Pub Date : 2024-12-09DOI: 10.1038/s41592-024-02555-5
Nicolò Caporale, Davide Castaldi, Marco Tullio Rigoli, Cristina Cheroni, Alessia Valenti, Sarah Stucchi, Manuel Lessi, Davide Bulgheresi, Sebastiano Trattaro, Martina Pezzali, Alessandro Vitriolo, Alejandro Lopez-Tobon, Matteo Bonfanti, Dario Ricca, Katharina T. Schmid, Matthias Heinig, Fabian J. Theis, Carlo Emanuele Villa, Giuseppe Testa
Dissecting human neurobiology at high resolution and with mechanistic precision requires a major leap in scalability, given the need for experimental designs that include multiple individuals and, prospectively, population cohorts. To lay the foundation for this, we have developed and benchmarked complementary strategies to multiplex brain organoids by pooling cells from different pluripotent stem cell (PSC) lines either during organoid generation (mosaic models) or before single-cell RNA sequencing (scRNA-seq) library preparation (downstream multiplexing). We have also developed a new computational method, SCanSNP, and a consensus call to deconvolve cell identities, overcoming current criticalities in doublets and low-quality cell identification. We validated both multiplexing methods for charting neurodevelopmental trajectories at high resolution, thus linking specific individuals’ trajectories to genetic variation. Finally, we modeled their scalability across different multiplexing combinations and showed that mosaic organoids represent an enabling method for high-throughput settings. Together, this multiplexing suite of experimental and computational methods provides a highly scalable resource for brain disease and neurodiversity modeling. This paper develops two approaches for multiplexing cortical organoids and SCanSNP, a method for deconvolving cell identities, to trace neurodevelopmental trajectories at scale.
{"title":"Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single-cell resolution","authors":"Nicolò Caporale, Davide Castaldi, Marco Tullio Rigoli, Cristina Cheroni, Alessia Valenti, Sarah Stucchi, Manuel Lessi, Davide Bulgheresi, Sebastiano Trattaro, Martina Pezzali, Alessandro Vitriolo, Alejandro Lopez-Tobon, Matteo Bonfanti, Dario Ricca, Katharina T. Schmid, Matthias Heinig, Fabian J. Theis, Carlo Emanuele Villa, Giuseppe Testa","doi":"10.1038/s41592-024-02555-5","DOIUrl":"10.1038/s41592-024-02555-5","url":null,"abstract":"Dissecting human neurobiology at high resolution and with mechanistic precision requires a major leap in scalability, given the need for experimental designs that include multiple individuals and, prospectively, population cohorts. To lay the foundation for this, we have developed and benchmarked complementary strategies to multiplex brain organoids by pooling cells from different pluripotent stem cell (PSC) lines either during organoid generation (mosaic models) or before single-cell RNA sequencing (scRNA-seq) library preparation (downstream multiplexing). We have also developed a new computational method, SCanSNP, and a consensus call to deconvolve cell identities, overcoming current criticalities in doublets and low-quality cell identification. We validated both multiplexing methods for charting neurodevelopmental trajectories at high resolution, thus linking specific individuals’ trajectories to genetic variation. Finally, we modeled their scalability across different multiplexing combinations and showed that mosaic organoids represent an enabling method for high-throughput settings. Together, this multiplexing suite of experimental and computational methods provides a highly scalable resource for brain disease and neurodiversity modeling. This paper develops two approaches for multiplexing cortical organoids and SCanSNP, a method for deconvolving cell identities, to trace neurodevelopmental trajectories at scale.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"358-370"},"PeriodicalIF":36.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02555-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142801714","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 : 2024-12-06DOI: 10.1038/s41592-024-02551-9
Arunima Singh
Algorithms help to capture macromolecular motion and structural heterogeneity in native cellular environments.
算法有助于在原生细胞环境中捕获大分子运动和结构异质性。
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Pub Date : 2024-12-06DOI: 10.1038/s41592-024-02550-w
Lei Tang
Advances aim to capture comprehensive, dynamic genome structures in living cells.
研究进展旨在捕捉活细胞中全面、动态的基因组结构。
{"title":"More dimensions of the 3D genome","authors":"Lei Tang","doi":"10.1038/s41592-024-02550-w","DOIUrl":"10.1038/s41592-024-02550-w","url":null,"abstract":"Advances aim to capture comprehensive, dynamic genome structures in living cells.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2229-2229"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789400","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 : 2024-12-06DOI: 10.1038/s41592-024-02548-4
Nina Vogt
Detailed biomechanical models of animal bodies can help to tackle questions about how the brain controls movement and bodily interactions with the environment.
