All microbes, including bacteria, viruses, and fungi, can be classified and identified by matching a few peptides known to be unique to each organism. Identifying Microbes by Mass Spectrometry Proteomics describes ways to identify microorganisms using powerful new techniques combining hardware and software and yielding highly accurate methods for detection, identification, and classification of microbes. This straightforward technology can be used to detect unknown and unsequenced microorganisms as well as microbes in complex environmental samples.
This book reviews various mass analyzers used for detection and describes ionization methods frequently used for analysis of microbial constituents, a necessary step in the preparation of mass spectrometry (MS) samples. The text also discusses diverse processing methods, which are used to analyze MS files for matching mass spectral profiles, and examines protein and nucleic acid sequence-based methods capable of classification and identification of microbial agents. The book also covers sample collection methods and specific sample preparation techniques.
The text addresses using computer software and bioinformatics approaches for data mining to discriminate microbes using mass spectrometry proteomics (MSP). It also discusses historical pattern recognition-based methods and other approaches such as analysis of pyrolysis products, chemical ionization (CI) of fatty acid methyl esters, and MALDI-MS. The text contains examples of the application of the MSP technique for microbe detection and includes a survey of suitable and commercially available MS-based platforms. Successful applications include the identification of unknown microbes in honey bees associated with colony collapse disorder and the analysis of virus strains from the 2009 influenza pandemic. The final chapter outlines future trends in these groundbreaking uses of MS techniques, which are fast, not limited by sample type, and show potential in answering complex environmental questions.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Microbiologists, chemists, healthcare professionals working in microbial detection, pharmaceutical companies, hospitals, vaccine producers, food production industries, and government agencies-USDA, HHS, CDC, DOD, and Homeland Security.
Illustrationen
98 s/w Abbildungen, 17 s/w Tabellen
17 Tables, black and white; 98 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
Dicke: 16 mm
Gewicht
ISBN-13
978-1-138-19986-6 (9781138199866)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Dr. Charles H. Wick, Ph.D., is a retired senior scientist from the U.S. Army Edgewood Chemical Biological Center, where he has served both as a manager and research physical scientist and has made significant contributions to forensic science. Although his 40-year professional career has spanned both the public sector and the military, his better-known work in the area of forensic science has occurred in concert with the Department of Defense. Throughout his career, Dr. Wick has made lasting and important contributions to forensic science and to the field of antiterrorism. He holds several U.S. patents in the area of microbe detection and classification. He has written more than 45 civilian and military publications and has received myriad awards and citations.
Herausgeber*in
Edgewood Chemical Biological Center, Aberdeen Proving Ground, Maryland, USA
Detection and Identification of Microbes Using Mass Spectrometry Proteomics. Mass Analyzers and MS/MS Methods for Microbial Detection and Identification. Matching Mass Spectral Profiles of Biomarkers. Sequence Information Derived from Proteins or Nucleic Acids. Collection and Processing of Microbial Samples. Computer Software Used for Chemometric and Bioinformatics to Discriminate Microbes. Applications. Survey of Commercially Available MS-Based Platforms Suitable for Bacterial Detection and Identification. Current and Future Trends in Using MS for Microbial Detection and Identification. References. Index.
