Biology subjects

Crow, M.

Publications and source records attributed to Crow, M..

3 recordsLinked to original sources

bioRxiv2017

Transcriptional Architecture of Synaptic Communication Delineates Cortical GABAergic Neuron Identity

Understanding the organizational logic of neural circuits requires deciphering the biological basis of neuron type diversity and identity, but there is no consensus on defining a neuron type. We analyzed single cell transcriptomes of anatomically and physiologically characterized cortical ground truth populations and conducted a computational genomic screen for transcription profiles that distinguish them. We discovered that cardinal GABAergic neuron types are delineated by a transcriptional architecture that encodes their synaptic communication patterns. This architecture comprises 6 categories of ~40 gene families including cell adhesion molecules, transmitter-modulator receptors, ion channels, signaling proteins, neuropeptides and vesicular release components, and transcription factors. Combinatorial expression of select members across families shapes a multi-layered molecular scaffold along cell membrane that may customize synaptic connectivity patterns and input-output signaling properties. This molecular genetic framework of neuronal identity integrates cell phenotypes along multiple axes and provides a foundation for discovering and classifying neuron types.

neuroscience↗

bioRxiv2017

Addressing the looming identity crisis in single cell RNA-seq

Single cell RNA-sequencing technology (scRNA-seq) provides a new avenue to discover and characterize cell types, but the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine the replicability of these studies. Meta-analysis of rapidly accumulating data is further hampered by the use of ad hoc naming conventions. Here we demonstrate our replication framework, MetaNeighbor, that allows researchers to quantify the degree to which cell types replicate across datasets, and to rapidly identify clusters with high similarity for further testing. We first measure the replicability of neuronal identity by comparing more than 13 thousand individual scRNA-seq transcriptomes, then assess cross-dataset evidence for novel pyramidal neuron and cortical interneuron subtypes identified by scRNA-seq. We find that 24/45 cortical interneuron subtypes and 10/48 pyramidal neuron subtypes have evidence of replication in at least one other study. Identifying these putative replicates allows us to re-analyze the data for differential expression and provide lists of robust candidate marker genes. Across tasks we find that large sets of variably expressed genes can identify replicable cell types and subtypes with high accuracy, indicating many of the transcriptional changes characterizing cell identity are pervasive and easily detected.

bioinformatics↗

bioRxiv2017

Not by systems alone: identifying functional outliers in rare disease pedigrees

In characterizing a disease, it is common to search for dysfunctional genes by assaying the transcriptome. The resulting differentially expressed genes are typically assessed for shared features, such as functional annotation or co-expression. While useful, the reliability of these systems methods is hard to evaluate. To better understand shared disease signals, we assess their replicability by first looking at gene-level recurrence and then pathway-level recurrence along with co-expression signals across six pedigrees of a rare homogeneous X-linked disorder, TAF1 syndrome. We find most differentially expressed genes are not recurrent between pedigrees, making functional enrichment largely distinct in each pedigree. However, we find two highly recurrent \"functional outliers\" (CACNA1I and IGFBP3), genes acting atypically with respect to co-expression and therefore absent from a systems-level assessment. We show this occurs in re-analysis of Huntingtons disease, Parkinsons disease and schizophrenia. Our results suggest a significant role for genes easily missed in systems approaches.

systems biology↗