Pub Date : 2025-12-12DOI: 10.1038/s41592-025-02931-9
Hao Yuan, Christopher A. Mancuso, Kayla Johnson, Ingo Braasch, Arjun Krishnan
Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that use transcriptome data and/or molecular networks. Our Perspective addresses four key areas: (1) transferring disease and gene annotation knowledge across species, (2) identifying functionally equivalent molecular components, (3) inferring equivalent perturbed genes or gene sets and (4) identifying equivalent cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer, including introducing the concept of ‘agnology’ to describe functional equivalence of biological entities, regardless of their evolutionary origins. This concept is becoming pervasive in integrative data-driven models in which evolutionary origins of functions can remain unresolved. This Perspective reviews computational methods for cross-species knowledge transfer and introduces ‘agnology’, a data-driven concept of functional equivalence independent of evolutionary origin.
{"title":"Computational strategies for cross-species knowledge transfer","authors":"Hao Yuan, Christopher A. Mancuso, Kayla Johnson, Ingo Braasch, Arjun Krishnan","doi":"10.1038/s41592-025-02931-9","DOIUrl":"10.1038/s41592-025-02931-9","url":null,"abstract":"Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that use transcriptome data and/or molecular networks. Our Perspective addresses four key areas: (1) transferring disease and gene annotation knowledge across species, (2) identifying functionally equivalent molecular components, (3) inferring equivalent perturbed genes or gene sets and (4) identifying equivalent cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer, including introducing the concept of ‘agnology’ to describe functional equivalence of biological entities, regardless of their evolutionary origins. This concept is becoming pervasive in integrative data-driven models in which evolutionary origins of functions can remain unresolved. This Perspective reviews computational methods for cross-species knowledge transfer and introduces ‘agnology’, a data-driven concept of functional equivalence independent of evolutionary origin.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"312-327"},"PeriodicalIF":32.1,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145743179","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 : 2025-12-12DOI: 10.1038/s41592-025-02966-y
Jiahao Liu, Xue Dong, Huaide Lu, Tao Liu, Wei Liu, Xinyao Hu, Quan Meng, Amin Jiang, Tao Jiang, Xiaohan Geng, Haosen Liu, Jun Cheng, Edmund Y. Lam, Yan-Jun Liu, Shan Tan, Dong Li
Deep-learning-based structured illumination microscopy (SIM) has demonstrated substantial potential in long-term super-resolution imaging of biostructures, enabling the study of subcellular dynamics and interactions in live cells. However, the acquisition of ground-truth (GT) data for training poses inherent challenges, limiting its universal applicability. Current approaches without using GT training data compromise reconstruction fidelity and resolution, and the lack of physical priors in end-to-end networks further limits these qualities. Here we developed self-supervised reconstruction (SSR)-SIM by combining statistical analysis of reconstruction artifacts with structured light modulation priors to eliminate the need for GT and improve reconstruction precision. We validated SSR-SIM on common biological datasets and demonstrated that SSR-SIM enabled long-term recording of dynamic events, including cytoskeletal remodeling in cell adhesion, mitochondrial cristae remodeling, interactions between viral glycoprotein and endoplasmic reticulum, endocytic recycling of transferrin receptors, vaccinia-virus-induced actin comet remodeling, and mitochondrial intercellular transfer through tunneling nanotubes. Self-supervised reconstruction structured illumination microscopy (SSR-SIM) is a reconstruction approach for SIM that improves image reconstruction by including light modulation priors and information on reconstruction artifacts, while simultaneously eliminating the need for ground-truth images. The improvements allow long-term imaging of sensitive cellular processes.
