A cap 0-dependent mRNA capture method to analyze the yeast transcriptome

Integrated analysis of single-cell RNA-seq and chipset data unravels PANoptosis-related genes in sepsis

Background

The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and development. Nevertheless, the role of PANoptosis in sepsis has not been fully elucidated.

Methods

We obtained Sepsis samples and scRNA-seq data from the GEO database. PANoptosis-related genes were subjected to consensus clustering and functional enrichment analysis, followed by identification of differentially expressed genes and calculation of the PANoptosis score. A PANoptosis-based prognostic model was developed. In vitro experiments were performed to verify distinct PANoptosis-related genes. An external scRNA-seq dataset was used to verify cellular localization.

Results

Unsupervised clustering analysis using 16 PANoptosis-related genes identified three subtypes of sepsis. Kaplan-Meier analysis showed significant differences in patient survival among the subtypes, with different immune infiltration levels. Differential analysis of the subtypes identified 48 DEGs. Boruta algorithm PCA analysis identified 16 DEGs as PANoptosis-related signature genes. We developed PANscore based on these signature genes, which can distinguish different PANoptosis and clinical characteristics and may serve as a potential biomarker. Single-cell sequencing analysis identified six cell types, with high PANscore clustering relatively in B cells, and low PANscore in CD16+ and CD14+ monocytes and Megakaryocyte progenitors. ZBP1, XAF1, IFI44L, SOCS1, and PARP14 were relatively higher in cells with high PANscore.

Conclusion

We developed a machine learning based Boruta algorithm for profiling PANoptosis related subgroups with in predicting survival and clinical features in the sepsis.

Transcriptome-Wide Analysis of mRNA Adenylation Status in Yeast Using Nanopore Sequencing

There are multiple methods for studying deadenylation, either in vitro or in vivo, which allow for observation of mRNA abundance or poly(A) tail dynamics. However, direct RNA sequencing using the Oxford Nanopore Technologies (ONT) platform makes it possible to conduct transcriptome-wide analyses at the single-molecule level without the PCR bias introduced by other methods. In this chapter, we provide a protocol to measure both RNA levels and poly(A)-tail lengths in the yeast Saccharomyces cerevisiae using ONT.