| Literature DB >> 27339989 |
Blue B Lake1, Rizi Ai2, Gwendolyn E Kaeser3, Neeraj S Salathia4, Yun C Yung5, Rui Liu1, Andre Wildberg2, Derek Gao1, Ho-Lim Fung1, Song Chen1, Raakhee Vijayaraghavan4, Julian Wong5, Allison Chen5, Xiaoyan Sheng5, Fiona Kaper4, Richard Shen4, Mostafa Ronaghi4, Jian-Bing Fan6, Wei Wang7, Jerold Chun8, Kun Zhang9.
Abstract
The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.Entities:
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Year: 2016 PMID: 27339989 PMCID: PMC5038589 DOI: 10.1126/science.aaf1204
Source DB: PubMed Journal: Science ISSN: 0036-8075 Impact factor: 47.728