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US Navy 070905-N-0194K-029 Lt. Paul Graf, a microbiology officer aboard Military Sealift Command hospital ship USNS Comfort (T-AH 20), examines wound cultures in the ship's microbiology laboratory.jpg

Title: What types of testing occur within a medical microbiology laboratory?

Author for citation: Shawn E. Douglas

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: April 2024

Introduction

The medical microbiology laboratory has a variety of testing and workflow requirements that manage to separate it from other biomedical labs.

This brief topical article will examine the typical types of testing that occur in medical microbiology labs.


The medical microbiology lab in general

A medical microbiology laboratory helps detect, identify, and characterize microorganisms for both individual patient treatment and broader population disease prevention and control. In the course of its work towards aiding in the diagnosis of individual patients' ailments, the lab may identify infectious agents of concern and trends in those infections as part of a greater public health effort. By extension, medical microbiology laboratories are also responsible for reporting those identification and trends to various public health agencies (city, county, state, and federal). These reports are then used by public health laboratories, in tandem with medical microbiology labs, to track incidences and attempt to identify outbreaks.[1] In particular, the medical microbiology lab is uniquely suited to confirming infectious disease cases as part of outbreak investigations, with its analytical and interpretive "methods that are not commonly available in a routine laboratory setting."[2]

A standard consolidated medical microbiology laboratory will have the facilities for rapid microbiology, microscopy, cell culturing, serology, molecular biology, parasitology, virology, communicable disease management (i.e., public health or reference activities[2]) and more, and it also may have the facilities for environmental microbiology.[3] A variety of specimen types will be tested, including urine, blood, stool, tissues, and precious fluids, as well as skin, mucosal, and genital swabs.[3]

Culture-based and other microbiology test methods have largely been performed manually up until recently. As Antonios et al. noted at the end of 2021, "the introduction of automation in microbiology was considered difficult to apply for several reasons such as the complexity and variability of sample types, the variations of specimens processing, the doubtful cost-effectiveness especially for small and average-sized laboratories, and the perception that machines could not exercise the critical decision-making skills required to process microbiological samples."[4] However, economic, employment, and other societal drivers have necessarily brought laboratory automation and large language models (LLMs) more fully to the medical microbiology lab in recent years.[3][4][5] This has allowed these labs to move from a traditional partial-day work schedule to a more 24-hour work schedule by, for example, the use of automated front-end plating systems.[4]

Whether manual or automated, successful medical microbiology workflows rely on specific quality controls, reporting, instruments, and test methods to achieve overall laboratory and healthcare objectives. The next section will specifically examine the types of testing that occur within a medical microbiology laboratory.

Medical microbiology testing

Within the scope of detecting, identifying, and characterizing microorganisms, medical microbiology labs depend on a variety of scientific subspecialties (e.g., bacteriology, mycology, virology) and test methods to achieve their goals. What follows are examples of the more common detection, identification, and characterization activities conducted in these labs.

Detection of microbial growth

By detecting the telltale signs of living microorganisms, such as growth (i.e., an increase in the number of cells), microbiologists can then make an initial diagnosis of microbiological infection and take a deeper dive into identifying the microorganism(s). (Note that measuring microbial growth is not a direct proxy for measuring microbial metabolism, however.[6]) Growth can be demonstrated in multiple ways, including[7]:

  • confirming turbidity, gas, or discrete colonies in broth;
  • confirming discrete colonies in on agar plates;
  • confirming cytopathic effects or inclusions that distort the structures of cells in culture; and
  • confirming "genus- or species-specific antigens or nucleotide sequences"[7] in the specimen, culture medium, or culture system.

