Documenting the extent of cellular diversity is a critical step in defining the functional organization of the nervous system. In this context, we sought to develop statistical methods capable of revealing underlying cellular diversity given incomplete data sampling - a common problem in biological systems, where complete descriptions of cellular characteristics are rarely available. We devised a sparse Bayesian framework that infers cell type diversity from partial or incomplete transcription factor expression data. This framework appropriately handles estimation uncertainty, can incorporate multiple cellular characteristics, and can be used to optimize experimental design. We applied this framework to characterize a cardinal inhibitory population in the spinal cord.
Animals generate movement by engaging spinal circuits that direct precise sequences of muscle contraction, but the identity and organizational logic of local interneurons that lie at the core of these circuits remain unresolved. By using our Sparse Bayesian approach, we showed that V1 interneurons, a major inhibitory population that controls motor output, fractionate into diverse subsets on the basis of the expression of nineteen transcription factors. Transcriptionally defined subsets exhibit highly structured spatial distributions with mediolateral and dorsoventral positional biases. These distinctions in settling position are largely predictive of patterns of input from sensory and motor neurons, arguing that settling position is a determinant of inhibitory microcircuit organization. Finally, we extensively validated inferred cell types by direct experimental measurement and then, extend our Bayesian framework to full transcriptome technologies. Together, these findings provide insight into the diversity and organizational logic through which inhibitory microcircuits shape motor output.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8PN95XC |
| Date | January 2016 |
| Creators | Gabitto, Mariano Ignacio |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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