An optimized workflow for single-cell transcriptomics and repertoire profiling of purified lymphocytes from clinical samples

Abstract

Establishing clinically relevant single-cell (SC) transcriptomic workflows from cryopreserved tissue is essential to move this emerging immune monitoring technology from the bench to the bedside. Improper sample preparation leads to detrimental cascades, resulting in loss of precious time, money and finally compromised data. There is an urgent need to establish protocols specifically designed to overcome the inevitable variations in sample quality resulting from uncontrollable factors in a clinical setting. Here, we explore sample preparation techniques relevant to a range of clinically relevant scenarios, where SC gene expression and repertoire analysis are applied to a cryopreserved sample derived from a small amount of blood, with unknown or partially known preservation history. We compare a total of ten cell-counting, viability-improvement, and lymphocyte-enrichment methods to highlight a number of unexpected findings. Trypan blue-based automated counters, typically recommended for single-cell sample quantitation, consistently overestimate viability. Advanced sample clean-up procedures significantly impact total cell yield, while only modestly increasing viability. Finally, while pre-enrichment of B cells from whole peripheral blood mononuclear cells (PBMCs) results in the most reliable BCR repertoire data, comparable T-cell enrichment strategies distort the ratio of CD4+ and CD8+ cells. Furthermore, we provide high-resolution analysis of gene expression and clonotype repertoire of different B cell subtypes. Together these observations provide both qualitative and quantitative sample preparation guidelines that increase the chances of obtaining high-quality single-cell transcriptomic and repertoire data from human PBMCs in a variety of clinical settings.

Unraveling B cell trajectories at single cell resolution

“…To solve this, in a different study, transcriptomic profiling of B cells from human and mouse BM was performed in an unbiased manner to study the effects of hypoxia-inducible factor-1a Box 1. Advances in BCR sequencing at single cell resolution Using scRNA-seq, repertoire profiling has been performed on purified B cells from human peripheral blood mononuclear cells (PMBCs) [9]. However, BCR cDNA is limited in length during scRNA-seq and might not always represent the entire antigen receptor repertoire.…”

“…Hence, several single cell methods are being increasingly used for B cell-specific lineage tracing and to resolve phenotypes, interactomes, and plasticity at the single cell level in a high-throughput manner. These methods can be divided into those that analyze (i) transcriptomic data, such as conventional single cell RNA sequencing (scRNA-seq) [8], single cell paired heavy and light chain sequencing [9], B cell receptor sequencing (BCR-seq) [10], and repertoire and gene expression by sequencing (RAGE-seq) [11], (ii) transcriptomic and proteomic data together, such as cellular indexing of transcriptomics and epitope sequencing (CITE-seq) [12] and RNA and protein sequencing (REAP-seq) [7], and lastly (iii) genomic data, such as VDJ sequencing (VDJ-seq) [13], single cell chromatin accessibility sequencing (scATAC-seq) [14] and single cell whole-genome sequencing (sc-WG-seq) [15] (see following sections for a detailed description of these methods). Together with spatial transcriptomics, these unbiased methods enable the Highlights Single cell techniques are used to study mammalian B cell signatures required for the differentiation of B cell subtypes within lymphoid organs.…”

Sampling time-dependent artifacts in single-cell genomics studies

“…Especially, large-scale consortia with multi-center sampling strategies, such as the Human Cell Atlas project [4] or the single-cell eQTLGen consortium [5], require dedicated standardization efforts to allow an informed decision-making process for guidelines and standards towards highquality data production. Previous work to determine sampling artifacts in scRNA-seq datasets identified profound alterations of gene expression signatures during sample preparation, storage, and processing, strongly underlining the importance of specific benchmarking efforts [6][7][8][9].…”

Single-Cell RNA Sequencing Approaches for Tracing T Cell Development

“…A VDJ enrichment step can be added to generate TCR-enriched libraries for 10X Genomics 59 library construction to obtain detailed information on TCR gene usage (85,86). This works by generating cDNA with a cellular barcode that is then divided, in which one portion is processed for 59 gene expression library, whereas the other is prepared with TCR sequence enrichment and VDJ segment library preparation (87). The two libraries are sequenced separately, and the transcriptome and VDJ data are integrated with downstream analysis (85).…”