Sangeeta Murugkar

Ionizing radiation elicits complex and dynamic cellular responses that are not fully captured by conventional analytic approaches. Many existing assays typically rely on population-averaged measurements, require extensive and/or destructive sample preparation, or depend on exogenous contrast agents for single-cell analysis. Additionally, they often focus on isolated biomarkers rather than concurrent changes in key macromolecular constituents such as lipids and proteins. To address these limitations, we developed a label-free, multimodal nonlinear optical (NLO) imaging platform that integrates stimulated Raman scattering (SRS) microscopy at Raman shifts of 2850 cm⁻¹ and 2926 cm⁻¹ with two-photon excitation fluorescence (TPEF) microscopy targeting reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] and flavin adenine dinucleotide (FAD). In foundational studies, we characterized the radiobiological effects of clinical X-ray doses (2–10 Gy) in human lung cancer A549 cells. Irradiated cells exhibited multiple quantifiable biomarkers that varied with radiation dose and post-irradiation time. These included changes in lipid and protein signals, differences in NAD(P)H and FAD intensities, higher lipid-to-protein ratios, enhanced optical redox ratio, enlarged cell and lipid droplet areas, and increased colocalization of lipid droplets with mitochondria. Collectively, these results establish NLO imaging as a sensitive and powerful modality for quantitatively assessing cellular responses to clinical doses of ionizing radiation.

Intraductal carcinoma of the prostate (IDC-P) is a very aggressive histopathological subtype of prostate cancer (PCa) that is strongly associated with poor clinical outcomes but for which no accurate biomarkers exist. Here, we demonstrate a novel application of texture analysis-based machine learning alongside multimodal nonlinear optical imaging using second-harmonic generation (SHG) and stimulated Raman scattering (SRS) at 1450 cm⁻¹ and 1668 cm⁻¹ Raman shifts to distinguish IDC-P from regular PCa and benign prostate. Images from each tissue type were analyzed to extract the first-order statistics and texture-based second-order statistics derived from the gray-level co-occurrence matrix of the images. A machine learning model was constructed using support vector machine (SVM) to classify the prostate tissue based on these statistics. Our results demonstrate that SVM models trained on either SHG or SRS images accurately classify IDC-P as well as high-grade PCa, low-grade PCa, and benign tissue with a mean classification accuracy exceeding 89%. Moreover, a mean classification accuracy of 98% was achieved using an SVM model trained on combined SHG and SRS images. Our study demonstrates that multimodal nonlinear optical imaging using SHG and SRS can be combined with texture analysis-based SVM classification to provide pathologists with a reliable biomarker of IDC-P.

Biodosimetry is a key technique for retrospective radiation dosimetry that provides individual estimates of the absorbed dose of ionising radiation based on the detection of biological indicators. A critical challenge confronting current biodosimetry methods is the time and technical expertise needed in processing and analysing samples, therefore new high-throughput techniques are required. In this pilot study, we demonstrate a technique based on RS and multivariate analysis of peripheral blood plasma from nine healthy male and female anonymous donors for the classification and biomarker identification of ex vivo irradiated blood exposed to 0 (control), 5 and 20 Gy of x-ray dose. After 4 h post-exposure, the blood was centrifuged, and the blood plasma samples were immediately frozen at −80 °C. Raman spectra were measured from thawed blood plasma using a custom benchtop Raman micro-spectroscopy setup. Data were preprocessed and analysed using partial least squares-discriminant analysis (PLS-DA). We applied a method based on a linear mixed-effects model to compensate for the differences in covariates such as gender, age and complete blood count between donors. After covariate adjustment, the application of PLS-DA to the residual Raman spectral intensities provided improved separation in the binary classification results (0 vs. 5 and 0 vs. 20 Gy). Raman spectral biomarkers responsible for the discrimination were extracted by evaluating the coefficients of the PLS-DA loading vectors. Sparse PLS-DA was demonstrated to be a promising method that offers the potential to further narrow down the regions in the Raman spectra that are dose discriminatory.

