Network-community analysis of cellular senescence (2406.13889v1)

Abstract: Most cellular phenotypes are genetically complex. Identifying the set of genes that are most closely associated with a specific cellular state is still an open question in many cases. Here we study the transcriptional profile of cellular senescence using a combination of network-based approaches, which include eigenvector centrality feature selection and community detection. We apply our method to cell-type-resolved RNA sequencing data obtained from injured muscle tissue in mice. The analysis identifies some genetic markers consistent with previous findings, and other previously unidentified ones, which are validated with previously published single-cell RNA sequencing data in a different type of tissue. The key identified genes, both those previously known and the newly identified ones, are transcriptional targets of factors known to be associated with established hallmarks of senescence, and can thus be interpreted as molecular correlates of such hallmarks. The method proposed here could be applied to any complex cellular phenotype even when only bulk RNA sequencing is available, provided the data is resolved by cell type.

• The paper introduces a network-community method that identifies a refined gene set to accurately distinguish senescent cell states.
• It employs RNA sequencing data and community detection via the Louvain algorithm to enhance gene selection and analysis.
• The study highlights key markers such as Lncpint and Cd59a, suggesting broader applications in aging research and therapeutic interventions.

Network-Community Analysis of Cellular Senescence

This paper presents a network-community approach to understanding cellular senescence's transcriptional landscape through analyzing RNA sequencing data from mouse muscle tissue. This technique, which combines network-based feature selection with community detection, yields insights into genes that distinguish senescent from non-senescent cell states. This methodology provides a refined filtering mechanism potentially applicable to other complex cellular phenotypes beyond senescence.

Background and Motivation

Cellular senescence, a stable and irreversible growth arrest state, results from unresolved molecular damage and is characterized by a secretive phenotype (SASP). It has implications in aging and age-related diseases. Despite its significance, identifying clear transcriptional markers for senescence remains a challenge due to its complex phenotype. This study uses network community analysis to enhance gene selection for profiling senescence, potentially distinguishing senescent cells by their gene expression patterns.

Methodology

Experimental Data

The study involves a comprehensive analysis of RNA sequencing data from cell types (macrophages, fibro-adipogenic progenitors, and satellite cells) obtained from the muscle tissue of young and geriatric mice. The dataset, consisting of 108 samples characterized by 46,078 genes, includes both injured and non-injured tissue, allowing for comparison across senescent, non-senescent, and basal states.

Data Filtering and Analysis

Initial data filtering reduced technical noise, resulting in a focused set of 28,603 genes. PCA revealed that this gene set effectively distinguished cell types but not senescent cell states. A subsequent Kolmogorov-Smirnov test, refining the number to 142 genes, improved state differentiation but required further refinement.

Centralized Feature Selection and Community Detection

Using the ECFS algorithm, the researchers ranked genes by their potential to differentiate senescent profiles, selecting the top 10 based on eigenvector centrality scores. This selection was pivotal in distinguishing senescent phenotypes in network analysis, where nodes represented cellular conditions linked by gene expression correlations. The Louvain algorithm further delineated communities correlating strongly with specific cell states.

Results

The optimized set of 10 genes demonstrated significant capability in distinguishing senescent cells, achieving an optimal separation entropy score. This analysis was validated through permutation tests, ensuring consistent separability beyond chance events. Notably, genes like Lncpint and Fabp3 featured prominently, consistent with prior reports linking them to cellular aging and SASP.

Discussion and Validation

Further analysis confirmed the relevance of identified genes, such as Lncpint's pivotal role in DNA damage response and senescence. Their transcription factors intersected with known senescence pathways, underpinning their biological significance. External validation using single-cell RNA-seq data from liver and kidney tissues underscored the newly identified gene Cd59a's potential as a senescence marker, reflecting results consistent across different tissues.

Conclusion

The paper illustrates a robust methodology for identifying state-specific genetic markers in complex cell phenotypes like senescence. By leveraging network-theory concepts, the approach enhances understanding of underlying transcriptional regulation, offering a scalable framework for similar analyses in other contexts. The identified gene set not only advanced the senescent phenotype's resolution but also provided novel insights into its molecular hallmarks, promising broader applications in aging research and therapeutic interventions.