Pub Date : 2025-02-07DOI: 10.1038/s41588-025-02116-2
Shengqian Xia, Jianhai Chen, Deanna Arsala, J. J. Emerson, Manyuan Long
Correction to: Nature Genetics https://doi.org/10.1038/s41588-024-02059-0, published online 28 January 2025.
{"title":"Publisher Correction: Functional innovation through new genes as a general evolutionary process","authors":"Shengqian Xia, Jianhai Chen, Deanna Arsala, J. J. Emerson, Manyuan Long","doi":"10.1038/s41588-025-02116-2","DOIUrl":"https://doi.org/10.1038/s41588-025-02116-2","url":null,"abstract":"<p>Correction to: <i>Nature Genetics</i> https://doi.org/10.1038/s41588-024-02059-0, published online 28 January 2025.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"26 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367405","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 : 2025-02-07DOI: 10.1038/s41588-024-02073-2
Madison Wahlsten, Sydney M. Shaffer
A study describes the development of PERFF-seq, a method to ‘FISH’ out rare cells using RNA markers, helping to solve the challenge in single-cell biology of studying cells that make up less than 1% of a sample.
{"title":"Unveiling heterogeneity in rare cells by combining RNA-based sorting and scRNA-seq","authors":"Madison Wahlsten, Sydney M. Shaffer","doi":"10.1038/s41588-024-02073-2","DOIUrl":"10.1038/s41588-024-02073-2","url":null,"abstract":"A study describes the development of PERFF-seq, a method to ‘FISH’ out rare cells using RNA markers, helping to solve the challenge in single-cell biology of studying cells that make up less than 1% of a sample.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 2","pages":"283-284"},"PeriodicalIF":31.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257835","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 : 2025-02-07DOI: 10.1038/s41588-025-02092-7
Phenotypic plasticity of cancer cells is increasingly recognized as a mechanism of tumor escape from targeted therapies. Yet, the phenotypic heterogeneity of colorectal cancer remains poorly explored. Our study identifies oncofetal reprogramming of neoplastic stem cells, driven by the transcription factors YAP and AP-1, as a critical driver of phenotypic heterogeneity, lineage plasticity and therapy resistance. Targeting the oncofetal program enhances the efficacy and durability of current treatments.
{"title":"Oncofetal reprogramming induces phenotypic heterogeneity in colorectal cancer","authors":"","doi":"10.1038/s41588-025-02092-7","DOIUrl":"10.1038/s41588-025-02092-7","url":null,"abstract":"Phenotypic plasticity of cancer cells is increasingly recognized as a mechanism of tumor escape from targeted therapies. Yet, the phenotypic heterogeneity of colorectal cancer remains poorly explored. Our study identifies oncofetal reprogramming of neoplastic stem cells, driven by the transcription factors YAP and AP-1, as a critical driver of phenotypic heterogeneity, lineage plasticity and therapy resistance. Targeting the oncofetal program enhances the efficacy and durability of current treatments.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 2","pages":"285-286"},"PeriodicalIF":31.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367404","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 : 2025-02-04DOI: 10.1038/s41588-025-02078-5
Martin Blohmer, David M. Cheek, Wei-Ting Hung, Maria Kessler, Foivos Chatzidimitriou, Jiahe Wang, William Hung, I-Hsiu Lee, Alexander N. Gorelick, Emma CE Wassenaar, Ching-Yeuh Yang, Yi-Chen Yeh, Hsiang-Ling Ho, Dorothee Speiser, Maria M. Karsten, Michael Lanuti, Sara I. Pai, Onno Kranenburg, Jochen K. Lennerz, Teh-Ying Chou, Matthias Kloor, Kamila Naxerova
Cell division drives somatic evolution but is challenging to quantify. We developed a framework to count cell divisions with DNA replication-related mutations in polyguanine homopolymers. Analyzing 505 samples from 37 patients, we studied the milestones of colorectal cancer evolution. Primary tumors diversify at ~250 divisions from the founder cell, while distant metastasis divergence occurs significantly later, at ~500 divisions. Notably, distant but not lymph node metastases originate from primary tumor regions that have undergone surplus divisions, tying subclonal expansion to metastatic capacity. Then, we analyzed a cohort of 73 multifocal lung cancers and showed that the cell division burden of the tumors’ common ancestor distinguishes independent primary tumors from intrapulmonary metastases and correlates with patient survival. In lung cancer too, metastatic capacity is tied to more extensive proliferation. The cell division history of human cancers is easily accessible using our simple framework and contains valuable biological and clinical information.
