Pub Date : 2025-05-20DOI: 10.1038/s41576-025-00857-8
Loïc Binan
In this Tools of the Trade article, Loic Binan explains Perturb-FISH, which measures genetic perturbations and gene expression in situ at high throughput to map gene regulatory networks at cellular and tissue scale.
{"title":"Investigating spatial gene circuits and gene–phenotype mechanisms with Perturb-FISH","authors":"Loïc Binan","doi":"10.1038/s41576-025-00857-8","DOIUrl":"10.1038/s41576-025-00857-8","url":null,"abstract":"In this Tools of the Trade article, Loic Binan explains Perturb-FISH, which measures genetic perturbations and gene expression in situ at high throughput to map gene regulatory networks at cellular and tissue scale.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 8","pages":"507-508"},"PeriodicalIF":52.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103599","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 : 2025-05-20DOI: 10.1038/s41576-025-00850-1
Jianzhi Zhang, Wenfeng Qian
Synonymous mutations are coding mutations that do not alter protein sequences. Commonly thought to have little to no functional consequence, synonymous mutations have been widely used in evolutionary analyses that require neutral markers, including those foundational for the neutral theory. However, recent studies suggest that synonymous mutations can influence nearly every step in the expression of genetic information and may often be strongly non-neutral. We review the extent and mechanisms of these phenotypic and fitness effects and discuss the implications of the functionality and non-neutrality of synonymous mutations for various analyses and conclusions pertinent to genetics, evolution, conservation and disease. Synonymous mutations, once deemed neutral, have been shown to influence gene expression and organismal fitness by affecting transcription, mRNA processing, translation and protein folding. In this Perspective, the authors highlight evidence for fitness effects of synonymous mutations and discuss resulting implications for evolutionary and disease genetics.
{"title":"Functional synonymous mutations and their evolutionary consequences","authors":"Jianzhi Zhang, Wenfeng Qian","doi":"10.1038/s41576-025-00850-1","DOIUrl":"10.1038/s41576-025-00850-1","url":null,"abstract":"Synonymous mutations are coding mutations that do not alter protein sequences. Commonly thought to have little to no functional consequence, synonymous mutations have been widely used in evolutionary analyses that require neutral markers, including those foundational for the neutral theory. However, recent studies suggest that synonymous mutations can influence nearly every step in the expression of genetic information and may often be strongly non-neutral. We review the extent and mechanisms of these phenotypic and fitness effects and discuss the implications of the functionality and non-neutrality of synonymous mutations for various analyses and conclusions pertinent to genetics, evolution, conservation and disease. Synonymous mutations, once deemed neutral, have been shown to influence gene expression and organismal fitness by affecting transcription, mRNA processing, translation and protein folding. In this Perspective, the authors highlight evidence for fitness effects of synonymous mutations and discuss resulting implications for evolutionary and disease genetics.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 11","pages":"789-804"},"PeriodicalIF":52.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144097132","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 : 2025-05-19DOI: 10.1038/s41576-025-00856-9
Nadiya Khyzha
In this Tools of the Trade article, Nadiya Khyzha describes SLAM-RT&Tag, a method for profiling RNA localization and dynamics within nuclear compartments, such as speckles.
{"title":"SLAM-RT&Tag: spatiotemporal profiling of RNA within nuclear compartments in situ","authors":"Nadiya Khyzha","doi":"10.1038/s41576-025-00856-9","DOIUrl":"10.1038/s41576-025-00856-9","url":null,"abstract":"In this Tools of the Trade article, Nadiya Khyzha describes SLAM-RT&Tag, a method for profiling RNA localization and dynamics within nuclear compartments, such as speckles.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 7","pages":"439-439"},"PeriodicalIF":52.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144087866","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 : 2025-05-15DOI: 10.1038/s41576-025-00839-w
Brieuc Lehmann, Leandra Bräuninger, Yoonsu Cho, Fabian Falck, Smera Jayadeva, Michael Katell, Thuy Nguyen, Antonella Perini, Sam Tallman, Maxine Mackintosh, Matt Silver, Karoline Kuchenbäcker, David Leslie, Nilanjan Chatterjee, Chris Holmes
The causes and consequences of inequities in genomic research and medicine are complex and widespread. However, it is widely acknowledged that underrepresentation of diverse populations in human genetics research risks exacerbating existing health disparities. Efforts to improve diversity are ongoing, but an often-overlooked source of inequity is the choice of analytical methods used to process, analyse and interpret genomic data. This choice can influence all areas of genomic research, from genome-wide association studies and polygenic score development to variant prioritization and functional genomics. New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging within the wider genomic research and genomic medicine ecosystems. At this crucial time point, it is important to clarify where improvements in methods and practices can, or cannot, have a role in improving equity in genomics. Here, we review existing approaches to promote equity and fairness in statistical analysis for genomics, and propose future methodological developments that are likely to yield the most impact for equity. New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging. The authors review how the choice of analytical methods used to process, analyse and interpret genomic data can influence genomic research, as well as existing methodological approaches to promote equity and fairness in genomics.
