Short title + project description: Comparative single-cell analysis of macrophages and macrophage subtypes in atherosclerotic mice and humans

Atherosclerosis is a lipid-driven inflammatory disease affecting more than 500 million people yearly. It is characterized by plaque formation within the arteries that can lead to blocked arteries and strokes. Risk factors include a high concentration of LDL and a low concentration of HDL. Additionally, excess LDL is prone to oxidation in the arteries, which can attract monocytes that differentiate into macrophages that can either be atheroprotective or atherogenic, depending on their phenotype. Currently, there are suggested to be six distinct macrophage subtypes: resident-like macrophages, inflammatory macrophages, LAMs, iLAMs, IFNIC macrophages, and proliferating macrophages. To study macrophage heterogeneity, mouse models are used due to the ability to study atherosclerosis in a controlled environment and reduce costs. Among different genetically modified mouse models, ApoE-/- and LDLR-/- are primarily used to study macrophage heterogeneity. Tissue samples from these models can be analyzed using scRNA-seq. However, not all findings using mouse models are translatable to humans. The aim of this study was to analyze and compare transcriptional differences between atherosclerotic mouse and human macrophages to evaluate the translational value of using mouse as a model organism in human atherosclerosis research. To achieve this, atherosclerotic plaque samples were collected and analyzed using a well-established scRNA-seq workflow. Analysis revealed that the resident-like macrophages, inflammatory macrophages, and LAMs were largely conserved across species. In contrast, the iLAMs, and IFNIC macrophages showed several species-specific differentially expressed genes. Additionally, the proliferating macrophages were not found in the mouse macrophage populations. Despite several methodological limitations, these findings suggest that mouse models show promising translatability of macrophage findings to humans in atherosclerosis research.

Main contact: Philip Ahmadzada

Team: The Bioinformatics Laboratory

Pre-processing the data.: In other words, executing a quality control, normalization, feature selection and performing a dimensionality reduction.

Integration of datasets: Integration of the two mice datasets and the human dataset

Clustering and annotating: Clustering and annotation of the integrated datasets.

Dependencies:

How to execute the code: