Differentiating bacteria by their unique surface interactions

New, rapid, and accessible approaches to bacterial detection are necessary to help curb the rising impacts of antimicrobial resistance. In this study, we introduce a technique that distinguishes bacteria through their unique surface interactions. By measuring and combining the interaction strengths of a bacterium across a set of chemically defined surfaces, we produced a novel bacterial identifier termed the surface interaction profile (SIP). The interaction strengths of twelve test bacteria across three discrete polyelectrolyte multilayer films (PEMs) were measured, facilitated by introducing each bacterial suspension to individual PEMs in microfluidic channels over a 10-minute interaction period and rinsing to remove bulk and loosely bound bacteria. The remaining surface-bound cells were counted via microscopy and plotted against suspension concentrations to build attachment curves whose slopes were measured as the strength of interaction for a given bacteria-PEM combination. These slopes were collected, per bacterial type, to produce each SIP. SIPs were capable of distinguishing between our pathogenic strains (Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterococcus faecalis, methicillin-resistant Staphylococcus aureus, and vancomycin-intermediate Staphylococcus aureus) by Gram stain and individual species, and each blind test pathogen was successfully identified through SIP comparison. Furthermore, SIPs were also successful at differentiating between select Staphylococcus aureus walKR mutants impacting cell wall metabolism and high-risk antibiotic resistance mutants (MRSA and VISA), highlighting the utility and future diagnostic potential of this technique.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-06-30
DOI
https://doi.org/10.1371/journal.pone.0327489
Primary Topic
Bacterial Identification and Susceptibility Testing
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Differentiating bacteria by their unique surface interactions

Nicholas Konstantine Kotoulas, M. Cynthia Goh, Stephanie Tan, Justin Rea Nodwell
PLoS ONE
Bacterial Identification and Susceptibility Testing
article

Differentiating bacteria by their unique surface interactions

Nicholas Konstantine Kotoulas, M. Cynthia Goh, Stephanie Tan, Justin Rea Nodwell
article en

Abstract

New, rapid, and accessible approaches to bacterial detection are necessary to help curb the rising impacts of antimicrobial resistance. In this study, we introduce a technique that distinguishes bacteria through their unique surface interactions. By measuring and combining the interaction strengths of a bacterium across a set of chemically defined surfaces, we produced a novel bacterial identifier termed the surface interaction profile (SIP). The interaction strengths of twelve test bacteria across three discrete polyelectrolyte multilayer films (PEMs) were measured, facilitated by introducing each bacterial suspension to individual PEMs in microfluidic channels over a 10-minute interaction period and rinsing to remove bulk and loosely bound bacteria. The remaining surface-bound cells were counted via microscopy and plotted against suspension concentrations to build attachment curves whose slopes were measured as the strength of interaction for a given bacteria-PEM combination. These slopes were collected, per bacterial type, to produce each SIP. SIPs were capable of distinguishing between our pathogenic strains (Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterococcus faecalis, methicillin-resistant Staphylococcus aureus, and vancomycin-intermediate Staphylococcus aureus) by Gram stain and individual species, and each blind test pathogen was successfully identified through SIP comparison. Furthermore, SIPs were also successful at differentiating between select Staphylococcus aureus walKR mutants impacting cell wall metabolism and high-risk antibiotic resistance mutants (MRSA and VISA), highlighting the utility and future diagnostic potential of this technique.

PLoS ONEVol. 20(6)
University of Toronto (CA)
Mitacs, Natural Sciences and Engineering Research Council of Canada
Life in Land
Openalex Percentile: Top 82%
Bacterial Identification and Susceptibility Testing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Differentiating bacteria by their unique surface interactions — Nicholas Konstantine Kotoulas, M. Cynthia Goh, et al. · PLoS ONE (2025) | TGRS Research Map | TGRS