Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

Biomarkers to Predict Lethal Radiation Injury to the Rat Lung

Currently, there are no biomarkers to predict lethal lung injury by radiation. Since it is not ethical to irradiate humans, animal models must be used to identify biomarkers. Injury to the female WAG/RijCmcr rat has been well-characterized after exposure to eight doses of whole thorax irradiation: 0-, 5-, 10-, 11-, 12-, 13-, 14- and 15-Gy. End points such as SPECT imaging of the lung using molecular probes, measurement of circulating blood cells and specific miRNA have been shown to change after radiation. Our goal was to use these changes to predict lethal lung injury in the rat model, 2 weeks post-irradiation, before any symptoms manifest and after which a countermeasure can be given to enhance survival. SPECT imaging with 99mTc-MAA identified a decrease in perfusion in the lung after irradiation. A decrease in circulating white blood cells and an increase in five specific miRNAs in whole blood were also tested. Univariate analyses were then conducted on the combined dataset. The results indicated that a combination of percent change in lymphocytes and monocytes, as well as pulmonary perfusion volume could predict survival from radiation to the lungs with 88.5% accuracy (95% confidence intervals of 77.8, 95.3) with a p-value of < 0.0001 versus no information rate. This study is one of the first to report a set of minimally invasive endpoints to predict lethal radiation injury in female rats. Lung-specific injury can be visualized by 99mTc-MAA as early as 2 weeks after radiation.

Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer

Delta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed to develop a model to predict Dll4 expression levels in tumors using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains of breast cancer with different Dll4 expression levels and eight congenic xenograft strains were studied. Principal component analysis (PCA) was used to visualize and segment tumors, and modified PCA techniques identified and analyzed tumor and normal regions of interest (ROIs). The average NIR intensity for each ROI was calculated from pixel brightness at each time interval, yielding easily interpretable features including the slope of initial ICG uptake, time to peak perfusion, and rate of ICG intensity change after reaching half-maximum intensity. Machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. The selected machine learning methods accurately identified host Dll4 expression alterations with sensitivity and specificity above 90%. This may enable stratification of patients for Dll4 targeted therapies. NIR imaging with ICG can noninvasively assess Dll4 expression levels in tumors and aid in effective decision making for cancer therapy.

A rapid dynamic in vivo near-infrared fluorescence imaging assay to track lung vascular permeability after acute radiation injury

To develop a dynamic in vivo near-infrared (NIR) fluorescence imaging assay to quantify sequential changes in lung vascular permeability-surface area product (PS) in rodents. Dynamic NIR imaging methods for determining lung vascular permeability-surface area product were developed and tested on non-irradiated and 13 Gy irradiated rats with/without treatment with lisinopril, a radiation mitigator. A physiologically-based pharmacokinetic (PBPK) model of indocyanine green (ICG) pulmonary disposition was applied to in vivo imaging data and PS was estimated. In vivo results were validated by five accepted assays: ex vivo perfused lung imaging, endothelial filtration coefficient (Kf) measurement, pulmonary vascular resistance measurement, Evan's blue dye uptake, and histopathology. A PBPK model-derived measure of lung vascular permeability-surface area product increased from 2.60 ± 0.40 [CL: 2.42-2.78] mL/min in the non-irradiated group to 6.94 ± 8.25 [CL: 3.56-10.31] mL/min in 13 Gy group after 42 days. Lisinopril treatment lowered PS in the 13 Gy group to 4.76 ± 6.17 [CL: 2.12-7.40] mL/min. A much higher up to 5× change in PS values was observed in rats exhibiting severe radiation injury. Ex vivo K_f (mL/min/cm H₂O/g dry lung weight), a measure of pulmonary vascular permeability, showed similar trends in lungs of irradiated rats (0.164 ± 0.081 [CL: 0.11-0.22]) as compared to non-irradiated controls (0.022 ± 0.003 [CL: 0.019-0.025]), with reduction to 0.070 ± 0.035 [CL: 0.045-0.096] for irradiated rats treated with lisinopril. Similar trends were observed for ex vivo pulmonary vascular resistance, Evan's blue uptake, and histopathology. Our results suggest that whole body dynamic NIR fluorescence imaging can replace current assays, which are all terminal. The imaging accurately tracks changes in PS and changes in lung interstitial transport in vivo in response to radiation injury.

