{"title":"STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning","authors":"Ying Wu, Jia-Yi Zhou, Bofei Yao, Guanshen Cui, Yong-Liang Zhao, Chun-Chun Gao, Ying Yang, Shihua Zhang, Yun-Gui Yang","doi":"10.1186/s13059-024-03421-5","DOIUrl":"https://doi.org/10.1186/s13059-024-03421-5","url":null,"abstract":"Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"13 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2024-10-21DOI: 10.1186/s13059-024-03420-6
Rima Mustafa, Michelle M. J. Mens, Arno van Hilten, Jian Huang, Gennady Roshchupkin, Tianxiao Huan, Linda Broer, Joyce B. J. van Meurs, Paul Elliott, Daniel Levy, M. Arfan Ikram, Marina Evangelou, Abbas Dehghan, Mohsen Ghanbari
{"title":"A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data","authors":"Rima Mustafa, Michelle M. J. Mens, Arno van Hilten, Jian Huang, Gennady Roshchupkin, Tianxiao Huan, Linda Broer, Joyce B. J. van Meurs, Paul Elliott, Daniel Levy, M. Arfan Ikram, Marina Evangelou, Abbas Dehghan, Mohsen Ghanbari","doi":"10.1186/s13059-024-03420-6","DOIUrl":"https://doi.org/10.1186/s13059-024-03420-6","url":null,"abstract":"MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms. We present genome-wide association studies of 2083 plasma circulating miRNAs in 2178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We identify 3292 associations between 1289 SNPs and 63 miRNAs, of which 65% are replicated in two independent cohorts. We demonstrate that plasma miR-eQTLs co-localise with gene expression, protein, and metabolite-QTLs, which help in identifying miRNA-regulated pathways. We investigate consequences of alteration in circulating miRNA levels on a wide range of clinical conditions in phenome-wide association studies and Mendelian randomisation using the UK Biobank data (N = 423,419), revealing the pleiotropic and causal effects of several miRNAs on various clinical conditions. In the Mendelian randomisation analysis, we find a protective causal effect of miR-1908-5p on the risk of benign colon neoplasm and show that this effect is independent of its host gene (FADS1). This study enriches our understanding of the genetic architecture of plasma miRNAs and explores the signatures of miRNAs across a wide range of clinical conditions. The integration of population-based genomics, other omics layers, and clinical data presents opportunities to unravel potential clinical significance of miRNAs and provides tools for novel miRNA-based therapeutic target discovery.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"33 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2024-10-21DOI: 10.1186/s13059-024-03422-4
Marius Lange, Zoe Piran, Michal Klein, Bastiaan Spanjaard, Dominik Klein, Jan Philipp Junker, Fabian J. Theis, Mor Nitzan
{"title":"Mapping lineage-traced cells across time points with moslin","authors":"Marius Lange, Zoe Piran, Michal Klein, Bastiaan Spanjaard, Dominik Klein, Jan Philipp Junker, Fabian J. Theis, Mor Nitzan","doi":"10.1186/s13059-024-03422-4","DOIUrl":"https://doi.org/10.1186/s13059-024-03422-4","url":null,"abstract":"Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information. We validate our approach in simulations and demonstrate on Caenorhabditis elegans embryonic development how moslin predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"79 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2024-10-18DOI: 10.1186/s13059-024-03418-0
Weixu Wang, Yichen Wang, Ruiqi Lyu, Dominic Grün
{"title":"Scalable identification of lineage-specific gene regulatory networks from metacells with NetID","authors":"Weixu Wang, Yichen Wang, Ruiqi Lyu, Dominic Grün","doi":"10.1186/s13059-024-03418-0","DOIUrl":"https://doi.org/10.1186/s13059-024-03418-0","url":null,"abstract":"The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene–gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"2 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2024-10-17DOI: 10.1186/s13059-024-03412-6
