Xenografted human microglia display diverse transcriptomic states in response to Alzheimer's disease-related amyloid-β pathology.

Microglia are central players in Alzheimer's disease pathology but analyzing microglial states in human brain samples is challenging due to genetic diversity, postmortem delay and admixture of pathologies. To circumvent these issues, here we generated 138,577 single-cell expression profiles of human stem cell-derived microglia xenotransplanted in the brain of the App model of amyloid pathology and wild-type controls. Xenografted human microglia adopt a disease-associated profile similar to that seen in mouse microglia, but display a more pronounced human leukocyte antigen or HLA state, likely related to antigen presentation in response to amyloid plaques.

S100A8-enriched microglia populate the brain of tau-seeded and accelerated aging mice.

Long considered to fluctuate between pro- and anti-inflammatory states, it has now become evident that microglia occupy a variegated phenotypic landscape with relevance to aging and neurodegeneration. However, whether specific microglial subsets converge in or contribute to both processes that eventually affect brain function is less clear. To investigate this, we analyzed microglial heterogeneity in a tauopathy mouse model (K18-seeded P301L) and an accelerated aging model (Senescence-Accelerated Mouse-Prone 8, SAMP8) using cellular indexing of transcriptomes and epitopes by sequencing.

A targeted sequencing extension for transcript genotyping in single-cell transcriptomics.

As no existing methods within the single-cell RNA sequencing repertoire combine genotyping of specific genomic loci with high throughput, we evaluated a straightforward, targeted sequencing approach as an extension to high-throughput droplet-based single-cell RNA sequencing. Overlaying standard gene expression data with transcript level genotype information provides a strategy to study the impact of genetic variants. Here, we describe this targeted sequencing extension, explain how to process the data and evaluate how technical parameters such as amount of input cDNA, number of amplification rounds, and sequencing depth influence the number of transcripts detected.