Name: Multimodal Optical Imaging Platform for Studying Cellular Metabolism (Video) | JoVE Journal
Uploaded: 2025-06-06T13:35:00.000+00:00
Duration: 4 min 47 s
Description: Explore multimodal optical imaging for cellular metabolism and disease research using label-free microscopy techniques. This platform enables simultaneous visualization of metabolism, morphology, and molecular composition in biological samples.

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5 minutes

We thank Dr. Gloria Pryhuber and her HuBMAP team members for providing human lung tissue slices. We thank Dr. Kun Zhang for providing human brain tissues. We also thank Dr. Gen-Sheng Feng for providing mouse liver samples. We acknowledge support from NIHU54DK134301, NIH R01GM149976, NIH U01AI167892, NIH R01HL170107,&#160;NIH 5R01NS111039, NIH R21NS125395, NIH U54CA132378, UCSD Startup funds, Sloan Research Fellow Award, and CZI DAF2023-328667 Award.

1. Label-free multimodal imaging experiments
NOTE: This protocol focuses on the setup and acquisition procedure of the label-free multimodal imaging.
<ol>
	<li>Setup of the multimodal microscope (Figure 1A) and system calibration
	<ol>
		<li>Warm up the laser and wait approximately 15-20 min.</li>
		<li>Power on the control units and monitors in the following sequence: Control box | Touch panel controller | AC adapter for main laser remote AC adapter for sub laser remote.</li>
		<li>Power on the Si photodiode detector and lock-in amplifier.</li>
		<li>Configure the pump laser beams and Stokes beam. Set up the laser system with a pump beam tunable from 780 nm to 990 nm, 5 - 6 ps pulse width, and 80 MHz repetition rate. The Stokes laser beam has a fixed wavelength of 1,031 nm with a 6 ps pulse and 80 MHz repetition rate. Ensure both pump and Stokes beams are at low power (at least 20 mW) to be visible on the alignment plate.</li>
		<li>Place one alignment plate in the optical path (right after the laser box) to check the spatial overlapping of the pumps and Stokes beams and adjust the mirror until both spots overlap at the center of the alignment plate. 
		NOTE: Although the pumps and Stokes beams are roughly overlapped, fine-tuning the spatial overlapping of two laser beams can achieve the optimal SRS signal.</li>
		<li>After spatial overlapping, fine tune with the PSD (Position Sensitive Detector) by first clicking on the OPO control software. Use the hex key to carefully adjust the optical mirror 1 (OM1) and watch the PSD display for the X-Y position change to aim for the minimal deviation from the center. Then, to make more precise adjustments, use the hex key on the optical mirror 2 (OM2) until the position indicator is centered on the PSD display. 
		NOTE: OM1 and OM2 are two mirrors in the OPO head to adjust the laser alignment.</li>
		<li>Finally, center the condenser by turning the two condenser-centering screws to move the iris diaphragm image to the center of the field of view. 
		NOTE: The field iris diaphragm can restrict the diameter of the beam of light entering the objective and thus exclude extraneous light, improving image contrast.</li>
	</ol>
	</li>
	<li>Label-free multimodal imaging acquisition steps
	<ol>
		<li>Apply oil to the high-numerical aperture (1.4 NA) oil condenser and mount the microscope slide onto the oiled condenser. Finally, place a large water droplet on the microscope slide for the 25x water objective. Ensure that the microscope is securely mounted and immovable and then adjust the z stage to tune the focus until the bright-field image of the biological sample can be seen under the 25x water objective.</li>
		<li>Begin the imaging process in the correct sequence: MPF, SHG, and SRS to avoid photobleaching, which will impact the image quality and precise analysis of MPF. To switch between MPF and SHG quickly, switch from the pump beam to the fixed Stokes beam. 
		NOTE: Tuning the pump laser from 800 nm (NADH and FAD autofluorescence) to 791.3 nm (CH3 Raman shift) can take 1-2 min.</li>
		<li>Select the image resolution (512 x 512 pixels) and suitable dwell time for each modality. Use 8 &#181;s/pixel for the MPF and SHG with average frame above 3. Use 40 &#181;s/pixel with average frame 2 for the SRS modality. 
		NOTE: The average frame means the number of images to be averaged before saving.</li>
		<li>To acquire autofluorescence with MPF, turn off the Stokes laser beam and tune the pump laser to 800 nm for exciting NADH and flavin. Install the FVOPT filter cube into the light path with 460 &#177; 10 nm and 515 &#177; 10 nm for the NADH and flavin, respectively. The power on the sample is thus approximately 15 mW.</li>
