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Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.

ABSTRACT: Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Here, we show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, we formulated plausible mixture models for cell-to-cell regulatory heterogeneity and maximized the resulting likelihood functions to infer model parameters. Inferences were validated both computationally and experimentally for different mixture models, which included regulatory states for multicellular function that were occupied by as few as 1 in 40 cells of the population. Importantly, when the method was extended to programs of heterogeneously coexpressed transcripts, we found that population-level inferences were much more accurate with pooled samples than with one-cell samples when the extent of sampling was limited. Our deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions.

SUBMITTER: Bajikar SS

PROVIDER: S-EPMC3918796 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:MotivationSingle cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.ResultsWe developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development.Availability and implementationMATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:We previously found higher level of endothelial cell (EC) activation in patients who suffered from in-stent restenosis after bare-metal stenting compared to subjects who underwent drug-eluting stenting (DES) showing no complications. Here we investigated the potential transcriptional and post-transcriptional regulatory mechanisms by which everolimus attenuated EC activation after DES. We studied the effect of everolimus on E-selectin (SELE) and VCAM1 mRNA levels when human coronary artery (HCAECs) and human umbilical vein ECs were challenged with recombinant TNF-? (100 ng/mL) for 1-24 hours in the presence or absence of everolimus using 0.5 ?M concentration locally maintained by DES. EC activation was evaluated via the levels of IL-1? and IL-6 mRNAs with miR-155 expression by RT-qPCR as well as the nuclear translocation of nuclear factor kappa beta (NF-?B) detected by fluorescence microscopy. To investigate the transcriptional regulation of E-selectin and VCAM-1, TNF-?-induced enhancer RNA (eRNA) expression at p65-bound enhancers in the neighboring genomic regions of SELE and VCAM1 genes, including SELE_-11Kb and VCAM1_-10Kb, were measured in HCAECs. Mature and precursor levels of E-selectin and VCAM-1 repressor miR-181b were quantified to analyze the post-transcriptional regulation of these genes in HCAECs. Circulating miR-181b was analyzed in plasma samples of stented subjects by stem-loop RT-qPCR. TNF-? highly elevated E-selectin and VCAM-1 expression at transcriptional level in ECs. Levels of mature, pre- and pri-miR-181b were repressed in ECs by TNF-?, while everolimus acted as a negative regulator of EC activation via inhibited translocation of NF-?B p65 subunit into cell nuclei, lowered eRNA expression at SELE and VCAM1 genes-associated enhancers and modulated expression of their post-transcriptional repressor miR-181b. Significant negative correlation was observed between plasma miR-181b and soluble E-selectin and VCAM-1 in patients. In conclusion, everolimus attenuates EC activation via reduced NF-?B p65 translocation causing decreased E-selectin and VCAM-1 expression at transcriptional and post-transcriptional level after DES.

Project description:Molecular mechanisms employed by individual multipotent cells at the point of lineage commitment remain largely uncharacterized. Current paradigms span from instructive to noise-driven mechanisms. Of considerable interest is also whether commitment involves a limited set of genes or the entire transcriptional program, and to what extent gene expression configures multiple trajectories into commitment. Importantly, the transient nature of the commitment transition confounds the experimental capture of committing cells. We develop a computational framework that simulates stochastic commitment events, and affords mechanistic exploration of the fate transition. We use a combined modeling approach guided by gene expression classifier methods that infers a time-series of stochastic commitment events from experimental growth characteristics and gene expression profiling of individual hematopoietic cells captured immediately before and after commitment. We define putative regulators of commitment and probabilistic rules of transition through machine learning methods, and employ clustering and correlation analyses to interrogate gene regulatory interactions in multipotent cells. Against this background, we develop a Monte Carlo time-series stochastic model of transcription where the parameters governing promoter status, mRNA production and mRNA decay in multipotent cells are fitted to experimental static gene expression distributions. Monte Carlo time is converted to physical time using cell culture kinetic data. Probability of commitment in time is a function of gene expression as defined by a logistic regression model obtained from experimental single-cell expression data. Our approach should be applicable to similar differentiating systems where single cell data is available. Within our system, we identify robust model solutions for the multipotent population within physiologically reasonable values and explore model predictions with regard to molecular scenarios of entry into commitment. The model suggests distinct dependencies of different commitment-associated genes on mRNA dynamics and promoter activity, which globally influence the probability of lineage commitment.

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Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.

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