Real-Time, Noninvasive Evaluation of Mitochondrial Function Using Resonance Raman Spectroscopy

Summary

We present a noninvasive, real-time method for monitoring mitochondrial health using Resonance Raman Spectroscopy (RRS). We used RRS to quantify mitochondrial redox states and developed a metric for mitochondrial health, the Resonance Raman reduced mitochondrial ratio (3RMR), which shows a faster response to changes in oxygenation than traditional blood-gas analytes.

Abstract

Although mitochondria are central to pathogenesis and disease progression, mechanistic insight into mitochondrial health dynamics remains costly and inaccessible. To address this gap, this study employs Resonance Raman Spectroscopy (RRS) to assess mitochondrial function, using a portable system that delivers real-time, noninvasive, and quantitative measurements of mitochondrial cytochrome redox states in rat livers. In this protocol, we demonstrate the use of this technology, including setup, data acquisition, and data processing. This study presents a proof-of-concept experiment that highlights RRS's ability to measure real-time changes in mitochondrial redox state– and, by extension, mitochondrial function. Briefly, the RRS device was connected to a laser pump as well as a data acquisition computer and placed 1 cm away from the rat liver.  Acquisition parameters were selected in accordance with the rat liver protocol; redox states were measured in oxygenated and ischemic conditions utilizing an oxygen stress test. Changes in mitochondrial redox states were tracked throughout the oxygen stress test.

Introduction

Mitochondria play a crucial role in cellular function and homeostasis. Mitochondria are the main drivers of cellular metabolism, regulating ATP generation via oxidative phosphorylation. They also serve as signaling organelles that dictate cell fate by regulating apoptotic pathways, as well as ROS and Ca⁺² signaling¹. Thus, mitochondrial dysfunction can lead to various neurodegenerative, muscular, and cardiovascular diseases^2,3. Despite the crucial role mitochondria play in disease progression, evaluating mitochondrial function can be invasive, costly, and limited.

Key indicators of mitochondrial function, such as oxygen consumption rate, oxidative phosphorylation, and the activity of various complexes within the electron transport chain (ETC), are measured through respirometry. The ETC is composed of four complexes (I–IV) embedded in the inner mitochondrial membrane (IMM). These complexes, with the help of other co-enzymes, transfer electrons from reduced substrates, such as NADH and FADH2, to the final electron acceptor, O₂, which is then reduced to water. Most contemporary respirometry assays, such as the Seahorse and Orobros O2k, require tissue processing, thereby restricting their application for translational research^4,5. Measuring mitochondrial membrane potential (MMP) is another method of evaluating mitochondrial dysfunction, which utilizes cationic lipophilic fluorophores, such as JC-1, along with microscopic imaging, where coupled (healthy) and uncoupled (damaged) mitochondria fluoresce with different wavelengths⁶. However, these assays can have low sensitivity⁵. The dye itself can also have high cytotoxicity at high concentrations⁵. Lastly, similarly to respirometry measurements, this technique also requires tissue processing. Spectroscopy-based techniques have recently been used as noninvasive methods of evaluating mitochondrial health. For instance, Phosphorus Magnetic Resonance Spectroscopy (P-MRS), a method that evaluates cellular metabolism by measuring the concentrations of phosphorus-containing metabolites, has been used to characterize engineered adipogenic tissue undergoing differentiation⁷. However, this technique also requires specialized equipment and training.

Our group recently demonstrated a real-time, noninvasive, and highly specific technique for measuring mitochondrial redox state as a marker of organ viability using Resonance Raman Spectroscopy (RRS). RRS uses inelastic scattering of photons for the quantification of redox states of mitochondrial cytochromes. Porphyrin rings in heme moieties of molecules such as mitochondrial cytochromes, hemoglobin, and myoglobin produce a characteristic Raman spectrum depending on their oxidation state when excited with a 441 nm wavelength laser⁸. Though the RRS system utilizes a 441 nm wavelength, lasers with 420 nm and 405 nm wavelengths can also be used, each of which preferentially enhances a different cytochrome complex. The spectrum obtained from an unknown sample of interest is cross-referenced with pre-recorded libraries of fully oxidized or reduced intact mitochondria, yielding the ratio of reduced to total mitochondria or the average redox state of the in situ ETC cytochromes. This ratio is defined as the Resonance Raman Reduced Mitochondrial Ratio (3RMR).

There are 3 distinct calculations/types of 3RMR: mitochondrial 3RMR (mito-3RMR), complex III 3RMR (cIII-3RMR), and complex IV 3RMR (cIV-3RMR). Each of these indices is calculated through the fraction of reduced to the total number of a given component. For instance, the 3RMR of cIII would be the ratio of reduced cIII to the total number of cIII in the tissue (cIII-R/(cIII-O+CIII-R). The same pattern applies to cIV. Thus, 3RMR-cIII would refer to the ratio of reduced cIII to the total amount of cIII, and 3RMR-cIV would refer to the ratio of reduced cIV to the total amount of cIV. Through regression analysis, either mitochondrial redox states (mito-3RMR) are calculated utilizing spectral libraries of intact mitochondria, or individual complex redox states (cIII-3RMR and cIV-3RMR) are calculated utilizing cIII and cIV spectral libraries. A recent publication⁹ demonstrated that mito-3RMR is representative of the weighted average of cIII and cIV redox states (or cIII-3RMR and cIV-3RMR). It is important to note that cI and cII are not Raman-active or resonantly enhanced through the 441 nm wavelength. Consequently, they are not computed when calculating the mito-3RMR.

Leveraging these types of 3RMR, we have been defining the optimal range of 3RMR. In "healthy" organs, mitochondria are intact and have sufficient oxygen supply. In contrast, damaged organs may have insufficient delivery or utilization of oxygen, thereby causing ETC complexes to accumulate electrons. Electron accumulation causes "reductive stress," shifting the mitochondria from oxidized to reduced state and increasing 3RMR values. Several studies^8,9,10,11,12 ranging from hearts and livers in rodent and porcine models, have begun to shed light on the optimal "healthy" range for 3RMR, which is proposed to be between 10 and 30. For example, Perry et al., subjected mammalian hearts to hypoxia, where a 3RMR of 30% was shown to yield the optimal balance for sensitivity and specificity to detect hypoxic tissue⁸. This trend was further confirmed in additional studies with cardiac tissue^11,12, and liver grafts¹⁰. Additionally, Nyugen et al.⁹, demonstrated that 3RMR values drop to very low (oxidized) values for tissues in which metabolism has halted, with 3RMR levels (7.667 ±± 4.926) of non-transplantable warm-ischemic livers after 3 h of normothermic machine perfusion, compared to 3RMR values above 10 (~12–15) for fresh and transplantable warm-ischemic livers⁹.

