concerns, study design limitations, insufficient incentives, inadequate infrastructure, and a shortage of trained personnel. These challenges compromise data reliability and obstruct integrative meta-analyses. Addressing these issues requires standardization, education, stronger roles for journals and funding agencies, and improved incentives and infrastructure. Looking ahead, emerging technologies such as artificial intelligence and machine learning may offer promising solutions to automate metadata processes, increasing accuracy and scalability. Fostering a collaborative culture of metadata sharing will maximize the value of omics data, accelerating innovation and scientific discovery.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100845"},"PeriodicalIF":11.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065378","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}
Cell genomicsPub Date : 2025-04-09Epub Date: 2025-03-20DOI: 10.1016/j.xgen.2025.100813
Catherine Do, Guimei Jiang, Giulia Cova, Christos C Katsifis, Domenic N Narducci, Theodore Sakellaropoulos, Raphael Vidal, Priscillia Lhoumaud, Aristotelis Tsirigos, Faye Fara D Regis, Nata Kakabadze, Elphege P Nora, Marcus Noyes, Anders S Hansen, Jane A Skok
{"title":"Binding domain mutations provide insight into CTCF's relationship with chromatin and its contribution to gene regulation.","authors":"Catherine Do, Guimei Jiang, Giulia Cova, Christos C Katsifis, Domenic N Narducci, Theodore Sakellaropoulos, Raphael Vidal, Priscillia Lhoumaud, Aristotelis Tsirigos, Faye Fara D Regis, Nata Kakabadze, Elphege P Nora, Marcus Noyes, Anders S Hansen, Jane A Skok","doi":"10.1016/j.xgen.2025.100813","DOIUrl":"10.1016/j.xgen.2025.100813","url":null,"abstract":"<p><p>Here we used a series of CTCF mutations to explore CTCF's relationship with chromatin and its contribution to gene regulation. CTCF's impact depends on the genomic context of bound sites and the unique binding properties of WT and mutant CTCF proteins. Specifically, CTCF's signal strength is linked to changes in accessibility, and the ability to block cohesin is linked to its binding stability. Multivariate modeling reveals that both CTCF and accessibility contribute independently to cohesin binding and insulation, but CTCF signal strength has a stronger effect. CTCF and chromatin have a bidirectional relationship such that at CTCF sites, accessibility is reduced in a cohesin-dependent, mutant-specific fashion. In addition, each mutant alters TF binding and accessibility in an indirect manner, changes which impart the most influence on rewiring transcriptional networks and the cell's ability to differentiate. Collectively, the mutant perturbations provide a rich resource for determining CTCF's site-specific effects.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100813"},"PeriodicalIF":11.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675086","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}
Cell genomicsPub Date : 2025-04-09Epub Date: 2025-03-21DOI: 10.1016/j.xgen.2025.100814
Michael Herger, Christina M Kajba, Megan Buckley, Ana Cunha, Molly Strom, Gregory M Findlay
{"title":"High-throughput screening of human genetic variants by pooled prime editing.","authors":"Michael Herger, Christina M Kajba, Megan Buckley, Ana Cunha, Molly Strom, Gregory M Findlay","doi":"10.1016/j.xgen.2025.100814","DOIUrl":"10.1016/j.xgen.2025.100814","url":null,"abstract":"<p><p>Multiplexed assays of variant effect (MAVEs) enable scalable functional assessment of human genetic variants. However, established MAVEs are limited by exogenous expression of variants or constraints of genome editing. Here, we introduce a pooled prime editing (PE) platform to scalably assay variants in their endogenous context. We first improve efficiency of PE in HAP1 cells, defining optimal prime editing guide RNA (pegRNA) designs and establishing enrichment of edited cells via co-selection. We next demonstrate negative selection screening by testing over 7,500 pegRNAs targeting SMARCB1 and observing depletion of efficiently installed loss-of-function (LoF) variants. We then screen for LoF variants in MLH1 via 6-thioguanine selection, testing 65.3% of all possible SNVs in a 200-bp region including exon 10 and 362 non-coding variants from ClinVar spanning a 60-kb region. The platform's overall accuracy for discriminating pathogenic variants indicates that it will be highly valuable for identifying new variants underlying diverse human phenotypes across large genomic regions.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100814"},"PeriodicalIF":11.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694606","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":"Cross-ancestry analyses of Chinese and European populations reveal insights into the genetic architecture and disease implication of metabolites.","authors":"Chenhao Lin, Mingfeng Xia, Yuxiang Dai, Qingxia Huang, Zhonghan Sun, Guoqing Zhang, Ruijin Luo, Qianqian Peng, Jinxi Li, Xiaofeng Wang, Huandong Lin, Xin Gao, Huiru Tang, Xia Shen, Sijia Wang, Li Jin, Xingjie Hao, Yan Zheng","doi":"10.1016/j.xgen.2025.100810","DOIUrl":"10.1016/j.xgen.2025.100810","url":null,"abstract":"<p><p>Differential susceptibilities to various diseases and corresponding metabolite variations have been documented across diverse ethnic populations, but the genetic determinants of these disparities remain unclear. Here, we performed large-scale genome-wide association studies of 171 directly quantifiable metabolites from a nuclear magnetic resonance-based metabolomics platform in 10,792 Han Chinese individuals. We identified 15 variant-metabolite associations, eight of which were successfully replicated in an independent Chinese population (n = 4,480). By cross-ancestry meta-analysis integrating 213,397 European individuals from the UK Biobank, we identified 228 additional variant-metabolite associations and improved fine-mapping precision. Moreover, two-sample Mendelian randomization analyses revealed evidence that genetically predicted levels of triglycerides in high-density lipoprotein were associated with a higher risk of coronary artery disease and that of glycine with a lower risk of heart failure in both ancestries. These findings enhance our understanding of the shared and specific genetic architecture of metabolites as well as their roles in complex diseases across populations.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100810"},"PeriodicalIF":11.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675087","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}