{"title":"Quantifying cell divisions along evolutionary lineages in cancer","authors":"Martin Blohmer, David M. Cheek, Wei-Ting Hung, Maria Kessler, Foivos Chatzidimitriou, Jiahe Wang, William Hung, I-Hsiu Lee, Alexander N. Gorelick, Emma CE Wassenaar, Ching-Yeuh Yang, Yi-Chen Yeh, Hsiang-Ling Ho, Dorothee Speiser, Maria M. Karsten, Michael Lanuti, Sara I. Pai, Onno Kranenburg, Jochen K. Lennerz, Teh-Ying Chou, Matthias Kloor, Kamila Naxerova","doi":"10.1038/s41588-025-02078-5","DOIUrl":"https://doi.org/10.1038/s41588-025-02078-5","url":null,"abstract":"<p>Cell division drives somatic evolution but is challenging to quantify. We developed a framework to count cell divisions with DNA replication-related mutations in polyguanine homopolymers. Analyzing 505 samples from 37 patients, we studied the milestones of colorectal cancer evolution. Primary tumors diversify at ~250 divisions from the founder cell, while distant metastasis divergence occurs significantly later, at ~500 divisions. Notably, distant but not lymph node metastases originate from primary tumor regions that have undergone surplus divisions, tying subclonal expansion to metastatic capacity. Then, we analyzed a cohort of 73 multifocal lung cancers and showed that the cell division burden of the tumors’ common ancestor distinguishes independent primary tumors from intrapulmonary metastases and correlates with patient survival. In lung cancer too, metastatic capacity is tied to more extensive proliferation. The cell division history of human cancers is easily accessible using our simple framework and contains valuable biological and clinical information.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"39 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083418","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 : 2025-02-03DOI: 10.1038/s41588-024-02069-y
Wenbin Guo, Miriam Schreiber, Vanda B. Marosi, Paolo Bagnaresi, Morten Egevang Jørgensen, Katarzyna B. Braune, Ken Chalmers, Brett Chapman, Viet Dang, Christoph Dockter, Anne Fiebig, Geoffrey B. Fincher, Agostino Fricano, John Fuller, Allison Haaning, Georg Haberer, Axel Himmelbach, Murukarthick Jayakodi, Yong Jia, Nadia Kamal, Peter Langridge, Chengdao Li, Qiongxian Lu, Thomas Lux, Martin Mascher, Klaus F. X. Mayer, Nicola McCallum, Linda Milne, Gary J. Muehlbauer, Martin T. S. Nielsen, Sudharsan Padmarasu, Pai Rosager Pedas, Klaus Pillen, Curtis Pozniak, Magnus W. Rasmussen, Kazuhiro Sato, Thomas Schmutzer, Uwe Scholz, Danuta Schüler, Hana Šimková, Birgitte Skadhauge, Nils Stein, Nina W. Thomsen, Cynthia Voss, Penghao Wang, Ronja Wonneberger, Xiao-Qi Zhang, Guoping Zhang, Luigi Cattivelli, Manuel Spannagl, Micha Bayer, Craig Simpson, Runxuan Zhang, Robbie Waugh
A pan-transcriptome describes the transcriptional and post-transcriptional consequences of genome diversity from multiple individuals within a species. We developed a barley pan-transcriptome using 20 inbred genotypes representing domesticated barley diversity by generating and analyzing short- and long-read RNA-sequencing datasets from multiple tissues. To overcome single reference bias in transcript quantification, we constructed genotype-specific reference transcript datasets (RTDs) and integrated these into a linear pan-genome framework to create a pan-RTD, allowing transcript categorization as core, shell or cloud. Focusing on the core (expressed in all genotypes), we observed significant transcript abundance variation among tissues and between genotypes driven partly by RNA processing, gene copy number, structural rearrangements and conservation of promotor motifs. Network analyses revealed conserved co-expression module::tissue correlations and frequent functional diversification. To complement the pan-transcriptome, we constructed a comprehensive cultivar (cv.) Morex gene-expression atlas and illustrate how these combined datasets can be used to guide biological inquiry. A long- and short-read barley pan-transcriptome assembled from 20 diverse barley genotypes offers insights into genotype- and tissue-dependent gene expression and function.
