Using Fourier transform IR spectroscopy to analyze biological materials

Abstract

IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.

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Figure 1: Typical biological spectrum showing biomolecular peak assignments from 3,000–800 cm−1, where ν = stretching vibrations, δ= bending vibrations, s = symmetric vibrations and as = asymmetric vibrations.
Figure 2: The instrumentation underlying the main forms of IR spectroscopic sampling.
Figure 3: FTIR spectroscopy work flow for imaging and diagnosis.
Figure 4: Visual effect of different pre-processing steps on a set of FTIR spectra.
Figure 5: Classification rates (% classification ± s.d.) of all possible combinations between three different pre-processing, three different feature extraction and two different supervised classifier options.
Figure 6: IR image reconstruction of a section of human colon mucosa.

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Acknowledgements

Over several years, work in F.L.M.'s laboratories has been funded by the UK Engineering and Physical Sciences Research Council (EPSRC), the Rosemere Cancer Foundation and the UK Biotechnology and Biological Sciences Research Council (BBSRC). Work in R.B.'s laboratories has been funded by the US National Institutes of Health (grants R01CA138882 and R01EB009745). M.J.W. acknowledges the Department of Pathology, University of Illinois at Chicago for funding. P.R.F. and P.G. acknowledge the EPSRC. M.J.B. acknowledges the Rosemere Cancer Foundation, the EPSRC, Brain Tumour North West, the Sydney Driscoll Neuroscience Foundation and the Defence Science and Technology (Dstl).

Author information

F.L.M. is the principal investigator who conceived the idea for the manuscript; M.J.B. and K.M.D. provided information regarding FTIR FPA imaging; and P.R.F. provided information regarding microfluidic devices; J.T. wrote sections regarding Data processing, ANTICIPATED RESULTS and Figures, as well as maintaining a working manuscript; H.J.B. wrote the Instrumentation and Spectral acquisition sections; K.A.H. and B.O. wrote the Sample preparation and MATERIALS sections; R.J.S. wrote the INTRODUCTION and PROCEDURE, and contributed to the ANTICIPATED RESULTS section; C.H. provided material for Sample preparation, PCA–k-means clustering and cross-validation; P.L. provided figure suggestions and information regarding water vapor; B.R.W. provided information regarding live-cell imaging; R.B. provided significant revisions to the final manuscript; M.J.B., P.B., K.M.D., N.J.F., C.H., P.L., P.L.M.-H., G.D.S., J.S.-S., M.J.W., S.W.F., B.R.W. and P.G. all provided feedback on the manuscript; and F.L.M. brought together the text and finalized the manuscript.

Correspondence to Francis L Martin.

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Competing interests

P.L. is the author and owner of CytoSpec, a software package for vibrational hyperspectral imaging.

Supplementary information

Supplementary Method 1

Direct drop ATR-FTIR spectroscopy biofluid analysis. (PDF 159 kb)

Supplementary Method 2

FTIR FPA imaging using Agilent 670-IR spectrometer coupled with Agilent 620-IR microscope and FPA detector. (PDF 803 kb)

Supplementary Method 3

SVM classification in MATLAB using the IRootLab toolbox. (PDF 332 kb)

Supplementary Method 4

Protocol for FTIR spectroscopy of single living cells using a synchrotron source. (PDF 1161 kb)

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Baker, M., Trevisan, J., Bassan, P. et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat Protoc 9, 1771–1791 (2014) doi:10.1038/nprot.2014.110

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