Rapid diagnosis and tumor margin assessment during pancreatic cancer surgery with the MasSpec Pen technology Mary E. King, Jialing Zhang, John Q. Lin, Kyana Y. Garza, Rachel J. DeHoog, Clara L. Feider, Alena Bensussan, Marta Sans, Anna Krieger, Sunil Badal, Michael F. Keating, Spencer Woody, Sadhna Dhingra, Wendong Yu, Christopher Pirko, Kirtan A. Brahmbhatt, George Van Buren, William E. Fisher, James Suliburk, and Livia S. Eberlin Proceedings of the National Academy of Sciences 2021
Surgical removal of pancreatic cancer remains the only option for a cure. To verify the extent of tumor removal, surgeons rely on pathologic evaluation of frozen sections of surgical margins. However, this process can be challenging, time consuming, and subjective. Here, we used the MasSpec Pen to rapidly distinguish pancreatic cancer from healthy pancreatic and bile duct tissues by generating classification models based on the molecular signatures acquired from tissue. We evaluated this technology in an operating room during pancreatic surgeries and used these classification models to predict on data obtained in vivo and ex vivo with high performance. Our results suggest that the MasSpec Pen platform has the potential to improve and expedite margin evaluation during pancreatic cancer surgery.Intraoperative delineation of tumor margins is critical for effective pancreatic cancer surgery. Yet, intraoperative frozen section analysis of tumor margins is a time-consuming and often challenging procedure that can yield confounding results due to histologic heterogeneity and tissue-processing artifacts. We have previously described the development of the MasSpec Pen technology as a handheld mass spectrometry–based device for nondestructive tissue analysis. Here, we evaluated the usefulness of the MasSpec Pen for intraoperative diagnosis of pancreatic ductal adenocarcinoma based on alterations in the metabolite and lipid profiles in in vivo and ex vivo tissues. We used the MasSpec Pen to analyze 157 banked human tissues, including pancreatic ductal adenocarcinoma, pancreatic, and bile duct tissues. Classification models generated from the molecular data yielded an overall agreement with pathology of 91.5%, sensitivity of 95.5%, and specificity of 89.7% for discriminating normal pancreas from cancer. We built a second classifier to distinguish bile duct from pancreatic cancer, achieving an overall accuracy of 95%, sensitivity of 92%, and specificity of 100%. We then translated the MasSpec Pen to the operative room and predicted on in vivo and ex vivo data acquired during 18 pancreatic surgeries, achieving 93.8% overall agreement with final postoperative pathology reports. Notably, when integrating banked tissue data with intraoperative data, an improved agreement of 100% was achieved. The result obtained demonstrate that the MasSpec Pen provides high predictive performance for tissue diagnosis and compatibility for intraoperative use, suggesting that the technology may be useful to guide surgical decision-making during pancreatic cancer surgeries.
Clinical Translation and Evaluation of a Handheld and Biocompatible Mass Spectrometry Probe for Surgical Use Jialing Zhang, Marta Sans, Rachel J DeHoog, Kyana Y Garza, Mary E King, Clara L Feider, Alena Bensussan, Michael F Keating, John Q Lin, Sydney C Povilaitis, Nitesh Katta, Thomas E Milner, Wendong Yu, Chandandeep Nagi, Sadhna Dhingra, Christopher Pirko, Kirtan A Brahmbhatt, George Van Buren, Stacey Carter, Alastair Thompson, Raymon H Grogan, James Suliburk, and Livia S Eberlin Clinical Chemistry 2021
Intraoperative tissue analysis and identification are critical to guide surgical procedures and improve patient outcomes. Here, we describe the clinical translation and evaluation of the MasSpec Pen technology for molecular analysis of in vivo and freshly excised tissues in the operating room (OR). An Orbitrap mass spectrometer equipped with a MasSpec Pen interface was installed in an OR. A “dual-path” MasSpec Pen interface was designed and programmed for the clinical studies with 2 parallel systems that facilitated the operation of the MasSpec Pen. The MasSpec Pen devices were autoclaved before each surgical procedure and were used by surgeons and surgical staff during 100 surgeries over a 12-month period.Detection of mass spectral profiles from 715 in vivo and ex vivo analyses performed on thyroid, parathyroid, lymph node, breast, pancreatic, and bile duct tissues during parathyroidectomies, thyroidectomies, breast, and pancreatic neoplasia surgeries was achieved. The MasSpec Pen enabled gentle extraction and sensitive detection of various molecular species including small metabolites and lipids using a droplet of sterile water without causing apparent tissue damage. Notably, effective molecular analysis was achieved while no limitations to sequential histologic tissue analysis were identified and no device-related complications were reported for any of the patients. This study shows that the MasSpec Pen system can be successfully incorporated into the OR, allowing direct detection of rich molecular profiles from tissues with a seconds-long turnaround time that could be used to inform surgical and clinical decisions without disrupting tissue analysis workflows.
