Pub Date : 2024-11-21DOI: 10.1186/s13059-024-03430-4
Francesco Ravasini, Helja Kabral, Anu Solnik, Luciana de Gennaro, Francesco Montinaro, Ruoyun Hui, Chiara Delpino, Stefano Finocchi, Pierluigi Giroldini, Oscar Mei, Michael Allen Beck De Lotto, Elisabetta Cilli, Mogge Hajiesmaeil, Letizia Pistacchia, Flavia Risi, Chiara Giacometti, Christiana Lyn Scheib, Kristiina Tambets, Mait Metspalu, Fulvio Cruciani, Eugenia D’Atanasio, Beniamino Trombetta
The Italic Iron Age is characterized by the presence of various ethnic groups partially examined from a genomic perspective. To explore the evolution of Iron Age Italic populations and the genetic impact of Romanization, we focus on the Picenes, one of the most fascinating pre-Roman civilizations, who flourished on the Middle Adriatic side of Central Italy between the 9th and the 3rd century BCE, until the Roman colonization. More than 50 samples are reported, spanning more than 1000 years of history from the Iron Age to Late Antiquity. Despite cultural diversity, our analysis reveals no major differences between the Picenes and other coeval populations, suggesting a shared genetic history of the Central Italian Iron Age ethnic groups. Nevertheless, a slight genetic differentiation between populations along the Adriatic and Tyrrhenian coasts can be observed, possibly due to different population dynamics in the two sides of Italy and/or genetic contacts across the Adriatic Sea. Additionally, we identify several individuals with ancestries deviating from their general population. Lastly, in our Late Antiquity site, we observe a drastic change in the genetic landscape of the Middle Adriatic region, indicating a relevant influx from the Near East, possibly as a consequence of Romanization. Our findings, consistently with archeological hypotheses, suggest genetic interactions across the Adriatic Sea during the Bronze/Iron Age and a high level of individual mobility typical of cosmopolitan societies. Finally, we highlight the role of the Roman Empire in shaping genetic and phenotypic changes that greatly impact the Italian peninsula.
{"title":"The genomic portrait of the Picene culture provides new insights into the Italic Iron Age and the legacy of the Roman Empire in Central Italy","authors":"Francesco Ravasini, Helja Kabral, Anu Solnik, Luciana de Gennaro, Francesco Montinaro, Ruoyun Hui, Chiara Delpino, Stefano Finocchi, Pierluigi Giroldini, Oscar Mei, Michael Allen Beck De Lotto, Elisabetta Cilli, Mogge Hajiesmaeil, Letizia Pistacchia, Flavia Risi, Chiara Giacometti, Christiana Lyn Scheib, Kristiina Tambets, Mait Metspalu, Fulvio Cruciani, Eugenia D’Atanasio, Beniamino Trombetta","doi":"10.1186/s13059-024-03430-4","DOIUrl":"https://doi.org/10.1186/s13059-024-03430-4","url":null,"abstract":"The Italic Iron Age is characterized by the presence of various ethnic groups partially examined from a genomic perspective. To explore the evolution of Iron Age Italic populations and the genetic impact of Romanization, we focus on the Picenes, one of the most fascinating pre-Roman civilizations, who flourished on the Middle Adriatic side of Central Italy between the 9th and the 3rd century BCE, until the Roman colonization. More than 50 samples are reported, spanning more than 1000 years of history from the Iron Age to Late Antiquity. Despite cultural diversity, our analysis reveals no major differences between the Picenes and other coeval populations, suggesting a shared genetic history of the Central Italian Iron Age ethnic groups. Nevertheless, a slight genetic differentiation between populations along the Adriatic and Tyrrhenian coasts can be observed, possibly due to different population dynamics in the two sides of Italy and/or genetic contacts across the Adriatic Sea. Additionally, we identify several individuals with ancestries deviating from their general population. Lastly, in our Late Antiquity site, we observe a drastic change in the genetic landscape of the Middle Adriatic region, indicating a relevant influx from the Near East, possibly as a consequence of Romanization. Our findings, consistently with archeological hypotheses, suggest genetic interactions across the Adriatic Sea during the Bronze/Iron Age and a high level of individual mobility typical of cosmopolitan societies. Finally, we highlight the role of the Roman Empire in shaping genetic and phenotypic changes that greatly impact the Italian peninsula.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"81 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678276","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}
Pub Date : 2024-11-21DOI: 10.1186/s13059-024-03436-y
Wenbo Guo, Xinqi Li, Dongfang Wang, Nan Yan, Qifan Hu, Fan Yang, Xuegong Zhang, Jianhua Yao, Jin Gu
Understanding tumor cell heterogeneity and plasticity is crucial for overcoming drug resistance. Single-cell technologies enable analyzing cell states at a given condition, but catenating static cell snapshots to characterize dynamic drug responses remains challenging. Here, we propose scStateDynamics, an algorithm to infer tumor cell state dynamics and identify common drug effects by modeling single-cell level gene expression changes. Its reliability is validated on both simulated and lineage tracing data. Application to real tumor drug treatment datasets identifies more subtle cell subclusters with different drug responses beyond static transcriptome similarity and disentangles drug action mechanisms from the cell-level expression changes.
