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bioRxiv2025

Identification of pyroptosis-related diagnostic model relate to immune infiltration in osteoarthritis

BackgroundOsteoarthritis (OA) is a common chronic diseases which were associated with aging and progressive joint dysfunction. Pyroptosis, a novel mode of cell death. The effect of pyroptosis on osteoarthritis (OA) progression is still in the exploratory stage, and the specific mechanism is unclear. MethodsOA data sets were obtained from the GEO databases website. Bioinformatics analysis was conducted to identify pyroptosis-related genes (PRGs), construct a pyroptosis-related diagnostic model, and identify subtypes. We established the random forest (RF) model as well as a nomogram of differentially expressed PRGs. The association between immunity and pyroptosis was comprehensively explored. Finally, the expression levels of 3 OA characteristic genes were verified by qRT-PCR and immunohistochemistry. ResultsSeven differentially expressed PRGs (CASP3, GPX4, NOD1, TIRAP, SCAF11, CASP4, and CASP9) were obtained by differential analysis to establish RF models, and three important OA genes (CASP9, TIRAP, and SCAF11) were obtained. ROC curve proved that the established model had high diagnostic accuracy. Based on the above three important PRGs, a high-precision nomogram model is constructed. Two PRG models are determined by consensus clustering method. The enrichment degree of immune cells in cluster A was higher than that in cluster B, suggesting that cluster A might be related to the occurrence and development of OA. GPX4 was found to be positively correlated with higher immune infiltration. Finally, the expression levels of 3 OA characteristic genes were verified by qRT-PCR and immunohistochemistry. ConclusionWe constructed an effective prognostic model according to PRGs in OA and integrated explored the association between pyroptosis and immunity-related elements. This article provides a strong point for investigating directed immunotherapy methods with novelty for OA.

immunology↗

bioRxiv2019

scCAT-seq：single-cell identification and quantification of mRNA isoforms by cost-effective short-read sequencing of cap and tail

The differences in transcription start sites (TSS) and transcription end sites (TES) among gene isoforms can affect the stability, localization, and translation efficiency of mRNA. Isoforms also allow a single gene different functions across various tissues and cells However, methods for efficient genome-wide identification and quantification of RNA isoforms in single cells are still lacking. Here, we introduce single cell Cap And Tail sequencing (scCAT-seq). In conjunction with a novel machine learning algorithm developed for TSS/TES characterization, scCAT-seq can demarcate transcript boundaries of RNA transcripts, providing an unprecedented way to identify and quantify single-cell full-length RNA isoforms based on short-read sequencing. Compared with existing long-read sequencing methods, scCAT-seq has higher efficiency with lower cost. Using scCAT-seq, we identified hundreds of previously uncharacterized full-length transcripts and thousands of alternative transcripts for known genes, quantitatively revealed cell-type specific isoforms with alternative TSSs/TESs in dorsal root ganglion (DRG) neurons, mature oocytes and ageing oocytes, and generated the first atlas of the non-human primate cornea. The approach described here can be widely adapted to other short-read or long-read methods to improve accuracy and efficiency in assessing RNA isoform dynamics among single cells.

genomics↗