Quantitative enrichment of amyloid precursors refines mass spectrometry-based amyloidosis diagnosis

Quantitative enrichment of amyloid precursors refines mass spectrometry-based amyloidosis diagnosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantitative enrichment of amyloid precursors refines mass spectrometry-based amyloidosis diagnosis Sylvaine Di Tommaso, Bertrand Chauveau, Cyril Dourthe, Jean-William Dupuy, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7999764/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Clinical Proteomics → Version 1 posted 11 You are reading this latest preprint version Abstract Introduction Amyloidosis typing is crucial to determine the best therapeutic strategy for patients. Since conventional histological techniques often fail, the identification of amyloid precursors by mass spectrometry became the new standard. However, without quantification, selecting the amyloid precursor from proteins that may be ubiquitous under non-pathological conditions may be equivocal. Therefore, we quantified protein enrichment in amyloid deposits to improve amyloidosis typing. Methods Protein enrichment was measured by extracted ion chromatogram-based label free quantification by comparing a microdissected amyloid area with a non-amyloid area. We assessed the discrimination ability of candidate precursors with this approach compared to the two practiced identification methods. Results As a proof-of-concept, we selected 9 cases including the most common amyloidosis subtypes, 6 typed by immunohistochemistry and 3 inconclusive by immunohistochemistry. Proteins associated with amyloid deposits were identified in all samples, confirming the pathology. Where the routine clinical mass spectrometric identification techniques allowed unambiguous conclusions for 3 of 9 cases, quantification of the enrichment ratio in amyloid deposits allowed unambiguous precursor selection in all cases. Conclusion Quantification of precursor enrichment in amyloid deposits is a promising optimization for amyloidosis typing. Incorporated into routine clinical processes, it will improve patient care in difficult diagnostic situations. amyloidosis typing label free quantification mass spectrometry proteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Amyloidoses are rare, heterogeneous diseases caused by the extracellular deposition of amyloid, a fibrillar material composed of twisted β-sheet protein aggregates ( 1 , 2 ). Depending on the site of deposition and the mechanism of protein misfolding, amyloidosis can be systemic or localized, acquired or hereditary. Amyloid deposits contain a disease-specific precursor protein along with common components such as heparan sulfate proteoglycan and serum amyloid P component ( 2 ). The International Amyloidosis Society maintains the list of amyloid precursors, which forms the basis for the nomenclature of amyloidoses. This list continues to expand, with 36 precursors identified in 2018 ( 3 ) and 42 to date ( 4 ). The main systemic precursors—immunoglobulin light chains (κ or λ, AL amyloidosis), transthyretin (ATTR, acquired or hereditary), and serum amyloid A (AA)—account for nearly 90% of cases diagnosed in routine practice ( 5 ). Treatment strategies depend on the nature of the precursor and the distribution of deposits, ranging from chemotherapy, immunotherapy, or autologous stem cell transplantation for AL amyloidosis, to inflammation control for AA amyloidosis, and siRNA therapy or liver transplantation in selected hereditary ATTR cases ( 6 ). Accurate amyloid typing is therefore essential for optimal patient management and genetic counseling. Diagnosis may be clinically suspected or incidentally discovered by pathologists, but confirmation requires histopathological evaluation of an affected tissue or organ. Minor salivary glands, abdominal fat, and colonic mucosa are common biopsy sites, as they are frequently involved and easily accessible. Amyloid deposits show characteristic Congo red positivity and yellow-green birefringence under polarized light. Amyloid typing can be performed by immunofluorescence on frozen tissues or by immunohistochemistry (IHC) on formalin-fixed paraffin-embedded (FFPE) samples. However, IHC often suffers from background noise—particularly for light chains and transthyretin—and from the limited availability of antibodies for rare amyloid subtypes. Tissue scarcity can further restrict the number of proteins tested. Immunoelectron microscopy offers an alternative but is available only in few expert centers and requires a dedicated sample. Over the past decade, mass spectrometry (MS)-based proteomics from FFPE samples has emerged as the gold standard for amyloid typing, representing the first clinical application of MS-based proteomics in diagnostic pathology ( 5 , 7 ). It is particularly valuable when conventional methods are inconclusive, contradictory to clinical findings, or non-informative ( 8 ). The Mayo Clinic and the UK National Amyloidosis Centre are the leading reference centers, each reporting hundreds to thousands of accurately typed cases ( 5 , 7 , 9 ). Rezk et al. demonstrated that MS successfully resolved 80% of previously uninformative cases by IHC ( 10 ), while the Mayo Clinic reported a 100% precursor identification rate ( 5 , 9 ). Both centers use a similar bottom-up proteomics workflow involving laser microdissection of Congo red-stained deposits, tryptic digestion, and peptide analysis by LC-MS/MS. Amyloid identity is confirmed by detection of shared amyloid-associated proteins, including serum amyloid P component, apolipoprotein E, and apolipoprotein A-IV ( 5 , 7 ). For precursor identification, the Mayo Clinic selects the protein with the highest number of peptide spectrum matches (PSMs), whereas the UK Centre uses the protein with the best Mascot identification score ( 7 , 11 , 12 ). Despite these robust pipelines, precursor assignment can remain challenging when multiple candidates show comparable PSM or Mascot scores, as observed in samples with scarce deposits, blood contamination, or proteins naturally present in tissues such as apolipoproteins A-I/A-IV, immunoglobulins, or transthyretin ( 7 , 10 , 13 ). An Australian group reported up to 40% of AL amyloidosis cases displaying secondary precursors with similar PSM values ( 14 ). Protein identification by MS depends on intrinsic physicochemical properties, such as peptide ionization efficiency and sequence length. Consequently, low-abundance proteins with well-ionizing peptides may yield stronger signals than abundant proteins with poor ionization. Conventional proteomic analysis therefore restricts quantification to comparisons of identical peptides (same sequence, charge, and modifications) and does not consider inter-protein comparisons of identification scores, PSMs, or relative intensities as valid measures of abundance ( 15 ). To enhance diagnostic confidence, targeted quantitative MS approaches have been proposed. These rely on isotopically labeled standard peptides to quantify specific amyloid precursors. Ogawa et al. provided the first proof-of-concept ( 16 ), and Park et al. demonstrated high sensitivity and specificity of targeted quantification compared with standard MS-based identification, though only for the three major systemic forms—AL, AA, and ATTR ( 17 ). These pseudo-absolute quantification methods require advanced MS expertise and specialized acquisition workflows, limiting their use in clinical laboratories. Label-free quantification (LFQ) is widely used in exploratory proteomics and increasingly applied to clinical studies ( 18 , 19 ). LFQ estimates protein abundance by extracting ion chromatograms (XICs) based on retention time and m/z coordinates, enabling relative quantification even when peptides are not consistently identified, provided they are sequenced at least once. This approach allows direct comparison of relative protein abundances between samples, making it particularly suitable for evaluating the enrichment of precursor proteins within amyloid deposits. To date, no study has assessed amyloid precursor enrichment quantification using a label-free approach in FFPE-derived samples. In this study, we propose to implement label-free enrichment quantification in routine practice to improve the robustness and reliability of amyloidosis typing. Materials and methods Study population This study relied on a retrospective inclusion of selected patients with an amyloidosis diagnosis performed at the Bordeaux University Hospital from 2013 to 2022, with consistent clinico-biological data. Archived formalin-fixed and paraffin embedded (FFPE) samples with sufficient remaining material were included. Main clinical, biological and pathological characteristics were retrieved from the institutional medical software. Congo red staining was performed for amyloidosis diagnosis in all samples and amyloidosis typing was performed by routine immunohistochemistry targeting kappa and lambda light chains (clone A21-Y and K22-Y from Clinisciences), serum amyloid A (clone MC 1 from Dako), transthyretin (polyclonal from Dako) and β2-microglobulin (polyclonal from Genetex). All immunostains were performed using an Omnis automate from Dako Agilent (EnVision Flex/horseradish peroxidase for signal amplification). All reagents were provided by Dako/Agilent. No frozen tissue was available for any case. For each case, the abundance of amyloid deposits was semi-quantitatively assessed by a pathologist in a 1 to 5 scale, where 5 represents a sample nearly exclusively affected by amyloid deposits. The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by a local ethic committee for the protection of persons (reference number CER-BDX-2023-02). Microdissection of selected area and sample preparation For each case, Hematoxylin eosin saffron and Congo red stains were used to determine the areas of interest to be dissected. Non-amyloid (NA) and Amyloid (A) areas were selected under the supervision of a pathologist (either B.LB. or B.C.). Between 0.1 and 1 mm² of tissue was microdissected from a FFPE 5 µm-thick section with a PALM type 4 (Zeiss) laser microdissector. Laser microdissected sections were collected and immerged in a 50 mM Ammonium bicarbonate buffer. Fragments were heated at 90°C for 120 min with occasional vortexing. Samples were reduced in 10mM dithiothreitol and alkyled in 100mM iodoacetamide before digestion into tryptic peptides overnight and analyzed by liquid chromatography and tandem mass spectrometry (LC-MS/MS). Mass spectrometry analysis NanoLC-MS/MS analysis was performed using an Ultimate 3000 RSLC Nano-UPHLC system (Thermo Scientific, USA) coupled to a nanospray Orbitrap Fusion™ Lumos™ Tribrid™ Mass Spectrometer (Thermo Fisher Scientific, California, USA). Each peptide extracts were loaded on a 300 µm ID x 5 mm PepMap C 18 precolumn (Thermo Scientific, USA) at a flow rate of 10 µL/min. After a 3 min desalting step, peptides were separated on a 50 cm EasySpray column (75 µm ID, 2 µm C 18 beads, 100 Å pore size, ES903, Thermo Fisher Scientific) with a 4–40% linear gradient of solvent B (0.1% formic acid in 80% ACN) in 57 min. The separation flow rate was set at 300 nL/min. The mass spectrometer operated in positive ion mode at a 2.0 kV needle voltage. Data was acquired using Xcalibur 4.4 software in a data-dependent mode. MS scans (m/z 375–1500) were recorded at a resolution of R = 120000 (@ m/z 200), a standard AGC target and an injection time in automatic mode, followed by a top speed duty cycle of up to 3 seconds for MS/MS acquisition. Precursor ions (2 to 7 charge states) were isolated in the quadrupole with a mass window of 1.6 Th and fragmented with HCD@28% normalized collision energy. MS/MS data was acquired in the Orbitrap cell with a resolution of R = 30000 (@m/z 200), an standard AGC target and a maximum injection time in automatic mode. For protein identification, Mascot 2.5 algorithm through Proteome Discoverer 2.5 Software (Thermo Fisher Scientific Inc.) was used in batch mode by searching against the UniProt Homo sapiens database (79 071 entries, Reference Proteome Set, release date: January, 2022) from http://www.uniprot.org/ website. Two missed enzyme cleavages were allowed for the trypsin. Mass tolerances in MS and MS/MS were set to 10 ppm and 0.02 Da. Oxidation of methionine and acetylation of lysine were searched as dynamic modifications. Carbamidomethylation on cysteine was searched as static modification. Raw LC-MS/MS data were imported in Proline Studio for feature detection, alignment, and quantification ( 20 ). Proteins identification was accepted only with at least 2 specific peptides with a pretty rank = 1 and with a protein False Discovery Rate (FDR) value less than 1.0% calculated using the “decoy” option in Mascot. Label free quantification of MS1 spectra by extracted ion chromatograms (XIC) was carried out with parameters indicated previously ( 21 ). