Digital signaling decouples activation probability and population heterogeneity

Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity.

Download Full-text

Automated cell cycle and cell size measurements for single-cell gene expression studies

10.1101/182766 ◽

2017 ◽

Author(s):

Anissa Guillemin ◽

Angelique Richard ◽

Sandrine Gonin-Giraud ◽

Olivier Gandrillon

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Single Cell ◽

Cell Size ◽

Cell Tracking ◽

Erythroid Progenitors ◽

Expression Variability ◽

Unbiased Gene ◽

Transcriptomic Level ◽

Cell To Cell Variability

AbstractRecent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. How cell cycle and cell size influences gene expression variability at single-cell level is not yet clearly understood. To deconvolute such influences, most of the single-cell studies used dedicated methods that could include some bias. Here, we provide a universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.

Download Full-text

Exploiting variability of single cells to uncover the in vivo hierarchy of miRNA targets

10.1101/035097 ◽

2015 ◽

Cited By ~ 2

Author(s):

Andrzej Jerzy Rzepiela ◽

Arnau Vina-Vilaseca ◽

Jeremie Breda ◽

Souvik Ghosh ◽

Afzal P Syed ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Cells ◽

Human Embryonic Kidney Cells ◽

Mirna Targets ◽

Wide Range ◽

Gene Expression Dynamics ◽

Cell To Cell Variability ◽

First Time

MiRNAs are post-transcriptional repressors of gene expression that may additionally reduce the cell-to-cell variability in protein expression, induce correlations between target expression levels and provide a layer through which targets can influence each other's expression as 'competing RNAs' (ceRNAs). Here we combined single cell sequencing of human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, with mathematical modeling, to estimate Michaelis-Menten (KM)-type constants for hundreds of evolutionarily conserved miRNA targets. These parameters, which we inferred here for the first time in the context of the entire network of endogenous miRNA targets, vary over ~2 orders of magnitude. They reveal an in vivo hierarchy of miRNA targets, defined by the concentration of miRNA-Argonaute complexes at which the targets are most sensitively down-regulated. The data further reveals miRNA-induced correlations in target expression at the single cell level, as well as the response of target noise to the miRNA concentration. The approach is generalizable to other miRNAs and post-transcriptional regulators and provides a deeper understanding of gene expression dynamics.

Download Full-text

Modeling of cytometry data in logarithmic space: when is a bimodal distribution not bimodal?

10.1101/150201 ◽

2017 ◽

Author(s):

Amir Erez ◽

Robert Vogel ◽

Andrew Mugler ◽

Andrew Belmonte ◽

Grégoire Altan-Bonnet

Keyword(s):

Single Cell ◽

Cell Activation ◽

Graphical Model ◽

Large Cell ◽

Bimodal Distribution ◽

Biological Information ◽

Logarithmic Scale ◽

Underlying Distribution ◽

Systems Immunology ◽

Cell To Cell Variability

AbstractRecent efforts in systems immunology lead researchers to build quantitative models of cell activation and differentiation. One goal is to account for the distributions of proteins from single-cell measurements by flow cytometry or mass cytometry as a readout of biological regulation. In that context, large cell-to-cell variability is often observed in biological quantities. We show here that these readouts, viewed in logarithmic scale may result in two easily-distinguishable modes, while the underlying distribution (in linear scale) is unimodal. We introduce a simple mathematical test to highlight this mismatch. We then dissect the flow of influence of cell-to-cell variability proposing a graphical model which motivates higher-dimensional analysis of the data. Finally we show how acquiring additional biological information can be used to reduce uncertainty introduced by cell-to-cell variability, helping to clarify whether the data is uni- or bimodal. This communication has cautionary implications for manual and automatic gating strategies, as well as clustering and modeling of single-cell measurements.

Download Full-text

Expression variation and covariation impair analog and enable binary signaling control

10.1101/244236 ◽

2018 ◽

Author(s):

Kyle M. Kovary ◽

Brooks Taylor ◽

Michael L. Zhao ◽

Mary N. Teruel

Keyword(s):

