scholarly journals No detectable alloreactive transcriptional responses under standard sample preparation conditions during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Christopher S. McGinnis ◽  
David A. Siegel ◽  
Guorui Xie ◽  
George Hartoularos ◽  
Mars Stone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sample Preparation ◽  
Single Cell ◽  
Mononuclear Cells ◽  
Peripheral Blood Mononuclear ◽  
Cross Sectional ◽  
Preparation Conditions ◽  
Single Cell Rna Sequencing ◽  
Unrelated Donors ◽  
Technical Artifacts

Abstract Background Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurements of transcript counts in individual cells. However, high assay costs and artifacts associated with analyzing samples across multiple sequencing runs limit the study of large numbers of samples. Sample multiplexing technologies such as MULTI-seq and antibody hashing using single-cell multiplexing kit (SCMK) reagents (BD Biosciences) use sample-specific sequence tags to enable individual samples to be sequenced in a pooled format, markedly lowering per-sample processing and sequencing costs while minimizing technical artifacts. Critically, however, pooling samples could introduce new artifacts, partially negating the benefits of sample multiplexing. In particular, no study to date has evaluated whether pooling peripheral blood mononuclear cells (PBMCs) from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures. Results Here, we applied the 10x Genomics scRNA-seq platform to MULTI-seq and/or SCMK-labeled PBMCs from a single donor with and without pooling with PBMCs from unrelated donors for 30 min at 4 °C. We did not detect any alloreactivity signal between mixed and unmixed PBMCs across a variety of metrics, including alloreactivity marker gene expression in CD4+ T cells, cell type proportion shifts, and global gene expression profile comparisons using Gene Set Enrichment Analysis and Jensen-Shannon Divergence. These results were additionally mirrored in publicly-available scRNA-seq data generated using a similar experimental design. Moreover, we identified confounding gene expression signatures linked to PBMC preparation method (e.g., Trima apheresis), as well as SCMK sample classification biases against activated CD4+ T cells which were recapitulated in two other SCMK-incorporating scRNA-seq datasets. Conclusions We demonstrate that (i) mixing PBMCs from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) does not cause an allogeneic response, and (ii) that Trima apheresis and PBMC sample multiplexing using SCMK reagents can introduce undesirable technical artifacts into scRNA-seq data. Collectively, these observations establish important benchmarks for future cross-sectional immunological scRNA-seq experiments.

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

2019 ◽  
Author(s):  
Richa Hanamsagar ◽  
Timothy Reizis ◽  
Mathew Chamberlain ◽  
Robert Marcus ◽  
Frank O. Nestle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sample Preparation ◽  
Single Cell ◽  
Mononuclear Cells ◽  
Immune Monitoring ◽  
Cell Counting ◽  
Clinical Samples ◽  
Peripheral Blood Mononuclear ◽  
Cd8 Cells ◽  
Unexpected Findings

AbstractEstablishing 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.

scholarly journals No detectable alloreactive transcriptional responses during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

2020 ◽  
Author(s):  
Christopher S. McGinnis ◽  
David A. Siegel ◽  
Guorui Xie ◽  
Mars Stone ◽  
Zev J. Gartner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Peripheral Blood Mononuclear Cells ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Transcriptional Responses ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Blood Mononuclear Cells

ABSTRACTSingle-cell RNA sequencing (scRNA-seq) provides high-dimensional measurement of transcript counts in individual cells. However, high assay costs limit the study of large numbers of samples. Sample multiplexing technologies such as antibody hashing and MULTI-seq use sample-specific sequence tags to enable individual samples (e.g., different patients) to be sequenced in a pooled format before downstream computational demultiplexing. Critically, no study to date has evaluated whether the mixing of samples from different donors in this manner results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self immune antigens). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures,. Here, we compared the expression profiles of peripheral blood mononuclear cells (PBMCs) from a single donor with and without pooling with PBMCs isolated from other donors with different blood types. We find that there was no evidence of alloreactivity in the multiplexed samples following three distinct multiplexing workflows (antibody hashing, MULTI-seq, and in silico genotyping using souporcell). Moreover, we identified biases amongst antibody hashing sample classification results in this particular experimental system, as well as gene expression signatures linked to PBMC preparation method (e.g., Ficoll-Paque density gradient centrifugation with or without apheresis using Trima filtration).

scholarly journals Single-cell multi-omics analysis of the immune response in COVID-19

2021 ◽  
Author(s):  
Emily Stephenson ◽  
◽  
Gary Reynolds ◽  
Rachel A. Botting ◽  
Fernando J. Calero-Nieto ◽  
...  
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Single Cell ◽  
Mononuclear Cells ◽  
Severe Disease ◽  
Effector Memory ◽  
Peripheral Blood Mononuclear ◽  
Cross Sectional ◽  
Hematopoietic Stem ◽  
Symptomatic Disease

AbstractAnalysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy.

scholarly journals Multiplexing droplet-based single cell RNA-sequencing using natural genetic barcodes

2017 ◽  
Author(s):  
Hyun Min Kang ◽  
Meena Subramaniam ◽  
Sasha Targ ◽  
Michelle Nguyen ◽  
Lenka Maliskova ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Specific Gene ◽  
Cell Capture ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Sample Identity

Droplet-based single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes from tens of thousands of cells. Multiplexing samples for single cell capture and library preparation in dscRNA-seq would enable cost-effective designs of differential expression and genetic studies while avoiding technical batch effects, but its implementation remains challenging. Here, we introduce an in-silico algorithm demuxlet that harnesses natural genetic variation to discover the sample identity of each cell and identify droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments where cells from unrelated individuals are pooled and captured at higher throughput than standard workflows. To demonstrate the performance of demuxlet, we sequenced 3 pools of peripheral blood mononuclear cells (PBMCs) from 8 lupus patients. Given genotyping data for each individual, demuxlet correctly recovered the sample identity of > 99% of singlets, and identified doublets at rates consistent with previous estimates. In PBMCs, we demonstrate the utility of multiplexed dscRNA-seq in two applications: characterizing cell type specificity and inter-individual variability of cytokine response from 8 lupus patients and mapping genetic variants associated with cell type specific gene expression from 23 donors. Demuxlet is fast, accurate, scalable and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

scholarly journals Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1297 ◽  
Author(s):  
Saskia Freytag ◽  
Luyi Tian ◽  
Ingrid Lönnstedt ◽  
Milica Ng ◽  
Melanie Bahlo
Keyword(s):  
Single Cell ◽  
Peripheral Blood Mononuclear Cells ◽  
Gold Standard ◽  
Mononuclear Cells ◽  
Rna Seq ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Silver Standard ◽  
Single Cell Rna Sequencing ◽  
Blood Mononuclear Cells

Background: The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods. Results: We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.

scholarly journals FEM: mining biological meaning from cell level in single-cell RNA sequencing data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12570
Author(s):  
Yunqing Liu ◽  
Na Lu ◽  
Changwei Bi ◽  
Tingyu Han ◽  
Guo Zhuojun ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Differentially Expressed Gene ◽  
Biological Significance ◽  
Human Pancreas ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Expression Matrix

Background One goal of expression data analysis is to discover the biological significance or function of genes that are differentially expressed. Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used. However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded. Methods We developed the functional expression matrix (FEM) algorithm to utilize the information from all expressed genes. The algorithm converts the gene expression matrix (GEM) into a FEM. The FEM algorithm can provide insight on the biological significance of a single cell. It can also integrate with GEM for downstream analysis. Results We found that FEM performed well with cell clustering and cell-type specific function annotation in three datasets (peripheral blood mononuclear cells, human liver, and human pancreas).

scholarly journals Scalable full-transcript coverage single cell RNA sequencing with Smart-seq3xpress

2021 ◽  
Author(s):  
Michael Hagemann-Jensen ◽  
Christoph Ziegenhain ◽  
Rickard Sandberg
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Peripheral Blood Mononuclear Cells ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Cell Types ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Blood Mononuclear Cells ◽  
Additional Protein

Plate-based single-cell RNA-sequencing methods with full-transcript coverage typically excel at sensitivity but are more resource and time-consuming. Here, we miniaturized and streamlined the Smart-seq3 protocol for drastically reduced cost and increased throughput. Applying Smart-seq3xpress to 16,349 human peripheral blood mononuclear cells revealed a highly granular atlas complete with both common and rare cell types whose identification previously relied on additional protein measurements or the integration with a reference atlas.

scholarly journals Single-Cell RNA-Sequencing of Peripheral Blood Mononuclear Cells Reveals Changes in Immune Cell Composition and Distinct Inflammatory Gene Expression Profiles during Idiopathic Multicentric Castleman Disease Flare

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2406-2406 ◽  
Author(s):  
Ruth-Anne Langan ◽  
Dustin Shilling ◽  
Michael Gonzalez ◽  
Charlly Kao ◽  
Hakon Hakonarson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Partial Remission ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Expression Profiles ◽  
Castleman Disease ◽  
Peripheral Blood Mononuclear ◽  
Disease Flare ◽  
Inflammatory Gene

