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Volume 34, Issue 4
Nick Stone of the University of Exeter explains why mid-infrared (mid-IR) spectroscopy is so valuable for spectral imaging in disease research and clinical diagnostics, and discusses his own recent work in this area.
Mid-infrared (mid-IR) spectroscopy excels in the speed of data acquisition, and this feature has led spectroscopists to pursue mid-IR spectral imaging in particular as a valuable future tool for medicine, in areas such histopathology and cytology for disease research, and clinical diagnostics. Nick Stone of the University of Exeter, UK, spoke to Spectroscopy about the advantages of the technique, and the latest technological development that are advancing the capabilities of mid-IR spectroscopy for use in medicine.
You are well known for your work in Raman spectroscopy. Recently, however, you have collaborated on some projects using mid-IR spectroscopy for biological samples. Are there circumstances where mid-IR offers specific advantages for biological or clinical applications?
Absolutely. Raman and IR spectroscopy are complementary techniques that are able to probe the vibrational modes of biomolecules of interest, and therefore are useful to study the composition and microenvironment of tissues and cells as disease develops (1). We have worked on Raman and IR in parallel for many years to ensure that complementary information is available to us in our work developing methods for clinical diagnostics. Working with both techniques has allowed us to assess the relative pros and cons of each approach for particular clinical applications. We previously undertook a parallel study on IR and Raman for identification of secondary cancers in mediastinal lymph nodes. Both approaches provided an almost identical performance when using multivariate prediction of disease (2).
There are, of course, pros and cons for each approach, particularly around sample preparation and use in vivo and ex vivo. Raman usually allows for little sample preparation, and IR usually requires dry, thin sections. However, mid-IR imaging excels in the speed of acquisition of the data, and this advantage has led us and other groups to consider mid-IR spectral histopathology as a key future tool for medicine (3). Current "gold standard" cancer diagnostics use formalin-fixed paraffin-embedded tissue sections for staining and histopathological analysis. These sections, when mounted on the appropriate IR transmitting slide, can be used unstained for analysis of disease-specific molecular changes.
My team in Exeter have been collaborating recently on two European projects seeking to develop IR technologies to further maximize the technique's potential for spectral histopathology. Rohit Bhargava of the University of Illinois at Urbana-Champaign, Wolfgang Petrich of the University of Heidelberg and pharmaceutical company Roche, in Frankfurt, Germany, and others, have shown the value of discrete frequency imaging with quantum cascade lasers (QCLs) as a method to speed up the collection of images at a set of specific wavelengths of interest (4,5).
We worked as part of the MINERVA consortium, a European Commission funded project that aims to develop photonic technology in the mid-IR to improve early cancer detection, to develop mid-IR supercontinuum sources with acousto-optical tunable filters for application in discrimination of malignancies in the colon (6). We are also working, as part of the Mid-TECH consortium, a consortium of academic and industrial partners in Europe to advance mid-IR spectroscopy, on a combination of tunable light sources (QCLs, or optical parametric oscillators [OPOs]), coupled with upconversion technologies, to enable detection of IR-converted light with silicon-based charge-coupled device (CCD) detectors, with the objective of simplifying systems and reducing costs by removing the need for cryogenic cooling of detectors (7).
Have there been any recent technological advances that have made mid-IR spectroscopy more viable as a technique in medicine?
Certainly, there are a number of recent developments that will revolutionize the use of IR for real-time microscopic imaging of biological specimens. As I mentioned previously, tunable laser light sources-QCLs, supercontinuum, or OPO systems-allow for significantly improved intensity and optimized high-resolution imaging, compared with globar, thermal IR sources.
The major clinical benefit of discrete frequency imaging is that clinicians can view molecular images in real time, much as they would now, but with the ability to dial up the molecular "stain" of interest by changing the wavelength.
Furthermore, chalcogenide fibers for transmission of mid-IR light and upconversion imaging are likely to have a major impact on this field (8).
The issue of coherence can limit image quality, and a number of strategies have been developed to overcome this. One approach is simply to measure all of the light in a single detector, and raster the illumination point; that is, to map it rather than image it. This overcomes variations in intensities measured at each pixel caused by speckle when using coherent sources to obtain wide-field images.
One of your recent collaborations involved the use of mid-infrared multispectral tissue imaging using a supercontinuum source. What was the aim of this project, and what were the benefits of using a chalcogenide fiber supercontinuum source?
