Sokka, M. | Search bioRxiv

bioRxiv2026

SenoQuant: One-stop AI software for senescence marker analysis and prediction

Senescent cells accumulate with age and contribute to tissue dysfunction, yet their identification in tissues is challenging due to low abundance, heterogeneous phenotypes, and the lack of specific markers. Senescence-associated features span multiple subcellular compartments, including nuclear DNA damage foci, cytosolic protein changes, and perinuclear alterations, each requiring tailored detection strategies. To overcome these challenges, we developed SenoQuant (https://github.com/HaamsRee/senoquant), a versatile software designed for comprehensive, accurate, and unbiased spatial quantification and prediction of senescence markers across diverse tissue contexts. Utilizing AI models, SenoQuant enables precise nuclear and cytoplasmic segmentation and detection of senescence markers across low- and high-plex imaging modalities, applicable to cultured cells and tissue sections from mice and humans. The platform also supports custom AI models; for example, we built SenCeption, a proof-of-concept predictor of single-cell p21 status from DAPI-stained nuclei in human skin. Available as a free napari plugin, SenoQuant is widely accessible to researchers. By providing a unified approach to senescence analysis and prediction, SenoQuant opens new opportunities for exploring the complex biology of senescence and its impacts on aging and disease.

Cell Biology↗

bioRxiv2025

Improved short nascent strand sequencing (iSNS-seq) enhances DNA replication origin detection and reduces non-origin biases

Identifying DNA replication origins in human and other metazoan genomes has been challenging, as highlighted by the fact that various methods for mapping them have produced conflicting results. A popular method, short nascent strand sequencing (SNS-seq), enriches newly replicated short single-stranded DNA by size selection and {lambda}-exonuclease ({lambda}-exo) digestion of parental DNA. Surprisingly, SNS-seq has never been validated in Saccharomyces cerevisiae where origins have been well characterized genome-wide. Here we improved the SNS-seq protocol through biochemical optimization and benchmarked its origin-mapping sensitivity and precision against traditional SNS-seq in asynchronous populations of S. cerevisiae, a genetically tractable system that allows direct comparison against a well-defined set of confirmed origins. Relative to traditional SNS-seq, the improved protocol substantially increased enrichment of origin-derived DNA. Strikingly, traditional SNS-seq failed to detect known origins and instead enriched non-origin DNA, likely arising from RNA:DNA hybrids. These findings have important implications for the interpretation of previously published datasets that rely on {lambda}-exo for origin mapping, and provide a proof-of-concept benchmark for extending this improved protocol to metazoan systems. Furthermore, our biochemical and genomic analyses help unravel the mystery of the inconsistencies between SNS-seq and other techniques used to map DNA replication origins genome-wide.

genomics↗