Microbiological Identification using MALDI-TOF and Tandem Mass Spectrometry
Description
Detailed resource presenting the capabilities of MALDI mass spectrometry (MS) to industrially and environmentally significant areas in the biosciences
Microbiological Identification using MALDI-TOF and Tandem Mass Spectrometry fulfills a need to bring the key analytical technique of MALDI mass spectrometric analysis into routine practice by specialists and non-specialists, and technicians. It informs and educates established researchers on the development of techniques as applied to industrially significant areas within the biosciences. Throughout the text, the reader is presented with recognized and emerging techniques of this powerful and continually advancing field of analytical science to key areas of importance.
While many scientific papers are reporting new applications of MS-based analysis in specific foci, this book is unique in that it draws together an incredibly diverse range of applications that are pushing the boundaries of MS across the broad field of biosciences.
Contributed to by recognized experts in the field of MALDI MS who have been key players in promoting the advancement and dissemination of authoritative information in this field, Microbiological Identification using MALDI-TOF and Tandem Mass Spectrometry covers sample topics such as:
* Oil microbiology, marine and freshwater ecosystems, agricultural and food microbiology, and industrial waste microbiology
* Bioremediation and landfill sites microbiology, microbiology of inhospitable sites (e.g. Arctic and Antarctic, and alkaline and acidic sites, and hot temperatures)
* Veterinary, poultry and animals, viral applications of MS, and antibiotic resistance using tandem MS methods
* Recent developments which are set to transform the use of MS from its success in clinical microbiology to a wide range of commercial and environmental uses
Bridging the gap between measurement and key applications, this text is an ideal resource for industrial and environmental analytical scientists, including technologists in the food industry, pharmaceuticals, and agriculture, as well as biomedical scientists, researchers, clinicians and academics and scientists in bio-resource centers.
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Haroun N. Shah led the establishment of unique laboratory capabilities, transforming Public Health Laboratory Services' identification of new and emerging threats through mass spectrometry combined with molecular technologies between 1999-2015. After his retirement, he continued to provide expert advice and training to industry and academia to advance innovations and embed new applications of proteomics across biosciences.
Saheer E. Gharbia is the Deputy Director of Gastrointestinal Infection and Food Safety for the UK Health Security Agency and has led the COVID-19 Genomics Programme to support the response to the COVID-19 pandemic. She continues to develop tools for the analysis and interpretation of complex biological and pathogenic traits and works across the One Health Scientific Community to embed common surveillance mechanisms to detect and track emerging threats.
Ajit J. Shah is a Professor in Bioanalytical Science in the Department of Natural Science, Middlesex University, UK.
Erika Y. Tranfield is Scientific Affairs Manager Microbiology at Bruker.
K. Clive Thompson is Chief Scientist at ALS, Life Sciences, UK, an analytical testing organisation in the UK and Ireland.
Content
List of Contributors xix
Preface xxiii
1 Progress in the Microbiological Applications of Mass Spectrometry: from Electron Impact to Soft Ionization Techniques, MALDI- TOF MS and Beyond 1
Emmanuel Raptakis, Ajit J. Shah, Saheer E. Gharbia, Laila M.N. Shah, Simona Francese, Erika Y. Tranfield, Louise Duncan, and Haroun N. Shah
1.1 Introduction 1
1.1.1 Algorithms Based upon Traditional Carbohydrate Fermentation Tests 1
1.1.2 Dynamic Changes in the Chemotaxonomic Era (c. 1970-1985) through the Lens of the Genus Bacteroides 2
