Biology subjects

Menon, V.

Publications and source records attributed to Menon, V..

4 recordsLinked to original sources

bioRxiv2018

A single cell-based atlas of human microglial states reveals associations with neurological disorders and histopathological features of the aging brain

Recent studies of bulk microglia have provided insights into the role of this immune cell type in central nervous system development, homeostasis and dysfunction. Nonetheless, our understanding of the diversity of human microglial cell states remains limited; microglia are highly plastic and have multiple different roles, making the extent of phenotypic heterogeneity a central question, especially in light of the development of therapies targeting this cell type. Here, we investigated the population structure of human microglia by single-cell RNA-sequencing. Using surgical- and autopsy-derived cortical brain samples, we identified 14 human microglial subpopulations and noted substantial intra- and inter-individual heterogeneity. These putative subpopulations display divergent associations with Alzheimers disease, multiple sclerosis, and other diseases. Several states show enrichment for genes found in disease-associated mouse microglial states, suggesting additional diversity among human microglia. Overall, human microglia appear to exist in different functional states with varying levels of involvement in different brain pathologies.

neuroscience↗

bioRxiv2017

Shared and distinct transcriptomic cell types across neocortical areas

Neocortex contains a multitude of cell types segregated into layers and functionally distinct regions. To investigate the diversity of cell types across the mouse neocortex, we analyzed 12,714 cells from the primary visual cortex (VISp), and 9,035 cells from the anterior lateral motor cortex (ALM) by deep single-cell RNA-sequencing (scRNA-seq), identifying 116 transcriptomic cell types. These two regions represent distant poles of the neocortex and perform distinct functions. We define 50 inhibitory transcriptomic cell types, all of which are shared across both cortical regions. In contrast, 49 of 52 excitatory transcriptomic types were found in either VISp or ALM, with only three present in both. By combining single cell RNA-seq and retrograde labeling, we demonstrate correspondence between excitatory transcriptomic types and their region-specific long-range target specificity. This study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct regions of the mouse cortex.

neuroscience↗

bioRxiv2017

Distinct descending motor cortex pathways and their roles in movement

Activity in motor cortex predicts specific movements, seconds before they are initiated. This preparatory activity has been observed in L5 descending pyramidal tract (PT) neurons. A key question is how preparatory activity can be maintained without causing movement, and how preparatory activity is eventually converted to a motor command to trigger appropriate movements. We used single cell transcriptional profiling and axonal reconstructions to identify two types of PT neuron. Both types share projections to multiple targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex. In a delayed-response task, these neurons produced early preparatory activity that persisted until the movement. The second type projects to motor centers in the medulla and produced late preparatory activity and motor commands. These results indicate that two motor cortex output neurons are specialized for distinct roles in motor control.

neuroscience↗

bioRxiv2017

Generalized Leaky Integrate-And-Fire Models Classify Multiple Neuron Types

In the mammalian neocortex, there is a high diversity of neuronal types. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with the Allen Cell Types Database. We construct a series of generalized integrate-and-fire (GLIF) models of increasing complexity aiming to reproduce the spiking behaviors of the recorded neurons. We test the performance of these GLIF models on data from 771 neurons from 14 transgenic lines, with increasing model performance for more complex models. To answer how complex a model needs to be to reproduce the number of electrophysiological cell types, we perform unsupervised clustering on the parameters extracted from these models. The number of clusters is smaller for individual model types, but when combining all GLIF parameters 18 clusters are obtained, while 11 clusters are obtained using 16 electrophysiological features. Therefore, these low dimensional models have the capacity to characterize the diversity of cell types without the need for a priori defined features.

neuroscience↗