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A genetic and computational approach to structurally classify neuronal types.

ABSTRACT: The importance of cell types in understanding brain function is widely appreciated but only a tiny fraction of neuronal diversity has been catalogued. Here we exploit recent progress in genetic definition of cell types in an objective structural approach to neuronal classification. The approach is based on highly accurate quantification of dendritic arbor position relative to neurites of other cells. We test the method on a population of 363 mouse retinal ganglion cells. For each cell, we determine the spatial distribution of the dendritic arbors, or arbor density, with reference to arbors of an abundant, well-defined interneuronal type. The arbor densities are sorted into a number of clusters that is set by comparison with several molecularly defined cell types. The algorithm reproduces the genetic classes that are pure types, and detects six newly clustered cell types that await genetic definition.

SUBMITTER: Sumbul U

PROVIDER: S-EPMC4164236 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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A genetic and computational approach to structurally classify neuronal types.

Sümbül Uygar U Song Sen S McCulloch Kyle K Becker Michael M Lin Bin B Sanes Joshua R JR Masland Richard H RH Seung H Sebastian HS

Nature communications 20140324

The importance of cell types in understanding brain function is widely appreciated but only a tiny fraction of neuronal diversity has been catalogued. Here we exploit recent progress in genetic definition of cell types in an objective structural approach to neuronal classification. The approach is based on highly accurate quantification of dendritic arbor position relative to neurites of other cells. We test the method on a population of 363 mouse retinal ganglion cells. For each cell, we determ ...[more]

PMID: 24662602

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Project description:ObjectiveIrritability, which is strongly associated with impairment and negative outcomes, is a common reason for referral to mental health services but is a nosological and treatment challenge. A major issue is how irritability should be conceptualized. The authors used a developmental approach to test the hypothesis that there are several forms of irritability, including a "neurodevelopmental/ADHD-like" type, with onset in childhood, and a "depression/mood" type, with onset in adolescence.MethodsData were analyzed from the Avon Longitudinal Study of Parents and Children, a prospective U.K. population-based cohort. Irritability trajectory classes were estimated for 7,924 individuals with data at multiple time points across childhood and adolescence (four possible time points from approximately ages 7 to 15). Psychiatric diagnoses were assessed at approximately ages 7 and 15. Psychiatric genetic risk was indexed by polygenic risk scores (PRSs) for attention deficit hyperactivity disorder (ADHD) and depression, derived using large genome-wide association study results.ResultsFive irritability trajectory classes were identified: low (81.2%), decreasing (5.6%), increasing (5.5%), late-childhood limited (5.2%), and high-persistent (2.4%). The early-onset high-persistent trajectory was associated with male preponderance, childhood ADHD (odds ratio=108.64, 95% CI=57.45-204.41), and ADHD PRS (odds ratio=1.31, 95% CI=1.09-1.58). The adolescent-onset increasing trajectory was associated with female preponderance, adolescent depression (odds ratio=5.14, 95% CI=2.47-10.73), and depression PRS (odds ratio=1.20, 95% CI=1.05-1.38). Both the early-onset high-persistent and adolescent-onset increasing trajectory classes were associated with adolescent depression diagnosis and ADHD PRS.ConclusionsThe developmental context of irritability may be important in its conceptualization: early-onset persistent irritability may be more neurodevelopmental/ADHD-like and later-onset irritability more depression/mood-like. These findings have implications for treatment as well as nosology.

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A genetic and computational approach to structurally classify neuronal types.

Publications

A genetic and computational approach to structurally classify neuronal types.

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