Detection and accurate identification of Mycobacterium species by flow injection tandem mass spectrometry (FIA-MS/MS) analysis of mycolic acids

Categories: Disease & Virus

April 17, 2025

Optimization of sample preparation

An extraction procedure adapted from published protocols was employed, involving successive alkaline hydrolysis in an organic solvent at elevated temperatures, followed by neutralization and liquid-liquid extraction¹². The original protocol for isolating mycolic acids underwent extensive optimization, incorporating 18 distinct modifications. These focused on critical steps such as cell pretreatment, including the extraction and removal of surface lipids using methanol; adjustments to alkali hydrolysis parameters, including alkali concentrations, reaction temperatures, and incubation times; and improvements to the extraction process, such as optimizing the solvent volumes and extraction durations to maximize yield and efficiency. Each modification was meticulously evaluated for reproducibility and repeatability to ensure reliable and consistent results. Compared to the original protocol, the optimized method demonstrated significantly enhanced sensitivity, robustness, and overall analytical performance.

Validation of the analytical method

The validation method focused on evaluating all parameters characterizing the entire procedure, including sample preparation and FIA-MS/MS analysis, as a diagnostic method for the identification of Mycobacterium species and the potential assessment of their drug resistance. Validation encompassed a comprehensive set of parameters: recovery, sensitivity, limit of detection (LOD), lower limit of quantitation (LLOQ), working range, precision, repeatability, reproducibility, relative diagnostic sensitivity, and diagnostic specificity. Each parameter was rigorously tested to ensure the method’s reliability and applicability in clinical diagnostics. Recovery was assessed to verify the efficiency of the extraction and analytical steps, while sensitivity and LOD were evaluated to confirm the method’s ability to detect low concentrations of mycolic acids. Precision and reproducibility analyses included intra- and inter-assay variability to guarantee consistent performance across different runs, laboratories, and operators. Diagnostic sensitivity and specificity were validated against a library of reference strains and clinical isolates to ensure accurate identification of Mycobacterium species and their differentiation from non-mycobacterial samples.

The validated method demonstrated high reliability and robustness, making it suitable for routine clinical diagnostics and research applications. A detailed summary of the validation criteria and performance metrics is presented in Table 1.

Table 1 Validation results summary.

Recovery rates and method performance

Recovery studies were performed concurrently on pooled urine and sputum samples collected from healthy volunteers. These samples were spiked with a commercially available mycolic acid mixture derived from Mycobacterium tuberculosis (Sigma-Aldrich) to evaluate the efficiency and accuracy of the extraction and quantification process. Enrichment levels of 1 µg/mL and 10 µg/mL of the mycolic acid mixture were used for the experiments. The recovery rates demonstrated excellent method performance, with values of 104.0% and 95.9% observed for sputum samples and 103.5% and 97.5% for urine samples at the 1 µg/mL and 10 µg/mL enrichment levels, respectively. These results indicate a robust and reliable method capable of efficiently extracting and quantifying mycolic acids across a range of concentrations. The slightly elevated recovery values at the lower enrichment level (1 µg/mL) suggest high sensitivity and minimal matrix interference, making this method particularly suited for detecting low concentrations of mycolic acids in clinical samples. Such consistent recovery rates across different biological matrices highlight the method’s versatility and potential applicability in diverse diagnostic and research settings.

Limits of detection, quantitation and working range

Tests were conducted to determine the analytical sensitivity and working range of the method for three selected reference strains of Mycobacterium: M. tuberculosis H37Rv ATCC 25618, M. smegmatis ATCC 19420, and M. abscessus ATCC 23045. Bacterial suspensions of defined densities were systematically diluted to achieve appropriate bacterial counts. In parallel, reduction cultures were performed on solid Löwenstein-Jensen (L-J) medium to confirm the exact number of bacteria. The resulting bacterial pellets were subjected to extraction and FIA-MS/MS analysis of mycolic acids. The working range limits of the method were established based on the XIC intensity for the most prominent mycolic acid in the profile of each selected strain. Detection limits (LOD) and the lower limit of quantification (LLOQ) were determined using the signal-to-noise ratio for the chosen XIC. The upper limit of quantification (ULOQ) was defined as the highest bacterial concentration at which the regression parameter (R²) remained greater than 0.990. As shown in Table 1, the developed method demonstrated exceptional analytical sensitivity, capable of detecting single mycobacterial cells. Additionally, it exhibited a wide working range spanning six orders of magnitude. These results highlight the robustness and reliability of the method for quantifying mycolic acids across diverse concentrations, making it well-suited for both low- and high-density bacterial samples in diagnostic and research applications.