{"title":"scStateDynamics: deciphering the drug-responsive tumor cell state dynamics by modeling single-cell level expression changes","authors":"Wenbo Guo, Xinqi Li, Dongfang Wang, Nan Yan, Qifan Hu, Fan Yang, Xuegong Zhang, Jianhua Yao, Jin Gu","doi":"10.1186/s13059-024-03436-y","DOIUrl":"https://doi.org/10.1186/s13059-024-03436-y","url":null,"abstract":"Understanding tumor cell heterogeneity and plasticity is crucial for overcoming drug resistance. Single-cell technologies enable analyzing cell states at a given condition, but catenating static cell snapshots to characterize dynamic drug responses remains challenging. Here, we propose scStateDynamics, an algorithm to infer tumor cell state dynamics and identify common drug effects by modeling single-cell level gene expression changes. Its reliability is validated on both simulated and lineage tracing data. Application to real tumor drug treatment datasets identifies more subtle cell subclusters with different drug responses beyond static transcriptome similarity and disentangles drug action mechanisms from the cell-level expression changes.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"19 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678435","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}
Pub Date : 2024-11-19DOI: 10.1186/s13059-024-03427-z
Marleen Balvert, Johnathan Cooper-Knock, Julian Stamp, Ross P. Byrne, Soufiane Mourragui, Juami van Gils, Stefania Benonisdottir, Johannes Schlüter, Kevin Kenna, Sanne Abeln, Alfredo Iacoangeli, Joséphine T. Daub, Brian L. Browning, Gizem Taş, Jiajing Hu, Yan Wang, Elham Alhathli, Calum Harvey, Luna Pianesi, Sara C. Schulte, Jorge González-Domínguez, Erik Garrisson, Michael P. Snyder, Alexander Schönhuth, Letitia M. F. Sng, Natalie A. Twine
Epistasis refers to changes in the effect on phenotype of a unit of genetic information, such as a single nucleotide polymorphism or a gene, dependent on the context of other genetic units. Such interactions are both biologically plausible and good candidates to explain observations which are not fully explained by an additive heritability model. However, the search for epistasis has so far largely failed to recover this missing heritability. We identify key challenges and propose that future works need to leverage idealized systems, known biology and even previously identified epistatic interactions, in order to guide the search for new interactions.
{"title":"Considerations in the search for epistasis","authors":"Marleen Balvert, Johnathan Cooper-Knock, Julian Stamp, Ross P. Byrne, Soufiane Mourragui, Juami van Gils, Stefania Benonisdottir, Johannes Schlüter, Kevin Kenna, Sanne Abeln, Alfredo Iacoangeli, Joséphine T. Daub, Brian L. Browning, Gizem Taş, Jiajing Hu, Yan Wang, Elham Alhathli, Calum Harvey, Luna Pianesi, Sara C. Schulte, Jorge González-Domínguez, Erik Garrisson, Michael P. Snyder, Alexander Schönhuth, Letitia M. F. Sng, Natalie A. Twine","doi":"10.1186/s13059-024-03427-z","DOIUrl":"https://doi.org/10.1186/s13059-024-03427-z","url":null,"abstract":"Epistasis refers to changes in the effect on phenotype of a unit of genetic information, such as a single nucleotide polymorphism or a gene, dependent on the context of other genetic units. Such interactions are both biologically plausible and good candidates to explain observations which are not fully explained by an additive heritability model. However, the search for epistasis has so far largely failed to recover this missing heritability. We identify key challenges and propose that future works need to leverage idealized systems, known biology and even previously identified epistatic interactions, in order to guide the search for new interactions.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"11 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671019","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}
Pub Date : 2024-11-18DOI: 10.1186/s13059-024-03433-1
B J Chabot, R Sun, A Amjad, S J Hoyt, L Ouyang, C Courret, R Drennan, L Leo, A M Larracuente, L J Core, R J O'Neill, B G Mellone
Background: Centromeres depend on chromatin containing the conserved histone H3 variant CENP-A for function and inheritance, while the role of centromeric DNA repeats remains unclear. Retroelements are prevalent at centromeres across taxa and represent a potential mechanism for promoting transcription to aid in CENP-A incorporation or for generating RNA transcripts to maintain centromere integrity.
