Disentangling neural cell diversity using single-cell transcriptomics
Autor: | Jens Hjerling-Leffler, Jeffrey M. Trimarchi, Jean-François Poulin, Bosiljka Tasic, Rajeshwar Awatramani |
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Rok vydání: | 2016 |
Předmět: |
0301 basic medicine
Nervous system Cell type Molecular neuroscience Biology Transcriptome 03 medical and health sciences 0302 clinical medicine Cellular neuroscience Specialization (functional) medicine Animals Humans Neural cell Cells Cultured Neurons Gene Expression Profiling General Neuroscience Brain Gene expression profiling 030104 developmental biology medicine.anatomical_structure Nerve Net Neuroscience 030217 neurology & neurosurgery |
Zdroj: | Nature Neuroscience. 19:1131-1141 |
ISSN: | 1546-1726 1097-6256 |
Popis: | Although single-cell gene expression profiling has been possible for the past two decades, a number of recent technological advances in microfluidic and sequencing technology have recently made the procedure much easier and less expensive. Awatramani and colleagues discuss the use of single-cell gene expression profiling for classifying neuronal cell types. Cellular specialization is particularly prominent in mammalian nervous systems, which are composed of millions to billions of neurons that appear in thousands of different 'flavors' and contribute to a variety of functions. Even in a single brain region, individual neurons differ greatly in their morphology, connectivity and electrophysiological properties. Systematic classification of all mammalian neurons is a key goal towards deconstructing the nervous system into its basic components. With the recent advances in single-cell gene expression profiling technologies, it is now possible to undertake the enormous task of disentangling neuronal heterogeneity. High-throughput single-cell RNA sequencing and multiplexed quantitative RT-PCR have become more accessible, and these technologies enable systematic categorization of individual neurons into groups with similar molecular properties. Here we provide a conceptual and practical guide to classification of neural cell types using single-cell gene expression profiling technologies. |
Databáze: | OpenAIRE |
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