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ADVANCES IN SPECTROSCOPY

A new e-book from

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Raman Advances Using SESORRS and SERS for Biomedical MeasurementsAn interview with Karen FauldsJerome Workman, Jr.

Revisiting Beer’s Law, Wave Optics, and Dispersion Theory for Infrared SpectroscopyAn interview with Thomas Mayerhöfer Jerome Workman, Jr.

Advancing Biomedical Tissue Analysis with Fluorescence-Enabled Virtual StainingAn interview with Aydogan OzcanLaura Bush

Achieving Quality Control in Continuous Pharmaceutical Manufacturing with NIR SpectroscopyAn interview with Steve HammondLaura Bush

A U G U S T 2 0 1 9

Modern Raman Spectroscopy at the Center of Biosensing and Medical DiagnosisAn interview with Juergen PoppJerome Workman, Jr.


THE SCIX 2019 PREVIEW AND COMPANION GUIDE

The annual SciX conference is always a vibrant event, with a week’s worth of scientific talks and posters, instruments to see in the exhibit hall, and ample networking opportunities. But for those who have not experienced the benefits of attending, and who are wondering whether they should register, we have prepared this e-book.

Prepared in collaboration with the SciX conference organization, this e-book presents interviews with five analytical spectroscopists who will give talks at this year’s event (October 13–18, 2019, in Palm Springs, California).

In addition to previewing the excellent science that will be presented this October, it also can serve as a conference companion guide during or after the event, or simply on its own, as a way to keep abreast of important developments in spectroscopy.

Three of the interviews address the ongoing trend of applying Raman spectroscopy techniques to biology and medicine. Karen Faulds of the University of Strathclyde, the winner of the 2019 FACSS Charles Mann Award for Applied Raman Spectroscopy, discusses her work using surface-enhanced spatially offset resonance Raman spectroscopy (SESORRS) to enable noninvasive, real-time measurements of living tissue and multiple bacterial pathogens. Aydogan Ozcan explains the new method that he and his team at the Bio- and Nano-Photonics Laboratory at the University of California Los Angeles (UCLA) have developed for virtually staining tissue samples, using deep learning and fluorescence microscopy. Juergen Popp of the Leibniz Institute of Photonic Technology in Jena, Germany explores the latest developments in Raman spectroscopy for medicine, such as in-vivo identification of tumor margins, the study of silicon nanoparticle uptake in breast cancer cells, and fast antibiotic susceptibility testing.

The other two interviews in the e-book address topics that range from pure theory to practical implementation of infrared (IR) spectroscopy techniques. On the theoretical side, Thomas Mayerhöfer, of the Leibniz Institute of Photonic Technologies explains his rigorous analysis of theoretical aspects of infrared (IR) spectroscopy, using wave optical and dispersion theory to examine Beer’s law, crystal orientation, absorbance band fitting, and Kramers-Kronig analysis. On the side of practical implementation, Steve Hammond, a process-analytical technology (PAT) consultant who recently retired from Pfizer, talks about using near-infrared (NIR) spectroscopy for process control, illustrating his points with examples from a recent implementation.

These interviews provide an excellent sampling of the fascinating information that will be presented this October at the SciX conference. We hope to see you there!

Biomedical SERS and SESORRS

IR TheoryFluorescence-Enabled Virtual Staining

Continuous QC with NIR

Raman in Biosensing and Diagnostics

4 AUGUST 2019 | SPECTROSCOPY

ecent advances in Raman spectroscopy, specifically using surface enhanced spatially off-set resonance Raman spectros-

copy (SESORRS), which is a combination of surface enhanced Raman scattering (SERS), and spatially offset Raman spec-troscopy (SORS) are enabling noninvasive, real-time measurements of living tissue and multiple bacterial pathogens. In an interview with Karen Faulds, the 2019 re-cipient of the FACSS Charles Mann Award for Applied Raman Spectroscopy, we explore the latest developments in Raman spectroscopy for biomedical analysis ap-plications. This interview is part of a series of interviews with the winners of awards presented at the SciX conference.

You have published research using handheld surface enhanced spatially offset resonance Raman spectros-copy (SESORRS) for noninvasive measurements of a live breast cancer tumor cells (1). What prompted you to investigate this specific problem?

What are the unique aspects of your measurement approach?We are very interested in being able to carry out Raman measurements at depth with a long term view that this approach could one day be used in vivo. My close collaborator at Strathclyde, Duncan Graham, and I published the first papers on the combination of spatially offset Ra-man spectroscopy (SORS) and surface enhanced Raman scattering (SERS), or SESORS, in collaboration with Nick Stone at the University of Exeter, and Pavel Matousek from Rutherford Apple-ton Laboratory, in 2010–2011 (2,3). This demonstrated the detection of nanotags buried at depths of 25 mm in tissue. Our recent work carried out by PhD student Fay Nicolson has extended this to use the resonance effect, where we use Ra-man reporters that are in resonance with the SORS excitation wavelength of 830 nm, to further enhance the signal to give SESORRS. This allowed detection of the resonant nanotags at depths of 25 mm

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Raman Advances Using SESORRS and SERS for Biomedical MeasurementsAn Interview with Karen FauldsJerome Workman, Jr.

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Raman Advances Using SESORRS and SERS for Biomedical Measurements






using a handheld instrument (the initial work was carried out using a benchtop instrument). We also extended the work to the detection of the resonant nanotags inside a cancer tumor model buried at depth. We grew 3D multicellular tumor spheroids (MTS) from breast cancer cells; these cell models resemble the 3D in vivo environment of a tumor more closely than 2D models, establishing character-istic concentration gradients in oxygen, nutrients, and metabolites. Our resonant nanotags were taken up into the spheroid model, and we were able to detect these at depths of up to 15 mm. This is the first report of the detection of nanoparticles using SESORRS within a tumor model that has potential applications for the noninvasive detection of breast cancer us-ing targeted nanoparticles combined with optical detection.

You have also recently developed a new bionanosensor for the isolation and detection of multiple bacterial pathogens via magnetic separation and SERS for analysis of multiple types of antimicrobial-resistant pathogens (4). How did you develop this approach? What unique discov-eries did you find in developing this technique?The main aim of this approach was to develop a method for the detection of bacteria in complex samples; for example, in clinical or food samples. We wanted to develop an approach that could be used to both isolate the bacteria, allowing it to be separated from the complex matrix,

and then to detect the bacteria using the same bionanosensor. Our postdoc, Hayleigh Kearns, was able to do this by making magnetic nanoparticles for isolation and separation that we then functionalized with lectins, which would non-specifically capture multiple bacteria. We then developed SERS nanotags that had specific Raman reporters and antibod-ies that could then be used to specifically identify the bacteria strains present. This allowed multiple bacteria to be detected in one sample, using one bionanosensor and one measurement very sensitively using portable detection. We have been collaborating with Roy Goodacre of the University of Liverpool on the deconvolu-tion of multiplexed SERS signals, and this has been an important step forward allow-ing us to both multiplex and quantify the components of a multiplex.

Would you explain the use of red-shifted chalcogen pyrylium-based Raman reporters in terms of their chemical properties and how are they used?The chalcogen dyes are made by our col-laborator, Professor Mike Detty, from the University at Buffalo, New York. These are excellent molecules that bind strongly to the metal surface to give a strong SERS response. However, their main advantage is that Mike can tune the molecules to absorb at NIR wavelengths. If we can tune the absorbance of the dyes to the laser excitation wavelength, then we can get excellent resonant enhancement from these wonderful molecules. There are not







many nonfluorescent, NIR absorbing dyes commercially available that attach strongly to metal surfaces, so these molecules make excellent resonant Raman reporters that give strong SERS in the NIR biological window, which is of interest for in vivo measurements.

What instrumentation or sampling advances in did you have to develop in order to perform your research?

We have only used commercially available instrumentation for this work, although we do have a homemade SORS system. The handheld SORS instrument is commercial-ly available, and we are working with an-other company on the bacteria detection work. Fay developed a set up that allowed the SORS instrument to be mounted above a microscope stage that allowed her to carry out crude imaging using the handheld instrument. The advantages of both these instruments is their portability, which makes them very attractive for use for in situ measurements, whether in the clinic or the food manufacturing environ-ment.

From your perspective, what are the most exciting developments in Ra-man spectroscopy for biomedical analysis over the past five years (in terms of both applications and instru-mentation development)?In terms of instrumentation, the develop-ment of portable instruments is of great interest. Small, portable instruments are now available that have excellent per-

formance, and are not compromised in terms of specification compared to some benchtop instruments. These are ideal for the type of aim and point, rapid measure-ments we are interested in for some of these in situ measurements. In terms of application, there is some really exciting work going on using Raman tags, which work in the cell silent region of the spec-trum (1800–2800 cm-1) for imaging applica-tions. This area of research was first pub-lished by Katsumasa Fujita and colleagues from Osaka University who used Raman combined with alkyne molecules to shift the bands into this cell silent region. He is also doing some excellent work on increasing the speed of Raman imaging to make acquisition of spectral maps much faster. Wei Min of Columbia University is now doing some really nice work using these biorthogonal labels using stimulated Raman spectroscopy (SRS), and this is a really exciting area of research that I think will become really important in biomedical imaging in the future.

