Discriminating Bacterial Phenotypes at the Population and Single‐Cell Level: A Comparison of Flow Cytometry and Raman Spectroscopy Fingerprinting. Issue 7 (30th December 2019)

• Record Type: Journal Article
• Authors: García‐Timermans, Cristina; Rubbens, Peter; Heyse, Jasmine; Kerckhof, Frederiek‐Maarten; Props, Ruben; Skirtach, Andre G.; Waegeman, Willem; Boon, Nico
• Other Names: Müeller Susan guestEditor.; Chang Hyun‐Dong guestEditor.
• Is Part Of: Cytometry. Volume 97:Issue 7(2020)
• Page Start: 713
• Page End: 726
• Publication Date: 2019-12-30
• Subjects: E. coli -- flow cytometry -- phenotypic heterogeneity -- Raman spectroscopy -- single‐cell technology -- microbial ecology -- growth phase; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.23952
• Languages: English
• ISSNs: 1552-4922

Abstract

Abstract: Investigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, such as flow cytometry and Raman spectroscopy, which describe optical properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth stages of three replicate Escherichia coli populations were characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high‐throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single‐cell level (i.e., more biochemical information is recorded) . Therefore, it can identify distinct phenotypic populations when coupled with analyses tailored toward single‐cell data. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose a computational workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external data set. We recommend using flow cytometry to quantify phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in‐depth analysis of heterogeneity at the single‐cell level. © 2019 International Society for Advancement of Cytometry Abstract :