Above and beyond these demonstrations of how 3RMR can measure tissue viability, the sensitivity, specificity and ease-of-use of RRS make it highly applicable to many other types of research where cellular metabolism and mitochondrial function are affected. In previous studies, RRS has been utilized to detect macular degeneration and glaucoma in mice through evaluating the metabolic activity of unmyelinated axons of retinal ganglion cells at the optic nerve head¹³. Additionally, it was used to detect coarctation of the aorta (CoA) in a rat model of congenital heart disease, a diagnosis that is often missed in newborn cardiac screenings¹². This technology was leveraged to understand organ function and viability under various experimental conditions that are relevant in the field of organ transplantation, including studies that assess organ viability on ex vivo machine perfusion^10,11, evaluate reoxygenation post-static cold storage¹⁴, or mitigate reperfusion injury of warm-ischemic livers⁹.

This paper seeks to establish RRS as a real-time, sensitive tool for assessing mitochondrial health. Whole livers from a rat model were subjected to varying levels of ischemia and oxygen reperfusion as a stress test to assess mitochondrial response. In this oxygen stress test, fresh livers are subjected to 5 min of initial perfusion flow, 5 min of ischemia, and 5 min of reperfusion. 3RMR, as well as lactate, K+, partial pressure of oxygen (pO2) measurements was taken throughout the stress test. A faster response to changes in oxygenation was observed with 3RMR compared with traditional blood-gas analytes. With these experiments, the use of RRS is highlighted not just for mechanistic studies, but also as a diagnostic method as well as a therapeutic testbed. Thus, the shared protocol may be useful to a variety of researchers working with mitochondria. Overall, RRS, with its noninvasive, real-time, and highly specific nature, could have a substantial impact on cellular metabolism.

Protocol

All animals for the experiments were maintained in accordance with National Research Council guidelines and were approved by the Institutional Animal Care and Use Committee (IACUC) at Massachusetts General Hospital (Boston, MA, USA).

1. Setting up and utilizing RRS

1. Device description:
   NOTE: The portable RRS system (Pendar Technologies; product is for research purposes only) uses a 441 nm laser due to a resonant enhancement of the Raman spectrum of mitochondrial cytochromes with this wavelength. The laser runs between 4 mW and 8 mW.
   1. Connect a laser excitation source to a custom-built probe via a fiber optic cable. The probe shines a 2 mm diameter laser spot on the tissue, as well as collects the emitted spectrum. The spectrum then passes through a bandpass filter and is collected by a charge-coupled device (CCD) array. The spectrum is collected every second over a 180-s timeframe and averaged to create a raw, noise-reduced spectrum for analysis.
   2. The software compares this obtained spectrum to pre-measured spectral libraries through a regression analysis.
2. Procedure for pre-recording libraries
   NOTE: For the experiments presented in this paper, reduced and oxidized forms of mitochondria, complex III, and complex IV, hemoglobin, and myoglobin libraries were created and utilized. The RRS signals of all libraries were taken in a black box at room temperature (21 °C). These library files are provided with the analysis software separately. However, depending on the tissue of interest and the presence of other Raman-active molecules, other libraries could also be created by suitably adapting the following protocol. Raw RRS spectra of the hemoglobin, myoglobin, and oxidized/reduced complex III/IV libraries are provided in Supplementary Figure 1.
   1. Hemoglobin libraries (Supplementary Figure 1C):
      1. Collect fresh rodent blood via animal exsanguination. Oxygenate the collected blood using 100% oxygen, or deoxygenate it using 100% nitrogen, in a glass cuvette, adding sodium dithionate.
      2. Measure the RRS of each state through the glass cuvette wall for 10 min, with the RRS probe positioned 9 mm away from the glass cuvette.
      3. Use spectral peaks at 1356 cm^-1 to verify the deoxygenated state, and use peaks at 1376 cm^-1 to confirm the oxygenated state.
   2. Reduced/oxidized mitochondrial libraries (Figure 1)
      1. Isolate mitochondria from homogenized and purified rodent hearts in a glass cuvette. Place the Raman probe 9 mm away from the glass cuvette and measure the spectra of these mitochondria through the glass wall.
      2. Create fully oxidized conditions by providing 100% oxygen flow to the mitochondria. Verify the oxidized state through the absence of the reduced RR peak at 1357 cm^-1.
      3. Create fully reduced conditions by exposing the mitochondria to 100% nitrogen with small amounts of hydrogen sulfide. Verify the reduced conditions through the absence of oxidized RR marker peak at 1371 cm^-1.
   3. Oxidized/reduced complex III/IV (Supplementary Figure 1A,B):
      1. Isolate Complex III and IV as described elsewhere^9,15,16,17. Briefly, use a stock sample of isolated mitochondria (corresponding to 200 nanomoles of cytochrome a) as the starting material.
      2. Dilute the mitochondria to 10 nmol cytochrome A/mL in ice-cold buffer C (50 mM Tris, 1 mM MgSO_4, pH 8.45). Then, add 10% w/v n-β-D-dodecyl maltoside (DM) drop by drop to a final concentration of 1%, while gently mixing on ice.
      3. Centrifuge the extract at 40,000 × g for 40 min at 4 °C. Apply the supernatant to a 2.5 cm × 20 cm glass column that has previously been packed with an exchange resin equilibrated with 5 column volumes of buffer C (containing 0.02% w/v DM).
      4. After washing with 2 column volumes of buffer C, elute mitochondrial complexes by applying a linear 0–400 mM NaCl gradient in the same buffer and collect the elute in 4 mL fractions.
      5. Green fractions containing Complex IV eluted close to the middle of the gradient (~180 mL NaCl), before the reddish fractions containing Complex III. Concentrate the pooled fractions containing each complex to 20–30 nmoles cytochrome a/mL.
      6. Once the individual complexes were isolated, expose each to oxidizing and reducing conditions. Reduce an 11 mM sample of Complex III in Tris buffer using 2 mL of ascorbate (1 M) to reduce cytochrome c1 and 2 mL of HS (hydrosulfite) to fully reduce cytochromes b_L and b_H.
      7. Reduce an 18.67 mM sample of Complex IV in Tris buffer in multiple steps, since Complex IV undergoes a multi-step catalytic oxidation process. To fully reduce Complex IV, add two 2 mL of H₂O₂ (10 mM), followed by 2 mL of HS.
      8. Collect library spectra after each addition to capture each step of the cycle. Add fully oxidized and reduced spectra to the final reference library to represent the steady states of complex IV.
      9. Prepare all samples in a 1 mL glass vial and hold them in a fixed position at 9 mm from the probe lens and enclose them in a black box to prevent light contamination. Collect RRS spectra for 600 s for each complex and oxidation state.
      10. Validate the purity of isolated cIII and cIV as described previously¹⁷, and validate the adequacy of isolated cIII and cIV spectral libraries in explaining the full mitochondrial spectra as described in a recent paper⁹.
   4. Myoglobin libraries (Supplementary Figure 1D):
      1. Using similar oxygen and nitrogen (with dissolved sodium dithionite) flows, vary the oxymyoglobin saturation of the myocardial myoglobin extract between 0% and 100%. Verify the Raman spectra through marker peaks at 1355 cm^-1 and 1376 cm^-1.

