Label-free Raman characterization of bacteria calls for standardized procedures.

Raman spectroscopy has gained relevance in single-cell microbiology for its ability to detect bacterial (sub)populations in a non-destructive and label-free way. However, the Raman spectrum of a bacterium can be heavily affected by abiotic factors, which may influence the interpretation of experimental results. Additionally, there is no publicly available standard for the annotation of metadata describing sample preparation and acquisition of Raman spectra. This article explores the importance of sample manipulations when measuring bacterial subpopulations using Raman spectroscopy. Based on the results of this study and previous findings in literature we propose a Raman metadata standard that incorporates the minimum information that is required to be reported in order to correctly interpret data from Raman spectroscopy experiments. Its aim is twofold: 1) mitigate technical noise due to sample preparation and manipulation and 2) improve reproducibility in Raman spectroscopy experiments studying microbial communities.

[1]  Sebastian Gibb,et al.  MALDIquant: a versatile R package for the analysis of mass spectrometry data , 2012, Bioinform..

[2]  Jürgen Popp,et al.  Recursive feature elimination in Raman spectra with support vector machines , 2017 .

[3]  P. Vandenabeele,et al.  Evaluation of an accurate calibration and spectral standardization procedure for Raman spectroscopy. , 2005, The Analyst.

[4]  A. Schintlmeister,et al.  Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells , 2014, Proceedings of the National Academy of Sciences.

[5]  R. Goodacre,et al.  Discrimination of bacteria using surface-enhanced Raman spectroscopy. , 2004, Analytical chemistry.

[6]  Peer Bork,et al.  Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees , 2016, Nucleic Acids Res..

[7]  Tianlun Li,et al.  Simultaneous analysis of microbial identity and function using NanoSIMS , 2008, Environmental microbiology.

[8]  Yizeng Liang,et al.  Automatic standardization method for Raman spectrometers with applications to pharmaceuticals , 2015 .

[9]  Anne-Laure Boulesteix,et al.  Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics , 2012, WIREs Data Mining Knowl. Discov..

[10]  Verónica Ambriz-Aviña,et al.  Applications of Flow Cytometry to Characterize Bacterial Physiological Responses , 2014, BioMed research international.

[11]  Elizabeth A. Suter,et al.  Single-Cell Growth Rates in Photoautotrophic Populations Measured by Stable Isotope Probing and Resonance Raman Microspectrometry , 2017, Front. Microbiol..

[12]  A. I. Athamneh,et al.  Phenotypic Profiling of Antibiotic Response Signatures in Escherichia coli Using Raman Spectroscopy , 2013, Antimicrobial Agents and Chemotherapy.

[13]  Holly J. Butler,et al.  Using Raman spectroscopy to characterize biological materials , 2016, Nature Protocols.

[14]  Jürgen Popp,et al.  Cultivation-Free Raman Spectroscopic Investigations of Bacteria. , 2017, Trends in microbiology.

[15]  O. Svensson,et al.  Exploring bacterial phenotypic diversity using factorial design and FTIR multivariate fingerprinting , 2014 .

[16]  Claudia Beleites,et al.  Assessing and improving the stability of chemometric models in small sample size situations , 2008, Analytical and bioanalytical chemistry.

[17]  J. Popp,et al.  Raman spectroscopy towards clinical application: drug monitoring and pathogen identification. , 2015, International journal of antimicrobial agents.

[18]  Bernd Bischl,et al.  mlr: Machine Learning in R , 2016, J. Mach. Learn. Res..

[19]  M. Uyttendaele,et al.  Small Bacillus cereus ATCC 14579 subpopulations are responsible for cytotoxin K production , 2013, Journal of applied microbiology.

[20]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[21]  Jürgen Popp,et al.  Towards an improvement of model transferability for Raman spectroscopy in biological applications , 2017 .

[22]  I. Vidavsky,et al.  Comparing similar spectra: From similarity index to spectral contrast angle , 2002, Journal of the American Society for Mass Spectrometry.

[23]  A. Whiteley,et al.  Chemical fixation methods for Raman spectroscopy-based analysis of bacteria. , 2015, Journal of microbiological methods.

[24]  R. Goodacre,et al.  Provided for Non-commercial Research and Educational Use Only. Not for Reproduction, Distribution or Commercial Use. Shining Light on the Microbial World: the Application of Raman Microspectroscopy , 2022 .

[25]  J. Kauffman,et al.  Standardization of Raman spectra for transfer of spectral libraries across different instruments. , 2011, The Analyst.

[26]  Kimberly M. Davis,et al.  Defining heterogeneity within bacterial populations via single cell approaches , 2016, BioEssays : news and reviews in molecular, cellular and developmental biology.

[27]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[28]  Per Ola Andersson,et al.  Explosive and chemical threat detection by surface-enhanced Raman scattering: a review. , 2015, Analytica chimica acta.