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Abstract Background Disease-associated microglia (DAMs), that surround beta-amyloid plaques, represent a transcriptionally-distinct microglial profile in Alzheimer’s disease (AD). Activation of DAMs is dependent on triggering receptor expressed on myeloid cells 2 (TREM2) in mouse models and the AD TREM2-R47H risk variant reduces microglial activation and plaque association in human carriers. Interestingly, TREM2 has also been identified as a microglial lipid-sensor, and recent data indicates lipid droplet accumulation in aged microglia, that is in turn associated with a dysfunctional proinflammatory phenotype. However, whether lipid droplets (LDs) are present in human microglia in AD and how the R47H mutation affects this remains unknown. Methods To determine the impact of the TREM2 R47H mutation on human microglial function in vivo, we transplanted wild-type and isogenic TREM2-R47H iPSC-derived microglial progenitors into our recently developed chimeric Alzheimer mouse model. At 7 months of age scRNA-seq and histological analyses were performed. Results Here we report that the transcriptome of human wild-type TREM2 and isogenic TREM2-R47H DAM xenografted microglia (xMGs), isolated from chimeric AD mice, closely resembles that of human atherosclerotic foam cells. In addition, much like foam cells, plaque-bound xMGs are highly enriched in lipid droplets. Somewhat surprisingly and in contrast to a recent in vitro study, TREM2-R47H mutant xMGs exhibit an overall reduction in the accumulation of lipid droplets in vivo. Notably, TREM2-R47H xMGs also show overall reduced reactivity to plaques, including diminished plaque-proximity, reduced CD9 expression, and lower secretion of plaque-associated APOE. Conclusions Altogether, these results indicate lipid droplet accumulation occurs in human DAM xMGs in AD, but is reduced in TREM2-R47H DAM xMGs, as it occurs secondary to TREM2-mediated changes in plaque proximity and reactivity.

Abstract Adipogenesis is essential in in vitro experimentation to assess differentiation capability of stem cells, and therefore, its accurate measurement is important. Quantitative analysis of adipogenic levels, however, is challenging and often susceptible to errors due to non-specific reading or manual estimation by observers. To this end, we developed a novel adipocyte quantification algorithm, named Fast Adipogenesis Tracking System (FATS), based on computer vision libraries. The FATS algorithm is versatile and capable of accurately detecting and quantifying percentage of cells undergoing adipogenic and browning differentiation even under difficult conditions such as the presence of large cell clumps or high cell densities. The algorithm was tested on various cell lines including 3T3-L1 cells, adipose-derived mesenchymal stem cells (ASCs), and induced pluripotent stem cell (iPSC)-derived cells. The FATS algorithm is particularly useful for adipogenic measurement of embryoid bodies derived from pluripotent stem cells and was capable of accurately distinguishing adipogenic cells from false-positive stains. We then demonstrate the effectiveness of the FATS algorithm for screening of nuclear receptor ligands that affect adipogenesis in the high-throughput manner. Together, the FATS offer a universal and automated image-based method to quantify adipocyte differentiation of different cell lines in both standard and high-throughput workflows.