Mitochondrial Effects on the Physiological Characteristics of <i>Lentinula edodes</i>

Kim, Minseek; Yang, Seong-Hyeok; Han, Hui-Gang; Kim, Eunbi; Kim, Sinil; Oh, Youngjae; Ro, Hyeon Su

doi:10.1080/12298093.2022.2138226

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Single-cell RNA sequencing (scRNA-seq) is capable of assessing the amount of gene expression within a single cell, not only to detect rare or low abundance cell populations, but also to label previously unknown cell types or subtypes [12,13]. In addition to this, scRNA-seq could help researchers to understand cell-to-cell information transfer, thus enabling the exploration of pathological mechanisms of diseases and the identification of new diagnostic markers or new therapeutic targets at the single-cell level [14][15][16]. For example, Li et al noted in their study that scRNA-seq could be used to identify transcriptional changes and levels of individual proteins that may contribute to the development of emphysema in a cell-type specific manner [17].…”

Section: Introductionmentioning

confidence: 99%

Potential marker genes for chronic obstructive pulmonary disease revealed based on single-cell sequencing and Mendelian randomization analysis

Sun,

Zhou,

Han

et al. 2024

Aging

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Background: Progress is being made in the prevention and treatment of chronic obstructive pulmonary disease (COPD), but it is still unsatisfactory. With the development of genetic technology, validated genetic information can better explain COPD. Objective: The study utilized scRNA-seq and Mendelian randomization analysis of eQTLs to identify crucial genes and potential mechanistic pathways underlying COPD pathogenesis. Mehods: Single-cell sequencing data were used to identify marker genes for immune cells in the COPD process. Data on eQTLs for immune cell marker genes were obtained from the eQTLGen consortium. To estimate the causal effect of marker genes on COPD, we selected an independent cohort (ukb-b-16751) derived from the UK Biobank database for two-sample Mendelian randomization analysis. Subsequently, we performed immune infiltration analysis, gene set enrichment analysis (GSEA), and co-expression network analysis on the key genes. Results: The 154 immune cell-associated marker genes identified were mainly involved in pathways such as vacuolar cleavage, positive regulation of immune response and regulation of cell activation. Mendelian randomization analysis screened four pairs of marker genes (GZMH, COTL1, CSTA and CD14) were causally associated with COPD. These four key genes were significantly associated with immune cells. In addition, we have identified potential transcription factors associated with these key genes using the Cistrome database, thus contributing to a deeper understanding of the regulatory network of these gene expressions. Conclusions: This eQTLs Mendelian randomization study identified four key genes (GZMH, COTL1, CSTA, and CD14) causally associated with COPD, providing new insights for prevention and treatment of COPD.

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Section: Introductionmentioning

confidence: 99%

Potential marker genes for chronic obstructive pulmonary disease revealed based on single-cell sequencing and Mendelian randomization analysis

Sun,

Zhou,

Han

et al. 2024

Aging

View full text Add to dashboard Cite

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“…Particularly, scRNA-seq can help us understand cell–cell communications ( 6 , 7 , 8 ). Therefore, it provides a new way to explore physiological processes and pathological mechanisms of diseases at the single-cell level and identify new diagnostic markers or new therapeutic targets ( 9 , 10 , 11 ).…”

Section: Introductionmentioning

confidence: 99%

scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data

Wang,

Xie,

Liu

et al. 2023

Life Sci. Alliance

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Single-cell RNA sequencing (scRNA-seq) enables researchers to reveal previously unknown cell heterogeneity and functional diversity, which is impossible with bulk RNA sequencing. Clustering approaches are widely used for analyzing scRNA-seq data and identifying cell types and states. In the past few years, various advanced computational strategies emerged. However, the low generalization and high computational cost are the main bottlenecks of existing methods. In this study, we established a novel computational framework, scFseCluster, for scRNA-seq clustering analysis. scFseCluster incorporates a metaheuristic algorithm (Feature Selection based on Quantum Squirrel Search Algorithm) to extract the optimal gene set, which largely guarantees the performance of cell clustering. We conducted simulation experiments in several aspects to verify the performance of the proposed approach. scFseCluster performed very well on eight benchmark scRNA-seq datasets because of the optimal gene sets obtained using the Feature Selection based on Quantum Squirrel Search Algorithm. The comparative study demonstrated the significant advantages of scFseCluster over seven State-of-the-Art algorithms. In addition, our analysis shows that feature selection on high-variable genes can significantly improve clustering performance. In conclusion, our study demonstrates that scFseCluster is a highly versatile tool for enhancing scRNA-seq data clustering analysis.

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Mitochondrial Effects on the Physiological Characteristics of Lentinula edodes

Cited by 2 publications

References 23 publications

Potential marker genes for chronic obstructive pulmonary disease revealed based on single-cell sequencing and Mendelian randomization analysis

Potential marker genes for chronic obstructive pulmonary disease revealed based on single-cell sequencing and Mendelian randomization analysis

scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data

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