Bridge, J. E. | Search bioRxiv

bioRxiv2026

A transcriptomic analysis reveals shared and inducer-specific expression patterns of cellular senescence

Cellular senescence is a heterogeneous cell state induced by diverse stressors, including telomere attrition, genotoxic agents, oxidative damage, and inflammation. Despite ongoing efforts to identify conserved senescence biomarkers, it remains unclear whether senescence-inducing stimuli converge at the level of individual genes or broader molecular processes. Here, we profiled transcriptomic changes in human primary lung fibroblasts (IMR-90) driven toward senescence by replicative exhaustion, bleomycin, H2O2, or ionizing radiation under matched, dose- or time-resolved conditions. Across all four senescent inducers, global transcriptomic variation aligned along a shared axis of senescence progression, consistent with established machine learning-based senescence classifiers. However, overlap at the level of individual genes was limited, with most responses being inducer-specific or only partially conserved. In contrast, pathway-level analysis revealed far more consistent enrichment across all conditions, including downregulation of proliferation-associated pathways and activation of stress-related and pro-inflammatory pathways, accompanied by distinct inducer-specific patterns. These results support a hierarchical organization of the senescent transcriptome, in which diverse senescence inducers converge on shared pathway-level features while maintaining gene-level heterogeneity. These results provide a foundational basis for interpreting senescence signatures and may facilitate the development of more robust transcriptome-based markers of cellular senescence in aging and disease.

genomics↗

bioRxiv2025

A benchmark of the somatic mutation landscape using single-cell and single-molecule whole-genome sequencing

Accurate detection of somatic mutations in noncancerous cells is critical for studying somatic mosaicism, a process implicated in aging and multiple chronic diseases. However, single-cell and single-molecule DNA sequencing platforms differ in their error profiles, coverage biases, and sensitivity to specific mutation types, complicating cross-platform comparisons. Here, we present in vitro and in silico benchmarks to quantify true-positive and false-positive rates in single-cell whole-genome sequencing using Single-Cell Multiple Displacement Amplification, and in single-molecule sequencing using Nanorate Sequencing (NS) and whole-genome NS (WGNS). Using standard cell lines, we show that all three methods detect single-nucleotide variants (sSNVs) and small insertions and deletions (sINDELs) with high accuracy, but differ in genomic coverage and susceptibility to artifacts. Method-specific biases influence mutational signatures and hotspot detection. Applying results of the benchmark to IMR-90 fibroblasts, we estimate higher in vitro mutation rates using NS than expected from in vivo data, consistent with potential replication stress and culture-associated DNA damage. Overall, our study highlights the substantial impact of sequencing platform-specific biases on somatic mutation detection and interpretation, and lays the foundation for standardized, cross-platform-comparable analyses of somatic mosaicism in normal human tissues.

genomics↗