Results: In this study, we probe into the transcription and RNA localization of the centromere-enriched retroelement G2/Jockey-3 (hereafter referred to as Jockey-3) in Drosophila melanogaster, currently the only in vivo model with assembled centromeres. We find that Jockey-3 is a major component of the centromeric transcriptome and produces RNAs that localize to centromeres in metaphase. Leveraging the polymorphism of Jockey-3 and a de novo centromere system, we show that these RNAs remain associated with their cognate DNA sequences in cis, suggesting they are unlikely to perform a sequence-specific function at all centromeres. We show that Jockey-3 transcription is positively correlated with the presence of CENP-A and that recent Jockey-3 transposition events have occurred preferentially at CENP-A-containing chromatin.
Conclusions: We propose that Jockey-3 preferentially inserts at the centromere to ensure its own selfish propagation, while contributing to transcription across these regions. Given the conservation of retroelements as centromere components through evolution, our findings may offer a basis for understanding similar associations in other species.
{"title":"Transcription of a centromere-enriched retroelement and local retention of its RNA are significant features of the CENP-A chromatin landscape.","authors":"B J Chabot, R Sun, A Amjad, S J Hoyt, L Ouyang, C Courret, R Drennan, L Leo, A M Larracuente, L J Core, R J O'Neill, B G Mellone","doi":"10.1186/s13059-024-03433-1","DOIUrl":"10.1186/s13059-024-03433-1","url":null,"abstract":"<p><strong>Background: </strong>Centromeres depend on chromatin containing the conserved histone H3 variant CENP-A for function and inheritance, while the role of centromeric DNA repeats remains unclear. Retroelements are prevalent at centromeres across taxa and represent a potential mechanism for promoting transcription to aid in CENP-A incorporation or for generating RNA transcripts to maintain centromere integrity.</p><p><strong>Results: </strong>In this study, we probe into the transcription and RNA localization of the centromere-enriched retroelement G2/Jockey-3 (hereafter referred to as Jockey-3) in Drosophila melanogaster, currently the only in vivo model with assembled centromeres. We find that Jockey-3 is a major component of the centromeric transcriptome and produces RNAs that localize to centromeres in metaphase. Leveraging the polymorphism of Jockey-3 and a de novo centromere system, we show that these RNAs remain associated with their cognate DNA sequences in cis, suggesting they are unlikely to perform a sequence-specific function at all centromeres. We show that Jockey-3 transcription is positively correlated with the presence of CENP-A and that recent Jockey-3 transposition events have occurred preferentially at CENP-A-containing chromatin.</p><p><strong>Conclusions: </strong>We propose that Jockey-3 preferentially inserts at the centromere to ensure its own selfish propagation, while contributing to transcription across these regions. Given the conservation of retroelements as centromere components through evolution, our findings may offer a basis for understanding similar associations in other species.</p>","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"25 1","pages":"295"},"PeriodicalIF":10.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1186/s13059-024-03419-z
Pierre Boyeau, Stephen Bates, Can Ergen, Michael I. Jordan, Nir Yosef
Unveiling functional relationships between various molecular cell phenotypes from data using machine learning models is a key promise of multiomics. Existing methods either use flexible but hard-to-interpret models or simpler, misspecified models. VI-VS (Variational Inference for Variable Selection) balances flexibility and interpretability to identify relevant feature relationships in multiomic data. It uses deep generative models to identify conditionally dependent features, with false discovery rate control. VI-VS is available as an open-source Python package, providing a robust solution to identify features more likely representing genuine causal relationships.
{"title":"VI-VS: calibrated identification of feature dependencies in single-cell multiomics","authors":"Pierre Boyeau, Stephen Bates, Can Ergen, Michael I. Jordan, Nir Yosef","doi":"10.1186/s13059-024-03419-z","DOIUrl":"https://doi.org/10.1186/s13059-024-03419-z","url":null,"abstract":"Unveiling functional relationships between various molecular cell phenotypes from data using machine learning models is a key promise of multiomics. Existing methods either use flexible but hard-to-interpret models or simpler, misspecified models. VI-VS (Variational Inference for Variable Selection) balances flexibility and interpretability to identify relevant feature relationships in multiomic data. It uses deep generative models to identify conditionally dependent features, with false discovery rate control. VI-VS is available as an open-source Python package, providing a robust solution to identify features more likely representing genuine causal relationships.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"11 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637555","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}
Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.