{"title":"Bio-friendly and high-precision super-resolution imaging through self-supervised reconstruction structured illumination microscopy","authors":"Jiahao Liu, Xue Dong, Huaide Lu, Tao Liu, Wei Liu, Xinyao Hu, Quan Meng, Amin Jiang, Tao Jiang, Xiaohan Geng, Haosen Liu, Jun Cheng, Edmund Y. Lam, Yan-Jun Liu, Shan Tan, Dong Li","doi":"10.1038/s41592-025-02966-y","DOIUrl":"10.1038/s41592-025-02966-y","url":null,"abstract":"Deep-learning-based structured illumination microscopy (SIM) has demonstrated substantial potential in long-term super-resolution imaging of biostructures, enabling the study of subcellular dynamics and interactions in live cells. However, the acquisition of ground-truth (GT) data for training poses inherent challenges, limiting its universal applicability. Current approaches without using GT training data compromise reconstruction fidelity and resolution, and the lack of physical priors in end-to-end networks further limits these qualities. Here we developed self-supervised reconstruction (SSR)-SIM by combining statistical analysis of reconstruction artifacts with structured light modulation priors to eliminate the need for GT and improve reconstruction precision. We validated SSR-SIM on common biological datasets and demonstrated that SSR-SIM enabled long-term recording of dynamic events, including cytoskeletal remodeling in cell adhesion, mitochondrial cristae remodeling, interactions between viral glycoprotein and endoplasmic reticulum, endocytic recycling of transferrin receptors, vaccinia-virus-induced actin comet remodeling, and mitochondrial intercellular transfer through tunneling nanotubes. Self-supervised reconstruction structured illumination microscopy (SSR-SIM) is a reconstruction approach for SIM that improves image reconstruction by including light modulation priors and information on reconstruction artifacts, while simultaneously eliminating the need for ground-truth images. The improvements allow long-term imaging of sensitive cellular processes.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"395-404"},"PeriodicalIF":32.1,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145743172","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}
Single-cell perturbation technologies enable systematic investigation of gene functions and regulatory networks with single-cell resolution. However, performing large-scale and combinatorial perturbation screens poses notable challenges due to their exponentially increased complexity. Computational methods, including foundation models, have been developed to predict perturbation effects. Yet despite claims of promising performance, concerns remain about their true efficacy, particularly when evaluated across diverse and previously unseen cellular contexts and perturbation scenarios. Here, we present a comprehensive benchmark of 27 methods for single-cell perturbation response prediction, evaluated across 29 datasets using 6 complementary performance metrics. By evaluating them under multiple scenarios, we systematically assess their generalizability, including that of emerging foundation models. Our results provide practical guidance for method selection and underscore the need for cellular context embedding approaches to enhance the generalizability of perturbation effect prediction in single-cell research. This analysis performs comprehensive comparisons of 27 single-cell perturbation response prediction methods using 29 datasets under different test scenarios and against multiple evaluation metrics.
{"title":"Benchmarking algorithms for generalizable single-cell perturbation response prediction","authors":"Zhiting Wei, Yiheng Wang, Yicheng Gao, Shuguang Wang, Ping Li, Duanmiao Si, Yuli Gao, Siqi Wu, Danlu Li, Kejing Dong, Xingbo Yang, Chen Tang, Shaliu Fu, Xiaohan Chen, Wannian Li, Yuzhou You, Chen Zhang, Aibin Liang, Guohui Chuai, Qi Liu","doi":"10.1038/s41592-025-02980-0","DOIUrl":"10.1038/s41592-025-02980-0","url":null,"abstract":"Single-cell perturbation technologies enable systematic investigation of gene functions and regulatory networks with single-cell resolution. However, performing large-scale and combinatorial perturbation screens poses notable challenges due to their