What have been your greatest chal-lenges in scientific discovery over your career? What is your general approach to problem solving in your scientific work?One of the greatest challenges in SERS is making the measurements reproducible and quantitative, as well as the complexi-ties in developing multiplexed SERS as-says and interpreting the data. We gener-ally have a good handle on this; however, there is a lot of work and problem solving that goes on by the researchers in our re-







search group to achieve this. The outside world see the final work in publications and presentations, but it often takes a lot of work to get there! It requires a lot of problem solving that involves working out what effect every component has on the final system. Multiplexed assays are very challenging to develop!

What are some major gaps in knowl-edge for Raman technology that you would like to see more research and development time devoted to?Raman technology has developed expo-nentially in the last 10–15 years, and has reduced dramatically in size and costs from when I first started doing Raman during my PhD. I think perhaps one of the areas that will be further developed over the next few years is further decreases in instrument size, and the performance of these small instruments. The cost needs to decrease before it can be used in some applications; for example, in food testing, as this can be prohibitive but I guess it is a bit of the-chicken-or-the-egg dilemma.If high demand for small portable instru-ments exists, then the cost of production of instruments should decrease. I think more development could go into the front-end sampling geometries of instruments; in portable commercial instruments, these tend to be fixed and less adaptable, and they are not always ideal for the sample, or assay the instrument is being used in terms of beam size and geometry and sample integration. Being able to integrate bespoke data analysis directly into instru-ments will also be important in the future,

particularly for handheld instruments where you may want to do testing in the field.

What do you anticipate is your next major area of research or application in your field?We are extremely interested in extending the SESORRS approach further into bio-medical applications, and moving towards using this approach to specifically detect and target disease states with a long term view to be able to do this in vivo one day.

References(1) F. Nicolson, L.E. Jamieson, S. Mabbott, K.

Plakas, N.C. Shand, M.R. Detty, D. Graham, and K. Faulds, Chem. Science 9(15), 3788–3792 (2018).

(2) N. Stone, K. Faulds, D. Graham, and P. Ma-tousek, Anal. Chem. 82(10), 3969–3973 (2010).

(3) N. Stone, M. Kerssens, G.R. Lloyd, K. Faulds, D. Graham, and P. Matousek, Chem. Science 2(4), 776–780 (2011).

(4) H. Kearns, R. Goodacre, L.E. Jamieson, D. Graham, and K. Faulds, Anal. Chem. 89(23), 12666–12673 (2017).

Karen Faulds is a Professor in the De-partment of Pure and Applied Chemistry at the University of Strathclyde, and an expert in the devel-opment of surface enhanced Raman

scattering (SERS) and other spectroscopic techniques for novel analytical detection strategies and in particular multiplexed bioanalytical applications. She has pub-







lished over 130 peer reviewed publica-tions, and has filed five patents. She has been awarded over £20M in funding as principal and co-investigator from EPSRC, BBSRC, charities, industry, and govern-mental bodies. Her group’s research has been recognized through multiple awards, including the Nexxus Young Life Scientist of the Year award (2009), Royal Society of Chemistry (RSC) Joseph Black Award (2013), Craver Award (2016), and Charles Mann Award (2019). She is a Fellow of the RSC (2012), the Society for Applied Spec-

troscopy (2017), and the Royal Society of Edinburgh (2018). She has given over 75 invited talks at national and international conferences. She is the Strathclyde Direc-tor of the Centre for Doctoral Training in Optical Medical Imaging, serves on the editorial board of RSC Advances, on the editorial advisory board for Analytical Chemistry, Analyst and Chemical Society Reviews, and is the current Chair of the UK’s Infrared and Raman Discussion Group (IRDG).







ritical analysis of the theoretical aspects of infrared (IR) spectros-copy has been the subject of research conducted by Thomas

Mayerhöfer, a scientist at the Leibniz In-stitute of Photonic Technologies, in Jena, Germany. Mayerhöfer conducted a rigorous analysis using wave optical and dispersion theory to examine Beer’s law, crystal ori-entation, absorbance band fitting, Kramers-Kronig analysis, and more. We recently interviewed Mayerhöfer about this work.

You have stated that your current work is to critically review the theoretical as-pects of IR spectra from the viewpoint of wave optics and dispersion theory, point-ing out what has been lost over the years, and adding what is still missing. What caused you to move your research in this direction? What is this important?One of my tasks for my PhD more than 20 years ago was to investigate the ori-entation of crystallites in oriented glass ceramic samples by IR spectroscopy. I recorded a multitude of spectra of

different cuts, and tried to explain my results by the so-called linear dichroism theory (LDT). An explanation based on this theory, however, was not possible, because comparisons among the spectra showed that not only intensity variations, but also band shape and position changes, occurred. In addition, I (re-)discovered that randomly oriented samples have two principal spectra, depending on the crys-tallite size relative to the resolution limit of light, something which can also not be explained by LDT. After I had acquired single crystal data, I tried to understand how all these findings could be explained, and how to do forward calculations based on these data. I came to a full understand-ing only after I had mastered and more fully comprehended wave optics; this occurred a couple of years after I had finished my PhD. Over the years I have found that, quite often in IR spectroscopy, we allow matter to have wave properties, but deny the same allowance to light. The theoretical approximation on which Beer’s law and LDT are founded works in many

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Revisiting Beer’s Law, Wave Optics, and Dispersion Theory for Infrared SpectroscopyAn Interview with Thomas MayerhöferJerome Workman, Jr.

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Revisiting Beer’s Law, Wave Optics, and Dispersion Theory for Infrared Spectroscopy






instances, but I think we must be aware of the limits of these approximations and know what to do if we go beyond the lim-its of these models.

You have published the first adapted gen-eralized dispersion analysis of a peptide crystal (1), and also room-temperature polarized IR reflectance spectra of α-POX (2). How did you select these samples for analysis? What is the significance of us-ing IR for analysis of these crystals?What I have learned is that, in general, sin-gle crystals are the most “well-behaved” samples. Even then, it is a challenge to properly analyze low symmetry crystals, that is those that are monoclinic and, in particular, triclinic. In case of the latter, we were the first to suggest a working measurement and evaluation scheme back in 2013. In the following years, we developed a generalized dispersion analy-sis that allows us to extract the dielectric tensor function, even without prior knowl-edge of the orientation and the symmetry of a crystal. In case of those two materi-als you mentioned, we were approached by colleagues to attempt analysis. For us, it is always exciting to obtain a (nearly) perfect sample, have the opportunity to be the first to analyze it, and help colleagues to understand the nature of the chemical bonds.

It may be surprising to some experienced spectroscopists that there is still much to discover and explore related to Beer’s law. You have written a recent paper demonstrating a rigorous derivation of

Beer's law from electromagnetic theory (3). In that paper, you have established the connection between wave optics and Maxwell's equations using quantita-tive ultraviolet-visible (UV-vis) and IR spectroscopy. What was the major insight gained from this work?I am a chemist by training, and I originally fully relied on the idea that all aspects of Beer’s law can be derived from Maxwell’s equations, believing that I did not have to understand these equations in order to interpret spectra. I soon discovered that Beer’s law in the form we know today (you will not find the modern form in Beer’s paper from 1852; the present form emerged around 1900, and was not well known before about 1920) must be an approximation, but as long as you work exclusively with organic or biologic materi-als, the errors are often subtle. I actually thought, until a couple of years ago, that these errors are always negligible, but when we began to actively search for and to investigate them, it became obvious that the errors often cannot be neglected.

The most prominent failure of Beer’s law is probably that it cannot predict the thickness dependence of absorbance spectra gained from thin layers on highly reflecting substrates. This problem is of great interest for the applicability of the technique; for example, in IR histopathol-ogy. With Beer’s law, it is hard to explain how sometimes increasing the thickness leads to a decrease in the absorbance. Using wave optics, you understand that this phenomenon is due to interference effects. Changing thickness will lower the







average electric field intensity in certain spectral ranges and, thereby, the absorp-tion. But it is not only that we cannot understand certain effects when we focus on Beer’s law and absorbance—we also miss opportunities. LDT, for example, predicts that we can determine the ori-entation distribution in a sample only to a very limited extent. By using a full wave optics-based approach, this limitation is in general removed.

In other papers, you explore Beer’s law again, and describe the linear depen-dence of the absorbance from concentra-tion (4,5). You have stated that chemical interactions and instrumental imperfec-tion are made responsible for experimen-tal deviations from theoretical linearity. What do you think are the major points of interest from this particular work? How would one make corrections to Beer’s law to achieve results more closely fitted to theory?What was always problematic for me as a chemist is that, chemically, very differ-ent moieties should all tend to have the same effect on absorbance as related to concentration. What we finally “discov-ered” is that the attenuation coefficient (absorptivity) is not, in general, a specific quantity, but only under certain conditions, which then, as a consequence, lead to an approximately linear dependence of absorbance based on concentration. In essence, if we have two molecules in-stead of one, absorbance may double only when the molecules are far apart, even in the absence of chemical interactions,

since the electric field changes around one molecule influence the absorption of the other molecule, and vice versa. It seems that most of the so-called chemi-cal interactions are actually caused by the wave nature of light. Therefore, if we base our interpretation on wave optics, we may be able in many instances to obtain the concentration from oscillator strength determined by dispersion analysis, instead of from absorbance.

