Deconvolution of Human Brain Cell Type Transcriptomes Unraveled Microglia-Specific Potential Biomarkers

Abstract

Microglial cells form a context-dependent network of brain immunoeffector cells. Despite their indispensable roles, unresolved questions exist around biomarker discovery relevant to their cellular localization, self-renewing potential, and brain developmental dynamics. To resolve the existent gap in the annotation of candidate biomarkers, we conducted a meta-analysis of brain cells using available high-throughput data sets for deciphering microglia-specific expression profiles. We have identified 3,290 significant genes specific to microglia and further selected the top 20 dysregulated genes on the basis of p-value and log2FC. To this list, we added 7 known microglia-specific markers making the candidate list comprising 27 genes for further downstream analyses. Next, we established a connectome of these potential markers with their putative protein partners, which demonstrated strong associations of upregulated genes like Dedicator of cytokinesis 2 (DOCK2) with early/mature microglial markers such as Sphingosine kinase 1 (SPHK1), CD68, and CD45. To elucidate their respective brain anatomical location, we deconvoluted the BrainSpan Atlas expression data. This analysis showed high expression of the majority of candidate genes in microglia-dense regions (Amygdala, Hippocampus, Striatum) in the postnatal brain. Furthermore, to decipher their localized expression across brain ages, we constructed a developmental dynamics map (DDM) comprising extensive gene expression profiles throughout prenatal to postnatal stages, which resulted in the discovery of novel microglia-specific gene signatures. One of the interesting readout from DDM is that all the microglia-dense regions exhibit dynamic regulation of few genes at 37 post conception week (pcw), the transition period between pre- and postnatal stages. To validate these findings and correlate them as potential biomarkers, we analyzed the expression of corresponding proteins in hESC-derived human microglia precursors. The cultured microglial precursors showed expression of Pentraxin 3 (PTX3) and SPHK1 as well as several known markers like CD68, Allograft inflammatory factor 1 (AIF1/IBA1). In summary, this study has furnished critical insights into microglia dynamics across human brain ages and cataloged potential transcriptomic fingerprints that can be further exploited for designing novel neurotherapeutics.

Gingival transcriptomics of follicular T cell footprints in progressing periodontitis

“…Examination of the existing literature provided a subset of 12 genes to provide a more directed description of changes in Tfh infiltrates or functional activities [47–49] in the gingival tissues, similar to approaches in complex tissues using molecular deconvolution approaches [25–30]. Figure 4 depicts individual adult and young animal gingival expression of the composite sum of transcript levels for these 12 genes.…”

“…It was also predicted that certain bacterial components of the complex oral microbiome would have the capacity to aid in triggering or dampening the various molecules required for functional Tfh cells in the gingival tissues. Examination of the characteristics of gene expression representing features of Tfh cells in the gingival tissues was implemented as reported for cellular transcriptomic studies in other systems [25–30]. However, a limitation of the use of this approach to examine tissue level presentation of Tfh genes is that a number of these gene transcripts would also be produced by other cell types that reside in the gingival tissues.…”

RHO to the DOCK for GDP disembarking: Structural insights into the DOCK GTPase nucleotide exchange factors

“…Multiple immunodeficiencies have also been linked to DOCK2 mutations ( 64 ). In the brain DOCK2 is exclusively expressed in microglia and has been identified as a biomarker of this cell type, as it is a molecular hub influencing both homeostatic and neural disease processes ( 65 ). It has been implicated in the pathogenesis of AD by enhancing amyloid beta (Aβ) plaque formation ( 66 , 67 ).…”

The Role of Scale in the Estimation of Cell-type Proportions

“…Let the amount of mRNA from gene n in the reference sample be η * nk . Since the mixture sample is comprised of a proportion p k of type k cells and the reference sample is 100% type k cells, then we expect that (3) η nk ≈ p k η * nk . Essentially, this assumes that type k cells in the mixture behave as if they were a random sample of the type k reference cells.…”