Cell culturing plays an important role, as hinted at above. Those cultures can occur in liquid broth, agar plates, or some other enhanced culture medium, as found with blood cultures in specialized bottles or tubes. Cultures are incubated to allow time for any microorganisms to multiply. Then signs of growth are sought out.[7] However, detecting this growth is rarely straightforward and has its own set of complications.[8][9] This may necessitate other methods such as Gram staining or fluorescence in situ hybridization (FISH) for quicker and more accurate detection of growth.[9]

Taxonomic identification and overall characterization

As an extension of detecting microbial growth, microbiologists can examine the growth characteristics of the microorganism(s) in order to identify what type of bacteria or fungi is growing. The identification of viruses, on the other hand, is typically done by examining the cytopathic effects or inclusions that affect cells in culture, or through detection of antigens or nucleotides specific to a viral genus or species.[7] Databases are commonly used as part of the identification process of microorganisms.[1] The sources used for these databases highlights some of the identification techniques used. For example, a "biochemical reaction" database implies microorganisms identified with techniques such as polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), fatty acid profiling (using gas chromatography [GC] and mass spectrometry [MS]), and metabolic/chemo profiling (using high-performance liquid chromatography [HPLC] and MS).[10] A "nucleic acid sequence" database implies microorganisms identified with PCR for a single pathogen[1], or DNA microarrays, metagenomics analysis, and next-generation sequencing (NGS) for identifying multiple pathogens at the same time.[1][11]

All of these techniques have their place in the microbiology lab, with genotypic methods in particular proving useful "for assessing sterility test and media fill failures, and for tracking the route of contamination as part of a contamination control strategy."[5] This type of contamination tracking and tracing is enabled by genotypic methods that allow microorganisms to be "characterized," i.e., grouped together based upon the shared characteristics of their DNA fragment patterns or antigenic profiles.[12] Other aspects of a culture may be characterized as well in order to provide a more accurate "description" of the microorganism for future identification efforts.[13]

Other analyses and techniques

Medical microbiology labs will perform antibiogram and antimicrobial susceptibility testing (AST) as part of their public health function. An antibiogram is a cumulative summary or "overall profile of [in vitro] susceptibility testing results for a specific microorganism to an array of antimicrobial drugs," often given in a tabular form.[14] Given that antibiotic resistance remains one of the primary challenges for global public health, determinations of how susceptible a microorganism is to certain antimicrobials before physician prescription or administration of an antibiotic is of significant value.[15] There are multiple approaches to antibiograms for a wide variety of susceptibility testing, common to microbiology labs, including broth and agar dilution, gradient strip test, disk diffusion test, chromogenic and colorimetric test, PCR, DNA microarray, and other methods.[15] The nuances of antibiograms and susceptibility testing drive reporting requirements, particularly to the standard CLSI M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data.[1][16] This highlights the importance of the lab not only accurately performing these analyses but also properly reporting the results for consistent and rapid interpretation.

Digital image analysis is another important technique used in the medical microbiology lab. This work has traditionally been performed with analog microscopy techniques to identify and characterize microorganisms, even into the early 2010s when digital imaging analysis was become more viable.[17] In 2014, Rhoads et al. characterized automated or semi-automated methods in image interpretation as not being widely implemented in the medical microbiology lab, while at the same time recognizing those methods' potential for screening slides for identifications or characterizations, as well as improving standardization and turnaround time for analyzed specimens.[1] Since then, laboratory automation, LLMs, and artificial intelligence (AI) tools—as well as the COVID-19 pandemic—have advanced the microbiology imaging paradigm sufficiently to arguably make digital image analysis more mainstream.[4][5][18][19] The introduction of automated microscopes "designed to collect high‑resolution image data from microscopic slides" and "high‑resolution image analysis systems that can detect small and mixed colonies, which a human eye cannot"[5] are examples of how modern medical microbiology labs are approaching their imaging work.

