scholarly journals Rheostat Coordination of Latent Kaposi Sarcoma-Associated Herpesvirus RNA expression in Single Cells

2021 ◽  
Author(s):  
Nicole C. Rondeau ◽  
JJ L. Miranda

We detected precise coordination of RNA levels between two latent genes of the Kaposi sarcoma-associated herpesvirus (KSHV) using single-cell RNA sequencing. LANA and vIL6 are expressed during latency by different promoters on remote regions of the episome.…

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sunny Z. Wu ◽  
Daniel L. Roden ◽  
Ghamdan Al-Eryani ◽  
Nenad Bartonicek ◽  
Kate Harvey ◽  
...  

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.


2019 ◽  
Author(s):  
Imad Abugessaisa ◽  
Shuhei Noguchi ◽  
Melissa Cardon ◽  
Akira Hasegawa ◽  
Kazuhide Watanabe ◽  
...  

AbstractAnalysis and interpretation of single-cell RNA-sequencing (scRNA-seq) experiments are compromised by the presence of poor quality cells. For meaningful analyses, such poor quality cells should be excluded to avoid biases and large variation. However, no clear guidelines exist. We introduce SkewC, a novel quality-assessment method to identify poor quality single-cells in scRNA-seq experiments. The method is based on the assessment of gene coverage for each single cell and its skewness as a quality measure. To validate the method, we investigated the impact of poor quality cells on downstream analyses and compared biological differences between typical and poor quality cells. Moreover, we measured the ratio of intergenic expression, suggesting genomic contamination, and foreign organism contamination of single-cell samples. SkewC is tested in 37,993 single-cells generated by 15 scRNA-seq protocols. We envision SkewC as an indispensable QC method to be incorporated into scRNA-seq experiment to preclude the possibility of scRNA-seq data misinterpretation.


2016 ◽  
Author(s):  
Hannah R. Dueck ◽  
Rizi Ai ◽  
Adrian Camarena ◽  
Bo Ding ◽  
Reymundo Dominguez ◽  
...  

AbstractRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.


2020 ◽  
Author(s):  
Felipe Vilella ◽  
Wanxin Wang ◽  
Inmaculada Moreno ◽  
Stephen R. Quake ◽  
Carlos Simon

AbstractWe investigated potential SARS-CoV-2 tropism in human endometrium by single-cell RNA-sequencing of viral entry-associated genes in healthy women. Percentages of endometrial cells expressing ACE2, TMPRSS2, CTSB, or CTSL were <2%, 12%, 80%, and 80%, respectively, with 0.7% of cells expressing all four genes. Our findings imply low efficiency of SARS-CoV-2 infection in the endometrium before embryo implantation, providing information to assess preconception risk in asymptomatic carriers.


Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 240 ◽  
Author(s):  
Prashant N. M. ◽  
Hongyu Liu ◽  
Pavlos Bousounis ◽  
Liam Spurr ◽  
Nawaf Alomran ◽  
...  

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.


2021 ◽  
Vol 41 (3) ◽  
pp. 1012-1018
Author(s):  
Jean Acosta ◽  
Daniel Ssozi ◽  
Peter van Galen

The blood system is often represented as a tree-like structure with stem cells that give rise to mature blood cell types through a series of demarcated steps. Although this representation has served as a model of hierarchical tissue organization for decades, single-cell technologies are shedding new light on the abundance of cell type intermediates and the molecular mechanisms that ensure balanced replenishment of differentiated cells. In this Brief Review, we exemplify new insights into blood cell differentiation generated by single-cell RNA sequencing, summarize considerations for the application of this technology, and highlight innovations that are leading the way to understand hematopoiesis at the resolution of single cells. Graphic Abstract: A graphic abstract is available for this article.


Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0003682021
Author(s):  
Rachel M B Bell ◽  
Laura Denby

Kidney disease represents a global health burden of increasing prevalence and is an independent risk factor for cardiovascular disease. Myeloid cells are a major cellular compartment of the immune system; they are found in the healthy kidney and in increased numbers in the damaged and/or diseased kidney, where they act as key players in the progression of injury, inflammation and fibrosis. They possess enormous plasticity and heterogeneity, adopting different phenotypic and functional characteristics in response to stimuli in the local milieu. Though this inherent complexity remains to be fully understood in the kidney, advances in single-cell genomics promises to change this. Specifically, single-cell RNA sequencing (scRNA-seq) has had a transformative effect on kidney research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput, and subsequent generation of cell atlases. Moving forward, combining scRNA- and single-nuclear RNA-seq with greater resolution spatial transcriptomics will allow spatial mapping of kidney disease of varying aetiology to further reveal the patterning of immune cells and non-immune renal cells. This review summarises the roles of myeloid cells in kidney health and disease, the experimental workflow in currently available scRNA-seq technologies and published findings using scRNA-seq in the context of myeloid cells and the kidney.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2756-2756
Author(s):  
Erin Guest ◽  
Byunggil Yoo ◽  
Rumen Kostadinov ◽  
Midhat S. Farooqi ◽  
Emily Farrow ◽  
...  

Introduction Infant acute lymphoblastic leukemia (ALL) with KMT2A rearrangement (KMT2A-r) is associated with a very poor prognosis. Disease free survival from the date of diagnosis is approximately 20% to 40%, depending on age, white blood cell count, and response to induction therapy. Refractory and relapsed infant ALL is often resistant to attempts at re-induction, and second remission is difficult to both achieve and maintain. Genomic sequencing studies of infant KMT2A-r ALL clinical samples have demonstrated an average of fewer than 3 additional non-silent somatic mutations per case at diagnosis, most commonly sub-clonal variants in RAS pathway genes. We previously reported relapse-associated gains in somatic variants associated with signaling, adhesion, and B-cell development pathways (Blood 2016 128:1735). We hypothesized that relapsed infant ALL is characterized by recurrent, altered patterns of gene expression. In this analysis, we utilized single cell RNA sequencing (scRNAseq) to identify candidate genes with differential expression in diagnostic vs. relapse leukemia specimens from 3 infants with KMT2A-r ALL. Methods Cryopreserved blood or bone marrow specimens from 3 infants enrolled in the Children's Oncology Group AALL0631 trial were selected for analysis. Samples from both diagnosis (DX) and relapse (RL) time points were thawed and checked for viability (>90% of cells viable) using trypan blue staining. Samples were multiplexed and processed for single cell RNA sequencing using the Chromium Single Cell 3' Library Kit (v2) and 10x Genomics Chromium controller per manufacturer's instructions (10x Genomics, Pleasanton, CA). Single cell libraries were converted to cDNA, amplified, and sequenced on an Illumina NovaSeq instrument. Two technical replicates were performed. Samples were de-multiplexed using genotype information acquired from previous whole exome sequencing (WES) and demuxlet software. Transcript alignment and counting were performed using the Cell Ranger pipeline (10x Genomics, default settings, Version 2.2.0, GRCh37 reference). Quality control, normalization, gene expression analysis, and unsupervised clustering were performed using the Seurat R package (Version 3.0). Dimensionality reduction and visualization were performed with the UMAP algorithm. Analyses were restricted to leukemia blasts with CD19 expression by scRNAseq. Results The clinical features for each case are shown in Table 1. Cells from the 3 infant ALL samples clustered together, distinct from cells of non-infant B-ALL, T-ALL, and mixed lineage acute leukemia biospecimens in the Children's Mercy scRNAseq database, but largely did not overlap with one another. For each of the 3 infant cases, cells from DX and RL time points could be distinguished by differential patterns of gene expression (Figure 1). Individual genes with statistically significant (p<0.05) log-fold change values were examined. Figure 2 summarizes the number of genes with up-regulation of expression by scRNAseq at RL compared to DX. Only 6 genes, DYNLL1, HMGB2, HMGN2, JUN, STMN1, and TUBA1B, were significantly increased at RL across all 3 cases. We repeated this analysis, restricting to leukemia blasts with CD79A expression, and identified these same 6 genes, and 4 additional genes: H2AFZ, NUCKS1, PRDX1, and TUBB, as consistently up-regulated in RL clusters. We examined the expression of candidate genes of interest, including clinically targetable genes, to compare the distribution of expression at DX and RL (Table 2). Conclusion Genomic factors underlying the aggressive, refractory clinical phenotype of relapsed infant ALL have yet to be defined. Each of these 3 cases demonstrates unique expression patterns at relapse, readily distinguishable from both the paired diagnostic sample and the other 2 relapse samples. Thus, scRNAseq is a powerful tool to identify heterogeneity in gene expression, with the potential to discover recurrent genomic drivers within resistant disease sub-clones. Ongoing analyses include scRNAseq in additional infant ALL samples, relative quantification of transcript expression in single cells, and comparison with bulk RNAseq data. Disclosures No relevant conflicts of interest to declare.