NOTE: Most spectral components detailed above, such as hemoglobin or oxidized/reduced mitochondria, are heme-containing molecules and are excited by the same wavelength. This RRS system avoids signal overlap by creating a regression library for each molecule based on its unique spectral signal. The obtained scattered spectra are run through a regression algorithm against the selected libraries. The regression algorithm yields a factor for each library spectrum that represents the best-fit estimate of its relative contribution in the recorded spectrum, and thus its relative concentration in the tissue. Because the algorithm uses the best fit across a range of wave numbers, small differences in the position or intensity of spectral peaks of the various library components allow the regression to discern even closely related chromophores. Through the regression algorithm and spectral libraries, the RRS system can break down the total, scattered signal into various components depending on their unique spectral peaks, avoiding signal overlap from heme-containing molecules. To show the relationship between hemoglobin and mitochondria, Supplementary Figure 2 is included, which provides images of spectra taken from a liver perfusion, broken into the measured signal, the line of best-fit, and the unexplained residual that results from differences in the individual spectral peaks. In panel A, reduced and oxidized hemoglobin and reduced and oxidized mitochondria regression libraries are selected. The line of best-fit curated through the libraries and the regression analysis neatly overlaps with the measured signal, and there is little unexplained residual. In panel B, only oxidized and reduced mitochondria libraries are selected. Since there is a large unexplained residual structure, the mitochondrial spectral peak is shifted several wave numbers from that of hemoglobin, which was not included in the regression analysis. Moreover, in the supplementary figures of Nguyen et al.⁹, the authors explain the relationship between mitochondria and the individual complexes (cIII and cIV), as well as the relationship between individual complexes¹⁶, whereby complex III exhibits sharper, lower-frequency peaks, while complex IV shows broader, upshifted bands. Further, the authors show that the reduced Complex III and Complex IV libraries fully explain the measured spectrum of reduced whole mitochondria, resulting in minimal unexplained residual.

Figure 1: Spectral libraries of fully oxidized and reduced mitochondria. RRS library spectra were obtained after isolated mitochondria were provided with (A) 100% oxygen, or 100% nitrogen (B) to reach oxidized and reduced states, respectively. These states were confirmed with the presence of RR (C) oxygenated and reduced (D) marker peaks. The shift in these peaks corresponds to changes in molecular vibrational modes in the presence of oxygen. The intensity of each peak is normalized to 1 arbitrary intensity unit (AU), as the enhancement of the reduced state is greater using this 441 nm excitation wavelength. Please click here to view a larger version of this figure.