The aim of this project was to provide a bright and tunable source as an alternative to QCLs. QCLs have been demonstrated to be most dominant at long wavelengths (5–12 µm), and following recent developments in the MINERVA project, supercontinuum sources are now dominant at shorter wavelengths (2–5 µm). However, the MINERVA consortium were able to deliver a supercontinuum source able to cover much of the fingerprint region of the spectrum (1000–1800 cm-1 or around 5–10 µm), albeit with low power at longer wavelengths (9). Further developments in this area are ongoing. One output from the project was a shorter wavelength supercontinuum source covering the high wavenumber region of the IR spectrum, and providing an intense source able to be used for a number of biomedical applications (10). Successful development of a broadband supercontinuum light source covering the fingerprint and high wavenumber region is likely to be much cheaper than the requirement for numerous QCLs to cover the same range. That said, a tunable filter is still needed, unless the supercontinuum source is used as the source for the Fourier-transform (FT) interferometer, which then removes the benefit of rapid discrete frequency imaging.
What were the main obstacles you had to overcome?
Technology! It is difficult to make chalcogenide fibers of sufficient quality. Angela Seddon's group in Nottingham were able to do this, and, with Ole Bang's group in DTU in Denmark, they were able to deliver the world's first supercontinuum light source in the fingerprint region (9). Christian Pederson and Peter Tideman-Lichtenberg's group, also at DTU, have been driving forward the upconversion detection instrumentation and methodology (11).
Coherence in wide-field imaging is also an issue that a number of groups have been seeking solutions for. As mentioned above, the simplest solution is to merge all the photons into one detector and remove the speckle problem, but this reduces other strengths of the approach for real-time imaging.
Could the approach using a chalcogenide fiber supercontinuum source be useful in other tissue imaging applications?
Yes. This approach is applicable to all biological tissues, although, to date, really only the high wavenumber region of the IR spectrum has had sufficient photons generated with a supercontinuum source to provide data of similar quality to an FT-IR instrument. Clinical diagnostics are possible in this wavelength range (12), but more subtle changes, such as early cancers, usually need the fingerprint data to improve the accuracy of discrimination.
You also used mid-IR for hyper-spectral imaging for label-free histopathology and cytology. What specific advantages does mid-IR offer for this these applications?
As mentioned before, mid-IR spectroscopy allows molecular analysis of unstained tissues, rapid analysis, and, potentially, the automated prediction of disease, and analysts only need to view a single section for numerous molecular stains of interest (13). Cytology is trickier because of Mie scattering effects, but Achim Kohler and others have worked hard to overcome this problem with post-measurement corrections (14).
What were your main findings?
Discrete frequency imaging approaches and upconversion detection can be readily used to provide molecular distributions across tissue sections. There is still some way to go to ensure that these instruments can deliver the spectral quality or reproducibility of FT-IR instruments, but this does not appear to be an impossible task (15). Ongoing work is showing the viability of generating similar IR spectral histopathological images using various instrument configurations (16).
Are you planning to explore your research in mid-IR further?
Yes. Now that we have systems that can provide the signals we need, we will begin to explore the clinical needs where this will be most useful, and seek to translate it to the clinic, if possible, for the benefit of patients.
In what specific area of clinical use do you think mid-IR spectroscopy has the greatest potential? Are there any misconceptions that are stopping mid-IR spectroscopy from being used in this field?
Linking mid-IR imaging with digital pathology is the next key step. Many experts in machine learning have been able to provide pretty good predictions of disease based on pattern recognition and two stains in an RGB image. A few more channels, albeit at lower spatial resolution, should provide much improved performance, particularly for the pathologies that are most difficult to reliably identify, such as dysplasias and early cancers.
The real power of vibrational spectroscopic measurements lies in their ability to measure the biochemistry of the tissues of interest; this is the downstream expression of any genetic mutation, and will provide us with information relating to the future outcome of that disease. We are at the birth of this field in one sense, even though IR and Raman have been around for decades. The real breakthroughs will come when tens to hundreds of thousands of patients' samples, with particular conditions and their outcomes, are measured, and machine learning approaches are used to extract the key prognostic markers. Then, and only then, will IR really be providing the significant added value that it has promised for so long.
(1) M.J. Baker, H.J. Byrne, J. Chalmers, P. Gardner, R. Goodacre, A. Henderson, S.G. Kazarian, F.L. Martin, J. Moger, N. Stone, and J. Sulé-Suso Analyst 143(8), 1735–1757, 2018.