1.1.3 Microbial Lipids as Diagnostic Biomarkers; Resurgence of Interest in MALDI- TOF MS with Advances in Lipidomics 3
1.2 The Dawn of MALDI- TOF MS: Establishing Proof of Concept for Diagnostic Microbiology 7
1.2.1 Development of a MALDI- TOF MS Database for Human Infectious Diseases 10
1.2.2 The Dilemma with Clostridium difficile: from Intact Cells to Intracellular Proteins, MALDI- TOF MS Enters a New Phase 13
1.3 Linear/Reflectron MALDI- TOF MS to Tandem Mass Spectrometry 15
1.3.1 Tandem MALDI- TOF Mass Spectrometry 17
1.3.2 Electrospray- based Mass Analysers 18
1.3.3 Tandem Mass Spectrometry 18
1.3.4 Mass Spectrometry- based Proteomics 19
1.3.5 Case Study: LC- MS/MS of Biothreat Agents, Proteomes of Pathogens and Strain- level Tying Using Bottom- up and Top- down Proteomics 19
1.3.6 Discovery Proteomics 21
1.3.7 Targeted Proteomics 22
1.3.8 Top- down Proteomics 23
1.3.9 Targeted Protein Quantitation 24
1.4 The Application of MALDI- MS Profiling and Imaging in Microbial Forensics: Perspectives 25
1.4.1 MALDI- MSP of Microorganisms and their Products 26
1.5 Hydrogen/Deuterium Exchange Mass Spectrometry in Microbiology 27
1.6 The Omnitrap, a Novel MS Instrument that Combines Many Applications of Mass Spectrometry 29
References 35
2 Machine Learning in Analysis of Complex Flora Using Mass Spectrometry 45
Luis Mancera, Manuel J. Arroyo, Gema Méndez, Omar Belgacem, Belén Rodríguez-Sánchez, and Marina Oviaño
2.1 Introduction 45
2.2 An Improved MALDI- TOF MS Data Analysis Pipeline for the Identification of Carbapenemase- producing Klebsiella pneumoniae 47
2.2.1 Motivation 47
2.2.2 Materials and Methods 47
2.2.3 Spectra Acquisition 50
2.2.4 Results 51
2.2.5 Discussion 54
2.3 Detection of Vancomycin- Resistant Enterococcus faecium 55
2.3.1 Motivation 55
2.3.2 Materials and Methods 56
2.3.3 Results and Discussion 59
2.4 Detection of Azole Resistance in Aspergillus fumigatus Complex Isolates 59
2.4.1 Introduction 59
2.4.2 Material and Methods 60
2.4.3 Results 60
2.4.4 Discussion 64
2.5 Peak Analysis for Discrimination of Cryptococcus neoformans Species Complex and their Interspecies Hybrids 64
2.5.1 Motivation 64
2.5.2 Material and Methods 65
2.5.3 Results and Discussion 65
2.6 Conclusions 66
References 67
3 Top- down Identification of Shiga Toxin (and Other Virulence Factors and Biomarkers) from Pathogenic E. coli using MALDI- TOF/TOF Tandem Mass Spectrometry 71
Clifton K. Fagerquist
3.1 Introduction 71
3.2 Decay of Metastable Peptide and Protein Ions by the Aspartic Acid Effect 72
3.3 Energy Deposition during Desorption/Ionization by MALDI 75
3.4 Protein Denaturation and Fragmentation Efficiency of PSD 76
3.5 Arginine and its Effect on Fragment Ion Detection and MS/MS Spectral Complexity 79
3.6 Inducing Gene Expression in Wild- type Bacteria for Identification by Top- Down Proteomic Analysis 82
3.7 Top- down Proteomic Identification of B- Subunit of Shiga Toxin from STEC Strains 83
3.8 Furin- digested Shiga Toxin and Middle- down Proteomics 85
3.9 Top- down Identification of an Immunity Cognate of a Bactericidal Protein Produced from a STEC Strain 87
3.10 Lc- Maldi- Tof/tof 88
3.11 Conclusions 89
References 94
4 Liquid Atmospheric Pressure (LAP) - MALDI MS(/MS) Biomolecular Profiling for Large- scale Detection of Animal Disease and Food Adulteration and Bacterial Identification 97
Cristian Piras and Rainer Cramer
4.1 Introduction 97
4.2 Background to LAP- MALDI MS 98
4.3 Bacterial Identification by LAP- MALDI MS 102
4.4 Food Adulteration and Milk Quality Analysis by LAP- MALDI MS 105
4.5 Animal Disease Detection by LAP- MALDI MS 108
4.6 Antibiotic Resistance Detection of Microbial Consortia by Lap- Maldi Ms 110
4.7 Future Directions for LAP- MALDI MS Applications 113
References 114
5 Development of a MALDI- TOF Mass Spectrometry Test for Viruses 117
Ray K. Iles, Jason K. Iles, and Raminta Zmuidinaite
5.1 Introduction 117
5.2 Understanding the Systems Biology of the Virus and Viral Infections 120
5.3 Understanding the Nature of Viral Proteins and Molecular Biology 121