2. Data collection using RRS

1. Device setup:
   1. Connect the spectrometer unit (housed inside a compact box) to the direct current (DC) power supply and to the power adapter using the supplied cables.The spectrometer unit has one round plug with alignment indentations.
   2. Align the indentations of the round plug with those of the cable that gets connected to the power adapter. No cleaning is required prior to connecting the ports (Figure 2). 
   3. Connect the computer running the analysis to the same power adaptor that connects the spectrometer and power supply using the cables supplied.
   4. Place the laser probe in the probe holder. Adjust the position of this probe at the start of the experiment.
   5. Open the spectrometer control software (Cellular Energetics Software).
2. Software setup:
   NOTE: Settings files and regression libraries for different types of tissue are provided with the system. These files include information about what libraries the RRS system should include in the regression, how many spectra are included in the regression window, and other parameters such as laser power. Default settings can be changed and a new settings file saved for use in future experiments. Screen captures of the software detailed below are presented in Supplementary Figure 3.
   1. Upon opening the spectrometer control software, a graph displaying percent 3RMR, Oxygen Saturation (StO₂) and myoglobin saturation will appear. This screen is where these metrics will be displayed, if selected, in real time throughout the measurements. Click on the Settings tab at the bottom left of the page.
   2. Under the Info tab, enter session information under Session Name, and any necessary details under the Session Description tab.
   3. There are two data collection modes. Use Spot Check mode to accumulate spectra until specified quality metrics are met (e.g., target sigma or regression window/number of spectra), then stop recording. Use Continuous Mode to record spectra until the stop button is pressed. In both modes, all data, both 3RMR results and individual spectra, are stored automatically. This experiment utilizes Continuous Mode.
   4. Once all session information is entered, click on the System tab.
   5. Under Settings File, select the protocol appropriate for the project (for acellular rat liver perfusions, choose the Rat Liver Acellular protocol). This will automatically adjust the measurement type, average length of measurement (regression window), as well as the target sigma.
      1. To create a new settings file, adjust any of the components listed above, then click the Save button. This should save these parameters as a separate settings file that can then be imported into the system.
      2. To import a new settings file, click the drop-down menu next to Default (Mod) and select Import New Settings File. Click on the new Settings File to be imported.
   6. Under Regression Libraries, select and confirm the combination of libraries to be used for data acquisition and analyses. The libraries for reduced and oxidized mitochondria [Mito Only] were selected for this experiment. Other users may select to use libraries that also include hemoglobin, myoglobin, or other complexes found under Advanced Library. 
   7. Select and confirm the preferred Window Width Mode.
      1. Adaptive width mode: Use the adaptive width mode to add individual spectra to the averaged spectrum until the maximum sigma target is reached. If the sigma target is not reached, the recording will stop when it reaches the upper limit of the set regression window (i.e., the maximum number of spectra it was told to record). The measurement results will be displayed on screen after the minimum sigma target is reached.
      2. Fixed width mode: Use the fixed width mode to add individual spectra to the averaged spectrum until the target number of spectra is included (set under average length).
   8. Once system settings are complete, click on the Plot tab.
   9. Under Main Series, select the components to be plotted and displayed on screen during the measurements.
   10. Confirm that all measurement components selected are in line with the protocol of interest.
3. Data acquisition:
   NOTE: Raman measurements should be taken in a dark space. Prior to starting data acquisition, ensure that the workspace is as shielded from light as possible. If the room cannot be shielded from light entirely, the RRS system comes with a black cover with a rectangular opening in the center (shown in Figure 2), through which the laser probe can be placed. The box can cover the sample/the section of the organ where RRS measurements are being taken from. When working with the RRS laser, avoid direct or prolonged eye exposure. Do not shine a laser directly in the eye or onto reflective surfaces. Only activate the laser when positioned directly on the tissue of interest. The RRS laser is a Class 3R laser when used at 4 mW, and Class 3B when used at 10 mW. The laser is safe for tissue contact.
   1. Select the appropriate, pre-saved settings, recording parameters, and library files.
   2. Place the laser probe directly above the tissue/organ of interest. Position the probe as perpendicular as possible and 1 cm away from the tissue. Use a ruler to accurately measure 1 cm from the surface of the organ. Adjust the probe to be positioned closer than 1 cm if the signal is not clear. However, ensure that the probe never touches the tissue.
      NOTE: The probe could be handheld, but it is recommended to use a probe holder/stand that can be adjusted and remains stable. Position the probe as perpendicular as possible and 1 cm away from the tissue. A ruler could be used to accurately measure 1cm from the surface of the organ. The probe could be adjusted to be positioned closer than 1 cm if the signal is not clear. However, the probe should never touch the tissue.
   3. Confirm that the laser spot is on the left lateral lobe, as close to the portal vein as possible.
   4. Confirm that all selected libraries, regression settings, target sigma, regression window, and acquisition modes are correct and as desired.
      1. Make sure the room is as dark as possible. Verify that the probe does not have any contamination, especially blood, on the window. In case of contamination, use a lint-free, non-abrasive wipe with 70% isopropyl alcohol to clean the Quartz window, followed by thorough drying with a dry wipe before proceeding.
      2. In case of heavy contamination, wipe the probe as thoroughly as possible, as described above. Then, calibrate the device by placing the laser in a dark enclosure lined with black tape and pressing the Take Dark button. Calibration takes about 5–10 min. Regardless of contamination, calibrate the device once a month, and it will send automatic reminder messages.
         NOTE: The explanations of each quality control metric, examples of such QC metrics, and the raw spectral signal obtained from optimal and sub-optimal recordings (Supplementary File 1, Figure 3, Supplementary Figure 3). These quality control criteria were optimized for whole livers from the rat model. Any newly developed protocol using a different organ/animal model should refine and adjust these metrics accordingly.
   5. On the main page, click the Start button to start acquiring data. Depending on the settings chosen, the software will take recordings until the target error value (sigma) or 180 s is reached. Data acquisition is now complete.
      1. Infer the quality of the RRS signal from these plots and values to allow adjustment of the probe position or measurement conditions (Figure 3). If the obtained RRS signal fails to meet these quality control criteria, adjust the probe to be as perpendicular as possible and about 9 mm from the surface to the probe window.
   6. Once data acquisition/experimental protocol is complete, discard the whole liver/tissue into a biohazard bin. Clean any system or surface that was in contact with the organ/tissue with 70% ethanol.
      1. If working with a perfusion system, run DI water mixed with enzymatic cleanser (7.8 mL diluted in 1 L of deionized [DI] water), followed by clean DI water.
         NOTE: Each recording will save as a separate analysis file under a new session file. Results for 3RMR and mitochondrial signal strength (r3RMR) are plotted in the main window. The raw spectrum can also be displayed. Other quality check metrics, such as residual signal strength and total signal strength, will be displayed under the Spectrum tab.
4. Data analysis:
   1. Once done with data acquisition, click the Review folder at the bottom of the screen to open the experimental file and see the results.
   2. Alternatively, open a text file containing the results. Select the session file of interest. An analysis file containing all the obtained measurements will open. Each parameter and its sigma value will have its own column. For example, 3RMR values obtained each second will be listed under the column "3RMR," and its sigma value will be listed under "sigma_3RMR".
   3. The last row of each column will contain the final values for each parameter of that session file. Use this value to plot.
   4. Repeat steps for each session file/each recording. Plot obtained values as desired.

Figure 2: RRS device setup. A schematic of the RRS system setup. The probe is connected via an optical fiber cable to the RRS system, which contains the laser excitation source and the spectrometer. This setup then connects to any laptop that contains the spectrometer control app for data collection and analysis. Both the computer and the spectrometer unit are connected to a power outlet. RRS measurements should be taken in the dark. If the room cannot be shielded from light entirely, the RRS system comes with a black cover with a rectangular opening in the center, where the laser probe can be placed. The box covers the sample/the section of the organ from which the RRS measurements are being taken. Please click here to view a larger version of this figure.

