Arina Nikitina, Bioinformatics Thesis Defense

In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics in the School of Biological Sciences.

Arina Nikitina
Defends her thesis:
Computational Analysis Methodologies for Evaluating Metabolism Changes in iPSCs Undergoing Differentiation
Tuesday, December 6, 2022
10:00am Eastern Time
Krone Engineered Biosystems Building, Children’s Healthcare of Atlanta Seminar Room (EBB 1005)
Snacks Provided
Zoom link:
Thesis Advisor: 
Dr. Melissa Kemp
School of Biomedical Engineering
Georgia Institute of Technology
Committee Members:
Dr. Matthew Torres 
School of Biological Sciences 
Georgia Institute of Technology 



Dr. Shuye Nie 
School of Biological Sciences 
Georgia Institute of Technology

Dr. Facundo Fernandez 
School of Chemistry and Biochemistry 
Georgia Institute of Technology 

Dr. Denis Tsygankov 
School of Biomedical Engineering 
Georgia Institute of Technology

Induced pluripotent stem cells (iPSCs) hold great promise as a regenerative medicine tool, allowing the creation of any type of tissue from a patient’s own cells. Clinical applications of iPSCs are still hampered by the great costs of cell manufacturing, as well as the poor understanding of the processes governing the differentiation process. Additionally, few non-invasive algorithms have been established to evaluate quality control at the earliest stage of differentiation before lengthy protocols expend resources. 

To identify quality control attributes and enhance knowledge of iPSC differentiation, I developed a multi-modal imaging analytical pipeline that resolves spatial metabolomics to single-cell resolution. In this work, I investigated changes in iPSC lipid profiles during the initial loss of pluripotency over the course of spontaneous differentiation using co-registration of confocal microscopy and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging. Despite lipids having known functions in cell signaling, their role in pluripotency maintenance and lineage specification is underexplored. Lipids that are highly informative of the temporal stage of the differentiation were identified through a variety of multivariate modeling methods and shown to reveal lineage bifurcation occurring metabolically. Among these lipids, several phosphatidylinositol (PI) species emerged as early metabolic markers of pluripotency loss, preceding changes in Oct4 - a transcription factor well known for its connection to pluripotency. In addition, continuous inhibition of phosphatidylethanolamine N-methyltransferase during differentiation enhanced pluripotency maintenance and increased levels of the same lipid pluripotency markers before transcriptional changes. This highlights a novel mechanism of pluripotency maintenance by upregulating PI production via an unknown pathway. The small subset of informative PI species can be used as novel targets for early quality control in a variety of protocols involving pluripotency loss and/or maintenance. 

In addition, I investigated the predictive power of real-time oxygen consumption measurements in iPSCs undergoing cardiomyocyte differentiation through machine learning algorithms. Oxygen consumption rate values in the first 4 days of the differentiation protocol were highly predictive of the cardiomyocyte yield by day 16, with several time series features capable of predicting the differentiation failure as early as in the first 48 hours. This approach can be easily scaled and translated to the regenerative medicine application, greatly reducing cell manufacturing costs by saving time and resources from being wasted on a failed batch. In summary, this work presents new analytical tools for cellular manufacturing that also yields novel knowledge about iPSCs metabolism during differentiation.   

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Arina Nikitina, Bioinformatics PhD
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