Operational integration of FT-IR spectroscopy into a genomics-based surveillance algorithm for Listeria monocytogenes

Abstract

ABSTRACT Rapid and cost-effective tools are needed to complement whole-genome sequencing (WGS) in Listeria monocytogenes surveillance, particularly in settings with limited sequencing capacity. We evaluated the operational integration of Fourier-transform infrared (FT-IR) spectroscopy as a first-line screening method within a genomics-based surveillance algorithm. A total of 24 L. monocytogenes isolates, including two epidemiologically confirmed outbreaks ( n = 15) and unrelated sporadic isolates ( n = 9), were analyzed. Species identification was performed by matrix-assisted laser desorption ionization–time-of-flight mass spectrometry, and genomic characterization was conducted using WGS, including multilocus sequence typing and single-nucleotide polymorphism (SNP) analysis. FT-IR spectra were acquired using the IR Biotyper system, and serogroup prediction and clustering were assessed using artificial neural network-based classification and multivariate analysis. FT-IR achieved 100% concordance with WGS for serogroup assignment, with consistently high confidence scores. Clustering analysis generated FT-IR groupings broadly concordant with WGS-defined outbreak clusters, correctly grouping isolates differing by ≤9 SNPs within the same outbreak-associated clusters while differentiating most genetically unrelated strains. FT-IR excluded seven of nine unrelated isolates from outbreak-associated clustering, although partial overlap was observed among highly clonal CC1 isolates. Operationally, FT-IR enabled preliminary results within 48–72 h after isolation, significantly reducing turnaround time compared to WGS. This tiered approach improves cost-efficiency by optimizing sequencing resource allocation, which is particularly relevant for low- and middle-income settings. These findings demonstrate that FT-IR spectroscopy provides rapid and reliable preliminary typing and can be effectively integrated into routine workflows, enhancing early outbreak detection and supporting more efficient measures. To our knowledge, this is among the first studies describing the structured integration of FT-IR into a genomics-based national surveillance workflow for L. monocytogenes , including its use as a real-time triage tool to optimize WGS allocation and accelerate epidemiological response in a public health reference laboratory setting. IMPORTANCE Using a well-characterized data set, including two epidemiologically confirmed outbreaks and sporadic isolates, we demonstrate complete concordance (100%) between Fourier-transform infrared (FT-IR)-based serogroup prediction and whole-genome sequencing, along with the capacity of FT-IR to support rapid preliminary clustering. Notably, this study was conducted following the implementation of the first FT-IR system installed in Argentina, representing a major step forward in the incorporation of innovative technologies into national surveillance programs. To our knowledge, this is the first manuscript reporting the combined use of FT-IR spectroscopy, matrix-assisted laser desorption ionization–time-of-flight mass spectrometry, and genomics within an integrated surveillance framework for Listeria monocytogenes . This approach provides a novel and pragmatic model for strengthening microbiological surveillance in resource-constrained settings. Beyond analytical performance, this work provides a real-world, operational framework for integrating rapid phenotypic screening with genomic confirmation, enabling earlier outbreak detection, optimized use of sequencing resources, and the potential to accelerate source-tracking investigations.

The authors' abstract, as published at the source. Microbiology Spectrum, 2026 · DOI ↗