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.

Update on: proteome analysis in thyroid pathology - part II: overview of technical and clinical enhancement of proteomic investigation of the thyroid lesions

INTRODUCTION

An accurate diagnostic classification of thyroid lesions remains an important clinical aspect that needs to be addressed in order to avoid 'diagnostic' thyroidectomies. Among the several 'omics' techniques, proteomics is playing a pivotal role in the search for diagnostic markers. In recent years, different approaches have been used, taking advantage of the technical improvements related to mass spectrometry that have occurred. Areas covered: The review provides an update of the recent findings in diagnostic classification, in genetic definition and in the investigation of thyroid lesions based on different proteomics approaches and on different type of specimens: cytological, surgical and biofluid samples. A brief section will discuss how these findings can be integrated with those obtained by metabolomics investigations. Expert commentary: Among the several proteomics approaches able to deepen our knowledge of the molecular alterations of the different thyroid lesions, MALDI-MSI is strongly emerging above all. In fact, MS-imaging has also been demonstrated to be capable of distinguishing thyroid lesions, based on their different molecular signatures, using cytological specimens. The possibility to use the material obtained by the fine needle aspiration makes MALDI-MSI a highly promising technology that could be implemented into the clinical and pathological units.

The potential of near infrared spectroscopy to estimate the content of cannabinoids in Cannabis sativa L.: A comparative study

Cannabis has been one of the oldest source of food, textile fiber and psychotropic substances. Cannabinoids are the main biologically active constituents of the Cannabis genus, with a demonstrated medicinal value. Its production is becoming legalized and regulated in many countries, thus increasing the need for a rapid analysis method to assess the content of cannabinoids. Gas chromatography (GC) is the preferred analytical method for the determination of these compounds, although is a slow and costly technique. Near infrared spectroscopy (NIR) has the potential for the quantitative prediction of quality parameters, and also of pharmacologically active compounds, but no references about cannabinoids prediction has been previously reported. The aim of the present research was to develop a fast, economical, robust and environmentally friendly method based on NIR technology that allow the quantification of the main cannabinoids present in Cannabis sativa L.

SAMPLES

A total of 189 grinded and dried samples from different genotypes and registered varieties were used. The content of the cannabinoids CBDV, Δ9-THCV, CBD, CBC, Δ8-THC, Δ9-THC, CBG and CBN were determined by gas chromatography. Spectra were collected in a dispersive NIR Systems 6500 instrument, and in a Fourier transform near Infrared (FT-NIR) equipment. The sample group was divided into calibration and validation sets, to develop modified partial lest squares (PLS) regression models with WINISI IV software with the dispersive data, and PLS models using OPUS 7.2 with the FT-NIR ones. Excellent coefficient of determination of cross validation (R²_CV from 0.91 to 0.99), were obtained for the prediction of CBD, CBC, Δ8-THC, Δ9-THC, CBG and CBN, with standard error of prediction (SEP) values among 1.5-3 times the standard error of laboratory (SEL); and good for CBDV and Δ9-THCV cannabinoids (R² values of 0.89 and 0.83, respectively) with the dispersive instrument. Similar calibration and validation statistics have been obtained with the FT-NIR instrument with the same sample sets, using its specific OPUS software. In conclusion, a methodology of quantitative determination of cannabinoids in Cannabis raw materials has been developed for the first time using NIR and FT-NIR instruments, with similar good predictive results. This new analytical method would allow a simpler, more robust and precise estimation than the current standard GC.