Frontiers | A brief history of somatostatin interneuron taxonomy or: how many somatostatin subtypes are there, really?

IF 3.4 3区 医学 Q2 NEUROSCIENCES
Ariel Agmon, Alison L. Barth
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Abstract

We provide a brief (and unabashedly biased) overview of the pre-transcriptomic history of somatostatin interneuron taxonomy, followed by a chronological summary of the large-scale, NIH-supported effort over the last ten years to generate a comprehensive, single-cell RNA-seq-based taxonomy of cortical neurons. Focusing on somatostatin interneurons, we present the perspective of experimental neuroscientists trying to incorporate the new classification schemes into their own research while struggling to keep up with the ever-increasing number of proposed cell types, which seems to double every two years. We suggest that for experimental analysis, the most useful taxonomic level is the subdivision of somatostatin interneurons into ten or so “supertypes,” which closely agrees with their more traditional classification by morphological, electrophysiological and neurochemical features. We argue that finer subdivisions (“t-types” or “clusters”), based on slight variations in gene expression profiles but lacking clear phenotypic differences, are less useful to researchers and may actually defeat the purpose of classifying neurons to begin with. We end by stressing the need for generating novel tools (mouse lines, viral vectors) for genetically targeting distinct supertypes for expression of fluorescent reporters, calcium sensors and excitatory or inhibitory opsins, allowing neuroscientists to chart the input and output synaptic connections of each proposed subtype, reveal the position they occupy in the cortical network and examine experimentally their roles in sensorimotor behaviors and cognitive brain functions.
前沿 | 体生长抑素中间神经元分类简史或:到底有多少种体生长抑素亚型?
我们简要概述了体视蛋白中间神经元分类的前转录组历史(毫不掩饰地带有偏见),然后按时间顺序总结了过去十年中美国国立卫生研究院(NIH)支持的大规模努力,以产生一种全面的、基于单细胞 RNA-seq 的皮层神经元分类法。我们以体脂素中间神经元为重点,介绍了实验神经科学家的观点,他们试图将新的分类方案纳入自己的研究,同时努力跟上不断增加的细胞类型(似乎每两年翻一番)。我们建议,对于实验分析而言,最有用的分类方法是将体视蛋白中间神经元细分为十个左右的 "超类型",这与它们更传统的形态学、电生理学和神经化学特征分类方法非常吻合。我们认为,基于基因表达谱的细微差别但缺乏明显表型差异的更精细的细分("t 型 "或 "群")对研究人员来说用处不大,而且实际上可能有悖于神经元分类的初衷。最后,我们强调有必要开发新的工具(小鼠品系、病毒载体),从基因上靶向表达荧光报告、钙离子传感器和兴奋或抑制性蛋白的不同超类型,使神经科学家能够绘制每个拟议亚型的输入和输出突触连接图,揭示它们在大脑皮层网络中的位置,并通过实验研究它们在感觉运动行为和大脑认知功能中的作用。
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来源期刊
CiteScore
6.00
自引率
5.70%
发文量
135
审稿时长
4-8 weeks
期刊介绍: Frontiers in Neural Circuits publishes rigorously peer-reviewed research on the emergent properties of neural circuits - the elementary modules of the brain. Specialty Chief Editors Takao K. Hensch and Edward Ruthazer at Harvard University and McGill University respectively, are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Frontiers in Neural Circuits launched in 2011 with great success and remains a "central watering hole" for research in neural circuits, serving the community worldwide to share data, ideas and inspiration. Articles revealing the anatomy, physiology, development or function of any neural circuitry in any species (from sponges to humans) are welcome. Our common thread seeks the computational strategies used by different circuits to link their structure with function (perceptual, motor, or internal), the general rules by which they operate, and how their particular designs lead to the emergence of complex properties and behaviors. Submissions focused on synaptic, cellular and connectivity principles in neural microcircuits using multidisciplinary approaches, especially newer molecular, developmental and genetic tools, are encouraged. Studies with an evolutionary perspective to better understand how circuit design and capabilities evolved to produce progressively more complex properties and behaviors are especially welcome. The journal is further interested in research revealing how plasticity shapes the structural and functional architecture of neural circuits.
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