Figure 3: Example traces and quality control metrics of suboptimal quality and optimal quality traces. The residual signal (noise) is marked with an orange bracket, and the mitochondrial signal with a green bracket. (A) Example trace from a suboptimal Raman signal. The residual signal is too large to differentiate from the mitochondrial signal. Spectral peaks are indistinguishable throughout the measured signal. (B) Example trace from optimal Raman signal. The mitochondrial signal is visibly larger and distinct from the residual signal. Spectral peaks from the obtained measurement are distinguishable. (C) QC metrics obtained from each signal. Overall, the total signal obtained in the suboptimal reading is less than the optimal reading. More mitochondrial signal is measured in the optimal recording in comparison to the suboptimal recording, with mitochondrial signal accounting for 17% and 14% of the total signal, respectively. Signal was measured with 4.3% error in the optimal reading, in contrast to 18.5% error in the suboptimal reading. Similarly, unexplained residual signal accounts for 11% of the total signal in the suboptimal recording, while it accounts for 3.6% in the optimal. Please click here to view a larger version of this figure.

3. Evaluating mitochondrial health using RRS

NOTE: Resonance Raman Spectroscopy (RRS) technology was employed to develop a mitochondrial viability metric (3RMR) that indicates the viability of transplant organs. This study aims to demonstrate how changes in oxygenation–and, by extension, cellular respiration– are reflected in 3RMR trends in comparison to traditional damage biomarkers, such as lactate and K⁺. An oxygen stress test is utilized here, where, after 5 min of continuous perfusion, the oxygenated perfusion flow is paused for 5 min, then resumed for 5 min. RRS measurements, as well as inflow and outflow perfusate samples, are taken throughout the stress test.

1. Liver procurement:
   1. Sedate animals in an induction chamber using 3%–5% isoflurane. Filter excess vapor with activated charcoal.
   2. Perform a full hepatectomy on male Lewis rats (150 g). Ligate the branches of the portal vein. Inject 50 U of sodium heparin through the IVC. Cannulate the portal vein with a 16 G catheter during surgery, and flush the liver with 50 mL of saline.
   3. Place the procured livers on ice in a Petri dish containing 30 mL of University of Wisconsin (UW) solution.
2. Subnormothermic machine perfusion (SNMP) setup and resonance Raman measurements:
   1. Set up the RRS device as previously described. Set the Acellular Rat Liver Protocol on the spectrometer control software, which automatically sets width mode to adaptive width, average length to 180, and target sigma to 1.5. It also selects 3RMR and Fluorescence as measurement parameters, as well as Mito only as the regression library. Set the acquisition mode to continuous mode.
   2. Set up the SNMP system as described elsewhere¹⁸.
      1. Briefly, ensure the perfusion setup consisted of a perfusion chamber, a peristaltic pump, a membrane oxygenator, and a bubble trap.
      2. Deliver oxygen with an atmospheric mix (95% O₂ and 5% CO₂); Keep pO₂ between 400–500 mmHg. Monitor the intrahepatic pressure with a pressure sensor and keep it between 3–5 mmHg. Collect the samples through sampling ports.
   3. Once the software and the perfusion system are set up, connect the cannula penetrating the PV to the inflow tube of the perfusion machine. Keep the organ, perfusate, and perfusion setup at room temperature (21 °C), without the help of a water bath.
   4. Deliver the perfusate to the organ through the cannula in the PV utilizing a peristaltic pump.
      1. To prepare the perfusate, mix 500 mL of phenol red-free Williams' Medium with 5 g of Bovine Serum Albumin (BSA), 12 mg of water-soluble dexamethasone, 5 mL of penicillin-streptomycin, 5 mL of L-glutamine, 2.5 µL of insulin, and 1000 U heparin.
      2. Stir all components on a stir plate for about 20 min, or until all components have dissolved. Increase the perfusion flows to yield pressures of 3–5 mmHg.
   5. Then, place the RRS laser source in the probe holder and position it at 1 cm above the largest lobe of the liver, as close to the PV as possible. Pause the perfusion flow at 5 min (T₅) and resume it at 10 min (T₁₀). Conduct the stress test at 15 min (T₁₅).
   6. Take RRS measurements throughout the whole stress test; take inflow and outflow measurements every 5 min, starting at T₀ (Figure 4). Plot the 3RMR values, as well as outflow K⁺ levels and lactate levels for T₀, T₅, T₁₀, T₁₅ (n = 3) (Figure 5).
   7. Calculate oxygen consumption rate (OCR) at T₀, T₅, T₁₀, T₁₅ by multiplying the inflow/outflow partial pressure of oxygen (pO₂) with the solubility coefficient of oxygen in plasma (0.00314). Plot the OCR for T₀, T₅, T₁₀, T₁₅ (Figure 5).
      NOTE: Exemplary Resonance Raman spectral traces at perfused (T₅), ischemic (T₁₀), and reperfused (T₁₅) states from one liver are shown (Figure 6)

Figure 4: Diagram depicting experimental flow. A full hepatectomy was performed on female Lewis rats. The procured liver was placed on subnormothermic machine perfusion and supplied with oxygenated perfusate for the first 5 min. Perfusate flows into the liver through the portal vein and recirculates through the peristaltic pump (perfusion flow depicted with blue arrows). Oxygenated perfusion was paused for the next 5 min at T5 and restarted for another 5 min at T10. 3RMR measurements taken throughout the stress test (depicted as the laser). Perfusate samples were obtained at T0, T5, T10, and T15. Please click here to view a larger version of this figure.

Results

Fresh livers were subjected to periods of ischemia and reperfusion utilizing an oxygen stress test. Then, the real-time changes in 3RMR were compared throughout the stress test to oxygen consumption rate, outflow lactate, and potassium obtained from perfusate analysis at T₀, T₅, T₁₀, T₁₅. Under healthy conditions, 3RMR values are expected to be between 10% and 30% (Figure 5A). As expected, the 3RMR values at T₀ and T₅ fall within this range, since the liver was continuously supplied with oxygenated perfusate between these time points (Figure 5A). However, the 3RMR value at the end of the ischemic period (T₁₀) is out of the optimal range (Figure 5A). This increase is indicative of mitochondria shifting to their reduced state, as the electrons build up throughout the transport chain due to deoxygenation. At T_10, the oxygenated flow is resumed; the reperfusion period begins. At the end of the reperfusion period (T₁₅), the 3RMR values return to optimal levels (Figure 5A) as the activity of the ETC is restored with the reintroduction of oxygen as the final electron acceptor. These changes in oxygen flow are visible in the raw spectra as well. Raw RRS spectra at T₀ and T₁₅ have spectral characteristics similar to those of oxidized mitochondria (Figure 6A,C), such as a spectral marker peak at 1371 cm^-1 (Figure 6D,F). Similarly, the raw Raman spectra at T₁₀ (Figure 6B) show reduced RR markers, such as a spectral marker peak at 1357 cm^-1 (Figure 6E). In conclusion, RRS identifies the shifts in mitochondrial redox states in real-time, which is demonstrated both in the raw spectral signal, and as a metric of mitochondrial health (3RMR).

