Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria

Abstract

Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically comparing the resulting single-cell expression distributions. Using these results, we develop methods for rigorous quantitative inference of single-cell expression distributions from fluorescence flow cytometry data. First, we present a Bayesian mixture model to separate debris from viable cells using all scattering signals. Second, we show that cytometry measurements of fluorescence are substantially affected by autofluorescence and shot noise, which can be mistaken for intrinsic noise in gene expression, and present methods to correct for these using calibration measurements. Finally, we show that because forward-and side-scatter signals scale non-linearly with cell size, and are also affected by a substantial shot noise component that cannot be easily calibrated unless independent measurements of cell size are available, it is not possible to accurately estimate the variability in the sizes of individual cells using flow cytometry measurements alone. To aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering debris and for estimating true biological expression means and variances from the fluorescence signal. The package is

Inferring gene regulation dynamics from static snapshots of gene expression variability

“…Similarly, experiments have shown high biological reproducibility, e.g., three repeats of identical flow-cytometry experiments exhibited a standard error of the CVs of around 10% [15]. However, a significant limitation remains for current experimental high-throughput methods like flowcytometry or single-cell sequencing: The measurements techniques themselves introduce significant noise, especially when used with bacteria [25,26], which means that technical variability can introduce a systematic error in estimates of biological variability. In such cases, one can attempt to deconvolve true biological variability from measurement noise using experimental and analytical techniques, e.g., by using calibration beads [25] or by using noise models [26].…”

“…However, a significant limitation remains for current experimental high-throughput methods like flowcytometry or single-cell sequencing: The measurements techniques themselves introduce significant noise, especially when used with bacteria [25,26], which means that technical variability can introduce a systematic error in estimates of biological variability. In such cases, one can attempt to deconvolve true biological variability from measurement noise using experimental and analytical techniques, e.g., by using calibration beads [25] or by using noise models [26]. Alternatively, one can opt for methods of lower throughput but greater precision, for example, mRNA measurements by smFISH are well suited for validation of our method given its accuracy and high sensitivity [27].…”

“…Fluorescent protein abundance CVs were obtained from the reported flow-cytometry fluorescence distributions. Under the assumption that reporter concentrations are cell-volume independent, i.e., transcription rates are cell-cycle independent, then the concentration CVs are related to the CVs in abundances and the CV in cell volume CV V [25]:…”

“…However, it is possible that measurement noise which scales inversely with the average is contributing to the measured variability. For example, in [25] it was shown that flow-cytometry measurements contain a significant amount of measurement noise when used with bacteria due to their small size. This measurement noise was shown to scale inversely with the average of the fluorescence signal, meaning it could mask itself as biological variability contributing to the first term on the right-hand side of Eq.…”

“…(L2). In [25] a rigorous method was developed to measure the true CVs using flow cytometry on bacteria, separating measurement noise and autofluorescence contributions using commonly used calibration beads.…”