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE ( 22 ) partner repository with the dataset identifier PXD039814. Bioinformatical and statistical analysis Statistical analyses and plots were performed using R, version 4.2.2 ( 23 ). Plots were performed using the ggplot2 package and the ggpubr package. Spearman's rank correlations were performed using the stats package. To assess the confidence in data interpretation, we defined a confidence score for each approach, expressed as a ratio (score of the first precursor divided by the mean of the next 2 scores). As all three methods of mass spectrometry-based amyloidosis typing hypothesized that, from all identified and/or quantified proteins, a protein precursor with a top score will emerge (PSM, Mascot or abundance ratio), confidence in results interpretation can be summarized, for each approach, as the relative difference between the protein precursor with the best score compared to the next two scores. Results Clinical and biological characteristics of the patients We selected a panel of 9 cases as a proof-of-concept, focusing on the most common amyloidosis types. For 6 of them, immunohistochemistry (IHC) was informative. For 3 of them, IHC was not informative but the associated clinical data of the patient strongly suggested the amyloid precursor determination. Of these 9 patients, 8 were cases of systemic amyloidosis (case numbers 1 to 6, 8 and 9) and 1 was a case of localized amyloidosis (case 7). Mean age of included patients was 68 years old (range 47–83). Available samples consisted of a biopsy for 6 cases and a surgical specimen for 3 cases, from various organ or tissue origin (Table 1 ). Amyloidosis typing based on clinicopathological findings were as follows: 2 AL lambda, 2 AL kappa, 1 ATTR, 1 Aβ2M and 1 AA. In three cases (cases 4,7 and 8), IHC was non-informative. Case 4 had an IgM kappa monoclonal gammopathy with an incidental finding of amyloid deposits in a duodenal ampulloma resection. Case 7 had a previous biopsy that favored kappa light chain deposits by IHC. Case 8 had a lambda light chain multiple myeloma. Main clinicopathological characteristics are summarized in Table 1 . Table 1 Relevant clinical data of the included patients Case No. Age (years) Gender Sample (surgical specimen, biopsy) Site Amyloid subtype by IHC Associated condition 1 66 F Surgical specimen (gangrene) Perineum β2-microglobulin End-stage renal disease with dialysis for 40 years 2 78 M Biopsy Minor salivary gland Light chain Lambda Multiple myeloma (IgG Lambda) 3 47 F Biopsy Rectum Serum amyloid A-1 protein Familial Mediterranean fever. Hemodialysis 4 83 M Surgical specimen Duodenum (ampulloma) Non-informative IgM monoclonal gammopathy (kappa). Suspicion of low-grade digestive lymphoma. 5 56 M Surgical specimen Liver (recipient) Transthyretin Transthyretin-related hereditary amyloidosis 6 69 F Biopsy Bone marrow Light chain lambda Monoclonal gammopathy (IgG lambda) 7 70 F Biopsy Larynx Undefined from this sample. A prior biopsy favored kappa light chain deposits. Localized amyloidosis 8 83 M Biopsy Minor salivary gland Doubtful (moderate staining with transthyretin). Light chain multiple myeloma (lambda) 9 62 F Biopsy Liver Light chain Kappa IgM monoclonal gammopathy (kappa). Quantification of protein enrichment in amyloid deposits and comparison with routine identification selection methods. For each case, two regions of interest of the same surface (1 mm²) were isolated by laser microdissection (Fig. 1 ), one with amyloid deposits (Fig. 1 b) and the other without morphological amyloid deposits (Fig. 1 c). These regions were previously annotated on a Congo red staining by an experienced pathologist (Fig. 1 d and e ). After microdissection (Fig. 1 f), proteins were extracted and digested, and proteolytic peptides were analyzed by high resolution tandem mass spectrometry (LC-MS/MS) (Fig. 2 a). In the amyloid area, identification of at least two of the three proteins associated with amyloid deposition (serum amyloid P-component/APCS, apolipoprotein E/APOE, and apolipoprotein A-IV/APOA4) was used as an initial quality control. For amyloidosis typing, the two methods routinely used in the expert centers are (i) the PSM, the protein precursor with the most peptide spectra matches (PSM, number of MS/MS spectra) in the amyloid area (A) and (ii) the Mascot score, the precursor with the highest Mascot identification score in the amyloid zone (A). We compared these methods to (iii) the quantification of the enrichment ratio by a label free approach: the precursor with the highest enrichment ratio calculated by comparing its relative abundance between the amyloid area (A) and the non-amyloid area (NA) (Fig. 2 b). A selection is first made from the 42 known amyloid precursors, which means that among the identified proteins we do not consider proteins that have never been associated with amyloidosis in first intention. Overall, in all analyzed cases, we identified between 4 and 34 amyloid protein precursors per case. The quantification of the protein enrichment in the deposits (ratio A/NA) revealed that several precursors could be enriched and allowed us to isolate the precursor with the highest enrichment rate. Some ubiquitous amyloid precursors were even more present in the non-amyloid tissue than in the deposits (ratio A/NA ≤ 0.5). These results demonstrate the interest of providing quantity information beyond the simple identification of precursors (Fig. 2 b). We then focused on each of the analyzed cases. As a first example, case 3 is an inflammatory AA amyloidosis that was previously characterized by IHC (Fig. 3 a). Amyloidosis-associated proteins (APOE, APOA4 and APCS) were identified and were enriched in the deposits (Fig. 3 b). The number of PSM and the Mascot score of the SAA2 precursor were ranked fifth and seventh, respectively, among the identified precursors. In contrast, with our quantification method, the highest A/NA enrichment among the amyloid precursors was SAA2 ratio (A/NA = 19.6) (Fig. 3 c). Another example is case 8. Here, unlike to the previous illustrated case, IHC was not informative for amyloidosis typing, with at best a doubtful and moderate staining with transthyretin, while the patient had a lambda light chain multiple myeloma (Fig. 4 a). The quantification ratio confirmed transthyretin as the leading enriched amyloid precursor (ratio A/NA 13.7), while both PSM and Mascot scores highlighted Gelsolin as the leading protein precursor of systemic amyloidosis, inconsistent with the clinical data. Of note, lambda light chain was not detected by mass spectrometry in this case, consistent with the IHC findings. These two examples illustrate the gain in robustness in quantifying protein enrichment in amyloid deposition compared to a simple identification approach. Considering case 9, a liver biopsy nearly entirely replaced by kappa light chain amyloid deposits, a microdissection of a non-amyloid area was not possible. Here, we used a pool of subnormal liver tissues as control, coming from surgical specimens of 3 patients, which led to the correct precursor determination of kappa light chain as the leading enriched protein by quantification ratio. Overall, identification and quantification results of the 9 cases analyzed are summarized in Table 2 . Proteins known to be associated with amyloidosis were systematically identified, with varying levels of enrichment in each case. We found a significant and positive correlation between the semi-quantitative histological abundance of amyloid deposits and the APOE PSM score (ρ = 0.82, p = 0.01) and with the APOE Mascot score (ρ = 0.86, p = 0.006). No significant correlations were observed with other amyloidosis-associated proteins (number of PSMs or Mascot score), probably due to a lack of statistical power: APCS (ρ = 0.28, p = 0.46 and ρ = 0.35, p = 0.35, respectively) and APOA4 (ρ = 0.29, p = 0.49 and ρ = 0.29, p = 0.49, respectively). As for the abundance ratio, no correlations were found between the histological amount of amyloid deposits and APOA4 (ρ=-0.25, p = 0.55), APCS (ρ = 0.009, p = 0.98) or APOE (ρ = 0.46, p = 0.26). Considering amyloidosis typing, the PSM number-based and Mascot methods retained a correct amyloid precursor in 3 cases out of 9, while results were equivocal in 5 and misleading in 1. Enrichment quantification retained a correct amyloid precursor in all cases (Table 2 ). Table 2 Identification and quantification proteomic results Case No. Amount of amyloid deposits Proteins (Peptide spectrum matches, Mascot score, Ratio Amyloid/non-Amyloid) Amyloid subtype by IHC Amyloid subtype by PSM Amyloid subtype by Mascot Amyloid subtype (Ratio) Common protein Precursors APCS APOE APOA4 SAA TTR B2M Lambda light chain Kappa light chain Other 1 ++++ 3/152/ 2.8 3/131/ 12.1 3/113 /4.9 IGKC 2/77/ 3.3 IGHG1 5/234/ 0.8 LYZ 5/239/ 0.6 IGHA2 1/37/ 1.6 Aβ2M Equivocal Equivocal Aβ2M 2 ++ 4/150/ 8.1 2/76/ 6.3 IGLC2 2/102/ 6.6 IGHG2 2/96/ 4.5 IGHA1 2/104/ 2.2 AL Lambda Equivocal Equivocal AL Lambda 3 +++ 9/498/ 279 14/592/ 339 10/469/ 125 SAA2 2/111/ 19.6 IGHG1 4/181/ 2.2 IGHG3 4/175/ 1.6 IGHA1 3/144/ 4.5 AA Equivocal Equivocal (AH ?) AA 4 +++++ 5/242/ 5.8 26/1214/ 185 33/1410/ 115 IGCL2 5/186/ 0.6 IGKC 4/344/ 0.2 IGHM 15/562/ 61.8 FGA 6/383/ 1.9 IGHG3 10/301/ 2.6 Non-informative AH IgM AH IgM AH IgM 5 +++ 45/663/ 45.5 30/1138/ 118.2 23/1051/ 104.2 26/735/ 94.2 IGLV1-47 1/77/ 2.4 IGKV3-20 3/174/ 4.6 EFEMP1 4/259/ 28.0 APOA1 17/753/ 0.9 IGHG3 16/508/ 2.5 ATTR ATTR Equivocal ATTR 6 +++++ 23/675/ 8.6 68/1816/ 13.6 15/515/ 2.0 6/259/ 3.4 IGLV3-9 1/129/ 5.5 IGKV3-20 3/168/ 2.1 APOC3 1/101/ 7.7 LYZ 8/257/ 1.9 FGA 26/813/ 0.6 AL Lambda Equivocal Equivocal AL Lambda 7 +++++ 12/614/ 40.5 41/1624/ 50.8 45/1713/ 43.7 12/549/ 3.1 IGLV3-9 1/152/ 0.9 IGLC2 5/188/ 0.44 IGKV6D-21 7/240/ 15.5 IGKC 35/768/ 6.5 APOC3 2/164/ 13.4 APOA1 49/1431/ 11.9 Non-informative AL Kappa* AL Kappa* AL Kappa 8 +++ 10/336/ 17.6 4/38/ 11.4 11/281/ 116.0 3/97/ 13.7 IGKV3D 1/22/ 2.9 IGKC 5/160 /1.9 IGHA2 NA/NA /5.8 LTF 14/497 /3.4 GSN 10/344/ 1.6 Doubtful. ATTR? AGel AGel ATTR 9 +++++ 11/416/ 369 55/1330/ 1363 5/170/ 169 9/332/ 72 IGKC 8/414/ 618 IGHM 9/296 /270 GSN 10/348/ 56 AL Kappa Equivocal AL Kappa AL Kappa The amount