{"title":"Methodological opportunities in genomic data analysis to advance health equity","authors":"Brieuc Lehmann, Leandra Bräuninger, Yoonsu Cho, Fabian Falck, Smera Jayadeva, Michael Katell, Thuy Nguyen, Antonella Perini, Sam Tallman, Maxine Mackintosh, Matt Silver, Karoline Kuchenbäcker, David Leslie, Nilanjan Chatterjee, Chris Holmes","doi":"10.1038/s41576-025-00839-w","DOIUrl":"10.1038/s41576-025-00839-w","url":null,"abstract":"The causes and consequences of inequities in genomic research and medicine are complex and widespread. However, it is widely acknowledged that underrepresentation of diverse populations in human genetics research risks exacerbating existing health disparities. Efforts to improve diversity are ongoing, but an often-overlooked source of inequity is the choice of analytical methods used to process, analyse and interpret genomic data. This choice can influence all areas of genomic research, from genome-wide association studies and polygenic score development to variant prioritization and functional genomics. New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging within the wider genomic research and genomic medicine ecosystems. At this crucial time point, it is important to clarify where improvements in methods and practices can, or cannot, have a role in improving equity in genomics. Here, we review existing approaches to promote equity and fairness in statistical analysis for genomics, and propose future methodological developments that are likely to yield the most impact for equity. New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging. The authors review how the choice of analytical methods used to process, analyse and interpret genomic data can influence genomic research, as well as existing methodological approaches to promote equity and fairness in genomics.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 9","pages":"635-649"},"PeriodicalIF":52.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979479","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 : 2025-05-14DOI: 10.1038/s41576-025-00845-y
Lucie C. Gaspard-Boulinc, Luca Gortana, Thomas Walter, Emmanuel Barillot, Florence M. G. Cavalli
Spatial transcriptomics is a powerful method for studying the spatial organization of cells, which is a critical feature in the development, function and evolution of multicellular life. However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe tissue architectures in health and disease. In this Review, we describe the varied types of cell-type deconvolution methods for spatial transcriptomics, contrast their capabilities and summarize them in a web-based, interactive table to enable more efficient method selection. Cell-type deconvolution methods are often needed to analyse spatial transcriptomic data to recover cell-type distributions. In this Review, the authors describe the process of cell-type deconvolution, contrast the tools available and highlight important considerations for which tool to use.
{"title":"Cell-type deconvolution methods for spatial transcriptomics","authors":"Lucie C. Gaspard-Boulinc, Luca Gortana, Thomas Walter, Emmanuel Barillot, Florence M. G. Cavalli","doi":"10.1038/s41576-025-00845-y","DOIUrl":"10.1038/s41576-025-00845-y","url":null,"abstract":"Spatial transcriptomics is a powerful method for studying the spatial organization of cells, which is a critical feature in the development, function and evolution of multicellular life. However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe tissue architectures in health and disease. In this Review, we describe the varied types of cell-type deconvolution methods for spatial transcriptomics, contrast their capabilities and summarize them in a web-based, interactive table to enable more efficient method selection. Cell-type deconvolution methods are often needed to analyse spatial transcriptomic data to recover cell-type distributions. In this Review, the authors describe the process of cell-type deconvolution, contrast the tools available and highlight important considerations for which tool to use.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 12","pages":"828-846"},"PeriodicalIF":52.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945698","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 : 2025-05-13DOI: 10.1038/s41576-025-00841-2
Lucía Barbadilla-Martínez, Noud Klaassen, Bas van Steensel, Jeroen de Ridder
Transcription of genes is regulated by DNA elements such as promoters and enhancers, the activity of which are in turn controlled by many transcription factors. Owing to the highly complex combinatorial logic involved, it has been difficult to construct computational models that predict gene activity from DNA sequence. Recent advances in deep learning techniques applied to data from epigenome mapping and high-throughput reporter assays have made substantial progress towards addressing this complexity. Such models can capture the regulatory grammar with remarkable accuracy and show great promise in predicting the effects of non-coding variants, uncovering detailed molecular mechanisms of gene regulation and designing synthetic regulatory elements for biotechnology. Here, we discuss the principles of these approaches, the types of training data sets that are available and the strengths and limitations of different approaches. Barbadilla-Martínez et al. review recent progress in deep-learning-based sequence-to-expression models, which predict gene expression levels solely from DNA sequence. These models are providing new insights into the complex combinatorial logic underlying cis-regulatory control of gene expression.
