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 ◽  
...  

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.

2018 ◽  
Author(s):  
Ken Jean-Baptiste ◽  
José L. McFaline-Figueroa ◽  
Cristina M. Alexandre ◽  
Michael W. Dorrity ◽  
Lauren Saunders ◽  
...  

ABSTRACTSingle-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach toA. thalianaroot cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.


2019 ◽  
Author(s):  
Richa Hanamsagar ◽  
Timothy Reizis ◽  
Mathew Chamberlain ◽  
Robert Marcus ◽  
Frank O. Nestle ◽  
...  

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.


2020 ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Jubao Duan ◽  
Kai Wang

AbstractThe importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, the vast majority of gene expression studies are conducted on bulk tissues, necessitating computational approaches to infer biological insights on cell type-specific contribution to diseases. Several computational methods are available for cell type deconvolution (that is, inference of cellular composition) from bulk RNA-Seq data, but cannot impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq (scRNA-seq) and population-wide expression profiles, it can be a computationally tractable and identifiable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations by employing genome-wide tissue-wise expression signatures from GTEx to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations, and uses a multi-variate stochastic search algorithm to estimate the expression level of each gene in each cell type. Extensive analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease, and type 2 diabetes validated efficiency of CellR, while revealing how specific cell types contribute to different diseases. We conducted numerical simulations on human cerebellum to generate pseudo-bulk RNA-seq data and demonstrated its efficiency in inferring cell-specific expression profiles. Moreover, we inferred cell-specific expression levels from bulk RNA-seq data on schizophrenia and computed differentially expressed genes within certain cell types. Using predicted gene expression profile on excitatory neurons, we were able to reproduce our recently published findings on TCF4 being a master regulator in schizophrenia and showed how this gene and its targets are enriched in excitatory neurons. In summary, CellR compares favorably (both accuracy and stability of inference) against competing approaches on inferring cellular composition from bulk RNA-seq data, but also allows direct imputation of cell type-specific gene expression, opening new doors to re-analyze gene expression data on bulk tissues in complex diseases.


2017 ◽  
Author(s):  
Hyun Min Kang ◽  
Meena Subramaniam ◽  
Sasha Targ ◽  
Michelle Nguyen ◽  
Lenka Maliskova ◽  
...  

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.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2406-2406 ◽  
Author(s):  
Ruth-Anne Langan ◽  
Dustin Shilling ◽  
Michael Gonzalez ◽  
Charlly Kao ◽  
Hakon Hakonarson ◽  
...  

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.


2021 ◽  
Vol 12 ◽  
Author(s):  
Haiyan Yu ◽  
Xiaoping Hong ◽  
Hongwei Wu ◽  
Fengping Zheng ◽  
Zhipeng Zeng ◽  
...  

ObjectiveSystemic lupus erythematosus (SLE) is a complex autoimmune disease, and various immune cells are involved in the initiation, progression, and regulation of SLE. Our goal was to reveal the chromatin accessibility landscape of peripheral blood mononuclear cells (PBMCs) in SLE patients at single-cell resolution and identify the transcription factors (TFs) that may drive abnormal immune responses.MethodsThe assay for transposase accessible chromatin in single-cell sequencing (scATAC-seq) method was applied to map the landscape of active regulatory DNA in immune cells from SLE patients at single-cell resolution, followed by clustering, peak annotation and motif analysis of PBMCs in SLE.ResultsPeripheral blood mononuclear cells were robustly clustered based on their types without using antibodies. We identified twenty patterns of TF activation that drive abnormal immune responses in SLE patients. Then, we observed ten genes that were highly associated with SLE pathogenesis by altering T cell activity. Finally, we found 12 key TFs regulating the above six genes (CD83, ELF4, ITPKB, RAB27A, RUNX3, and ZMIZ1) that may be related to SLE disease pathogenesis and were significantly enriched in SLE patients (p <0.05, FC >2). With qPCR experiments on CD83, ELF4, RUNX3, and ZMIZ1 in B cells, we observed a significant difference in the expression of genes (ELF4, RUNX3, and ZMIZ1), which were regulated by seven TFs (EWSR1-FLI1, MAF, MAFA, NFIB, NR2C2 (var. 2), TBX4, and TBX5). Meanwhile, the seven TFs showed highly accessible binding sites in SLE patients.ConclusionsThese results confirm the importance of using single-cell sequencing to uncover the real features of immune cells in SLE patients, reveal key TFs in SLE-PBMCs, and provide foundational insights relevant for epigenetic therapy.


BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Christopher S. McGinnis ◽  
David A. Siegel ◽  
Guorui Xie ◽  
George Hartoularos ◽  
Mars Stone ◽  
...  

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.


2017 ◽  
Author(s):  
Garth R. Ilsley ◽  
Ritsuko Suyama ◽  
Takeshi Noda ◽  
Nori Satoh ◽  
Nicholas M. Luscombe

AbstractSingle-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.


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