of amyloid deposits was semi-quantitively assessed in a + to +++++ scale where +++++ represents a sample with > 80% of amyloid deposits. For each case, results from the common proteins found in amyloid deposits are firstly displayed, followed by the results of the top precursors of amyloidosis. For each protein, the number of peptide spectrum matches (PSM number of detected MS/MS spectra) is displayed, followed by the Mascot identification score and finally the enrichment in the amyloid area assessed by label free enrichment quantification (abundance ratio between the amyloid microdissected area and the non-amyloid area, highlighted using bold fonts). Interpretations are provided for each approach, as well as the immunohistochemical findings. For each approach, the precursor with the best score was considered as the amyloid subtype (except for APOA1, commonly seen in amyloid deposits, asterisk). An equivocal result was considered when the second-best precursor score was in a 10% range from the first best precursor score (including ties). Abbreviations: Ig, immunoglobulin; IHC, immunohistochemistry; PSM, peptide spectrum matches; XIC, extracted ion chromatogram. At this stage, we wanted to define an indicator allowing us to compare the different approaches based on mass spectrometry. Whatever the approach used, from all identified and/or quantified proteins, a protein precursor with a top score will emerge (number of PSM, Mascot score or enrichment ratio). Confidence in results interpretation can be summarized, for each approach, as the relative difference between the hit protein compared to the other proteins. We defined a confidence score for each approach calculated by dividing the value associated with the hit protein by the average value of the next 2 proteins. In this way, label free quantification of enrichment in this cohort showed the most confident results compared to PSM- and Mascot-based approaches (Mann-Whitney U tests, p = 0.002 and p = 0.0005 respectively, Supplemental Fig. 1 ). No significant correlations were found between the abundance of histological deposits and the Mascot confidence score (ρ = 0.58, p = 0.10), the PSM score (ρ = 0.30, p = 0.43) and the enrichment quantification (ρ=-0.17, p = 0.66). In the end, the contribution of quantification in the selection of the amyloid precursor by mass spectrometry allows to avoid any ambiguity. Discussion Accurate amyloidosis typing remains essential for guiding optimal therapeutic strategies. However, conventional histological methods, particularly immunohistochemistry on FFPE samples, are often limited by background staining, antibody unavailability, or tissue exhaustion when multiple stains are required. Consequently, laser microdissection of amyloid deposits followed by tandem mass spectrometry (MS/MS) has become the gold standard when conventional techniques fail. Two major MS-based workflows have been described for precursor protein selection, based on either Mascot identification scores or peptide spectrum match (PSM) counts. Yet, data interpretation can remain challenging, especially when multiple potential precursors are detected, partly due to the absence of true quantification. Although MS-based proteomics has revolutionized amyloid typing, only a few specialized centers have incorporated it into clinical practice. Even among these, difficult cases with several precursors of comparable identification confidence have been reported ( 7 , 14 , 24 , 25 ). In such studies, precursor selection relied primarily on either Mascot scores ( 7 ) or PSM numbers ( 14 , 24 , 26 , 27 ) from a single microdissected region. While these methods are simple and require minimal postprocessing, their outputs reflect identification confidence rather than protein abundance, limiting interpretability in complex cases. In this work, we introduce label-free enrichment quantification as an additional layer of information for amyloid precursor selection and demonstrate its value in challenging diagnostic contexts. Quantifying the enrichment of proteins between amyloid deposits and adjacent non-amyloid tissue provides a straightforward and robust means to discriminate the true precursor among multiple candidates. Moreover, unlike Mascot or PSM-based approaches, enrichment quantification remains reliable in samples with limited deposits, as supported by previous studies highlighting decreased PSM sensitivity in such conditions ( 11 , 28 ). We observed lower overall PSM counts compared with published datasets, likely due to differences in dynamic exclusion parameters, which were not always specified in previous reports. Our analyses were performed on microdissected amyloid and non-amyloid regions of equal surface area (1 mm² each), ensuring consistent material input and direct comparison between deposits and controls. This sampling strategy captures a deeper and more quantitative proteomic profile than the narrower sampling often used in reference workflows at the Mayo Clinic and UK National Amyloidosis Centre. By prioritizing quantitative over purely qualitative identification, our approach increases the likelihood of accurate precursor assignment. Even when additional potential precursors are detected, enrichment ratios clearly distinguish the true amyloidogenic protein. In our series, enrichment quantification demonstrated superior robustness compared with Mascot or PSM-based methods. In all nine cases, the expected precursor was consistently identified as the most enriched protein within amyloid deposits, regardless of deposit size. In contrast, Mascot- and PSM-based approaches yielded less confident assignments in six cases. By applying a confidence score to precursor selection, we showed that enrichment quantification provided a higher level of diagnostic reliability, particularly in intermediate cases (e.g., cases 3–5), where traditional methods were ambiguous. Although our study was limited to a small cohort of nine patients, it successfully establishes a proof-of-concept for the diagnostic value of label-free enrichment quantification. Beyond its current application to typing success rates, the broader use of this approach in large-scale studies could enhance the reliability of clinical proteomics and refine amyloidosis classification. The method requires the availability of non-amyloid control tissue, yet even a region with lower amyloid content can serve as a suitable reference for quantification. Interestingly, we also observed enrichment of multiple precursor proteins within some deposits, including proteins not previously associated with amyloidosis. Expanding proteomic profiling to larger patient cohorts could deepen our understanding of amyloid pathophysiology and contribute to the identification of novel precursor proteins, whose number continues to increase over time ( 3 , 4 ). In conclusion, our study provides a proof-of-concept that label-free enrichment quantification significantly improves the accuracy and confidence of amyloidosis typing by MS-based proteomics. This method offers a practical and quantitative enhancement to current workflows at the modest cost of analyzing an additional non-amyloid region and requiring basic bioinformatics integration. Enrichment quantification can thus be readily implemented as a first-line approach to minimize equivocal results, strengthen diagnostic confidence, and ultimately improve patient management. Abbreviations A Amyloid FDR False Discovery Rate FFPE formalin-fixed and paraffin-embedded Ig immunoglobulin IHC immunohistochemistry LC-MS/MS liquid chromatography and tandem mass spectrometry NA Non-amyloid PSM peptide spectrum matches XIC extracted ion chromatogram Declarations Funding: Fondation ARC, Cancéropôle Grand Sud-Ouest (GSO), Région Nouvelle Aquitaine. Author Contribution SDT and BC participate on acquisition, analysis, interpretation of data and wrote the manuscriptSCT, BC and CD prepared figuresCD, JWD participate on acquisition and analysis of the dataFS, BLB and AAR design the project , wrote the manuscript, draft the work and revise itAll authors reviewed the manscript Acknowledgements : The authors wish to thank Sylvaine Di Tommaso, supported by the ARC Foundation, and Cyril Dourthe, supported by the Cancéropôle Grand Sud-Ouest (GSO). We are also grateful to Marlène Maître (Laser Microdissection Platform, Magendie Institute, Bordeaux) and Nathalie Senant (Histology Platform, UAR TBMcore) for their valuable technical support. Data Availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (22) partner repository with the dataset identifier PXD039814. References Merlini G, Bellotti V. Molecular mechanisms of amyloidosis. N Engl J Med. 7 août. 2003;349(6):583–96. 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Supplementary Files GA.png Graphical Abstract : The identification of amyloid precursors by mass spectrometry has become the new standard, selecting the best protein identified in the deposit. We have improved standard protocols by microdissecting and quantifying protein enrichment ratios between amyloid deposits with a null/weak amyloid zone. This methodology avoids the identification of false precursors that could be confused with ubiquitous proteins under non-pathological conditions. placeholderimage.png Supplemental Figure 1: Comparison of confidence scores of amyloidosis precursor selection depending on the method. The confidence score in interpreting the results was summarized as the relative difference between the three protein precursors with the highest score/ratio for each approach, by dividing the score/ratio of the first precursor divided by the average of the next 2 scores/ratios. A higher confidence score means a greater confidence in selecting the protein precursor from the observed data. Mann Whitney U tests were performed to compare the three amyloid precursor ranking methods: Mascot score-based method, number of PSM (peptide spectrum matches)-based method and enrichment quantification (abundance ratio). Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Clinical Proteomics → Version 1 posted Editorial decision: Revision requested 08 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviews received at journal 25 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 02 Nov, 2025 Submission checks completed at journal 02 Nov, 2025 First submitted to journal 31 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7999764","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542921989,"identity":"a9a54029-65bb-4b2f-874d-ba66395964ed","order_by":0,"name":"Sylvaine Di Tommaso","email":"","orcid":"","institution":"Oncoprot Platform, TBM-Core UAR 005, F-33000 Bordeaux, France","correspondingAuthor":false,"prefix":"","firstName":"Sylvaine","middleName":"Di","lastName":"Tommaso","suffix":""},{"id":542921994,"identity":"24bf87ed-3414-45c9-9a0e-7a15fcfbdf72","order_by":1,"name":"Bertrand Chauveau","email":"","orcid":"","institution":"Department of 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07:17:02","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120020,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7999764/v1/dfa29d1d058a1223dbdb3af9.html"},{"id":96244689,"identity":"4b2906d1-60c2-4ea5-af18-046f4179ee3a","added_by":"auto","created_at":"2025-11-19 07:19:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":320624,"visible":true,"origin":"","legend":"\u003cp\u003eLaser capture of amyloid (A) and non-amyloid (NA) tissue areas in case number 4. (a): Hematoxylin-Eosin-Saffron (HES) staining from a surgical resection of ampulloma. Focus on amyloid deposits seen in the lamina propria and on a non-amyloid area (b and c, HES staining), and with Congo red staining (d and e). (f): HES section after laser capture. The arrows indicate the microdissected areas. Scale bar = 100 µm.