RRS can also calculate 3RMR values based on the redox states of cIII and cIV. A similar pattern was observed in the cIII and cIV 3RMR values (Figure 5B). At T₀, fully oxidized 3RMR values for cIV, and 3RMR values within the optimal range for cIII were observed. This redox balance between the complexes is physiological, as electrons flow from cIII to cIV to be rapidly oxidized. Upon the onset ischemia, an increase in 3RMR levels for each complex was observed. During the ischemic period, a build-up of electrons through the ETC increases the reductive stress on cIII and cIV, which consequently increases cIII-3RMR and cIV-3RMR. However, upon reintroduction of oxygen, the 3RMR values of both complexes decrease back to optimal levels: fully oxidized cIV and physiologically reduced cIII (Figure 5B). The 3RMR trends in both complexes show that the cessation of oxygen impacts the entire ETC, and no individual complex is especially vulnerable to no-flow ischemia.

On the other hand, the changes in oxygenation dynamics were not reflected in damage biomarkers and blood-gas analytes obtained from perfusate analysis. Upon onset ischemia, lactate and K⁺ are expected to rise, as the cell starts relying on aerobic respiration, and slowly degrades to release intercellular K⁺. We also expect OCR to decrease, as the cells do not produce enough energy to continue cellular respiration. Even after a period of no-flow ischemia, oxygen consumption rates showed no signs of damage. Outflow lactate and K⁺ levels stay relatively consistent throughout the stress test, despite the changes in oxygenation (Figure 5D–E).

Figure 5: Changes in 3RMR and damage biomarkers throughout the oxygen stress test. Start of ischemia and reperfusion periods are depicted through dotted lines at minutes 5 and 10, respectively. Additionally, periods of oxygenated flow are depicted with blue lines, and periods of ischemia are depicted with red lines. (A) 3RMR values throughout the mitochondrial assay. The 3RMR values increase when oxygenated flow is paused and decrease back to optimal levels when flow is restarted. (B) 3RMR values of complex III and complex IV. Cessation of flow leads to a reduction in both complexes. (C) Average outflow K⁺ values throughout the stress test. K⁺ values stay consistent throughout the stress test, despite changes in oxygenation dynamics. (D) Average outflow lactate levels throughout the stress test. Lactate output stays consistent throughout the stress test. (E) Oxygen consumption rates at T₀, T₁₀, T₁₅. Since pO₂ values are flow-based measurements, OUR could not be calculated for a no-flow ischemia period. No significant changes were observed (p-value: 0.2500) (n = 3). Please click here to view a larger version of this figure.

Figure 6: Exemplary raw spectral traces of a fresh rat liver throughout the stress test. Full spectral profiles of a liver in (A) oxidized, (B) ischemic, and (C) reperfused states. The shift from the oxygenated RR marker to the reduced RR marker is visible when zooming into the spectral peak of the mitochondria, marked with a red box in each spectral profile. (D–F) Oxidized RR marker peaks (1371 cm^-1) are present in (D) oxidized and (F) reperfused states and are marked with a red dotted line. Reduced RR marker peak (1357 cm^-1) is present in (E) ischemic state and is marked with a blue dashed line. Please click here to view a larger version of this figure.

Supplemental Figure 1: Spectral traces of reduced and oxidized complex III, complex IV, hemoglobin, and myoglobin. (A) Complex III, (B) complex IV, (C) hemoglobin, and (D) myoglobin. The regression analysis runs the collected spectra against these spectral libraries (if selected), yielding a factor for each library spectrum that represents the best fit estimate of its relative contribution in the recorded spectrum and thus its relative concentration in the tissue. Please click here to download this file.

Supplementary Figure 2: Example of utilizing regression libraries to break down overlapping heme-containing molecules (hemoglobin and oxidized/reduced mitochondria). (A) Raw spectral traces from liver tissue perfused with cellular perfusate, with all necessary regression libraries, such as hemoglobin, oxidized hemoglobin, reduced mitochondria, and oxidized mitochondria, were selected. It is clear that the line of best fit, derived from the libraries and the regression analysis, neatly overlaps with the measured signal, with little unexplained residual. (B) Raw spectral trace from the same tissue, but with only oxidized and reduced mitochondria libraries selected. Since there is a large unexplained residual structure, it is clear that the mitochondrial spectral peak is shifted several wave numbers from that of hemoglobin, which was not included in the regression analysis. This figure shows that the high-resolution spectrometer and regression analysis against the entire spectrum are able to distinguish closely related spectra.Please click here to download this file.

Supplementary Figure 3: Screen captures the software. (A) Main window that displays measurement values across time. 3RMR, StO2, and any other selected series will be displayed here during the measurement. Additionally, the signal strength of each series will be displayed as a dashed line on the graph, and a value underneath on the right side of the screen. For instance, the solid line captured here represents the 3RMR of a tissue, and the dashed line represents r3RMR (signal strength of 3RMR). If one wanted to see the quality of the recorded spectra, they could click on the Show spectrum tab (circled in purple). (B) Screen that displays the raw spectra of the tissue. This is the tab where one could assess the quality of the signal through the strength of the measured spectra versus the residual noise. (C) Tab that shows trends in series across the experiment. If taking, for example, multiple 3RMR measurements throughout an experiment, each measurement will be displayed across time in this tab. Trends in other series, if selected, will also be displayed here. This tab could be accessed by clicking the Trends button on the main screen (circled in green on A). (D) Info tab, where one can enter experimental information, such as session name and description. (E) System tab, where one can upload pre-loaded protocols. For instance, if selecting the protocol LiverContAcell, this protocol will set the regression library, target regression window and sigma according to the acellular perfusion protocol for rat livers. To import a protocol, one can click Import Settings from File and import the protocol of interest. If one wanted to create their own protocol, they can select necessary libraries from the (F) Regression Library drop-down menu, adjust target sigma and regression window, as well as alarms, and click the Save (represented with a disc) on the bottom left. (F) Plot tab, where one can select which series to display on the main window. If reanalyzing a spectrum, and one wanted to add more series to their previous measurements, they can select the series of interest from under the Recal section. During recalibration (Recal), the software will reanalyze the measured raw spectra and display the results of the newly selected series. One can also select to show the signal strength of selected series from this tab.Please click here to download this file.