动物身体的详细生物力学模型可以帮助解决大脑如何控制运动以及身体与环境相互作用的问题。
{"title":"Biomechanical modeling of whole bodies","authors":"Nina Vogt","doi":"10.1038/s41592-024-02548-4","DOIUrl":"10.1038/s41592-024-02548-4","url":null,"abstract":"Detailed biomechanical models of animal bodies can help to tackle questions about how the brain controls movement and bodily interactions with the environment.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2228-2228"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789393","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 : 2024-12-06DOI: 10.1038/s41592-024-02539-5
Yuval Bussi, Leeat Keren
Multiplexed tissue imaging has transformed tissue biology by revealing cellular diversity and interactions, but the analysis of its massive datasets remains a bottleneck. Here, we provide an overview of computational advancements, discuss current challenges and envision an AI-driven future in which integrated tools streamline analysis and visualization, unlocking the full potential of multiplexed imaging for breakthroughs in spatial biology.
{"title":"Multiplexed image analysis: what have we achieved and where are we headed?","authors":"Yuval Bussi, Leeat Keren","doi":"10.1038/s41592-024-02539-5","DOIUrl":"10.1038/s41592-024-02539-5","url":null,"abstract":"Multiplexed tissue imaging has transformed tissue biology by revealing cellular diversity and interactions, but the analysis of its massive datasets remains a bottleneck. Here, we provide an overview of computational advancements, discuss current challenges and envision an AI-driven future in which integrated tools streamline analysis and visualization, unlocking the full potential of multiplexed imaging for breakthroughs in spatial biology.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2212-2215"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789395","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 : 2024-12-06DOI: 10.1038/s41592-024-02545-7
Arunima Singh
Experimental and computational studies are paving way for a deeper understanding of the dynamic nature of protein–protein interactions.
实验和计算研究为更深入地理解蛋白质-蛋白质相互作用的动态本质铺平了道路。
{"title":"Understanding protein interaction dynamics","authors":"Arunima Singh","doi":"10.1038/s41592-024-02545-7","DOIUrl":"10.1038/s41592-024-02545-7","url":null,"abstract":"Experimental and computational studies are paving way for a deeper understanding of the dynamic nature of protein–protein interactions.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2226-2227"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789405","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 : 2024-12-06DOI: 10.1038/s41592-024-02565-3
Approaches for profiling the spatial proteome in tissues are the basis of atlas-scale projects that are delivering on their promise for understanding biological complexity in health and disease.
{"title":"Method of the Year 2024: spatial proteomics","authors":"","doi":"10.1038/s41592-024-02565-3","DOIUrl":"10.1038/s41592-024-02565-3","url":null,"abstract":"Approaches for profiling the spatial proteome in tissues are the basis of atlas-scale projects that are delivering on their promise for understanding biological complexity in health and disease.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2195-2196"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02565-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789392","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 : 2024-12-06DOI: 10.1038/s41592-024-02536-8
Vivien Marx
Hundreds of researchers collaborate on maps of the human body and the subcellular realm. As they scout out their next mapping expeditions, they take stock of atlas-making.
{"title":"Atlases galore: where to next?","authors":"Vivien Marx","doi":"10.1038/s41592-024-02536-8","DOIUrl":"10.1038/s41592-024-02536-8","url":null,"abstract":"Hundreds of researchers collaborate on maps of the human body and the subcellular realm. As they scout out their next mapping expeditions, they take stock of atlas-making.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2203-2208"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789394","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 : 2024-12-06DOI: 10.1038/s41592-024-02542-w
Daniela F. Quail, Logan A. Walsh
Spatial proteomics has transformed cancer research by providing unparalleled insights into the microenvironmental landscape of tumors. Here we discuss how these technologies have significantly advanced our understanding of cell–cell interactions, tissue organization and spatially coordinated mechanisms underlying antitumor immune responses, and will pave the way for emerging breakthroughs in cancer research.
{"title":"Revolutionizing cancer research with spatial proteomics and visual intelligence","authors":"Daniela F. Quail, Logan A. Walsh","doi":"10.1038/s41592-024-02542-w","DOIUrl":"10.1038/s41592-024-02542-w","url":null,"abstract":"Spatial proteomics has transformed cancer research by providing unparalleled insights into the microenvironmental landscape of tumors. Here we discuss how these technologies have significantly advanced our understanding of cell–cell interactions, tissue organization and spatially coordinated mechanisms underlying antitumor immune responses, and will pave the way for emerging breakthroughs in cancer research.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2216-2219"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789379","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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