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7999764/v1/2c0e993efcb4ec9318518253.png"},{"id":95938876,"identity":"5b699f73-e094-4bf3-90aa-5be7745b7d64","added_by":"auto","created_at":"2025-11-14 16:05:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72045,"visible":true,"origin":"","legend":"\u003cp\u003ea- Analytical workflow. Step 1 - Microdissection of an amyloid (A) and a non-amyloid (NA) area. Proteins were extracted, digested with trypsin and peptides analyzed by high resolution tandem mass spectrometry (LC-MS/MS). Step 2 - Processing of mass spectrometry (MS) data, the two methods currently practiced in clinical routine are based on the quality of identification from an amyloid area (Mascot score and Peptide Spectrum Match/PSM number). The proposed new method (in green) integrates a quantification of the relative abundances of proteins to quantify an enrichment ratio in the amyloid deposits compared to a non-amyloid area (A/NA). b- Global representation of the identified and quantified proteins for the 9 analyzed cases. Each point represents a protein distributed according to its A/NA ratio. Proteins above the bar have an A/NA ratio greater than or equal to 2 and therefore the protein is enriched in the amyloid deposits. Proteins colored in purple are known precursors of amyloidosis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7999764/v1/b6f6b897e2178f1ca020de42.png"},{"id":96244653,"identity":"a083e0bc-82dc-44f6-87de-8c844b6ede56","added_by":"auto","created_at":"2025-11-19 07:19:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122808,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the results of case 3. \u003cstrong\u003ea\u003c/strong\u003e- Congo Red staining and Serum amyloid A (SAA) immunostaining that allowed amyloidosis diagnosis and typing in clinical practice. \u003cstrong\u003eb- \u003c/strong\u003eIdentification and quantification of the enrichment of amyloid-associated proteins. At least two of the three must be found enriched to confirm the presence of amyloid deposits in the sample. Enriched proteins have a relative abundance ratio (amyloid area (A) to non-amyloid area (NA)) far greater than 2 (red bar). \u003cstrong\u003ec-\u003c/strong\u003eRanking results according to the 2 clinical routine identification methods (Mascot score and number of PSM) and ranking of enrichment ratios in the amyloid deposits. The protein associated with the known amyloidosis type is colored in red and its ranking is indicated below. The enrichment ratio is of nearly 20 for serum amyloid A, while other described amyloid precursors only showed a comparatively mild enrichment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7999764/v1/7209508d391e460be5ca2e5e.png"},{"id":95938882,"identity":"3e9ee253-db2f-47cf-ac1e-8a58bd5e8930","added_by":"auto","created_at":"2025-11-14 16:05:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102924,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the results of the analysis of case 8. a- Congo Red staining, showing amyloid deposits in a minor salivary gland sample, with corresponding lambda and transthyretin immunostains. No significant staining is seen with lambda while transthyretin shows at best a moderate staining. b- Identification and quantification of the enrichment of amyloid-associated proteins. At least two of the three must be found enriched to confirm the presence of amyloid deposits in the sample. Enriched proteins have a relative abundance ratio (amyloid area (A) to non-amyloid area (NA)) far greater than 2 (red bar). c- Ranking results according to the 2 clinical routine identification methods (Mascot score and number of PSM) and ranking of enrichment ratios in the amyloid deposits. The protein associated with the amyloidosis type is colored in red and the first ranking is indicated below. The enrichment ratio is of nearly 14 for transthyretin, while other amyloid precursors only showed a comparatively mild or no enrichment.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7999764/v1/1461c7d44c11a9c3f2775eea.png"},{"id":105223721,"identity":"cb6f74c0-bbd4-490e-b8c7-a69ec2724438","added_by":"auto","created_at":"2026-03-23 16:09:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1568669,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7999764/v1/c3cb0f2b-d35c-4ffc-9763-83c17c13087a.pdf"},{"id":95938877,"identity":"223a1b5c-8535-44dd-95d4-e64ff140598a","added_by":"auto","created_at":"2025-11-14 16:05:06","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":183944,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract :\u003c/p\u003e\n\u003cp\u003eThe identification of amyloid precursors by mass spectrometry has become the new standard, selecting the best protein identified in the deposit. We have improved standard protocols by microdissecting and quantifying protein enrichment ratios between amyloid deposits with a null/weak amyloid zone. This methodology avoids the identification of false precursors that could be confused with ubiquitous proteins under non-pathological conditions.\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-7999764/v1/e3f1e23a066ce32c370324d6.png"},{"id":95938880,"identity":"529fd6c7-f5d8-4b15-a321-2dab11438101","added_by":"auto","created_at":"2025-11-14 16:05:06","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 1:\u003c/strong\u003e \u003cstrong\u003eComparison of confidence scores of amyloidosis precursor selection depending on the method.\u003c/strong\u003e The confidence score in interpreting the results was summarized as the relative difference between the three protein precursors with the highest score/ratio for each approach, by dividing the score/ratio of the first precursor divided by the average of the next 2 scores/ratios. A higher confidence score means a greater confidence in selecting the protein precursor from the observed data. Mann Whitney U tests were performed to compare the three amyloid precursor ranking methods: Mascot score-based method, number of PSM (peptide spectrum matches)-based method and enrichment quantification (abundance ratio).\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-7999764/v1/9a62d05dcaabcf78b42ecef2.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative enrichment of amyloid precursors refines mass spectrometry-based amyloidosis diagnosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmyloidoses are rare, heterogeneous diseases caused by the extracellular deposition of amyloid, a fibrillar material composed of twisted β-sheet protein aggregates (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Depending on the site of deposition and the mechanism of protein misfolding, amyloidosis can be systemic or localized, acquired or hereditary. Amyloid deposits contain a disease-specific precursor protein along with common components such as heparan sulfate proteoglycan and serum amyloid P component (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The International Amyloidosis Society maintains the list of amyloid precursors, which forms the basis for the nomenclature of amyloidoses. This list continues to expand, with 36 precursors identified in 2018 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and 42 to date (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The main systemic precursors\u0026mdash;immunoglobulin light chains (κ or λ, AL amyloidosis), transthyretin (ATTR, acquired or hereditary), and serum amyloid A (AA)\u0026mdash;account for nearly 90% of cases diagnosed in routine practice (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Treatment strategies depend on the nature of the precursor and the distribution of deposits, ranging from chemotherapy, immunotherapy, or autologous stem cell transplantation for AL amyloidosis, to inflammation control for AA amyloidosis, and siRNA therapy or liver transplantation in selected hereditary ATTR cases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Accurate amyloid typing is therefore essential for optimal patient management and genetic counseling.\u003c/p\u003e\u003cp\u003eDiagnosis may be clinically suspected or incidentally discovered by pathologists, but confirmation requires histopathological evaluation of an affected tissue or organ. Minor salivary glands, abdominal fat, and colonic mucosa are common biopsy sites, as they are frequently involved and easily accessible. Amyloid deposits show characteristic Congo red positivity and yellow-green birefringence under polarized light. Amyloid typing can be performed by immunofluorescence on frozen tissues or by immunohistochemistry (IHC) on formalin-fixed paraffin-embedded (FFPE) samples. However, IHC often suffers from background noise\u0026mdash;particularly for light chains and transthyretin\u0026mdash;and from the limited availability of antibodies for rare amyloid subtypes. Tissue scarcity can further restrict the number of proteins tested. Immunoelectron microscopy offers an alternative but is available only in few expert centers and requires a dedicated sample.\u003c/p\u003e\u003cp\u003eOver the past decade, mass spectrometry (MS)-based proteomics from FFPE samples has emerged as the gold standard for amyloid typing, representing the first clinical application of MS-based proteomics in diagnostic pathology (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). It is particularly valuable when conventional methods are inconclusive, contradictory to clinical findings, or non-informative (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The Mayo Clinic and the UK National Amyloidosis Centre are the leading reference centers, each reporting hundreds to thousands of accurately typed cases (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Rezk et al. demonstrated that MS successfully resolved 80% of previously uninformative cases by IHC (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), while the Mayo Clinic reported a 100% precursor identification rate (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBoth centers use a similar bottom-up proteomics workflow involving laser microdissection of Congo red-stained deposits, tryptic digestion, and peptide analysis by LC-MS/MS. Amyloid identity is confirmed by detection of shared amyloid-associated proteins, including serum amyloid P component, apolipoprotein E, and apolipoprotein A-IV (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). For precursor identification, the Mayo Clinic selects the protein with the highest number of peptide spectrum matches (PSMs), whereas the UK Centre uses the protein with the best Mascot identification score (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Despite these robust pipelines, precursor assignment can remain challenging when multiple candidates show comparable PSM or Mascot scores, as observed in samples with scarce deposits, blood contamination, or proteins naturally present in tissues such as apolipoproteins A-I/A-IV, immunoglobulins, or transthyretin (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). An Australian group reported up to 40% of AL amyloidosis cases displaying secondary precursors with similar PSM values (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProtein identification by MS depends on intrinsic physicochemical properties, such as peptide ionization efficiency and sequence length. Consequently, low-abundance proteins with well-ionizing peptides may yield stronger signals than abundant proteins with poor ionization. Conventional proteomic analysis therefore restricts quantification to comparisons of identical peptides (same sequence, charge, and modifications) and does not consider inter-protein comparisons of identification scores, PSMs, or relative intensities as valid measures of abundance (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo enhance diagnostic confidence, targeted quantitative MS approaches have been proposed. These rely on isotopically labeled standard peptides to quantify specific amyloid precursors. Ogawa et al. provided the first proof-of-concept (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and Park et al. demonstrated high sensitivity and specificity of targeted quantification compared with standard MS-based identification, though only for the three major systemic forms\u0026mdash;AL, AA, and ATTR (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). These pseudo-absolute quantification methods require advanced MS expertise and specialized acquisition workflows, limiting their use in clinical laboratories. Label-free quantification (LFQ) is widely used in exploratory proteomics and increasingly applied to clinical studies (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). LFQ estimates protein abundance by extracting ion chromatograms (XICs) based on retention time and m/z coordinates, enabling relative quantification even when peptides are not consistently identified, provided they are sequenced at least once. This approach allows direct comparison of relative protein abundances between samples, making it particularly suitable for evaluating the enrichment of precursor proteins within amyloid deposits.