Baumgart, S. | bioRxiv Science

bioRxiv2023

Encompassing view of spatial and single-cell RNA-seq renews the role of the microvasculature in human atherosclerosis

Atherosclerosis is a pervasive contributor to cardiovascular diseases including ischemic heart disease and stroke. Despite the advance and success of effective lipid lowering-therapies and hypertensive agents, the residual risk of an atherosclerotic event remains high and improving disease understanding and development of novel therapeutic strategies has proven to be challenging. This is largely due to the complexity of atherosclerosis with a spatial interplay of multiple cell types within the vascular wall. Here, we generated an integrative high-resolution map of human atherosclerotic plaques by combining single-cell RNA-seq from multiple studies and novel spatial transcriptomics data from 12 human specimens to gain insights into disease mechanisms. Comparative analyses revealed cell-type and atherosclerosis-specific expression changes and associated alterations in cell-cell communication. We highlight the possible recruitment of lymphocytes via different endothelial cells of the vasa vasorum, the migration of vascular smooth muscle cells towards the lumen to become fibromyocytes, and cell-cell communication in the plaque, indicating an intricate cellular interplay within the adventitia and the subendothelial space in human atherosclerosis.

bioinformatics↗

bioRxiv2023

A Benchmark of state-of-the-art Deconvolution Methods in Spatial Transcriptomics: Insights from Cardiovascular Disease and Chronic Kidney Disease

A major challenge in sequencing based spatial transcriptomics (ST) is resolution limitations. Tissue sections are divided into hundreds-to thousands of spots, where each spot invariably contains a mixture of cell types. Methods have been developed to deconvolute the mixed transcriptional signal into its constituents. While ST is becoming essential for drug discovery especially in Cardiometabolic diseases, to date no deconvolution benchmark has been performed on these types of tissues and diseases. However, the three methods Cell2location, RCTD and spatialDWLS have previously been shown to perform well in brain tissue and simulated data. Here, we compare these methods to assess best performance when using human data from Cardiovascular Disease (CVD) data and Chronic Kidney Disease (CKD) from patients at different pathological states, evaluated using expert annotation. In this benchmark, we found that all three methods performed comparably well in deconvoluting verifiable cell types including smooth muscle cells and macrophages in vascular samples and podocytes in kidney samples. RCTD shows the best performance accuracy scores in CVD samples while Cell2location on average achieved the highest performance across test experiments. While all three methods had similar accuracies Cell2location need less reference data to converge at the expense of higher computational intensity. Finally, we also report that RCTD has the fastest computational time and the simplest workflow requiring fewer computational dependencies. In conclusion, we find that each method has particular advantages, and the optimal choice depends on the use case.

bioinformatics↗