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Spatial resolution of renal amyloid deposits through MALDI-MSI: a combined digital and molecular approach to monoclonal gammopathies
  1. Greta Bindi1,
  2. Andrew Smith1,
  3. Glenda Oliveira1,
  4. Albino Eccher2,
  5. Simona Vatrano3,
  6. Federico Alberici4,
  7. Giorgio Cazzaniga5,
  8. Stefania Galimberti6,
  9. Giulia Capitoli6,
  10. Fulvio Magni1,
  11. Fabio Pagni5,
  12. Vincenzo L'Imperio5
  1. 1Department of Medicine and Surgery, Proteomics and Metabolomics Units, University of Milano-Bicocca, Monza, Italy
  2. 2Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
  3. 3Pathology Unit, ASP Catania, "Gravina" Hospital, Caltagirone, Italy
  4. 4Nephrology Unit, Spedali Civili Hospital, ASST Spedali Civili di Brescia, Brescia, Italy
  5. 5Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
  6. 6Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
  1. Correspondence to Dr Vincenzo L'Imperio, Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, Monza, 20900, Italy; vincenzo.limperio{at}


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.

  • amyloid
  • glomerulonephritis
  • hematology

Data availability statement

Data are available on reasonable request.

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Data availability statement

Data are available on reasonable request.

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  • Handling editor Runjan Chetty.

  • GB and AS contributed equally.

  • Contributors VL'I and AS defined the study design; GCaz, AE, SV and FA performed the data collection; GB, AS and GO carried out experiments; SG and GCap performed the data analysis; VL, GB and AS performed the data interpretation; FM, FP and VL'I provided the funding acquisition and administrative support. VL'I is responsible for the overall content as guarantor. All authors were involved in writing the paper and had final approval of the submitted and published versions.

  • Funding This work was supported by the Regione Lombardia, regional law n° 9/2020, resolution n° 3776/2020; Italian Ministry of Health under the grant 'Ricerca Finalizzata' GR-2021-12374235 and PNRR-MR1-2022-12375735.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.