Abstract Idiopathic multicentric Castleman disease (iMCD) is a rare and deadly hematologic illness involving episodic disease flares with polyclonal lymphoproliferation, systemic inflammation, and multiple organ system dysfunction. A cytokine storm involving interleukin(IL)-6 is believed to drive disease pathogenesis in some patients. However, only 34% of patients were found to respond to anti-IL-6 therapy with siltuximab in its registrational clinical trial; no other FDA approved treatments exist for iMCD. With the 5- and 10-year mortality rates reported as 35% and 60%, respectively, there is a clear need for additional treatment options. However, the development of next generation therapeutics is challenging as the etiology, pathological cell types, and signaling pathways involved in iMCD are largely unknown. To identify pathophysiological mechanisms and cellular drivers of iMCD, we applied cutting edge single-cell RNA-sequencing (scRNA-seq) technology to investigate bulk peripheral blood mononuclear cells (PBMCs) isolated from an iMCD patient at two distinct stages of disease activity. The first sample was collected during a short remission period following the patient's first disease flare (partial remission) (clinical symptom: fatigue; laboratory tests: hemoglobin 11.2 g/dL, platelets: 225,000/µL; albumin 4.2 g/dL, creatinine 0.73 mg/dL) and a second sample was collected at the start of his second flare (flare 2) (clinical symptoms: fatigue, fever, night sweats and fluid accumulation; laboratory tests: hemoglobin 12.9 g/dL, platelets: 122,000/µL; albumin 2.3 g/dL, creatinine 1.48 mg/dL). We utilized the Cellranger pipeline (10x Genomics, v.2.1.0) for aggregation of single-cell transcriptomes and Loupe Cell Browser (10x Genomics, v.2.1.0) for initial analysis of 20,135 recovered cells from partial remission (16,283 means reads/cell, 799 median genes/cell) and 19,322 recovered cells in flare 2 (17,327 reads/cell, 823 median genes/cell). Initial analyses of clusters revealed changes in the composition and frequency of immune cell subsets between the two samples. Plasmablasts (identified as expressing CD19, CD27, CD38, CD79a, CD79b) increased 7-fold in number during flare 2 with 28 cells in partial remission and 216 cells in flare 2. Similarly, monocyte and macrophage cell populations increased in frequency from 9% of all PBMCs in the partial remission sample to 15% of all PBMCs in the flare 2 sample. Conversely, CD8+ T cell frequency in the dataset decreased from 22% of the partial remission sample to 13% in flare 2. Interrogation of gene expression profiles of immune cell clusters identified highly activated CD8+ T cells which increased in frequency during flare 2 and are characterized by an inflammatory gene signature including expression of perforin and granzyme. Additionally, inflammatory gene signatures within the myeloid cell compartment during flare were identified, including elevated expression of S100 family members. S100 proteins are implicated in the pathogenesis of a number of autoimmune diseases and contribute to immune cell migration, chemotaxis, and leukocyte invasion. To our knowledge this is the first application of cutting edge single-cell sequencing technology to PBMCs obtained from an iMCD patient in flare and remission. Our observations support a role for both T and B cell activation in iMCD flare and lead us to hypothesize that CD8+ T cells may have left circulation and migrated to sites of active inflammation during this patient's disease flare. This dataset demonstrates involvement of multiple immune cell populations and inflammatory gene programs during disease flare in this patient and provides a novel resource for understanding gene expression and cell population changes in Castleman disease. Disclosures Fajgenbaum: Janssen Pharmaceuticals, Inc.: Research Funding.

scholarly journals Single-cell RNA-sequencing reveals widespread personalized, context-specific gene expression regulation in immune cells.

2021 ◽  
Author(s):  
Roy Oelen ◽  
Dylan H. de Vries ◽  
Harm Brugge ◽  
Gracie Gordon ◽  
Martijn Vochteloo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Lupus Erythematosus ◽  
Mononuclear Cells ◽  
Gene Expression Regulation ◽  
Specific Gene ◽  
Specific Expression ◽  
Peripheral Blood Mononuclear ◽  
Cell Type Specific Expression ◽  
Context Specific

Gene expression and its regulation can be context-dependent. To dissect this, using samples from 120 individuals, we single-cell RNA-sequenced 1.3M peripheral blood mononuclear cells exposed to three different pathogens at two time points or left unexposed. This revealed thousands of cell type-specific expression changes (eQTLs) and pathogen-induced expression changes (response QTLs) that are influenced by genetic variation. In monocytes, the strongest responder to pathogen stimulations, genetics also affected co-expression of 71.4% of these eQTL genes. For example, the pathogen recognition receptor CLEC12A showed many such co-expression interactions, but only in monocytes after 3h pathogen stimulation. Further analysis linked this to interferon-regulating transcription factors, a finding that we recapitulated in an independent cohort of patients with systemic lupus erythematosus, a condition characterized by increased interferon activity. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease.

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