Advances in Spontaneous Raman Scattering for Biological and Chemical Imaging in Cells and Tissues

Biomedical imaging using Raman spectroscopy has been advancing significantly in recent years. Expanding the capabilities of Raman spectroscopy for biomedical imaging depends on developments in various aspects of the technique. Katsumasa Fujita, a professor of applied physics at Osaka University, has been working to improve techniques for imaging biological samples using spontaneous Raman scattering. His emphasis is on improving image acquisition speed, which allows high spatial resolution observations and analysis of molecular dynamics during biological events. His current development work is focused on improvements of both the spatial and temporal resolution of Raman imaging. We recently spoke to him about his latest work.

In a research article, you applied surface-enhanced Raman scattering (SERS), along with Ag nano-assemblies functionalized with 4-mercaptobenzoic acid (p-MBA), to monitor intracellular pH (1). How are samples prepared and analyzed?

The silver (Ag) nano assemblies were prepared as three steps: 1) We synthesized Ag nanoparticles with silver ion reduction and 2) assembled the Ag nanoparticles with linker agent molecules, and then 3) we put p-MBA molecules into the assembles. Finally, we have the Ag nano assembles for pH sensing. HeLa cells were cultured with the Ag nano assembles under appropriate condition for several hours—enough to introduce the nano assembles to the cell bodies. The SERS measurement was performed by using a home-build Raman microscope. We then measured the intensity of SERS peaks of p-MBA that are pH dependent. The pH was calibrated from the SERS intensity of the ratio at 1390 cm-1 (COO-) against the intensity at 1690 cm-1 (C=O).

What lead you to selecting p-MBA as a reporter (or tag) molecule?

The range of detectable pH is important for our research since we set our goal to be the long-term measurement of intracellular pH in dynamic cellular events. The pH probe is sensitive within the range of pH from 3.0 to 9.0 (pKa 6.05), which covers the typical range of intracellular pH (pH 4.8-8.0). In terms of the chemical structure, p-MBA has a thiol group which can bind tightly to metal surfaces and provide stable and reproducible pH measurement.

What was the most surprising discovery from your intracellular pH analysis?

We achieved the optimum measurement of the pH response from one particular Ag nano assembly. Simultaneous measurement of fluorescence images of lysosomes and SERS spectra indicated that the decrease in pH was due to the lysosome internalization. The data indicates a clear difference between the inside and outside of the lysosome. We believe that knowing the chemical environment in a live cell precisely is important for understanding biological processes, demonstrated as a series of chemical reactions.

You have published research on the use of SERS imaging for detecting low–molecular weight compounds in their original tissue environment (2). How are samples prepared and measured using this technique?

An alkyne-incorporated drug was administrated with an intraperitoneal injection to a living mouse. Then the mouse brain was sliced to 20 µm after fixation and put on a SERS substrate in order to detect the alkyne signal with enhancement. The SERS measurement was performed using a home-built Raman microscope.

Have you created special imaging software for this application?

We used MATLAB (Mathworks) for data analysis and ImageJ (NIH) for visualization. We have built simple auxiliary codes for data processing and visualization that run on these software platforms.

One of your papers describes the application of narrowband Raman spectroscopy for breast nontumorigenic epithelial and cancer cell classification (3). This method is reported as a label-free imaging technique that uses principal component regression (PCR) and linear discriminant analysis (LDA) as classification algorithms. What key aspects were learned from this research with respect to classification accuracy in terms of Raman spectral regions selected, spectral resolution, imaging speed, sample preparation, and data analysis methods?

One of the issues in Raman imaging is imaging speed, and, to improve the imaging speed, there are many parameters we can still optimize. Spectral resolution and the wide spectral range are the advantages of Raman spectroscopy. However, depending on the purpose, we do not need such a high spectral resolution and wide spectral range. In this research, we targeted the discrimination of two cell types and optimized the parameters of spectral detection for this purpose. This approach allowed us to reduce the exposure and readout time and drastically shortened the time required for imaging. The technique also allows us to perform further signal multiplexing in imaging since it does not require many pixels on a camera for spectral detection. This research suggests that introducing flexibility in parameter setting in Raman imaging expands the application of Raman spectroscopy and microscopy.

