SnRNA-Seq analysis reveals ten hub genes associated with alveolar epithelial cell injury during pulmonary acute respiratory distress syndrome

snCED-seq: high-fidelity cryogenic enzymatic dissociation of nuclei for single-nucleus RNA-seq of FFPE tissues

Recent advances have shown that single-nucleus RNA sequencing (snRNA-seq) can be applied to formalin-fixed, paraffin-embedded (FFPE) tissues, opening avenues for transcriptomic analysis of archived specimens. Yet, isolating intact nuclei remains difficult due to RNA cross-linking. Here, we introduce a cryogenic enzymatic dissociation (CED) strategy for rapid, high-yield and fidelity nuclei extraction from FFPE samples and validate its utility with snRandom-seq (snCED-seq) using male C57/BL6 mice. Compared with conventional approaches, CED delivers a tenfold increase in nuclei yield with significantly reduced hands-on time, while minimizing secondary RNA degradation and preserving intranuclear transcripts. snCED-seq enhances gene detection sensitivity, lowers mitochondrial and ribosomal contamination, and increases overall gene expression quantification. In Alzheimer's disease studies, it distinguished two astrocyte subpopulations, microglia, and oligodendrocytes, revealing cellular heterogeneity. Additionally, snCED-seq identify major cell types in a single 50 μm FFPE human lung section. Our results demonstrate that snCED-seq is robust for FFPE specimens and poised to enable multi-omics analyses of clinical samples.

Single nuclear RNA sequencing and analysis of basal cells in pulmonary acute respiratory distress syndrome

OBJECTIVE

This study aims to find the gene expression profile specifically in basal cells from pulmonary acute respiratory distress syndrome (ARDSp) patients using single-cell level analysis.

METHODS

Single nuclear RNA sequencing (snRNA-seq) data of lung samples, including 18 ARDSp participants and 7 healthy participants, were sourced from the GEO database (GSE171524). The differentially expressed genes (DEGs) were screened by | log2FC | >1 and P < 0.05. Functional enrichment was constructed via Gene Ontology (GO) analysis. Pathway enrichment was conducted via Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The protein-protein interaction (PPI) network of the DEGs was performed via the STRING database. Cytoscape software was employed to find hub genes. The hub genes were sequenced and validated via data set after constructing the rat model of ARDSp.

RESULTS

Using DESeq2 package, 299 genes were disclosed to be downregulated, while 228 were upregulated in ARDSp participants. GO analysis disclosed DEGs were enriched in processes like actin filament organization, regulation of small GTPase-mediated signal transduction, response to unfolded protein, wound healing, and response to oxygen levels. Meanwhile, KEGG analysis disclosed DEGs were involved in protein digestion and absorption, Th17 cell differentiation, iron death, and other biological effects. Ten hub genes, including FN1, HIF1A, HSP90AA1, SMAD3, FOS, CDKN2A, COL1A1, HSPA8, FLNA, and NFKBIA were highlighted based on their network centrality and biological significance. HIF1A, HSPA8, NFKBIA, and CDKN2A were differentially expressed in the validation dataset.

CONCLUSIONS

Basal cells in ARDSp exhibit significant changes in gene expression, with ten hub genes identified. Among them, four (HIF1A, HSPA8, NFKBIA, CDKN2A) were validated experimentally using RNA-Seq data from an ARDSp rat model. This study emphasizes the role of basal cells in ARDSp, highlighting the altered gene networks involved in repair and inflammatory responses, providing potential targets for further therapeutic exploration. These findings suggest that alterations in these hub genes may be crucial to basal cell-driven inflammatory and reparative responses in ARDSp.

Investigation of the Mechanism of SEMA5A and Its Associated Autophagy-Related Genes in Gastric Cancer

Purpose

Semaphorin 5A (SEMA5A) and autophagy-related genes (ARGs) are pivotal in the pathogenesis of gastric cancer (GC). However, the potential regulatory role of SEMA5A in autophagy via its associated ARGs and the underlying molecular mechanisms remain unresolved.

Patients and Methods

GC-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify differentially expressed genes (DEGs) between GC and control samples. The intersection of DEGs with ARGs produced candidate genes, which were further analyzed using Spearman correlation with SEMA5A to identify signature genes. Stratification of GC samples based on signature gene expression, followed by Kaplan-Meier survival analysis, identified key genes. Subsequent analyses, including gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint evaluation, were conducted on the key genes and SEMA5A. The mRNA expression level was quantified using real-time quantitative polymerase chain reaction (RT-qPCR).