In another paper of keen interest to infra-red spectroscopists, you presented the theoretical basis to improve the standard methods of absorbance band fitting. This you refer to as “band deconvolu-tion” (6). What was the main discovery encountered in this work? Does your recommended method improve current techniques used for band deconvolution based strictly on the Lorentz oscillator?

Here I have to admit that the goal of this paper is not to improve the method of “band deconvolution” or “band fitting.” If you compare band deconvolution and dispersion analysis, then the difference between the two is that the latter relies mostly on the Lorentz-oscillator instead of the Lorentz-profile, and is a fully wave optics–based approach. If you go back in history, band deconvolution relying on Lorentz-profiles was, as far as I know, introduced in 1938, while the first disper-sion analysis was carried out by Marianus Czerny, a former PhD student of Heinrich Rubens (“Reststrahlen”!), already in 1930, so it is essentially older! What I actually







recommend is to use dispersion analysis wherever possible, but since I remember how I struggled with it in the beginning, it might be preferable to improve band fitting gradually until it can no longer be distinguished from dispersion analysis. The above paper can be seen as a step in this direction.

What are some major gaps in knowledge about IR measurement theory that you would like to see more research and de-velopment time devoted to? What do you anticipate will be your next major area of research or application in your field?Generally, spectacular progress was made in the last decade concerning equipment for IR spectroscopy, based on quantum cascade lasers (QCLs), frequency combs, new detection schemes like photothermal infrared (PTIR), and so on, which will al-low a cornucopia of new applications. On the other hand, I feel that there are still some open questions on a fundamental level. For example, we understand how to analyze partly oriented samples if they consist of domains that are larger than the resolution limit, but not how to do the same if the samples are smaller than the resolution limit. For randomly oriented ma-terials consisting of small domains, two fundamentally different methods exist for how to predict their spectra from optical constants. Which one is “correct”? The spectra of homogenous solid mixtures of two materials are still not fully under-stood. How shall we be able to interpret spectra if we do not know how to calcu-late them from single crystal data? In the

limit of vanishing oscillator strength, most of these problems seem to go away, but, for me, it is unsatisfactory that we do not have a more precise, generalized solution. Apart from looking for ways to separate optical from chemical effects in spectra, we will, for example, also search to ex-plain recent findings in plasmon-enhanced vibrational circular dichroism. Substrates that are not supposed to work according to theory actually do work—but in surpris-ing ways.

What have been your greatest challenges in scientific discovery over your career? What is your general approach to prob-lem solving in your scientific work?

The biggest challenge for me was to search the literature and to find exactly what I needed. Quite often, I have rein-vented the wheel, and found out about this years later by accident. In this context, I would highly appreciate a prob-ably AI-based search engine that could solve this problem. Generally, I have to admit I believe that, to solve problems, you should not concentrate on them too much. Whenever I had an idea of how to solve a problem, it never came to me when I was in the lab or in the office think-ing hard about the solution—the answer came only when I relaxed and allowed my thoughts to stray.

References1. S. Höfer, A Berg, H Brückner, and T.G. Mayer-

höfer, Spectrochim. Acta, Part A 191, 283–289 (2018). https://doi.org/10.1016/j.saa.2017.10.033


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2. V. Ivanovski, T.G. Mayerhöfer, J. Stare, M.K. Gunde, and J. Grdadolnik, Spectrochim. Acta, Part A 218, 1–8 (2019).

3. T.G. Mayerhöfer and J. Popp, Spectrochim. Acta, Part A 215, 345–347 (2019).

4. T.G. Mayerhöfer and J. Popp, Chem. Phys. Chem. 20(4), 511–515 (2019).

5. T.G. Mayerhöfer, S. Höfer, and J. Popp, Phys. Chem. Chem. Phys. 21(19), 9793–9801 (2019).

6. T.G. Mayerhöfer and J. Popp, Chem. Phys. Chem. 20(1), 31–36 (2019).

Thomas Mayerhöfer obtained his diploma in chemistry in 1996 at the University of Regensburg and his PhD at the Friedrich-Schiller University in Jena, Germany, in 1999. In 2006 he finished his habilita-

tion on "Optics and IR-Spectroscopy of Polydomain Materials" in physical chemistry. Since then he has been contributing to the field of crystal optics, mainly by working on the ad-

vancement of dispersion analysis, and to the field of surface-enhanced vibrational spectroscopy. Recently, he dedicated a part of his work to promoting understand-ing of the Bouguer-Beer-Lambert law from a wave optics point of view.







xtensive research is being con-ducted into applying various spectroscopic techniques to many areas of medical diagnostics, such

as the identification of infectious pathogens and defining the edges of tumors during surgery. Until now, none of these advances has truly addressed an important challenge in medical pathology: the manual process of staining tissue samples for analysis—a process that is labor-intensive, and highly dependent on the expertise of the personnel involved. Aydogan Ozcan and his team at the Bio- and Nano-Photonics Laboratory at the University of California Los Angeles (UCLA), have now developed a method for virtually staining tissue samples, using deep learning and fluorescence microscopy. The technique also converts low-resolution fluorescence microscopy images to high-resolution imag-es, making the approach accessible to those without high-end instrumentation. Ozcan recently spoke to us about this work.

You have developed a new approach for clinical tissue imaging that involves virtual

histological staining using autofluorescence of unlabeled tissue (1). First, why did you pursue this project?Histopathology, which dates back to the 19th century, has been one of the gold-standard diagnostic methods used in pathology. If a biopsy is needed following a medical examination, or during a surgical operation, a tissue section is taken from the patient, which is then sectioned into micrometer-thin layers. These sections contain microscopic information regarding the pathological state of the tissue, but, unfortunately, such thin tissue sections are transparent, and do not present suf-ficient contrast under a standard light mi-croscope. Histochemistry uses the cellular and subcellular chemical environment of the specimen to attach chromophores to specific tissue constituents, creating color contrast under a visible light microscope that forms the basis for expert diagnosti-cians and pathologists to diagnose abnor-malities in patient specimens.

However, this process of staining tis-sue samples in histopathology is as time

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Advancing Biomedical Tissue Analysis with Fluorescence-Enabled Virtual Staining






consuming as it is labor intensive, and re-quires a dedicated laboratory setting, with chemical reagents and trained personnel, such as histotechnologists. Staining vari-ability among pathology labs and histo-technologists may lead to misdiagnoses, and creates quality control challenges. Furthermore, currently used staining methods do not preserve tissue samples, which is a limitation, since advanced mo-lecular analysis of the same tissue sample cannot be easily performed after the initial staining process.

Recognizing these bottlenecks, our research team focused on creating a machine learning framework to perform virtual staining of label-free tissue.

What is the role of deep learning in your approach? How do you define a deep learning network?

At first, we wanted to find a robust and simple way to introduce contrast in the microscopic images of label-free tissue sections. To do so, we chose to use tissue autofluorescence, which results from en-dogenous fluorophores, naturally embed-ded within the specimen. We decided to use the near-ultraviolet (UV) fluorescence excitation band, since it can be used to effectively excite various tissue constitu-ents, and can be easily acquired using any standard fluorescence microscope. Fol-lowing a multistage deep neural network training process based on the concept of generative adversarial networks, we developed a deep learning-based method to take a microscopic image of the natu-rally present fluorescent compounds in unstained, label-free tissue sections, and transform this autofluorescence image into a bright-field microscope equivalent

Figure 1: The steps in virtually histology, from biopsy to virtually stained image, enabled by deep learning. The virtual staining process replaces the manual and laborious processing and staining steps that are normally performed by medical personnel.

Figure 2: Virtually stained and chemically stained images of tissue samples from H&E stained salivary gland tissue, Jones Silver stained kidney tissue, Masson’s trichrome stained lung tissue, and Masson’s trichrome stained liver tissue (from left to right).







image of the same sample, as if it had been taken after the standard tissue stain-ing process. Stated differently, we used deep learning to virtually stain label-free tissue samples, replacing the manual and laborious processing and staining steps that are normally performed by medical personnel, saving labor, cost, and time by substituting most of the tasks performed a histotechnologist with a trained neural network (Figure 1).

The success of this deep-learning–powered virtual staining method was demonstrated for multiple tissue types (kidney, lung, liver, ovary, salivary gland, and thyroid) and three different stains, hematoxylin and eosin (H&E), Masson’s trichrome, and Jones’ silver stain (some examples are shown in Figure 2). The ef-ficacy of our virtual staining results was independently evaluated by a panel of board-certified pathologists who were blinded to the origin of the examined im-ages, such that the pathologists did not know which images were actually stained by expert technicians, and which im-ages were virtually stained by our neural network. The conclusion of this blinded study revealed no clinically significant difference in the staining quality and the medical diagnoses resulting from the two sets of images.

