This work lead by Alex Van Grouw entitled “Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency” published in Stem Cells establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high-potency MSC lines and metabolic engineering.

https://doi.org/10.3390/cancers14092262

This work lead by Daniel Vallejo entitled “Native triboelectric nanogenerator ion mobility-mass spectrometry of egg proteins relevant to objects of cultural heritage at picoliter and nanomolar quantities” published in Analytica Chemica Acta demonstrates that triboelectric nanogenerators (TENG) can generate protein ions suitable for native mass spectrometry at nanomolar and picoliter quantities, and its potential applications towards cultural heritage research. Check it out using the link below:

https://doi.org/10.3390/cancers14092262

This work lead by Olatomiwa Bifarin, Samyukta Sah, David Gaul, and Samuel Moore entitled “Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer” published in Journal of Proteome Research tracks the alterations in the metabolome of mice models using machine learning and demonstrates how differences in metabolite flux between ovarian cancer severity/outcome can be leveraged to develop more accurate early detection methodologies. Check it out using the link below:

https://doi.org/10.3390/cancers14092262

Carter Asef began a 12-week experiential internship program at Abbvie Pharmaceuticals in the analytical R&D division. His research project will involve using recently published techniques [1] to perform ion molecule reactions for the structural elucidation of nitrosamine compounds.

A Diagnostic Nitrosamine Detection Approach for Pharmaceuticals by Using Tandem Mass Spectrometry Based on Diagnostic Gas-Phase Ion-Molecule Reactions

Judy Kuan-Yu Liu, Erlu Feng, Yue Fu, Wanru Li, Xin Ma, Huaming Sheng, John Kong, Yong Liu, Michael Hicks, Bangping Xiang, Zhijian Liu, Justin Pennington, and Hilkka I. Kenttämaa

Analytical Chemistry 2022 94 (40), 13795-13803

DOI: 10.1021/acs.analchem.2c02282

Samyukta Sah is the newest minted Ph.D. from the Fernandez group! Congratulations again Dr. Sah, and we wish you the best in all your future endeavors. We know you’re going to be an amazing success

This work lead by Carter Asef and Markace Rainey entitled “Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry” published in Analytical Chemistry demonstrates that ion mobility is capable of providing both high error, but also highly accurate filters through collision cross section measurements for metabolite annotation. Check it out using the link below:

https://doi.org/10.3390/cancers14092262

This work lead by Markace Rainey and Carter Asef entitled “CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics” published in Analytical Chemistry provides an open source Jupyter Notebook format for prediction high accuracy collision cross section measurements and predictions for metabolites. Check it out using the link below:

https://doi.org/10.3390/cancers14092262

Daniel visited the University of Bordeaux in Professor Caroline Tokarski’s group under the Nerem International Travel Award through IBB at Georgia Tech. During his time at UBordeaux he learned state-of-the-art Top-down, HDX, and MALDI applications towards objects of cultural heritage such as paintings and gums to support his ongoing NSF funded projects.

A warm welcome to our newest graduate student Jada Gray!

This work by Xin MA entitled “Advances in mass spectrometry imaging for spatial cancer metabolomics” published in Mass Spectrometry Reviews provides a comprehensive review of the fundamentals, recent advancements in imaging applications generally as well as with particular interest in cancer research, and the implementation of emerging research fields such as machine learning/artificial intelligence. Check it out using the link below:

https://doi.org/10.3390/cancers14092262