		<li>Acquire the collagen fiber signal using SHG. Turn off the pump laser beam, only use stokes laser beam, and set the power to be 500 mW. Only acquire the channel related to the 515 nm filter.</li>
		<li>Obtain the spatial distribution of proteins and lipids using SRS. Keep both laser beams on and adjust the laser beam frequency to match the specific vibrational mode for each molecule. 
		NOTE: Typically, 791.3 nm is used for CH3, 797 nm is used for CH2, 787 nm is for unsaturated lipids, and 794.6 is for saturated lipids. The power on the sample is thus approximately 40 mW.</li>
		<li>To acquire the SRS hyperspectral image datasets, open the OPO control software to select sweep, set the wavelength range from 780 nm to 806.5 nm, and choose a stack number of at least 60. Then, capture the hyperspectral image stack. 
		NOTE: The resulting dataset contains 60 images, each representing the spatial distribution at a specific Raman shift (from 2,700 cm-1 to 3,150 cm-1). The time for hyperspectral acquisition is dependent on the field of view (FOV) of the image and scan time but will be approximately between 5 min and 8 min for a 512 x 512 image. For larger images, care should be taken to minimize the duration under the laser to prevent any photodamage, which can occur after 2 h (depending on the sample&#39;s thickness and FOV size).</li>
		<li>Save all images of the same regions of interest (ROIs) in the same folder. The image format is Olympus .oir file.</li>
	</ol>
	</li>
</ol>
2. Image analysis
<ol>
	<li>Use image processing software to open all saved raw images to assign the color and add the scale bar for the image display. Also use it to generate the binary mask of NADH and saturation fatty acids (SFAs, 2,880 cm-1) images for optical redox ratio and lipid unsaturation analysis. 
	NOTE: Make sure the mask intensity only has 0 and 1.</li>
	<li>Use the home-built python script to do any downstream analysis (the code is available at&#160;https://github.com/lingyanshi2020/HSI_Analysis).&#160; 
	NOTE: This script is designed to process multiple regions of interest (ROIs) from both control and cancer samples. The script&#39;s main function requires only two inputs: the file paths for the control and cancer image folders.
	<ol>
		<li>Iterate through the folders, matching images with the same base name (e.g., &#34;roi_1_flavin.tif&#34;, &#34;roi_1_nadh.tif&#34;, &#34;roi1_mask.tif&#34;).</li>
		<li>For each set of matched images, let the script perform ratiometric analysis and generate new ratiometric images based on these calculations. 
		The optical redox ratio is calculated as flavin / (NADH + flavin). 
		&#8203;Lipid unsaturation is calculated as: USFAs / (SFAs + USFAs) where USFAs represent unsaturated fatty acids.</li>
		<li>All ratiometric values from the images are then stored in a pandas DataFrame for easy manipulation. Perform one-way ANOVA and use matplotlib with styple &#34;ggplot&#34; or seaborn to do the barplot or boxplot for quantification.</li>
		<li>Use the 2,930 cm-1 (CH3) and 2,850 cm-1 (CH2) images to generate a subtracted (CH3 - CH2) image. Then, blend CH2 image CH3 - CH2 image in RGB color space with a custom LUT that mimics H&#38;E staining to generate the digital histology image46.</li>
	</ol>
	</li>
	<li>After acquisition of hyperspectral SRS images, employ the home-built python script to do the k-means clustering to segment the images based on spectral similarities (the code is available at&#160;https://github.com/lingyanshi2020/HSI_Analysis).&#8203;
	<ol>
		<li>Load the .tiff image stacks with the tifffile package. Reshape the SRS hyperspectral image stack (x, y, spectra) into a 2D array (pixels, spectra).</li>
		<li>Use the scikit-learn package to deploy k-means algorithm to this 2D array, with the defined number of clusters.</li>
		<li>Reshape the resulting cluster labels back into the original image dimensions, creating a segmentation map.</li>
		<li>Assign unique colors to each cluster, generating a false-color image that represents different biochemical compositions within the tissue.</li>
		<li>Use matplotlib to plot the spectra of each cluster with mean value (solid line) and standard deviation (shadow area)</li>
		<li>Follow the protocol of the penalized reference matching SRS (PRM-SRS) protocol using MATLAB to create the spatial label-free lipid subtype detection with SRS hyperspectral image datasets47.</li>
		<li>Organize the images and save them in a 300 dpi .tif format.</li>
	</ol>
	</li>
</ol>

The authors have no conflicts of interest to declare.

