Histoproteomic Characterization of Localized Cutaneous Amyloidosis in X-Linked Reticulate Pigmentary Disorder

L’Imperio, Vincenzo; Bruno, Irene; Rabach, Ingrid; Smith, Andrew; Chinello, Clizia; Stella, Martina; Magni, Fulvio; Pagni, Fabio

doi:10.1159/000464336

Cited by 3 publications

(1 citation statement)

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“…In contrast, the use of matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) provides a natural solution by combining the molecular power of MS with the underlying morphological data directly in situ 9. Pivotal experiments have demonstrated the feasibility of the approach10 11 through its use in tandem with parallel liquid chromatography-mass spectrometry (LC-MS)12 and prior identification of amyloidosis by annotation based on Congo red staining 13. Therefore, the aim of the present study is to develop an automated pipeline based on MALDI-MSI for the detection of renal amyloidosis in the complex molecular background of the kidney.…”

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confidence: 99%

Spatial resolution of renal amyloid deposits through MALDI-MSI: a combined digital and molecular approach to monoclonal gammopathies

et al. 2023

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AimsIdentification and characterisation of monoclonal gammopathies of renal significance (MGRS) is critical for therapeutic purposes. Amyloidosis represents one of the most common forms of MGRS, and renal biopsy remains the gold standard for their classification, although mass spectrometry has shown greater sensitivity in this area.MethodsIn the present study, a new in situ proteomic technique, matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI), is investigated as an alternative to conventional laser capture microdissection MS for the characterisation of amyloids. MALDI-MSI was performed on 16 cases (3 lambda light chain amyloidosis (AL), 3 AL kappa, 3 serum amyloid A amyloidosis (SAA), 2 lambda light chain deposition disease (LCDD), 2 challenging amyloid cases and 3 controls). Analysis began with regions of interest labelled by the pathologist, and then automatic segmentation was performed.ResultsMALDI-MSI correctly identified and typed cases with known amyloid type (AL kappa, AL lambda and SAA). A ‘restricted fingerprint’ for amyloid detection composed of apolipoprotein E, serum amyloid protein and apolipoprotein A1 showed the best automatic segmentation performance (area under the curve >0.7).ConclusionsMALDI-MSI correctly assigned minimal/challenging cases of amyloidosis to the correct type (AL lambda) and identified lambda light chains in LCDD cases, highlighting the promising role of MALDI-MSI for amyloid typing.

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