biostudies-arrayexpressMetabolomicsUnknownTranscriptomicsGenomicsProteomicsDNBSEQ-T7RNA-seq of coding RNA from single cellsHomo sapiensHomo sapienshttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-16932Cell heterogeneity is a fundamental feature of biological systems, driving diverse responses to stimuli and stressors, including developmental cues, diseases, and drug treatments. While single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to characterize this diversity by profiling gene expression at single cell levels, a critical gap remains in that it cannot directly link transcriptional profiles to functional cellular outcomes, such as stress-induced damage. In this work, we developed DISC-seq (Damage Identification in Single-Cell RNA sequencing), a method compatible with standard scRNA-seq workflows that simultaneously quantifies transcriptome-wide gene expression and evaluates the extent of cell damage at single-cell resolution. Applied to both cancer cell lines and clinical peripheral blood mononuclear cells (PBMCs) from pediatric hematology patients, DISC-seq uncovered key molecular pathways and gene expression determinants that govern heterogeneous treatment and stress responses. Our approach enables the systematic discovery of regulatory mechanisms underlying heterogeneous cellular stress sensitivity within and across cell types, providing a powerful tool for dissecting the molecular basis of cell heterogeneity.biostudies-arrayexpressNucleic Acid Extraction - Single-cell RNA-seq libraries were prepared using C4 scRNA-seq kit (MGI). Barcoded mRNA capture beads, droplet generation oil and the single-cell suspension were loaded into the corresponding reservoirs on chip for droplet generation. The droplets were gently removed to the collection vial and placed at room temperature for 20 minutes. Droplets were then broken and collected by the bead filter (MGI). The supernatant was removed, and the bead pellet was resuspended with 100 μl RT mix. The mixture was then thermal cycled as follows: 42 °C for 90 minutes, 10 cycles of 50 °C for 2 minutes, 42 °C for 2 minutes. The bead pellet was then resuspended in 200 μl of exonuclease mix and incubated at 37 °C for 45 minutes. Afterward the PCR master mix was added to the beads pellet and thermal cycled as follows: 95 °C for 3 minutes, 13 cycles of 98 °C for 20 s, 58 °C for 20 s, 72 °C for 3 minutes, and finally 72 °C for 5 minutes. Amplified cDNA was purified using 60 μl of AMPure XP beads.Sequencing - All libraries were further prepared based on BGISEQ-500 sequencing platform. The DNA nanoballs were loaded into the patterned nanoarrays and sequenced on the DNBSEQ-T7 sequencer.Sample Collection - Cells were grown to a confluence of 50-60%. Cells were treated with Trypsin-EDTA for 5 minutes, quenched with equal volume of complete growth medium, and spun down at 300 x g for 5 minutes. The supernatant was removed, and cells were washed twice with 1X phosphate buffered saline (PBS). Then cells were resuspended in 1X PBS containing 0.01% bovine serum albumin (BSA), passed through a 40 μm cell strainer and then centrifuged at 300 x g for 5 minutes. Cells were resuspended with cell resuspension buffer (MGI) at a concentration of 1,000 cells/μl.Library Construction - The cDNA was subsequently fragmented to 400-600bp with NEBNext dsDNA Fragmentase according to the manufacturer’s protocol. Indexed sequencing libraries were constructed using the reagents in the C4 scRNA-seq kit following the steps: (1) post fragmentation size selection with AMPure XP beads; (2) end repair and A-tailing; (3) adapter ligation; (4) post ligation purification with AMPure XP beads; (5) sample index PCR and size selection with AMPure XP beads. The barcode sequencing libraries were quantified by Qubit.MINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesData Transformation - The endogenous library was analyzed using the Seurat package (v5.3.0). After filtering, the data underwent log-normalization. The top 2,000 highly variable genes were selected, and PCA was applied to reduce the data to 30 dimensions. Cells were clustered within the reduced-dimensional space using the Leiden algorithm and visualized with UMAP. Marker genes for each cluster were identified by both using Seurat's FindAllMarkers function (parameters: min.pct = 0.1, logfc.threshold = 0.1) and COSG package(v0.9.0)falseDISC-seq: deciphering cell stress heterogeneity through joint mapping of cellular damage and transcriptomic landscapes in scRNA-seqCell heterogeneity is a fundamental feature of biological systems, driving diverse responses to stimuli and stressors, including developmental cues, diseases, and drug treatments. While single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to characterize this diversity by profiling gene expression at single cell levels, a critical gap remains in that it cannot directly link transcriptional profiles to functional cellular outcomes, such as stress-induced damage. In this work, we developed DISC-seq (Damage Identification in Single-Cell RNA sequencing), a method compatible with standard scRNA-seq workflows that simultaneously quantifies transcriptome-wide gene expression and evaluates the extent of cell damage at single-cell resolution. Applied to both cancer cell lines and clinical peripheral blood mononuclear cells (PBMCs) from pediatric hematology patients, DISC-seq uncovered key molecular pathways and gene expression determinants that govern heterogeneous treatment and stress responses. Our approach enables the systematic discovery of regulatory mechanisms underlying heterogeneous cellular stress sensitivity within and across cell types, providing a powerful tool for dissecting the molecular basis of cell heterogeneity.2026-04-19T00:00:00Z2026-04-19T01:38:32.416Z2026-04-17T12:05:41.723ZE-MTAB-16932ERP192296EFO_0002944EFO_0004170EFO_0005684EFO_0005518EFO_0003816EFO_0004184