5.4 Virion Protein Solubilization and Extraction 123
5.5 Sampling and Virion Enrichment 123
5.6 Peak Identification: Quantification and Bioinformatics 125
5.7 Promise and Pitfalls of Machine Learning Bioinformatics 126
5.8 Accelerating MALDI- TOF Assay Protocol Development Using Pseudotypes/ pseudoviruses 128
5.9 Understanding the Operational Parameters of your MALDI- TOF MS 130
5.10 Understanding the Operational Requirements of the Clinical Testing Laboratory: Validation and International Accreditation 131
5.10.1 Limitation and Advantages of CLIA LDTs 131
5.11 MALDI- TOF MS Screening Test for SARS- CoV- 2s 132
5.11.1 Prepare Positive Control 132
5.11.2 Prepare Gargle- saliva Samples 132
5.11.3 Viral Particle Enrichment 132
5.11.4 Dissolution of Virions and Solubilization of Viral Proteins 133
5.11.5 Maldi- Tof Ms 133
5.12 CLIA LDT Validation of a MALDI- TOF MS Test for SARS- CoV- 2 133
5.12.1 Limit of Detection 134
5.12.2 Interfering Substances and Specificity 134
5.12.3 Clinical Performance Evaluation 136
5.12.3.1 Establishing Operational Cut- off Values 137
5.12.3.2 Direct comparison with an RT- PCR SARS- CoV- 2 test 138
5.12.3.3 Internal Sampling Quality Control 138
5.12.3.4 Daily System Quality Control 138
5.12.4 Reproducibility 139
5.12.5 Stability 139
5.12.6 Validation Disposition 141
5.12.6.1 Global Biosecurity 141
References 142
6 A MALDI- TOF MS Proteotyping Approach for Environmental, Agricultural and Food Microbiology 147
Hiroto Tamura
6.1 Introduction 147
6.2 Serotyping of Salmonella enterica Subspecies enterica 151
6.3 Discrimination of the Lineages of Listeria monocytogenes and Species of
Listeria 161
6.4 Discrimination of the Bacillus cereus Group and Identification of Cereulide 167
6.5 Identification of Alkylphenol Polyethoxylate- degrading Bacteria in the Environment 171
6.6 Conclusions and Future Perspectives 173
References 175
7 Diversity, Transmission and Selective Pressure on the Proteome of Pseudomonas aeruginosa 183
Louise Duncan, Ajit J. Shah, Malcolm Ward, Radhey S. Gupta, Bashudev Rudra, Alvin Han, Ken Bruce, and Haroun N. Shah
7.1 Introduction: Diversity 183
7.1.1 P. aeruginosa: from 'Atypical' to Diverse 183
7.1.2 Phenotypical Diversity in Isolates from Different Environments 183
7.1.2.1 Clinical Isolates 183
7.1.2.2 Environmental Isolates 184
7.1.2.3 Veterinary Isolates 184
7.1.2.4 Comparing P. aeruginosa Phenotypical Profiles from Different Environments 184
7.1.2.5 Antibiotic Resistance in P. aeruginosa from Different Environments 186
7.1.3 The Relationship Between Phenotypical and Proteomic Diversity 186
7.1.4 Techniques and Practical Considerations for Studying Proteomic Diversity 186
7.1.5 Proteomic Diversity and MS Applications 189
7.2 Transmission 189
7.2.1 The History of P. aeruginosa Transmission 189
7.2.2 Proteomics and P. aeruginosa Transmission 191
7.2.3 The Impact of Proteomic Diversity on Transmission 191
7.3 Selective Pressures on the Proteome 192
7.3.1 Tandem MS Systems for Studying Selected Proteomes 192
7.3.2 Microenvironment Selection 192
7.3.2.1 The Human Body and CF Lung 192
7.3.2.2 The Natural Environment 192
7.3.3 Antimicrobial Selection 193
7.4 Conclusions on Studies of the Proteome 193
7.5 Genomic Studies on Pseudomonas aeruginosa Strains Revealing the Presence of Two Distinct Clades 195
7.5.1 Phylogenomic Analysis Reveals the Presence of Two Distinct Clades Within
P. aeruginosa 196
7.5.2 Identification of Molecular Markers Distinguishing the Two P. aeruginosa
Clades 198
7.6 Final Conclusions 201
References 201
8 Characterization of Biodegradable Polymers by MALDI- TOF MS 211
Hiroaki Sato
8.1 Introduction 211
8.2 Structural Characterization of Poly(e- caprolactone) Using Maldi- Tof Ms 212
8.3 Biodegradation Profiles of a Terminal- modified PCL Observed by Maldi- Tof Ms 216
8.4 Bacterial Biodegradation Mechanisms of Non- ionic Surfactants 218
8.5 Advanced Molecular Characterization by High- resolution MALDI- TOF MS Combined with KMD Analysis 221
8.6 Structural Characterization of High- molecular- weight Biocopolyesters by High- resolution MALDI- TOF MS Combined with KMD Analysis 225
