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. 2025 Jun 27;10(26):27869-27882.
doi: 10.1021/acsomega.5c00894. eCollection 2025 Jul 8.

Untargeted Lipidomics in Fabry Disease of Urine Samples by Low-Resolution Flow Injection Mass Spectrometry (ESI(±)-LTQ MS)

Rafael Arruda Foletto  1   2   3 , Augusto Santos Borges  1   2 , LarissaCampos Motta  3 , Marcos Valério Vieira Lyrio  3 , Carolina Teles Baretto  4 , Luciene Cristina Gastalho Campos  4 , Paulo Roberto Filgueiras  3 , Valério Garrone Barauna  1 , Wanderson Romão  2   3
Affiliations

Untargeted Lipidomics in Fabry Disease of Urine Samples by Low-Resolution Flow Injection Mass Spectrometry (ESI(±)-LTQ MS)

Rafael Arruda Foletto et al. ACS Omega. .

Abstract

Background: Fabry disease (FD) is a lysosomal storage disease caused by genetic mutations related to the coding of the enzyme α-galactosidase A, which is responsible for the metabolism of glycosphingolipids such as globotriaosylceramide and globotriaosylsphingosine. The accumulation of these and other metabolites can occur in various types of cells and impair the functioning of multiple organs and systems, such as the heart, brain, and kidneys. However, with early diagnosis and appropriate therapeutic intervention, the clinical outcome can be significantly improved. This study aimed to analyze the performance of new diagnostic methods for FD using the broad field of lipidomics combined with multivariate analyses, proposing the use of urine as a specimen.

Materials and methods: urine samples were collected from patients with both confirmed (Case) and negative (Control) diagnoses of FD, which were later processed for specific lipid extraction. After extraction, 81 samples (44 cases and 37 controls) were subjected to mass spectrometry analysis, with direct infusion and electrospray ionization in both positive and negative modes (ESI(±)). After spectral acquisition, the data were processed and analyzed using multivariate analysis methods such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA).

Results: the combination of both ionization modes for PLS-DA was able to differentiate between the Case and Control groups with 92% accuracy.

Conclusion: this paper suggests that the proposed method of application of lipidomics combined with multivariate analyses as a tool for early diagnosis of FD is promising, enabling and contributing to the improvement of healthcare for these patients.

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Figures

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1
Representative mass spectra obtained by LTQ-MS in the ESI­(+) mode of samples from patients in the Case (left) and Control (right) groups.
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2
Representative mass spectra obtained by LTQ-MS in the ESI(−) mode from samples from patients in the Case (left) and Control (right) groups.
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3
Fragmentation spectra of the highest intensity ions obtained by LTQ-MS in the ESI­(+) mode.
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4
Fragmentation spectra of the highest intensity ions obtained by LTQ-MS in the ESI(−) mode.
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5
PCA and PLS-DA scatter plots of samples obtained from ESI(±)-LTQ MS. The PC1 and PC2 components and the latent variables LV1 and LV2 represent the percentages of the variation in the data set. The red dots represent samples from the Case group and the blue dots represent samples from the Control group. The circles represent training samples (used in the construction of the classification models) and the squares represent test samples (used to evaluate the quality of the model).
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6
Calculated response plot, coefficients, and VIP scores of PLS-DA of lipid profiles, differentiating the Control and Case (Fabry) groups. The red dots represent samples from the Case group, and the blue dots represent samples from the Control group. The circles represent training samples (used in the construction of classification models) and the rhombuses represent test samples (used to evaluate the quality of the model). The dashed red line represents the limit set by PLS-DA.
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7
Fragmentation spectra of the main compounds identified as discriminant by the PLS-DA model from ESI­(+) and ESI(−) ionization modes.
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