Image Analysis for Sliding Motility of <i>Clostridium perfringens</i>

The research investigates the sliding motility of Clostridium perfringens by employing machine learning-based image segmentation techniques and tracking to extract key quantitative characteristics of the movement of the bacteria. C. perfringens cells maintain end-to-end connections after cell divisions and form elongated chains that expand in a one-dimensional fashion. Cells in the elongating chains are pushed by each other to achieve a sliding movement at potentially high speeds. However, these chains are susceptible to breakage due to stress accumulation from rapid growth, which would undermine efficiency of the passive sliding motility. Utilizing AI-powered image analysis, this research aims to obtain detailed quantification of these dynamics and generate data for future mechanistic studies of the sliding motility. Results from this work highlight the effectiveness of machine learning in detecting individual cells from microscopy images. The accurately segmented cells enable enhanced tracking and detailed analysis of bacterial motility. The results generate useful quantitative data such as growth rate, velocity, and division frequency of C. perfringens.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Department dc:contributor.department

Biological Sciences

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Chopdekar, Nidhi

Chair dc:contributor.committeechair

• Chen, Jing

Committee members dc:contributor.committeemember

• Melville, Stephen B.
• Hauf, Silke

• image analysis
• image segmentation
• sliding motility
• live-cell images
• cell tracking

Statement dc:rights

• Creative Commons Attribution-NonCommercial 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10919/119406

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119406