(2) M. Isabelle, N. Stone, H. Barr, M. Vipond, N. Shepherd, and K. Rogers, J. Spectrosc. 22 (2–3), 97–104, 2008.
(3) J. Nallala, G.R. Lloyd, M. Hermes, N. Shepherd, and N. Stone, Vib. Spectrosc. 91, 83-91, 2017.
(4) K. Yeh, S. Kenkel, J.N. Liu, and R. Bhargava, Anal Chem. 87(1):485–493 (2015 Jan 6). doi: 10.1021/ac5027513. Epub 2014 Dec 22.
(5) N. Kröger, A, Egl, M. Engel, N. Gretz, K. Haase, I. Herpich, B. Kränzlin, S. Neudecker, A Pucci, A. Schönhals, J. Vogt, and W. Petrich. J. Biomed. Opt. 19, 111607 (2014).
(6) MINERVA project, http://minerva-project.eu/.
(7) Mid-TECH project, http://www.midtech-itn.eu/
(8) L. Sójka, Z. Tang, D. Furniss, H. Sakr, E. BereÅ -Pawlik, A.B. Seddon, T.M. Benson, and S. Sujecki, Opt. Quant. Electron. 49(21). (2017). doi: 10.1007/s11082-016-0827-0.
(9) C.R. Petersen, U.V. MØller, I. Kubat, B. Zhou, S. Dupont, J. Ramsay, T Benson, S. Sujecki, N. Abdel-Moneim, Z. Tang, D. Furniss, A. Seddon, and O. Bang, Nature Photonics , 8, 830–834 (2014).
(10) C.R. Petersen, N. Prtljaga, M. Farries, J. Ward, B. Napier, G.R. Lloyd, J. Nallala, N. Stone, and O. Bang, Optics Letters 43(5), 999–1002 (2018).
(11) S. Junaid, J. Tomko, M.P. Semtsiv, J. Kischkat, W.T. Masselink, C. Pedersen, and P. Tidemand-Lichtenberg, Optics express 26(3), 2203–2211 (2018).
(12) G.R. Lloyd and N. Stone, Appl. Spectrosc. 69(9), 1066–1073 (2015).
(13) F Penaranda, V Naranjo, R Verdu-Monedero, GR Lloyd, J Nallala, and N. Stone, Digital Signal Processing 68, 1–15 (2017).
(14) T. Konevskikh, R. Lukacs, R. Blümel, A. Ponossova, and A. Kohlera Faraday Discuss. 187, 235-257 (2016). Doi: 10.1039/C5FD00171D.
(15) M. Hermes, R.B. Morrish, L. Huot, L. Meng, S. Junaid, J. Tomko, G.R. Lloyd, W. T. Masselink, P. Tidemand-Lichtenberg, C. Pedersen, F. Palombo, and N. Stone, J. Optics 20(2), 023002 (2018).
(16) Y.P. Tseng, P. Bouzy, C. Pedersen, N. Stone, and P. Tidemand-Lichtenberg, Biomed. Optics Express 9(10), 4979–4987 (2018).
Nick Stone holds the position of professor of Biomedical Imaging and Biosensing and NHS Consultant Clinical Scientist at the University of Exeter. He recently led the Department of Physics and Astronomy for three years after holding the role of director of research. Stone has worked to pioneer the field of novel optical diagnostics within the clinical environment, moving from the NHS (Gloucestershire Hospitals), after almost 20 years of working closely at the clinical–academic–commercial interface to pull through novel technologies to be used where they have most clinical need. He is an internationally recognized leader in biomedical applications of vibrational spectroscopy (Raman and infrared).
Stone graduated with a BSc (Hons) from Bath University in Applied Physics with Industrial Training in 1992. Since then he has undertaken numerous studies which include an MSc with Distinction from Heriot-Watt and St Andrews Universities in Laser Engineering with Applications; an MSc with Distinction in Applied Radiation Physics with Medical Physics at Birmingham University; a PhD in the application of Raman spectroscopy for cancer diagnostics at Cranfield University and an MBA (Health Executive) at Keele University.
Stone has received numerous awards for his research both personally and within his research group. He recently won the International Raman Award for Most Innovative Technological Development 2014 and was the runner up in the 2013 NHS Innovation Challenge Prize. He won the Chief Scientific Officer's National R&D Award for 2009. He has published over 150 papers and book chapters.