TLM-Quant: An Open-Source Pipeline for Visualization and Quantification of Gene Expression Heterogeneity in Growing Microbial Cells

Abstract

Gene expression heterogeneity is a key driver for microbial adaptation to fluctuating environmental conditions, cell differentiation and the evolution of species. This phenomenon has therefore enormous implications, not only for life in general, but also for biotechnological applications where unwanted subpopulations of non-producing cells can emerge in large-scale fermentations. Only time-lapse fluorescence microscopy allows real-time measurements of gene expression heterogeneity. A major limitation in the analysis of time-lapse microscopy data is the lack of fast, cost-effective, open, simple and adaptable protocols. Here we describe TLM-Quant, a semi-automatic pipeline for the analysis of time-lapse fluorescence microscopy data that enables the user to visualize and quantify gene expression heterogeneity. Importantly, our pipeline builds on the open-source packages ImageJ and R. To validate TLM-Quant, we selected three possible scenarios, namely homogeneous expression, highly ‘noisy’ heterogeneous expression, and bistable heterogeneous expression in the Gram-positive bacterium Bacillus subtilis. This bacterium is both a paradigm for systems-level studies on gene expression and a highly appreciated biotechnological ‘cell factory’. We conclude that the temporal resolution of such analyses with TLM-Quant is only limited by the numbers of recorded images.

Small Regulatory RNA-Induced Growth Rate Heterogeneity of Bacillus subtilis

“…The Δ spo0A background was used to elevate AbrB-GFP levels and thereby to facilitate fluorescence measurements. Cells were pre-cultured in M9G as was done for the FC measurements and applied to agarose pads (at OD 600 ∼0.15) essentially as was described by Piersma et al [ 52 ]. From these experiments, and consistent with FC data in Fig.…”

“…Live imaging analysis was conducted on aerated agarose cover slips as described previously [ 52 ]. Segmentation, calculation of Feret diameter, and auto-fluorescence correction for every microcolony were performed with ImageJ also as described by Piersma et al [ 52 ].…”

“…Live imaging analysis was conducted on aerated agarose cover slips as described previously [ 52 ]. Segmentation, calculation of Feret diameter, and auto-fluorescence correction for every microcolony were performed with ImageJ also as described by Piersma et al [ 52 ]. Subsequent computations and plotting was done with R. The specific cell length (Feret diameter) increase per hour was computed as follows: ((cumulative Feret diameter at t 20 min / number of cells at t 0 min ) – (cumulative Feret diameter at t 0 min / number of cells at t 0 min )) / ((t 20 min —t 0 min ) / 60 min).…”

Homogeneity and heterogeneity in amylase production by Bacillus subtilis under different growth conditions

“…For industrial protein production, the question whether target gene expression is homogeneous or heterogeneous is highly relevant [ 21 ]. Clearly, to obtain the highest yields possible, homogeneous high-level target gene-expressing populations are most desirable.…”

When Phase Contrast Fails: ChainTracer and NucTracer, Two ImageJ Methods for Semi-Automated Single Cell Analysis Using Membrane or DNA Staining

“…To obtain data from sufficient numbers of cells, it is desirable to have automatic analysis software that can interpret the microscopy images. Several software packages have been developed to do this, including CellProfiler , MicrobeTracker and plugins for ImageJ like TLM-Quant [ 6 – 8 ]. These methods use thresholding of the phase contrast image to outline cells.…”