Simultaneous RNA and protein profiling using a TurboID proximity‐labeling strategy

Abstract

AbstractBackgroundBiologically relevant insights into cellular disease mechanisms of neurons and glia can be obtained by complimentary molecular profiling of the transcriptome and proteome of these cells. While mutually exclusive pipelines are available, the information surveyed is often from different samples leading to poor correlation between the transcriptome and proteome. Our goal was to develop a method for concomitant cell type‐specific analyses of RNA and protein.MethodWe utilized a proximity‐labeling strategy that uses the biotin ligase, TurboID, to efficiently label the proteome, in a mouse microglial BV2 cell line. We created a stable BV2‐TurboID cell line that expresses TurboID fused to a nuclear export sequence. Biotin treatment of BV2‐TurboID cells resulted in robust biotinylation of the cellular proteome as confirmed by Western blot and by label‐free quantitation mass spectrometry (LFQ‐MS) of biotinylated proteins enriched with streptavidin beads. LFQ‐MS revealed that TurboID biotinylates several RNA‐binding proteins (RBPs) including ribosomal units, suggesting that transcripts associated with RBPs may also be pulled down simultaneously with proteins. To test this, we homogenized BV2‐Turbo and control BV2 cells, enriched biotinylated proteins with streptavidin beads while maintaining RNA‐protein interactions, and then eluted RNA.ResultQuality control studies showed negligible mRNA from streptavidin pulldowns from control BV2 cells, while BV2‐TurboID pulldowns had larger mRNA yields with high quality. NanoString neuroinflammatory profiling (800 genes) of whole cell RNA from control BV2 and BV2‐TurboID cells and streptavidin pulldowns from both cell types were performed. We observed a 23‐fold higher mRNA yield in the BV2‐TurboID pulldowns compared to control BV2 cells for 550 genes included in the analyses. Transcript abundances from total RNA and BV2‐TurboID pulldowns were highly comparable (R2=0.97; no differentially expressed genes) with equivalent abundance of microglial genes (e.g., Spp1 and Apoe) suggesting a faithful transcriptome capture.ConclusionOur novel TurboID proximity labeling approach can simultaneously capture cell type‐specific transcriptomes and proteomes. We are now validating this method in other cell types using RNA‐sequencing and MS approaches. Once validated, this concurrent RNA and protein profiling approach can be applied to in vivo and ex vivo model systems to investigate the distinct roles brain cell types play in development, aging, and disease.