Conclusion

References

  1. ↑ 1.0 1.1 1.2 1.3 1.4 1.5 Rhoads, Daniel D.; Sintchenko, Vitali; Rauch, Carol A.; Pantanowitz, Liron (1 October 2014). "Clinical Microbiology Informatics" (in en). Clinical Microbiology Reviews 27 (4): 1025–1047. doi:10.1128/CMR.00049-14. ISSN 0893-8512. PMC PMC4187636. PMID 25278581. https://journals.asm.org/doi/10.1128/CMR.00049-14. 
  2. ↑ 2.0 2.1 European Centre for Disease Prevention and Control (June 2010). "Core functions of microbiology reference laboratories for communicable diseases" (PDF). European Centre for Disease Prevention and Control. doi:10.2900/29017. ISBN 9789291932115. https://www.ecdc.europa.eu/sites/default/files/media/en/publications/Publications/1006_TER_Core_functions_of_reference_labs.pdf. Retrieved 24 April 2024. 
  3. ↑ 3.0 3.1 3.2 Vandenberg, Olivier; Durand, Géraldine; Hallin, Marie; Diefenbach, Andreas; Gant, Vanya; Murray, Patrick; Kozlakidis, Zisis; van Belkum, Alex (18 March 2020). "Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies" (in en). Clinical Microbiology Reviews 33 (2): e00057–19. doi:10.1128/CMR.00057-19. ISSN 0893-8512. PMC PMC7048017. PMID 32102900. https://journals.asm.org/doi/10.1128/CMR.00057-19. 
  4. ↑ 4.0 4.1 4.2 4.3 Antonios, Kritikos; Croxatto, Antony; Culbreath, Karissa (30 December 2021). "Current State of Laboratory Automation in Clinical Microbiology Laboratory" (in en). Clinical Chemistry 68 (1): 99–114. doi:10.1093/clinchem/hvab242. ISSN 0009-9147. https://academic.oup.com/clinchem/article/68/1/99/6490228. 
  5. ↑ 5.0 5.1 5.2 5.3 Sandle, T. (22 December 2021). "Enhancing rapid microbiology methods: how AI is shaping microbiology". European Pharmaceutical Review. https://www.europeanpharmaceuticalreview.com/article/166302/enhancing-rapid-microbiology-methods-how-ai-is-shaping-microbiology/. Retrieved 17 April 2024. 
  6. ↑ Braissant, Olivier; Astasov-Frauenhoffer, Monika; Waltimo, Tuomas; Bonkat, Gernot (17 November 2020). "A Review of Methods to Determine Viability, Vitality, and Metabolic Rates in Microbiology". Frontiers in Microbiology 11: 547458. doi:10.3389/fmicb.2020.547458. ISSN 1664-302X. PMC PMC7705206. PMID 33281753. https://www.frontiersin.org/articles/10.3389/fmicb.2020.547458/full. 
  7. ↑ 7.0 7.1 7.2 7.3 Washington, J.A. (1996). "Chapter 10: Principles of Diagnosis". In Baron, Samuel. Medical microbiology (4th ed ed.). Galveston, Tex: University of Texas Medical Branch at Galveston. ISBN 978-0-9631172-1-2. PMID 21413287. 
  8. ↑ Zengler, Karsten (1 December 2009). "Central Role of the Cell in Microbial Ecology" (in en). Microbiology and Molecular Biology Reviews 73 (4): 712–729. doi:10.1128/MMBR.00027-09. ISSN 1092-2172. PMC PMC2786577. PMID 19946138. https://journals.asm.org/doi/10.1128/MMBR.00027-09. 
  9. ↑ 9.0 9.1 Źródłowski, Tomasz; Sobońska, Joanna; Salamon, Dominika; McFarlane, Isabel M.; Ziętkiewicz, Mirosław; Gosiewski, Tomasz (29 February 2020). "Classical Microbiological Diagnostics of Bacteremia: Are the Negative Results Really Negative? What is the Laboratory Result Telling Us About the “Gold Standard”?" (in en). Microorganisms 8 (3): 346. doi:10.3390/microorganisms8030346. ISSN 2076-2607. PMC PMC7143506. PMID 32121353. https://www.mdpi.com/2076-2607/8/3/346. 
  10. ↑ Moore, S. (14 January 2021). "Biochemical Tests for Microbial Identification". News-Medical Life Sciences. https://www.news-medical.net/life-sciences/Biochemical-Tests-for-Microbial-Identification.aspx. Retrieved 26 April 2024. 
