Aguti, S. | Search bioRxiv

bioRxiv2026

PDGFRα-directed fibroblast-homing peptides for targeted delivery of nucleic acid therapeutics

Efficient and cell type-specific delivery remains a major barrier to the clinical translation of nucleic acid therapeutics (NATs), particularly for fibroblast-driven diseases in which target cells are poorly accessible in vivo. By integrating single-cell transcriptomics with cell-surface protein profiling, we identified platelet-derived growth factor receptor alpha (PDGFR) as a fibroblast-enriched surface receptor. A panel of PDGFA-derived short peptides was designed and screened across multiple human and mouse cell types, leading to the identification of two fibroblast-homing peptides, Pep3 and Pep5, which displayed rapid and preferential uptake in fibroblasts. PDGFR; knockdown reduced peptide internalisation as measured by live-cell imaging, confirming receptor-mediated uptake. To evaluate their potential as delivery vehicles for NATs, a fibroblast-based cellular model of collagen VI-related congenital muscular dystrophy (COL6-CMD) was used for further validation. Lead peptides were conjugated to a splice-switching phosphorodiamidate morpholino oligomer (PMO) targeting the recurrent COL6A1 c.930+189C>T variant. Peptide-PMO conjugates retained exon-skipping activity and Pep3-PMO enhanced collagen VI protein deposition in patient-derived fibroblasts following repeated treatment compared to unconjugated PMO. Transcriptomic profiling demonstrated no activation of inflammatory, oncogenic, or PDGFR; receptor-associated signalling pathways following peptide-PMO treatment. Collectively, these findings establish PDGFR-targeting fibroblast-peptides as a promising platform for selective delivery of NATs to fibroblasts.

molecular biology↗

bioRxiv2021

Detection of pathogenic splicing events from RNA-sequencing data using dasper

Although next-generation sequencing technologies have accelerated the discovery of novel gene-to-disease associations, many patients with suspected Mendelian diseases still leave the clinic without a genetic diagnosis. An estimated one third of these patients will have disorders caused by mutations impacting splicing. RNA-sequencing has been shown to be a promising diagnostic tool, however few methods have been developed to integrate RNA-sequencing data into the diagnostic pipeline. Here, we introduce dasper, an R/Bioconductor package that improves upon existing tools for detecting aberrant splicing by using machine learning to incorporate disruptions in exon-exon junction counts as well as coverage. dasper is designed for diagnostics, providing a rank-based report of how aberrant each splicing event looks, as well as including visualization functionality to facilitate interpretation. We validate dasper using 16 patient-derived fibroblast cell lines harbouring pathogenic variants known to impact splicing. We find that dasper is able to detect pathogenic splicing events with greater accuracy than existing LeafCutterMD or z-score approaches. Furthermore, by only applying a broad OMIM gene filter (without any variant-level filters), dasper is able to detect pathogenic splicing events within the top 10 most aberrant identified for each patient. Since using publicly available control data minimises costs associated with incorporating RNA-sequencing into diagnostic pipelines, we also investigate the use of 504 GTEx fibroblast samples as controls. We find that dasper leverages publicly available data effectively, ranking pathogenic splicing events in the top 25. Thus, we believe dasper can increase diagnostic yield for a pathogenic splicing variants and enable the efficient implementation of RNA-sequencing for diagnostics in clinical laboratories.

bioinformatics↗