Richard Zare, Postdoctoral Faculty Sponsor
Hydrogen-Deuterium Exchange Desorption Electrospray Ionization Mass Spectrometry Visualizes an Acidic Tumor Microenvironment.
We report that microdroplet hydrogen-deuterium exchange (HDX) detected by desorption electrospray ionization mass spectrometry imaging (DESI-MSI) allows the measurement of the acidity of a tissue sample. The integration of HDX and DESI-MSI has been applied to visualize the acidic tumor microenvironment (TME). HDX-DESI-MSI enables the simultaneous collection of regional pH variation and its corresponding in-depth metabolomic changes. This technique is a cost-effective tool for providing insight into the pH-dependent tumor metabolism heterogeneity.
View details for DOI 10.1021/acs.analchem.1c02026
View details for PubMedID 34279072
Distinguishing between Isobaric Ions Using Microdroplet Hydrogen–Deuterium Exchange Mass Spectrometry
View details for DOI 10.3390/metabo11110728
In situ DESI-MSI lipidomic profiles of mucosal margin of oral squamous cell carcinoma.
2021; 70: 103529
Although there is consensus that the optimal safe margin is ≥ 5mm, obtaining clear margins (≥5 mm) intraoperatively seems to be the major challenge. We applied a molecular diagnostic method at the lipidomic level to determine the safe surgical resection margin of OSCC by desorption electrospray ionisation mass spectrometry imaging (DESI-MSI).By overlaying mass spectrometry images with hematoxylin-eosin staining (H&E) from 18 recruited OSCC participants, the mass spectra of all pixels across the diagnosed tumour and continuous mucosal margin regions were extracted to serve as the training and validation datasets. A Lasso regression model was used to evaluate the test performance.By leave-one-out validation, the Lasso model achieved 88.6% accuracy in distinguishing between tumour and normal regions. To determine the safe surgical resection distance and margin status of OSCC, a set of 14 lipid ions that gradually decreased from tumour to normal tissue was assigned higher weight coefficients in the Lasso model. The safe surgical resection distance of OSCC was measured using the developed 14 lipid ion molecular diagnostic model for clinical reference. The overall accuracy of predicting tumours, positive margins, and negative margins was 92.6%.The spatial segmentation results based on our diagnostic model not only clearly delineated the tumour and normal tissue, but also distinguished the different status of surgical margins. Meanwhile, the safe surgical resection margin of OSCC on frozen sections can also be accurately measured using the developed diagnostic model.This study was supported by Nanjing Municipal Key Medical Laboratory Constructional Project Funding (since 2016) and the Centre of Nanjing Clinical Medicine Tumour (since 2014).
View details for DOI 10.1016/j.ebiom.2021.103529
View details for PubMedID 34391097
- Big cohort metabolomic profiling of serum for oral squamous cell carcinoma screening and diagnosis Natural Sciences 2021; 1 (1)
Oral squamous cell carcinoma diagnosed from saliva metabolic profiling.
Proceedings of the National Academy of Sciences of the United States of America
Saliva is a noninvasive biofluid that can contain metabolite signatures of oral squamous cell carcinoma (OSCC). Conductive polymer spray ionization mass spectrometry (CPSI-MS) is employed to record a wide range of metabolite species within a few seconds, making this technique appealing as a point-of-care method for the early detection of OSCC. Saliva samples from 373 volunteers, 124 who are healthy, 124 who have premalignant lesions, and 125 who are OSCC patients, were collected for discovering and validating dysregulated metabolites and determining altered metabolic pathways. Metabolite markers were reconfirmed at the primary tissue level by desorption electrospray ionization MS imaging (DESI-MSI), demonstrating the reliability of diagnoses based on saliva metabolomics. With the aid of machine learning (ML), OSCC and premalignant lesions can be distinguished from the normal physical condition in real time with an accuracy of 86.7%, on a person by person basis. These results suggest that the combination of CPSI-MS and ML is a feasible tool for accurate, automated diagnosis of OSCC in clinical practice.
View details for DOI 10.1073/pnas.2001395117
View details for PubMedID 32601197
Coulometry-assisted quantitation in spray ionization mass spectrometry.
Journal of mass spectrometry : JMS
The concentration of target analyte in a mixture can be quantified by combining coulometric measurements with spray ionization mass spectrometry. A three-electrode system screen printed on the polymer support acts both as the coulometry platform for electrochemical oxidation and the sample loading tip for spray ionization. After loading a droplet of the analyte solution onto the tip, two steps were taken to implement quantitation. First, the electrochemical oxidation potential was optimized with cyclic voltammetry followed by coulometric measurements to calculate the amount of oxidized analyte under a constant low voltage within a fixed period of time (5 s). Then, a high voltage (+4.5 kV) was applied to the tip to trigger spray ionization for measuring the oxidation yield from the native analyte ion and its oxidized product ion intensities by mass spectrometry. The analyte's native concentration is quantified by dividing the oxidized product's concentration (based on Coulomb's law) and the oxidation yield (estimated from mass spectrometry [MS] assuming that the parent and oxidation product have nearly the same ionization efficiencies). The workflow has an advantage in being free of any standard for constructing the quantitation curve. Several model compounds (tyrosine, dopamine, and angiotensin II) were selected for method validation. It was demonstrated that this strategy was feasible with an accuracy of ~15% for a wide coverage of different species including endogenous metabolites and peptides. As an example of its possible practical use, it was initially employed to make a bilirubin assay in urine.
View details for DOI 10.1002/jms.4628
View details for PubMedID 33245185