John L. Robertson

Raman spectroscopy (RS) is used increasingly for disease detection, including diseases of the nervous system (CNS). This Perspective presents RS basics and how it has been applied to disease detection. Research that focused on using a novel Raman-based technology—Rametrix^® Molecular Urinalysis (RMU)—for systemic disease detection is presented, demonstrated by an example of how the RS/RMU technology could be used for detection and management of diseases of the CNS in companion animals.

Introduction: The presence of cancer in dogs was detected by Raman spectroscopy of urine samples and chemometric analysis of spectroscopic data. The procedure created a multimolecular spectral fingerprint with hundreds of features related directly to the chemical composition of the urine specimen. These were then used to detect the broad presence of cancer in dog urine as well as the specific presence of lymphoma, urothelial carcinoma, osteosarcoma, and mast cell tumor.Methods: Urine samples were collected via voiding, cystocentesis, or catheterization from 89 dogs with no history or evidence of neoplastic disease, 100 dogs diagnosed with cancer, and 16 dogs diagnosed with non-neoplastic urinary tract or renal disease. Raman spectra were obtained of the unprocessed bulk liquid urine samples and were analyzed by ISREA, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were applied using the Rametrix^®Toolbox software.Results and discussion: The procedure identified a spectral fingerprint for cancer in canine urine, resulting in a urine screening test with 92.7% overall accuracy for a cancer vs. cancer-free designation. The urine screen performed with 94.0% sensitivity, 90.5% specificity, 94.5% positive predictive value (PPV), 89.6% negative predictive value (NPV), 9.9 positive likelihood ratio (LR+), and 0.067 negative likelihood ratio (LR-). Raman bands responsible for discerning cancer were extracted from the analysis and biomolecular associations were obtained. The urine screen was more effective in distinguishing urothelial carcinoma from the other cancers mentioned above. Detection and classification of cancer in dogs using a simple, non-invasive, rapid urine screen (as compared to liquid biopsies using peripheral blood samples) is a critical advancement in case management and treatment, especially in breeds predisposed to specific types of cancer.

Neurotropic oncolytic viruses are appealing agents to treat brain tumors as they penetrate the blood–brain barrier and induce preferential cytolysis of neoplastic cells. The pathobiological similarities between human and canine brain tumors make immunocompetent dogs with naturally occurring tumors attractive models for the study of oncolytic virotherapies. In this dose-escalation/expansion study, an engineered Lasota NDV strain targeting the urokinase plasminogen activator system (rLAS-uPA) was administered by repetitive intravenous infusions to 20 dogs with intracranial tumors with the objectives of characterizing toxicities, immunologic responses, and neuroradiological anti-tumor effects of the virus for up to 6 months following treatment. Dose-limiting toxicities manifested as fever, hematologic, and neurological adverse events, and the maximum tolerated dose (MTD) of rLAS-uPA was 2 × 10⁷ pfu/mL. Mild adverse events, including transient infusion reactions, diarrhea, and fever were observed in 16/18 of dogs treated at or below MTD. No infectious virus was recoverable from body fluids. Neutralizing antibodies to rLAS-uPA were present in all dogs by 2 weeks post-treatment, and viral genetic material was detected in post-treatment tumors from six dogs. Tumor volumetric reductions occurred in 2/11 dogs receiving the MTD. Systemically administered rLAS-uPA NDV was safe and induced anti-tumor effects in canine brain tumors, although modifications to evade host anti-viral immunity are needed to optimize this novel therapy.

Objectives: To test whether mitochondrial transplantation (MITO) mitigates damage in two models of acute kidney injury (AKI). Summary Background Data: MITO is a process where exogenous isolated mitochondria are taken up by cells. As virtually any morbid clinical condition is characterized by mitochondrial distress, MITO may find a role as a treatment modality in numerous clinical scenarios including AKI. Methods: For the in vitro experiments, human proximal tubular cells were damaged and then treated with mitochondria or placebo. For the ex vivo experiments, we developed a non-survival ex vivo porcine model mimicking the donation after cardiac death (DCD) renal transplantation scenario. One kidney was treated with mitochondria, while the mate organ received placebo, before being perfused at room temperature for 24 hours. Perfusate samples were collected at different time points and analyzed with Raman spectroscopy. Biopsies taken at baseline and 24 hours were analyzed with standard pathology, immunohistochemistry and RNA sequencing analysis. Results: In vitro, cells treated with MITO showed higher proliferative capacity and ATP production, preservation of physiological polarization of the organelles and lower toxicity and reactive oxygen species production. Ex vivo, kidneys treated with MITO shed fewer molecular species, indicating stability. In these kidneys, pathology showed less damage while RNAseq analysis showed modulation of genes and pathways most consistent with mitochondrial biogenesis and energy metabolism and downregulation of genes involved in neutrophil recruitment, including IL1A, CXCL8, and PIK3R1. Conclusions: MITO mitigates AKI both in vitro and ex vivo.

