8 research outputs found
Reconstructing the Forest of Lineage Trees of Diverse Bacterial Communities Using Bio-inspired Image Analysis
Cell segmentation and tracking allow us to extract a plethora of cell
attributes from bacterial time-lapse cell movies, thus promoting computational
modeling and simulation of biological processes down to the single-cell level.
However, to analyze successfully complex cell movies, imaging multiple
interacting bacterial clones as they grow and merge to generate overcrowded
bacterial communities with thousands of cells in the field of view,
segmentation results should be near perfect to warrant good tracking results.
We introduce here a fully automated closed-loop bio-inspired computational
strategy that exploits prior knowledge about the expected structure of a
colony's lineage tree to locate and correct segmentation errors in analyzed
movie frames. We show that this correction strategy is effective, resulting in
improved cell tracking and consequently trustworthy deep colony lineage trees.
Our image analysis approach has the unique capability to keep tracking cells
even after clonal subpopulations merge in the movie. This enables the
reconstruction of the complete Forest of Lineage Trees (FLT) representation of
evolving multi-clonal bacterial communities. Moreover, the percentage of valid
cell trajectories extracted from the image analysis almost doubles after
segmentation correction. This plethora of trustworthy data extracted from a
complex cell movie analysis enables single-cell analytics as a tool for
addressing compelling questions for human health, such as understanding the
role of single-cell stochasticity in antibiotics resistance without losing site
of the inter-cellular interactions and microenvironment effects that may shape
it
Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data
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
Tracking single-cells in overcrowded bacterial colonies
Cell tracking enables data extraction from timelapse "cell movies" and promotes modeling biological processes at the single-cell level. We introduce a new fully automated computational strategy to track accurately cells across frames in time-lapse movies. Our method is based on a dynamic neighborhoods formation and matching approach, inspired by motion estimation algorithms for video compression. Moreover, it exploits "divide and conquer" opportunities to solve effectively the challenging cells tracking problem in overcrowded bacterial colonies. Using cell movies generated by different labs we demonstrate that the accuracy of the proposed method remains very high (exceeds 97%) even when analyzing large overcrowded microbial colonies
Facial expression and gesture analysis for emotionally-rich man-machine interaction
This chapter presents a holistic approach to emotion modeling and analysis
and their applications in Man-Machine Interaction applications. Beginning
from a symbolic representation of human emotions found in this context,
based on their expression via facial expressions and hand gestures, we
show that it is possible to transform quantitative feature information from
video sequences to an estimation of a user’s emotional state. While these
features can be used for simple representation purposes, in our approach
they are utilized to provide feedback on the users’ emotional state, hoping
to provide next-generation interfaces that are able to recognize the
emotional states of their users