Automated in vivo Assessment of Vascular Response to Radiation using a Hybrid Theranostic X-ray Irradiator/Fluorescence Molecular Imaging System

Hypofractionated stereotactic body radiotherapy treatments (SBRT) have demonstrated impressive results for the treatment of a variety of solid tumors. The role of tumor supporting vasculature damage in treatment outcome for SBRT has been intensely debated and studied. Fast, non-invasive, longitudinal assessments of tumor vasculature would allow for thorough investigations of vascular changes correlated with SBRT treatment response. In this paper, we present a novel theranostic system which incorporates a fluorescence molecular imager into a commercial, preclinical, microCT-guided, irradiator and was designed to quantify tumor vascular response (TVR) to targeted radiotherapy. This system overcomes the limitations of single-timepoint imaging modalities by longitudinally assessing spatiotemporal differences in intravenously-injected ICG kinetics in tumors before and after high-dose radiation. Changes in ICG kinetics were rapidly quantified by principle component (PC) analysis before and two days after 10 Gy targeted tumor irradiation. A classifier algorithm based on PC data clustering identified pixels with TVR. Results show that two days after treatment, a significant delay in ICG clearance as measured by exponential decay (40.5±16.1% P=0.0405 Paired t-test n=4) was observed. Changes in the mean normalized first and second PC feature pixel values (PC1 & PC2) were found (P=0.0559, 0.0432 paired t-test), suggesting PC based analysis accurately detects changes in ICG kinetics. The PC based classification algorithm yielded spatially-resolved TVR maps. Our first-of-its-kind theranostic system, allowing automated assessment of TVR to SBRT, will be used to better understand the role of tumor perfusion in metastasis and local control.

Improving Diagnosis of Cervical Pre-Cancer: Combination of PCA and SVM Applied on Fluorescence Lifetime Images

We report a significant improvement in the diagnosis of cervical cancer through a combined application of principal component analysis (PCA) and support vector machine (SVM) on the average fluorescence decay profile of Fluorescence Lifetime Images (FLI) of epithelial hyperplasia (EH) and CIN-I cervical tissue samples, obtained ex-vivo. The fast and slow components of double exponential fitted fluorescence lifetimes were found to be higher for EH compared to the lifetimes of CIN-I samples. Application of PCA to the average time-resolved fluorescence decay profiles showed that the 2nd PC, in combination with 1st PC, enhanced the discrimination between EH and CIN-I tissues. Fluorescence lifetime and PC scores were then classified separately by using SVM support vector machine to identify the two. On applying SVM to a combination of fluorescence lifetime and PC scores, diagnostic capability improved significantly.

Spatio-temporal map for early cancer detection: Proof of concept

A spatio-temporal map of human cervical tissue is obtained from time-resolved fluorescence images with the dynamic contrast enhanced through principal component analysis (PCA) for clear demarcation of regions of normal and pre-cancerous conditions. Changes in the properties of fluorescence in different environments are captured through fluorescence lifetime maps in the human cervical tissue sample. The correlation embodied in the second principal component (PC) representing sectorial information free of background of the first PC, segregates fluorescence activities, as illustrated in the PC maps. It significantly enhances the contrast of the images which are majorly handicapped by the variations in fluorophore environment. The result is validated on phantoms, mimicking the changes in the environment of normal and abnormal tissues. This spatio-temporal map illustrates the potential of time resolved auto-fluorescence imaging of cervical tissue in combination with PCA to clearly demarcate normal and abnormal regions with enhanced contrast.

Perfusion-based fluorescence imaging method delineates diverse organs and identifies multifocal tumors using generic near-infrared molecular probes

Rapid detection of multifocal cancer without the use of complex imaging schemes will improve treatment outcomes. In this study, dynamic fluorescence imaging was used to harness differences in the perfusion kinetics of near-infrared (NIR) fluorescent dyes to visualize structural characteristics of different tissues. Using the hydrophobic nontumor-selective NIR dye cypate, and the hydrophilic dye LS288, a high tumor-to-background contrast was achieved, allowing the delineation of diverse tissue types while maintaining short imaging times. By clustering tissue types with similar perfusion properties, the dynamic fluorescence imaging method identified secondary tumor locations when only the primary tumor position was known, with a respective sensitivity and specificity of 0.97 and 0.75 for cypate, and 0.85 and 0.81 for LS288. Histological analysis suggests that the vasculature in the connective tissue that directly surrounds the tumor was a major factor for tumor identification through perfusion imaging. Although the hydrophobic dye showed higher specificity than the hydrophilic probe, use of other dyes with different physical and biological properties could further improve the accuracy of the dynamic imaging platform to identify multifocal tumors for potential use in real-time intraoperative procedures.