  11. ↑ Yadav, Brijesh Singh; Ronda, Venkateswarlu; Vashista, Dinesh P.; Sharma, Bhaskar (1 January 2013). "Sequencing and Computational Approaches to Identification and Characterization of Microbial Organisms" (in en). Biomedical Engineering and Computational Biology 5: BECB.S10886. doi:10.4137/BECB.S10886. ISSN 1179-5972. PMC PMC4147756. PMID 25288901. http://journals.sagepub.com/doi/10.4137/BECB.S10886. 
  12. ↑ Kim, Young Ran; Lee, Shee Eun; Kim, Choon Mee; Kim, Soo Young; Shin, Eun Kyoung; Shin, Dong Hyeon; Chung, Sun Sik; Choy, Hyon E. et al. (1 October 2003). "Characterization and Pathogenic Significance of Vibrio vulnificus Antigens Preferentially Expressed in Septicemic Patients" (in en). Infection and Immunity 71 (10): 5461–5471. doi:10.1128/IAI.71.10.5461-5471.2003. ISSN 0019-9567. PMC PMC201039. PMID 14500463. https://journals.asm.org/doi/10.1128/IAI.71.10.5461-5471.2003. 
  13. ↑ Trüper, Hans G.; Krämer, Johannes (1981), Starr, Mortimer P.; Stolp, Heinz; Trüper, Hans G. et al.., eds., "Principles of Characterization and Identification of Prokaryotes" (in en), The Prokaryotes (Berlin, Heidelberg: Springer Berlin Heidelberg): 176–193, doi:10.1007/978-3-662-13187-9_6, ISBN 978-3-662-13189-3, http://link.springer.com/10.1007/978-3-662-13187-9_6. Retrieved 2024-04-26 
  14. ↑ Antimicrobial Resistance and Stewardship Initiative, University of Minnesota (February 2020). "How to Use a Clinical Antibiogram" (PDF). https://arsi.umn.edu/sites/arsi.umn.edu/files/2020-02/How_to_Use_a_Clinical_Antibiogram_26Feb2020_Final.pdf. Retrieved 17 April 2024. 
  15. ↑ 15.0 15.1 Gajic, Ina; Kabic, Jovana; Kekic, Dusan; Jovicevic, Milos; Milenkovic, Marina; Mitic Culafic, Dragana; Trudic, Anika; Ranin, Lazar et al. (23 March 2022). "Antimicrobial Susceptibility Testing: A Comprehensive Review of Currently Used Methods" (in en). Antibiotics 11 (4): 427. doi:10.3390/antibiotics11040427. ISSN 2079-6382. PMC PMC9024665. PMID 35453179. https://www.mdpi.com/2079-6382/11/4/427. 
  16. ↑ Simner, Patricia J.; Hindler, Janet A.; Bhowmick, Tanaya; Das, Sanchita; Johnson, J. Kristie; Lubers, Brian V.; Redell, Mark A.; Stelling, John et al. (19 October 2022). Humphries, Romney M.. ed. "What’s New in Antibiograms? Updating CLSI M39 Guidance with Current Trends" (in en). Journal of Clinical Microbiology 60 (10): e02210–21. doi:10.1128/jcm.02210-21. ISSN 0095-1137. PMC PMC9580356. PMID 35916520. https://journals.asm.org/doi/10.1128/jcm.02210-21. 
  17. ↑ Pasulka, Alexis L.; Hood, Jonathan F.; Michels, Dana E.; Wright, Mason D. (19 January 2023). "Flexible and open-source programs for quantitative image analysis in microbial ecology". Frontiers in Marine Science 10: 1052119. doi:10.3389/fmars.2023.1052119. ISSN 2296-7745. https://www.frontiersin.org/articles/10.3389/fmars.2023.1052119/full. 
  18. ↑ Burns, Bethany L.; Rhoads, Daniel D.; Misra, Anisha (21 September 2023). Humphries, Romney M.. ed. "The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology" (in en). Journal of Clinical Microbiology 61 (9): e02336–21. doi:10.1128/jcm.02336-21. ISSN 0095-1137. PMC PMC10575257. PMID 37395657. https://journals.asm.org/doi/10.1128/jcm.02336-21. 
  19. ↑ Yakimovich, Artur (28 February 2024). Imperiale, Michael J.. ed. "Toward the novel AI tasks in infection biology" (in en). mSphere 9 (2): e00591–23. doi:10.1128/msphere.00591-23. ISSN 2379-5042. PMC PMC10900907. PMID 38334404. https://journals.asm.org/doi/10.1128/msphere.00591-23. 


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