Supplementary File 1: Explanations of each quality control metric.Please click here to download this file.

Discussion

This study highlights a noninvasive, real-time, and highly sensitive method of quantifying mitochondrial health using a portable Resonance Raman Spectroscopy system that is comprised of a laser in a compact probe, connected with a fiber optic cable to a laser pump. This method used a 441 nm laser to excite mitochondria, which produce unique spectral signals depending on their oxidation state. Thus, RRS is able to quantify each state of mitochondria through their spectral signatures and compare the emitted spectra with pre-measured spectral libraries using regression analysis. A mitochondrial health metric was then created based on the ratio of reduced to total mitochondria: the Reduced Resonance Raman Mitochondrial Ratio (3RMR).

This paper focuses on demonstrating real-time changes in 3RMR as a function of oxygenation with native tissue without the need for sample processing. This was achieved through an oxygen stress test: after 5 min of continuous oxygenated perfusion, perfusion flow is paused for 5 min and restarted for another 5 min. 3RMR measurements are taken throughout the stress test. A stress test model, where we shift between ischemic and oxygenated periods, allows us to track continuous changes in mitochondrial redox states– and, by extension, mitochondrial function. In other words, the oxygen stress test allows us to track the trends in 3RMR as a function of oxygenation. Our group has attributed increasing 3RMR values to ischemic conditions, disrupting perfusion flow, which causes electrons passing through the ETC to build up, as O₂ is not available as the final electron acceptor. This build-up of electrons causes a reduced state, which translates to an increase in 3RMR values. However, upon reintroduction of O₂, healthy mitochondria are expected to resume ETC activity and recover to physiological redox states, as their structural and functional elements are intact.

As expected, in the present paper, a real-time, continuous increase in 3RMR was observed throughout the ischemic period (Figure 5A). Similarly, 3RMR gradually returns to optimal levels throughout the reperfusion period (Figure 5A). This real-time change in mitochondrial redox states is also visible in the spectral graph, as the mitochondrial peak shifts from oxidized (1371 cm^-1) to reduced (1357 cm^-1) states throughout the ischemic period, and returns to oxidized at the end of reperfusion (Figure 6). Other reduced RR marker peaks appear during the ischemic period, as well. Unlike traditional mitochondrial health assays, Resonance Raman is sensitive enough to quantify the redox states of complex III and complex IV, which could, and has provided, mechanistic insight into the specific impact of ischemia-reperfusion injuries to the ETC. In healthy mitochondria, electrons are passed through various complexes in the ETC, starting from complex I, until they reach O₂ as the final electron acceptor. A pause in oxygen flow can shift each complex to a reduced state, as electrons get backed up along the ETC. As expected, a real-time increase was observed in 3RMR values of cIII and cIV upon the onset of ischemia. This indicates that the lack of O₂ impacts the whole ETC, rather than a specific complex (Figure 5B). As we will discuss below, the sensitivity of RRS facilitates detailed mechanistic studies and informs the development of targeted therapies for IRI.

Traditionally, we also expect to observe elevated lactate levels in ischemic organs, since they rely on anaerobic metabolism in the absence of oxygen to produce ATP. Similarly, an increase in K⁺ levels was expected in damaged organs, since the membrane integrity of cells is disrupted, allowing an efflux of intercellular K⁺. Lastly, severely damaged cells may have lower metabolism, since their metabolic pathways are impacted by ischemia-induced metabolic waste and ROS, leading to a diminished consumption of oxygen. However, no changes in lactate, K⁺, or oxygen consumption are observed during or after the ischemic period. In fact, values do not indicate any cessation of oxygen flow, as they stay relatively consistent throughout the stress test (Figure 5B–D). In essence, using an oxygen stress test, this study shows that 3RMR values reflect shifts in mitochondrial redox states during ischemia-reperfusion more quickly, in real time, and more sensitively than traditional damage biomarkers (Figure 5). We recognize that this study compared 3RMR dynamics with indirect indicators of mitochondrial health, such as lactate and OCR, rather than more direct downstream comparators, such as NAD+/NADH and ATP. Although this study does not explicitly correlate changes in 3RMR to ATP/NADH concentrations, previous work^9,14 has shown 3RMR to be predictive of changes in these downstream biomarkers. For instance, Nguyen et al.⁹ demonstrated that 3 h warm-ischemic (WI) livers with low 3RMR values (<10%) have significantly lower ATP concentrations and NAD/NADH ratios than 1 h WI livers (3RMR values of ~15%). Additionally, Jain et al.¹⁴ saw no significant differences in the NAD+/NADH ratio or EC in groups that had no significant differences in their 3RMR. They also observed the NAD+/NADH ratios and EC to be significantly different in 24 h cold-ischemic (CI) livers upon acellular perfusion in comparison to cellular perfusion, which also showed statistically significant 3RMR values. Taken together, these results demonstrate 3RMR values to reflect changes in more direct measures of mitochondrial and ETC function.

