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Biology subjects

Pavelko, V.

Publications and source records attributed to Pavelko, V..

2 recordsLinked to original sources

Targeted proteomics informed by long-read RNA-sequencing reveals protein isoform expression associated with bone mineral density

The expanding catalog of sQTLs from projects like GTEx has established that genetic risk for complex traits is frequently mediated through RNA splicing. Yet it is generally unknown whether disease-associated splice events produce stable, distinct protein isoforms and whether they are functionally consequent. Even the deepest shotgun proteomics efforts only capture a minority of annotated splice events; isoform-specific peptides are systematically under-sampled by nature of being lower abundance and intrinsically harder to detect. Here, we implement a genetics-guided targeted mass spectrometry (MS) strategy to detect splice-derived protein isoforms prioritized by sQTL colocalization. Using internal standard parallel reaction monitoring (IS-PRM), we targeted 288 peptides corresponding to 166 putative BMD-associated isoforms in differentiating osteoprogenitor cells and patient-derived primary mesenchymal stem cells (MSCs). Targeted acquisition increased recovery of isoform-informative peptides relative to conventional DDA, rescued splice events that were undetected or sparsely sampled under global acquisition, and confirmed protein-level expression of genetically prioritized isoforms in clinical material. Together, this work establishes that targeted MS is a useful approach to support validation of protein expression in studies of the genetics of splicing.

systems biology↗

Quantitative isoform profiling using deep coverage long-read RNA sequencing across early endothelial differentiation

PacBio long-read RNA sequencing resolves transcripts with greater clarity than short-read technologies, yet its quantitative performance remains under-evaluated at scale. Here, we benchmark the high-throughput PacBio Kinnex platform against Illumina short-read RNA-seq using matched, deeply sequenced datasets across a time course of endothelial cell differentiation. Compared to Illumina, Kin-nex achieved comparable gene-level quantification and more accurate transcript discovery and transcript quantification. While Illumina detected more transcripts overall, many reflected potentially unstable or ambiguous estimates in complex genes. Kinnex largely avoids these issues, producing more reliable differential transcript expression (DTE) calls, despite a mild bias against short transcripts (shorter than 1.25 kb). When correcting Illumina for inferential variability, Kinnex and Illumina quantifications were highly concordant, demonstrating equivalent performance. We also benchmarked long-read tools, nominating Oarfish as the most efficient for our Kinnex data. Together, our results establish Kinnex as a reliable platform for full-length transcript quantification.

bioinformatics↗