SpringerLink
Log in

Differentiating Live Versus Dead Gram-Positive and Gram-Negative Bacteria With and Without Oxidative Stress Using Buoyant Mass Measurements

  • Published:
Current Microbiology Aims and scope Submit manuscript

Abstract

Expeditious and accurate determination of pathogenic bacteria cell viability is of great importance to public health for numerous areas including medical diagnostics, food safety, and environmental monitoring. In this work a cell buoyant mass classifier approach is presented to assess bacteria cell viability in real time. Buoyant mass measurements for live and dead Gram-positive and Gram-negative bacteria populations were acquired with a commercial suspended microchannel resonator, Archimedes, to generate receiver operating characteristic (ROC) curves. To quantitatively assess the difference in buoyant mass for live and dead bacteria populations, ROC curves were generated to demonstrate cell viability determination. The results are presented as a binary classifier with a decision boundary, above which cells are considered live and below which cells are considered dead. A decision threshold value is evaluated with consideration that a certain true positive rate (correct classification of a live cell) is maintained with an acceptable false positive rate. The potential for this approach to monitor cell viability in real time is significant, especially when considering multiple classifier dimensions such as buoyant mass and density. This classifier approach represents a next generation technique for rapid and label-free diagnostics based on cell feature measurements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code Availability

The code generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Scallan E, Hoekstra RM, Angulo FJ, Tauxe RV, Widdowson MA, Roy SL, Jones JL, Griffin PM (2011) Foodborne illness acquired in the United States—major pathogens. Emerg Infect Dis 17(1):7–15. https://doi.org/10.3201/eid1701.P11101

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lagier J-C, Edouard S, Pagnier I, Mediannikov O, Drancourt M, Raoult D (2015) Current and past strategies for bacterial culture in clinical microbiology. Clin Microbiol Rev 28(1):208. https://doi.org/10.1128/CMR.00110-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Robertson J, McGoverin C, Vanholsbeeck F, Swift S (2019) Optimisation of the protocol for the LIVE/DEAD® BacLightTM bacterial viability kit for rapid determination of bacterial load. Front Microbiol. https://doi.org/10.3389/fmicb.2019.00801

    Article  PubMed  PubMed Central  Google Scholar 

  4. Zeng D, Chen Z, Jiang Y, Xue F, Li B (2016) Advances and challenges in viability detection of foodborne pathogens. Front Microbiol 7:1833. https://doi.org/10.3389/fmicb.2016.01833

    Article  PubMed  PubMed Central  Google Scholar 

  5. Law JW-F, Ab Mutalib N-S, Chan K-G, Lee L-H (2015) Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations. Front Microbiol 5:770. https://doi.org/10.3389/fmicb.2014.00770

    Article  PubMed  PubMed Central  Google Scholar 

  6. Elizaquível P, Aznar R, Sánchez G (2014) Recent developments in the use of viability dyes and quantitative PCR in the food microbiology field. J Appl Microbiol 116(1):1–13. https://doi.org/10.1111/jam.12365

    Article  CAS  PubMed  Google Scholar 

  7. Sundaram J, Park B, Hinton A, Yoon SC, Windham WR, Lawrence KC (2012) Classification and structural analysis of live and dead Salmonella cells using Fourier transform infrared spectroscopy and principal component analysis. J Agric Food Chem 60(4):991–1004. https://doi.org/10.1021/jf204081g

    Article  CAS  PubMed  Google Scholar 

  8. Wang Y, Lee K, Irudayaraj J (2010) Silver nanosphere SERS probes for sensitive identification of pathogens. J Phys Chem C 114(39):16122–16128. https://doi.org/10.1021/jp1015406

    Article  CAS  Google Scholar 

  9. Nocker A, Caspers M, Esveld-Amanatidou A, van der Vossen J, Schuren F, Montijn R, Kort R (2011) A multiparameter viability assay for stress profiling applied to the food pathogen Listeria monocytogenes F2365. Appl Environ Microbiol 77(18):6433–6440. https://doi.org/10.1128/AEM.00142-11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cerf A, Cau J-C, Vieu C, Dague E (2009) Nanomechanical properties of dead or alive single-patterned bacteria. Langmuir 25(10):5731–5736. https://doi.org/10.1021/la9004642

    Article  CAS  PubMed  Google Scholar 

  11. Xu S, Mutharasan R (2011) Cell viability measurement using 2′,7′-bis-(2-carboxyethyl)-5-(and-6)-carboxyfluorescein acetoxymethyl ester and a cantilever sensor. Anal Chem 83(4):1480–1483. https://doi.org/10.1021/ac102757q

    Article  CAS  PubMed  Google Scholar 

  12. Lewis CL, Craig CC, Senecal AG (2014) Mass and density measurements of live and dead Gram-negative and Gram-positive bacterial populations. Appl Environ Microbiol 80(12):3622–3631. https://doi.org/10.1128/aem.00117-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cui Y, Oh YJ, Lim J, Youn M, Lee I, Pak HK, Park W, Jo W, Park S (2012) AFM study of the differential inhibitory effects of the green tea polyphenol (−)-epigallocatechin-3-gallate (EGCG) against Gram-positive and Gram-negative bacteria. Food Microbiol 29(1):80–87. https://doi.org/10.1016/j.fm.2011.08.019