What have you learned by applying Raman spectroscopy to understand mitochondrial activity in glutamate-stressed neuronal cells (4)? What special methods had to be applied to measure high-quality Raman spectra for this application?

From this research, we have learned that the redox state of cytochromes is significantly affected by the cellular or mitochondrial functionalities. The Raman signal of reduced cytochromes showed strong correlations with many different cell activity assays, which requires chemical treatment of cells. Raman microscopy provides similar results without chemical treatment. Raman microscopy can also show the difference in activities between cells or organelles. This does not require a special method in both measurement and data analysis. The technique would be useful for label-free monitoring of cell responses under various stimuli, such as drug screening.

In your work to develop and synthesize plasmonic gold and silver nanoparticles (NPs) (5), what have you discovered in terms of material type, size, shape, aggregation properties, and so forth, that provides optimized performance for any specific SERS application?

We concluded that the Ag chain, Ag core/satellites, Au-coated Ag core/satellites, and Au core/satellites nanoassemblies were useful for stable pH sensing of local positions in living cells. These structures have nano-scale gaps which enhance electric field efficiency and are beneficial for measurement with a short exposure time. We applied the nanoparticles for a pH sensing probe for live cells. Therefore, finally, we chose Au-coated Ag core/satellites because of their stability of enhancement and the long-term stability in intracellular conditions.

Do different SERS applications require different NPs?

We think they do. We have to consider the environment for the application. In terms of the signal enhancement, for example, Ag chain showed highest intensity from our result. However, Ag has less chemical stability compared with gold (Au), and Ag ion can cause damage to the cell. Therefore, it is better to choose Au-based nanoparticles for live cell application. Another point is the excitation wavelength. Each nanoparticle has appropriate wavelength (plasmon resonance wavelength) to maximize the signal enhancement. The wavelength should be chosen depending materials, shape and state of metal aggregation. Confirming that the sample and nanoparticle does not show fluorescence under irradiation of the laser used for the experiment.

Would you explain what is meant by hybrid fluorescence-Raman microscopy? How can this technique be used to measure the micro-environment associated with protein expression in living cells (6)?

Yes, the combination with fluorescence imaging is useful for accurate interpretation of Raman data in cell or tissue imaging. Raman microscopy can provide spatial distribution of signals, which helps to the peak assignment by comparing known biological information derived by fluorescence microscopy. Fluorescence and Raman microscopy should be used complementarily.

For label-free spontaneous Raman microscopy, would you explain to our readers what structured illumination is and how it is being used to increase spatial resolution for biological and chemical component mapping (7)?

Structured illumination is a spatially patterned illumination used to improve the spatial resolution in optical microscopy. The technique is known as structured illumination microscopy, and a sinusoidal pattern is used for structured illumination. Under this illumination, the signal from the sample produces moiré fringes in the spatial distribution of the signal, which highlights structures with a size similar to or even smaller than the periods of sinusoidal pattern and results in the improvement of the spatial resolution. The technique is widely used in fluorescence imaging. In our research, we combined this technique with line illumination to improve the spatial resolution in hyperspectral Raman imaging.

What are some of the key challenges that you have encountered during your recent research?

The key challenges in our recent research are the improvement of the imaging speed and the sensitivity in Raman imaging. For the first challenge, we are trying to push our technique for multiplex spectral detection further to achieve at least ten-times increase of imaging speed. As for the sensitivity, we are trying to utilize SERS. However, SERS has issues in reproducibility of signal and quantitative measurements. We are trying to tackle this issue by using high-speed imaging and Raman tag technique.

What would you consider to be the most meaningful contributions of your Raman work?

I believe that the capability of high spatial resolution imaging has expanded the application of Raman spectroscopy to biomedical fields, where imaging techniques have been taking significant roles to understand their targets. It was actually also useful to interpret Raman spectra from a sample with complex mixtures of different molecules as mentioned above. Our demonstration of Raman tag imaging is another meaningful contribution to the bioimaging field. It has opened a way of small molecule imaging in living cells and tissues, which was difficult for fluorescence microscopy.