Kristen J. Wade, Rayo Suseno, Kerry Kizer, Jacqueline Williams, Juliano Boquett, Stacy Caillier, Nicholas R. Pollock, Adam Renschen, Adam Santaniello, Jorge R. Oksenberg, Paul J. Norman, Danillo G. Augusto, Jill A. Hollenbach
{"title":"MHConstructor: a high-throughput, haplotype-informed solution to the MHC assembly challenge","authors":"Kristen J. Wade, Rayo Suseno, Kerry Kizer, Jacqueline Williams, Juliano Boquett, Stacy Caillier, Nicholas R. Pollock, Adam Renschen, Adam Santaniello, Jorge R. Oksenberg, Paul J. Norman, Danillo G. Augusto, Jill A. Hollenbach","doi":"10.1186/s13059-024-03412-6","DOIUrl":"https://doi.org/10.1186/s13059-024-03412-6","url":null,"abstract":"The extremely high levels of genetic polymorphism within the human major histocompatibility complex (MHC) limit the usefulness of reference-based alignment methods for sequence assembly. We incorporate a short-read, de novo assembly algorithm into a workflow for novel application to the MHC. MHConstructor is a containerized pipeline designed for high-throughput, haplotype-informed, reproducible assembly of both whole genome sequencing and target capture short-read data in large, population cohorts. To-date, no other self-contained tool exists for the generation of de novo MHC assemblies from short-read data. MHConstructor facilitates wide-spread access to high-quality, alignment-free MHC sequence analysis.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"11 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2024-10-15DOI: 10.1186/s13059-024-03411-7
Youshu Cheng, Biao Cai, Hongyu Li, Xinyu Zhang, Gypsyamber D’Souza, Sadeep Shrestha, Andrew Edmonds, Jacquelyn Meyers, Margaret Fischl, Seble Kassaye, Kathryn Anastos, Mardge Cohen, Bradley E. Aouizerat, Ke Xu, Hongyu Zhao
{"title":"HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data","authors":"Youshu Cheng, Biao Cai, Hongyu Li, Xinyu Zhang, Gypsyamber D’Souza, Sadeep Shrestha, Andrew Edmonds, Jacquelyn Meyers, Margaret Fischl, Seble Kassaye, Kathryn Anastos, Mardge Cohen, Bradley E. Aouizerat, Ke Xu, Hongyu Zhao","doi":"10.1186/s13059-024-03411-7","DOIUrl":"https://doi.org/10.1186/s13059-024-03411-7","url":null,"abstract":"Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data. Through simulations, we show that HBI enhances the estimation of cell-type-specific meQTLs. In real data analyses, we demonstrate that HBI can further improve the functional annotation of genetic variants and identify biologically relevant cell types for complex traits.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"32 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2024-10-15DOI: 10.1186/s13059-024-03383-8
Fengyang Jing, Lijing Zhu, Jianyun Zhang, Xuan Zhou, Jiaying Bai, Xuefen Li, Heyu Zhang, Tiejun Li
{"title":"Multi-omics reveals lactylation-driven regulatory mechanisms promoting tumor progression in oral squamous cell carcinoma","authors":"Fengyang Jing, Lijing Zhu, Jianyun Zhang, Xuan Zhou, Jiaying Bai, Xuefen Li, Heyu Zhang, Tiejun Li","doi":"10.1186/s13059-024-03383-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03383-8","url":null,"abstract":"Lactylation, a post-translational modification, is increasingly recognized for its role in cancer progression. This study investigates its prevalence and impact in oral squamous cell carcinoma (OSCC). Immunohistochemical staining of 81 OSCC cases shows lactylation levels correlate with malignancy grading. Proteomic analyses of six OSCC tissue pairs reveal 2765 lactylation sites on 1033 proteins, highlighting its extensive presence. These modifications influence metabolic processes, molecular synthesis, and transport. CAL27 cells are subjected to cleavage under targets and tagmentation assay for accessible-chromatin with high-throughput sequencing, and transcriptomic sequencing pre- and post-lactate treatment, with 217 genes upregulated due to lactylation. Chromatin immunoprecipitation-quantitative PCR and real-time fluorescence quantitative PCR confirm the regulatory role of lactylation at the K146 site of dexh-box helicase 9 (DHX9), a key factor in OSCC progression. CCK8, colony formation, scratch healing, and Transwell assays demonstrate that lactylation mitigates the inhibitory effect of DHX9 on OSCC, thereby promoting its occurrence and development. Lactylation actively modulates gene expression in OSCC, with significant effects on chromatin structure and cellular processes. This study provides a foundation for developing targeted therapies against OSCC, leveraging the role of lactylation in disease pathogenesis.