{"title":"IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model","authors":"Dongjoo Lee, Jeongbin Park, Seungho Cook, Seongjin Yoo, Daeseung Lee, Hongyoon Choi","doi":"10.1186/s13059-024-03380-x","DOIUrl":"https://doi.org/10.1186/s13059-024-03380-x","url":null,"abstract":"Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"409 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598333","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}
Pub Date : 2024-11-11DOI: 10.1186/s13059-024-03434-0
Leilei Wu, Shutan Jiang, Meisong Shi, Tanglong Yuan, Yaqin Li, Pinzheng Huang, Yingqi Li, Erwei Zuo, Changyang Zhou, Yidi Sun
The safety of CRISPR-based gene editing methods is of the utmost priority in clinical applications. Previous studies have reported that Cas9 cleavage induced frequent aneuploidy in primary human T cells, but whether cleavage-mediated editing of base editors would generate off-target structure variations remains unknown. Here, we investigate the potential off-target structural variations associated with CRISPR/Cas9, ABE, and CBE editing in mouse embryos and primary human T cells by whole-genome sequencing and single-cell RNA-seq analyses. The results show that both Cas9 and ABE generate off-target structural variations (SVs) in mouse embryos, while CBE induces rare SVs. In addition, off-target large deletions are detected in 32.74% of primary human T cells transfected with Cas9 and 9.17% of cells transfected with ABE. Moreover, Cas9-induced aneuploid cells activate the P53 and apoptosis pathways, whereas ABE-associated aneuploid cells significantly upregulate cell cycle-related genes and are arrested in the G0 phase. A percentage of 16.59% and 4.29% aneuploid cells are still observable at 3 weeks post transfection of Cas9 or ABE. These off-target phenomena in ABE are universal as observed in other cell types such as B cells and Huh7. Furthermore, the off-target SVs are significantly reduced in cells treated with high-fidelity ABE (ABE-V106W). This study shows both CRISPR/Cas9 and ABE induce off-target SVs in mouse embryos and primary human T cells, raising an urgent need for the development of high-fidelity gene editing tools.
{"title":"Adenine base editors induce off-target structure variations in mouse embryos and primary human T cells","authors":"Leilei Wu, Shutan Jiang, Meisong Shi, Tanglong Yuan, Yaqin Li, Pinzheng Huang, Yingqi Li, Erwei Zuo, Changyang Zhou, Yidi Sun","doi":"10.1186/s13059-024-03434-0","DOIUrl":"https://doi.org/10.1186/s13059-024-03434-0","url":null,"abstract":"The safety of CRISPR-based gene editing methods is of the utmost priority in clinical applications. Previous studies have reported that Cas9 cleavage induced frequent aneuploidy in primary human T cells, but whether cleavage-mediated editing of base editors would generate off-target structure variations remains unknown. Here, we investigate the potential off-target structural variations associated with CRISPR/Cas9, ABE, and CBE editing in mouse embryos and primary human T cells by whole-genome sequencing and single-cell RNA-seq analyses. The results show that both Cas9 and ABE generate off-target structural variations (SVs) in mouse embryos, while CBE induces rare SVs. In addition, off-target large deletions are detected in 32.74% of primary human T cells transfected with Cas9 and 9.17% of cells transfected with ABE. Moreover, Cas9-induced aneuploid cells activate the P53 and apoptosis pathways, whereas ABE-associated aneuploid cells significantly upregulate cell cycle-related genes and are arrested in the G0 phase. A percentage of 16.59% and 4.29% aneuploid cells are still observable at 3 weeks post transfection of Cas9 or ABE. These off-target phenomena in ABE are universal as observed in other cell types such as B cells and Huh7. Furthermore, the off-target SVs are significantly reduced in cells treated with high-fidelity ABE (ABE-V106W). This study shows both CRISPR/Cas9 and ABE induce off-target SVs in mouse embryos and primary human T cells, raising an urgent need for the development of high-fidelity gene editing tools.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"2 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598474","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}
Pub Date : 2024-11-11DOI: 10.1186/s13059-024-03428-y
Eloise Withnell, Maria Secrier
Spatial transcriptomics is revolutionizing the exploration of intratissue heterogeneity in cancer, yet capturing cellular niches and their spatial relationships remains challenging. We introduce SpottedPy, a Python package designed to identify tumor hotspots and map spatial interactions within the cancer ecosystem. Using SpottedPy, we examine epithelial-mesenchymal plasticity in breast cancer and highlight stable niches associated with angiogenic and hypoxic regions, shielded by CAFs and macrophages. Hybrid and mesenchymal hotspot distribution follows transformation gradients reflecting progressive immunosuppression. Our method offers flexibility to explore spatial relationships at different scales, from immediate neighbors to broader tissue modules, providing new insights into tumor microenvironment dynamics.
