Quin F. Wills

Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments

Gene expression in multiple individual cells from a tissue or culture sample varies according to cell-cycle, genetic, epigenetic and stochastic differences between the cells. However, single-cell differences have been largely neglected in the analysis of the functional consequences of genetic variation. Here we measure the expression of 92 genes affected by Wnt signaling in 1,440 single cells from 15 individuals to associate single-nucleotide polymorphisms (SNPs) with gene-expression phenotypes, while accounting for stochastic and cell-cycle differences between cells. We provide evidence that many heritable variations in gene function--such as burst size, burst frequency, cell cycle-specific expression and expression correlation/noise between cells--are masked when expression is averaged over many cells. Our results demonstrate how single-cell analyses provide insights into the mechanistic and network effects of genetic variability, with improved statistical power to model these effects on gene expression.

MicroRNA-125a is over-expressed in insulin target tissues in a spontaneous rat model of Type 2 Diabetes

BackgroundMicroRNAs (miRNAs) are non-coding RNA molecules involved in post-transcriptional control of gene expression of a wide number of genes, including those involved in glucose homeostasis. Type 2 diabetes (T2D) is characterized by hyperglycaemia and defects in insulin secretion and action at target tissues. We sought to establish differences in global miRNA expression in two insulin-target tissues from inbred rats of spontaneously diabetic and normoglycaemic strains.MethodsWe used a miRNA microarray platform to measure global miRNA expression in two insulin-target tissues: liver and adipose tissue from inbred rats of spontaneously diabetic (Goto-Kakizaki [GK]) and normoglycaemic (Brown-Norway [BN]) strains which are extensively used in genetic studies of T2D. MiRNA data were integrated with gene expression data from the same rats to investigate how differentially expressed miRNAs affect the expression of predicted target gene transcripts.ResultsThe expression of 170 miRNAs was measured in liver and adipose tissue of GK and BN rats. Based on a p-value for differential expression between GK and BN, the most significant change in expression was observed for miR-125a in liver (FC = 5.61, P = 0.001, Padjusted= 0.10); this overexpression was validated using quantitative RT-PCR (FC = 13.15, P = 0.0005). MiR-125a also showed over-expression in the GK vs. BN analysis within adipose tissue (FC = 1.97, P = 0.078, Padjusted= 0.99), as did the previously reported miR-29a (FC = 1.51, P = 0.05, Padjusted= 0.99). In-silico tools assessing the biological role of predicted miR-125a target genes suggest an over-representation of genes involved in the MAPK signaling pathway. Gene expression analysis identified 1308 genes with significantly different expression between GK and BN rats (Padjusted < 0.05): 233 in liver and 1075 in adipose tissue. Pathways related to glucose and lipid metabolism were significantly over-represented among these genes. Enrichment analysis suggested that differentially expressed genes in GK compared to BN included more predicted miR-125a target genes than would be expected by chance in adipose tissue (FDR = 0.006 for up-regulated genes; FDR = 0.036 for down-regulated genes) but not in liver (FDR = 0.074 for up-regulated genes; FDR = 0.248 for down-regulated genes).ConclusionMiR-125a is over-expressed in liver in hyperglycaemic GK rats relative to normoglycaemic BN rats, and our array data also suggest miR-125a is over-expressed in adipose tissue. We demonstrate the use of in-silico tools to provide the basis for further investigation of the potential role of miR-125a in T2D. In particular, the enrichment of predicted miR-125a target genes among differentially expressed genes has identified likely target genes and indicates that integrating global miRNA and mRNA expression data may give further insights into miRNA-mediated regulation of gene expression.

Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells

Highlights ► Microfluidic arrays enable analysis of 96 qPCR assays on 1440 single cells. ► Detailed methods on obtaining qPCR data and performing preliminary data processing. ► Data from sufficient cells to address noise inherent in single-cell transcription. ► Methods used for conventional qPCR do not necessarily apply to single-cell qPCR.

Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.

Motivation: Single‐cell RNA sequencing (scRNA‐seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre‐processing, quality control (QC) and normalization. Results: We have developed the R/Bioconductor package scater to facilitate rigorous pre‐processing, quality control, normalization and visualization of scRNA‐seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high‐quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single‐cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. Availability and Implementation: The open‐source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater. Contact: davis@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes.

Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS–associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (DABD-GLU = 0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response–related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS–associated versus un-associated modules (ABD: 0.48 versus 0.18, P = 0.08; GLU: 0.54 versus 0.20, P = 7.8×10−4). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS–related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P = 6.0×10−4); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10−4) and BMI–adjusted waist-to-hip ratio (P = 2.4×10−4). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.