2018 ◽  
Vol 31 (Supplement_1) ◽  
pp. 190-190
Author(s):  
Robert Walker ◽  
Maria Secrier ◽  
Jack Harrington ◽  
Rachel Parker ◽  
Jamie Kelly ◽  
...  

Abstract Background Esophageal adenocarcinoma (EAC) develops in a complex ecosystem that defines tumour evolution, response to treatment and patient outcomes. We have shown that high levels of cancer associated fibroblast (CAF) in the tumour microenvironment (TME) predicts poor outcome and is inversely related to tumour infiltrating lymphocyte (TILs) abundance. Bulk sequencing studies lack the resolution to dissect the phenotypic and functional heterogeneity and cell-cell interactions of the TME. We have applied single cell RNA sequencing to 12 EAC patients to address this challenge. Methods A total of 24 single-cell suspensions were prepared from resected specimens and paired normal tissue. Single cells and barcoded mRNA-binding micro-particles were combined in droplets containing cell lysis buffer using a custom-built microfluidic platform. Captured mRNA with a cell barcode and unique molecular identifier was reverse transcribed, amplified and sequenced. SEURAT (v2.1, R-package) was used to identify highly-variable genes and perform cell clustering. Results Analysis of 6859 of the highest quality cells using the 4167 most variable genes revealed 46 clusters which were divided into 12 broad populations. Antigen Presenting Cells (n = 189), B Cells (n = 265), Cancer Cells (n = 1449), Endothelial Cells (321), Fibroblasts (1690), Mast Cells (n = 184), Monocytes/Macrophages (n = 254), Plasma Cells (n = 208), Smooth Muscle, (n = 115), Squamous Epithelium (n = 751), T Cells (n = 1433). Analysis of publicly available bulk RNAseq datasets (TCGA) of EAC showed that tumours that were ‘hot’ for a CAF gene signature were ‘cold’ for a T-Cell signature. Subset analysis of the fibroblasts from tumour samples that were enriched for the same CAF signature revealed 3 subtly different clusters. One of these sub-populations differentially expressed genes associated with the Gene Ontology terms GO:00,40011 (locomotion) GO:0,006928 (movement of cell or subcellular component) and GO:00,48870 (cell motility). The two tumours with the highest ratio of this type of CAF to T-Cells were both found to have distant metastasis at resection. Conclusion EAC CAFs are a heterogeneous population with distinct biological functions which may have different implications for prognosis. These early results suggest that we are able to identify candidate biological processes that may describe the mechanisms through which CAFs and TILs influence outcome. Disclosure All authors have declared no conflicts of interest.


2019 ◽  
Vol 28 (21) ◽  
pp. 3569-3583 ◽  
Author(s):  
Patricia M Schnepp ◽  
Mengjie Chen ◽  
Evan T Keller ◽  
Xiang Zhou

Abstract Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.


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