Reagent-Free Identification of Clinical Yeasts by Use of Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy

Development and validation of a quick, automated, and reproducible ATR FT-IR spectroscopy machine-learning model for Klebsiella pneumoniae typing

The reliability of Fourier-transform infrared (FT-IR) spectroscopy for Klebsiella pneumoniae typing and outbreak control has been previously assessed, but issues remain in standardization and reproducibility. We developed and validated a reproducible FT-IR with attenuated total reflectance (ATR) workflow for the identification of K. pneumoniae lineages. We used 293 isolates representing multidrug-resistant K. pneumoniae lineages causing outbreaks worldwide (2002-2021) to train a random forest classification (RF) model based on capsular (KL)-type discrimination. This model was validated with 280 contemporaneous isolates (2021-2022), using wzi sequencing and whole-genome sequencing as references. Repeatability and reproducibility were tested in different culture media and instruments throughout time. Our RF model allowed the classification of 33 capsular (KL)-types and up to 36 clinically relevant K. pneumoniae lineages based on the discrimination of specific KL- and O-type combinations. We obtained high rates of accuracy (89%), sensitivity (88%), and specificity (92%), including from cultures obtained directly from the clinical sample, allowing to obtain typing information the same day bacteria are identified. The workflow was reproducible in different instruments throughout time (>98% correct predictions). Direct colony application, spectral acquisition, and automated KL prediction through Clover MS Data analysis software allow a short time-to-result (5 min/isolate). We demonstrated that FT-IR ATR spectroscopy provides meaningful, reproducible, and accurate information at a very early stage (as soon as bacterial identification) to support infection control and public health surveillance. The high robustness together with automated and flexible workflows for data analysis provide opportunities to consolidate real-time applications at a global level. IMPORTANCE We created and validated an automated and simple workflow for the identification of clinically relevant Klebsiella pneumoniae lineages by FT-IR spectroscopy and machine-learning, a method that can be extremely useful to provide quick and reliable typing information to support real-time decisions of outbreak management and infection control. This method and workflow is of interest to support clinical microbiology diagnostics and to aid public health surveillance.

Automated Characterisation of Neutrophil Activation Phenotypes in Ex Vivo Human Candida Blood Infections

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Candida bloodstream infections are difficult to diagnose and treat in humans.

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Infection processes give rise to activation of host immune cells.

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Immune cell activation is reflected by characteristic cell morphology.

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Neutrophils exhibit distinct morphodynamics for different Candida species.

Rapid identification of pathogens is required for early diagnosis and treatment of life-threatening bloodstream infections in humans. This requirement is driving the current developments of molecular diagnostic tools identifying pathogens from human whole blood after successful isolation and cultivation. An alternative approach is to determine pathogen-specific signatures from human host immune cells that have been exposed to pathogens. We hypothesise that activated immune cells, such as neutrophils, may exhibit a characteristic behaviour — for instance in terms of their speed, dynamic cell morphology — that allows (i) identifying the type of pathogen indirectly and (ii) providing information on therapeutic efficacy. In this feasibility study, we propose a method for the quantitative assessment of static and morphodynamic features of neutrophils based on label-free time-lapse imaging data. We investigate neutrophil activation phenotypes after confrontation with fungal pathogens and isolation from a human whole-blood assay. In particular, we applied a machine learning supported approach to time-lapse microscopy data from different infection scenarios and were able to distinguish between Candida albicans and C. glabrata infection scenarios with test accuracies well above 75%, and to identify pathogen-free samples with accuracy reaching 100%. These results significantly exceed the test accuracies achieved using state-of-the-art deep neural networks to classify neutrophils by their morphodynamics.

Multicenter evaluation of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy-based method for rapid identification of clinically relevant yeasts

Fourier transform infrared (FTIR) spectroscopy has demonstrated applicability as a reagent-free whole-organism fingerprinting technique for both microbial identification and strain typing. For routine application of this technique in microbiology laboratories, acquisition of FTIR spectra in the attenuated total reflectance (ATR) mode simplifies the FTIR spectroscopy workflow, providing results within minutes after initial culture without prior sample preparation. In our previous central work, 99.7% correct species identification of clinically relevant yeasts was achieved by employing an ATR-FTIR-based method and spectral database developed by our group. In this study, ATR-FTIR spectrometers were placed in 6 clinical microbiology laboratories over a 16-month period and were used to collect spectra of routine yeast isolates for on-site identification to the species level. The identification results were compared to those obtained from conventional biochemical tests and/or matrix-assisted laser desorption/ionization time of flight mass spectrometry. Isolates producing discordant results were reanalyzed by routine identification methods, ATR-FTIR spectroscopy and PCR gene sequencing of the D1/D2 and ITS regions. Among the 573 routine clinical yeast isolates collected and identified by the ATR-FTIR-based method, 564 isolates (98.4%) were correctly identified at the species level while the remaining isolates were inconclusive with no misidentifications. Due to the low prevalence of Candida auris in routine isolates, additional randomly selected C. auris (n = 24) isolates were obtained for evaluation and resulted in 100% correct identification. Overall, the data obtained in our multicenter evaluation study using multiple spectrometers and system operators indicate that ATR-FTIR spectroscopy is a reliable, cost-effective yeast identification technique that provides accurate and timely (∼3 minutes/sample) species identification promptly after the initial culture.