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"4 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation","authors":"Xiuqi Gui, Jing Huang, Linjie Ruan, Yanjun Wu, Xuan Guo, Ruifang Cao, Shuhan Zhou, Fengxiang Tan, Hongwen Zhu, Mushan Li, Guoqing Zhang, Hu Zhou, Lixing Zhan, Xin Liu, Shiqi Tu, Zhen Shao","doi":"10.1186/s13059-024-03382-9","DOIUrl":"https://doi.org/10.1186/s13059-024-03382-9","url":null,"abstract":"Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"23 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2024-10-14DOI: 10.1186/s13059-024-03403-7
Chris Papadopoulos, Hugo Arbes, David Cornu, Nicolas Chevrollier, Sandra Blanchet, Paul Roginski, Camille Rabier, Safiya Atia, Olivier Lespinet, Olivier Namy, Anne Lopes
{"title":"The ribosome profiling landscape of yeast reveals a high diversity in pervasive translation","authors":"Chris Papadopoulos, Hugo Arbes, David Cornu, Nicolas Chevrollier, Sandra Blanchet, Paul Roginski, Camille Rabier, Safiya Atia, Olivier Lespinet, Olivier Namy, Anne Lopes","doi":"10.1186/s13059-024-03403-7","DOIUrl":"https://doi.org/10.1186/s13059-024-03403-7","url":null,"abstract":"Pervasive translation is a widespread phenomenon that plays a critical role in the emergence of novel microproteins, but the diversity of translation patterns contributing to their generation remains unclear. Based on 54 ribosome profiling (Ribo-Seq) datasets, we investigated the yeast Ribo-Seq landscape using a representation framework that allows the comprehensive inventory and classification of the entire diversity of Ribo-Seq signals, including non-canonical ones. We show that if coding regions occupy specific areas of the Ribo-Seq landscape, noncoding regions encompass a wide diversity of Ribo-Seq signals and, conversely, populate the entire landscape. Our results show that pervasive translation can, nevertheless, be associated with high specificity, with 1055 noncoding ORFs exhibiting canonical Ribo-Seq signals. Using mass spectrometry under standard conditions or proteasome inhibition with an in-house analysis protocol, we report 239 microproteins originating from noncoding ORFs that display canonical but also non-canonical Ribo-Seq signals. Each condition yields dozens of additional microprotein candidates with comparable translation properties, suggesting a larger population of volatile microproteins that are challenging to detect. Our findings suggest that non-canonical translation signals may harbor valuable information and underscore the significance of considering them in proteogenomic studies. Finally, we show that the translation outcome of a noncoding ORF is primarily determined by the initiating codon and the codon distribution in its two alternative frames, rather than features indicative of functionality. Our results enable us to propose a topology of a species’ Ribo-Seq landscape, opening the way to comparative analyses of this translation landscape under different conditions.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"123 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2024-10-14DOI: 10.1186/s13059-024-03410-8
Luxiao Chen, Zhenxing Guo, Tao Deng, Hao Wu
{"title":"scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq","authors":"Luxiao Chen, Zhenxing Guo, Tao Deng, Hao Wu","doi":"10.1186/s13059-024-03410-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03410-8","url":null,"abstract":"Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"55 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}