Scientific Understanding of Consciousness
Consciousness as an Emergent Property of Thalamocortical Activity

Neuronal Diversity


Science 16 August 2013: Vol. 341 no. 6147 pp. 726-727

Mapping Neuronal Diversity One Cell at a Time

Hynek Wichterle, David Gifford, Esteban Mazzoni

Departments of Pathology and Cell Biology, Neurology, and Neuroscience, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Medical Center, 630 168 Street, New York, NY 10032, USA.

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA.

Department of Biology, New York University. 100 Washington Square East, New York, NY 10003, USA.


How many types of nerve cells are there in the mammalian central nervous system (CNS)? We still do not have a satisfactory answer to this deceptively simple question, and yet the precise assignment of nerve cells to well-defined subtype categories is critical both for elucidating the function of neural circuits and for the success of neural regenerative medicine. Amid the anatomical, electrophysiological, and biochemical diversity of nerve cells, the field is struggling to devise simple and clear criteria for neuronal classification. A universally applicable classification system should be based on traits that are objectively quantifiable, sufficiently diverse, and reproducible in independent laboratories. Such a classification method would provide new insights into CNS organization, development, and function, and might reveal unexpected relationships between neuronal subtypes.

To fully characterize nerve cells and appreciate their diversity, they are analyzed at all three phenotypic levels—anatomical, biochemical, and electrophysiological. Complete anatomical mapping was accomplished for the CNS of the worm Caenorhabditis elegans by reconstruction of serial electron micrographs.

Ultrastructural mapping is complemented by analysis of anatomy and connectivity based on cell type–specific expression of reporter genes to effectively study the much larger mammalian CNS.

At the biochemical level, ongoing efforts to map expression patterns of developmentally regulated genes provide fundamental insights into molecular diversity and developmental programs of individual nerve cells.

At the electrophysiological level, new research programs, such as the recently announced Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative, are supporting development of technologies for global mapping of neuronal activity in behaving animals. Although integration of the three complementary approaches is essential for the ultimate understanding of brain structure and function at the single nerve cell level such detailed analysis poses a problem as it allows assignment of each nerve cell to multiple different subtype groups.

Currently we do not have a system to provide a definitive count of neuronal subtypes, even in a small region of the mammalian CNS. A recent review on subtype diversity of neocortical interneurons provided a partial solution by proposing to focus on a few easily distinguishable morphological phenotypes to categorize inhibitory interneurons. Although such an approach is practical and immediately applicable to classification of cortical interneurons, it is not sufficiently universal to be easily transferable to other types of neurons and might miss important functional differences among interneurons that are not manifested by anatomically discernible features.

We propose that single-cell expression profiling be used to characterize neuronal diversity. Single-cell RNA sequencing (RNA-seq) provides an unbiased and systematic method of “fingerprinting” an aspect of cellular state that enables similar cells to be computationally identified. Once subtype identities are determined, it will be possible to extract a core set of differentially expressed genes defining neuronal subtype identity (subtype signature). The flexibility of this system will make it easy to continuously add expression profiles, update models, and define finer neuronal subtype categories.

[end of paraphrase]

[Seems like a “Dewey Decimal System” for neuron types assigned into an expandable core set of types.]


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