Background Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets have enabled an increased understanding of senescent cell heterogeneity. Methods Here we present SenPred, a machine-learning pipeline which identifies fibroblast senescence based on single-cell transcriptomics from fibroblasts grown in 2D and 3D. Results Using scRNA-seq of both 2D and 3D deeply senescent fibroblasts, the model predicts intra-experimental fibroblast senescence to a high degree of accuracy (> 99% true positives). Applying SenPred to in vivo whole skin scRNA-seq datasets reveals that cells grown in 2D cannot accurately detect fibroblast senescence in vivo. Importantly, utilising scRNA-seq from 3D deeply senescent fibroblasts refines our ML model leading to improved detection of senescent cells in vivo. This is context specific,

Background Neoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development

Background Circular RNAs (circRNAs) are highly stable regulators, often accumulated in mammalian brains and thought to serve as “memory molecules” that govern the long process of aging. Mounting evidence demonstrated circRNA dysregulation in the brains of Alzheimer’s disease (AD) patients. However, whether and how circRNA dysregulation underlies AD progression remains unexplored. Methods We combined Poly(A)-tailing/RNase R digestion experimental approach with CARP, our published computational framework using pseudo-reference alignment for more sensitive and accurate circRNA detection to identify genome-wide circRNA dysregulation and their downstream pathways in the 5xFAD mouse cerebral cortex between 5 and 7 months of age, a critical window marks the transition from reversible to irreversible pathogenic progression. Dysregulated circRNAs and pathways associated with disease progression in 5xFAD cortex were systematically compared with circRNAs affected in postmortem subcortical areas o