Detection of antibiotic heteroresistance in clinical microbiology: current and emerging methodologies

Antibiotic heteroresistance (HR) is characterised by the coexistence of susceptible and resistant subpopulations within an apparently isogenic bacterial isolate. Because routine antimicrobial susceptibility testing (AST) primarily assesses the dominant population, HR may escape detection, potentially leading to discrepancies between laboratory susceptibility categorisation and the underlying bacterial population structure.

To provide a critical and practice-oriented evaluation of current and emerging methodologies for HR detection and to discuss their strengths, limitations, and potential for clinical implementation.

Narrative review based on PubMed searches, complemented by screening of key reference lists and relevant EUCAST and CLSI documents. Peer-reviewed literature was prioritised.

Phenotypic approaches, particularly population analysis profiling, remain the reference method for HR definition, but their labour-intensive workflows, long turnaround times, and limited standardisation restrict routine implementation. Alternative strategies, including modified AST assays, metabolic assays, and single-cell platforms, offer gains in speed or throughput but require broader validation. Molecular approaches such as quantitative PCR, droplet digital PCR, targeted deep sequencing, and whole-genome sequencing improve detection of minority resistance determinants. Emerging computational frameworks, including machine learning models integrating phenotypic and genomic data, represent a promising frontier for scalable HR prediction.

Available evidence supports the clinical relevance of HR, although its association with adverse outcomes varies across bacterial species and antibiotic classes. Harmonised methodologies and clinically validated interpretive criteria are needed to support integration of HR assessment into routine diagnostics. Prospective multicentre studies and further standardisation, including engagement with EUCAST and CLSI, will be important to advance clinical implementation.