exponentially increased complexity. Computational methods, including foundation models, have been developed to predict perturbation effects. Yet despite claims of promising performance, concerns remain about their true efficacy, particularly when evaluated across diverse and previously unseen cellular contexts and perturbation scenarios. Here, we present a comprehensive benchmark of 27 methods for single-cell perturbation response prediction, evaluated across 29 datasets using 6 complementary performance metrics. By evaluating them under multiple scenarios, we systematically assess their generalizability, including that of emerging foundation models. Our results provide practical guidance for method selection and underscore the need for cellular context embedding approaches to enhance the generalizability of perturbation effect prediction in single-cell research. This analysis performs comprehensive comparisons of 27 single-cell perturbation response prediction methods using 29 datasets under different test scenarios and against multiple evaluation metrics.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"451-464"},"PeriodicalIF":32.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145743224","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 : 2025-12-11DOI: 10.1038/s41592-025-02981-z
Matthew Aton, Daniel McDonald, Jorge Cañardo Alastuey, Raeed Azom, Paarth Batra, Valentyn Bezshapkin, Evan Bolyen, Alexander Cagle, J. Gregory Caporaso, Justine W. Debelius, Kestrel Gorlick, Nirmitha Hamsanipally, Lars Hunger, Aryan Keluskar, Disen Liao, Yang Young Lu, Jose A. Navas-Molina, Anders Pitman, Jai Ram Rideout, Anton Sazonov, Bharath Sathappan, Karen Schwarzberg Lipson, Igor Sfiligoi, Chris Tapo, Yoshiki Vázquez-Baeza, Zijun Wu, Zhenjiang Zech Xu, Mingsong Sam Ye, Jianshu Zhao, Rob Knight, James T. Morton, Qiyun Zhu
{"title":"Scikit-bio: a fundamental Python library for biological omic data analysis","authors":"Matthew Aton, Daniel McDonald, Jorge Cañardo Alastuey, Raeed Azom, Paarth Batra, Valentyn Bezshapkin, Evan Bolyen, Alexander Cagle, J. Gregory Caporaso, Justine W. Debelius, Kestrel Gorlick, Nirmitha Hamsanipally, Lars Hunger, Aryan Keluskar, Disen Liao, Yang Young Lu, Jose A. Navas-Molina, Anders Pitman, Jai Ram Rideout, Anton Sazonov, Bharath Sathappan, Karen Schwarzberg Lipson, Igor Sfiligoi, Chris Tapo, Yoshiki Vázquez-Baeza, Zijun Wu, Zhenjiang Zech Xu, Mingsong Sam Ye, Jianshu Zhao, Rob Knight, James T. Morton, Qiyun Zhu","doi":"10.1038/s41592-025-02981-z","DOIUrl":"10.1038/s41592-025-02981-z","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"274-276"},"PeriodicalIF":32.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145743238","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 : 2025-12-10DOI: 10.1038/s41592-025-02899-6
Quentin Blampey, Hakim Benkirane, Nadège Bercovici, Kevin Mulder, Grégoire Gessain, Florent Ginhoux, Fabrice André, Paul-Henry Cournède
Spatial transcriptomics is advancing molecular biology by providing high-resolution insights into gene expression within the spatial context of tissues. This context is essential for identifying spatial domains, enabling the understanding of microenvironment organizations and their implications for tissue function and disease progression. To improve current model limitations on multiple slides, we have designed Novae ( https://github.com/MICS-Lab/novae ), a graph-based foundation model that extracts representations of cells within their spatial contexts. Our model was trained on a large dataset of nearly 30 million cells across 18 tissues, allowing Novae to perform zero-shot domain inference across multiple gene panels, tissues and technologies. Unlike other models, it also natively corrects batch effects and constructs a nested hierarchy of spatial domains. Furthermore, Novae supports various downstream tasks, including spatially variable gene or pathway analysis and spatial domain trajectory analysis. Overall, Novae provides a robust and versatile tool for advancing spatial transcriptomics and its applications in biomedical research. Novae, a self-supervised graph attention network, is a foundation model excelling at a diverse spectrum of spatial transcriptomics modeling and analysis tasks.
