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. 2014 Jan 22;34(4):1420-31.
doi: 10.1523/JNEUROSCI.4488-13.2014.

Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders

Xiaoxiao Xu  1 , Alan B Wells, David R O'Brien, Arye Nehorai, Joseph D Dougherty
Affiliations

Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders

Xiaoxiao Xu et al. J Neurosci. .

Abstract

Recent advances have substantially increased the number of genes that are statistically associated with complex genetic disorders of the CNS such as autism and schizophrenia. It is now clear that there will likely be hundreds of distinct loci contributing to these disorders, underscoring a remarkable genetic heterogeneity. It is unclear whether this genetic heterogeneity indicates an equal heterogeneity of cellular mechanisms for these diseases. The commonality of symptoms across patients suggests there could be a functional convergence downstream of these loci upon a limited number of cell types or circuits that mediate the affected behaviors. One possible mechanism for this convergence would be the selective expression of at least a subset of these genes in the cell types that comprise these circuits. Using profiling data from mice and humans, we have developed and validated an approach, cell type-specific expression analysis, for identifying candidate cell populations likely to be disrupted across sets of patients with distinct genetic lesions. Using human genetics data and postmortem gene expression data, our approach can correctly identify the cell types for disorders of known cellular etiology, including narcolepsy and retinopathies. Applying this approach to autism, a disease where the cellular mechanism is unclear, indicates there may be multiple cellular routes to this disorder. Our approach may be useful for identifying common cellular mechanisms arising from distinct genetic lesions.

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Figures

Figure 1.
Figure 1.
A topography of cell-specific and enriched transcripts in the mouse brain. A, Hierarchical clustering of cell types by transcript levels recapitulates known biological relationships. B, Example of a single bullseye plot. For each cell type, the size of the bullseye in A is scaled to the number of specific and enriched transcripts at different stringency thresholds. For example, Purkinje neurons have many unique transcripts (large central hexagon, pSI < 0.0001)), while cortical projection neurons (in A) have few. For CSEAs in later figures, bullseyes will be color coded by Fisher's exact test p values as shown.
Figure 2.
Figure 2.
The cell-specific and enriched transcript lists are robust and unrelated to transcript length. A, Venn diagram showing little change in transcript lists for cortical astrocytes at pSI = 0.01, when pSI is recalculated with or without the closely related cerebellar astrocyte sample. B, Across all cell types and pSI thresholds, gene lists are >89% identical when calculated with or without cerebellar astrocytes. C, Density plot of distributions of transcript lengths colored by pSI value shows no length bias.
Figure 3.
Figure 3.
CSEA correctly identifies retinopathies as diseases of rods and cones, and CSEA of narcoleptic transcriptomic data identifies a loss of hypocretin neurons. A, Bullseye plot of the output of CSEA reveals a substantial over-representation of retinopathy disease genes (n = 120) producing transcripts enriched in rods and cones, regardless of the threshold chosen for pSI. B, Output of CSEA reveals an over-representation of hypocretin neuron cell transcripts among those transcripts (n = 9) that were decreased in the hypothalami of human narcoleptic subjects or in mice with Hcrt neuron ablation (n = 63).
Figure 4.
Figure 4.
Impact of candidate gene list size and purity on sensitivity and specificity of CSEA. A, Box-and-whisker plots illustrating the distribution of CSEA p values (log base 10 scale, y-axis) for rods from retinopathy gene lists sampled at various sizes (100 samplings per size). B, From random subsets of retinopathy gene lists mixed with random sets of nonretinopathy genes. The x-axis (purity) indicates what fraction of the list derives from the original retinopathy set (100 samplings per purity). A pSI threshold of <0.01 is shown here. Results are substantially similar across all pSI thresholds from 0.05 to 0.0001 (data not shown).
Figure 5.
Figure 5.
CSEA of autism cortical transcriptomic data suggest increased signals from astrocytes and immune cells, and a disruption of interneurons. A, Output of CSEA reveals a substantial over-representation at multiple pSI levels for astrocyte and immune cell transcripts among those transcripts found to be increased (n = 184) in cortices from individuals with autism. B, Output of CSEA reveals a substantial over-representation of Pnoc+ interneuron cell transcripts at multiple pSI levels among those transcripts (n = 174) found to be decreased in cortices from individuals with autism (axes are as shown in Fig. 1; analysis restricted to cortical cell types).
Figure 6.
Figure 6.
CSEA of autism candidate genes curated from human genetic studies suggests a disruption of cortical neurons and striatal circuitry. Output of CSEA reveals a substantial over-representation at multiple pSI levels for cortical interneurons (Pnoc+ and Cort+), layer 5b neurons, and striatal medium spiny neurons.
Figure 7.
Figure 7.
Analysis of enrichment in human brain regions supports the disruption of cortex and striatum. A, Curated autism genes are over-represented at multiple pSI levels in striatally and cortically enriched transcripts calculated from Brainspan, 2013 young adult RNAseq data. B, Rare, protein-disrupting de novo variants identified in probands with autism (n = 122) have enriched expression during fetal cortical and striatal development.
Figure 8.
Figure 8.
Autism genes and transcripts enriched in particular cell types. A, Transcriptionally upregulated transcripts that overlap with astrocyte-enriched genes, corresponding to Figure 5A. Right column, pSI in cortical astrocytes. B, Transcriptionally downregulated transcripts that overlap with Pnoc+ neuron-enriched transcripts, corresponding to Figure 5B. C, Autism candidate genes that overlap with Pnoc+ neuron-enriched transcripts, corresponding to Figure 6.
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