biostudies-arrayexpressWesley AbplanalpHomo sapienshttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-15820PBMCs of patients with AVS having TET2 mutations versus those with no known CHIP mutations were utilized for scRNA-seqbiostudies-arrayexpressSample Collection - We analyzed single-cell sequencing signatures of gene expression in circulating immune cells obtained from patients with severe degenerative aortic valve stenosis. Patients with evidence for acute inflammatory or hematological disease were excluded, and all patients were recruited prior to the outbreak of COVID-19 in Europe (August 2019, and November 2019). Patients were enrolled 5-26 months after undergoing TAVI procedure. All patients provided written informed consent for the study. The ethics review board of the Goethe University of Frankfurt, Germany, approved the protocol, and the study complies with the Declaration of Helsinki. For single-cell RNA sequencing, blood was obtained from patients and centrifuged on a Ficoll gradient, and mononuclear cells were used for droplet single-cell RNA sequencing.Nucleic Acid Extraction - Blood was collected and transported on ice. All steps were performed using RNase-free reagents and plastics. Whole blood: Peripheral blood mononuclear cells were isolated by density gradient centrifugation using [Ficoll] according to the manufacturer’s instructions. Washes and debris cleanup Cells were washed twice with ice-cold PBS + 0.04% BSA and filtered through a 40 µm strainer. If high debris was present, a brief low-speed spin (for example 300 g, 1 min) was used to enrich intact cells from fine debris. Optional dead-cell removal was performed with [Miltenyi Dead Cell Removal Kit] following the user guide. Cell counting and viability Cells were counted with a hemocytometer or automated counter using trypan blue. Viability exceeded 85% for libraries included in this study. Cell concentration was adjusted to the target capture of ~6,000–10,000 cells per library for 10x Chromium. Nucleic acid preservation No RNA was extracted in bulk. RNA molecules were preserved in intact cells kept on ice in PBS + 0.04% BSA until loading. Final preparation for GEM generation Cells were centrifuged at 300–400 g for 5 min at 4°C and resuspended at [700–1,200] cells/µl in PBS + 0.04% BSA. Large aggregates were removed by a final pass through a 40 µm strainer immediately before loading onto the Chromium Controller.Library Construction - Single cell suspensions were prepared in nuclease-free PBS with 0.04% BSA. Cell viability exceeded 85%. Cells were counted on an automated counter, debris was removed as needed, and the suspension was adjusted to the target recovery of ~6,000–10,000 cells per library (10x Genomics user guide, Chromium Next GEM Single Cell 3’ v3.1). GEM generation and reverse transcription Cells were loaded into a Chromium Controller with the Chromium Next GEM Single Cell 3’ Library and Gel Bead Kit v3.1 and Next GEM Chip G according to the manufacturer’s instructions. Gel beads carrying oligos with cell barcode, UMI, and poly(dT) were combined with cells and partitioning oil to form GEMs. Reverse transcription was performed in GEMs to generate barcoded first-strand cDNA (template switch). GEMs were broken, and cDNA was purified with silica membrane or SPRIselect cleanups per kit instructions. cDNA amplification and QC Barcoded cDNA was amplified using the kit master mix. Cycle number followed the 10x decision tree for targeted cell recovery and cDNA yield (typically 10–14 cycles). Amplified cDNA was purified with SPRIselect. cDNA quality was assessed by Bioanalyzer High Sensitivity DNA (broad peak from ~400 bp to >1 kb expected). 3’ gene expression library construction Amplified cDNA was enzymatically fragmented. End repair and A-tailing were performed. Adapters were ligated. Libraries were enriched by limited-cycle PCR with sample index primers. Final libraries were purified with SPRIselect and evaluated by BioanalyzerSequencing - Libraries were pooled equimolarly and sequenced with paired-end reads on an Illumina NovaSeq 6000 at GenomeScan, Leiden, Netherlands. Flow cells were NovaSeq S4 with four lanes [adjust if S1 or S2], and libraries were distributed across lanes to balance yield. Standard 10x Genomics 3’ v3.1 read geometry was used: Read 1 = 28 bp containing cell barcode and UMI, Index 1 = 8 bp sample index, Index 2 = 0 bp if not used, Read 2 = 91–100 bp cDNA insert. A 1–5% PhiX spike-in was included for run quality control. Target sequencing depth was ~20,000–50,000 read pairs per cell depending on sample. Base calls (BCL) were converted to FASTQ with bcl2fastq v2.x [or bcl-convert v3.x if applicable], using the 10x-recommended base mask and allowing a single mismatch in the sample index. Per-lane FASTQs for the same library were concatenated after demultiplexing. Read quality was assessed with vendor metrics and FastQC.OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesData Transformation - Raw base calls were demultiplexed to FASTQ with Cell Ranger mkfastq (Illumina bcl2fastq v2.x). Reads were processed with Cell Ranger count (vX.Y.Z) using the 10x Genomics scRNA-seq pipeline: Read 1 contained cell barcode and UMI, Read 2 the cDNA insert, and I1 the sample index. Reads were aligned to [reference genome/transcriptome] (10x reference refdata-gex-GRCh38-2020-A, Ensembl annotation) with the pipeline’s STAR-based aligner; multi-mapping and low-quality reads were excluded per default settings. UMIs were error-corrected and collapsed per gene and cell barcode, and cell calling was performed by Cell Ranger’s algorithm. The primary processed output is the filtered_feature_bc_matrix(.h5) containing raw UMI counts per gene per cell (no downstream normalization, scaling, batch correction, or clustering applied here). Web summary metrics and QC files are included as produced by Cell Ranger.UnknownTranscriptomicsGenomicsProteomicsIllumina NovaSeq 6000Chromium ControllerChromium Controller / Agilent BioanalyzerNoneRNA-seq of coding RNA from single cellsHomo sapiensClonal hematopoiesis activates pro-calcific pathways in macrophages and promotes aortic valve stenosisWesley T. Abplanalp1,2,3+*†, Michael A. Raddatz4,5,6*, Bianca Schuhmacher1,3*, Silvia Mas-Peiro7, María A. Zuriaga8, Nuria Matesanz8, José J. Fuster8,9, Yash Pershad6, Caitlyn Vlasschaert10, Alexander J. Silver4,11,12, Eric Farber-Eger13, Yaomin Xu14,15,16, Quinn S. Wells13,15,17, Delara Shahidi1,3, Sameen Fatima1,3, Xiao Yang1,3, Adwitiya A. P. Boruah1,3, Akshay Ware1,3, Maximilian Merten1,2,3, Moritz von Scheidt18, David John 1,2,3, Mariana Shumliakivska1,2,3, Marion Muhly-Reinholz1,3, Mariuca Vasa-Nicotera7, Stefan Guenter2,3,19, Michael R. Savona11,12,20,21, Brian R. Lindman13,22, Stefanie Dimmeler1,2,3#, Alexander G. Bick6,21#+, Andreas M. Zeiher1,2,3# * and #: contributed equally †: Corresponding authorWesley AbplanalpfalseClonal hematopoiesis activates pro-calcific pathways in macrophages and promotes aortic valve stenosisPBMCs of patients with AVS having TET2 mutations versus those with no known CHIP mutations were utilized for scRNA-seq2025-10-22T00:00:00Z2026-05-27T14:32:22.43Z2025-10-22T14:47:33.834ZE-MTAB-15820ERP182715EFO_0002944EFO_0004170EFO_0005684EFO_0005518EFO_0003816EFO_0004184