What challenges does this approach overcome?When preparing the image data for this blind comparison, we also realized that another important advantage of our virtual staining method is the standardization of

the staining process, because a trained neural network also eliminates the stain-ing variability that is frequently observed among technicians and histopathology lab-oratories, which can cause misdiagnosis or misclassification of tissue specimens.

This virtual staining process powered by deep learning will significantly reduce cost and sample preparation time, while also saving expert labor. Since it only requires a standard fluorescence microscope and a simple computer (such as a laptop), it will be especially transformative for pathology needs in resource-limited settings and in developing countries.

Are the advancements related to super-resolution microscopy methods (2) mostly hardware or software? What is a generative adversarial network (GAN), and how does one train a GAN to transform diffraction-limited input images into super-resolved images?Following a similar route as in tissue stain-ing, we have created a new technique that uses deep learning to transform lower-reso-lution fluorescence microscopy images into super resolution. The framework takes imag-es from a simple, inexpensive microscope, and produces images that mimic those from more advanced and expensive ones.

Similar to the virtual staining case, to learn from those images, the system uses a generative adversarial network, a model for artificial intelligence in which two algorithms compete. One algorithm tries to create computer-generated super-resolution images from a low-resolution input image, while the second algorithm tries to differentiate between those







computer-generated images and existing super-resolution images that are obtained from advanced microscopes.

That training needs to be done only once for each type of subject the system needs to learn. After that, the network can improve a low-resolution image it has never “seen” before to match the image resolution from a super-resolution micro-scope, which eliminates the need for an expensive, high-resolution microscope.

Through this project, we successfully en-hanced the resolution, contrast, and depth of field of original images, which were of cell and tissue samples.

You applied this deep-learning frame-work to images acquired by different imaging modalities, including wide-field fluorescence, confocal, and total internal reflection fluorescence (TIRF) micro-scopes. What adaptations did you have to make for each of these modalities?We pretty much followed a similar general workflow of image registration, training, validation, and blind testing in each case, as illustrated in Figure 1.

How difficult would it be for others to ap-ply this framework? Have other research-ers approached you for help since your publications came out?Yes, many. In fact, we have made our net-work models publicly available, and they can be found through the Nature Methods and Nature Biomedical Engineering websites.

What are your next steps in this work?Looking forward, we envision that this

AI-based virtual staining technology, after going through further development and validation with large-scale clinical studies, will create a paradigm shift in the field of histopathology. In addition to replacing the standard workflow in a histopathology lab with a much simpler, faster, and more cost-effective alternative, it will also lead to new capabilities that are nearly impos-sible to achieve with today’s standard methods, such as the simultaneous virtual staining of the same sectioned tissue with multiple types of stains, while also pre-serving the unstained sample for further molecular analysis, if needed. In addition to these very exciting opportunities en-abled by our method, deep-learning–based virtual staining also eliminates the need for a well-trained histotechnologist, help-ing us with another bottleneck for running pathology services in resource-limited countries since the availability of expert medical personnel is often restricted. This AI-based virtual staining technology can also be used in surgery rooms to rapidly assess tumor margins, providing highly needed and critical guidance for surgeons during an operation. All of these are excit-ing directions that we are exploring.

References(1) Y. Rivenson, H. Wang, Z. Wei, K. de Haan, Y.

Zhang, Y. Wu, H. Günaydın, J.E. Zuckerman, T. Chong, A.E. Sisk, L.M. Westbrook, W.D. Wallace, and A. Ozcan, Nat. Biomed. Eng. 3, 466–477 (2019). https://doi.org/10.1038/s41551-019-0362-y

(2) H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydın, L.A. Bentolila, C. Kural, and A. Ozcan, Nat. Methods 16, 103–110 (2019). https://doi.org/10.1038/s41592-018-0239-0







Aydogan Ozcan, PhD, is the Chancellor’s Pro-fessor at UCLA and an HHMI Professor with the Howard Hughes Medical Institute, leading the Bio- and Nano-Photonics Labo-ratory at the University

of California Los Angeles (UCLA) School of Engineering. He is also the Associate Director of the California NanoSystems Institute. Ozcan is an elected Fellow of the National Academy of Inventors (NAI), and holds 41 issued patents and more than 20 pending patent applications. He is the au-thor of one book and the co-author of over 700 peer-reviewed publications. Dr. Ozcan is the founder and a member of the Board of Directors of Lucendi Inc., as well as Holomic/Cellmic LLC, which was named a Technology Pioneer by the World Economic Forum in 2015. Dr. Ozcan is also a Fellow of the American Association for the Ad-vancement of Science (AAAS), the Interna-tional Photonics Society (SPIE), the Optical

Society of America (OSA), the American Institute for Medical and Biological Engi-neering (AIMBE), the Institute of Electri-cal and Electronics Engineers (IEEE), the Royal Society of Chemistry (RSC), and the Guggenheim Foundation. He has received major awards, including the Presidential Early Career Award for Scientists and En-gineers, the International Commission for Optics Prize, the Biophotonics Technology Innovator Award, the Rahmi M. Koc Sci-ence Medal, the International Photonics Society Early Career Achievement Award, the Army Young Investigator Award, an NSF CAREER Award, an NIH Director’s New In-novator Award, the Navy Young Investigator Award, the IEEE Photonics Society Young Investigator Award and Distinguished Lec-turer Award, National Geographic Emerg-ing Explorer Award, the National Academy of Engineering The Grainger Foundation Frontiers of Engineering Award, and MIT’s TR35 Award for his seminal contributions to computational imaging, sensing and diagnostics.







mplementing process analytical tech-nology (PAT) became a goal in the phar-maceutical industry 15 years ago, with the publication of a guidance document

from the U.S. Food and Drug Administration (1). Recently, the industry’s drive to use con-tinuous manufacturing—instead of batch pro-cessing—has given new impetus to PAT ap-plications. Steve Hammond, a PAT consultant who recently retired from Pfizer, has spent four decades focused on process-based analytics, using techniques like near-infrared (NIR) spectroscopy. He recently spoke to us about this work, including the current status of PAT implementation in the pharmaceutical industry and the challenges involved, illustrat-ing his points with examples from a recent implementation at Pfizer.

The FDA released its guidance for indus-try on process analytical technology (PAT) 15 years ago. How would you describe the current state of implementation of PAT at this point?I was a founding member of the FDA team that wrote that FDA Guidance. PAT

is now established, and many applications have been registered. In recent years, PAT applications have received a boost, from the development of continuous process-ing for solid dosage forms. New spec-trometer types, with better performance and robustness, have helped with the deployment of PAT. Much better software platforms have also increased the prob-ability of success for PAT applications.

There is a drive in the pharmaceutical industry toward continuous manufactur-ing. How does the shift toward continu-ous manufacturing change the way one implements PAT? What new challenges does it present?In the continuous manufacturing scenario, there is a need to continuously monitor the performance of each step in the man-ufacturing stream, to ensure equipment is performing within expected tolerances. This is particularly important for the feeder mixer units. See Figure 1.

This has led to development of better PAT interfaces that ensure PAT probes

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perform in an optimum way in a dynamic environment. That means probes are collecting good quality spectra, and that fresh material is presented to the probe window to ensure good discrete sampling of the process step. It is one of the obvi-ous developments with continuous pro-cessing that the engineering of sensors into the process has been given particular attention and investment. See Figures 2–5.

Another important point is the recogni-tion of the need for software platforms that can simultaneously take sensor data from multiple sources, and turn data into information that a control system can use.

In a continuous manufacturing system, PAT and the data handling software platform have to be engineered into the process and be a part of the process. Validation of a continuous manufacturing system is focused on the control system,

which, in turn, is based on timely process measurements. Those measurements can be made with PAT sensors, other sensors like load cells used in mass balance mod-els, or, hybrid models, with a combination of equipment sensors and PAT devices.

Why is NIR, particularly in diffuse reflec-tance mode using fiber optic probes, a key analytical method for PAT in continu-ous pharmaceutical manufacturing—compared to other techniques, such as Raman or infrared spectroscopy?When using NIR in diffuse reflectance mode, it is easy to fashion fiber optic probes that can be engineered into the processing equipment used for continu-ous processing.

The wavelengths of NIR radiation are easily transported through fiber optics ca-bles, and sensitive detectors are available for NIR wavelengths. The availability of these sensitive detectors is due, in part, to the telecommunication industry, which uses the NIR region for telephone signals. For example, the Viavi spectrometers are a spinout of JDSU, a telecom company. The JDSU spectrometers were originally designed to test telephone signals trans-ported through fiber optic cables.

Raman instruments have slower scan-ning speeds and a lower signal-to-noise (S/N) ratio than NIR. Raman spectroscopy tends to be more useful for liquid media. Fiber optics for Raman spectroscopy are good, as Raman tends to use the 1–2 μm wavelength light that NIR also does.

Fiber optics for mid-IR exist, but are low capability and very expensive compared to

Figure 1: Placement of process analytical technologies in a continuous manufacturing rig to confirm conformance of the output of each unit operation.







those used with NIR. Mid-IR fibers tend to be only a few meters in length, which reduces their viability for use in a manu-facturing plant, although applications in reactor monitoring are very successful.