Rapid Analysis and Authentication of Meat Using the MasSpec Pen Technology Abigail N. Gatmaitan, John Q. Lin, Jialing Zhang, and Livia S. Eberlin Journal of Agricultural and Food Chemistry 2021
Food authenticity and safety are major public concerns due to the increasing number of food fraud cases. Meat fraud is an economically motivated practice of covertly replacing one type of meat with a cheaper alternative raising health, safety, and ethical concerns for consumers. In this study, we implement the MasSpec Pen technology for rapid and direct meat analysis and authentication. The MasSpec Pen is an easy-to-use handheld device connected to a mass spectrometer that employs a solvent droplet for gentle chemical analysis of samples. Here, MasSpec Pen analysis was performed directly on several meat and fish types including grain-fed beef, grass-fed beef, venison, cod, halibut, Atlantic salmon, sockeye salmon, and steelhead trout, with a total analysis time of 15 s per sample. Statistical models developed with the Lasso method using a training set of samples yielded per-sample accuracies of 95% for the beef model, 100% for the beef versus venison model, and 84% for the multiclass fish model. Predictors of meat type selected included several molecules previously reported in the skeletal muscles of animals, including carnosine, anserine, succinic acid, xanthine, and taurine. When testing the models on independent test sets of samples, per-sample accuracies of 100% were achieved for all models, demonstrating the robustness of our method for unadulterated meat authentication. MasSpec Pen feasibility testing for classifying venison and grass-fed beef samples adulterated with grain-fed beef achieved per-sample prediction accuracies of 100% for both classifiers using test sets of samples. Altogether, the results obtained in this study provide compelling evidence that the MasSpec Pen technology is a powerful alternative analytical method for meat analysis and investigation of meat fraud.
Distinguishing Non-Small Cell Lung Cancer Subtypes in Fine Needle Aspiration Biopsies by Desorption Electrospray Ionization Mass Spectrometry Imaging Alena V Bensussan, John Lin, Chunxiao Guo, Ruth Katz, Savitri Krishnamurthy, Erik Cressman, and Livia S Eberlin Clinical Chemistry 2020
Distinguishing adenocarcinoma and squamous cell carcinoma subtypes of non-small cell lung cancers is critical to patient care. Preoperative minimally-invasive biopsy techniques, such as fine needle aspiration (FNA), are increasingly used for lung cancer diagnosis and subtyping. Yet, histologic distinction of lung cancer subtypes in FNA material can be challenging. Here, we evaluated the usefulness of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to diagnose and differentiate lung cancer subtypes in tissues and FNA samples.
DESI-MSI was used to analyze 22 normal, 26 adenocarcinoma, and 25 squamous cell carcinoma lung tissues. Mass spectra obtained from the tissue sections were used to generate and validate statistical classifiers for lung cancer diagnosis and subtyping. Classifiers were then tested on DESI-MSI data collected from 16 clinical FNA samples prospectively collected from 8 patients undergoing interventional radiology guided FNA.
Various metabolites and lipid species were detected in the mass spectra obtained from lung tissues. The classifiers generated from tissue sections yielded 100% accuracy, 100% sensitivity, and 100% specificity for lung cancer diagnosis, and 73.5% accuracy for lung cancer subtyping for the training set of tissues, per-patient. On the validation set of tissues, 100% accuracy for lung cancer diagnosis and 94.1% accuracy for lung cancer subtyping were achieved. When tested on the FNA samples, 100% diagnostic accuracy and 87.5% accuracy on subtyping were achieved per-slide.
DESI-MSI can be useful as an ancillary technique to conventional cytopathology for diagnosis and subtyping of non-small cell lung cancers.
Metastatic cutaneous squamous cell carcinoma responsive to cemiplimab in a patient with multiple myeloma Nareh Valerie Marukian, John Q. Lin, A. Dimitrios Colevas, Steven Coutre, and Anne Lynn S. Chang JAAD Case Reports 2020
Multiplatform Investigation of Plasma and Tissue Lipid Signatures of Breast Cancer Using Mass Spectrometry Tools Alex Ap. Rosini Silva, Marcella R. Cardoso, Luciana Montes Resende, John Q. Lin, Fernando Guimaraes, Geisilene R. Paiva Silva, Michael Murgu, Denise Gonçalves Priolli, Marcos N. Eberlin, Alessandra Tata, Livia S. Eberlin, Sophie F. M. Derchain, and Andreia M. Porcari International Journal of Molecular Sciences 2020
Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.