{"title":"Novae: a graph-based foundation model for spatial transcriptomics data","authors":"Quentin Blampey, Hakim Benkirane, Nadège Bercovici, Kevin Mulder, Grégoire Gessain, Florent Ginhoux, Fabrice André, Paul-Henry Cournède","doi":"10.1038/s41592-025-02899-6","DOIUrl":"10.1038/s41592-025-02899-6","url":null,"abstract":"Spatial transcriptomics is advancing molecular biology by providing high-resolution insights into gene expression within the spatial context of tissues. This context is essential for identifying spatial domains, enabling the understanding of microenvironment organizations and their implications for tissue function and disease progression. To improve current model limitations on multiple slides, we have designed Novae ( https://github.com/MICS-Lab/novae ), a graph-based foundation model that extracts representations of cells within their spatial contexts. Our model was trained on a large dataset of nearly 30 million cells across 18 tissues, allowing Novae to perform zero-shot domain inference across multiple gene panels, tissues and technologies. Unlike other models, it also natively corrects batch effects and constructs a nested hierarchy of spatial domains. Furthermore, Novae supports various downstream tasks, including spatially variable gene or pathway analysis and spatial domain trajectory analysis. Overall, Novae provides a robust and versatile tool for advancing spatial transcriptomics and its applications in biomedical research. Novae, a self-supervised graph attention network, is a foundation model excelling at a diverse spectrum of spatial transcriptomics modeling and analysis tasks.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2539-2550"},"PeriodicalIF":32.1,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145719853","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 : 2025-12-10DOI: 10.1038/s41592-025-02974-y
Tingting Luo, Moping Xu, Miao Wang, Faying Chen, Jiejun Shi
Nanopore direct RNA sequencing offers a versatile approach for detecting multiple types of RNA modifications at a single-base resolution. In this study, we systematically evaluate 86 computational tools for detecting six RNA modifications (m6A, Ψ, m5C, A-to-I editing, m7G and m1A) using direct RNA sequencing data from both RNA002 and RNA004 chemistries. We demonstrate that retraining tools with a combination of in vitro transcription and real biological samples notably enhances both accuracy and generalizability over their original implementations, especially for Ψ, m5C and A-to-I. Evaluations on real biological samples reveal that while m6A detection tools generally achieve high accuracy, non-m6A tools struggle with precision–recall balance, quantification accuracy and biological validity. Our findings highlight the importance of incorporating diverse training data and stress the need for tools capable of reliably distinguishing between modification types at single-base resolution. These insights provide a foundation for advancing RNA modification detection. This analysis benchmarked computational tools for RNA modification detection using nanopore direct RNA sequencing and showed that retraining with mixed in vitro transcription and real data improves performance.
{"title":"Systematic evaluation of computational tools for multitype RNA modification detection using nanopore direct RNA sequencing","authors":"Tingting Luo, Moping Xu, Miao Wang, Faying Chen, Jiejun Shi","doi":"10.1038/s41592-025-02974-y","DOIUrl":"10.1038/s41592-025-02974-y","url":null,"abstract":"Nanopore direct RNA sequencing offers a versatile approach for detecting multiple types of RNA modifications at a single-base resolution. In this study, we systematically evaluate 86 computational tools for detecting six RNA modifications (m6A, Ψ, m5C, A-to-I editing, m7G and m1A) using direct RNA sequencing data from both RNA002 and RNA004 chemistries. We demonstrate that retraining tools with a combination of in vitro transcription and real biological samples notably enhances both accuracy and generalizability over their original implementations, especially for Ψ, m5C and A-to-I. Evaluations on real biological samples reveal that while m6A detection tools generally achieve high accuracy, non-m6A tools struggle with precision–recall balance, quantification accuracy and biological validity. Our findings highlight the importance of incorporating diverse training data and stress the need for tools capable of reliably distinguishing between modification types at single-base resolution. These insights provide a foundation for advancing RNA modification detection. This analysis benchmarked computational tools for RNA modification detection using nanopore direct RNA sequencing and showed that retraining with mixed in vitro transcription and real data improves performance.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"438-450"},"PeriodicalIF":32.1,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145724208","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 : 2025-12-09DOI: 10.1038/s41592-025-02925-7
Jian Zhong, Ryan G Natan, Qinrong Zhang, Justin S J Wong, Christoph Miehl, Krishnashish Bose, Xiaoyu Lu, François St-Pierre, Su Guo, Brent Doiron, Kevin K Tsia, Na Ji
Monitoring neuronal activity at large scale and high spatiotemporal resolution is crucial for understanding information processing within the brain. Here we optimized a kilohertz-frame-rate two-photon fluorescence microscope with an all-optical megahertz line-scan rate to achieve ultrafast imaging across large areas and volumes at subcellular resolution. Applying this technique to in vivo voltage and calcium imaging, we demonstrated simultaneous recording of voltage activity over 200 neurons and calcium activity over 14,000 neurons from the mouse visual cortex, as well as volumetric calcium imaging of the larval zebrafish brain.
