New Tools for Comparing Microscopy Images: Quantitative Analysis of Cell Types in Bacillus subtilis

Abstract

bFluorescence microscopy is a method commonly used to examine individual differences between bacterial cells, yet many studies still lack a quantitative analysis of fluorescence microscopy data. Here we introduce some simple tools that microbiologists can use to analyze and compare their microscopy images. We show how image data can be converted to distribution data. These data can be subjected to a cluster analysis that makes it possible to objectively compare microscopy images. The distribution data can further be analyzed using distribution fitting. We illustrate our methods by scrutinizing two independently acquired data sets, each containing microscopy images of a doubly labeled Bacillus subtilis strain. For the first data set, we examined the expression of srfA and tapA, two genes which are expressed in surfactin-producing and matrix-producing cells, respectively. For the second data set, we examined the expression of eps and tapA; these genes are expressed in matrix-producing cells. We show that srfA is expressed by all cells in the population, a finding which contrasts with a previously reported bimodal distribution of srfA expression. In addition, we show that eps and tapA do not always have the same expression profiles, despite being expressed in the same cell type: both operons are expressed in cell chains, while single cells mainly express eps. These findings exemplify that the quantification and comparison of microscopy data can yield insights that otherwise would go unnoticed. Bacterial cells can develop into distinct and discrete phenotypic states, which are typically referred to as cell types (1-4). Even when cells experience nearly identical environmental conditions, differentiation is possible (i.e., probabilistic cell differentiation) due to regulatory feedback loops that amplify inherent cellular noise (5-8). In many cases, however, cell differentiation is triggered by environmental changes (2, 9-12). Besides responding to environmental conditions, cells can also modify their environment. For example, they can produce extracellular polysaccharides, communication signals, and antimicrobials (13-15). The feedback between cells and their environment drives colony development (14, 16).In Bacillus subtilis, cell behavior is often studied in the context of colony development (17). B. subtilis cells can differentiate into a number of cell types, and each of them is associated with a unique set of phenotypes (1, 2, 18). The regulatory mechanisms underlying cell differentiation are often studied using time-lapse fluorescence microscopy, in which gene expression is monitored using fluorescent reporters (8,(18)(19)(20)(21). Microscopy images can be analyzed using advanced image-analysis software, which allows the detailed quantification of gene expression along time (22,23). In this way, Veening and colleagues (24) showed that the timing of sporulation in B. subtilis depends on epigenetic inheritance. In a similar way, Levine and colleagues (25) showed that positive-feedback loops affect the timing of s...

Applying the handicap principle to biofilms: condition‐dependent signalling inBacillus subtilismicrobial communities

“…This function is consistent with recent findings that identify surfactin as a self-identity determinant involved in the clustering of kin B. subtilis isolates with each other (Lyons et al, 2016). The second is that, as surfactin can promote a collective non-flagellar motility of extracellular matrix (ECM) producers (van Gestel et al, 2015a), it may facilitate transitions of collectives composed of both cell types within the biofilm microenvironment, generating a propagating front of ECM cells that can colonize new niches (Srinivasan et al, 2018).…”

“…Surfactin is an enzymatically‐synthesized lipopeptide of seven amino acids with a lipid side‐chain, encoded by the srfAA operon (Nakano et al ., ), which is highly expressed in a specific ‘surfactin producer’ subpopulation (López et al ., ). Interestingly, srfAA expression has a unimodal distribution in the biofilm population (van Gestel et al ., ,b). The accumulation of surfactin triggers the differentiation of a subpopulation (distinct from the surfactin producer subpopulation) into matrix producers (López et al ., ).…”

“…Cell–cell variability for TasA was also observed at the transcription level. Single‐cell analysis indicates that there are at least three phenotypic states for TasA expression: cells that do not express tapA‐sipW‐tasA , cells that weakly express tapA‐sipW‐tasA and cells that strongly express tapA‐sipW‐tasA (van Gestel et al ., ,b). Among ECM operons studied, this was a unique property of TasA, as EPS production was more classically bimodal.…”

“…The initial developmental transition in biofilm formation involves the peptide ComX. ComX is expressed by the entire biofilm population; at a certain cell density, its concentration activates the production of the lipopeptide surfactin in a subpopulation of 'surfactin producers' (López et al, 2009b;van Gestel et al, 2015b) (Fig. 1).…”

“…Surfactin induces potassium-selective pore formation in the cell membrane (Sheppard et al, 1991); moreover, its signalling function is mediated by its toxicity, through the response to potassium leakage (López et al, 2009a). These properties are also crucial for its non-signalling roles: surfactin facilitates expansion of the biofilm by lowering surface tension (Angelini et al, 2009;van Gestel et al, 2015a) and may also serve as a means of predation in interspecies competition . It has been suggested that surfactin producers are immune to its effects, as these cells do not differentiate into matrix producers (López et al, 2009b).…”