TY - JOUR
T1 - Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery
AU - Bonev, Boyan
AU - Gonçalo, Castelo Branco
AU - Chen, Fei
AU - Codeluppi, Simone
AU - Corces, M. Ryan
AU - Fan, Jean
AU - Heiman, Myriam
AU - Harris, Kenneth
AU - Inoue, Fumitaka
AU - Kellis, Manolis
AU - Levine, Ariel
AU - Lotfollahi, Mo
AU - Luo, Chongyuan
AU - Maynard, Kristen R.
AU - Nitzan, Mor
AU - Ramani, Vijay
AU - Satijia, Rahul
AU - Schirmer, Lucas
AU - Shen, Yin
AU - Sun, Na
AU - Green, Gilad S.
AU - Theis, Fabian
AU - Wang, Xiao
AU - Welch, Joshua D.
AU - Gokce, Ozgun
AU - Konopka, Genevieve
AU - Liddelow, Shane
AU - Macosko, Evan
AU - Bayraktar, Omer
AU - Habib, Naomi
AU - Nowakowski, Tomasz J.
N1 - Publisher Copyright:
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85211137231&partnerID=8YFLogxK
U2 - 10.1038/s41593-024-01806-0
DO - 10.1038/s41593-024-01806-0
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C2 - 39627587
AN - SCOPUS:85211137231
SN - 1097-6256
VL - 27
SP - 2292
EP - 2309
JO - Nature Neuroscience
JF - Nature Neuroscience
IS - 12
M1 - eadd7046
ER -