Use of high-content analysis and machine learning to characterize complex microbial samples via morphological analysis

Autoři: Jennifer Petitte aff001;  Michael Doherty aff001;  Jacob Ladd aff001;  Cassandra L. Marin aff001;  Samuel Siles aff001;  Vanessa Michelou aff001;  Amanda Damon aff001;  Erin Quattrini Eckert aff001;  Xiang Huang aff001;  John W. Rice aff001
Působiště autorů: Novozymes North America, Inc., Durham, North Carolina, United States of America aff001
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222528


High Content Analysis (HCA) has become a cornerstone of cellular analysis within the drug discovery industry. To expand the capabilities of HCA, we have applied the same analysis methods, validated in numerous mammalian cell models, to microbiology methodology. Image acquisition and analysis of various microbial samples, ranging from pure cultures to culture mixtures containing up to three different bacterial species, were quantified and identified using various machine learning processes. These HCA techniques allow for faster cell enumeration than standard agar-plating methods, identification of “viable but not plate culturable” microbe phenotype, classification of antibiotic treatment effects, and identification of individual microbial strains in mixed cultures. These methods greatly expand the utility of HCA methods and automate tedious and low-throughput standard microbiological methods.

Klíčová slova:

Antibiotics – Cell staining – DAPI staining – Flow cytometry – Machine learning – Machine learning algorithms – Protein synthesis – Image analysis


1. Jones LH, Bunnage ME. Applications of chemogenomic library screening in drug discovery. Nat Rev Drug Discov. Nature Publishing Group; 2017;16: 285–296. doi: 10.1038/nrd.2016.244 28104905

2. Dürr O, Murina E, Siegismund D, Tolkachev V, Steigele S, Sick B. Know When You Don’t Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes. Assay Drug Dev Technol. Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA; 2018;16: 343–349. doi: 10.1089/adt.2018.859 30148665

3. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. American Association for the Advancement of Science; 2015;349: 255–60. doi: 10.1126/science.aaa8415 26185243

4. Grote M. Petri dish versus Winogradsky column: a longue durée perspective on purity and diversity in microbiology, 1880s–1980s. Hist Philos Life Sci. Springer International Publishing; 2018;40: 11. doi: 10.1007/s40656-017-0175-9 29188459

5. Stiefel P, Schmidt-Emrich S, Maniura-Weber K, Ren Q. Critical aspects of using bacterial cell viability assays with the fluorophores SYTO9 and propidium iodide. BMC Microbiol. BioMed Central; 2015;15: 36. doi: 10.1186/s12866-015-0376-x 25881030

6. Li M, Liu L, Xi N, Wang Y. Nanoscale monitoring of drug actions on cell membrane using atomic force microscopy. Acta Pharmacol Sin. Nature Publishing Group; 2015;36: 769–782. doi: 10.1038/aps.2015.28 26027658

7. Vives-Rego J, Lebaron P, Nebe-von Caron G. Current and future applications of flow cytometry in aquatic microbiology. FEMS Microbiol Rev. Oxford University Press; 2000;24: 429–448. doi: 10.1111/j.1574-6976.2000.tb00549.x 10978545

8. Moter A, Göbel UB. Fluorescence in situ hybridization (FISH) for direct visualization of microorganisms. J Microbiol Methods. Elsevier; 2000;41: 85–112. doi: 10.1016/S0167-7012(00)00152-4

9. Rohde A, Hammerl JA, Appel B, Dieckmann R, Al Dahouk S. FISHing for bacteria in food–A promising tool for the reliable detection of pathogenic bacteria? Food Microbiol. Academic Press; 2015;46: 395–407. doi: 10.1016/J.FM.2014.09.002 25475309

10. Wang Y, Huang WE, Cui L, Wagner M. Single cell stable isotope probing in microbiology using Raman microspectroscopy. Curr Opin Biotechnol. Elsevier Current Trends; 2016;41: 34–42. doi: 10.1016/J.COPBIO.2016.04.018 27149160