Veterans Affairs Insurance Disparities for Metastatic Lung Cancer in the Hawaiian Islands John Q. Lin, Shirley Q. Li, Todd A. Pezzi, Abdallah S.R. Mohamed, Clifton D. Fuller, Aileen B. Chen, Bruce D. Minsky, David L. Schwartz, Brenda Y. Hernandez, and Stephen G. Chun JTO Clinical and Research Reports 2020
The highest concentration of military personnel in the United States is located in Hawaii where occupational exposures, such as to asbestos in the Pacific Fleet shipyards, predispose them to thoracic malignancies. For this reason, Veterans Affairs (VA) insurance outcomes for lung cancer in Hawaii are of interest.
All cases of lung cancer in the Hawaii Tumor Registry from 2000 to 2015 were evaluated. The selection criterion included evidence of extensive-stage SCLC (ES-SCLC) or metastatic NSCLC. Overall survival was compared using the Kaplan-Meier log-rank method. Univariate analysis and multivariable analysis (MVA) were carried out to understand the variables associated with overall survival.
There were 434 cases of ES-SCLC and 2139 cases of metastatic NSCLC identified. VA insurance (median survival [MS], 2 mo), Medicaid (MS, 4 mo), and Medicare (MS, 4 mo) had worse survival (log-rank p < 0.001) than private insurance (MS, 8 mo). In ES-SCLC, VA insurance (hazard ratio [HR], 2.74; 95% confidence interval [CI]: 1.50–5.01; p = 0.001) and Medicaid (HR, 1.46; 95% CI: 1.04–2.03; p = 0.027) had significantly worse survival compared with private insurance on MVA. VA insurance (HR, 1.84; 95% CI: 1.34–2.53; p < 0.001) and Medicaid (HR, 1.40; 95% CI: 1.20–1.63; p < 0.001) also had worse survival compared with private insurance in metastatic NSCLC on MVA.
VA insurance coverage was associated with dismal survival for metastatic lung cancer that was effectively similar to hospice or supportive care, compelling further investigation to identify reasons for this disparity.
Mass Spectrometry Imaging enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues Jialing Zhang, Shirley Q. Li, John Q. Lin, Wendong Yu, and Livia S. Eberlin Cancer Research 2019
Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor.
Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies Rachel J. DeHoog, Jialing Zhang, Elizabeth Alore, John Q. Lin, Wendong Yu, Spencer Woody, Christopher Almendariz, Monica Lin, Anton F. Engelsman, Stan B. Sidhu, Robert Tibshirani, James Suliburk, and Livia S. Eberlin Proceedings of the National Academy of Sciences 2019
Thyroid neoplasia is common and requires appropriate clinical workup with imaging and fine-needle aspiration (FNA) biopsy to evaluate for cancer. Yet, up to 20% of thyroid nodule FNA biopsies will be indeterminate in diagnosis based on cytological evaluation. Genomic approaches to characterize the malignant potential of nodules showed initial promise but have provided only modest improvement in diagnosis. Here, we describe a method using metabolic analysis by desorption electrospray ionization mass spectrometry (DESI-MS) imaging for direct analysis and diagnosis of follicular cell-derived neoplasia tissues and FNA biopsies. DESI-MS was used to analyze 178 tissue samples to determine the molecular signatures of normal, benign follicular adenoma (FTA), and malignant follicular carcinoma (FTC) and papillary carcinoma (PTC) thyroid tissues. Statistical classifiers, including benign thyroid versus PTC and benign thyroid versus FTC, were built and validated with 114,125 mass spectra, with accuracy assessed in correlation with clinical pathology. Clinical FNA smears were prospectively collected and analyzed using DESI-MS imaging, and the performance of the statistical classifiers was tested with 69 prospectively collected clinical FNA smears. High performance was achieved for both models when predicting on the FNA test set, which included 24 nodules with indeterminate preoperative cytology, with accuracies of 93% and 89%. Our results strongly suggest that DESI-MS imaging is a valuable technology for identification of malignant potential of thyroid nodules.
Performance of the MasSpec Pen for Rapid Diagnosis of Ovarian Cancer Marta Sans, Jialing Zhang, John Q. Lin, Clara L. Feider, Noah Giese, Michael T. Breen, Katherine Sebastian, Jinsong Liu, Anil K. Sood, and Livia S. Eberlin Clinical Chemistry 2019
Background: Accurate tissue diagnosis during ovarian cancer surgery is critical to maximize cancer excision and define treatment options. Yet, current methods for intraoperative tissue evaluation can be time intensive and subjective. We have developed a handheld and biocompatible device coupled to a mass spectrometer, the MasSpec Pen, which uses a discrete water droplet for molecular extraction and rapid tissue diagnosis. Here we evaluated the performance of this technology for ovarian cancer diagnosis across different sample sets, tissue types, and mass spectrometry systems.