Single Cell ◽

Cell Fate ◽

Cell Activation ◽

Signal Transmission ◽

Population Level ◽

Stimulus Range ◽

Level Control ◽

Cell Fate Decisions ◽

Cell Functions ◽

Cell To Cell Variability

AbstractDue to noise in the synthesis and degradation of proteins, the concentrations of individual vertebrate signaling proteins were estimated to vary with a coefficient of variation (CV) of approximately 25% between cells. This high variation enables population-level regulation of cell functions but abolishes accurate single-cell signal transmission. Here we measure cell-to-cell variability of relative protein abundance using quantitative proteomics of individual Xenopus laevis eggs and cultured human cells and show that variation is typically much lower, in the range of 5–15%, compatible with accurate single-cell transmission. Furthermore, we show that MEK and ERK expression covary which improves controllability of the fraction of cells that activate bimodal ERK signaling, arguing that covariation has a role in facilitating population-level control of binary cell-fate decisions. Together, our experimental and model data argues for a control principle whereby low covariation limits signaling noise for accurate control analog single-cell signaling. In contrast, increased covariation widens the stimulus-range over which external inputs can regulate binary cell activation, thereby enabling accurate control of the fraction of activated cells at the population level.

Download Full-text

Stochastic holin expression can account for lysis time variation in the bacteriophage λ

Journal of The Royal Society Interface ◽

10.1098/rsif.2014.0140 ◽

2014 ◽

Vol 11 (95) ◽

pp. 20140140 ◽

Cited By ~ 34

Author(s):

Abhyudai Singh ◽

John J. Dennehy

Keyword(s):

Gene Expression ◽

Single Cell ◽

First Passage Time ◽

Passage Time ◽

Critical Threshold ◽

Biochemical Processes ◽

Lysis Time ◽

Analytical Formulae ◽

Mean And Variance ◽

Cell To Cell Variability

The inherent stochastic nature of biochemical processes can drive differences in gene expression between otherwise identical cells. While cell-to-cell variability in gene expression has received much attention, randomness in timing of events has been less studied. We investigate event timing at the single-cell level in a simple system, the lytic pathway of the bacterial virus phage λ . In individual cells, lysis occurs on average at 65 min, with an s.d. of 3.5 min. Interestingly, mutations in the lysis protein, holin, alter both the lysis time (LT) mean and variance. In our analysis, LT is formulated as the first-passage time (FPT) for cellular holin levels to cross a critical threshold. Exact analytical formulae for the FPT moments are derived for stochastic gene expression models. These formulae reveal how holin transcription and translation efficiencies independently modulate the LT mean and variation. Analytical expressions for the LT moments are used to evaluate previously published single-cell LT data for λ phages with mutations in the holin sequence or its promoter. Our results show that stochastic holin expression is sufficient to account for the intercellular LT differences in both wild-type phages, and phage variants where holin transcription and the threshold for lysis have been experimentally altered. Finally, our analysis reveals regulatory motifs that enhance the robustness of lysis timing to cellular noise.

Download Full-text

A multi-scale integrated analysis identifies KRT8 as a pan-cancer early biomarker

10.1101/2020.10.01.20205450 ◽

2020 ◽

Author(s):

Madeleine K. D. Scott ◽

Michael G. Ozawa ◽

Pauline Chu ◽

Maneesha Limaye ◽

Viswam S. Nair ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Clinical Practice ◽

Protein Expression ◽

Single Cell ◽

Population Heterogeneity ◽

Integrated Analysis ◽

Multi Scale ◽

Public Data ◽

Early Biomarker

An early biomarker would transform our ability to screen and treat patients with cancer. The large amount of multi-scale molecular data in public repositories from various cancers provide unprecedented opportunities to find such a biomarker. However, despite identification of numerous molecular biomarkers using these public data, fewer than 1% have proven robust enough to translate into clinical practice1. One of the most important factors affecting the successful translation to clinical practice is lack of real-world patient population heterogeneity in the discovery process. Almost all biomarker studies analyze only a single cohort of patients with the same cancer using a single modality. Recent studies in other diseases have demonstrated the advantage of leveraging biological and technical heterogeneity across multiple independent cohorts to identify robust disease biomarkers. Here we analyzed 17149 samples from patients with one of 23 cancers that were profiled using either DNA methylation, bulk and single-cell gene expression, or protein expression in tumor and serum. First, we analyzed DNA methylation profiles of 9855 samples across 23 cancers from The Cancer Genome Atlas (TCGA). We then examined the gene expression profile of the most significantly hypomethylated gene, KRT8, in 6781 samples from 57 independent microarray datasets from NCBI GEO. KRT8 was significantly over-expressed across cancers except colon cancer (summary effect size=1.05; p < 0.0001). Further, single-cell RNAseq analysis of 7447 single cells from lung tumors showed that genes that significantly correlated with KRT8 (p < 0.05) were involved in p53-related pathways. Immunohistochemistry in tumor biopsies from 294 patients with lung cancer showed that high protein expression of KRT8 is a prognostic marker of poor survival (HR = 1.73, p = 0.01). Finally, detectable KRT8 in serum as measured by ELISA distinguished patients with pancreatic cancer from healthy controls with an AUROC=0.94. In summary, our analysis demonstrates that KRT8 is (1) differentially expressed in several cancers across all molecular modalities and (2) may be useful as a biomarker to identify patients that should be further tested for cancer.