Method precision, repeatability, and reproducibility

Precision, repeatability, and reproducibility studies were conducted in parallel on three Mycobacterium strains previously selected and used for analytical sensitivity testing. Mycobacterial pellets of 10⁶ to 10⁷ CFU (colony forming units), were subjected to mycolic acid extraction and FIA-MS/MS analysis. The area under the XIC curve for the most prominent mycolic acid signal of each strain was used to calculate these validation parameters. The method’s precision was assessed by performing 10 consecutive analyses of the same sample, while repeatability and reproducibility were evaluated through six independent extractions and FIA-MS/MS analyses conducted daily. Repeatability involved a single analyst performing all procedures, whereas reproducibility included the involvement of two different analysts to simulate variations in laboratory handling. The results, expressed as relative standard deviation (RSD), are summarized in Table 1. The findings demonstrate that the developed method, despite being based on the FIA-MS/MS technique, exhibits exceptional precision, repeatability, and reproducibility, ensuring its reliability for robust diagnostic and research applications.

Mycolic acids as chemotaxonomic biomarkers in the Mycobacterium genus

The PCA analysis of the obtained mycolic acid profiles from the reference strains (Fig. 1a) clearly demonstrates a distinct separation of all analyzed species, based on the simultaneous evaluation of 52 mycolic acids. These findings were further validated by an extended PCA analysis (Fig. 1b), which incorporated the MA profiles of 32 reference strains and 299 clinical isolates. Notably, the analysis of clinical isolates highlights the significant biological diversity of MA profiles, reinforcing their value as chemotaxonomic biomarkers within the Mycobacterium genus. Species such as M. xenopi and M. malmoense are characterized by relatively low heterogeneity and highly conserved MA profiles, reflecting their stable evolutionary traits. Conversely, members of the Mycobacterium tuberculosis complex exhibit a mix of high heterogeneity and conserved MA profiles, a pattern distinct from the Mycobacterium avium-intracellulare complex group, which is notable for its elevated heterogeneity and reduced profile conservativeness. Other non-tuberculous mycobacterial species display significant diversity among themselves while clustering according to their chemotaxonomic similarities.

As shown in Fig. 2a-b, the heat map illustrates the distribution of individual mycolic acids across different Mycobacterium groups and strains. Mycolic acid profiles typically consist of MAs from multiple classes present simultaneously, regardless of strain classification. However, an exception is observed with class α₂-MAs, which are exclusively found in the MAI group and certain NTM strains. Among these, the highest proportions are detected in M. gordonae, M. fortuitum, and M. smegmatis. A much higher level of conservatism is noted in the relationship between α-alkyl chain lengths. Profiles dominated by molecules with the longest α-alkyl chain (C₂₄) are characteristic of the MTB group, with only a few exceptions (M. xenopi, M. malmoense, and M. simiae). Mycolic acids with a C₂₂ α-alkyl chain length are the most common across strains, while no strain with a predominant C₂₀ α-alkyl chain was observed among the tested reference strains. The identification of pulmonary tuberculosis depends on detecting the distinctive MA profile specific to the MTB group. This profile is characterized by the presence of total mycolic acids ranging from C₇₄ to C₈₈, with a predominance of MAs containing a C₂₄ α-alkyl chain.