Modern diode array NIR spectrometers are capable of very fast data acquisition, while maintaining good selectivity and accuracy. For example, the Sentronic NIR probe system can scan at a rate of one acquisition scan in 20 ms, so several acquisitions (scans) can be integrated over 0.5–1.0 s to obtain high quality, low noise spectra. Mid-IR and Raman are not capable of this fast data acquisition at the same S/N.

Mid-IR is very difficult to interface with powders, because the intensity of mid-IR bands is about 100 times greater than in NIR. This means the mid-IR light intensity

returning from the surface of a sample to a detector is very low, as most of the mid-IR radiation is absorbed by the sample ma-terial. The only solution to this with solid samples is to dilute them, traditionally in sodium chloride or Nujol. That is totally impractical in a processing environment. Mid-IR works well in a liquid environment where ATR crystals enable undiluted samples to be scanned effectively.

You have worked on deploying PAT at five different steps in the manufacture of pharmaceutical tablets, where four of the steps used NIR (Figure 1). What types of challenges were presented by those different steps? Why was NIR not the technique chosen for the other step?The one application that did not use NIR used a focused beam reflectance mea-surement (FBRM), which is a probe that can measure the physical size of particles. This is not a chemical composition mea-surement, which is what NIR is generally used for. FBRM can directly estimate the particle size distribution of powders. The direct FBRM measurements can be used to calibrate an indirect NIR method for particle size. See Figure 4.

The four NIR applications had challenges related to sampling. The mixer we used had high throughput and little retained mass, so material passed through the mix-er in seconds. That meant the measure-ment systems had to be fast to monitor a fast-throughput process. A measurement that took less than 1 s was imperative to provide timely data on the function of the mixer and the feeders placing ingredients

Figure 2: Spider wheel interface for medium to high flow rate powder streams. The spider wheel catches the process powder or granules over the probe window, long enough for data acquisition, and then moves the material on, ensuring fresh sample covers the window.







into the mixer.Wet powders exiting a granulator are

sticky. So development of ways of making sure the sticky material did not blind a probe window, and good sampling could be achieved, were the secrets of success of all the NIR applications. In addition, the process engineers warned the PAT people of dire consequences if the probe inter-faces blocked the flow of material in the processing equipment.

The devices in Figures 2–5 illustrate how these challenges were met. Devices were installed that captured the powder

to the correct mass and density in front of a probe to ensure good quality spectra, and wiped the window, but also ensured material moved on down chutes.

Powder exiting a dryer or a mill tends to be in the form of a dispersed cloud. The device shown in Figure 3 answered this need. The spoon device captures the powder into good mass and density to be scanned in an optimum way for the NIR probe.

Developing systems to optimize the spectroscopy of the tablet feed frame, where product release decisions are made, was critical. Figuring out how to engineer a probe into the tablet feed system, and not have the moving parts interfere with the spectroscopy, required some engineering modifications to the tablet press feeder system. Historically, it has been very difficult to have engineers agree to modifications to equipment to accommodate sensors. Continuous manu-facturing changed all that, because the equipment could not be operated without continuous monitoring of performance. The results of our designs are shown in Figures 2–5.

How do you ensure that your NIR probes are collecting spectra on representative samples? The FDA has indicated that, for blend uniformity analysis, the effective sample size should be comparable to a unit dose. How did you ensure and docu-ment that?The solution was to engineer devices that could ensure unit dose sampling. The combination of mechanical movement of

Figure 3: The dispersed powder collection spoon and integral window wiper.







powder married with a fast scanning spec-trometer can achieve unit dose sampling.

Experiments showed a typical fiber optic probe scanning a typical pharma-ceutical powder interrogates about 3 mg of powder each time it scans. In other words, only 3 mg of powder contributes to a single scan. Typical tablet weights are 100–300 mg, so to obtain a tablet-weight spectrum, the fiber optic probe is set to scan 50 times, and the 50 scans are integrated to produce the “spectrum” of a virtual 150 mg tablet that is used in the calculation of a concentration result. See Figure 7.

How do you minimize interferences like specular reflectance and scattering when making measurements?The spider wheel fingers were modified to avoid the spectrometer scanning bare metal. Either 2-mm deep notches were cut in the fingers, or the back of the fin-gers was coated with white PTFE, similar to the reference material. See Figures 2 and 6.

What was your process or protocol for developing your chemometric models for this work? What do you think is the FDA’s approach toward chemometric methods, and what could be done to improve pro-cedures using chemometrics?Pfizer does have standard protocols for development of models and their valida-tion. These are based on the many text-books and publications on this subject. One important point about calibration development using a continuous manufac-turing rig is that the loss-in-weight feeders can be programmed to transition across the target concentration, from minus 10% to plus 10% of the nominal, thus providing calibration samples that can be sampled and analyzed using conventional methods.

When applying data preprocessing, SIMCA, MLR, PCA, and PLS models, is there a recommended document or set of documents that gives guidance on pre-cisely how those models are developed, validated, and maintained? Does the FDA give guidance on this process?

Figure 4: Action of the spinning disc focused beam reflectance measurement (FBRM) interface.

Figure 5: The placement of an NIR probe into a tablet press feed frame.







Again, Pfizer does have standard protocols for development of models and their vali-dation. These are based on the many text books and publications on this subject.

The FDA will not provide guidance on these processes, mainly because there are so many possible ways to do them that are all “right.” As computer systems develop, more possibilities for producing models become available. The develop-ment of artificial intelligence systems will almost certainly produce the calibration algorithms of the future—artificial neural networks (ANNs), for example.

FDA will expect the company to submit the protocols used, and a detailed expla-nation of what was done, as well as a scientific explanation that validates that the system works.

For new algorithms, or data treatments,

the FDA has set up a team, called the “Emerging Technologies Team” to exam-ine these new systems and provide feed-back in a neutral environment. That team can be approached to examine new tech-nologies, and will provide feedback, but that feedback does not in any way give official FDA approval to any technology.

References(1) US Food and Drug Administration, Guidance

for Industry: PAT — A Framework for Innova-tive Pharmaceutical Development, Manufactur-ing, and Quality Assurance (FDA, Rockville, MD, 2004). https://www.fda.gov/regulatory-informa-tion/search-fda-guidance-documents/pat-frame-work-innovative-pharmaceutical-development-manufacturing-and-quality-assurance

Steve Hammond is an independent consul-tant, working in the area of process-based analytics. His work includes the develop-ment of PAT interfaces using systems from ExpoPharma (www.expopharma.ie). In May

Figure 6: The notched “spider wheel” design of the powder interface. Notches cut into the top of the wheel fingers avoid interference of the wheel fingers in the collected spectra. The arrow shows the path of the scan point as the paddle and blend circulate in the feed frame. The design of the spider wheel is press and spectrometer dependent.

Figure 7: Typical tablet weights are 100–300 mg, so to obtain a tablet weight spectrum, the fiber optic probe is set to scan 50 times and the 50 scans are inte-grated to produce the “spectrum” of a virtual 150 mg tablet, that is used in the calculation of a con-centration result.







2018, he retired from Pfizer, where he spent 39 years as the senior director or team leader of the Process Analyti-cal Sciences Group, in Peapack, New Jersey. During his career with Pfizer, he authored or

co-authored many scientific publications on the pharmaceutical applications of process analytical technologies, and contributed many oral presentations on this subject as an in-

vited speaker. In his most recent work there, Hammond took responsibility for develop-ing a suite of process analytical technology sensors and their interfaces for the monitor-ing and control of continuous manufactur-ing streams, for both solid dose forms and continuous flow reactors. Hammond is a graduate of the Royal Institute of Chemistry in London, and has a master’s degree in ana-lytical chemistry from the University of Kent, UK. He was appointed to the Royal Society on Chemistry in 1981, and held the status of Chartered Chemist in the UK.







odern Raman spectroscopy is at the center of investiga-tions into nanotechnology characterization tools for

biosensing and medical diagnosis. Ra-man studies have included insights and discovery into aging-associated diseases, cancer diagnosis, pathogen detection, and environmental contaminants at nanomolar concentrations. Raman spectroscopy is being used for characterization of athero-sclerotic plaque, antibiotic susceptibility testing, high-throughput screening for label-free cellomics, and real-time histology of disease activity by non-linear multimodal imaging. The applications push for Raman has led to both hardware and software ad-vances, including new fiber probe designs and novel data processing techniques for improved signal discrimination and imag-ing. This interview with Juergen Popp of the Leibniz Institute of Photonic Technol-ogy, in Jena, Germany, explores the latest developments in Raman spectroscopy for medicine and other research applications.