This multimodal system is a powerful imaging platform for capturing a holistic visualization of the molecular environment of samples across a wide range of biological origins and pathological conditions. The advantage of leveraging different label-free modalities lies in the capacity to acquire complementary information and target specific analytes that might otherwise be difficult or impossible in a single label-free imaging technique. Specifically, the three nonlinear imaging techniques (SRS, MPF, SHG) mentioned in thi...

Optical biomedical imaging has been pivotal in advancing our understanding of biological structure and function. The images are generated by modulating excitation light and detecting signals from light-tissue interactions. The first compound microscope, developed by Hans and Zacharias Janssen around 1590, utilized two convex lenses in a tube, providing magnification up to 30x1. Modern optical microscopes, following centuries of advancements, can now achieve resolutions as fine as 1-3 nm2,3. In addition to offering high resolution, advanced imaging systems now provide deeper tissue penetration, greater efficiency, and minimal sample damage, making them especially suited for live cell and tissue imaging. Label-free imaging is particularly advantageous as it captures information without disrupting intracellular processes or compromising sample integrity.
Multiphoton fluorescence (MPF) microscopy, particularly two-photon fluorescence microscopy, has been extensively used for label-free imaging. Unlike conventional fluorescence microscopy, which relies on linear single-photon absorption and emission, MPF excitation involves the simultaneous absorption of multiple photons, whose combined energy excites a single fluorophore molecule4,5. These photons, typically in the infrared spectrum, possess half or less of the energy required for single-photon excitation. The longer wavelengths and localized excitation at the focal point in this nonlinear process result in lower scattering, deeper tissue penetration, and reduced phototoxicity.
Cellular metabolic information can be captured by label-free MPF microscopy through the detection of autofluorescence signals from endogenous metabolic substrates, such as reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD). These coenzymes exhibit distinct excitation and emission spectra, and their fluorescence intensity ratio, known as the redox ratio (NADH/FAD), reflects the cell&#39;s oxidative state. Since Britton Chance first introduced the concept of the redox ratio in 1979, additional ratios, including NAD(P)H/FAD, NAD(P)H/(FAD + NAD(P)H), and FAD/(FAD + NAD(P)H), have been proposed6,7,8,9. Quantifying these optical redox ratios via MPF imaging provides valuable insight into metabolic dynamics. For instance, MPF imaging can distinguish cancer cells from normal cells based on their altered metabolism, demonstrating its potential for cancer diagnosis10,11,12. However, MPF-based autofluorescence detection has limitations. Other intrinsic fluorophores, such as keratin, may contribute to fluorescence intensity, leading to spectral crosstalk and inaccurate signal interpretation13. Additionally, the redox ratio only reflects overall cellular oxidation-reduction changes and does not distinguish between NADH from different sources (e.g., cytoplasmic or mitochondrial) or between NADH and NAD(P)H, as both exhibit similar spectral peaks at 450 nm, resulting in blended intensity signals14.
Second Harmonic Generation (SHG), a nonlinear optical process first demonstrated in the biomedical field in the 1980s, has been widely utilized for label-free imaging of cellular structures15,16. Similar to MPF, SHG involves the simultaneous absorption of two photons of the same energy from an ultrafast pulsed laser. These photons are recombined to emit a new photon twice the frequency of the incident light, resulting in the detection of the second-harmonic signal. This non-linear optical interaction occurs exclusively in non-centrosymmetric materials that exhibit a non-zero second-order susceptibility to induce a polarization for generating the second harmonic signal17,18. This makes SHG particularly effective for imaging the filamentous proteins and fibrillar structures, such as collagen, myosin, and tubulin, without requiring exogenous fluorescence dyes15,17,19,20. The abnormality in abundance, stiffness, alignment, and structure of fibrosis and collagen are prevalent in many conditions such as inflammation and cancer, making SHG a promising tool for efficient and non-invasive detection for certain disease conditions21,22,23. The widespread application of SHG imaging in oncological research, including studies on breast, ovarian, and skin cancers, has highlighted its crucial role in both fundamental research and potential clinical applications24,25,26,27.