Background: Micrometer‐scale dosimetry is crucial when estimating the energy deposited within micrometer‐scale biological targets exposed to low doses or high dose gradients. Raman micro‐spectroscopy read‐out of radiochromic films (RCFs) permits micrometer‐scale resolution; this presents a novel opportunity to explore its feasibility for experimental microdosimetry. Purpose: The purpose of this work was to develop a novel approach towards generating data for experimental microdosimetry. The objective was to develop a method based on high (1–2 µm) spatial resolution Raman micro‐spectroscopy of RCFs, ensuring reproducibility of data while producing two‐dimensional intensity maps of the Raman response. Methods: EBT3 RCFs were irradiated to doses between 0.2 Gy and 2 Gy using a clinical linear accelerator. Raman spectra were collected using a custom Raman microscope fitted with 40× and 60× water immersion (WI) objectives, and a commercial Raman microscope utilizing a 100× dry objective. The excitation source of the custom setup was circularly polarized to minimize the influence of polarization on the film read‐out. The Raman response of the RCFs was measured over a 100 × 100 µm ² region of interest (ROI) with a 10 × 10 grid. The Raman response of the active layer of the film was normalized to the radiation‐insensitive monomer peak at 2260 cm ⁻¹ . The Raman intensities of the 1445 cm ⁻¹ and 2060 cm ⁻¹ peaks were used to generate dose response curves for each microscope setup. Maps of the Raman intensity over the 100 × 100 µm ² ROI for the 60× WI setup were used to quantify the heterogeneity in the film response. Higher resolution point‐scans were performed over a 20 × 20 µm ² ROI for 0 Gy and 2 Gy samples. Results: The dose response of each Raman microscope setup over the 0–2 Gy dose range was linear ( r ² of 0.98) after normalization to the 2260 cm ⁻¹ Raman peak in the active layer. The slope of the dose response curve of the commercial microscope exhibited dependence on the film orientation; this was minimized with the custom Raman setup by using the circularly polarized excitation source. The relative standard deviation (RSD) of the 1445 cm ⁻¹ peak Raman intensity over the 100 × 100 µm ² ROIs was significant (∼11%) for each microscope setup, and independent of dose. The Raman intensity distribution maps revealed that the heterogeneity in Raman response across the ROI was on the same size‐scale (1.62 µm × 9.4 µm) of the lithium salt of pentacosa‐10,12‐diynoic acid (LiPCDA) crystals comprising the active layer of the film. Conclusions: This work explored the feasibility of a Raman micro‐spectroscopy‐based read‐out technique of RCFs for experimental microdosimetry. Utilizing the 2260 cm ⁻¹ peak in the Raman spectrum as an internal standard for normalization produced a linear ( r ² of 0.98) dose‐response curve in the 0–2 Gy dose range. Utilizing circularly polarized laser excitation minimized the polarization dependence of the film and increased the reproducibility of the Raman measurements. Spatial heterogeneity in the concentration of PCDA crystals in the active layer was visualized based on two‐dimensional maps of the Raman intensity response to explore the implications on microdosimetry.

SignificanceCellular metabolism plays a central role in health and disease, making its study critical for advancing diagnostics and therapies. Label-free optical metabolic imaging using endogenous fluorescence from reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] and flavin adenine dinucleotide (FAD) provides nondestructive, high-resolution insights into metabolic function and heterogeneity from the sub-cellular to the tissue level. Standardized approaches are essential to ensure reproducibility and comparability across studies.AimWe aim to establish a consensus framework for the acquisition, calibration, and reporting of microscopic imaging metabolic function assessments based on fluorescence intensity and lifetime measurements of NAD(P)H and FAD.ApproachWe present best practices for calibrating, analyzing, and reporting fluorescence intensity-based optical redox ratios and fluorescence lifetime data using multiexponential fitting and phasor analysis. Guidelines for validation experiments and cross-system standardization are provided to improve accuracy and reproducibility.ResultsWe demonstrate the importance of calibration procedures and normalization strategies for intensity-based optical redox measurements. We highlight needed calibration, signal-to-noise ratio considerations, and the impact of distinct analytical approaches on fluorescence lifetime-based metabolic function metrics.ConclusionWe recommend a consistent, practical framework for reproducible, label-free, optical metabolic imaging, facilitating robust comparisons across studies and supporting the broader adoption of optical metabolic imaging technologies for biomedical research and clinical translation.

Intraductal carcinoma of the prostate (IDC-P) is a very aggressive histopathological subtype of prostate cancer (PCa) for which no accurate biomarkers exist. In our work, we apply a multimodal nonlinear optical imaging approach that uses second-harmonic generation (SHG) and stimulated Raman scattering (SRS) imaging to distinguish IDC-P from regular PCa and benign prostate tissue. Images from each tissue type were classified using support vector machine (SVM). The technique classified the images from each region based on first-order statistics and texture-based second-order statistics derived from the gray-level co-occurrence matrix (GLCM) of the images. Our results demonstrate that SVM models trained on either SHG or SRS images accurately classify IDC-P as well as high-grade PCa, low-grade PCa, and benign tissue with a mean classification accuracy of over 89%. Furthermore, a classification model combining both SHG and SRS imaging modalities can accurately classify all tissue types with a mean classification accuracy of 98%.