You have written a book chapter covering the use of surface enhanced Raman spectroscopy (SERS) for medical diagnostics application (1). Your summary discusses aging-associated diseases, cancer diagnostics, pathogen detection, and other applications. What are the differences in the SERS technique for these varied applications? Which medical diagnostic application do you feel will have the greatest potential impact in medicine?In general, various SERS modalities are available for medical diagnostic applications, such as label-free SERS, SERS-based im-munoassays, and SERS molecular sensors. The desired application scenario defines the SERS detection scheme. For example, in the case of the detection of a low-molecular weight substance, such as drugs or metabo-lites, with high affinity toward the metallic surface used to enhance the weak Raman signal, label-free SERS approaches are pre-ferred. Thus, elaborate sample clean-up pro-tocols are not needed, and the analysis time and costs can be considerably reduced. An application scenario might be the detection

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Modern Raman Spectroscopy at the Center of Biosensing and Medical DiagnosisAn Interview with Juergen PoppJerome Workman, Jr.

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of antibiotics in human urine, or the detec-tion of metabolites of pathogens in saliva and sputum.

In the field of the detection of pathogens, viruses, or tumor margins in tissue, immu-noassays might be applied. Here, SERS tags are used. The tags are modified with a Ra-man reporter, showing a strong and stable SERS spectrum as well as recognition elements such as antibodies or short DNA sequences. Thus, unlike in fluorescence-based immunoassays, no bleaching occurs. Moreover, multiplexing with a high number of different labels is possible due to the sharp and narrow Raman modes of various Raman reporter molecules. Hence, the technique takes advantage from the strong antigen–antibody binding reaction and a fast and sensitive SERS read-out. Finally, SERS-based molecular sensors have gained interest in the last few years. They are used to indirectly detect small molecules such as CO, H2O2, or glucose, which have a weak or undetectable SERS signal. Here, the plasmonic active material is equipped with a specific molecule undergoing a chemical reaction with the target analyte. Thus, the detected SERS signal is related with the molecular changes of the sensor molecule or its changed orientation toward the metal-lic surface upon the interaction with the target analyte.

Within the last decade, SERS research has shown that the application of SERS tags in immunoassays shows high potential for the in-vivo identification of tumor margins. It is expected that these detection schemes will come to real applications in the near future. However, to validate this technique,

a much larger number of clinical samples and patients need to be investigated. Beside label-based SERS approaches, the direct detection of metabolites and drugs in body fluids via SERS is also promising. For example, for the therapeutic drug monitor-ing of pharmacological substances with a narrow therapeutic range, high performance liquid chromatography with mass spectrom-etry detection (HPLC–MS) is still the gold standard. Here, by carefully designing a label-free SERS approach, a faster and cost-effective analytical tool could be developed for a bedside determination of the drug concentration.

Lab-on-a-chip-surface enhanced Raman scattering (LoC-SERS) has been developed for the identification of various drugs in bio-logical fluids, such as nitroxoline in urine (2). This method has also been applied to sulfamethoxazole as an environmental pol-lutant in drinking and surface water sourc-es (3). Are the limits of detection (LOD), the limits of quantification (LOQ), and the linear dynamic range for these SERS methods compatible with clinical and environmental requirements?In the case of the detection of nitroxoline in human urine, the LOD is ∼3 μM and the LOQ is ∼6.5 μM, while the linear range is between 4.28 and 42.8 μM. This covers relevant treatment concentrations, as the minimum inhibitory concentration values of the most commonly encountered uro-pathogens, such as various E. coli strains, K. pneumoniae, and for Proteus mirabilis and S. saprophyticus, are currently below 42 μM. Therefore, the LoC-SERS-based







detection platform is in agreement with the clinical requirements.

For the detection of sulfamethoxazole in water samples, a LOD under lab conditions of 2.2·10-10 M and of 2.2·10-9 M in tap water has been demonstrated. For a matrix of tap, lake, and river water, the blank sample and the spiked sulfamethoxazole concentration were differentiated by 2.2·10-9 M, which is below the legal required sensitivity of 2·10-7 M. For environmental samples, the limit value monitoring is of high importance and thus, the applied detection scheme needs to show sufficient sensitivity and no large linear dynamic range is required.

Multimodal visualization studies of silicon nanoparticle uptake in breast cancer cells have been completed using Raman spectroscopy (4). How could this work lead to customized diagnostics and treatment (theranostic) application and what are the current limitations?The main goal of this work was to prove that Raman spectroscopy can be utilized to monitor the uptake of nanoparticles in cells. Here, Raman spectroscopy has been combined with structured illumina-tion microscopy (SIM) to monitor possible morphological alterations of cells due to an interaction of the cells with the nanopar-ticles. Currently, we are trying, together with one of the pioneers of SIM, Prof. Dr. Rainer Heintzmann, to combine SIM with Raman spectroscopy; that is, to transfer the concept of SIM to Raman microscopy. This is conceptually feasible, due to the incoherence of the Raman scattering process. This would allow to obtain chemi-

cal Raman information with high spatial resolution.

The targeted treatment of diseases via the application of specially tailored nanoparticles or nanocarriers loaded with drugs for a selective drug release at the point of ac-tion is highly promising. To characterize the interaction mechanism of nanoparticles and active substances with cells and tissue, and to monitor their uptake kinetics, aggregation behavior, and so on within cells, appropriate molecular selective monitoring approaches are required. That is exactly what we are working on—utilizing linear and non-linear Raman approaches to study nanoparticular systems in cells and tissue (5,6). We are not involved in synthesizing such systems. How-ever, our work is embedded in a collabora-tive research center here in Jena called Poly-Target (https://www.polytarget.uni-jena.de/en/), where a large, highly interdisciplinary consortium of scientists (chemists, pharma-cologists, physicians, physicists, and so on researches polymer-based, nanoparticulate carrier materials for the targeted application of active pharmaceutical ingredients. One of the big obstacles to overcome here is to get medical approval once promising nanopar-ticular system have been identified.

In other publications, you describe the development and optimization of shifted-excitation Raman difference spectroscopy (SERDS) for isolating the pure Raman spectra from heavily fluorescence-inter-fered raw spectra (7,8,9). When optimized, this technique uses specified multiple preprocessing steps, such as 1) z-score normalization, 2) zero-centering of the dif-







ference spectra, 3) a piecewise baseline correction of the pure Raman spectra, and 4) analysis of the influence of the shift of the excitation wavelength on the quality of the reconstructed spectra. How did you derive this set of techniques for the optimized Raman spectra?In our research, we try to address different aspects, which include device development and translation to analytical and clinical ap-plications, but also focusing heavily on data processing and analysis. In recent years, we have been continuously improving compu-tational approaches for preprocessing and analyzing of Raman data, and the above-mentioned derived techniques are the result of those research efforts. We combine our analytical know-how with our strong background in application development to address basic questions in our field. For ex-ample, when looking at the definition of the best shift for SERDS, the common wisdom throughout literature is that the shift should be quite small, typically less than one nanometer. It is not particularly mentioned why, though one can easily imagine a few reasonable scenarios why that could be important. For a very simple sample with nonoverlapping Raman bands and similar bandwidths at an excitation wavelength of 785 nm, a shift by one nanometer would correspond to approximately 17 cm-1. It ap-pears sound to use the typical shift in this situation, because the typical bandwidths are on the same order. Looking at real world scenarios, the truth is quite different. Here, we rarely have individual nonoverlapping Raman bands, and using small shifts will potentially reduce the remaining signals.

We have explicitly performed some tests for common biomolecules that can be found in tissue sample, and realized that small shifts, as described in literature, are actually not all that good. That is just one example, but we raise and answer similar questions in other situations as well.

Infrared spectra have been applied to de-velop surface-enhanced infrared absorption (SEIRA) combined with vibrational circular dichroism (VCD), sensing in the spectral range ∼1000–4000 cm–1 (SEIRA) and ∼2000–4000 cm–1 (VCD) (10). What is the advantage of combining these techniques? Why do you feel that this has great potential for use as a sensor in the analysis of biologic and pharmaceutic substances?Attenuation coefficients of vibrational bands are usually much weaker than those of the bands in the UV-Vis range. In addition, VCD bands are about four to five orders of mag-nitude weaker than vibrational bands. This is why it is not possible to just equip a conven-tional spectrometer with a circular polarizer to measure VCD. Instead, we need special equipment, which is comparably expensive. Furthermore, the use of neat substance is of advantage, which means that a lot of substance is needed. As in surface enhanced IR-spectroscopy, our dream is to enhance the signals to an extent where small amounts of analytes, in this case enantiomers, are sufficient to provide enough signal to be able to identify and distinguish them. In addition, it might be even possible to replace the VCD spectrometers by conventional ones with the help of SEIRA, which would enable the routine use of this method.







How could Raman spectroscopy be used for fast antibiotic susceptibility testing and determination of the minimal inhibitory concentration (MIC) for an antibiotic (11–14)? Do you think this is a viable clinical diagnostic tool?Raman spectroscopy probes specifically—without the need of any label—the chemical composition of complex bio-samples, such as bacterial pathogens. During the action of the antibiotics, the chemical composition of the bacteria is altered, depending on the mode of action of the drug; for example, some drugs interfere with the cell wall syn-thesis (such as vancomycin, [11,12]), while others interfere with structure and function of the DNA (such as ciprofloxacin, [13,14]). These changes on the molecular level can be detected as changes in the Raman spectra recorded from the bacteria, just by illuminating the bacteria for a few seconds with laser light and analyzing the inelastically scattered light. If the bacteria are resistant to the specific drug, they are not affected in the same way, thus also the specific spec-tral changes cannot be detected, but those bacteria give Raman spectra that are rather similar to untreated controls (or show other spectral changes that are very specific for the resistance mechanisms). To determine the minimal inhibitory concentration (MIC), the bacteria are confronted with different concentrations of the drug. The lowest concentration of the antibiotic that results in the spectral changes in the bacterial cells that are specific for the mode of action of this drug is directly related to the MIC. With classical microbiological methods this assay takes ~24 h (broth microdilution assay) or at

least 8 h (automated systems, such as Vi-tek2), while the Raman spectroscopic assay needs less than 3.5 h.