\u003c/p\u003e\u003cp\u003eTo date, no study has assessed amyloid precursor enrichment quantification using a label-free approach in FFPE-derived samples. In this study, we propose to implement label-free enrichment quantification in routine practice to improve the robustness and reliability of amyloidosis typing.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThis study relied on a retrospective inclusion of selected patients with an amyloidosis diagnosis performed at the Bordeaux University Hospital from 2013 to 2022, with consistent clinico-biological data. Archived formalin-fixed and paraffin embedded (FFPE) samples with sufficient remaining material were included. Main clinical, biological and pathological characteristics were retrieved from the institutional medical software. Congo red staining was performed for amyloidosis diagnosis in all samples and amyloidosis typing was performed by routine immunohistochemistry targeting kappa and lambda light chains (clone A21-Y and K22-Y from Clinisciences), serum amyloid A (clone MC 1 from Dako), transthyretin (polyclonal from Dako) and β2-microglobulin (polyclonal from Genetex). All immunostains were performed using an Omnis automate from Dako Agilent (EnVision Flex/horseradish peroxidase for signal amplification). All reagents were provided by Dako/Agilent. No frozen tissue was available for any case. For each case, the abundance of amyloid deposits was semi-quantitatively assessed by a pathologist in a 1 to 5 scale, where 5 represents a sample nearly exclusively affected by amyloid deposits.\u003c/p\u003e\u003cp\u003e The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by a local ethic committee for the protection of persons (reference number CER-BDX-2023-02).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMicrodissection of selected area and sample preparation\u003c/h3\u003e\n\u003cp\u003eFor each case, Hematoxylin eosin saffron and Congo red stains were used to determine the areas of interest to be dissected. Non-amyloid (NA) and Amyloid (A) areas were selected under the supervision of a pathologist (either B.LB. or B.C.). Between 0.1 and 1 mm\u0026sup2; of tissue was microdissected from a FFPE 5 \u0026micro;m-thick section with a PALM type 4 (Zeiss) laser microdissector.\u003c/p\u003e\u003cp\u003eLaser microdissected sections were collected and immerged in a 50 mM Ammonium bicarbonate buffer. Fragments were heated at 90\u0026deg;C for 120 min with occasional vortexing. Samples were reduced in 10mM dithiothreitol and alkyled in 100mM iodoacetamide before digestion into tryptic peptides overnight and analyzed by liquid chromatography and tandem mass spectrometry (LC-MS/MS).\u003c/p\u003e\n\u003ch3\u003eMass spectrometry analysis\u003c/h3\u003e\n\u003cp\u003eNanoLC-MS/MS analysis was performed using an Ultimate 3000 RSLC Nano-UPHLC system (Thermo Scientific, USA) coupled to a nanospray Orbitrap Fusion\u0026trade; Lumos\u0026trade; Tribrid\u0026trade; Mass Spectrometer (Thermo Fisher Scientific, California, USA). Each peptide extracts were loaded on a 300 \u0026micro;m ID x 5 mm PepMap C\u003csub\u003e18\u003c/sub\u003e precolumn (Thermo Scientific, USA) at a flow rate of 10 \u0026micro;L/min. After a 3 min desalting step, peptides were separated on a 50 cm EasySpray column (75 \u0026micro;m ID, 2 \u0026micro;m C\u003csub\u003e18\u003c/sub\u003e beads, 100 \u0026Aring; pore size, ES903, Thermo Fisher Scientific) with a 4\u0026ndash;40% linear gradient of solvent B (0.1% formic acid in 80% ACN) in 57 min. The separation flow rate was set at 300 nL/min. The mass spectrometer operated in positive ion mode at a 2.0 kV needle voltage. Data was acquired using Xcalibur 4.4 software in a data-dependent mode. MS scans (m/z 375\u0026ndash;1500) were recorded at a resolution of R\u0026thinsp;=\u0026thinsp;120000 (@ m/z 200), a standard AGC target and an injection time in automatic mode, followed by a top speed duty cycle of up to 3 seconds for MS/MS acquisition. Precursor ions (2 to 7 charge states) were isolated in the quadrupole with a mass window of 1.6 Th and fragmented with HCD@28% normalized collision energy. MS/MS data was acquired in the Orbitrap cell with a resolution of R\u0026thinsp;=\u0026thinsp;30000 (@m/z 200), an standard AGC target and a maximum injection time in automatic mode. For protein identification, Mascot 2.5 algorithm through Proteome Discoverer 2.5 Software (Thermo Fisher Scientific Inc.) was used in batch mode by searching against the UniProt Homo sapiens database (79 071 entries, Reference Proteome Set, release date: January, 2022) from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"http://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e website. Two missed enzyme cleavages were allowed for the trypsin. Mass tolerances in MS and MS/MS were set to 10 ppm and 0.02 Da. Oxidation of methionine and acetylation of lysine were searched as dynamic modifications. Carbamidomethylation on cysteine was searched as static modification. Raw LC-MS/MS data were imported in Proline Studio for feature detection, alignment, and quantification (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Proteins identification was accepted only with at least 2 specific peptides with a pretty rank\u0026thinsp;=\u0026thinsp;1 and with a protein False Discovery Rate (FDR) value less than 1.0% calculated using the \u0026ldquo;decoy\u0026rdquo; option in Mascot. Label free quantification of MS1 spectra by extracted ion chromatograms (XIC) was carried out with parameters indicated previously (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) partner repository with the dataset identifier PXD039814.\u003c/p\u003e\n\u003ch3\u003eBioinformatical and statistical analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analyses and plots were performed using R, version 4.2.2 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Plots were performed using the ggplot2 package and the ggpubr package. Spearman's rank correlations were performed using the stats package. To assess the confidence in data interpretation, we defined a confidence score for each approach, expressed as a ratio (score of the first precursor divided by the mean of the next 2 scores). As all three methods of mass spectrometry-based amyloidosis typing hypothesized that, from all identified and/or quantified proteins, a protein precursor with a top score will emerge (PSM, Mascot or abundance ratio), confidence in results interpretation can be summarized, for each approach, as the relative difference between the protein precursor with the best score compared to the next two scores.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eClinical and biological characteristics of the patients\u003c/h2\u003e\u003cp\u003eWe selected a panel of 9 cases as a proof-of-concept, focusing on the most common amyloidosis types. For 6 of them, immunohistochemistry (IHC) was informative. For 3 of them, IHC was not informative but the associated clinical data of the patient strongly suggested the amyloid precursor determination. Of these 9 patients, 8 were cases of systemic amyloidosis (case numbers 1 to 6, 8 and 9) and 1 was a case of localized amyloidosis (case 7). Mean age of included patients was 68 years old (range 47\u0026ndash;83). Available samples consisted of a biopsy for 6 cases and a surgical specimen for 3 cases, from various organ or tissue origin (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Amyloidosis typing based on clinicopathological findings were as follows: 2 AL lambda, 2 AL kappa, 1 ATTR, 1 Aβ2M and 1 AA. In three cases (cases 4,7 and 8), IHC was non-informative. Case 4 had an IgM kappa monoclonal gammopathy with an incidental finding of amyloid deposits in a duodenal ampulloma resection. Case 7 had a previous biopsy that favored kappa light chain deposits by IHC. Case 8 had a lambda light chain multiple myeloma. Main clinicopathological characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelevant clinical data of the included patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCase No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSample (surgical specimen, biopsy)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAmyloid subtype by IHC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAssociated condition\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSurgical specimen (gangrene)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePerineum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eβ2-microglobulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEnd-stage renal disease with dialysis for 40 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinor salivary gland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLight chain Lambda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMultiple myeloma (IgG Lambda)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRectum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSerum amyloid A-1 protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFamilial Mediterranean fever. Hemodialysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSurgical specimen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDuodenum (ampulloma)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-informative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIgM monoclonal gammopathy (kappa). Suspicion of low-grade digestive lymphoma.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSurgical specimen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLiver (recipient)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTransthyretin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTransthyretin-related hereditary amyloidosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBone marrow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLight chain lambda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMonoclonal gammopathy (IgG lambda)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLarynx\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUndefined from this sample. A prior biopsy favored kappa light chain deposits.