scholarly journals Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics

2021 ◽  
Vol 23 (1) ◽  
pp. 319
Author(s):  
Nicolai Bjødstrup Palstrøm ◽  
Aleksandra M. Rojek ◽  
Hanne E. H. Møller ◽  
Charlotte Toftmann Hansen ◽  
Rune Matthiesen ◽  
...  

Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype.

2015 ◽  
Vol 7 (23) ◽  
pp. 9808-9816 ◽  
Author(s):  
Steven L. Reeber ◽  
Sneha Gadi ◽  
Sung-Ben Huang ◽  
Gary L. Glish

Paper spray ionization enables the rapid mass spectrometric analysis of environmental samples without the use of chromatography or sample cleanup techniques.


Author(s):  
D. V. Ul’shina ◽  
D. A. Kovalev ◽  
I. V. Kuznetsova ◽  
O. V. Bobrysheva ◽  
T. L. Krasovskaya ◽  
...  

The effectiveness of differentiation of bacterial pathogens using MALDI-TOF mass spectrometry depends on the quality of sample preparation, compliance with mass spectrometric analysis parameters and statistical approaches used, implemented by various modern software tools. The review provides a brief description of the most known software used in the processing and bioinformation analysis of time-of-flight mass spectrometry data. A list of computer platforms, programs and environments, both commercial and publicly available, is presented. The results of indication and identification of pathogens of particularly dangerous and natural-focal infections by MALDI-TOF mass spectrometry using publicly available software – programming language R, Mass-Up, MicrobeMS, licensed – MatLab, ClinProTools, as well as free web applications, including, Speclust, Ribopeaksare provided. The data on usage of such well-known platforms as MALDI BioTyper, SARAMIS Vitek-MS and Andromas (Andromas SAS, France) for inter- and intra-specific differentiation of closely related species are presented. Results of identification and differentiation of microorganisms applying MALDI-TOF mass spectrometry based on detection of specific proteins for cross-comparison – biomarkers – are given. The analysis shows that the programming language R environment is one of the publicly available universal platforms with an optimal combination of algorithms for processing and interpreting of a large array of mass spectrometric data.


The Analyst ◽  
2016 ◽  
Vol 141 (19) ◽  
pp. 5520-5526 ◽  
Author(s):  
Eric Janusson ◽  
G. Bryce McGarvey ◽  
Farhana Islam ◽  
Christine Rowan ◽  
J. Scott McIndoe

A simple chemical derivatization technique was developed for electrospray ionization mass spectrometry (ESI-MS) in which thiols and disulfides may be selectively analyzed in a complex matrix and easily characterized.


Parasitology ◽  
2009 ◽  
Vol 137 (9) ◽  
pp. 1409-1423 ◽  
Author(s):  
D. G. WATSON

SUMMARYThe strengths and limitations of existing mass spectrometry methods for metabolite detection and identification are discussed. A brief review is made of the methods available for quenching and extraction of cells or organisms prior to instrumental analysis. The techniques available for carrying out mass spectrometry-based profiling of metabolomes are discussed using the analysis of extracts from trypanosomes to illustrate various points regarding methods of separation and mass spectrometric analysis. The advantages of hydrophilic interaction chromatography (HILIC) for the analysis of polar metabolites are discussed. The challenges of data processing are outlined and illustrated using the example of ThermoFisher's Sieve software. The existing literature on applications of mass spectrometry to the profiling of parasite metabolomes is reviewed.


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