Results

Ninety candidate genes were identified for Spearman correlation with SEMA5A, revealing TNFSF11, BMP6, ITPR1, and DLC1 with correlation coefficients exceeding 0.3. Survival analysis underscored DLC1 and BMP6 as key genes due to significant prognostic differences. GSEA implicated SEMA5A, BMP6, and DLC1 in the ECM receptor interaction pathway. Immune infiltration analysis indicated a negative correlation of SEMA5A and BMP6 with M1 macrophages, while DLC1 exhibited the strongest association with the immune checkpoint PDCD1LG2 (p < 0.05, cor = 0.43). The mRNA expression level of SEMA5A was significantly upregulated in AGS parental cells compared to GES-1 cells (p < 0.01), whereas DLC1 and BMP6 mRNA levels were markedly downregulated in AGS parental cells relative to GES-1 (p < 0.0001).

Conclusion

ARGs BMP6 and DLC1, associated with SEMA5A, were identified, and their prognostic significance in GC was demonstrated. Additionally, their regulatory mechanisms were further elucidated through immune infiltration analysis and molecular network construction, providing a theoretical foundation for future research on the molecular mechanisms in patients with GC.

Transcriptomic Approaches to Cardiomyocyte-Biomaterial Interactions: A Review

Biomaterials, essential for supporting, enhancing, and repairing damaged tissues, play a critical role in various medical applications. This Review focuses on the interaction of biomaterials and cardiomyocytes, emphasizing the unique significance of transcriptomic approaches in understanding their interactions, which are pivotal in cardiac bioengineering and regenerative medicine. Transcriptomic approaches serve as powerful tools to investigate how cardiomyocytes respond to biomaterials, shedding light on the gene expression patterns, regulatory pathways, and cellular processes involved in these interactions. Emerging technologies such as bulk RNA-seq, single-cell RNA-seq, single-nucleus RNA-seq, and spatial transcriptomics offer promising avenues for more precise and in-depth investigations. Longitudinal studies, pathway analyses, and machine learning techniques further improve the ability to explore the complex regulatory mechanisms involved. This review also discusses the challenges and opportunities of utilizing transcriptomic techniques in cardiomyocyte-biomaterial research. Although there are ongoing challenges such as costs, cell size limitation, sample differences, and complex analytical process, there exist exciting prospects in comprehensive gene expression analyses, biomaterial design, cardiac disease treatment, and drug testing. These multimodal methodologies have the capacity to deepen our understanding of the intricate interaction network between cardiomyocytes and biomaterials, potentially revolutionizing cardiac research with the aim of promoting heart health, and they are also promising for studying interactions between biomaterials and other cell types.

Knockdown of KIF23 alleviates the progression of asthma by inhibiting pyroptosis

BACKGROUND

Asthma is a chronic disease affecting the lower respiratory tract, which can lead to death in severe cases. The cause of asthma is not fully known, so exploring its potential mechanism is necessary for the targeted therapy of asthma.

METHOD

Asthma mouse model was established with ovalbumin (OVA). H&E staining, immunohistochemistry and ELISA were used to detect the inflammatory response in asthma. Transcriptome sequencing was performed to screen differentially expressed genes (DEGs). The role of KIF23 silencing in cell viability, proliferation and apoptosis was explored by cell counting kit-8, EdU assay and flow cytometry. Effects of KIF23 knockdown on inflammation, oxidative stress and pyroptosis were detected by ELISA and western blot. After screening KIF23-related signalling pathways, the effect of KIF23 on p53 signalling pathway was explored by western blot.

RESULTS

In the asthma model, the levels of caspase-3, IgG in serum and inflammatory factors (interleukin (IL)-1β, KC and tumour necrosis factor (TNF)-α) in serum and bronchoalveolar lavage fluid were increased. Transcriptome sequencing showed that there were 352 DEGs in the asthma model, and 7 hub genes including KIF23 were identified. Knockdown of KIF23 increased cell proliferation and inhibited apoptosis, inflammation and pyroptosis of BEAS-2B cells induced by IL-13 in vitro. In vivo experiments verified that knockdown of KIF23 inhibited oxidative stress, inflammation and pyroptosis to alleviate OVA-induced asthma mice. In addition, p53 signalling pathway was suppressed by KIF23 knockdown.

CONCLUSION

Knockdown of KIF23 alleviated the progression of asthma by suppressing pyroptosis and inhibited p53 signalling pathway.