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLocalized amyloidosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinor salivary gland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDoubtful (moderate staining with transthyretin).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLight chain multiple myeloma (lambda)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLight chain Kappa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIgM monoclonal gammopathy (kappa).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantification of protein enrichment in amyloid deposits and comparison with routine identification selection methods.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor each case, two regions of interest of the same surface (1 mm\u0026sup2;) were isolated by laser microdissection (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), one with amyloid deposits (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) and the other without morphological amyloid deposits (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). These regions were previously annotated on a Congo red staining by an experienced pathologist (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ed \u003cb\u003eand e\u003c/b\u003e). After microdissection (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ef), proteins were extracted and digested, and proteolytic peptides were analyzed by high resolution tandem mass spectrometry (LC-MS/MS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In the amyloid area, identification of at least two of the three proteins associated with amyloid deposition (serum amyloid P-component/APCS, apolipoprotein E/APOE, and apolipoprotein A-IV/APOA4) was used as an initial quality control. For amyloidosis typing, the two methods routinely used in the expert centers are (i) the PSM, the protein precursor with the most peptide spectra matches (PSM, number of MS/MS spectra) in the amyloid area (A) and (ii) the Mascot score, the precursor with the highest Mascot identification score in the amyloid zone (A). We compared these methods to (iii) the quantification of the enrichment ratio by a label free approach: the precursor with the highest enrichment ratio calculated by comparing its relative abundance between the amyloid area (A) and the non-amyloid area (NA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). A selection is first made from the 42 known amyloid precursors, which means that among the identified proteins we do not consider proteins that have never been associated with amyloidosis in first intention. Overall, in all analyzed cases, we identified between 4 and 34 amyloid protein precursors per case. The quantification of the protein enrichment in the deposits (ratio A/NA) revealed that several precursors could be enriched and allowed us to isolate the precursor with the highest enrichment rate. Some ubiquitous amyloid precursors were even more present in the non-amyloid tissue than in the deposits (ratio A/NA\u0026thinsp;\u0026le;\u0026thinsp;0.5). These results demonstrate the interest of providing quantity information beyond the simple identification of precursors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). We then focused on each of the analyzed cases. As a first example, case 3 is an inflammatory AA amyloidosis that was previously characterized by IHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Amyloidosis-associated proteins (APOE, APOA4 and APCS) were identified and were enriched in the deposits (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The number of PSM and the Mascot score of the SAA2 precursor were ranked fifth and seventh, respectively, among the identified precursors. In contrast, with our quantification method, the highest A/NA enrichment among the amyloid precursors was SAA2 ratio (A/NA\u0026thinsp;=\u0026thinsp;19.6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Another example is case 8. Here, unlike to the previous illustrated case, IHC was not informative for amyloidosis typing, with at best a doubtful and moderate staining with transthyretin, while the patient had a lambda light chain multiple myeloma (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The quantification ratio confirmed transthyretin as the leading enriched amyloid precursor (ratio A/NA 13.7), while both PSM and Mascot scores highlighted Gelsolin as the leading protein precursor of systemic amyloidosis, inconsistent with the clinical data. Of note, lambda light chain was not detected by mass spectrometry in this case, consistent with the IHC findings. These two examples illustrate the gain in robustness in quantifying protein enrichment in amyloid deposition compared to a simple identification approach. Considering case 9, a liver biopsy nearly entirely replaced by kappa light chain amyloid deposits, a microdissection of a non-amyloid area was not possible. Here, we used a pool of subnormal liver tissues as control, coming from surgical specimens of 3 patients, which led to the correct precursor determination of kappa light chain as the leading enriched protein by quantification ratio.\u003c/p\u003e\u003cp\u003eOverall, identification and quantification results of the 9 cases analyzed are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Proteins known to be associated with amyloidosis were systematically identified, with varying levels of enrichment in each case. We found a significant and positive correlation between the semi-quantitative histological abundance of amyloid deposits and the APOE PSM score (ρ\u0026thinsp;=\u0026thinsp;0.82, p\u0026thinsp;=\u0026thinsp;0.01) and with the APOE Mascot score (ρ\u0026thinsp;=\u0026thinsp;0.86, p\u0026thinsp;=\u0026thinsp;0.006). No significant correlations were observed with other amyloidosis-associated proteins (number of PSMs or Mascot score), probably due to a lack of statistical power: APCS (ρ\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;=\u0026thinsp;0.46 and ρ\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;=\u0026thinsp;0.35, respectively) and APOA4 (ρ\u0026thinsp;=\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.49 and ρ\u0026thinsp;=\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.49, respectively). As for the abundance ratio, no correlations were found between the histological amount of amyloid deposits and APOA4 (ρ=-0.25, p\u0026thinsp;=\u0026thinsp;0.55), APCS (ρ\u0026thinsp;=\u0026thinsp;0.009, p\u0026thinsp;=\u0026thinsp;0.98) or APOE (ρ\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;=\u0026thinsp;0.26). Considering amyloidosis typing, the PSM number-based and Mascot methods retained a correct amyloid precursor in 3 cases out of 9, while results were equivocal in 5 and misleading in 1. Enrichment quantification retained a correct amyloid precursor in all cases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIdentification and quantification proteomic results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCase No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAmount of amyloid deposits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003eProteins (Peptide spectrum matches, Mascot score, Ratio Amyloid/non-Amyloid)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAmyloid subtype by IHC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAmyloid subtype by PSM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAmyloid subtype by Mascot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAmyloid subtype (Ratio)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eCommon protein\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003ePrecursors\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPCS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAPOE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAPOA4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSAA\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eTTR\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eB2M\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eLambda light chain\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eKappa light chain\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eOther\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e++++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3/152/\u003cb\u003e2.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3/131/\u003cb\u003e12.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3/113\u003cb\u003e/4.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eIGKC\u003c/b\u003e 2/77/\u003cb\u003e3.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eIGHG1\u003c/b\u003e 5/234/\u003cb\u003e0.8\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLYZ\u003c/b\u003e 5/239/\u003cb\u003e0.6\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGHA2\u003c/b\u003e 1/37/\u003cb\u003e1.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eAβ2M\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eEquivocal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eEquivocal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eAβ2M\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4/150/\u003cb\u003e8.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2/76/\u003cb\u003e6.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eIGLC2\u003c/b\u003e 2/102/\u003cb\u003e6.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eIGHG2\u003c/b\u003e 2/96/\u003cb\u003e4.5\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGHA1\u003c/b\u003e 2/104/\u003cb\u003e2.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eAL Lambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eEquivocal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eEquivocal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eAL Lambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9/498/\u003cb\u003e279\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14/592/\u003cb\u003e339\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10/469/\u003cb\u003e125\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSAA2\u003c/b\u003e 2/111/\u003c/p\u003e\u003cp\u003e\u003cb\u003e19.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eIGHG1\u003c/b\u003e\u0026nbsp;4/181/\u003cb\u003e2.2\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGHG3\u003c/b\u003e 4/175/\u003cb\u003e1.6\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGHA1\u003c/b\u003e 3/144/\u003cb\u003e4.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eEquivocal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eEquivocal (AH\u0026nbsp;?)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+++++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5/242/\u003cb\u003e5.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26/1214/\u003cb\u003e185\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33/1410/\u003cb\u003e115\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eIGCL2\u003c/b\u003e 5/186/\u003cb\u003e0.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eIGKC\u003c/b\u003e 4/344/\u003cb\u003e0.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eIGHM\u003c/b\u003e 15/562/\u003cb\u003e61.8\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFGA\u003c/b\u003e 6/383/\u003cb\u003e1.9\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGHG3\u003c/b\u003e 10/301/\u003cb\u003e2.