Even though this paper utilizes RRS throughout a 15-min perfusion to demonstrate the performance of the system, other published studies have demonstrated the utility of RRS in longitudinal studies. More specifically, in a previous work, we demonstrated that RRS complements traditional biomarkers throughout longer organ perfusions, as it provides insight into mitochondrial health and function that differ significantly from traditional methods. For instance, the study utilized RRS to assess the viability of transplantable (24 h SCS) and non-transplantable (72 h SCS) cold-ischemic rat livers throughout subnormothermic perfusion¹⁰. Although the initial response to oxygenation in both groups was similar, the 3RMR of non-transplantable livers increased with perfusion, while the 3RMR of transplantable liver remained consistent through SNMP. Similarly, potassium levels and overall weight gain for 72 h CI livers were higher throughout SNMP in comparison to 24 h CI livers. The study demonstrated RRS' utility in complementing traditional biomarkers throughout long perfusions. In another study, we utilized RRS to understand reoxygenation dynamics and metabolic recovery of cold-ischemic (CI) rat livers¹⁴. We perfused minimally ischemic (15 min of cold-ischemia) and 24-h cold-ischemic livers with either an acellular or an RBC-packed perfusate (pRBC). When 24-h CI livers were perfused with acellular perfusate, we observed a latent decrease in 3RMR compared to when perfused with pRBCs. The study attributed this delayed increase in 3RMR to the accumulation of electrons at the mitochondrial cytochromes during cold ischemia that can be rapidly transferred to the abundant oxygen molecules in the case of the pRBC-based perfusate. However, in combination with other markers of injury, we observe greater hepatic damage in 24 h CI livers perfused with pRBCs. The study concluded that measuring mitochondrial oxygenation in real time via RRS can provide insight into mitochondrial recovery, which could be beneficial for optimizing long perfusion strategies. In essence, the findings indicate that integrating traditional biomarkers (e.g., lactate, OCR, ATP) with RRS-driven insights into mitochondrial redox states and oxygenation dynamics provides a more comprehensive assessment of mitochondrial recovery.

Other studies have used RRS as a testbed for therapeutics that may mitigate ischemia-reperfusion injury to the ETC⁹. In prolonged periods of ischemia, electrons accumulate within the ETC, with the capacity for electrons to escape through the various complexes, especially during reperfusion. Understanding the redox state of individual complexes as a function of ischemia-reperfusion can offer a deeper understanding of the location of injury with potential for more targeted interventions. For instance, our group has utilized RRS to develop therapeutic interventions for complex-III dysfunction in warm-ischemic porcine livers. In a study conducted by Nguyen et al. (2026)⁹, 1 h and 3 h WI livers were placed onto SNMP for 3 h. As previously mentioned, three-hour WI livers are known to suffer from damage to the ETC, resulting in overoxidation and reduced metabolism. Although 3RMR values for fresh and 1 h WI livers were within the optimal range throughout the perfusion, we observed 3RMR values of 3 h WI livers to continuously drop, reaching below the optimal range (10%) at 3 h. Additionally, we observed no differences in the 3RMR values of cIV between either group, with cIV being fully oxidized in each group. The hyperoxidization of cIII, combined with a healthy redox state observed in cIV indicates reperfusion-induced overoxidation at complex III. Armed with this knowledge, Nyugen et al. (2026)⁹were able to select a therapeutic that could overcome the dysfunction of cIII: methylene blue (MB), which acts as an electron shuttle between cI and cytochrome c, bypassing cIII and transferring electrons directly to cIV. The 3RMR results reflected the therapeutic effect of MB on the mitochondria, as 3RMR values during SNMP of 3h WI livers increased to reflect electrons being transported directly to cIV, and hepatic metabolism restarting. The mitigating effect of MB was also validated by decreasing outflow lactate and improving oxygen consumption rates. In essence, the specificity of RRS enabled the development of a therapeutic pathway tailored to mitochondrial needs following warm ischemia.

Throughout this paper, we present Resonance Raman Spectroscopy as a noninvasive, real-time, and highly specific method to measure mitochondrial health. The study demonstrates how RRS can measure subtle shifts in redox states without disturbing the tissue before such physiological changes have downstream effects. However, it is important to note the several limitations of the Resonance Raman system. Firstly, RRS requires all molecules that are resonantly enhanced in a tissue to be identified and accounted for in the regression, with each molecule having different enhancement factors to account for relative signal strengths. Although we have created spectral libraries for all such components at a given wavelength and tissue, the user may need to create and validate new libraries for each tissue and wavelength of interest. Further, some tissues may have complicated spectral elements, which can make it challenging to measure mitochondrial cytochromes that exist in lower concentrations. For example, pig or human livers contain beta-carotene, and blood contains hemoglobin, which both have strong spectral signals that add to the number of spectral signatures RRS needs to account for in the regression. Lastly, the 441 nm laser used for these measurements has a beam diameter of 2 mm and a penetration depth of less than 1 mm. Such localized, surface measurements may promote sampling error, as differences in redox state in other parts of the organ may be overlooked. To overcome such challenges, measurements could be taken from biopsies taken from the core of the organ, or a multi-laser setup could enable measurements over a larger surface area to provide a more complete understanding of whole organ health.  

In conclusion, we show that Resonance Raman Spectroscopy is sensitive to subtle changes in mitochondrial dynamics in comparison to blood-gas analytes. A detailed explanation of setting up and operating the system is provided here. We highlight the noninvasive, highly specific, and real-time evaluation nature of RRS, and explain how it could be used as a diagnostic and mechanistic approach. We also emphasize its use in developing specific therapeutic pathways for mitochondrial dysfunction. Future studies may focus on adapting the RRS system to evaluate mitochondrial health from biopsies, precision-cut tissue slices, or other tissue-derived products, including human tissue for translational research. The adoption of RRS offers a powerful approach in metabolic research, improving our understanding of cellular bioenergetic processes with greater ease than traditional methods.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
16 G Catheter	BD Insyte Authoguard	381454	Used to cannulate the portal vein
Activated Charcoral	Vet Equip Vapor Guard Activated Charcoal	931401	Used to filter excess vapor during anasthesia
BSA	Sigma-Aldrich	A7906	Perfusate composition
Bubble Trap	Radnoti	130149
Dexamethasone	Sigma-Aldrich	D2915	Perfusate composition
Glutamax	Thermo Fisher Scientific	35050061	Perfusate composition
Masterflex L/S Precision Pump Tubing (24 G)	Fisher Scientific	13-200-292	Used to flow perfusate through the liver
Masterflex peristaltic pumps	Cole Parmer, Vernon Hill, IL	7528-30	Used to flow perfusate through the liver
Membrane Oxygenator	Radnoti	130144	Used to oxygenate perfusate.
Penicillin-streptomycin	Sigma-Aldrich	P4458	Perfusate composition
RAPIDPoint 500 Blood Gas System	Siemens Healthineers	41115805	For blood-gas analysis
Resonance Raman System	Pendar Technologies	A4D441	Contact Pendar Technologies on inquiries about this product