biostudies-arrayexpressInga-Lill MårtenssonHomo sapiensCell Ranger v.3.1.0 (10x Genomics)https://www.ebi.ac.uk/biostudies/studies/E-MTAB-12346B and T cells play central roles in immune responses and, as such, are integral in many diseases, such as autoimmunity. Characterizing these cells, their subsets, and exploring their transcriptomes to understand the underlying biology, can be achieved using single-cell RNA sequencing (scRNA-seq). While some studies report removing the transcripts encoding the variable regions of the B- and T-cell antigen receptors prior to downstream analyses, a systematic analysis of the effects of retaining versus removing these genes has been lacking. We investigated the effects these transcripts in B and T cells impose on supervised clustering and downstream analyses of scRNA-seq data.biostudies-arrayexpressSequencing - 5' gene expression libraries were sequenced on a NextSeq (Illumina) with NextSeq 500/550 v2.5 Sequencing Reagent Kit (Illumina).Sample Collection - Venous blood was collected in Lithium-Heparin tubes from the antecubital area of the arm of four patients with untreated early rheumatoid arthritis (RA). PBMCs were isolated using Ficoll (GE Healthcare) and frozen in 10% dimethylsulphoxide (DMSO, Sigma-Aldrich) to be stored at -150° C. Frozen PBMCs were thawed within 50 days of freezing and resuspended in RPMI-1640 10% FCS (Gibco, Thermofisher). Samples were stained with Fixable Viability Dye eFluor™ 506 (Invitrogen) to exclude dead cells.Nucleic Acid Extraction - CD20+CD3- B cells were electronically gated and CD27 versus IgD displayed. In this gate memory B cells were isolated by excluding CD27-IgD+ B cells and sorted as a single population using a Sony H800 cell sorter. Cell suspensions were loaded into a Chromium cell processing unit (10x Genomics, Pleasanton, CA, USA), generating gel bead-in emulsions (GEMs).Nucleic Acid Extraction - CD24hiCD38hi early B-lineage cells were electronically sorted with a Sony SH800. Staining with Fixable Viability Dye eFluor(TM) 506 (Invitrogen) was used to exclude dead cells. Cell suspensions were loaded into a Chromium cell processing unit (10x Genomics, Pleasanton, CA, USA), generating gel bead-in emulsions (GEMs).Library Construction - 5' gene expression libraries were constructed according to standard 10x protocol, using the Chromium Single Cell V(D)J Reagent Kits (v1.1 chemistry, 10x Genomics) GEMs were used to construct sequencing libraries using the standard 10x protocol. Genomics Chromium Single Cell V(D)J Reagent Kits (5', chemistry 1.1).Sample Collection - Bone marrow samples were collected mechanically using a cordless drill from the femoral head of patients undergoing hip-replacement surgery. Larger debris was removed through filtering. After centrigufation and washing, the samples were layered carefully over Ficoll-Hypaque (GE Healthcare). Following centrifugation, bone marrow mononuclear cells were harvested and washedin PBS, before being frozen in aliquots and stored at -150°C.OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesSequence Alignment - Sequencing files were de-multiplexed, filtered, trimmed, and aligned to the GRCh38 genome using the Cell Ranger software (v.3.1.0; 10x Genomics).Data Transformation - Downstream normalization for analysis was carried out outside of Cell Ranger, as described in the Materials and Methods section in the associated publication, and is not reflected in the deposited count matrices.MetabolomicsUnknownTranscriptomicsGenomicsProteomicsNextSeq 500B and T cells are integral parts of the immune system and are implicated in many diseases, e.g. autoimmunity. Towards understanding the biology of B and T cells and subsets thereof, their transcriptomes can be analyzed using single-cell RNA sequencing. In some studies, the V(D)J transcripts encoding the variable regions of the B- and T-cell antigen receptors have been removed before the analyses. However, a systematic analysis of the effects of including versus excluding these genes is currently lacking. We have investigated the effects of these transcripts on unsupervised clustering and down-stream analyses of single-cell RNA sequencing data from B and T cells. We found that exclusion of the B-/T-cell receptor genes prior to unsupervised clustering resulted in clusters that represented biologically meaningful subsets, such as subsets of memory B and memory T cells. Furthermore, pseudo-time and trajectory inference analyses of early B-lineage cells resulted in a developmental pathway from progenitor to immature B cells. In contrast, when the B-/T-cell receptor genes were not removed, with the PCs used for clustering consisting of up to 70% V-genes, this resulted in some clusters being defined exclusively by V-gene segments. These did not represent biologically meaningful subsets; for instance in the early B-lineage cells, these clusters contained cells representing all developmental stages. Thus, in studies of B and T cells, to derive biologically meaningful results, it is imperative to remove the gene sequences that encode B- and T-cell receptors.RNA-seq of coding RNA from single cellsHomo sapiensSingle-cell RNA sequencing analyses: interference by the genes that encode the B-cell and T-cell receptorsTimothy Sundell, Kristoffer Grimstad, Alessandro Camponeschi, Andreas Tilevik, Inger Gjertsson, Inga-Lill MårtenssonInga-Lill MårtenssonfalseInterference of B-Cell and T-Cell Receptor Genes in Single-Cell RNA Sequencing DataB and T cells play central roles in immune responses and, as such, are integral in many diseases, such as autoimmunity. Characterizing these cells, their subsets, and exploring their transcriptomes to understand the underlying biology, can be achieved using single-cell RNA sequencing (scRNA-seq). While some studies report removing the transcripts encoding the variable regions of the B- and T-cell antigen receptors prior to downstream analyses, a systematic analysis of the effects of retaining versus removing these genes has been lacking. We investigated the effects these transcripts in B and T cells impose on supervised clustering and downstream analyses of scRNA-seq data.2025-12-16T00:00:00Z2026-05-27T16:06:22.457Z2025-12-16T16:03:57.306ZE-MTAB-1234636473726EFO_0002944EFO_0004170EFO_0005684EFO_0004917EFO_0005518EFO_0003816EFO_000418410.1093/bfgp/elac044