References 228
9 Phytoconstituents and Antimicrobiological Activity 231
Philip L. Poole and Giulia T.M. Getti
9.1 Introduction to Phytochemicals 231
9.2 An Application to Bacteriology 233
9.2.1 Allicin Leads to a Breakdown of the Cell Wall of Staphylococcus aureus 234
9.3 Applications to Parasitology 239
9.3.1 Drug Discovery 239
9.3.2 Parasite Characterization 240
9.4 A Proteomic Approach: Leishmania Invasion of Macrophages 240
9.5 Intracellular Leishmania Amastigote Spreading between Macrophages 243
9.6 Potential Virus Applications 244
Acknowledgements 246
References 246
10 Application of MALDI- TOF MS in Bioremediation and Environmental Research 255
Cristina Russo and Diane Purchase
10.1 Introduction 255
10.2 Microbial Identification: Molecular Methods and MALDI- TOF MS 257
10.2.1 PCR- based Methods 258
10.2.2 Maldi- Tof Ms 260
10.3 Combination of MALDI- TOF MS with Other Methods for the Identification of Microorganisms 261
10.4 Application of MALDI- TOF MS in Environmental and Bioremediation Studies 263
10.4.1 The Atmospheric Environment 263
10.4.2 The Aquatic Environment 263
10.4.3 The Terrestrial Environment 265
10.4.4 Bioremediation Research Applications 266
10.5 Microbial Products and Metabolite Activity 268
10.6 Challenges of Environmental Applications 270
10.7 Opportunities and Future Outlook 271
10.8 Conclusions 272
References 273
11 From Genomics to MALDI- TOF MS: Diagnostic Identification and Typing of Bacteria in Veterinary Clinical Laboratories 283
John Dustin Loy and Michael L. Clawson
11.1 Introduction 283
11.2 Genomics 284
11.3 Defining Bacterial Species Through Genomics 286
11.4 Maldi- Tof Ms 287
11.5 Combining Genomics with MALDI- TOF MS to Classify Bacteria at the Subspecies Level 290
11.6 Data Exploration with MALDI- TOF MS 292
11.7 Validation of Typing Strategies 294
11.8 Future Directions 294
References 295
12 MALDI- TOF MS Analysis for Identification of Veterinary Pathogens from Companion Animals and Livestock Species 303
Dorina Timofte, Gudrun Overesch, and Joachim Spergser
12.1 Veterinary Diagnostic Laboratories and the MALDI- TOF Clinical Microbiology Revolution 303
12.1.1 MALDI- TOF MS: Reshaping the Workflow in Clinical Microbiology 304
12.1.2 Identification of Bacterial Pathogens Directly from Clinical Specimens 305
12.1.3 Prediction of Antimicrobial Resistance 307
12.1.4 Impact in Veterinary Hospital Biosecurity and Epidemiological Surveillance 308
12.2 Identification of Campylobacter spp. and Salmonella spp. in Routine Clinical Microbiology Laboratories 309
12.2.1 General Aspects on the Importance of Species/Subspecies and Serovar Identification of Campylobacter spp. and Salmonella spp. 309
12.2.2 General Aspects on Influence of Media/Culture Environment on Bacterial Species Identification by MALDI- TOF MS 311
12.2.3 Possibilities and Limits of Identification of Campylobacter spp. by Maldi- Tof Ms 312
12.2.3.1 Thermophilic Campylobacter spp. 312
12.2.3.2 Human- hosted Campylobacter Species 313
12.2.3.3 Campylobacter spp. of Veterinary Importance 313
12.2.4 Possibilities and Limits of Identification of Salmonella spp. by Maldi- Tof Ms 314
12.3 Identification and Differentiation of Mycoplasmas Isolated from Animals 316
12.3.1 Animal Mycoplasmas at a Glance 316
12.3.2 Laboratory Diagnosis of Animal Mycoplasmas 317
12.3.3 MALDI- TOF MS for the Identification of Animal Mycoplasmas 318
References 322
13 MALDI- TOF MS: from Microbiology to Drug Discovery 333
Ruth Walker, Maria E. Dueñas, Alan Ward, and Kaveh Emami
13.1 Introduction 333
13.2 Microbial Fingerprinting 334
13.2.1 Environmental 335
13.2.1.1 Actinobacteria 335
13.2.1.2 Aquatic Microorganisms 335
13.2.2 Terrestrial Microbiology 337
13.2.3 Food and Food Safety 338
13.2.3.1 Food Storage Effect on Identification 338
13.2.3.2 Insects 339
13.3 Mammalian Cell Fingerprinting 339
13.3.1 Differentiation of Cell Lines and Response to Stimuli 339
13.3.2 Cancer Diagnostics 341
13.3.3 Biomarkers 342
13.4 Drug Discovery Using MALDI- TOF 342
13.4.1 Enzymatic Assays 343