Would you share with our readers to describe your work ethic, philosophy, and how you plan your daily or weekly work schedules?

With the advance of the technology, science is becoming interdisciplinary more and more. We have to keep learning things from the outside of our original major. Fortunately, I have many friends who give me many interesting inputs from different fields that always give me a new idea. My part in the collaboration is mainly optical engineering and spectrum analysis, but I am always enjoying biological and medical aspects of the research. It is really difficult to tell how I plan my schedule. I can tell that I am not good at planning.

What words of wisdom or advice do you have for any young people interested in a scientific research career?

Focus on the moment. The future is unpredictable, and what we can do is try to increase the choice for the future by developing ourselves, not searching for something else around us. Probably, I am still enjoying research because I was not good at planning. Learn to help others. Learn from everyone—I have learned a lot of things that I could not purposefully plan to learn, and those things have eventually helped me personally and in my work.

References

(1) K. Bando, Z. Zhang, D. Graham, K. Faulds, K. Fujita, and S. Kawata, "Dynamic pH measurement of intracellular pathways using nano-plasmonic assemblies," Analyst 145, 5768-5775 (2020).

(2) M. Tanuma, A. Kasai, K. Bando, N. Kotoku, K. Harada, M. Minoshima, K. Higashino, A. Kimishima, M. Arai, Y. Ago, K. Seiriki, K. Kikuchi, S. Kawata, K. Fujita, and H. Hashimoto, “Direct visualization of an antidepressant analog using surface-enhanced Raman scattering in the brain,” JCI Insight 5(6), e133348 (2020).

(3) Y. Kumamoto, K. Mochizuki, K. Hashimoto, Y. Harada, H. Tanaka, and K. Fujita, "High-Throughput cell imaging and classification by narrowband and low-spectral-resolution Raman microscopy," J. Phys. Chem. B. 123(12), 2654 (2019).

(4) T. Morimoto, L.-d. Chiu*, H. Kanda, H. Kawagoe, T. Ozawa, M. Nakamura, K. Nishida, K. Fujita, and T. Fujikado, "Using redox-sensitive mitochondrial cytochrome Raman bands for label-free detection of mitochondrial dysfunction," Analyst 144, 2540 (2019).

(5) Z. Zhang, K. Bando, K. Mochizuki, A. Taguchi, K. Fujita, and S. Kawata, "Quantitative evaluation of SERS nanoparticles for intracellular pH sensing at a single particle level," Anal. Chem. 91(5), 3254–3262 (2019).

(6) L.-d. Chiu, T. Ichimura, T. Sekiya, H. Machiyama, T. Watanabe, H. Fujita, T. Ozawa, and K. Fujita, "Protein expression guided chemical profiling of living cells by hybrid fluorescence-Raman microscopy," Sci. Rep. 7, 43569 (2017).

(7). K. Watanabe, A. F. Palonpon, N. I. Smith, L.-d. Chiu, A. Kasai, H. Hashimoto, S. Kawata, and K. Fujita, "Structured line illumination Raman microscopy," Nat. Commun. 6,10095 (2015).

Katsumasa Fujita

Katsumasa Fujita is a professor of applied physics at Osaka University. He received his BSc, MSc, and Ph.D. all in applied physics in 1995, 1997, and 2000, respectively, from Osaka University. After working as a JSPS postdoctoral fellow at Kyoto Prefectural University of Medicine and as a research associate at Frontier Research Center in Osaka University, he was appointed as an assistant professor in Osaka University in 2002 and was promoted to a full professor in 2018. His research interest includes optical microscopy, non-linear optics, vibrational spectroscopy and their applications for biomedical sciences. He developed techniques for imaging biological samples using spontaneous Raman scattering. His technique improved the image acquisition speed in Raman microscopy, which allowed us to observe and analyze molecular dynamics during biological events. He also demonstrated the use of alkyne as a small tag for imaging small molecules by using the unique Raman peak of alkyne. His current development is focused on the further improvement of the spatial and temporal resolution of Raman imaging.