ViSCAR

ViSCAR analyzes and visualizes single-cell attributes from bacterial time-lapse microscopy to characterize spatiotemporal dynamics, stochasticity, and heterogeneity in microbial communities.

Key Features:

• Spatiotemporal Visualization: Models and visualizes the spatiotemporal evolution of single-cell attributes across cell populations, colonies, and generations from time-lapse single-cell movies.
• Correlation of Single-Cell Attributes: Provides functions to visually explore and correlate single-cell attributes to detect potential epigenetic information transfer across generations.
• Modeling Stochastic Phenomena: Supports inference of mathematical and statistical models describing stochastic phenomena such as cell growth and division.
• Error Correction in Bioimage Analysis: Identifies and auto-corrects errors introduced during the bioimage analysis of dense movies containing thousands of overcrowded cells.
• Exploration of Biological Noise: Analyzes single-cell movie datasets to investigate biological noise in gene regulation, cell growth, cell division, and intra- and inter-subpopulation heterogeneity.
• Applications to Human Health Research: Analyzes interactions within microbial communities relevant to human health, including pathogen competition with benign microbiome cells, emergence of dormant "persister" cells, and biofilm formation under stress conditions.

Scientific Applications:

• Capturing Stochasticity: Characterizes stochastic variation in bacterial populations and its impact on population-level behaviors.
• Mechanistic Phenotype Discovery: Links single-cell behaviors and lineage correlations to mechanisms producing specific cellular phenotypes.
• Community Dynamics: Deciphers the dynamic behavior and heterogeneity of large microbial communities at single-cell resolution.
• Health-Relevant Microbial Interactions: Investigates pathogen–microbiome competition, persister cell emergence, and stress-induced biofilm formation.

Methodology:

Implements analysis and visualization routines in R, integrates visualization with advanced analytics, supports inference of mathematical and statistical models for stochastic phenomena, and includes routines to identify and auto-correct errors from bioimage analysis of dense time-lapse movies.