Transcriptomics

Transcriptome sequencing analyses gene expression by providing an unbiased approach to gene detection and quantification, enabling discovery of novel isoforms, alternative splicing events, and fusion transcripts. ^[1] Single-cell gene expression data, or transcriptomics, capture the RNA expression of cells and are increasingly used to track organisms along their life-course. ^[2]

Recent transcriptomic technologies include bulk RNA sequencing (RNA-seq), single-cell transcriptomics, and spatial transcriptomics, and can survey different transcript types such as mRNA, long noncoding RNA, tRNA, and miRNA. ^[3] Short-read sequencing surpassed the limited dynamic range of earlier technologies such as microarrays but has limitations in resolving full-length transcripts and complex isoforms, which long-read RNA-seq now helps address. ^[1] Single-cell RNA sequencing identifies cell subpopulations within tissue but does not capture their spatial distribution, motivating integration with spatial transcriptomics techniques that localize RNA within tissue. ^[4]

Advances in transcriptomic technologies have revolutionized the ability to study aging at unprecedented resolution and scale, opening opportunities for biomarker discovery, elucidation of molecular pathways, and targeted therapies for age-related disorders. ^[3] Single-cell transcriptomics allows investigation of how aging affects cellular transcriptomes and how transcriptome changes may underlie aging, chronic inflammation (inflammaging), immunosenescence, and cellular senescence. ^[2] Many studies report that transcriptional heterogeneity, or expression variability, increases with age, an insight that is not easily obtained from bulk data. ^[2]

Transcriptomic characterization of senescent cells has produced signature gene sets such as CellAge, SeneQuest, and SenMayo, and machine-learning tools trained on RNA sequencing data to detect cellular senescence. ^[5] The classic in vitro observation that fibroblasts halt replication after approximately 50 population doublings first defined replicative senescence, a state now profiled transcriptomically. ^[5] In oncology, single-cell RNA sequencing of tumour and blood samples has created potential for identifying predictive biomarkers that select patients most likely to benefit from immune-checkpoint inhibitors. ^[6]