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Human cardiomyocytes derived from pluripotent stem cells (hPSC-CMs) have great potential as a tool to study cardiovascular disease and related treatments, but more information on the biology of hPSC-CMs is needed before they are fully developed. potential can be realized. In a to study recently published in the Proteome Research Journal, a novel multiomic method for analyzing the metabolome and proteome of hPSC-CM is described, which could greatly help advance the understanding of the biology of hPSC-CM.
Technological networks spoke with the study’s lead author, Elizabeth F. Bayne, a doctoral candidate in the Ge Laboratory at the University of Wisconsin-Madison, to learn more about the method and its meaning. In this interview, Bayne also discusses the benefits of taking a multiomic approach and outlines the next steps in the research.
Ash Board (AB): Why is it important to study the structure and functional properties of hPSC-CM? What applications can this have?
Elizabeth F. Bayne (EB): HPSC-CMs hold great promise in the field of precision medicine for cardiovascular disease, which is one of the leading causes of death worldwide. Specifically, hPSC-CMs are useful for screening cardiotoxicity for pharmaceutical applications, models of heart disease from patient-derived cell lines, and regenerative therapies such as cell replacement therapies.
AB: Why is hPSC-CM data replication in in vivo proven models?
EB: One of the obstacles to realizing the full potential of stem cell-derived cardiomyocytes in the clinic is their phenotypic immaturity compared to adult CMs. What is considered a “late” stage cardiomyocyte in culture resembles a fetal phenotype in vivo and lack of sarcomere and metabolic networks organized on the same scale as adult CMs. This relative immaturity of cardiomyocytes derived from stem cells makes it difficult to model genetic cardiac diseases that occur later in life or to use these cells as part of a regenerator therapy.
AB: What advantages does a multiomic approach offer in this context?
EB: Since the maturation of hPSC-CMs involves complex signaling networks, new discovery approaches are urgently needed to gain system-level information on the biology of hPSC-CMs. we analysed metabolites, lipids and proteins to discover the drivers of the development of CM maturation. Mass spectrometry-based methods provide unbiased measurements of high throughput biomolecules. The changes measured in the metabolome and the proteome can be correlated with changes in signal transduction and cell metabolism. Our goal was to integrate these measurements from a single cell culture in order to maximize the information obtained from each valuable sample and to create a complete picture of cardiomyocyte phenotypes as they mature in culture.
AB: Can you describe the sequential approach you took?
EB: The sequential approach encapsulates how metabolites, lipids, and proteins were harvested from 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-CM. After a brief centrifugation step, the metabolite-rich supernatant and the dehydrated cell pellet are easily separated. The metabolite-rich supernatant is analyzed by ultra high resolution Fourier transform ion cyclotron resonance mass spectrometry using automated flow injection. From there, we processed and annotated hundreds of metabolites and lipids in each spectrum and correlated the annotated metabolites to metabolic networks. During this time, the residual cellular material was resolubilized and subjected to shotgun proteomics by the PASEF compatible Bruker timsTOF Pro. To resolubilize the proteins, we used Azo, a photocleavable ionic surfactant that allows very efficient enzymatic digestion while achieving quantitative reproducibility for bottom-up proteomics. Data were searched using MaxQuant, transformed and organized into KEGG lanes. Finally, we combined our metabolites and proteins to create a combined profile of these cardiomyocytes.
AB: Why is it advantageous to adopt a sequential multiomic data collection strategy?
EB: A sequential strategy for multiomics allows us to maximize the information obtained from each valuable cell culture, which takes enormous effort, resources and time to cultivate. It also allows us to create multidimensional images of CM phenotypes throughout the different stages of maturation while eliminating the possibility of variation from sample to sample.
AB: Are there any data management issues associated with sequential data collection? How did you overcome this?
EB: The most difficult part of data management is ensuring that the processing of metabolomics and proteomics data is closely mirrored while ensuring that the data is analyzed appropriately to account for differences in the nature of the data. the composition of the sample, instrumentation and data acquisition methods. For example, accounting for missing values ​​in an ascending proteomics dataset may require a different statistical approach than for missing values ​​in a metabolomics dataset. To overcome this challenge, we first processed the datasets independently according to accepted standards in each area and integrated the protein and metabolite hits thereafter.
AB: What are your next steps in this research space?
EB: In the future, we anticipate that this sequential extraction strategy will be transposable to tissues with some minor modifications to account for the nature of 3D tissues. This multiomic extraction strategy and mass spectrometry platform will be useful for analyzing small amounts of tissue for clinically relevant analyzes, such as human heart biopsy samples.
Elizabeth Bayne was speaking to Ash Board, Editorial Director of Technology Networks.
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