Methods: MasSpec Pen analyses were performed on 192 ovarian, fallopian tube, and peritoneum tissue samples. Samples were evaluated by expert pathologists to confirm diagnosis. Performance using an orbitrap and a linear ion trap mass spectrometer was tested. Statistical models were generated using machine learning and evaluated using validation and test sets.
Results: High performance for high-grade serous carcinoma (n = 131; clinical sensitivity, 96.7%; specificity, 95.7%) and overall cancer (n = 138; clinical sensitivity, 94.0%; specificity, 94.4%) diagnoses was achieved using orbitrap data. Variations in the mass spectra from normal tissue, low-grade, and high-grade serous ovarian cancers were observed. Discrimination between cancer and fallopian tube or peritoneum tissues was also achieved with accuracies of 92.6% and 87.9%, respectively, and 100% clinical specificity for both. Using ion trap data, excellent results for high-grade serous cancer vs normal ovarian differentiation (n = 40; clinical sensitivity, 100%; specificity, 100%) were obtained.
Conclusions: The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.
Multicenter Study Using Desorption-Electrospray-Ionization-Mass-Spectrometry Imaging for Breast-Cancer Diagnosis Andreia M. Porcari, Jialing Zhang, Kyana Y. Garza, Raquel M. Rodrigues-Peres, John Q. Lin, Jonathan H. Young, Robert Tibshirani, Chandandeep Nagi, Geisilene R. Paiva, Stacey A. Carter, Luis Otavio Sarian, Marcos N. Eberlin, and Livia S. Eberlin Analytical Chemistry 2018
The histological and molecular subtypes of breast cancer demand distinct therapeutic approaches. Invasive ductal carcinoma (IDC) is subtyped according to estrogen-receptor (ER), progesterone-receptor (PR), and HER2 status, among other markers. Desorption-electrospray-ionization-mass-spectrometry imaging (DESI-MSI) is an ambient-ionization MS technique that has been previously used to diagnose IDC. Aiming to investigate the robustness of ambient-ionization MS for IDC diagnosis and subtyping over diverse patient populations and interlaboratory use, we report a multicenter study using DESI-MSI to analyze samples from 103 patients independently analyzed in the United States and Brazil. The lipid profiles of IDC and normal breast tissues were consistent across different patient races and were unrelated to country of sample collection. Similar experimental parameters used in both laboratories yielded consistent mass-spectral data in mass-to-charge ratios (m/z) above 700, where complex lipids are observed. Statistical classifiers built using data acquired in the United States yielded 97.6% sensitivity, 96.7% specificity, and 97.6% accuracy for cancer diagnosis. Equivalent performance was observed for the intralaboratory validation set (99.2% accuracy) and, most remarkably, for the interlaboratory validation set independently acquired in Brazil (95.3% accuracy). Separate classification models built for ER and PR statuses as well as the status of their combined hormone receptor (HR) provided predictive accuracies (>89.0%), although low classification accuracies were achieved for HER2 status. Altogether, our multicenter study demonstrates that DESI-MSI is a robust and reproducible technology for rapid breast-cancer-tissue diagnosis and therefore is of value for clinical use.
Desorption Electrospray Ionization Coupled with Ultraviolet Photodissociation for Characterization of Phospholipid Isomers in Tissue Sections Dustin R. Klein, Clara L. Feider, Kyana Y. Garza, John Q. Lin, Livia S. Eberlin, and Jennifer S. Brodbelt Analytical Chemistry 2018
Desorption electrospray ionization (DESI) mass spectrometry imaging has become a powerful strategy for analysis of tissue sections, enabling differentiation of normal and diseased tissue based on changes in the lipid profiles. The most common DESI workflow involves collection of MS1 spectra as the DESI spray is rastered over a tissue section. Relying on MS1 spectra inherently limits the ability to differentiate isobaric and isomeric species or evaluate variations in the relative abundances of key isomeric lipids, such as double-bond positional isomers which may distinguish normal and diseased tissues. Here, 193 nm ultraviolet photodissociation (UVPD), a technique capable of differentiating double-bond positional isomers, is coupled with DESI to map differences in the double-bond isomer composition in tissue sections in a fast, high throughput manner compatible with imaging applications.
Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system Jialing Zhang, John Rector, John Q. Lin, Jonathan H. Young, Marta Sans, Nitesh Katta, Noah Giese, Wendong Yu, Chandandeep Nagi, James Suliburk, Jinsong Liu, Alena Bensussan, Rachel J. DeHoog, Kyana Y. Garza, Benjamin Ludolph, Anna G. Sorace, Anum Syed, Aydin Zahedivash, Thomas E. Milner, and Livia S. Eberlin Science Translational Medicine 2017
Conventional methods for histopathologic tissue diagnosis are labor- and time-intensive and can delay decision-making during diagnostic and therapeutic procedures. We report the development of an automated and biocompatible handheld mass spectrometry device for rapid and nondestructive diagnosis of human cancer tissues. The device, named MasSpec Pen, enables controlled and automated delivery of a discrete water droplet to a tissue surface for efficient extraction of biomolecules. We used the MasSpec Pen for ex vivo molecular analysis of 20 human cancer thin tissue sections and 253 human patient tissue samples including normal and cancerous tissues from breast, lung, thyroid, and ovary. The mass spectra obtained presented rich molecular profiles characterized by a variety of potential cancer biomarkers identified as metabolites, lipids, and proteins. Statistical classifiers built from the histologically validated molecular database allowed cancer prediction with high sensitivity (96.4%), specificity (96.2%), and overall accuracy (96.3%), as well as prediction of benign and malignant thyroid tumors and different histologic subtypes of lung cancer. Notably, our classifier allowed accurate diagnosis of cancer in marginal tumor regions presenting mixed histologic composition. Last, we demonstrate that the MasSpec Pen is suited for in vivo cancer diagnosis during surgery performed in tumor-bearing mouse models, without causing any observable tissue harm or stress to the animal. Our results provide evidence that the MasSpec Pen could potentially be used as a clinical and intraoperative technology for ex vivo and in vivo cancer diagnosis.
Cardiolipins Are Biomarkers of Mitochondria-Rich Thyroid Oncocytic Tumors Jialing Zhang, Wendong Yu, Seung Woo Ryu, John Lin, Gerardo Buentello, Robert Tibshirani, James Suliburk, and Livia S. Eberlin Cancer Research 2016
Oncocytic tumors are characterized by an excessive eosinophilic, granular cytoplasm due to aberrant accumulation of mitochondria. Mutations in mitochondrial DNA occur in oncocytic thyroid tumors, but there is no information about their lipid composition, which might reveal candidate theranostic molecules. Here, we used desorption electrospray ionization mass spectrometry (DESI-MS) to image and chemically characterize the lipid composition of oncocytic thyroid tumors, as compared with nononcocytic thyroid tumors and normal thyroid samples. We identified a novel molecular signature of oncocytic tumors characterized by an abnormally high abundance and chemical diversity of cardiolipins (CL), including many oxidized species. DESI-MS imaging and IHC experiments confirmed that the spatial distribution of CLs overlapped with regions of accumulation of mitochondria-rich oncocytic cells. Fluorescent imaging and mitochondrial isolation showed that both mitochondrial accumulation and alteration in CL composition of mitochondria occurred in oncocytic tumors cells, thus contributing the aberrant molecular signatures detected. A total of 219 molecular ions, including CLs, other glycerophospholipids, fatty acids, and metabolites, were found at increased or decreased abundance in oncocytic, nononcocytic, or normal thyroid tissues. Our findings suggest new candidate targets for clinical and therapeutic use against oncocytic tumors.
Development of the MasSpec Pen Technology Towards Clinical Translation John Q. Lin, Jialing Zhang, Marta Sans, Noah Giese, Clara Feider, Nitesh Katta, Kevin Jian Yee, Rachel J. DeHoog, Kyana Garza, Mary King, Anna C. Krieger, Alena Bensussan, Qiuyu Li, Wendong Yu, James Suliburk, Thomas Milner, and Livia S. Eberlin Oral at 53rd Annual ACLPS Meeting 2018
Lipid Characterization in Genetically Engineered Yarrowia lipolytica by Desorption Electrospray Ionization Mass Spectrometry John Q. Lin, Lauren T. Cordova, Hal S. Alper, and Livia S. Eberlin Poster at ASMS 65th Conference on Mass Spectrometry and Allied Topics 2017
Collection probe and methods for the use thereof Livia S. Eberlin, Thomas Milner, Jialing Zhang, John Lin, John Rector, Nitesh Katta, and Aydin Zahedivash US Patent 10,943,775 2021
Collection probe and methods for the use thereof Livia S. Eberlin, Thomas Milner, Jialing Zhang, John Lin, John Rector, Nitesh Katta, and Aydin Zahedivash US Patent 10,643,832 2020