John Lin

Mr. John Lin

orcid

Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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6 September 2017 in Science Translational Medicine
Verified
Journal Article
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JZJialing Zhang
Jialing Zhang
JRJohn Rector
John Rector
JLJohn Lin
John Lin
JYJonathan H. Young
Jonathan H. Young
MSMarta Sans
Marta Sans
LELivia Eberlin
Livia Eberlin
A handheld and biocompatible mass spectrometry system provides nondestructive molecular diagnosis of cancer tissue samples.

1 May 2019 in Clinical Chemistry
Verified
Journal Article
Open access
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MSMarta Sans
Marta Sans
JZJialing Zhang
Jialing Zhang
JLJohn Lin
John Lin
CFClara L Feider
Clara L Feider
NGNoah Giese
Noah Giese
LELivia Eberlin
Livia Eberlin
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.

6 August 2018 in Analytical Chemistry
Verified
Journal Article
Closed access
DKDustin R. Klein
Dustin R. Klein
CFClara Leigh Feider
Clara Leigh Feider
KGKyana Y. Garza
Kyana Y. Garza
JLJohn Lin
John Lin
LELivia Eberlin
Livia Eberlin
JBJennifer S. Brodbelt
Jennifer S. Brodbelt

31 August 2018 in Analytical Chemistry
Verified
Journal Article
Closed access
APANDREIA M. PORCARI
ANDREIA M. PORCARI
JZJialing Zhang
Jialing Zhang
KGKyana Y. Garza
Kyana Y. Garza
RRRaquel M. Rodrigues-Peres
Raquel M. Rodrigues-Peres
JLJohn Lin
John Lin
LELivia Eberlin
Livia Eberlin

6 July 2021 in Proceedings of the National Academy of Sciences
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Journal Article
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MKMary E. King
Mary E. King
JZJialing Zhang
Jialing Zhang
JLJohn Lin
John Lin
KGKyana Y. Garza
Kyana Y. Garza
RDRachel J. DeHoog
Rachel J. DeHoog
LELivia Eberlin
Livia Eberlin
Significance: 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.

14 November 2016 in Cancer Research
Verified
Journal Article
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JZJialing Zhang
Jialing Zhang
WYWendong Yu
Wendong Yu
SRSeung Woo Ryu
Seung Woo Ryu
JLJohn Lin
John Lin
GBGerardo Buentello
Gerardo Buentello
LELivia Eberlin
Livia Eberlin
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. Cancer Res; 76(22); 6588–97. ©2016 AACR.