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eNon-informative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eAH IgM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eAH IgM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eAH IgM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45/663/\u003cb\u003e45.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30/1138/\u003cb\u003e118.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23/1051/\u003cb\u003e104.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26/735/\u003cb\u003e94.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eIGLV1-47\u003c/b\u003e 1/77/\u003cb\u003e2.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eIGKV3-20\u003c/b\u003e 3/174/\u003cb\u003e4.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eEFEMP1\u003c/b\u003e 4/259/\u003cb\u003e28.0\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAPOA1\u003c/b\u003e 17/753/\u003cb\u003e0.9\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGHG3\u003c/b\u003e 16/508/\u003cb\u003e2.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eATTR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eATTR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eEquivocal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eATTR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+++++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23/675/\u003cb\u003e8.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68/1816/\u003cb\u003e13.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15/515/\u003cb\u003e2.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6/259/\u003cb\u003e3.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eIGLV3-9\u003c/b\u003e 1/129/\u003cb\u003e5.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eIGKV3-20\u003c/b\u003e 3/168/\u003cb\u003e2.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eAPOC3\u003c/b\u003e 1/101/\u003cb\u003e7.7\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLYZ\u003c/b\u003e 8/257/\u003cb\u003e1.9\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFGA\u003c/b\u003e 26/813/\u003cb\u003e0.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eAL Lambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eEquivocal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eEquivocal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eAL Lambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+++++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12/614/\u003cb\u003e40.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41/1624/\u003cb\u003e50.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45/1713/\u003cb\u003e43.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12/549/\u003cb\u003e3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eIGLV3-9\u003c/b\u003e 1/152/\u003cb\u003e0.9\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGLC2\u003c/b\u003e 5/188/\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eIGKV6D-21\u003c/b\u003e 7/240/\u003cb\u003e15.5\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGKC\u003c/b\u003e 35/768/\u003cb\u003e6.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eAPOC3\u003c/b\u003e 2/164/\u003cb\u003e13.4\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAPOA1\u003c/b\u003e 49/1431/\u003cb\u003e11.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eNon-informative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eAL Kappa*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eAL Kappa*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eAL Kappa\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10/336/\u003cb\u003e17.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4/38/\u003cb\u003e11.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11/281/\u003cb\u003e116.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3/97/\u003cb\u003e13.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eIGKV3D\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1/22/\u003cb\u003e2.9\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGKC\u003c/b\u003e\u003c/p\u003e\u003cp\u003e5/160\u003cb\u003e/1.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eIGHA2\u003c/b\u003e NA/NA\u003cb\u003e/5.8\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLTF\u003c/b\u003e 14/497\u003cb\u003e/3.4\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGSN\u003c/b\u003e 10/344/\u003cb\u003e1.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eDoubtful. ATTR?\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eAGel\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eAGel\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eATTR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+++++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11/416/\u003cb\u003e369\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55/1330/\u003cb\u003e1363\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5/170/\u003cb\u003e169\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9/332/\u003cb\u003e72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eIGKC\u003c/b\u003e\u003c/p\u003e\u003cp\u003e8/414/\u003cb\u003e618\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eIGHM\u003c/b\u003e 9/296\u003cb\u003e/270\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGSN\u003c/b\u003e 10/348/\u003cb\u003e56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eAL Kappa\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eEquivocal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eAL Kappa\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eAL Kappa\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"15\"\u003eThe amount of amyloid deposits was semi-quantitively assessed in a\u0026thinsp;+\u0026thinsp;to +++++ scale where +++++ represents a sample with \u0026gt;\u0026thinsp;80% of amyloid deposits. For each case, results from the common proteins found in amyloid deposits are firstly displayed, followed by the results of the top precursors of amyloidosis. For each protein, the number of peptide spectrum matches (PSM number of detected MS/MS spectra) is displayed, followed by the Mascot identification score and finally the enrichment in the amyloid area assessed by label free enrichment quantification (abundance ratio between the amyloid microdissected area and the non-amyloid area, highlighted using bold fonts). Interpretations are provided for each approach, as well as the immunohistochemical findings. For each approach, the precursor with the best score was considered as the amyloid subtype (except for APOA1, commonly seen in amyloid deposits, asterisk). An equivocal result was considered when the second-best precursor score was in a 10% range from the first best precursor score (including ties). Abbreviations: Ig, immunoglobulin; IHC, immunohistochemistry; PSM, peptide spectrum matches; XIC, extracted ion chromatogram.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAt this stage, we wanted to define an indicator allowing us to compare the different approaches based on mass spectrometry. Whatever the approach used, from all identified and/or quantified proteins, a protein precursor with a top score will emerge (number of PSM, Mascot score or enrichment ratio). Confidence in results interpretation can be summarized, for each approach, as the relative difference between the hit protein compared to the other proteins. We defined a confidence score for each approach calculated by dividing the value associated with the hit protein by the average value of the next 2 proteins. In this way, label free quantification of enrichment in this cohort showed the most confident results compared to PSM- and Mascot-based approaches (Mann-Whitney U tests, p\u0026thinsp;=\u0026thinsp;0.002 and p\u0026thinsp;=\u0026thinsp;0.0005 respectively, \u003cb\u003eSupplemental Fig.\u0026nbsp;1\u003c/b\u003e). No significant correlations were found between the abundance of histological deposits and the Mascot confidence score (ρ\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;=\u0026thinsp;0.10), the PSM score (ρ\u0026thinsp;=\u0026thinsp;0.30, p\u0026thinsp;=\u0026thinsp;0.43) and the enrichment quantification (ρ=-0.17, p\u0026thinsp;=\u0026thinsp;0.66).\u003c/p\u003e\u003cp\u003eIn the end, the contribution of quantification in the selection of the amyloid precursor by mass spectrometry allows to avoid any ambiguity.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccurate amyloidosis typing remains essential for guiding optimal therapeutic strategies. However, conventional histological methods, particularly immunohistochemistry on FFPE samples, are often limited by background staining, antibody unavailability, or tissue exhaustion when multiple stains are required. Consequently, laser microdissection of amyloid deposits followed by tandem mass spectrometry (MS/MS) has become the gold standard when conventional techniques fail. Two major MS-based workflows have been described for precursor protein selection, based on either Mascot identification scores or peptide spectrum match (PSM) counts. Yet, data interpretation can remain challenging, especially when multiple potential precursors are detected, partly due to the absence of true quantification. Although MS-based proteomics has revolutionized amyloid typing, only a few specialized centers have incorporated it into clinical practice. Even among these, difficult cases with several precursors of comparable identification confidence have been reported (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In such studies, precursor selection relied primarily on either Mascot scores (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) or PSM numbers (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) from a single microdissected region. While these methods are simple and require minimal postprocessing, their outputs reflect identification confidence rather than protein abundance, limiting interpretability in complex cases. In this work, we introduce label-free enrichment quantification as an additional layer of information for amyloid precursor selection and demonstrate its value in challenging diagnostic contexts. Quantifying the enrichment of proteins between amyloid deposits and adjacent non-amyloid tissue provides a straightforward and robust means to discriminate the true precursor among multiple candidates. Moreover, unlike Mascot or PSM-based approaches, enrichment quantification remains reliable in samples with limited deposits, as supported by previous studies highlighting decreased PSM sensitivity in such conditions (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We observed lower overall PSM counts compared with published datasets, likely due to differences in dynamic exclusion parameters, which were not always specified in previous reports. Our analyses were performed on microdissected amyloid and non-amyloid regions of equal surface area (1 mm\u0026sup2; each), ensuring consistent material input and direct comparison between deposits and controls. This sampling strategy captures a deeper and more quantitative proteomic profile than the narrower sampling often used in reference workflows at the Mayo Clinic and UK National Amyloidosis Centre. By prioritizing quantitative over purely qualitative identification, our approach increases the likelihood of accurate precursor assignment. Even when additional potential precursors are detected, enrichment ratios clearly distinguish the true amyloidogenic protein. In our series, enrichment quantification demonstrated superior robustness compared with Mascot or PSM-based methods. In all nine cases, the expected precursor was consistently identified as the most enriched protein within amyloid deposits, regardless of deposit size. In contrast, Mascot- and PSM-based approaches yielded less confident assignments in six cases. By applying a confidence score to precursor selection, we showed that enrichment quantification provided a higher level of diagnostic reliability, particularly in intermediate cases (e.g., cases 3\u0026ndash;5), where traditional methods were ambiguous.