Because of the aforementioned major ad-vantages of the Raman-based method, we believe that it can become a viable diagnostic tool for three main reasons:

• The Raman method is fast and can provide the result within 3.5 h from taking the sample to the result. Further-more, the method can be applied directly on the patient’s sample, such as urine samples, making time-consuming cultivation steps unnecessary.

• The method determines antibiotic susceptibility and can also be used with the same method setup to identify pathogens. In contrast to the Raman technique, the current clinical routine uses several devices, starting with a machine for plating and culturing followed by MALDI-TOF for identification and finally Vitek2 or related systems for antimicrobial susceptibility testing (AST). With the new Raman method accomplishing these tests, clinical analysis can be hugely simplified.

• The Raman-based method utilizes light–matter interactions. Thus, it can be done cheaply, reducing costs for the health care system.

For this work utilizing Raman spectroscopy for fast antibiotic susceptibility testing, my colleague Prof. Dr. Ute Neugebauer and I were recently awarded the third prize of the “Berthold Leibinger Innovationspreis (2018)” (https://www.leibinger-stiftung.de/de/innova-tionspreis-zukunftspreis/preistraeger-preis-verleihung-2018/).







In one breakthrough paper, you described using a high-throughput screening Raman spectroscopy (HTS-RS) platform for a rapid and label-free macromolecular finger-printing of tens of thousands of eukaryotic cells (15). How do you think this develop-ment will lead to a significant expansion of Raman spectroscopy in biomedical and clinical cell research and diagnostics?Biomedical Raman spectroscopy has seen prodigious developments, both on the tech-nological and the application side, signify-ing the extensive opportunities this method has to offer. Nevertheless, the direct transformation to translational applications has been more cumbersome, especially for the investigation of eukaryotic and pro-karyotic cells. To address current obstacles, my team, led by Dr. Iwan Schie, has spent significant efforts to identify the key barri-ers that hinder Raman spectroscopy from becoming a common tool for the label-free molecular analysis of cells. The three most decisive aspects that hamper this develop-ment are: human dependency during data acquisition; commonly acquired data sizes (the number of sampled cells and batches); and experimental design (the common ex-perimental settings in Raman spectroscopy do not resemble real-world scenarios). One quickly realizes that the aforementioned points are heavily interconnected and inter-dependent. Raman spectroscopy has been hailed for not requiring any sample prepara-tion steps, which makes it quite attractive in comparison to current modalities. How-ever, current Raman-based implementa-tions have tediously relied on manpower by trained scientists to perform experiments,

making the method less attractive to biolo-gists or clinical researchers. We success-fully addressed this point, and were able to develop a dedicated device that almost completely removes our dependence on humans for data acquisition. For example, with our current approach, the user has only to supply the samples and initiate the measurements; the rest is done au-tomatically. We combined this with some additional technical modifications that further helped to increase the throughput. The two aspects, combined, directly ad-dress the second point of data size. In the abovementioned publication (15), we were able to measure more than 100,000 cells with spontaneous Raman spectroscopy in a reasonable time frame. Here, one has to keep in mind that the current standard in the Raman community is only around 100 cells. In a recently accepted publication we have pushed the hands-off approach even further, and were able to perform an entire experimental series to characterize phar-macological effects on cells. Experiments that have taken weeks in the past can now be performed in a matter of minutes. Obviously, we do not want to stop here, but develop the instrumentation to a such a degree that users only supply samples, and after a few minutes, readily receive their analytical results in a clear and compre-hensive way. In this context, I would like to emphasize that Raman spectroscopy ac-cesses the intrinsic molecular composition of cells and changes thereof, which means that, when done correctly, a single device can address a variety of applications without any particular sample preparation steps, can







use a single device to provide answers to a variety of questions, and become a real analytical workhorse in cell biology.

What are some of the unique hardware or instrumentation developments that you have been involved with? Was your fiber probe design a significant improvement over existing probes?Besides the development of the aforemen-tioned high-throughput screening Raman spectroscopy (HTS-RS) platform (15), we have also been focusing on improving fiber-probe-based applications for in vivo Raman diagnostics. Here, we focused on two separate aspects: in vivo Raman fiber-probe and device developments, and implementa-tion of a Raman fiber-probe–based imaging scheme in combination with augmented and mixed chemical reality. Raman-based applications for in vivo diagnostics have tremendous potential to complement, and even to substitute, traditional histopatho-logical analysis of malignant tissue. The advantage of optical methods, especially Raman spectroscopy, is that information on the presence and grade of a tumor can be established in vivo in real-time without the need to extract a biopsy, which has potential harmful effects on the patient. There are a few aspects that have to be considered in terms of device development, not only from the reproducibility side, but also from the regulatory side. There are currently no specific commercial suppliers that can pro-vide designated endoscopic Raman probes for our applications, which is why we have established a fiber-optic probe development facility at the Leibniz Institute of Photonic

Technology. This allows us to develop and to tailor endoscopic Raman probes for clini-cal applications. There are certain aspects that we have tried to address in the design of those probes. Specifically, the probes should be plug-and-play, mechanically robust, and sterilizable. Furthermore, they should be self-aligning when switching between individual probes. Typical Raman probes found in the literature are designed in such a way that the excitation and collec-tion paths are connected separately to the backend unit, where the collection line has to be correctly aligned. This works all right in the lab, but in a clinical environment, where every minute counts and the highest qual-ity of data is key, there is no time for such alignment procedures that can significantly affect the reproducibility and correlation of the data from different probes. Furthermore, the separation of the excitation and collec-tion fibers creates a nightmare for steriliza-tion in a clinical setting, because the clinical personal performs their standard approach for cleaning the device, and one point is certain—they are not always gentle. My team has designed and developed a probe that addresses all the above mentioned points, and the result is an endoscopic Ra-man probe that is in a mechanically robust single unit that can simply be plugged to the backend device, is always aligned, and is fully sterilizable using standard clinical procedures. Due to the new European Medical Device Regulation (MDR 17/745), which significantly complicates and restricts the application of research tools in a clinical setting, we have designed an entire in vivo Raman endoscopy platform, which is called







invaScope, according the MDR 17/745, in-cluding a conform documentation. This goes beyond the path that typical researchers are willing to go, but is, in my opinion, the quint-essential step that has to be taken to move Raman spectroscopy to the clinic for routine diagnostics.

We also look to the future, and work hard to establish new research areas that are not short of technology of science-fiction novels. Here the prime example is the Ra-man ChemLighter. It is a tool that allows you to “paint” chemical information on your sample. By using an in-house–developed Raman probe, in combination with exten-sive computational processing adopted from the gaming industry, we are able to freely determine the molecular information of a sample, and create a chemical image either on a computer screen, or directly overlay the information for the user on the sample. This device is like a magic wand that allows you to visually display the chemi-cal content directly in the sample plane. Think of some transparent materials, such as glass and some polymers, lying side-by-side; it is impossible to differentiate those two by the naked eye. Now, take the Chem-Lighter and move it above these materials. What will happen is that, while you move our chemically specific magic wand, a color will appear instantaneously on the sample, showing the chemical present everywhere the probe was moved. Now, if you have your transparent samples side-by-side you will instantly visualize the chemical composi-tion, and easily and precisely determine the boundary between the materials. Of course, we not developing this technology just for

fun, but also want to move this directly into clinical applications, where this can help surgeons to differentiate tumor margins.

Furthermore, we developed a compact nonlinear multimodal microscope for intraoperative frozen section analysis. The multimodal microscope combines the imaging methods of two-photon excited autofluorescence (TPEF), second harmonic generation (SHG), and coherent anti-Stokes Raman scattering (CARS) to determine the morphological and chemical composition (morphochemistry) of unfixed frozen sec-tions. Utilizing this microscope together with specifically developed automated im-age analysis routines offers the potential to overcome current limitations of frozen sec-tion analysis with respect to achieving a reli-able intraoperative tumor margin detection. This work has been recently awarded by the “Kaiser-Friedrich-Forschungspreis (2018)” (http://kaiserfriedrichforschungspreis.de/preistraeger-2018-2/).

What are the greatest challenges you have faced in developing Raman applications for medicine?Despite technological challenges of coming up with compact, easy-to-use, and cost-efficient Raman devices that combine the complete diagnostic process chain, starting from automated sampling approaches, via the measurement procedure towards an automated data analysis (see also answers to questions above), there are many other challenges to be addressed. It is very impor-tant to have medical experts on board from the very beginning, instead of developing clinical Raman approaches that prove to be







incompatible with clinical routine in hospi-tals. In addition, one repeatedly encounters physicians who are initially skeptical about the performance of new diagnostic meth-ods, which must then be demonstrated.

