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Advancing the Understanding of hPSC-CM Biology With Multiomics

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Human pluripotent stem-cell derived cardiomyocytes (hPSC-CMs) have great potential as a tool for studying cardiovascular disease and associated treatments, but more insights into hPSC-CM biology are needed before their full potential can be realized. In a study recently published in the Journal of Proteome Research, a new multiomics method for analyzing the hPSC-CM metabolome and proteome is described, which could greatly help to advance the understanding of hPSC-CM biology.

Technology Networks
spoke to the lead author of the study, Elizabeth F. Bayne, a PhD candidate in the Ge Lab at the University of Wisconsin–Madison, to learn more about the method and its significance. In this interview, Bayne also discusses the benefits of adopting a multiomics approach and outlines the next steps for the research.

Ash Board (AB): Why is it important to study the structure and functional properties of hPSC-CMs? What applications might this have?

Elizabeth F. Bayne (EB):
hPSC-CMs show immense promise in the field of precision medicine for cardiovascular disease, which is a leading cause of death worldwide. Specifically, hPSC-CMs are useful for cardiotoxicity screening for pharmaceutical applications, cardiac disease models from patient-derived cell lines, and regenerative therapies such as cell replacement therapies.

AB: Why has replicating hPSC-CMs data in in vivo models proven challenging?

One of the roadblocks to realizing the full potential of stem cell derived cardiomyocytes in the clinic is their phenotypic immaturity compared with adult CMs. What is considered “late” stage cardiomyocyte in culture resembles a fetal phenotype in vivo and lacks organized sarcomere and metabolic networks on the same scale as adult CMs. This relative immaturity of stem cell derived cardiomyocytes makes it difficult to model cardiac genetic diseases that occur later in life or use these cells as part of a regenerative therapy.

AB: What benefits does a multiomics approach offer in this context?

Because hPSC-CM maturation entails complex signaling networks, new discovery approaches are urgently needed to gain systems-level insights into hPSC-CM biology. We analyzed metabolites, lipids and proteins to uncover developmental drivers of CM maturation. Mass spectrometry-based methods offer unbiased measurements of biomolecules in a high-throughput manner. Measured changes in the metabolome and proteome can be correlated to changes in signal transduction and cellular metabolism. We aimed to integrate these measurements from a single cell culture to maximize the information gained from each precious sample and create a comprehensive picture of cardiomyocyte phenotypes as they mature in culture.

AB: Can you describe the sequential approach you adopted?

The sequential approach encapsulates the way the metabolites, lipids and proteins were harvested from the cell culture. Here, we used a solvent-based quenching technique to harvest metabolites and lipids while simultaneously precipitating proteins from the same well of hPSC-CMs. After a brief centrifugation step, the metabolite-rich supernatant and the dehydrated cell pellet are easily separated. The metabolite-rich supernatant is analyzed by ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry using an automated flow injection. From there, we processed and annotated hundreds of metabolites and lipids in each spectrum and correlated annotated metabolites to metabolic networks. Meanwhile, the residual cellular material was resolubilized and subjected to shotgun proteomics by the PASEF-enabled Bruker timsTOF Pro. To resolubilize the proteins, we used Azo, an ionic, photocleavable surfactant that enables highly efficient enzymatic digestion while achieving quantitative reproducibility for bottom-up proteomics. The data was searched using MaxQuant, transformed, and organized into KEGG pathways. Finally, we combined our metabolite and protein hits to create a combined profile of these cardiomyocytes.

AB: Why is advantageous to adopt a sequential strategy for gathering multiomics data?

A sequential strategy for multiomics allows us to maximize the information gained from each precious cell culture, which takes enormous effort, resource and time to cultivate. It also allows us to create multidimensional pictures of the CM phenotypes throughout various stages of maturation while eliminating the possibility of sample-to-sample variation.

AB: Are there data handling challenges associated with collecting data sequentially? How did you overcome this?

The most challenging part of handling the data is ensuring that the metabolomics and proteomics data processing mirrors each other closely while still ensuring the data is analysed appropriately to account for differences in the nature of the sample composition, instrumentation and data acquisition methods. For example, accounting for missing values in a bottom-up proteomics dataset may require a different statistical approach than missing values in a metabolomics dataset. To overcome this challenge, we processed the datasets first independently according to accepted standards in each field and integrated the protein and metabolite hits afterwards.

AB: What are your next steps in this research space?

In the future, we foresee this sequential extraction strategy to be translatable to tissue with some minor modification to account for the nature of 3D tissues. This multiomics extraction strategy and mass spectrometry platform will be useful to analyze small amounts of tissue for clinically-relevant analyses, such as human cardiac biopsy samples.

Elizabeth Bayne was speaking to Ash Board, Editorial Director for Technology Networks.