Related Experiment Video
Updated: Jun 22, 2025

Population and Single-Cell Analysis of Antibiotic Persistence in Escherichia coli
Published on: March 24, 2023
Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping
Sharath Narayana Iyengar1, Brianna Dowden1, Kathy Ragheb1
1Department of Basic Medical Sciences, Purdue University, West Lafayette, IN, 47907, USA.
Abstract:
The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony's unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. KEY POINTS: • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes.
Insights
A novel method uses a cell sorter to place individual bacteria on gradient agar plates, combined with elastic-light scattering and machine learning to rapidly detect and identify antibiotic-resistant strains, addressing a global health challenge.
Area of Science:
- Microbiology
- Biotechnology
- Analytical Chemistry
Background:
- Antimicrobial resistance (AMR) is a growing global threat, driven by antibiotic misuse.
- Current detection methods like agar plating are slow, while genotypic methods require pure samples and cannot distinguish live from dead bacteria.
Purpose of the Study:
- To develop a rapid, accurate method for detecting and identifying antibiotic-resistant bacterial strains.
- To overcome limitations of existing phenotypic and genotypic detection techniques.
Main Methods:
- A cell sorter precisely deposits individual bacterial cells onto antibiotic-gradient agar plates.
- Elastic-light scattering is employed to analyze unique colony scatter patterns.
- Machine learning algorithms classify bacterial strains based on scatter patterns for rapid identification.
Main Results:
- The cell sorter and gradient plate method reduced subculturing steps for direct detection of resistant strains.
- Elastic-light scattering provided rapid, label-free, non-destructive classification of bacterial colonies.
- Scatter patterns correlated with antimicrobial resistance phenotypes.
Conclusions:
- This combined approach offers a faster, more efficient way to identify antibiotic-resistant bacteria.
- The method enables direct qualitative binary detection of resistance.
- Elastic-light scattering technology is a promising tool for real-time pathogen classification.

