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Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.

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Title: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.
Authors: Bonev, Boyan (AUTHOR), Gonçalo, Castelo-Branco (AUTHOR), Chen, Fei (AUTHOR), Codeluppi, Simone (AUTHOR), Corces, M. Ryan (AUTHOR), Fan, Jean (AUTHOR), Heiman, Myriam (AUTHOR), Harris, Kenneth (AUTHOR), Inoue, Fumitaka (AUTHOR), Kellis, Manolis (AUTHOR), Levine, Ariel (AUTHOR), Lotfollahi, Mo (AUTHOR), Luo, Chongyuan (AUTHOR), Maynard, Kristen R. (AUTHOR), Nitzan, Mor (AUTHOR), Ramani, Vijay (AUTHOR), Satijia, Rahul (AUTHOR), Schirmer, Lucas (AUTHOR), Shen, Yin (AUTHOR), Sun, Na (AUTHOR)
Source: Nature Neuroscience. Dec2024, Vol. 27 Issue 12, p2292-2309. 18p.
Abstract: Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited. The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells. Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets. We highlight how data sharing and integration, documentation, visualization and benchmarking of results contribute to transparency, reproducibility, collaboration and democratization in neuroscience, and discuss needs and opportunities for future technology development and analysis. This review provides an overview of analysis and experimental design of single-cell omics in the brain, emphasizing epigenomics and spatial omics. The authors discuss how the computational and experimental designs are interlinked, with both being guided by the biological questions. [ABSTRACT FROM AUTHOR]
Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="JN" term="%22Nature+Neuroscience%22">Nature Neuroscience</searchLink>. Dec2024, Vol. 27 Issue 12, p2292-2309. 18p.
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  Data: Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited. The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells. Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets. We highlight how data sharing and integration, documentation, visualization and benchmarking of results contribute to transparency, reproducibility, collaboration and democratization in neuroscience, and discuss needs and opportunities for future technology development and analysis. This review provides an overview of analysis and experimental design of single-cell omics in the brain, emphasizing epigenomics and spatial omics. The authors discuss how the computational and experimental designs are interlinked, with both being guided by the biological questions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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