Download Full-text

Inferring kinetic parameters of oscillatory gene regulation from single cell time series data

10.1101/2021.05.12.443895 ◽

2021 ◽

Author(s):

Joshua Burton ◽

Cerys S Manning ◽

Magnus Rattray ◽

Nancy Papalopulu ◽

Jochen Kursawe

Keyword(s):

Gene Expression ◽

Time Series ◽

Single Cell ◽

Kinetic Parameters ◽

Cell Fate ◽

Time Series Data ◽

Single Cells ◽

Population Heterogeneity ◽

Series Data ◽

Gene Expression Dynamics

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation, and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

Download Full-text

Gene expression distribution deconvolution in single-cell RNA sequencing

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1721085115 ◽

2018 ◽

Vol 115 (28) ◽

pp. E6437-E6446 ◽

Cited By ~ 40

Author(s):

Jingshu Wang ◽

Mo Huang ◽

Eduardo Torre ◽

Hannah Dueck ◽

Sydney Shaffer ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Population Heterogeneity ◽

Noise Model ◽

Estimation Accuracy ◽

Marker Genes ◽

Batch Effects ◽

Differentiation Markers ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND’s noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers.

Download Full-text

Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

Journal of The Royal Society Interface ◽

10.1098/rsif.2021.0393 ◽

2021 ◽

Vol 18 (182) ◽

Author(s):

Joshua Burton ◽

Cerys S. Manning ◽

Magnus Rattray ◽

Nancy Papalopulu ◽

Jochen Kursawe

Keyword(s):

Gene Expression ◽

Time Series ◽

Single Cell ◽

Kinetic Parameters ◽

Cell Fate ◽

Time Series Data ◽

Single Cells ◽

Population Heterogeneity ◽

Series Data ◽

Gene Expression Dynamics

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time-series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

Download Full-text

Cell-to-Cell Heterogeneity in Growth Rate and Gene Expression in Methylobacterium extorquens AM1

Journal of Bacteriology ◽

10.1128/jb.00746-07 ◽

2007 ◽

Vol 189 (19) ◽

pp. 7127-7133 ◽

Cited By ~ 44

Author(s):

Tim J. Strovas ◽

Linda M. Sauter ◽

Xiaofeng Guo ◽

Mary E. Lidstrom

Keyword(s):

Gene Expression ◽

Growth Rate ◽

Single Cell ◽

Cell Size ◽

Growth Rates ◽

Laser Scanning Microscopy ◽

Cell Heterogeneity ◽

Methylobacterium Extorquens ◽

Subsequent Growth ◽

Cell To Cell Variability

ABSTRACT Cell-to-cell heterogeneity in gene expression and growth parameters was assessed in the facultative methylotroph Methylobacterium extorquens AM1. A transcriptional fusion between a well-characterized methylotrophy promoter (PmxaF ) and gfpuv (encoding a variant of green fluorescent protein [GFPuv]) was used to assess single-cell gene expression. Using a flowthrough culture system and laser scanning microscopy, data on fluorescence and cell size were obtained over time through several growth cycles for cells grown on succinate or methanol. Cells were grown continuously with no discernible lag between divisions, and high cell-to-cell variability was observed for cell size at division (2.5-fold range), division time, and growth rate. When individual cells were followed over multiple division cycles, no direct correlation was observed between the growth rate before a division and the subsequent growth rate or between the cell size at division and the subsequent growth rate. The cell-to-cell variability for GFPuv fluorescence from the PmxaF promoter was less, with a range on the order of 1.5-fold. Fluorescence and growth rate were also followed during a carbon shift experiment, in which cells growing on succinate were shifted to methanol. Variability of the response was observed, and the growth rate at the time of the shift from succinate to methanol was a predictor of the response. Higher growth rates at the time of the substrate shift resulted in greater decreases in growth rates immediately after the shift, but full induction of PmxaF -gfpuv was achieved faster. These results demonstrate that in M. extorquens, physiological heterogeneity at the single-cell level plays an important role in determining the population response to the metabolic shift examined.

Download Full-text