Project description:Amyloidoses are characterized by the pathological deposition of non-degradable misfolded protein fibrils. Precise identification of the fibril-forming protein is crucial for prognosis and correct therapeutic intervention. Here, we present a reproduceable method for amyloid typing using relative quantification to enhance the accuracy and reliability of proteomic amyloid typing. In this study, we analyzed 62 FFPE tissue samples (+4 replicates of one tissue sample using different set-ups), using liquid chromatography-tandem mass spectrometry and employed internal normalization of iBAQ values of amyloid-related proteins relative to serum amyloid-P component (APCS) for amyloidosis typing. This method demonstrated robust performance across multiple LC-MS/MS platforms, as well as for samples with low amounts of amyloid, and achieved complete concordance with IHC typed amyloidosis cases. More importantly, it resolved several unclear amyloid cases with inconclusive staining results. Finally, we established machine learning approach (XGBoost) achieving 94% accuracy by using ~160 amyloid-related proteins as input variables.
Project description:Purpose: This study uses a high-throughput glycan microarray to develop a novel method to assign ABO blood type. The method will then be applied to samples from patients treated with PROSTVAC to determine if blood type correlates with survival Results: Many blood group A and B antigens correlate with blood type. Blood typing is best achieved using a combination of 10 signals Conclusion: ABO blood type can be determined with greater than 97% accuracy using only 4 microliters of serum.
Project description: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 typing. Protein enrichment was measured by extracted ion chromatogram based label-free (LFX) 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. As proof of concept, we selected seven cases, 5 typical of the most common amyloidosis subtypes and typed by immunostainings (IHC), 2 unconclusive after immunohistochemistry. Proteins associated with amyloid deposits were identified in all samples confirming the pathology. When the routine clinical mass spectrometric identification techniques allowed unambiguous conclusions for 2/3 of 7 cases, quantification of the enrichment ratio in the amyloid deposit allowed unambiguous precursor selection in all cases. 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.
Project description:Comparison of High Resolution Viruelnce Allelic Profiling (HReVAP) typing with Multilocus Sequence Typing and Whole Genome SNPs analysis for typing VTEC strains