13.4.1.1 Targeting Antibiotic Resistance Using MALDI- TOF MS Enzymatic Assays 343
13.4.2 Cellular- based Assays for Drug Discovery 344
13.4.3 Automation in Drug Discovery 345
13.4.4 Assay Multiplexing 345
13.4.5 MS Imaging in Drug Discovery 346
13.4.6 Maldi- 2 346
13.5 Limitations/Challenges, Future Outlook, and Conclusions 347
13.5.1 Sample Preparation Limitations 347
13.5.1.1 Matrix 347
13.5.1.2 Interference from Low- molecular- mass Matrix Clusters 348
13.5.1.3 Buffer Compatibility 348
13.5.1.4 TOF Mass Resolution Limitations 348
13.5.2 Data Analysis and Application of Machine Learning 348
13.6 Future Outlook/Conclusions 349
References 350
14 Rapid Pathogen Identification in a Routine Food Laboratory Using High- throughput MALDI- TOF Mass Spectrometry 359
Andrew Tomlin
14.1 Introduction 359
14.2 MALDI- TOF MS in Food Microbiology 359
14.3 Review of Existing Confirmation Techniques and Comparison to Maldi- Tof Ms 362
14.4 Strain Typing Using MALDI- TOF MS 364
14.5 Verification Trial 365
14.6 Limitations of MALDI- TOF MS Strain Typing and Future Studies 369
14.7 Listeria Detection by MALDI- TOF MS 370
14.8 Trial Sample Preparation Procedure 370
14.9 Initial Trial 374
14.10 Limit of Detection Trial 375
14.11 Method Optimization, Further Prospects, and Conclusions 376
References 379
15 Detection of Lipids in the MALDI Negative Ion Mode for Diagnostics, Food Quality Control, and Antimicrobial Resistance 381
Yi Liu, Jade Pizzato, and Gerald Larrouy-Maumus
15.1 Introduction 381
15.2 Applications of Lipids in Clinical Microbiology Diagnostics 382
15.2.1 Use of Cell Envelope Lipids for Bacterial Identification 382
15.2.2 Detection of Cell Envelope Lipids and their Modifications to Determine Bacterial Drug Susceptibility 384
15.2.3 Detection of Lipids in MALDI Negative Ion Mode for Fungal Identification 387
15.2.4 Detection of Lipids in MALDI Negative Ion Mode for Parasite Identification 387
15.2.5 Detection of Lipids in MALDI Negative Ion Mode for Virus Identification 388
15.3 Applications of the Detection of Lipids in Negative Ion Mode MALDI- MS in Cancer Studies 388
15.3.1 Lipids and MALDI Negative Ion Mode for Diagnosis of Lung Cancer 389
15.3.2 Lipids and MALDI Negative Ion Mode for the Diagnosis of Breast Cancer 390
15.3.3 Lipids and MALDI Negative Ion Mode for Diagnosis of Other Cancers 391
15.3.4 Lipids and MALDI Negative Ion Mode for Drug-Cell Interactions and Prognosis 392
15.4 Applications of the Detection of Lipids and MALDI- MS in Alzheimer's Disease Studies 392
15.5 Applications of MALDI in Negative Ion Mode and the Detection of Lipids in Toxicology 393
15.6 Lipids and MALDI Negative Ion Mode for Food Fraud Detection 394
15.7 Conclusions and Future Development of Lipids and their Detection in MALDI in Negative Ion Mode 395
Acknowledgments 395
References 397
16 Use of MALDI- TOF MS in Water Testing Laboratories 405
Matthew Jones, Nadia Darwich, Rachel Chalmers, K. Clive Thompson, and Bjorn Nielsen
16.1 Introduction 405
16.2 Application in a Drinking Water Laboratory 408
16.2.1 Introduction 408
16.2.2 Method Validation 409
16.2.2.1 Reference Database Validation 410
16.2.2.2 Method Comparison 411
16.2.2.3 Agar Assessment 412
16.2.3 Application Within Drinking Water Laboratory 412
16.3 Application in Water Hygiene and Environmental Laboratory Testing 413
16.3.1 Introduction 413
16.3.2 Legionella Testing 414
16.3.3 Wastewater and Sewage Sludge Microbiology 415
16.3.4 Healthcare Water Testing 416
16.3.5 Investigative Analysis 417
16.3.6 Method Validation 417
16.3.6.1 Characterization of Intended Use 417
16.3.6.2 Library Assessment 418
16.3.6.3 Assessment of Variables 418
16.3.6.4 Comparison Assessment 419
16.3.6.5 Ongoing Verification 420
16.3.7 Conclusion on Suitability for Use in an Environmental Testing Laboratory 422
16.4 Potential Application for Cryptosporidium Identification 423
References 425
17 A New MALDI- TOF Database Based on MS Profiles of Isolates in Icelandic Seawaters for Rapid Identification of Marine Strains 431
Sibylle Lebert, Viggó Þór Marteinsson, and Pauline Vannier
