Disentangling neural cell diversity using single-cell transcriptomics

Abstract

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.

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Figure 1: Neural cell diversity in the visual cortex.
Figure 2: Experimental approaches commonly used for single-cell gene expression profiling.
Figure 3: Examples of approaches for neural cell classification in various regions of the nervous system.

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Acknowledgements

We would like to thank L. Laboissonniere, R. Chowdhury, N. Skene, V. Menon and G. Caronia-Brown for comments on the manuscript. J.-F.P. is supported by FRSQ and CIHR. R.A. is supported by the Paul Ruby Foundation, NARSAD, NIH R21NS092034-01A1 and R01 NS096240-01. B.T. is supported by the Allen Institute for Brain Science. J.H.-L. is supported by Swedish Research Council (VR), StratNeuro, Hjärnfonden, and EU FP7/Marie Curie Actions.

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Correspondence to Rajeshwar Awatramani.

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Poulin, J., Tasic, B., Hjerling-Leffler, J. et al. Disentangling neural cell diversity using single-cell transcriptomics. Nat Neurosci 19, 1131–1141 (2016). https://doi.org/10.1038/nn.4366

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