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Advances in automated fluorescence microscopy have made snapshot and time-lapse imaging of bacterial cells commonplace, yet fundamental challenges remain in analysis. The vast quantity of data collected in high-throughput experiments requires a fast and reliable automated method to analyze fluorescence intensity and localization, cell morphology and proliferation as well as other descriptors. Inspired by effective yet tractable methods of population-level analysis using flow cytometry, we have developed a framework and tools for facilitating analogous analyses in image cytometry. These tools can both visualize and gate (generate subpopulations) more than 70 cell descriptors, including cell size, age and fluorescence. The method is well suited to multi-well imaging, analysis of bacterial cultures with high cell density (thousands of cells per frame) and complete cell cycle imaging. We give a brief description of the analysis of four distinct applications to emphasize the broad applicability of the tool.

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment micro-colonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter, and neighboring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of post-processing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies, and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution. This article is protected by copyright. All rights reserved.

Bacterial cells display both spatial and temporal organization, and this complex structure is known to play a central role in cellular function. Although nearly one-fifth of all proteins in Escherichia coli localize to specific subcellular locations, fundamental questions remain about how cellular-scale structure is encoded at the level of molecular-scale interactions. One significant limitation to our understanding is that the localization behavior of only a small subset of proteins has been characterized in detail. As an essential step toward a global model of protein localization in bacteria, we capture and quantitatively analyze spatial and temporal protein localization patterns throughout the cell cycle for nearly every protein in E. coli that exhibits nondiffuse localization. This genome-scale analysis reveals significant complexity in patterning, notably in the behavior of DNA-binding proteins. Complete cell-cycle imaging also facilitates analysis of protein partitioning to daughter cells at division, revealing a broad and robust assortment of asymmetric partitioning behaviors.

Summary Bacterial genomes are organized by a plethora of chromatin proteins and physical mechanisms. This organization appears to be hierarchical with DNA folding events at the nm scale influencing higher levels of chromosome organization. Besides acting in shaping the genome these factors also play important regulatory roles in numerous DNA transactions. While DNA folding mechanisms operating at the nm scale are fairly well understood, it has been hard to translate this knowledge into accurate models that describe the complete dynamics of the genome. In recent years new techniques have evolved that are key to filling the current gaps in understanding. Particularly insightful in this light appear techniques that probe architectural properties of chromatin proteins on single molecules, techniques that map the binding of protein components and spatial structure on a genome-wide basis and improved imaging techniques that provide resolutions capable of resolving substructures/heterogeneities in the nucleoid. Moreover, bioinformatic and polymer physics approaches are starting to provide novel insights. In our opinion, an important aim in the field is to generate an accurate and complete description of the nucleoid and its dynamics at all scales. A first step towards this aim has now been set by bringing together people from diverse disciplinary backgrounds at the Lorentz centre workshop ‘Biology and Physics of Bacterial Genome Organization’ in Leiden, the Netherlands from 18 to 22 June 2012.

Pseudomonas aeruginosa responds to growth on agar surfaces to produce cyclic-di-GMP, which stimulates biofilm formation. This is mediated by an alternative cellular function chemotaxis-like system called Wsp. The receptor protein WspA, is bioinformatically indistinguishable from methyl-accepting chemotaxis proteins. However, unlike standard chemoreceptors, WspA does not form stable clusters at cell poles. Rather, it forms dynamic clusters at both polar and lateral subcellular locations. To begin to study the mechanism of Wsp signal transduction in response to surfaces, we carried out a structure-function study of WspA and found that its C-terminus is important for its lateral subcellular localization and function. When this region was replaced with that of a chemoreceptor for amino acids, WspA became polarly localized. In addition, introduction of mutations in the C-terminal region of WspA that rendered this protein able to form more stable receptor-receptor interactions, also resulted in a WspA protein that was less capable of activating signal transduction. Receptor chimeras with a WspA C-terminus and N-terminal periplasmic domains from chemoreceptors that sense amino acids or malate responded to surfaces to produce c-di-GMP. Thus, the amino acid sequence of the WspA periplasmic region did not need to be conserved for the Wsp system to respond to surfaces.