We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine ‘molecular fingerprint’, representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016–2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9–1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) 2 (range 59–84 mL/min/1.73m²). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis—Rametrix^®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of ‘Long COVID-19’ symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography–Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.

Glioblastoma is the deadliest malignant brain tumor. Its location behind the blood–brain barrier (BBB) presents a therapeutic challenge by preventing effective delivery of most chemotherapeutics. H-FIRE is a novel tumor ablation method that transiently disrupts the BBB through currently unknown mechanisms. We hypothesized that H-FIRE mediated BBB disruption (BBBD) occurs via cytoskeletal remodeling and alterations in tight junction (TJ) protein regulation. Intracranial H-FIRE was delivered to Fischer rats prior to sacrifice at 1-, 24-, 48-, 72-, and 96 h post-treatment. Cytoskeletal proteins and native and ubiquitinated TJ proteins (TJP) were evaluated using immunoprecipitation, Western blotting, and gene-expression arrays on treated and sham control brain lysates. Cytoskeletal and TJ protein expression were further evaluated with immunofluorescent microscopy. A decrease in the F/G-actin ratio, decreased TJP concentrations, and increased ubiquitination of TJP were observed 1–48 h post-H-FIRE compared to sham controls. By 72–96 h, cytoskeletal and TJP expression recovered to pretreatment levels, temporally corresponding with increased claudin-5 and zonula occludens-1 gene expression. Ingenuity pathway analysis revealed significant dysregulation of claudin genes, centered around claudin-6 in H-FIRE treated rats. In conclusion, H-FIRE is capable of permeating the BBB in a spatiotemporal manner via cytoskeletal-mediated TJP modulation. This minimally invasive technology presents with applications for localized and long-lived enhanced intracranial drug delivery.

Hematuria refers to the presence of blood in urine. Even in small amounts, it may be indicative of disease, ranging from urinary tract infection to cancer. Here, Raman spectroscopy was used to detect and quantify macro- and microhematuria in human urine samples. Anticoagulated whole blood was mixed with freshly collected urine to achieve concentrations of 0, 0.25, 0.5, 1, 2, 6, 10, and 20% blood/urine (v/v). Raman spectra were obtained at 785 nm and data analyzed using chemometric methods and statistical tests with the Rametrix toolboxes for Matlab. Goldindec and iterative smoothing splines with root error adjustment (ISREA) baselining algorithms were used in processing and normalization of Raman spectra. Rametrix was used to apply principal component analysis (PCA), develop discriminate analysis of principal component (DAPC) models, and to validate these models using external leave-one-out cross-validation (LOOCV). Discriminate analysis of principal component models were capable of detecting various levels of microhematuria in unknown urine samples, with prediction accuracies of 91% (using Goldindec spectral baselining) and 94% (using ISREA baselining). Partial least squares regression (PLSR) was then used to estimate/quantify the amount of blood (v/v) in a urine sample, based on its Raman spectrum. Comparing actual and predicted (from Raman spectral computations) hematuria levels, a coefficient of determination (R ² ) of 0.91 was obtained over all hematuria levels (0–20% v/v), and an R ² of 0.92 was obtained for microhematuria (0–1% v/v) specifically. Overall, the results of this preliminary study suggest that Raman spectroscopy and chemometric analyses can be used to detect and quantify macro- and microhematuria in unprocessed, clinically relevant urine specimens.

A urine-based screening technique for Lyme disease (LD) was developed in this research. The screen is based on Raman spectroscopy, iterative smoothing-splines with root error adjustment (ISREA) spectral baselining, and chemometric analysis using Rametrix software. Raman spectra of urine from 30 patients with positive serologic tests (including the US Centers for Disease Control [CDC] two-tier standard) for LD were compared against subsets of our database of urine spectra from 235 healthy human volunteers, 362 end-stage kidney disease (ESKD) patients, and 17 patients with active or remissive bladder cancer (BCA). We found statistical differences ( p < 0.001) between urine scans of healthy volunteers and LD-positive patients. We also found a unique LD molecular signature in urine involving 112 Raman shifts (31 major Raman shifts) with significant differences from urine of healthy individuals. We were able to distinguish the LD molecular signature as statistically different ( p < 0.001) from the molecular signatures of ESKD and BCA. When comparing LD-positive patients against healthy volunteers, the Rametrix-based urine screen performed with 86.7% for overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively. When considering patients with ESKD and BCA in the LD-negative group, these values were 88.7% (accuracy), 83.3% (sensitivity), 91.0% (specificity), 80.7% (PPV), and 92.4% (NPV). Additional advantages to the Raman-based urine screen include that it is rapid (minutes per analysis), is minimally invasive, requires no chemical labeling, uses a low-profile, off-the-shelf spectrometer, and is inexpensive relative to other available LD tests.