MicroRNA Expression in Abdominal and Gluteal Adipose Tissue Is Associated with mRNA Expression Levels and Partly Genetically Driven

To understand how miRNAs contribute to the molecular phenotype of adipose tissues and related traits, we performed global miRNA expression profiling in subcutaneous abdominal and gluteal adipose tissue of 70 human subjects and characterised which miRNAs were differentially expressed between these tissues. We found that 12% of the miRNAs were significantly differentially expressed between abdominal and gluteal adipose tissue (FDR adjusted p<0.05) in the primary study, of which 59 replicated in a follow-up study of 40 additional subjects. Further, 14 miRNAs were found to be associated with metabolic syndrome case-control status in abdominal tissue and three of these replicated (primary study: FDR adjusted p<0.05, replication: p<0.05 and directionally consistent effect). Genome-wide genotyping was performed in the 70 subjects to enable miRNA expression quantitative trait loci (eQTL) analysis. Candidate miRNA eQTLs were followed-up in the additional 40 subjects and six significant, independent cis-located miRNA eQTLs (primary study: p<0.001; replication: p<0.05 and directionally consistent effect) were identified. Finally, global mRNA expression profiling was performed in both tissues to enable association analysis between miRNA and target mRNA expression levels. We find 22% miRNAs in abdominal and 9% miRNAs in gluteal adipose tissue with expression levels significantly associated with the expression of corresponding target mRNAs (FDR adjusted p<0.05). Taken together, our results indicate a clear difference in the miRNA molecular phenotypic profile of abdominal and gluteal adipose tissue, that the expressions of some miRNAs are influenced by cis-located genetic variants and that miRNAs are associated with expression levels of their predicted mRNA targets.

Beta-Poisson model for single-cell RNA-seq data analyses

MOTIVATION Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property. RESULTS We introduce a beta-Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution. We further integrate the model into the generalized linear model framework in order to perform differential expression analyses. The whole analytical procedure is called BPSC. The results from several real single-cell RNA-seq datasets indicate that ∼90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in > 80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data. AVAILABILITY AND IMPLEMENTATION An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq data is available under GPL-3 license at https://github.com/nghiavtr/BPSC CONTACT: yudi.pawitan@ki.se or mattias.rantalainen@ki.se SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq.

BackgroundSingle-cell RNA-Seq can be a valuable and unbiased tool to dissect cellular heterogeneity, despite the transcriptome’s limitations in describing higher functional phenotypes and protein events. Perhaps the most important shortfall with transcriptomic ‘snapshots’ of cell populations is that they risk being descriptive, only cataloging heterogeneity at one point in time, and without microenvironmental context. Studying the genetic (‘nature’) and environmental (‘nurture’) modifiers of heterogeneity, and how cell population dynamics unfold over time in response to these modifiers is key when studying highly plastic cells such as macrophages.ResultsWe introduce the programmable Polaris™ microfluidic lab-on-chip for single-cell sequencing, which performs live-cell imaging while controlling for the culture microenvironment of each cell. Using gene-edited macrophages we demonstrate how previously unappreciated knockout effects of SAMHD1, such as an altered oxidative stress response, have a large paracrine signaling component. Furthermore, we demonstrate single-cell pathway enrichments for cell cycle arrest and APOBEC3G degradation, both associated with the oxidative stress response and altered proteostasis. Interestingly, SAMHD1 and APOBEC3G are both HIV-1 inhibitors (‘restriction factors’), with no known co-regulation.ConclusionAs single-cell methods continue to mature, so will the ability to move beyond simple ‘snapshots’ of cell populations towards studying the determinants of population dynamics. By combining single-cell culture, live-cell imaging, and single-cell sequencing, we have demonstrated the ability to study cell phenotypes and microenvironmental influences. It’s these microenvironmental components - ignored by standard single-cell workflows - that likely determine how macrophages, for example, react to inflammation and form treatment resistant HIV reservoirs.

Statistical approaches and software for clustering islet cell functional heterogeneity

ABSTRACT Worldwide efforts are underway to replace or repair lost or dysfunctional pancreatic β-cells to cure diabetes. However, it is unclear what the final product of these efforts should be, as β-cells are thought to be heterogeneous. To enable the analysis of β-cell heterogeneity in an unbiased and quantitative way, we developed model-free and model-based statistical clustering approaches, and created new software called TraceCluster. Using an example data set, we illustrate the utility of these approaches by clustering dynamic intracellular Ca2+ responses to high glucose in ∼300 simultaneously imaged single islet cells. Using feature extraction from the Ca2+ traces on this reference data set, we identified 2 distinct populations of cells with β-like responses to glucose. To the best of our knowledge, this report represents the first unbiased cluster-based analysis of human β-cell functional heterogeneity of simultaneous recordings. We hope that the approaches and tools described here will be helpful for those studying heterogeneity in primary islet cells, as well as excitable cells derived from embryonic stem cells or induced pluripotent cells.

Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer

We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was applied to MDA-MB-231 breast cancer cells treated with the antidiabetic drug metformin, which is being repurposed for treatment of triple-negative breast cancer. Unsupervised learning identified a cluster of metformin-treated cells characterized by a significant suppression of 230 genes (p-value < 2E-16). This analysis corroborates known studies of metformin action: a) pathway analysis indicated known mechanisms related to metformin action, including the citric acid (TCA) cycle, oxidative phosphorylation, and mitochondrial dysfunction (p-value < 1E-9); b) 70% of these 230 genes were functionally implicated in metformin response; c) among remaining lesser functionally-studied genes for metformin-response was CDC42, down-regulated in breast cancer treated with metformin. However, CDC42s mechanisms in metformin response remained unclear. Our functional studies showed that CDC42 was involved in metformin-induced inhibition of cell proliferation and cell migration mediated through an AMPK-independent mechanism. Our results points to 230 genes that might serve as metformin response signatures, which needs to be tested in patients treated with metformin and, further investigation of CDC42 and AMPK-independences role in metformins anticancer mechanisms.