Co-expression patterns of microglia markers Iba1, TMEM119 and P2RY12 in Alzheimer's disease

Microglia have been identified as key players in Alzheimer's disease pathogenesis, and other neurodegenerative diseases. Iba1, and more specifically TMEM119 and P2RY12 are gaining ground as presumedly more specific microglia markers, but comprehensive characterization of the expression of these three markers individually as well as combined is currently missing. Here we used a multispectral immunofluorescence dataset, in which over seventy thousand microglia from both aged controls and Alzheimer patients have been analysed for expression of Iba1, TMEM119 and P2RY12 on a single-cell level. For all markers, we studied the overlap and differences in expression patterns and the effect of proximity to β-amyloid plaques. We found no difference in absolute microglia numbers between control and Alzheimer subjects, but the prevalence of specific combinations of markers (phenotypes) differed greatly. In controls, the majority of microglia expressed all three markers. In Alzheimer patients, a significant loss of TMEM119+-phenotypes was observed, independent of the presence of β-amyloid plaques in its proximity. Contrary, phenotypes showing loss of P2RY12, but consistent Iba1 expression were increasingly prevalent around β-amyloid plaques. No morphological features were conclusively associated with loss or gain of any of the markers or any of the identified phenotypes. All in all, none of the three markers were expressed by all microglia, nor can be wholly regarded as a pan- or homeostatic marker, and preferential phenotypes were observed depending on the surrounding pathological or homeostatic environment. This work could help select and interpret microglia markers in previous and future studies.Comp Graphics & Visualisatio

Co-expression Patterns between <i>ATN1</i> and <i>ATXN2</i> Coincide with Brain Regions Affected in Huntington’s Disease

Cytosine-adenine-guanine (CAG) repeat expansions in the coding regions of nine polyglutamine (polyQ) genes (HTT, ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, ATN1, AR, and TBP) are the cause of several neurodegenerative diseases including Huntington’s disease (HD), six different spinocerebellar ataxias (SCAs), dentatorubral-pallidoluysian atrophy, and spinobulbar muscular atrophy. The expanded CAG repeat length in the causative gene is negatively related to the age-at-onset (AAO) of clinical symptoms. In addition to the expanded CAG repeat length in the causative gene, the normal CAG repeats in the other polyQ genes can affect the AAO, suggesting functional interactions between the polyQ genes. However, there is no detailed assessment of the relationships among polyQ genes in pathologically relevant brain regions. We used gene co-expression analysis to study the functional relationships among polyQ genes in different brain regions using the Allen Human Brain Atlas (AHBA), a spatial map of gene expression in the healthy brain. We constructed co-expression networks for seven anatomical brain structures, as well as a region showing a specific pattern of atrophy in HD patients detected by magnetic resonance imaging (MRI) of the brain. In this HD-associated region, we found that ATN1 and ATXN2 were co-expressed and shared co-expression partners which were enriched for DNA repair genes. We observed a similar co-expression pattern in the frontal lobe, parietal lobe, and striatum in which this relation was most pronounced. Given that the co-expression patterns for these anatomical structures were similar to those for the HD-associated region, our results suggest that their disruption is likely involved in HD pathology. Moreover, ATN1 and ATXN2 also shared many co-expressed genes with HTT, the causative gene of HD, across the brain. Although this triangular relationship among these three polyQ genes may also be dysregulated in other polyQ diseases, stronger co-expression patterns between ATN1 and ATXN2 observed in the HD-associated region, especially in the striatum, may be more specific to HD.Pattern Recognition and Bioinformatic