{"title":"SpottedPy quantifies relationships between spatial transcriptomic hotspots and uncovers environmental cues of epithelial-mesenchymal plasticity in breast cancer","authors":"Eloise Withnell, Maria Secrier","doi":"10.1186/s13059-024-03428-y","DOIUrl":"https://doi.org/10.1186/s13059-024-03428-y","url":null,"abstract":"Spatial transcriptomics is revolutionizing the exploration of intratissue heterogeneity in cancer, yet capturing cellular niches and their spatial relationships remains challenging. We introduce SpottedPy, a Python package designed to identify tumor hotspots and map spatial interactions within the cancer ecosystem. Using SpottedPy, we examine epithelial-mesenchymal plasticity in breast cancer and highlight stable niches associated with angiogenic and hypoxic regions, shielded by CAFs and macrophages. Hybrid and mesenchymal hotspot distribution follows transformation gradients reflecting progressive immunosuppression. Our method offers flexibility to explore spatial relationships at different scales, from immediate neighbors to broader tissue modules, providing new insights into tumor microenvironment dynamics.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"71 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598334","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}
Pub Date : 2024-11-08DOI: 10.1186/s13059-024-03426-0
Nam D. Nguyen, Lorena Rosas, Timur Khaliullin, Peiran Jiang, Euxhen Hasanaj, Jose A. Ovando-Ricardez, Marta Bueno, Irfan Rahman, Gloria S. Pryhuber, Dongmei Li, Qin Ma, Toren Finkel, Melanie Königshoff, Oliver Eickelberg, Mauricio Rojas, Ana L. Mora, Jose Lugo-Martinez, Ziv Bar-Joseph
The low resolution of spatial transcriptomics data necessitates additional information for optimal use. We developed scDOT, which combines spatial transcriptomics and single cell RNA sequencing to improve the ability to reconstruct single cell resolved spatial maps and identify senescent cells. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to lung spatial transcriptomics data improves on prior methods and allows the identification of the spatial organization of senescent cells, their neighboring cells and novel genes involved in cell-cell interactions that may be driving senescence.
{"title":"scDOT: optimal transport for mapping senescent cells in spatial transcriptomics","authors":"Nam D. Nguyen, Lorena Rosas, Timur Khaliullin, Peiran Jiang, Euxhen Hasanaj, Jose A. Ovando-Ricardez, Marta Bueno, Irfan Rahman, Gloria S. Pryhuber, Dongmei Li, Qin Ma, Toren Finkel, Melanie Königshoff, Oliver Eickelberg, Mauricio Rojas, Ana L. Mora, Jose Lugo-Martinez, Ziv Bar-Joseph","doi":"10.1186/s13059-024-03426-0","DOIUrl":"https://doi.org/10.1186/s13059-024-03426-0","url":null,"abstract":"The low resolution of spatial transcriptomics data necessitates additional information for optimal use. We developed scDOT, which combines spatial transcriptomics and single cell RNA sequencing to improve the ability to reconstruct single cell resolved spatial maps and identify senescent cells. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to lung spatial transcriptomics data improves on prior methods and allows the identification of the spatial organization of senescent cells, their neighboring cells and novel genes involved in cell-cell interactions that may be driving senescence.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"10 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597550","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}
Pub Date : 2024-11-07DOI: 10.1186/s13059-024-03429-x
Jiyuan Yang, Lu Wang, Lin Liu, Xiaoqi Zheng
The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm that leverages the strengths of graphical regularization and principal component analysis. Comprehensive evaluations on simulated and multi-resolution spatial transcriptomic datasets generated from various platforms demonstrate the capacity of GraphPCA to enhance downstream analysis tasks including spatial domain detection, denoising, and trajectory inference compared to other state-of-the-art methods.
{"title":"GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data","authors":"Jiyuan Yang, Lu Wang, Lin Liu, Xiaoqi Zheng","doi":"10.1186/s13059-024-03429-x","DOIUrl":"https://doi.org/10.1186/s13059-024-03429-x","url":null,"abstract":"The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm that leverages the strengths of graphical regularization and principal component analysis. Comprehensive evaluations on simulated and multi-resolution spatial transcriptomic datasets generated from various platforms demonstrate the capacity of GraphPCA to enhance downstream analysis tasks including spatial domain detection, denoising, and trajectory inference compared to other state-of-the-art methods.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"3 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594708","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}
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