17.1 Introduction 431
17.2 Selection and Cultivation of the Strains 432
17.3 Genotypic Identification 433
17.4 MALDI- TOF MS Data Acquisition and Database Creation 438
17.5 Verification of the Accuracy of the Home- made Database 441
17.6 Conclusions 448
Funding 448
References 449
18 MALDI- TOF MS Implementation Strategy for a Pharma Company Based upon a Network Microbial Identification Perspective 453
Lynn Johnson, Christoph Hansy, and Hilary Chan
18.1 Introduction 453
18.1.1 Microbial Identifications from a Pharmaceutical Industry Perspective 453
18.1.2 Historical Evolution 453
18.2 Regulatory Requirements/Guidance for Microbial Identification 455
18.3 Strategic Approaches to MALDI- TOF Implementation Within the Modern Microbial Methods Framework 455
18.3.1 Incorporation of MALDI- TOF into a Technical Evaluation Roadmap 455
18.3.2 Initial Implementation Planning Stage 456
18.3.2.1 Roles and Responsibilities (Global/Local, Partners/IT, Stakeholders) 456
18.3.2.2 Considerations When Selecting a Vendor/Model 457
18.3.2.3 Overall Identification Process Flow and MALDI- TOF as the Defined Application 458
18.3.2.4 Benefits of an In- house System for Pharmaceutical Companies Compared with Outsourcing 458
18.3.2.5 The Center of Excellence (CoE) Approach 460
18.3.2.6 Building a Business Case for the MALDI- TOF as a Network Strategy 461
18.3.3 Implementation Strategy - From Feasibility Studies to Global Deployment 463
18.3.3.1 Pilot Trials/Feasibility 463
18.3.3.2 Risk Assessment/Risk- based Validation Approach 463
18.3.3.3 Network Validation Approach 464
18.4 Conclusions 467
18.a Appendix 468
References 470
19 MALDI- TOF MS - Microbial Identification as Part of a Contamination Control Strategy for Regulated Industries 473
Christine E. Farrance and Prasanna D. Khot
19.1 Industry Perspective 473
19.1.1 Introduction to Regulated Industries 473
19.1.2 Contamination Control Strategy 474
19.1.3 Tracking and Trending EM Data 474
19.1.4 Drivers for Microbial Identification 476
19.1.5 Level of Resolution of an Identification 476
19.1.6 Global Harmonization 477
19.1.7 Validation Requirements for Regulated Industries 477
19.1.8 Summary 478
19.2 Technical Perspective 478
19.2.1 Identification Technologies 478
19.2.2 Phenotypic Systems 479
19.2.3 Proteotypic Systems 479
19.2.4 Genotypic Systems 479
19.2.5 The Importance of the Reference Database 480
19.2.6 MALDI- TOF in Regulated Industries 480
19.2.7 Outsourcing 480
19.2.8 Summary 481
19.3 MALDI- TOF MS Microbial Identification Workflow at a High- throughput Laboratory 481
19.3.1 MALDI- TOF MS Principles for Microbial Identification 481
19.3.2 Organism Cultivation for Microbial Identification with MALDI- TOF MS 482
19.3.3 Sample Preparation for Microbial Identification with MALDI- TOF MS 482
19.3.4 Sample Processing Workflow for Microbial Identification 482
19.3.5 Data Interpretation 483
19.3.6 Importance of a Sequence- based Secondary (or Fall- through) Identification System 484
19.4 MALDI- TOF MS Library Development and Coverage 485
19.4.1 Importance of Library Development Under a Quality System 485
19.4.2 Targeted Library Development for Gram- positive Bacteria and Water Organisms 488
19.4.2.1 Case Study 1: Impact of MALDI- TOF MS Library Coverage for Organisms of the Family Bacillaceae 488
19.4.2.2 Case Study 2: Impact of MALDI- TOF MS Library Coverage for Organisms Recovered from Water Systems 489
19.4.3 Supplemental and Custom MALDI- TOF MS Libraries 489
19.5 Comparison of MALDI- TOF MS with Other Microbial Identification Methods 490
19.6 Future Perspectives 490
References 491
20 Identification of Mold Species and Species Complex from the Food Environment Using MALDI- TOF MS 497
Victoria Girard, Valérie Monnin, Nolwenn Rolland, Jérôme Mounier, and Jean-Luc Jany
20.1 Fungal Taxonomy 497
20.1.1 Defining What Is a Fungal Species 497
20.1.2 Fungal Speciation within a Food Context 498
20.1.3 Delimiting Species 498
20.1.4 Foodborne Fungi within the Fungal Tree of Life 499
20.2 Impact of Molds in Food 500
20.2.1 Filamentous Fungi in Fermented Foods 500
