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Aims: Identification 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., Methods: In 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., Results: MALDI-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)., Conclusions: MALDI-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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Renal amyloidosis is a rare condition caused by the progressive accumulation of misfolded proteins within glomeruli, vessels, and interstitium, causing functional decline and requiring prompt treatment due to its significant morbidity and mortality. Congo red (CR) stain on renal biopsy samples is the gold standard for diagnosis, but the need for polarized light is limiting the digitization of this nephropathology field. This study explores the feasibility and reliability of CR fluorescence on virtual slides (CRFvs) in evaluating the diagnostic accuracy and proposing an automated digital pipeline for its assessment. Whole-slide images from 154 renal biopsies with CR were scanned through a Texas red fluorescence filter (NanoZoomer S60, Hamamatsu) at the digital Nephropathology Center of the Istituto di Ricovero e Cura a Carattere Scientifico San Gerardo, Monza, Italy, and evaluated double-blinded for the detection and quantification through the amyloid score and a custom ImageJ pipeline was built to automatically detect amyloid-containing regions. Interobserver agreement for CRFvs was optimal (k = 0.90; 95% CI, 0.81-0.98), with even better concordance when consensus-based CRFvs evaluation was compared to the standard CR birefringence (BR) (k = 0.98; 95% CI, 0.93-1). Excellent performance was achieved in the assessment of amyloid score overall by CRFvs (weighted k = 0.70; 95% CI, 0.08-1), especially within the interstitium (weighted k = 0.60; 95% CI, 0.35-0.84), overcoming the misinterpretation of interstitial and capsular collagen BR. The application of an automated digital pathology pipeline (Streamlined Pipeline for Amyloid detection through CR fluorescence Digital Analysis, SPADA) further increased the performance of pathologists, leading to a complete concordance with the standard BR. This study represents an initial step in the validation of CRFvs, demonstrating its general reliability in a digital nephropathology center. The computational method used in this study has the potential to facilitate the integration of spatial omics and artificial intelligence tools for the diagnosis of amyloidosis, streamlining its detection process., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)