Single Cell RNAseq for Serial Samples from Cutaneous T-Cell Lymphoma (CTCL)

LAIR1 prevents excess inflammatory tissue damage in Staphylococcus aureus skin infection and Cutaneous T-cell Lymphoma Hannah K. Dorando1, Evan C. Mutic1, Kelly L. Tomaszewski2, Yulia Korshunova1, Ling Tian1, Mellisa K. Stefanov1, Chaz C. Quinn1, Deborah J. Veis2, Juliane Bubeck Wardenburg3, Amy C. Musiek2, Neha Mehta-Shah2, Jacqueline E. Payton1 1 Washington University School of Medicine, Department of Pathology and Immunology 2 Washington University School of Medicine, Department of Medicine 3 Washington University School of Medicine, Department of Pediatrics # Correspondence: Jacqueline E. Payton 660 S. Euclid Ave, Box 8118 St. Louis, MO 63110 [email protected]du 314-362-5935 bioRxiv: doi: https://doi.org/10.1101/2024.06.13.598864 Methods related to the scRNA-sequencing datasets: Patient sample collection De-identified peripheral blood was obtained from patients seen at the Washington University School of Medicine Cutaneous Lymphoma Clinic under IRB-approved protocols with patients providing informed consent. Peripheral blood mononuclear cell isolation and scRNA + scTCR-sequencing PBMCs were subjected to enrichment and/or depletion using antibody cocktails as detailed below to enable purification of the desired cells. Monocytes and neutrophils were depleted by incubation of peripheral blood samples with RosetteSep Human Monocyte (CD36) Depletion Cocktail (StemCell Technologies, 15628) for 15 minutes at room temperature, layered onto a Histopaque1077 gradient, and centrifuged at 400g for 30 minutes with no brake. The interphase was collected and washed with 10 mL of sort buffer (PBS, 1% FBS, 2 mM EDTA), followed by red blood cell lysis (155 mM NH4Cl, 10 mM KHCO3, 0.1 mM EDTA) for 10 minutes. For each sample, 10,000 to 20,000 viable cells were submitted for processing using the 10x Genomics Chromium Controller and the Chromium Single Cell 5′ Library & Gel Bead Kit v2 (PN-1000006), Chromium Single Cell A Chip Kit (PN-1000152), Chromium Single Cell V(D)J Enrichment Kit, Human, Tcell (96rxns)(PN1000005), and Chromium Single Index Kit T (PN-1000213) following the manufacturer’s protocols. Normalized libraries were sequenced on a NovaSeq6000 S4 Flow Cell using the XP workflow and a 151x10x10x151 sequencing recipe according to manufacturer protocol. A median sequencing depth of 50,000 reads/cell was targeted for each sample. scRNA-sequencing data processing and analysis Alignment and gene counting were performed using the Cell Ranger pipeline (10x Genomics, v3.0, Pleasanton, CA). Genes found in fewer than 15 cells in a given sample were removed. For each patient, gene counts and cells were pooled into a single Seurat (v3.1.4) object (1), and cells containing fewer than 200 or more than 3000-3750 expressed genes, more than 8-10% mitochondrial reads, fewer than 300 or more than 10000-20000 UMIs, or classification as a doublet by the R package scDblFinder with parameters dims = 30, clust.method = “fast_greedy” were removed. Normalization and regression of technical variation due to mitochondrial read percentage and read depth was performed with the SCTransform function with variables.features.n = 4000. Integration to account for experimental variability due to differences in ficoll or buffy coat preparation and batch effects was performed using the Seurat wrapper around the fastMNN function from the batchelor R package (v1.4.0) with n.features = 3000. Gene expression was normalized and the top 1500 variable using the “VST” method were calculated. Data was integrated using the harmony (v1.0.0) R package (2) using both patient and batch information to correct for batch effect with up to 10 iterations. The UMAP and neighbors were calculated with Seurat, using 20 dimensions of the harmony calculations. Cell annotation was performed using the singler (v1.4.1) R package (3) using the highest spearman rho of purified immune populations in the Human Primary Cell Atlas (4). Cell type designations with less than 50 cells in the entire cohort were reduced to "other". Automated annotations were checked manually using canonical marker genes. All single cell visualizations were performed with Seurat and ggplot2 R package (v3.5.1) (5). scRNA-sequencing datasets in this repository Barcodes, features, and matrix .tsv files for scRNA and TCR sequencing for 16 samples from 6 CTCL patients. Samples are labeled in order of collection. References Cited 1. Stuart T, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):18881902.e21. 2. Korsunsky I, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289–1296. 3. Aran D, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163–172. 4. Mabbott NA, et al. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics. 2013;14:632. 5. Wickham H. ggplot2. Cham: Springer International Publishing; 2016.