20.2.2 Filamentous Fungi with Undesirable Impacts on Food Quality and Safety 500
20.3 Identification of Fungi 505
20.4 Identification of Foodborne Molds Using MALDI- TOF MS 506
20.4.1 Sample Preparation 506
20.4.2 Database Building and Performance of MALDI- TOF for Identification of Foodborne Molds 507
20.4.2.1 Database Building 507
20.4.2.2 Performance of Foodborne Mold Database 508
References 509
Index 515
Preface
Clinical symptoms provide clues to the aetiology of infections. However, there is no substitute for accurate identification to confirm disease and enable appropriate treatment to be confidently applied. Consequently, microbiological identification has been the cornerstone of clinical microbiology since Gram introduced his differential staining technique in 1884. Along with morphological characters, phenotypic and metabolic fermentation profiles grew in significance and appeared in the first edition of Bergey's Manual of Determinative Bacteriology in 1923. This remained the foundation of diagnostic microbiology and the basis for classification and identification of bacteria up until eighth edition of Bergey's Manual in 1974. This framework was incorporated into automated and miniaturized commercial kits (e.g. API® ID strip range by bioMérieux) for clinical and subsequently industrial and environmental laboratories to identify microbial species.
In the late 1970s, a new era of mass spectrometry enhanced microbial identification through higher-resolution analysis of microbial cellular components. Extensive analyses of polar and non-polar lipids revealed immense diversity in the microbial kingdom and specific molecular compositions were found to be unique to taxa. A new phase of chemical structure analysis dominated microbial taxonomy and classification and Bergey's Manual transitioned to a systematic approach for classification of the microbial kingdom, reflected in a change of the title of the new first edition to Bergey's Manual of Systematic Bacteriology (1984). However, implementation of chemical/bioanalytical methods and, in particular, electrophoresis, chromatography and mass spectrometry into diagnostic laboratories remained limited to specialized research laboratories with access to high-resolution analytical resources. Co-editor HNS's laboratory at the Royal London Hospital Medical College, University of London, was one of the pioneering teams to implement chemotaxonomy for the analysis of atypical and metabolically inert bacteria, including anaerobic pathogens. Wider adoption was found too cumbersome and technically demanding and limited their broader endorsement. However, a breakthrough was achieved by gas chromatography of long-chained fatty acid (LCFA) where analysis was partially automated by MIDI Inc., who developed the first dedicated database to drive analysis of lipid profiles. On the other hand, pyrolysis mass spectrometry failed to establish a wider base and was explored mainly by research laboratories. But lack of interlaboratory reproducibility, high cost and their cumbersome nature curtailed its development.
In 1973, Franz Hillenkamp developed a high-performance laser microprobe mass spectrometer with a spatial resolution of 0.5 µm and sub-attogram limit of detection for lithium atoms. This instrument was commercialized as the LAMMA 500. The more advanced 1000 was one of the first laser desorption mass spectrometers to be used for mass spectrometry imaging of tissues. In 1985, Hillenkamp and Michael Karas used a LAMMA 1000 mass spectrometer to demonstrate for the first time the technique of matrix-assisted laser desorption/ionization (MALDI), which allowed the analysis of large biopolymers. By the mid-1990s several researchers analysed bacterial cells and demonstrated that unique mass spectral profiles could be obtained from different species with MALDI. The concept of exploring the technology for a clinical diagnostic laboratory was not pursued, partly due to a history of failure of mass spectrometry (MS)-based techniques in clinical microbiology.