{"title":"FACED 2.0 enables large-scale voltage and calcium imaging in vivo.","authors":"Jian Zhong, Ryan G Natan, Qinrong Zhang, Justin S J Wong, Christoph Miehl, Krishnashish Bose, Xiaoyu Lu, François St-Pierre, Su Guo, Brent Doiron, Kevin K Tsia, Na Ji","doi":"10.1038/s41592-025-02925-7","DOIUrl":"10.1038/s41592-025-02925-7","url":null,"abstract":"<p><p>Monitoring neuronal activity at large scale and high spatiotemporal resolution is crucial for understanding information processing within the brain. Here we optimized a kilohertz-frame-rate two-photon fluorescence microscope with an all-optical megahertz line-scan rate to achieve ultrafast imaging across large areas and volumes at subcellular resolution. Applying this technique to in vivo voltage and calcium imaging, we demonstrated simultaneous recording of voltage activity over 200 neurons and calcium activity over 14,000 neurons from the mouse visual cortex, as well as volumetric calcium imaging of the larval zebrafish brain.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145715380","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 : 2025-12-08DOI: 10.1038/s41592-025-02952-4
Arunima Singh
Methods to study the structural and functional properties of proteins that contain intrinsically disordered regions at the proteome scale are on the rise.
在蛋白质组尺度上研究含有内在无序区域的蛋白质的结构和功能特性的方法正在兴起。
{"title":"Intrinsic protein disorder at scale","authors":"Arunima Singh","doi":"10.1038/s41592-025-02952-4","DOIUrl":"10.1038/s41592-025-02952-4","url":null,"abstract":"Methods to study the structural and functional properties of proteins that contain intrinsically disordered regions at the proteome scale are on the rise.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2493-2494"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708588","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 : 2025-12-08DOI: 10.1038/s41592-025-02903-z
Palash Sashittal, Richard Y. Zhang, Benjamin K. Law, Henri Schmidt, Alexander Strzalkowski, Adriano Bolondi, Michelle M. Chan, Benjamin J. Raphael
During development, cells differentiate through a hierarchy of increasingly restricted cell types, a process that is summarized by a cell differentiation map. Recent technologies profile lineages and cell types at scale, but existing methods to infer cell differentiation maps from these data rely on heuristic models with restrictive assumptions about the developmental process. Here we introduce a quantitative framework to evaluate cell differentiation maps and develop an algorithm, called Carta, that infers an optimal differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the tradeoff between the complexity of the map and the number of unobserved cell type transitions on the lineage tree. We show that, in models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods, including convergent differentiation of cell types, progenitor differentiation dynamics and new intermediate progenitors. Carta leverages lineage tracing data to infer the optimal trajectory of cell differentiation.
{"title":"Inferring cell differentiation maps from lineage tracing data","authors":"Palash Sashittal, Richard Y. Zhang, Benjamin K. Law, Henri Schmidt, Alexander Strzalkowski, Adriano Bolondi, Michelle M. Chan, Benjamin J. Raphael","doi":"10.1038/s41592-025-02903-z","DOIUrl":"10.1038/s41592-025-02903-z","url":null,"abstract":"During development, cells differentiate through a hierarchy of increasingly restricted cell types, a process that is summarized by a cell differentiation map. Recent technologies profile lineages and cell types at scale, but existing methods to infer cell differentiation maps from these data rely on heuristic models with restrictive assumptions about the developmental process. Here we introduce a quantitative framework to evaluate cell differentiation maps and develop an algorithm, called Carta, that infers an optimal differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the tradeoff between the complexity of the map and the number of unobserved cell type transitions on the lineage tree. We show that, in models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods, including convergent differentiation of cell types, progenitor differentiation dynamics and new intermediate progenitors. Carta leverages lineage tracing data to infer the optimal trajectory of cell differentiation.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 3","pages":"532-541"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02903-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708637","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 : 2025-12-08DOI: 10.1038/s41592-025-02971-1
R. Clay Reid
Functional connectomics is a new approach, involving calcium imaging of neuronal activity followed by correlated electron microscopy connectomics of the same neurons, that relates connections made by neurons to their in vivo function. I believe that this combined approach for studying structure and function will continue with ever-larger networks, including entire nervous systems.
{"title":"The past and future of functional connectomics","authors":"R. Clay Reid","doi":"10.1038/s41592-025-02971-1","DOIUrl":"10.1038/s41592-025-02971-1","url":null,"abstract":"Functional connectomics is a new approach, involving calcium imaging of neuronal activity followed by correlated electron microscopy connectomics of the same neurons, that relates connections made by neurons to their in vivo function. I believe that this combined approach for studying structure and function will continue with ever-larger networks, including entire nervous systems.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2479-2480"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708771","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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