Granular Transcriptomic Signatures Derived from Independent Component Analysis of Bulk Nervous Tissue for Studying Labile Brain Physiologies

Abstract

Multicellular organisms employ concurrent gene regulatory programs to control development and physiology of cells and tissues. The Drosophila melanogaster model system has a remarkable history of revealing the genes and mechanisms underlying fundamental biology yet much remains unclear. In particular, brain xenobiotic protection and endobiotic regulatory systems that require transcriptional coordination across different cell types, operating in parallel with the primary nervous system and metabolic functions of each cell type, are still poorly understood. Here we use the unsupervised machine learning method independent component analysis (ICA) on majority freshfrozen, bulk tissue microarrays to define biologically pertinent gene expression signatures which are sparse, i.e. each involving only a fraction of all fly genes. We optimize the gene expression signature definitions partly through repeated application of a stochastic ICA algorithm to a compendium of 3,346 microarrays from 221 experiments provided by the Drosophila research community. Our optimized ICA model of pan fly gene expression consists of 850 modules of co-regulated genes that map to tissue developmental stages, disease states, cell-autonomous pathways and presumably novel processes. Importantly, we show biologically relevant gene modules expressed at varying amplitudes in whole brain and isolated adult blood-brain barrier cell levels.Thus, whole tissue derived ICA transcriptional signatures that transcend single cell type boundaries provide a window into the transcriptional states of difficult to isolate cell ensembles maintaining delicate brain physiologies. We believe the fly ICA gene expression signatures set, by virtue of the success of ICA at inferring robust often low amplitude patterns across large datasets and the quality of the input samples, to be an important asset for analyzing compendium and newly generated microarray or RNA-seq expression datasets.

The neurodevelopmental transcriptome of theDrosophila melanogastermicrocephaly geneabnormal spindlereveals a role for temporal transcription factors and the immune system in regulating brain size

“…To complement our functional enrichment analysis, we also utilized a Drosophila -specific gene signature set consisting of 850 modules of co-regulated genes identified through Independent Component Analysis (ICA) [34]. The advantage of this analysis is that it utilizes gene co-expression patterns identified in flies from thousands of high throughput datasets, and therefore may have additional analytical power to uncover subtle expression perturbations and the underlying gene regulatory networks.…”

“…The advantage of this analysis is that it utilizes gene co-expression patterns identified in flies from thousands of high throughput datasets, and therefore may have additional analytical power to uncover subtle expression perturbations and the underlying gene regulatory networks. This is particularly true in the brain, where it has been used to identify transcriptional signatures unique to individual brain cell types [34] and thus can provide deeper insight into fly-specific brain function and physiology [19]. We therefore searched for enriched co-regulatory signatures that could be biologically relevant for MCPH.…”

“…However, it is important to note that the construction of these co-expressed networks are dependent on the input datasets used (microarray in this case) [34], and further refinement of them could be achieved through additional datasets, especially from the tissues of interest. Thus, there is a possibility that the modules containing a large number of genes (>200) and different GO associations could be further resolved into their own unique modules.…”

“…A second analysis pipeline was used to assess functional enrichment of differentially expressed genes (DEGs) based on Drosophila -specific gene expression signatures [34]. These gene expression signatures were identified through Independent Component Analysis (ICA) of 3,346 microarray datasets generated by the fly community and consist of 850 modules of co-regulated genes.…”

“…These gene expression signatures were identified through Independent Component Analysis (ICA) of 3,346 microarray datasets generated by the fly community and consist of 850 modules of co-regulated genes. ICA modules were encoded as a sparse non-negative 18,952 microarray probe sets by 850 modules weight matrix derived from a 18,952 microarray probe sets by 425 independent components ICA S matrix (see [34] for details). 850 sets of the most heavily-weighted co-regulated genes were created by discretizing (probe set weight ≥ 3) each of the ICA module matrix vectors and used to determine module GO term and KEGG pathway enrichments (Supplementary File 4).…”