The UK's Public Health Laboratory Service (PHLS, later PHE and now UK Health Security Agency), a 100-year-old institute that focused on the analysis of pathogens, was structured along the lines of human pathogens/infections; thus a Staphylococcus laboratory, enteric or respiratory infections units etc. were led by specialist scientists for principal clinical pathogens. This permitted a high degree of expertise of various pathogens, yet each laboratory still retained a significant level of 'unknown' species in storage that were designated incertae sedis. Therefore, in 1997, PHLS established a new laboratory, designated the 'Molecular Identification Services Unit' (MISU), under the directorship of co-editor HNS. The function of MISU was to improve the level of species identification of atypical, rarely isolated and emerging human pathogens through research programmes while providing a more comprehensive diagnostic service function for the organization. Coming from a research background in which 16S rRNA sequence analysis was being developed as a tool for studying microbial phylogenetics, the technique was adopted for bacterial identification and MISU became the first accredited laboratory to implement this approach for human clinical samples. The laboratory also incorporated LCFA profiles as an adjunct to its newly employed 16S rRNA and was therefore well positioned to assess the potential of emerging technologies.
MISU was fortuitously given the opportunity to field-test the first benchtop MALDI-time-of-flight MS (MALDI TOF MS) (Kratos Analytical Inc.) and organized a conference on 27 October 1998 jointly with Kratos Analytical and Manchester Metropolitan University to explore and demonstrate potential applications of MALDI-TOF MS for clinical laboratories. An instrument was placed at the conference lecture theatre and the technique was demonstrated live during the meeting. Its speed of analysis and simplicity had a huge impact on the audience, but the general comment was that it was a new platform for research applications. Undeterred by the negative views, MISU would go on to relentlessly pursue the technology for the next decade for microbial identification of clinical samples and eventually implement it as its frontline approach.
These were interesting times, as concurrently there were two major developments of the technology, one designated surface-enhanced laser desorption/ionization (SELDI)-TOF MS that selectively captured proteins on ProteinChip Arrays prior to MALDI-TOF MS analysis and SEQUENOM's MassArray for genotyping that used reverse transcriptase to produce the more stable RNA for analysis. To our knowledge, MISU was the only laboratory to have implemented the three approaches at the time. The strategy envisioned was that MALDI-TOF MS would be explored for general microbial identification, SELDI-TOF MS for proteotyping of strains, while the MassArray was employed for genotyping of strains by the Genomics, Proteomics and Bioanalytical Laboratory led by co-editor SEG. Both laboratories jointly organized annual conferences from 1998 to the present to promote and develop new applications of these technologies. These were later supported by co-authors AJS, EYT and KCT until the hiatus caused by the COVID-19 pandemic.
Poor uptake of the ProteinChip and MassArray technologies by microbiologists led to their eventual cessation, while MALDI-TOF MS grappled to gain acceptance. The MS company Micromass (later Waters Inc.) designed the first upright, more compact MS benchtop instrument and was placed at MISU in 2000 to develop the method for clinical microbiology. Because microbial identification was historically based upon patterns of carbohydrate fermentation, the major problem encountered by diagnostic laboratories was the differentiation of non-fermentative species that produced uniformly negative results. However, the introduction of comparative 16S rRNA sequencing permitted insight into the immense diversity of these highly complex microorganisms. Species of the non-fermentative genus Porphyromonas that were difficult to distinguish by diagnostic laboratories were used by MISU to establish proof of concept of MALDI-TOF MS for microbial identification. With all 18 species of the genus being unambiguously delineated, work began to standardize protocols and assemble a database using strains from an accredited source, the National Collection of Type Cultures. To assess the potential of MALDI-TOF MS, 16S rRNA and LCFA profiles were included as complementary methods and, by 2004, the first microbial database of 3500 mass spectral profiles were reported and MISU later field-tested the method at the Royal London Hospital. This demonstrated not only the pragmatism of the method but also its value for a clinical laboratory as it was found to be accurate and rapid and offered significantly lower cost.
The annual European Congress of Clinical Microbiology and Infectious Diseases (ECCMID) conferences became the major forum for communications on the development on MALDI-TOF MS. Progress at first was slow and circumspect, with very poor attendance in 2003 in Glasgow to a packed main auditorium capacity in Vienna in 2010 in which data on the success of MALDI-TOF MS to rapidly identify Clostridium difficile during an epidemic in UK hospitals were reported by PHE. This congress was a watershed moment for MALDI-TOF MS for several reasons. For example, in the midst of the meeting, bioMérieux entered the field by acquiring the successful diagnostic company AnagnosTec GmbH (Potsdam Golm, Germany), while ThermoFisher announced its engagement with MISU to explore the wider potential of MS for microbial diagnostics. It was also at this meeting that Wiley launched our...
