Image analysis driven single-cell analytics for systems microbiology

Balomenos et al. BMC Systems Biology (2017) 11:43 DOI 10.1186/s12918-017-0399-z METHODOLOGY ARTICLE Open Access Image analysis driven single-cell analytics for systems microbiology Athanasios D. Balomenos1, Panagiotis Tsakanikas2, Zafiro Aspridou3, Anastasia P. Tampakaki4, Konstantinos P. Koutsoumanis3 and Elias S. Manolakos1,5,6* Abstract Background: Time-lapse microscopy is an essential tool for capturing and correlating bacterial morphology and gene expression dynamics at single-cell resolution. However state-of-the-art computational methods are limited in terms of the complexity of cell movies that they can analyze and lack of automation. The proposed Bacterial image analysis driven Single Cell Analytics (BaSCA) computational pipeline addresses these limitations thus enabling high throughput systems microbiology. Results: BaSCA can segment and track multiple bacterial colonies and single-cells, as they grow and divide over time (cell segmentation and lineage tree construction) to give rise to dense communities with thousands of interacting cells in the field of view. It combines advanced image processing and machine learning methods to deliver very accurate bacterial cell segmentation and tracking (F-measure over 95%) even when processing images of imperfect quality with several overcrowded colonies in the field of view. In addition, BaSCA extracts on the fly a plethora of single-cell properties, which get organized into a database summarizing the analysis of the cell movie. We present alternative ways to analyze and visually explore the spatiotemporal evolution of single-cell properties in order to understand trends and epigenetic effects across cell generations. The robustness of BaSCA is demonstrated across different imaging modalities and microscopy types. Conclusions: BaSCA can be used to analyze accurately and efficiently cell movies both at a high resolution (single-cell level) and at a large scale (communities with many dense colonies) as needed to shed light on e.g. how bacterial community effects and epigenetic information transfer play a role on important phenomena for human health, such as biofilm formation, persisters’ emergence etc. Moreover, it enables studying the role of single-cell stochasticity without losing sight of community effects that may drive it. Keywords: Time-lapse microscopy, Machine learning, Bacterial image analysis, Colonies segmentation, Cell segmentation, Lineage tree construction, Visualization, Single-cell informatics, Single-cell analytics Background Systems biology is an interdisciplinary field with ultimate goal to elucidate the relationships between molecular states and higher order properties of complex biological systems. Microbial communities are such systems and the study of their collective behavior is a major challenge in the post-genomic era [1–5] in order to identify the sources and role of heterogeneity in the behavior of microbial populations and uncover the mechanisms that lead to * Correspondence: 1 Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Ilissia, Greece 5 Northeastern University, Boston, USA Full list of author information is available at the end of the article specific phenotypes of interest, such as persister cells [6] and biofilms [7]. It has also become clear that deciphering the dynamics of evolving bacterial communities requires multidisciplinary approaches [8, 9]. Microscopy is an important tool that can help us capture data and correlate information at multiple scales, from cell populations to molecules [9]. In particular, time lapse microscopy allows us to monitor the evolution of bacterial communities and generate "cell movies" massively [10]. However, accurate and fully automated image analysis and single-cell analytics methods are required before we can really exploit this abundance of "big data" [9, 10] for systems biology. It is a fact that we currently know very little on the role single-cell heterogeneity plays in the dynamic © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Balomenos et al. BMC Systems Biology (2017) 11:43 behaviour of microbial communities. Technical difficulties hamper the automatic monitoring and tracking of subpopulations and individual cells in growing bacterial colonies at a large scale [9]. Studies on the variability of individual cells behaviour rely on laborious manual annotation of cell movies with only a small number of cells per frame [9–12]. Tracking cells across image frames in overcrowded (dense) bacterial communities with many colonies and thousands of cells in the field of view, extracting automatically single-cell attributes (e.g. size, elongation rate, division time, etc.) and correlating them to molecular and other signatures (e.g. expression of fluorescently tagged proteins) remains elusive. Developing robust and high throughput image analysis pipelines that routinely accomplish these tasks effortlessly will enable single-cell analytics and provide new insights to compelling open questions. It is the combination of accurate single-cell image analysis and single-cell analytics that will empower the development of effective stochastic modeling and systems microbiology approaches. This new capability will allow us to characterize stochasticity in colonial growth dynamics of single-cells [13, 14], model stochastic gene expression in single-cells [15], measure phenotypic variation in bacteria [16], model bacterial state transitions from regular to persister cells [6, 17], or from planktonic to biofilm cells [6]. Bioimage analysis has evolved to become an important discipline in bioinformatics and computer vision [18]. For bacterial image processing, well known open source software packages for analyzing cell movies are the TLM-Tracker [19], CellTracer [20], MicrobeTracker [21] and its successor Oufti [22], and Schnitzcells [23]. The TLM-Tracker [19] uses multiple alternative algorithms for cell segmentation, such as threshold-based, watershed transform and level-set methods. To construct the lineage tree of a colony, it matches overlapping cells in consecutive movie frames. The CellTracer [20] employs the concept of hybrid grey-scale/black-white images and extends image filtering and mathematical morphology operators developed for grey-scale images to work with such hybrid images. This allows it to extract cells iteratively as it gradually conver (...truncated)