Single-cell RNA sequencing allows researchers to profile gene expression at the level of individual cells. Recently, Louis Saddic and colleagues at Cedars-Sinai developed a high-throughput single-cell proteomics by mass spectrometry, or SCP-MS, workflow. The study was published in Molecular & Cellular Proteomics and used a mouse model of Marfan syndrome, or MFS, which is characterized by a mutation in the fibrillin 1 gene. This leads to dysregulation of elasticity in the vasculature and cardiovascular complications. The group found that, using their single-cell protein quantification and quality control methods, they were able to optimize sample preparation to prevent the production of contaminants and stress protein signals during cell dissociation compared to bulk proteomic analysis.
Illustration of the human heart and circulatory system. The cardiovascular tissues affected by Marfan syndrome can become abnormally elastic, increasing the risk of cardiovascular complications.
When cells were separated into subtypes, they were confirmed to closely match well-established phenotypes that have been previously identified in the aorta, further validating the novel methodology. Researchers noticed a large quantity of differentially expressed proteins, or DEPs, in endothelial cells, or ECs. They found that many of these DEPs are associated with an endothelial–mesenchymal transition, marked by a reduction of EC markers and enhanced mesenchymal markers. This EC phenotype was corroborated with previously established transcriptional datasets and further validated using spatial proteomics.
Next, the group sought to determine if multiomic analysis of smooth muscle cells, or SMCs, would better identify subcellular phenotypes. Similar to the previous comparison, there appeared to be minimal overlap between transcriptional and proteomic datasets within this specific cell type. However, when performing a Kyoto Encyclopedia of Genes and Genomes analysis on clusters with shared RNA transcripts and proteins between datasets, the group detected key differences between control and MFS mice.
While transcriptional datasets are an integral component of cellular profiling and functional characterization of tissue, this new methodology offers a unique perspective on disease progression and cellular arrangement within a complex tissue. This work additionally opens the door to SCP-MS integration with other omic technologies as an additional metric of tissue analysis for more complex tissues in other diseases.