{"title":"Predicting gene expression from DNA sequence using deep learning models","authors":"Lucía Barbadilla-Martínez, Noud Klaassen, Bas van Steensel, Jeroen de Ridder","doi":"10.1038/s41576-025-00841-2","DOIUrl":"10.1038/s41576-025-00841-2","url":null,"abstract":"Transcription of genes is regulated by DNA elements such as promoters and enhancers, the activity of which are in turn controlled by many transcription factors. Owing to the highly complex combinatorial logic involved, it has been difficult to construct computational models that predict gene activity from DNA sequence. Recent advances in deep learning techniques applied to data from epigenome mapping and high-throughput reporter assays have made substantial progress towards addressing this complexity. Such models can capture the regulatory grammar with remarkable accuracy and show great promise in predicting the effects of non-coding variants, uncovering detailed molecular mechanisms of gene regulation and designing synthetic regulatory elements for biotechnology. Here, we discuss the principles of these approaches, the types of training data sets that are available and the strengths and limitations of different approaches. Barbadilla-Martínez et al. review recent progress in deep-learning-based sequence-to-expression models, which predict gene expression levels solely from DNA sequence. These models are providing new insights into the complex combinatorial logic underlying cis-regulatory control of gene expression.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 10","pages":"666-680"},"PeriodicalIF":52.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143939954","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 : 2025-05-12DOI: 10.1038/s41576-025-00840-3
Philip M. Boone, Teresa Buenaventura, James W. D. King, Matthias Merkenschlager
During early mammalian female development, X chromosome inactivation leads to random transcriptional silencing of one of the two X chromosomes. This inactivation is maintained through subsequent cell divisions, leading to intra-individual diversity, whereby cells express either the maternal or paternal X chromosome. Differences in X chromosome sequence content can trigger competitive interactions between clones that may alter organismal development and skew the representation of X-linked sequence variants in a cell-type-specific manner — a recently described phenomenon termed X-linked competition in analogy to existing cell competition paradigms. Skewed representation can define the phenotypic impact of X-linked variants, for example, the manifestation of disease in female carriers of X-linked disease alleles. Here, we review what is currently known about X-linked competition, reflect on what remains to be learnt and map out the implications for X-linked human disease. Differences in X chromosome sequence content can trigger competitive interactions between clones that may alter organismal development and skew the representation of X-linked sequence variants in a cell-type-specific manner. The authors review this recently described phenomenon of X-linked competition and map out the implications for X-linked human diseases.
{"title":"X-linked competition — implications for human development and disease","authors":"Philip M. Boone, Teresa Buenaventura, James W. D. King, Matthias Merkenschlager","doi":"10.1038/s41576-025-00840-3","DOIUrl":"10.1038/s41576-025-00840-3","url":null,"abstract":"During early mammalian female development, X chromosome inactivation leads to random transcriptional silencing of one of the two X chromosomes. This inactivation is maintained through subsequent cell divisions, leading to intra-individual diversity, whereby cells express either the maternal or paternal X chromosome. Differences in X chromosome sequence content can trigger competitive interactions between clones that may alter organismal development and skew the representation of X-linked sequence variants in a cell-type-specific manner — a recently described phenomenon termed X-linked competition in analogy to existing cell competition paradigms. Skewed representation can define the phenotypic impact of X-linked variants, for example, the manifestation of disease in female carriers of X-linked disease alleles. Here, we review what is currently known about X-linked competition, reflect on what remains to be learnt and map out the implications for X-linked human disease. Differences in X chromosome sequence content can trigger competitive interactions between clones that may alter organismal development and skew the representation of X-linked sequence variants in a cell-type-specific manner. The authors review this recently described phenomenon of X-linked competition and map out the implications for X-linked human diseases.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 8","pages":"571-580"},"PeriodicalIF":52.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933472","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 : 2025-05-12DOI: 10.1038/s41576-025-00853-y
Timothy R. Olsen
In this Tools of the Trade article, Timothy Olsen introduces DEFND-seq, a scalable method for co-sequencing RNA and DNA in single cells using commercially available high-throughput kits.
