Tufariello, Jessica A.

Abstract Background The legality and use of marijuana is a controversial topic. With increased prevalence of usage, cannabis detection for legal, medical, and workforce drug testing is on the rise. LC-MS/MS quantification of delta 9-carboxy tetrahydrocannabinol (delta 9-THC-COOH) in urine is accepted as the standard to detect marijuana use. One challenge faced by testing laboratories has been identifying adulterated samples. Many products/reagents can be used to manipulate the urine to avoid positive drug testing results. In this study, we developed a dilute-and-shoot strategy to separate and quantify both the delta 8- and 9-THC-COOH in urine samples and tested the effects of adulteration using a combination of nitrite and/or acids which can be used to mask cannabinoid use. Methods Five authentic delta 8- and 9-THC-COOH positive specimens, and one spiked sample were treated with strong acids (1N HCL or 10% TCA), 500 µg/mL nitrite, or a combination. After hydrolysis with sodium hydroxide, the analytes were quantified using a 6495 Mass Spectrometer coupled to a StreamSelect LC system (Agilent). LC separation was performed with a CORTECS T3 column utilizing a shallow gradient. In addition, twelve urine specimens with varying concentrations of delta 8- and 9-THC-COOH were treated with nitrite and screened using a delta 9-THC-COOH immunoassay on the Roche Cobas C501 and analyzed by LC-MS/MS. Results Nitrite did not change the physical appearance or the pH of the urine samples. Strong acid treatment also did not alter the appearance but did drop the urine pH ≤2. Pre-treated specimens with 1N HCL, 10% TCA, or 500 µg/mL of nitrite did not affect the delta 8- and 9-THC-COOH concentrations up to 6 days (refrigerated); however, specimens treated with strong acid combined with 500 µg/mL of nitrite for 30 min made both analytes undetectable. Additional experiments confirmed that 0.5 and 1 mol/L of nitrite lowered the concentration of all analytes, including internal standard, to undetectable levels in 48 h. Twelve additional specimens were collected, 3 with delta 8-THC-COOH, 3 with delta 9-THC-COOH, and 6 with both. All 12 samples screened positive using the Roche immunoassay and were quantified using the LC-MS/MS method. After treatment with 0.5 mol/L of nitrite, 10 of the 12 samples were negative using the immunoassay, but 2 samples remained positive. However, all nitrite treated specimens were negative (<LLOQ) using the LC-MS/MS method and the internal standard was eliminated. Later experiments confirmed that the delta 8- and 9-THC-COOH could be eliminated with as little as 0.1 mol/L nitrite for 30 min. Conclusion The unique dilute-and-shoot method can separate and quantify delta 8- and 9-THC-COOH. In addition, high concentrations of nitrite were shown to degrade both analytes along with the isotopically labeled internal standard. As a result, labs can distinguish true negative delta 8- and 9-THC-COOH (no analyte peaks) from nitrite adulterated specimens since the adulterated samples have no peaks for both the analytes and internal standard. A limitation of the study is that only sodium nitrite was tested. Other adulterants, such as potassium nitrite, pyridinium chlorochromate, peroxide etc. require further investigation.

Abstract Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 um section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and fail to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire intact NSCLC tissue samples. Methods: Human NSCLC tissue, stored frozen in OCT, was fixed in 4% paraformaldehyde, stained with nuclear (TOPRO-3) and general protein (Eosin) fluorescent dyes, and optically cleared using a modified iDISCO protocol with ethyl cinnamate as the refractive index matching solution. Whole, intact tissue samples, roughly 1-5mm3 in volume, were imaged at 2 microns/pixel resolution with an open-top light-sheet microscope (3Di, Alpenglow Biosciences). Smaller regions of interest (ROIs) with key pathologic features were reimaged at higher resolution, 0.17 microns/pixel, to reveal subnuclear features and for cell typing. Visualization was performed using Aivia software. Results: NSCLC tissue samples were successfully imaged in 3D. Low resolution images (2 microns/pixel) were obtained within 4-31 minutes, depending on the tissue volume. The 3D distribution of cancer cells, immune cells, vessels, and fibrosis varied substantially throughout the volume of the tissue. Recognizable histologic features, including nests of tumor cells surrounded by vasculature and immune cells, were readily visualized. Squamous and adenocarcinoma with its subtypes (solid, acinar, lepidic, and micropapillary) morphologies were recognizable in 2D optical sections of the 3D datasets. Imaging quality degraded in tissue deeper than 1 mm due to light scattering. Conclusion: We assessed intact NSCLC tissue samples measuring up to 5 mm3 using our custom light-sheet microscope and tissue clearing techniques. This novel method enables us to visualize key features of NSCLC such as the tumor interfaces, tertiary lymphoid structures, vessels and fibrosis in the entire tissue sample, preventing under sampling error, and potentially enabling new biologic insights. Next steps include segmentation and quantification of key tissue structures such as tumor volume, immune cell distribution, and fibrosis/immune cell exclusion. This proof-of-concept study provides motivation for further investigation into the significance of 3D tissue features in NSCLC tissue samples. Citation Format: Alexandra Alvarsson, Carl Storey, Brandy Olin Pope, Caleb Stoltzfus, Robert Vierkant, Jessica Tufariello, Aaron Bungum, Julia Naso, Cheuk Ki Chan, Eric Edell, Christopher Hartley, Janani Reisenauer, Nicholas Reder. 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6624.

2616 Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 µm section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and failure to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire, intact lung tissue samples. Herein, we present analysis methods for comprehensive 3D assessment of intact NSCLC tissue. Methods: Human NSCLC tissue, stored frozen in optimal cutting temperature (OCT) compound, was fixed in 4% paraformaldehyde, stained with nuclear (TOPRO-3) and general protein (Eosin) fluorescent dyes, and optically cleared using a modified iDISCO protocol with ethyl cinnamate as the refractive index matching solution. Twenty tissue samples measuring 1-5 mm3 in volume were imaged at 2 µm/pixel resolution with a hybrid open-top light-sheet microscope. Smaller regions of interest (ROIs) with interesting pathologic features were re-imaged at higher resolution, 0.17 µm/pixel. Visualization and analysis was performed using Aivia software, custom spatial analysis software and python scripts. Results: All NSCLC tissue samples were successfully imaged in 3D at both low and high resolution. Computational approaches, such as machine learning, were used to segment and classify tumor cells and lymphocytes within the high resolution ROIs. In one example we quantified a 0.0045 mm3 ROI containing 1x109 voxels. We segmented 1,474 unique objects, and classified 683 (46%) of the objects as lymphocytes and 309 (20%) of the cells as tumor cells. We found that on average the lymphocytes were within 12 µm of the nearest tumor cell. Additionally the tissue was broken into separate lymphocyte dense (58% of volume) and tumor dense regions (42% of volume). Further analysis is in progress to quantify more features, such as tertiary lymphoid structures, stromal cells, and fibrosis over the full 20 sample dataset. Conclusions: We successfully implemented 3D machine learning analysis pipelines using images from intact NSCLC tissue samples measuring up to 5 mm3. The imaging was performed using our custom hybrid open-top light-sheet microscope. This novel method enables us to identify and quantify key histological features such as tumor cells and lymphocytes within the tumor microenvironment. In addition, machine learning driven algorithms clustered cells into distinct tissue regions in 3D which enables a more precise assessment of disease state and heterogeneity. Further investigation is ongoing to link these results to patient outcomes.