Background: Time-lapse microscopy live-cell imaging is essential for
studying the evolution of bacterial communities at single-cell
resolution. It allows capturing detailed information about the
morphology, gene expression, and spatial characteristics of individual
cells at every time instance of the imaging experiment. The image
analysis of bacterial “single-cell movies” (videos) generates big
data in the form of multidimensional time series of measured bacterial
attributes. If properly analyzed, these datasets can help us decipher
the bacterial communities’ growth dynamics and identify the sources and
potential functional role of intra- and inter-subpopulation
heterogeneity. Recent research has highlighted the importance of
investigating the role of biological “noise” in gene regulation,
cell growth, cell division, etc. Single-cell analytics of complex
single-cell movie datasets, capturing the interaction of multiple
micro-colonies with thousands of cells, can shed light on essential
phenomena for human health, such as the competition of pathogens and
benign microbiome cells, the emergence of dormant cells
(”persisters”), the formation of biofilms under different stress
conditions, etc. However, highly accurate and automated bacterial
bioimage analysis and single-cell analytics methods remain elusive, even
though they are required before we can routinely exploit the plethora of
data that single-cell movies generate.
Results: We present visualization and single-cell analytics using R
(ViSCAR), a set of methods and corresponding functions, to visually
explore and correlate single-cell attributes generated from the image
processing of complex bacterial single-cell movies. They can be used to
model and visualize the spatiotemporal evolution of attributes at
different levels of the microbial community organization (i.e., cell
population, colony, generation, etc.), to discover possible epigenetic
information transfer across cell generations, infer mathematical and
statistical models describing various stochastic phenomena (e.g., cell
growth, cell division), and even identify and auto-correct errors
introduced unavoidably during the bioimage analysis of a dense movie
with thousands of overcrowded cells in the microscope’s field of view.
Conclusions: ViSCAR empowers researchers to capture and characterize the
stochasticity, uncover the mechanisms leading to cellular phenotypes of
interest, and decipher a large heterogeneous microbial communities’
dynamic behavior. ViSCAR source code is available from GitLab at