{"title":"Simultaneous single-cell sequencing of RNA and DNA at scale with DEFND-seq","authors":"Timothy R. Olsen","doi":"10.1038/s41576-025-00853-y","DOIUrl":"10.1038/s41576-025-00853-y","url":null,"abstract":"In this Tools of the Trade article, Timothy Olsen introduces DEFND-seq, a scalable method for co-sequencing RNA and DNA in single cells using commercially available high-throughput kits.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 7","pages":"440-440"},"PeriodicalIF":52.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933473","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 : 2025-05-12DOI: 10.1038/s41576-025-00843-0
Michael J. Owen, Nicholas J. Bray, James T. R. Walters, Michael C. O’Donovan
Schizophrenia, bipolar disorder and major depressive disorder — which are the most common adult disorders requiring psychiatric care — contribute substantially to premature mortality and morbidity globally. Treatments for these disorders are suboptimal, there are no diagnostic pathologies or biomarkers and their pathophysiologies are poorly understood. Novel therapeutic and diagnostic approaches are thus badly needed. Given the high heritability of psychiatric disorders, psychiatry has potentially much to gain from the application of genomics to identify molecular risk mechanisms and to improve diagnosis. Recent large-scale, genome-wide association studies and sequencing studies, together with advances in functional genomics, have begun to illuminate the genetic architectures of schizophrenia, bipolar disorder and major depressive disorder and to identify potential biological mechanisms. Genomic findings also point to the aetiological relationships between different diagnoses and to the relationships between adult psychiatric disorders and childhood neurodevelopmental conditions. Genomic advances have enhanced our understanding of schizophrenia, bipolar disorder and major depressive disorder, revealing genetic architectures and risk mechanisms through large-scale genome-wide association studies and sequencing, which could address limitations in current diagnostic frameworks and treatment strategies in the future.
{"title":"Genomics of schizophrenia, bipolar disorder and major depressive disorder","authors":"Michael J. Owen, Nicholas J. Bray, James T. R. Walters, Michael C. O’Donovan","doi":"10.1038/s41576-025-00843-0","DOIUrl":"10.1038/s41576-025-00843-0","url":null,"abstract":"Schizophrenia, bipolar disorder and major depressive disorder — which are the most common adult disorders requiring psychiatric care — contribute substantially to premature mortality and morbidity globally. Treatments for these disorders are suboptimal, there are no diagnostic pathologies or biomarkers and their pathophysiologies are poorly understood. Novel therapeutic and diagnostic approaches are thus badly needed. Given the high heritability of psychiatric disorders, psychiatry has potentially much to gain from the application of genomics to identify molecular risk mechanisms and to improve diagnosis. Recent large-scale, genome-wide association studies and sequencing studies, together with advances in functional genomics, have begun to illuminate the genetic architectures of schizophrenia, bipolar disorder and major depressive disorder and to identify potential biological mechanisms. Genomic findings also point to the aetiological relationships between different diagnoses and to the relationships between adult psychiatric disorders and childhood neurodevelopmental conditions. Genomic advances have enhanced our understanding of schizophrenia, bipolar disorder and major depressive disorder, revealing genetic architectures and risk mechanisms through large-scale genome-wide association studies and sequencing, which could address limitations in current diagnostic frameworks and treatment strategies in the future.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 12","pages":"862-877"},"PeriodicalIF":52.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933444","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 : 2025-05-09DOI: 10.1038/s41576-025-00852-z
Tracy A. Bedrosian
In this Journal Club, Tracy Bedrosian describes a 2001 paper by Rehen et al. that provided early evidence of pervasive somatic variation throughout the human brain.
{"title":"One but not the same — the many genomes of the brain","authors":"Tracy A. Bedrosian","doi":"10.1038/s41576-025-00852-z","DOIUrl":"10.1038/s41576-025-00852-z","url":null,"abstract":"In this Journal Club, Tracy Bedrosian describes a 2001 paper by Rehen et al. that provided early evidence of pervasive somatic variation throughout the human brain.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"26 7","pages":"442-442"},"PeriodicalIF":52.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927344","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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