11. Koydemir HC, Gorocs Z, Tseng D, Cortazar B, Feng S, Chan RYL, et al. Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning. Lab Chip. The Royal Society of Chemistry; 2015;15: 1284–1293. doi: 10.1039/C4LC01358A 25537426

12. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. The Human Microbiome Project. Nature. 2007;449: 804–810. doi: 10.1038/nature06244 17943116

13. Gilbert JA, Jansson JK, Knight R. The Earth Microbiome project: successes and aspirations. BMC Biol. BioMed Central; 2014;12: 69. doi: 10.1186/s12915-014-0069-1 25184604

14. Berg G, Grube M, Schloter M, Smalla K. Unraveling the plant microbiome: looking back and future perspectives. Front Microbiol. Frontiers; 2014;5: 148. doi: 10.3389/fmicb.2014.00148 24926286

15. van der Heijden MGA, Hartmann M. Networking in the Plant Microbiome. PLOS Biol. Public Library of Science; 2016;14: e1002378. doi: 10.1371/journal.pbio.1002378 26871440

16. Berg G, Grube M, Schloter M, Smalla K. The plant microbiome and its importance for plant and human health. Front Microbiol. Frontiers; 2014;5: 1. doi: 10.3389/fmicb.2014.00491 25278934

17. Sorensen K, Brodbeck U. Assessment of coating-efficiency in ELISA plates by direct protein determination. J Immunol Methods. Elsevier; 1986;95: 291–293. doi: 10.1016/0022-1759(86)90419-9

18. Barros RC, Gelens E, Bulten E, Tuin A, de Jong MR, Kuijer R, et al. Self-assembled nanofiber coatings for controlling cell responses. J Biomed Mater Res Part A. 2017;105: 2252–2265. doi: 10.1002/jbm.a.36092 28513985

19. da Silva Domingues JF, Roest S, Wang Y, van der Mei HC, Libera M, van Kooten TG, et al. Macrophage phagocytic activity toward adhering staphylococci on cationic and patterned hydrogel coatings versus common biomaterials. Acta Biomater. Elsevier; 2015;18: 1–8. doi: 10.1016/J.ACTBIO.2015.02.028 25752975

20. Sincock SA, Robinson JP. Flow cytometric analysis of microorganisms. Methods Cell Biol. Academic Press; 2001;64: 511–537. doi: 10.1016/S0091-679X(01)64027-5

21. Sutton S. [Microbiology Topics. Accuracy of Plate Counts [Internet].

22. Riss TL, Moravec RA, Niles AL, Duellman S, Benink HA, Worzella TJ, et al. Cell Viability Assays [Internet]. Assay Guidance Manual. Eli Lilly & Company and the National Center for Advancing Translational Sciences; 2004.

23. Hein CD, Liu X-M, Wang D. Click chemistry, a powerful tool for pharmaceutical sciences. Pharm Res. NIH Public Access; 2008;25: 2216–30. doi: 10.1007/s11095-008-9616-1 18509602

24. Moellering RC. Vancomycin: A 50-Year Reassessment. Clin Infect Dis. Oxford University Press; 2006;42: S3–S4. doi: 10.1086/491708 16323117

25. Watanakunakorn C. Mode of action and in-vitro activity of vancomycin. J Antimicrob Chemother. 1984;14 Suppl D: 7–18.

26. Brodersen DE, Clemons WM, Carter AP, Morgan-Warren RJ, Wimberly BT, Ramakrishnan V. The Structural Basis for the Action of the Antibiotics Tetracycline, Pactamycin, and Hygromycin B on the 30S Ribosomal Subunit. Cell. Cell Press; 2000;103: 1143–1154. doi: 10.1016/S0092-8674(00)00216-6

27. Zoffmann S, Vercruysse M, Benmansour F, Maunz A, Wolf L, Blum Marti R, et al. Machine learning-powered antibiotics phenotypic drug discovery. Sci Rep. Nature Publishing Group; 2019;9: 5013. doi: 10.1038/s41598-019-39387-9 30899034

Článek vyšel v časopise


2019 Číslo 9

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…

Kurzy Doporučená témata