SignificanceAltered lipid metabolism of cancer cells has been implicated in increased radiation resistance. A better understanding of this phenomenon may lead to improved radiation treatment planning. Stimulated Raman scattering (SRS) microscopy enables label-free and quantitative imaging of cellular lipids but has never been applied in this domain.AimWe sought to investigate the radiobiological response in human breast cancer MCF7 cells using SRS microscopy, focusing on how radiation affects lipid droplet (LD) distribution and cellular morphology.ApproachMCF7 breast cancer cells were exposed to either 0 or 30 Gy (X-ray) ionizing radiation and imaged using a spectrally focused SRS microscope every 24 hrs over a 72-hr time period. Images were analyzed to quantify changes in LD area per cell, lipid and protein content per cell, and cellular morphology. Cell viability and confluency were measured using a live cell imaging system while radiation-induced lipid peroxidation was assessed using BODIPY C11 staining and flow cytometry.ResultsThe LD area per cell and total lipid and protein intensities per cell were found to increase significantly for irradiated cells compared to control cells from 48 to 72 hrs post irradiation. Increased cell size, vacuole formation, and multinucleation were observed as well. No significant cell death was observed due to irradiation, but lipid peroxidation was found to be greater in the irradiated cells than control cells at 72 hrs.ConclusionsThis pilot study demonstrates the potential of SRS imaging for investigating ionizing radiation-induced changes in cancer cells without the use of fluorescent labels.

Spectral focusing is a well-established technique for increasing spectral resolution in coherent Raman scattering microscopy. However, current methods for tuning optical chirp in setups using spectral focusing, such as glass rods, gratings, and prisms, are very cumbersome, time-consuming to use, and difficult to align, all of which limit more widespread use of the spectral focusing technique. Here, we report a stimulated Raman scattering (SRS) configuration which can rapidly tune optical chirp by utilizing compact adjustable-dispersion TIH53 glass blocks. By varying the height of the blocks, the number of bounces in the blocks and therefore path length of the pulses through the glass can be quickly modulated, allowing for a convenient method of adjusting chirp with almost no necessary realignment. To demonstrate the flexibility of this configuration, we characterize our system’s signal-to-noise ratio and spectral resolution at different chirp values and perform imaging in both the carbon-hydrogen stretching region (MCF-7 cells) and fingerprint region (prostate cores). Our findings show that adjustable-dispersion glass blocks allow the user to effortlessly modify their optical system to suit their imaging requirements. These blocks can be used to significantly simplify and miniaturize experimental configurations utilizing spectral focusing.

Biological dosimetry is a key technique for retrospective radiation dosimetry that provides individual estimates of absorbed dose of ionizing radiation, applicable for use in a large scale radiological/nuclear event. Current techniques for biodosimetry are labor intensive and time consuming and not high through-put. In this proof-of-concept study, we developed a new approach for detecting irradiated blood based on Raman spectroscopy of blood combined with multivariate analysis. Peripheral blood samples from 8 healthy male and female, anonymous donors, were exposed to either 5 Gy X ray radiation or unirradiated (0 Gy). At 3 h postirradiation, the blood was immediately frozen at –80°C. Raman spectra were measured from thawed blood using a portable spectrometer system. Data were preprocessed and analyzed using principal component analysis (PCA) to observe trends in the data, and by using partial least squares-discriminant analysis (PLS-DA) to build a model to discriminate between Raman spectra of control (0 Gy) and irradiated (5 Gy) blood. We found strong evidence of inter-donor variability in the form of donor-wise clustering of PCA scores corresponding to the control Raman spectra, in addition to the poor separation of PLS-DA scores associated with Raman intensities of 0 Gy vs. 5 Gy spectra. However, after adjustment for donor covariates using a linear mixed-effects model, we obtained a better separation between control and irradiated blood using PLS-DA. Evaluation of the coefficients of the PLS-DA loading vectors indicated radiation-induced changes in proteins, lipids and hemoglobin to be major contributors for this discrimination. This pilot study demonstrates the potential of application of Raman spectroscopy to support biodosimetry of blood and blood components.