\u003c/p\u003e\u003cp\u003eAlthough our study was limited to a small cohort of nine patients, it successfully establishes a proof-of-concept for the diagnostic value of label-free enrichment quantification. Beyond its current application to typing success rates, the broader use of this approach in large-scale studies could enhance the reliability of clinical proteomics and refine amyloidosis classification. The method requires the availability of non-amyloid control tissue, yet even a region with lower amyloid content can serve as a suitable reference for quantification.\u003c/p\u003e\u003cp\u003eInterestingly, we also observed enrichment of multiple precursor proteins within some deposits, including proteins not previously associated with amyloidosis. Expanding proteomic profiling to larger patient cohorts could deepen our understanding of amyloid pathophysiology and contribute to the identification of novel precursor proteins, whose number continues to increase over time (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn conclusion, our study provides a proof-of-concept that label-free enrichment quantification significantly improves the accuracy and confidence of amyloidosis typing by MS-based proteomics. This method offers a practical and quantitative enhancement to current workflows at the modest cost of analyzing an additional non-amyloid region and requiring basic bioinformatics integration. Enrichment quantification can thus be readily implemented as a first-line approach to minimize equivocal results, strengthen diagnostic confidence, and ultimately improve patient management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmyloid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFalse Discovery Rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFFPE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eformalin-fixed and paraffin-embedded\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIg\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eimmunoglobulin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eimmunohistochemistry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLC-MS/MS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eliquid chromatography and tandem mass spectrometry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-amyloid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePSM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epeptide spectrum matches\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eXIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eextracted ion chromatogram\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eFondation ARC, Canc\u0026eacute;rop\u0026ocirc;le Grand Sud-Ouest (GSO), R\u0026eacute;gion Nouvelle Aquitaine.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSDT and BC participate on acquisition, analysis, interpretation of data and wrote the manuscriptSCT, BC and CD prepared figuresCD, JWD participate on acquisition and analysis of the dataFS, BLB and AAR design the project , wrote the manuscript, draft the work and revise itAll authors reviewed the manscript\u003c/p\u003e\u003ch2\u003eAcknowledgements :\u003c/h2\u003e\u003cp\u003eThe authors wish to thank Sylvaine Di Tommaso, supported by the ARC Foundation, and Cyril Dourthe, supported by the Canc\u0026eacute;rop\u0026ocirc;le Grand Sud-Ouest (GSO). We are also grateful to Marl\u0026egrave;ne Ma\u0026icirc;tre (Laser Microdissection Platform, Magendie Institute, Bordeaux) and Nathalie Senant (Histology Platform, UAR TBMcore) for their valuable technical support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (22) partner repository with the dataset identifier PXD039814.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMerlini G, Bellotti V. Molecular mechanisms of amyloidosis. N Engl J Med. 7 ao\u0026ucirc;t. 2003;349(6):583\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenson MD, Buxbaum JN, Eisenberg DS, Merlini G, Saraiva MJM, Sekijima Y, et al. Amyloid nomenclature 2020: update and recommendations by the International Society of Amyloidosis (ISA) nomenclature committee. Amyloid d\u0026eacute;c. 2020;27(4):217\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenson MD, Buxbaum JN, Eisenberg DS, Merlini G, Saraiva MJM, Sekijima Y, et al. Amyloid nomenclature 2018: recommendations by the International Society of Amyloidosis (ISA) nomenclature committee. Amyloid d\u0026eacute;c. 2018;25(4):215\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuxbaum JN, Dispenzieri A, Eisenberg DS, F\u0026auml;ndrich M, Merlini G, Saraiva MJM, et al. Amyloid nomenclature 2022: update, novel proteins, and recommendations by the International Society of Amyloidosis (ISA) Nomenclature Committee. Amyloid d\u0026eacute;c. 2022;29(4):213\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDasari S, Theis JD, Vrana JA, Rech KL, Dao LN, Howard MT et al. sept. Amyloid Typing by Mass Spectrometry in Clinical Practice: a Comprehensive Review of 16,175 Samples. 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Mayo Clin Proc. mai. 2021;96(5):1122\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRezk T, Gilbertson JA, Mangione PP, Rowczenio D, Rendell NB, Canetti D, et al. The complementary role of histology and proteomics for diagnosis and typing of systemic amyloidosis. J Pathol Clin Res juill. 2019;5(3):145\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnkney JA, Muneer A, Chen X. Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics. Annu Rev Anal Chem (Palo Alto Calif). 12 juin. 2018;11(1):49\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification by searching sequence databases using mass spectrometry data. 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Mass spectrometry-based absolute quantification of amyloid proteins in pathology tissue specimens: Merits and limitations. PLoS ONE. 2020;15(7):e0235143.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark J, Lee GY, Choi JO, Jeon ES, Kim K, Kim JS, et al. Development and Validation of Mass Spectrometry-Based Targeted Analysis for Amyloid Proteins. Proteom Clin Appl mai. 2018;12(3):e1700106.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDourthe C, Julien C, Di Tommaso S, Dupuy JW, Dugot-Senant N, Brochard A et al. Proteomic profiling of hepatocellular adenomas paves the way to new diagnostic and prognostic approaches. Hepatology. 23 mars 2021;.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaubon T, Guyon J, Raymond AA, Dartigues B, Rudewicz J, Ezzoukhry Z, et al. The invasive proteome of glioblastoma revealed by laser-capture microdissection. 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Anal Bioanal Chem sept. 2012;404(4):939\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-proteomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clip","sideBox":"Learn more about [Clinical Proteomics](http://clinicalproteomicsjournal.biomedcentral.com/)","snPcode":"12014","submissionUrl":"https://submission.nature.com/new-submission/12014/3","title":"Clinical Proteomics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"amyloidosis typing, label free quantification, mass spectrometry, proteomics","lastPublishedDoi":"10.21203/rs.3.rs-7999764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7999764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\u003cp\u003eAmyloidosis typing is crucial to determine the best therapeutic strategy for patients. Since conventional histological techniques often fail, the identification of amyloid precursors by mass spectrometry became the new standard. However, without quantification, selecting the amyloid precursor from proteins that may be ubiquitous under non-pathological conditions may be equivocal. Therefore, we quantified protein enrichment in amyloid deposits to improve amyloidosis typing.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eProtein enrichment was measured by extracted ion chromatogram-based label free quantification by comparing a microdissected amyloid area with a non-amyloid area. We assessed the discrimination ability of candidate precursors with this approach compared to the two practiced identification methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAs a proof-of-concept, we selected 9 cases including the most common amyloidosis subtypes, 6 typed by immunohistochemistry and 3 inconclusive by immunohistochemistry. Proteins associated with amyloid deposits were identified in all samples, confirming the pathology. Where the routine clinical mass spectrometric identification techniques allowed unambiguous conclusions for 3 of 9 cases, quantification of the enrichment ratio in amyloid deposits allowed unambiguous precursor selection in all cases.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eQuantification of precursor enrichment in amyloid deposits is a promising optimization for amyloidosis typing. Incorporated into routine clinical processes, it will improve patient care in difficult diagnostic situations.\u003c/p\u003e","manuscriptTitle":"Quantitative enrichment of amyloid precursors refines mass spectrometry-based amyloidosis diagnosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 16:05:01","doi":"10.21203/rs.3.rs-7999764/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-08T15:22:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T10:22:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T16:45:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T18:38:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305690742448877057984016645885627839221","date":"2025-11-05T14:15:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48672776991522225829221003924831683106","date":"2025-11-04T06:07:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137948862821080866877381694232933915480","date":"2025-11-03T16:19:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T15:46:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T04:38:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T04:37:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Proteomics","date":"2025-10-31T15:03:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-proteomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clip","sideBox":"Learn more about [Clinical Proteomics](http://clinicalproteomicsjournal.biomedcentral.com/)","snPcode":"12014","submissionUrl":"https://submission.nature.com/new-submission/12014/3","title":"Clinical Proteomics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14253254-df0e-4332-82fc-a07b75307788","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:06:42+00:00","versionOfRecord":{"articleIdentity":"rs-7999764","link":"https://doi.org/10.1186/s12014-026-09598-0","journal":{"identity":"clinical-proteomics","isVorOnly":false,"title":"Clinical Proteomics"},"publishedOn":"2026-03-17 15:57:45","publishedOnDateReadable":"March 17th, 2026"},"versionCreatedAt":"2025-11-14 16:05:01","video":"","vorDoi":"10.1186/s12014-026-09598-0","vorDoiUrl":"https://doi.org/10.1186/s12014-026-09598-0","workflowStages":[]},"version":"v1","identity":"rs-7999764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7999764","identity":"rs-7999764","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}