Fig. 1: Overall workflow of hUSI.

The workflow of hUSI encompasses four major steps: data collection, feature learning, hUSI computation and hUSI application. 1. Data collection: We collected bulk RNA-seq data (843 samples) from 73 studies. After quality control, 770 samples (385 senescent and 385 non-senescent) from 64 studies were retained across 34 distinct cell types and 13 senescence types. The distribution of cell lines and senescence types is shown in pie charts (lower left and lower right, respectively). 2. Feature learning: A uniform RNA-seq data processing pipeline was employed to create a standardized gene expression matrix. We selected senescent samples and filtered cell type signatures to generate a universal senescence transcriptome profile. Then, an OCLR model was applied to this profile and ultimately produced a weighted gene vector, referred to as the OCLR-learned senescence features. 3. hUSI computation: hUSI can be calculated for both bulk and single-cell samples by calculating the Spearman correlation coefficient between the OCLR-learned senescence features and gene expression value. 4. hUSI application: The effectiveness of hUSI was validated in distinguishing senescent samples across multiple bulk and single-cell datasets, demonstrating superior performance compared to other methods. Applying hUSI to various datasets can elucidate characteristics, interactions, clinical relationships and regulators of senescent cells as well as their accumulative changes in diseases. Figure created with BioRender.