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@ARTICLE{Bonev:273924,
      author       = {Bonev, Boyan and Gonçalo, Castelo-Branco and Chen, Fei and
                      Codeluppi, Simone and Corces, M Ryan and Fan, Jean and
                      Heiman, Myriam and Harris, Kenneth and Inoue, Fumitaka and
                      Kellis, Manolis and Levine, Ariel and Lotfollahi, Mo and
                      Luo, Chongyuan and Maynard, Kristen R and Nitzan, Mor and
                      Ramani, Vijay and Satijia, Rahul and Schirmer, Lucas and
                      Shen, Yin and Sun, Na and Green, Gilad S and Theis, Fabian
                      and Wang, Xiao and Welch, Joshua D and Gokce, Ozgun and
                      Konopka, Genevieve and Liddelow, Shane and Macosko, Evan and
                      Bayraktar, Omer and Habib, Naomi and Nowakowski, Tomasz J},
      title        = {{O}pportunities and challenges of single-cell and spatially
                      resolved genomics methods for neuroscience discovery.},
      journal      = {Nature neuroscience},
      volume       = {27},
      number       = {12},
      issn         = {1097-6256},
      address      = {New York, NY},
      publisher    = {Nature America},
      reportid     = {DZNE-2024-01398},
      pages        = {2292 - 2309},
      year         = {2024},
      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.},
      keywords     = {Single-Cell Analysis: methods / Humans / Genomics: methods
                      / Neurosciences: methods / Animals / Epigenomics: methods},
      cin          = {AG Gokce},
      ddc          = {610},
      cid          = {I:(DE-2719)1013041},
      pnm          = {351 - Brain Function (POF4-351)},
      pid          = {G:(DE-HGF)POF4-351},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39627587},
      doi          = {10.1038/s41593-024-01806-0},
      url          = {https://pub.dzne.de/record/273924},
}