{"title":"A barley pan-transcriptome reveals layers of genotype-dependent transcriptional complexity","authors":"Wenbin Guo, Miriam Schreiber, Vanda B. Marosi, Paolo Bagnaresi, Morten Egevang Jørgensen, Katarzyna B. Braune, Ken Chalmers, Brett Chapman, Viet Dang, Christoph Dockter, Anne Fiebig, Geoffrey B. Fincher, Agostino Fricano, John Fuller, Allison Haaning, Georg Haberer, Axel Himmelbach, Murukarthick Jayakodi, Yong Jia, Nadia Kamal, Peter Langridge, Chengdao Li, Qiongxian Lu, Thomas Lux, Martin Mascher, Klaus F. X. Mayer, Nicola McCallum, Linda Milne, Gary J. Muehlbauer, Martin T. S. Nielsen, Sudharsan Padmarasu, Pai Rosager Pedas, Klaus Pillen, Curtis Pozniak, Magnus W. Rasmussen, Kazuhiro Sato, Thomas Schmutzer, Uwe Scholz, Danuta Schüler, Hana Šimková, Birgitte Skadhauge, Nils Stein, Nina W. Thomsen, Cynthia Voss, Penghao Wang, Ronja Wonneberger, Xiao-Qi Zhang, Guoping Zhang, Luigi Cattivelli, Manuel Spannagl, Micha Bayer, Craig Simpson, Runxuan Zhang, Robbie Waugh","doi":"10.1038/s41588-024-02069-y","DOIUrl":"10.1038/s41588-024-02069-y","url":null,"abstract":"A pan-transcriptome describes the transcriptional and post-transcriptional consequences of genome diversity from multiple individuals within a species. We developed a barley pan-transcriptome using 20 inbred genotypes representing domesticated barley diversity by generating and analyzing short- and long-read RNA-sequencing datasets from multiple tissues. To overcome single reference bias in transcript quantification, we constructed genotype-specific reference transcript datasets (RTDs) and integrated these into a linear pan-genome framework to create a pan-RTD, allowing transcript categorization as core, shell or cloud. Focusing on the core (expressed in all genotypes), we observed significant transcript abundance variation among tissues and between genotypes driven partly by RNA processing, gene copy number, structural rearrangements and conservation of promotor motifs. Network analyses revealed conserved co-expression module::tissue correlations and frequent functional diversification. To complement the pan-transcriptome, we constructed a comprehensive cultivar (cv.) Morex gene-expression atlas and illustrate how these combined datasets can be used to guide biological inquiry. A long- and short-read barley pan-transcriptome assembled from 20 diverse barley genotypes offers insights into genotype- and tissue-dependent gene expression and function.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 2","pages":"441-450"},"PeriodicalIF":31.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-02069-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077198","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 : 2025-02-03DOI: 10.1038/s41588-024-02066-1
Jasmine T. Plummer, Ioannis S. Vlachos, Luciano G. Martelotto
This Comment outlines the creation of the Global Alliance for Spatial Technologies (GESTALT), a collaborative initiative aimed at fostering the growth and standardization of spatial tissue profiling technologies. It explores the need for GESTALT, its community-driven structure and its goals, spanning from the immediate to the long term.
