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At least 127 records · Page 7

Contributions of immunoaffinity chromatography to deep proteome profiling of human biofluids

Human biofluids, especially blood plasma or serum, hold great potential as the sources of potential biomarkers for various diseases; however, the enormous dynamic range of protein concentrations in biofluids represents a significant analytical challenge to detect promising low-abundance protein biomarkers. Over the last decade, various immunoaffinity chromatographic methods have been developed and routinely applied for separating low-abundance proteins from the high and moderate-abundance proteins, thus enabling more effective detection of low-abundance proteins. Herein, we review the advances of immunoaffinity separation methods and their contributions to the proteomics applications of different human biofluids. The limitations and future perspective of immunoaffinity separation methods are also discussed.

Environmental Molecular Sciences Laboratory↗

A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers

A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant ( Ricinus communis ) cultivars by four different sample preparation methods (PM1 – PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods and robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of a test set of ricin preparations were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved.

Fredriksson, Sten-Åke (ORCID:0000000237131990)↗