Different molecules exhibit distinct vibrational energy levels, which induce varying degrees of inelastic scattering when excited by incident light-a phenomenon first characterized by C. V. Raman in 192828. The Raman effect has since been extensively utilized in optical microscopy for the detection of molecular and tissue compositions without exogenous labeling. Both Stimulated Raman Scattering (SRS) and coherent anti-Stokes Raman Scattering (CARS) excite molecular vibrations coherently and leverage the nonlinear interaction of light to produce a stronger signal compared to conventional Spontaneous Raman Spectroscopy. The SRS phenomenon was first reported in 196229. In 2008, this mechanism was integrated into three-dimensional multiphoton imaging, allowing for selective detection of chemicals based on intensity changes in the Pump and Stokes beams due to molecular vibrational transitions30. This method minimizes non-resonant background interference, generating a clean intensity signal that surpasses that of CARS. SRS imaging excels in providing multiplexed and hyperspectral imaging, enabling simultaneous detection of multiple chemical bonds and allowing high-resolution visualization of molecular composition in specimens with considerable penetration depth. Although a relatively new technique, SRS imaging has proven effective in both clinical diagnostics and metabolic research, in vivo and in vitro30,31,32,33,34,35,36. For example, SRS can differentiate brain tumor-infiltrated tissues from the cortex and white matter by quantifying the lipid-to-protein ratio, enabling the delineation of tumor margins in a label-free, non-invasive manner37,38. Additionally, metabolic alterations, often considered hallmarks of aging-related and cancer-associated diseases, can be quantitatively assessed using SRS achieved by detecting carbon-deuterium bonds in samples treated with heavy water (D2O), allowing quantitative measurement of protein synthesis, lipogenesis, and other macromolecular metabolic processes31,33,34,35,36. The ability to track metabolites with high temporal and spatial resolution positions SRS as a promising tool for disease investigation and diagnosis, with potential for broader clinical applications.
Multimodal imaging has emerged as a powerful approach in biomedical research, integrating two or more imaging modalities to gain a more comprehensive understanding of complex biological systems within the same specimen. In 2018, a label-free autofluorescence-multiharmonic (SLAM) microscopy technique was introduced, integrating two-photon fluorescence (2PF), three-photon fluorescence (3PF), SHG, and third harmonic generation (THG)39. This approach facilitates the simultaneous visualization of cellular interactions, dynamic processes, and individual components within the tumor microenvironment. SLAM microscopy offers minimal perturbation and reduced laser power requirements for the sample, enabling deep tissue profiling and providing a safer method for intravital monitoring40. Another multimodal modality, combining intrinsic fluorescence spectroscopy, diffuse reflectance spectroscopy, and Raman spectroscopy, has been developed for in situ cancer detection during surgical procedures41. Additionally, a recently designed multimodal non-linear endoscopy system, which integrates CARS, SHG, and two-photon fluorescence (TPF), has demonstrated the capability to image biological samples at sub-micron and sub-cellular spatial resolution42. Combined 2PF and SRS microscopy has similarly been utilized for high-resolution, in-vivo imaging of tissues, cells, and organelles42,43,44,45. These emerging multimodal imaging techniques harness the strengths of individual modalities, leading to improved resolution, penetration depth, and image acquisition efficiency, thus showing considerable potential for clinical and surgical applications.
This multi-modality approach is increasingly favored over single-modality imaging because it provides a broader range of measurements while mitigating the limitations associated with individual techniques. As previously discussed, MPF measures endogenous fluorescence to reflect metabolic changes, SHG can image non-centrosymmetric structures such as collagen in biological samples, and SRS predominantly detects proteins and lipids due to the high density of chemical bonds that generate distinctive Raman signals based on their vibrational modes. Given their coherent properties and the shared principle of nonlinear optical properties, these imaging modalities can be integrated into a single microscope setup utilizing ultrashort pulsed lasers, allowing for the acquisition of various biomarkers at localized regions to provide a more complete view of biological processes44,45. This paper outlines a protocol for implementing a multimodal imaging platform that integrates MPF, SHG, and SRS for biomedical research applications.