Determination of the drug resistance based on mycolic acids profiles

Based on the culture extraction procedure, MA profiles from MTB and NTM strains with varying drug resistance (see Supplementary Table S1 and S2 online) profiles were examined using both the LC-MS/MS targeted method (MRM) and non-targeted methods (Precursor Ion). In the analysis of MTB strains, neither the targeted method (52 MRM pairs) nor the non-targeted methods yielded specific MA profiles that could precisely define the drug resistance profile (data not shown). However, the obtained profiles did allow for the determination of whether a tested strain is drug-resistant, which may hold significant value as supplementary information alongside species identification. This additional insight could facilitate both immediate clinical decision-making and partial personalization of antibiotic therapy. The observed relationship is effectively illustrated by the PCA analysis presented in Fig. 3, where, despite the inclusion of additional drug-sensitive reference strains, drug-resistant M. tuberculosis isolates form a distinct group within the PCA space. Non-targeted analyses also revealed that, in addition to changes in the intensities of certain MTB MAs used for species identification, there were notable variations in the relative intensities of additional MAs with masses above 1200 m/z. These variations were particularly evident in the C₂₂ and C₂₄ chains. These additional mycolic acids differentiate drug-resistant mycobacteria more effectively than those traditionally used for genus identification and could be incorporated into targeted methods specifically designed to identify general drug resistance based on the MA profile. In contrast, for NTM strains, no MA profiles were identified that could differentiate between drug-sensitive and drug-resistant strains or determine the drug resistance profile.

Interference testing and assay selectivity for mycolic acid profiling

To ensure the exclusion of false positives arising from the presence of mycolic acids in bacteria outside the Mycobacterium genus, mycolic acid extraction and FIA-MS/MS analysis were conducted on other mycolic acid-containing bacteria within the suborder Corynebacterinae. These included R. equi, N. asteroides, C. glutamicum, D. maris, G. bronchialis, G. sputi, and T. paurometabola. Remarkably, no false-positive signals were observed for any of these strains, underscoring the high specificity and reliability of the developed assay. This high degree of selectivity highlights the robustness of the method, particularly in differentiating Mycobacterium species from other genera that share similar biochemical characteristics. Such specificity is critical for ensuring the accuracy of diagnostic and research applications, reducing the likelihood of misidentification or erroneous conclusions.

Clinical validation of FIA-MS/MS mycolic acid profiling for the detection of Mycobacterium infections

The utility of mycolic acid profiling by FIA-MS/MS for detecting Mycobacterium infections was evaluated by analyzing clinical samples from patients with mycobacterial infections (n = 36), lower respiratory tract infections not caused by Mycobacterium (n = 235), and healthy volunteers (n = 94). Among patients with mycobacterial infections, a Mycobacterium-specific mycolic acid profile was detected in 33 samples using FIA-MS/MS. Of these profiles, 9 were identified as NTM strains, including M. avium (n = 5), M. chimaera (n = 2), M. malmoense (n = 1), and M. haemophilum (n = 1). The remaining 24 profiles were identified as M. tuberculosis. In three samples from patients with tuberculosis, M. tuberculosis-specific mycolic acids were not detected. Furthermore, no mycolic acids were detected by FIA-MS/MS in any of the samples from patients with lower respiratory tract infections caused by non-mycobacterial bacteria or in samples from healthy volunteers. The analysis of mycolic acid profiles was performed using M-Typer software, which demonstrated a high degree of similarity between the detected MA profiles and those in the reference library. Notably, in all positive samples, the match between the MA profiles and the library exceeded 90%, underscoring the accuracy and reliability of the method.

In summary, based on the analysis of the results obtained for the clinical samples presented in Table 2, the clinical performance parameters of the FIA-MS/MS test for the detection of Mycobacterium infections were calculated. The test demonstrated a specificity of 100%, a sensitivity of 91.6%, a negative predictive value (NPV) of 98.2%, and a positive predictive value (PPV) of 100%.

Table 2 Summary of FIA-MS/MS results for clinical samples describing numbers of true positive (TP), true negative (TN), false positive (FP) and false negative (FN) results.

These results highlight the high reliability of the FIA-MS/MS method, particularly its exceptional ability to exclude false positives due to its perfect specificity. The strong NPV further underscores its utility in confidently ruling out Mycobacterium infections in negative cases, while the PPV confirms its precision in accurately identifying positive cases. Such robust diagnostic performance supports the method’s potential as a valuable tool in clinical settings for the rapid and accurate identification of Mycobacterium infections, ultimately facilitating timely and targeted patient management.

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