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      Neuronal cell-type classification: challenges, opportunities and the path forward

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      Journal: Nature Reviews Neuroscience
      Publisher: Springer Nature

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          Abstract

          Attempts to group the cells of the nervous system into classes or types face technical and conceptual barriers. Zeng and Sanes consider the current approaches to classification and propose a strategy and set of principles to guide future classification efforts.

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          Interneurons of the neocortical inhibitory system.

          Henry Markram, Maria Toledo-Rodriguez, Yun Wang … (2004)
          Mammals adapt to a rapidly changing world because of the sophisticated cognitive functions that are supported by the neocortex. The neocortex, which forms almost 80% of the human brain, seems to have arisen from repeated duplication of a stereotypical microcircuit template with subtle specializations for different brain regions and species. The quest to unravel the blueprint of this template started more than a century ago and has revealed an immensely intricate design. The largest obstacle is the daunting variety of inhibitory interneurons that are found in the circuit. This review focuses on the organizing principles that govern the diversity of inhibitory interneurons and their circuits.
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            A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex.

            Hiroki Taniguchi, Miao He, Priscilla Wu … (2011)
            A key obstacle to understanding neural circuits in the cerebral cortex is that of unraveling the diversity of GABAergic interneurons. This diversity poses general questions for neural circuit analysis: how are these interneuron cell types generated and assembled into stereotyped local circuits and how do they differentially contribute to circuit operations that underlie cortical functions ranging from perception to cognition? Using genetic engineering in mice, we have generated and characterized approximately 20 Cre and inducible CreER knockin driver lines that reliably target major classes and lineages of GABAergic neurons. More select populations are captured by intersection of Cre and Flp drivers. Genetic targeting allows reliable identification, monitoring, and manipulation of cortical GABAergic neurons, thereby enabling a systematic and comprehensive analysis from cell fate specification, migration, and connectivity, to their functions in network dynamics and behavior. As such, this approach will accelerate the study of GABAergic circuits throughout the mammalian brain. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Comparative Analysis of Single-Cell RNA Sequencing Methods.

              Christoph Ziegenhain, Beate Vieth, Swati Parekh … (2017)
              Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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                Author and article information

                Journal
                Title: Nature Reviews Neuroscience
                Abbreviated Title: Nat Rev Neurosci
                Publisher: Springer Nature
                ISSN (Print): 1471-003X
                ISSN (Electronic): 1471-0048
                Publication date Created: August 3 2017
                Publication date (Print): August 3 2017
                Volume:
                Issue:
                Article
                DOI: 10.1038/nrn.2017.85
                PubMed ID: 28775344
                SO-VID: 7f7a351c-9cd6-4fb6-9242-2367be8621f3
                Copyright © © 2017
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