Another major challenge is the work fol-lowing successful proof-of-concept studies after it has been shown that the Raman spectroscopic approaches investigated are potentially able to meet unmet medical needs. Now their actual effectiveness has to be tested on a large patient cohort to be incorporated into everyday medicine. In other words, the Raman approaches have to be validated under routine clinical conditions against the current medical gold standard in preclinical validation studies. However, it is very difficult to obtain funding for such preclinical validation studies.

Overall, Raman spectroscopic approaches have shown their potential to meet current diagnostic challenges in various fields of medical need (such as infectious diseases and pathology). One of the biggest chal-lenges is the effective transfer of excellent research results into marketable products. Too often, innovations get stuck in the “val-ley of death” on their way to the market or before reaching a clinical setting. This is caused by gaps in the research transfer process and missing handover points be-tween partners. Overcoming the challenge of the valley of death to apply promising Raman approaches for clinical routine requires novel infrastructures. To reach this goal, we established the research campus InfectoGnostics to safeguard the transfer from fundamental research into diagnostic systems (http://www.infectognostics.de/

en.html). In this campus technologists and scientists from academic institutions, clini-cians, and companies are teaming up to develop rapid point-of-care diagnostics us-ing photonics technologies (such as Raman spectroscopy in combination with novel optoelectronic-based enrichment methods). In this context, basic research, technology development, regulatory competency, and clinical practice are being brought closely together, which increases the efficiency of research and shortens development times. Through cross-disciplinary scientific exchange and early discussion with experts from the industry, research and develop-ment are aligned along the value creation chain to implement technological ap-proaches as real clinical solutions or product innovation.

Furthermore, to achieve faster dissemina-tion of Raman technological innovations in the user community, a network has to be also established among policy makers, health insurance bodies, and others. In this context, I would like to mention the Leibniz Health Technologies Research Alliance we established (http://www.leibniz-healthtech.de/en/). Using an interdisciplinary approach, the alliance aims to draw together preven-tion, diagnostics, and therapy. Expertise is pooled from a wide range of scientific disciplines: photonics, medicine, micro-electronics, materials research, economic research, and applied mathematics. In this way, innovative health technologies are guided to market maturity along a seamless innovation chain, with the help of industry, hospitals, insurance companies, and policy makers.







What do you foresee as some of the most important medical uses for Raman in the future?As mentioned above, Raman spectroscopic approaches have shown their potential for routine clinical applications for early diag-nosis and targeted therapy of infectious diseases and cancer. Infectious diseases are one of the major causes of death worldwide. Successful treatment relies on timely identification of the pathogen and its antibiotic resistance pattern to select the appropriate antibiotic treatment as early as possible, as mentioned above. Classical mi-crobiological analysis methods rely on time-consuming overnight cultures. Fast culture-independent analysis methods could thus lead to huge improvements, and help save lives because early and targeted treatment improves the prognosis of the patients. I am convinced that Raman spectroscopy, in combination with chemometric strategies and chip-based sampling, will play a key role in turning this vision into reality in the near future.

Furthermore, I am sure that Raman-based approaches will be able to solve challenges currently faced by clinical pathology, like intraoperative determination of the tumor type and grade, or a better delineation of tumor margins, also mentioned above.

What method do you use for your scientific inquiry? Do you have a specific approach to scientific problem solving and daily work challenges?Biophotonics or optical health technolo-gies—the research area we are working on—is highly interdisciplinary, and requires

the involvement of many different disci-plines. Thus, the most important issue is a fruitful scientific discourse with colleagues, and here, most importantly, with clinical researchers. Overall, in my opinion the best approach to scientific problem solving is discussing things with my research team. I am very lucky to have an interdisciplinary team of very gifted researchers. Further-more, a constant exchange with national and international colleagues is also very important, and here, the SciX conference usually offers a very good platform for such an exchange. Overall, for me, communica-tion is the key to success.

What are your next planned steps in your research work?As mentioned above, our next steps in-volve initiating preclinical validation studies to validate our clinical Raman approaches (Raman antibiotics susceptibility testing and Raman spectral histopathology) on a larger database and patient cohort. These studies are a necessary prerequisite to get these approaches in routine clinical use. Furthermore, we also want to focus not only on diagnosis, but also on therapy and therapy monitoring. In this context, we recently got funding for a large nation-al project for image-guided tissue removal via laser ablation.

References(1) I.J. Jahn, A.I. Radu, K. Weber, D. Cialla-May, and

J. Popp, Nanotechnology Characterization Tools for Biosensing and Medical Diagnosis (Springer-Verlag, Germany, 2018), p.1.

(2) I.J. Hidi, M. Jahn, K. Weber, T. Bocklitz, M.W. Pletz, D. Cialla-May, and J. Popp, Anal. Chem. 88(18),







9173–9180 (2016).

(3) S. Patze, U. Huebner, F. Liebold, K. Weber, D. Cialla-May, and J. Popp, Anal. Chim. Acta, 949, 1–7 (2017).

(4) E. Tolstik, L.A. Osminkina, C. Matthäus, M. Bur-khardt, K.E. Tsurikov, U.A. Natashina, V.Y. Timosh-enko, R. Heintzmann, J. Popp, and V. Sivakov, Nanomedicine: NBM 12(7), 1931–1940 (2016).

(5) C. Matthäus, S. Schubert, M. Schmitt, C. Krafft, B. Dietzek, U.S. Schubert, and J. Popp, ChemPhy-sChem, 14, 155–161 (2013).

(6) T. Yildirim, C. Matthäus, A.T. Press, S. Schubert, M. Bauer, J. Popp, and U.S. Schubert, Macro-mol. Biosci. 17, 1700064 (2017). DOI: 10.1002/mabi.201700064.

(7) M.T. Gebrekidan, C. Knipfer, F. Stelzle, J. Popp, S. Will, and A. Braeuer, J. Raman Spectrosc. 47(2), 198–209 (2016).

(8) E. Cordero, F. Korinth, C. Stiebing, C. Krafft, I.W. Schie, and J. Popp, Sensors 17, 1724 (2017).

(9) S. X. Guo, O. Chernavskaia, J. Popp, and T. Bock-litz, Talanta 186, 372–380 (2018).

(10) R. Knipper, V. Kopecky Jr, U. Huebner, J. Popp, and T.G. Mayerhöfer, ACS Photonics 5(8), 3238–3245 (2018).

(11) C. Assmann, J. Kirchhoff, C. Beleites, J. Hey, S. Kostudis, W. Pfister, P. Schlattmann, J. Popp, and U. Neugebauer, Anal. Bioanal. Chem. 407(27), 8343-52 (2015).

(12) U.-Ch. Schröder, C. Beleites, C. Assmann, U. Glaser, U. Hübner, W. Pfister, W. Fritzsche, J. Popp, and U. Neugebauer, Scientific Reports 5, 8271 (2015).

(13) J. Kirchhoff, U. Glaser, J.A. Bohnert, M.W. Pletz, J. Popp, and U. Neugebauer, Anal. Chem. 90(3), 1811–1818 (2018).

(14) U.-Ch. Schröder, J. Kirchhoff, U. Hübner, G. Mayer, U. Glaser, T. Henkel, W. Pfister, W. Fritzsche, J. Popp, and U. Neugebauer, J. Biophotonics 10(11), 1547–1557 (2017).

(15) I.W. Schie, J. Rüger, A.S. Mondol, A. Ramoji, U. Neugebauer, C. Krafft, and J. Popp, Anal. Chem. 90(3), 2023-2030 (2018).

Juergen Popp studied chemistry at the uni-versities of Erlangen and Würzburg. After completing his PhD in chemistry, he joined Yale University to do postdoctoral work. He then returned to

Würzburg University, where he finished his habilitation in 2002. Since 2002, he has held a chair for physical chemistry at the Fried-rich-Schiller University, in Jena, Germany. Since 2006, he has also been the scientific director of the Leibniz Institute of Photonic Technology, in Jena, Germany. His research interests are mainly concerned with biopho-tonics. In particular, he focuses on the devel-opment and application of innovative Raman techniques for biomedical diagnosis.

He has published more than 800 journal papers and has been named as an inven-tor on 12 patents. He is the Editor-in-Chief of the Journal of Biophotonics. In 2012, he received an honorary doctoral degree from Babes-Bolyai University, in Cluj-Napoca, Romania. Professor Popp is the recipient of the 2013 Robert Kellner Lecture Award, and the 2016 Pittsburgh Spectroscopy Award. In 2016, he was elected to the American In-stitute for Medical and Biological Engineer-ing (AIMBE) College of Fellows. In 2018, Popp was awarded the renowned Johannes Marcus Marci Medal of the Czechoslovak Spectroscopy Society. He also won the third prize of the Berthold Leibinger Innovation-spreis and was also awarded the "Kaiser-Friedrich-Forschungspreis".


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