{"title":"Introducing the Global Alliance for Spatial Technologies (GESTALT)","authors":"Jasmine T. Plummer, Ioannis S. Vlachos, Luciano G. Martelotto","doi":"10.1038/s41588-024-02066-1","DOIUrl":"10.1038/s41588-024-02066-1","url":null,"abstract":"This Comment outlines the creation of the Global Alliance for Spatial Technologies (GESTALT), a collaborative initiative aimed at fostering the growth and standardization of spatial tissue profiling technologies. It explores the need for GESTALT, its community-driven structure and its goals, spanning from the immediate to the long term.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 2","pages":"275-279"},"PeriodicalIF":31.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077193","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 : 2025-02-03DOI: 10.1038/s41588-024-02035-8
Sile Hu, Lino A. F. Ferreira, Sinan Shi, Garrett Hellenthal, Jonathan Marchini, Daniel J. Lawson, Simon R. Myers
Understanding genetic differences between populations is essential for avoiding confounding in genome-wide association studies and improving polygenic score (PGS) portability. We developed a statistical pipeline to infer fine-scale Ancestry Components and applied it to UK Biobank data. Ancestry Components identify population structure not captured by widely used principal components, improving stratification correction for geographically correlated traits. To estimate the similarity of genetic effect sizes between groups, we developed ANCHOR, which estimates changes in the predictive power of an existing PGS in distinct local ancestry segments. ANCHOR infers highly similar (estimated correlation 0.98 ± 0.07) effect sizes between UK Biobank participants of African and European ancestry for 47 of 53 quantitative phenotypes, suggesting that gene–environment and gene–gene interactions do not play major roles in poor cross-ancestry PGS transferability for these traits in the United Kingdom, and providing optimism that shared causal mutations operate similarly in different populations. This study introduces the concept of Ancestry Components and shows that they can offer improved population stratification correction for geographically correlated traits. By using ancestry-aware polygenic score construction in admixed individuals, the authors find that effect sizes are conserved across ancestry groups.
{"title":"Fine-scale population structure and widespread conservation of genetic effect sizes between human groups across traits","authors":"Sile Hu, Lino A. F. Ferreira, Sinan Shi, Garrett Hellenthal, Jonathan Marchini, Daniel J. Lawson, Simon R. Myers","doi":"10.1038/s41588-024-02035-8","DOIUrl":"10.1038/s41588-024-02035-8","url":null,"abstract":"Understanding genetic differences between populations is essential for avoiding confounding in genome-wide association studies and improving polygenic score (PGS) portability. We developed a statistical pipeline to infer fine-scale Ancestry Components and applied it to UK Biobank data. Ancestry Components identify population structure not captured by widely used principal components, improving stratification correction for geographically correlated traits. To estimate the similarity of genetic effect sizes between groups, we developed ANCHOR, which estimates changes in the predictive power of an existing PGS in distinct local ancestry segments. ANCHOR infers highly similar (estimated correlation 0.98 ± 0.07) effect sizes between UK Biobank participants of African and European ancestry for 47 of 53 quantitative phenotypes, suggesting that gene–environment and gene–gene interactions do not play major roles in poor cross-ancestry PGS transferability for these traits in the United Kingdom, and providing optimism that shared causal mutations operate similarly in different populations. This study introduces the concept of Ancestry Components and shows that they can offer improved population stratification correction for geographically correlated traits. By using ancestry-aware polygenic score construction in admixed individuals, the authors find that effect sizes are conserved across ancestry groups.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 2","pages":"379-389"},"PeriodicalIF":31.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-02035-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077194","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 : 2025-02-03DOI: 10.1038/s41588-025-02075-8
Ikram Chekroun, Shruti Shenbagam, Mohamed A. Almarri, Younes Mokrab, Mohammed Uddin, Omer S. Alkhnbashi, Maha S. Zaki, Hossein Najmabadi, Kimia Kahrizi, Khalid A. Fakhro, Naif A. M. Almontashiri, Fahad R. Ali, Uğur Özbek, Bruno Reversade, Fowzan S. Alkuraya, Alawi Alsheikh-Ali, Ahmad N. Abou Tayoun
The Greater Middle East (GME) represents a concentrated region of unparalleled genetic diversity, characterized by an abundance of distinct alleles, founder mutations and extensive autozygosity driven by high consanguinity rates. These genetic hallmarks present a unique, yet vastly untapped resource for genomic research on Mendelian diseases. Despite this immense potential, the GME continues to face substantial challenges in comprehensive data collection and analysis. This Perspective highlights the region’s unique position as a natural laboratory for genetic discovery and explores the challenges that have stifled progress thus far. Importantly, we propose strategic solutions, advocating for an all-inclusive research approach. With targeted investment and focused efforts, the latent genetic wealth in the GME can be transformed into a global hub for genomic research that will redefine and advance our understanding of the human genome.