Blood flow characteristics of diabetic patients with complications detected by optical measurement

Background

Diabetes mellitus (DM) is one of the most common diseases worldwide. Uncontrolled and prolonged hyperglycemia can cause diabetic complications, which reduce the quality of life of patients. Diabetic complications are common in DM patients. Because it is impossible to completely recover from diabetic complications, it is important for early detection. In this study, we suggest a novel method of determining blood flow characteristics based on fluorescence image analysis with indocyanine green and report that diabetic complications have unique blood flow characteristics.

Methods

We analyzed time-series fluorescence images obtained from controls, DM patients, and DM patients with complications. The images were segmented into the digits and the dorsum of the feet and hands, and each part has been considered as arterial and capillary flow. We compared the blood flow parameters in each region among the three groups.

Results

The DM patients with complications showed similar blood flow parameters to the controls, except the area under the curve and the maximum intensity, which indicate the blood flow volume. These parameters were significantly decreased in DM patients with complications. Although some blood flow parameters in the feet of DM patients with complications were close to normal blood flow, the vascular response of the macrovessels and microvessels to stimulation of the hands was significantly reduced, which indicates less reactivity in DM patients with complications.

Conclusions

Our results suggest that DM patients, and DM patients with complications, have unique peripheral blood flow characteristics.

Methods for detecting host genetic modifiers of tumor vascular function using dynamic near-infrared fluorescence imaging

Vascular supply is a critical component of the tumor microenvironment (TME) and is essential for tumor growth and metastasis, yet the endogenous genetic modifiers that impact vascular function in the TME are largely unknown. To identify the host TME modifiers of tumor vascular function, we combined a novel genetic mapping strategy [Consomic Xenograft Model] with near-infrared (NIR) fluorescence imaging and multiparametric analysis of pharmacokinetic modeling. To detect vascular flow, an intensified cooled camera based dynamic NIR imaging system with 785 nm laser diode based excitation was used to image the whole-body fluorescence emission of intravenously injected indocyanine green dye. Principal component analysis was used to extract the spatial segmentation information for the lungs, liver, and tumor regions-of-interest. Vascular function was then quantified by pK modeling of the imaging data, which revealed significantly altered tissue perfusion and vascular permeability that were caused by host genetic modifiers in the TME. Collectively, these data demonstrate that NIR fluorescent imaging can be used as a non-invasive means for characterizing host TME modifiers of vascular function that have been linked with tumor risk, progression, and response to therapy.

Establishment and phenotyping of disease model cells created by cell-resealing technique

Cell-based assays are growing in importance for screening drugs and investigating their mechanisms of action. Most of the assays use so-called “normal” cell strain because it is difficult to produce cell lines in which the disease conditions are reproduced. In this study, we used a cell-resealing technique, which reversibly permeabilizes the plasma membrane, to develop diabetic (Db) model hepatocytes into which cytosol from diabetic mouse liver had been introduced. Db model hepatocytes showed several disease-specific phenotypes, namely disturbance of insulin-induced repression of gluconeogenic gene expression and glucose secretion. Quantitative image analysis and principal component analysis revealed that the ratio of phosphorylated Akt (pAkt) to Akt was the best index to describe the difference between wild-type and Db model hepatocytes. By performing image-based drug screening, we found pioglitazone, a PPARγ agonist, increased the pAkt/Akt ratio, which in turn ameliorated the insulin-induced transcriptional repression of the gluconeogenic gene phosphoenolpyruvate carboxykinase 1. The disease-specific model cells coupled with image-based quantitative analysis should be useful for drug development, enabling the reconstitution of disease conditions at the cellular level and the discovery of disease-specific markers.

Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

Tracking Equilibrium and Nonequilibrium Shifts in Data with TREND

Principal component analysis (PCA) discovers patterns in multivariate data that include spectra, microscopy, and other biophysical measurements. Direct application of PCA to crowded spectra, images, and movies (without selecting peaks or features) was shown recently to identify their equilibrium or temporal changes. To enable the community to utilize these capabilities with a wide range of measurements, we have developed multiplatform software named TREND to Track Equilibrium and Nonequilibrium population shifts among two-dimensional Data frames. TREND can also carry this out by independent component analysis. We highlight a few examples of finding concurrent processes. TREND extracts dual phases of binding to two sites directly from the NMR spectra of the titrations. In a cardiac movie from magnetic resonance imaging, TREND resolves principal components (PCs) representing breathing and the cardiac cycle. TREND can also reconstruct the series of measurements from selected PCs, as illustrated for a biphasic, NMR-detected titration and the cardiac MRI movie. Fidelity of reconstruction of series of NMR spectra or images requires more PCs than needed to plot the largest population shifts. TREND reads spectra from many spectroscopies in the most common formats (JCAMP-DX and NMR) and multiple movie formats. The TREND package thus provides convenient tools to resolve the processes recorded by diverse biophysical methods.