MALDI-MS Imaging in the Study of Glomerulonephritis

Mass Spectrometry Imaging Differentiates Chromophobe Renal Cell Carcinoma and Renal Oncocytoma with High Accuracy

Background: While subtyping of the majority of malignant chromophobe renal cell carcinoma (cRCC) and benign renal oncocytoma (rO) is possible on morphology alone, additional histochemical, immunohistochemical or molecular investigations are required in a subset of cases. As currently used histochemical and immunohistological stains as well as genetic aberrations show considerable overlap in both tumors, additional techniques are required for differential diagnostics. Mass spectrometry imaging (MSI) combining the detection of multiple peptides with information about their localization in tissue may be a suitable technology to overcome this diagnostic challenge.

Patients and Methods: Formalin-fixed paraffin embedded (FFPE) tissue specimens from cRCC (n=71) and rO (n=64) were analyzed by MSI. Data were classified by linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) algorithm with internal cross validation and visualized by t-distributed stochastic neighbor embedding (t-SNE). Most important variables for classification were identified and the classification algorithm was optimized.

Results: Applying different machine learning algorithms on all m/z peaks, classification accuracy between cRCC and rO was 85%, 82%, 84%, 77% and 64% for RF, SVM, KNN, CART and LDA. Under the assumption that a reduction of m/z peaks would lead to improved classification accuracy, m/z peaks were ranked based on their variable importance. Reduction to six most important m/z peaks resulted in improved accuracy of 89%, 85%, 85% and 85% for RF, SVM, KNN, and LDA and remained at the level of 77% for CART. t-SNE showed clear separation of cRCC and rO after algorithm improvement.

Conclusion: In summary, we acquired MSI data on FFPE tissue specimens of cRCC and rO, performed classification and detected most relevant biomarkers for the differential diagnosis of both diseases. MSI data might be a useful adjunct method in the differential diagnosis of cRCC and rO.

MALDI imaging in Fabry nephropathy: a multicenter study

BACKGROUND

The current study evaluates the application of histology and in situ proteomics (MALDI-MSI) in Fabry nephropathy (FN), showing investigative and classification role for this coupled approach.

METHODS

A retrospective series of 14 formalin fixed paraffin embedded (FFPE) renal biopsies with diagnosis of FN and 1 biopsy from a patient bearing a galactosidase-α (GLA) genetic variant of unknown significance (GVUS, c.376A>G) have been classified for clinical characteristics. Groups were compared for histological differences (following the ISGFN scoring system). Moreover, renal biopsies from these cases have been analyzed with MALDI-MSI as previously described to find proteomic signatures among different mutations and phenotypes.

RESULTS

Comparison of clinical features revealed lower mean 24 h proteinuria in females (225 mg/24 h) than in males (1477.5 mg/24 h, p = 0.006). As for clinical characteristics, females significantly differed from males only for lower arterial sclerosis, with a mean value of 0.82 vs. 1.05 (p = 0.001). Proteomic analysis demonstrated specific signatures in different subgroups of FN patients. Moreover, MALDI correctly classified cases with undetermined mutation or GVUS.

CONCLUSIONS

The present study demonstrated the feasible application of MALDI-MSI in the analysis of FN FFPE renal biopsies, allowing the detection of putative signatures for phenotypic distinction and demonstrating genetic classification capabilities.