{"title":"Genomics of rare diseases in the Greater Middle East","authors":"Ikram Chekroun, Shruti Shenbagam, Mohamed A. Almarri, Younes Mokrab, Mohammed Uddin, Omer S. Alkhnbashi, Maha S. Zaki, Hossein Najmabadi, Kimia Kahrizi, Khalid A. Fakhro, Naif A. M. Almontashiri, Fahad R. Ali, Uğur Özbek, Bruno Reversade, Fowzan S. Alkuraya, Alawi Alsheikh-Ali, Ahmad N. Abou Tayoun","doi":"10.1038/s41588-025-02075-8","DOIUrl":"https://doi.org/10.1038/s41588-025-02075-8","url":null,"abstract":"<p>The Greater Middle East (GME) represents a concentrated region of unparalleled genetic diversity, characterized by an abundance of distinct alleles, founder mutations and extensive autozygosity driven by high consanguinity rates. These genetic hallmarks present a unique, yet vastly untapped resource for genomic research on Mendelian diseases. Despite this immense potential, the GME continues to face substantial challenges in comprehensive data collection and analysis. This Perspective highlights the region’s unique position as a natural laboratory for genetic discovery and explores the challenges that have stifled progress thus far. Importantly, we propose strategic solutions, advocating for an all-inclusive research approach. With targeted investment and focused efforts, the latent genetic wealth in the GME can be transformed into a global hub for genomic research that will redefine and advance our understanding of the human genome.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"47 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077195","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 : 2025-02-03DOI: 10.1038/s41588-025-02080-x
Annika Vannan, Ruqian Lyu, Arianna L. Williams, Nicholas M. Negretti, Evan D. Mee, Joseph Hirsh, Samuel Hirsh, Niran Hadad, David S. Nichols, Carla L. Calvi, Chase J. Taylor, Vasiliy. V. Polosukhin, Ana P. M. Serezani, A. Scott McCall, Jason J. Gokey, Heejung Shim, Lorraine B. Ware, Matthew J. Bacchetta, Ciara M. Shaver, Timothy S. Blackwell, Rajat Walia, Jennifer M. S. Sucre, Jonathan A. Kropski, Davis J. McCarthy, Nicholas E. Banovich
Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecular changes associated with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially resolved view of PF and establish methods that could be applied to other spatial transcriptomic studies.
{"title":"Spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis","authors":"Annika Vannan, Ruqian Lyu, Arianna L. Williams, Nicholas M. Negretti, Evan D. Mee, Joseph Hirsh, Samuel Hirsh, Niran Hadad, David S. Nichols, Carla L. Calvi, Chase J. Taylor, Vasiliy. V. Polosukhin, Ana P. M. Serezani, A. Scott McCall, Jason J. Gokey, Heejung Shim, Lorraine B. Ware, Matthew J. Bacchetta, Ciara M. Shaver, Timothy S. Blackwell, Rajat Walia, Jennifer M. S. Sucre, Jonathan A. Kropski, Davis J. McCarthy, Nicholas E. Banovich","doi":"10.1038/s41588-025-02080-x","DOIUrl":"https://doi.org/10.1038/s41588-025-02080-x","url":null,"abstract":"<p>Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecular changes associated with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially resolved view of PF and establish methods that could be applied to other spatial transcriptomic studies.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"4 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077196","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}