<table><tbody><tr><td>460 nm Filter Cube</td><td>Olympus</td><td></td><td>OCT-ET 460/50M32</td></tr><tr><td>Bandpass Filter</td><td>KR Electronics</td><td>KR2724</td><td>8 MHz</td></tr><tr><td>BNC 50 Ohm Terminator</td><td>Mini Circuits</td><td>STRM-50</td><td></td></tr><tr><td>DC power supply</td><td>TopWard</td><td>6302D</td><td></td></tr><tr><td>Dichroic Mount</td><td>Thorlabs</td><td>KM100CL</td><td></td></tr><tr><td>FIJI ImageJ</td><td>ImageJ</td><td></td><td></td></tr><tr><td>High NA oil condenser</td><td>Olympus</td><td>6-U130</td><td></td></tr><tr><td>High O.D. shortpass filter</td><td>Thorlabs</td><td></td><td>950 nm</td></tr><tr><td>Inverted Laser Scanning Microscope</td><td>Olympus</td><td>FV1200MPE</td><td></td></tr><tr><td>Lock-in Amplifier</td><td>Zurich Instruments</td><td></td><td></td></tr><tr><td>picoEmerald Laser System</td><td>Applied Physics &amp;amp; Electronics, Inc.</td><td></td><td>&amp;nbsp;Synchronized pulsed pump beam (tunable 720&amp;ndash;990&amp;thinsp;nm wavelength, 5&amp;ndash;6&amp;thinsp;ps pulse width, and 80&amp;thinsp;MHz repetition rate) and Stokes (wavelength at 1032&amp;thinsp;nm, 6&amp;thinsp;ps pulse width, and 80&amp;thinsp;MHz repetition rate)</td></tr><tr><td>SI Photodiode Detector</td><td>Home Built</td><td></td><td></td></tr><tr><td>Touch Panel Controller</td><td>Olympus</td><td></td><td></td></tr><tr><td>VF-300 Compresstome&amp;nbsp;</td><td>Precisionary</td><td></td><td></td></tr></tbody></table>

multimodal optical imaging platform for studying cellular metabolism

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Optical imaging technologies are critical in biomedical studies for their ability to obtain both morphological and functional information from biological specimens at high spatial resolution. These optical processes exploit various light-molecule interactions, such as scattering, absorption, emission, and harmonic generation, between photons and the molecules within cells, tissues, or organs. While conventional biomedical imaging has historically focused on applying a single modality, recent research has shown that these diverse techniques provide complementary insights, and their combined outputs offer a more comprehensive understanding of molecular changes in aging processes and disease development and fundamentals in cell biology. 
In the past decades, label-free optical imaging methods have advanced, enabling detailed exploration of cellular and subcellular environments. For instance, multiphoton fluorescence (MPF) not only facilitates targeted protein imaging but also quantifies metabolic activity through autofluorescent coenzymes, achieving high penetration depth and spatial resolution. Second Harmonic Generation (SHG) is used to image structures like collagen in the extracellular matrix, while Stimulated Raman Scattering (SRS) maps chemical bonds and molecular composition in situ with subcellular resolution.
We have developed a multimodal imaging platform that combines MPF, SHG, and SRS modalities. The integration of these modalities into a single platform enables the acquisition of multifaceted information from the same localization within cells, tissues, organs, or even bodies, facilitating a more detailed exploration of the intricate relationships between cellular metabolism, extracellular matrix structure, and molecular composition. This multimodal system offers subcellular resolution, deep tissue penetration, in situ live-cell/tissue imaging, as well as label-free detection and instantaneous coregistration without the need for position adjustments, device switching, or postanalysis alignment. Here, we present a protocol for label-free imaging with this multimodal platform and demonstrate its application in characterizing cellular metabolism, and molecular heterogeneity in cells and tissues for studying aging and diseases.

The images in Figure 2 are representative of the results obtained from following the protocol for acquiring autofluorescence of FAD and NADH as well as the four SRS channels for protein, total lipids, unsaturated lipids, and saturated lipids. Here we also generate a pseudo-histological image as described in the protocol through RGB color blending. The acquisition of the MPF and SRS channels provides the image files that will be later utilized in ratiometric analysis. An example of this analy...

Utilizing a multimodal platform combining label-free optical imaging modalities, we have developed a protocol for visualizing and quantifying cellular dynamics and metabolism. Through imaging via multiphoton fluorescence, second harmonic generation, and stimulated Raman scattering microscopy, we can generate a holistic overview of the cellular and molecular environment.

Optical imaging technologies are critical in biomedical studies for their ability to obtain both morphological and functional information from biological ...

multimodal-optical-imaging-platform-for-studying-cellular-metabolism

Research

JoVE Journal

Bioengineering

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Explore multimodal optical imaging for cellular metabolism and disease research using label-free microscopy techniques. This platform enables simultaneous visualization of metabolism, morphology, and molecular composition in biological samples.

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

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