    Article  CAS  PubMed  Google Scholar 

  14. Jasson V, Uyttendaele M, Rajkovic A, Debevere J (2007) Establishment of procedures provoking sub-lethal injury of Listeria monocytogenes, Campylobacter jejuni and Escherichia coli O157 to serve method performance testing. Int J Food Microbiol 118(3):241–249. https://doi.org/10.1016/j.ijfoodmicro.2007.07.016

    Article  CAS  PubMed  Google Scholar 

  15. Thompson M (2006) Representing data distributions with kernal density estimates. Royal Society of Chemistry Analytical Methods Committee (AMC) Technical Brief (4):1–2

  16. Limpert E, Stahel WA, Abbt M (2001) Log-normal distributions across the sciences: keys and clues: on the charms of statistics, and how mechanical models resembling gambling machines offer a link to a handy way to characterize log-normal distributions, which can provide deeper insight into variability and probability—normal or log-normal: that is the question. BioScience 51(5):341–352. https://doi.org/10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2

    Article  Google Scholar 

  17. Feng PW, Weagent SD, **neman K (2020) Bacteriological analytical manual (BAM). In: Diarrheagenic Escherichia coli. https://www.fda.gov/food/laboratory-methodsfood/bam-chapter-4a-diarrheagenic-escherichia-coli. Accessed 20 Jan 2022

  18. Cermak N, Becker JW, Knudsen SM, Chisholm SW, Manalis SR, Polz MF (2017) Direct single-cell biomass estimates for marine bacteria via Archimedes’ principle. ISME J 11(3):825–828. https://doi.org/10.1038/ismej.2016.161

    Article  PubMed  Google Scholar 

  19. Cermak N, Olcum S, Delgado FF, Wasserman SC, Payer KR, Murakami MA, Knudsen SM, Kimmerling RJ, Stevens MM, Kikuchi Y (2016) High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays. Nat Biotechnol 34(10):1052–1059. https://doi.org/10.1038/nbt.3666

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Olcum S, Cermak N, Wasserman SC, Manalis SR (2015) High-speed multiple-mode mass-sensing resolves dynamic nanoscale mass distributions. Nat Commun 6:7070. https://doi.org/10.1038/ncomms8070

    Article  CAS  PubMed  Google Scholar 

  21. Byun S, Hecht Vivian C, Manalis Scott R (2015) Characterizing cellular biophysical responses to stress by relating density, deformability, and size. Biophys J 109(8):1565–1573. https://doi.org/10.1016/j.bpj.2015.08.038

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bagnall JS, Byun S, Begum S, Miyamoto DT, Hecht VC, Maheswaran S, Stott SL, Toner M, Hynes RO, Manalis SR (2015) Deformability of tumor cells versus blood cells. Sci Rep 5:18542. https://doi.org/10.1038/srep18542

    Article  CAS  Google Scholar 

  23. Bryan AK, Hecht VC, Shen W, Payer K, Grover WH, Manalis SR (2014) Measuring single cell mass, volume, and density with dual suspended microchannel resonators. Lab Chip 14(3):569–576. https://doi.org/10.1039/C3LC51022K

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Grover WH, Bryan AK, Diez-Silva M, Suresh S, Higgins JM, Manalis SR (2011) Measuring single-cell density. Proc Natl Acad SciUSA 108(27):10992–10996. https://doi.org/10.1073/pnas.1104651108

    Article  Google Scholar 

Download references

Funding

This research was supported by In-House Laboratory Independent Research (ILIR) Program at U.S. Army Combat Capabilities and Development Command Soldier Center. Christina L. Lewis was supported by the National Research Council Research Associateship Program for this work. No funding or resources from MIT Lincoln Laboratory were used to produce the results and findings reported in this publication.

Author information

Authors and Affiliations

  1. U.S. Army Combat Capabilities and Development Command Soldier Center, 10 General Greene Ave, Natick, MA, 01760, USA

    Christina L. Lewis, Andre G. Senecal & Michael S. Wiederoder

  2. MIT Lincoln Laboratory, 244 Wood Street, Lexington, MA, 02421, USA

    Brian M. Lewis

Authors
  1. Christina L. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andre G. Senecal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael S. Wiederoder
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brian M. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by CLL, BML, and AGS. The first draft of the manuscript was written by CLL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Michael S. Wiederoder.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare.

Ethical Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 676 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lewis, C.L., Senecal, A.G., Wiederoder, M.S. et al. Differentiating Live Versus Dead Gram-Positive and Gram-Negative Bacteria With and Without Oxidative Stress Using Buoyant Mass Measurements. Curr Microbiol 79, 74 (2022). https://doi.org/10.1007/s00284-022-02764-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00284-022-02764-1

Search

Navigation