Construction of bioinformatic infrastructure for single-cell analysis to improve induced pluripotent stem cell systems

The human heart has limited regenerative capacity. Consequently, patients who have suffered from myocardial infarction (MI) are unable to endogenously replace lost tissue. MI patients have a significantly increased risk of heart failure that can ultimately lead to death. Induced pluripotent stem cell cardiomyocytes (iPSC-CMs) provide an inexhaustible source of cells for replacing lost ventricular CMs (vCMs) and restoring full heart function. Currently, the clinical translation of iPSC-CMs is halted by the immaturity of the existing iPSC-CM derivation protocols. Previous work with transplanting iPSC-CMs into large animal models has observed cardiac arrhythmias as a direct consequence of engrafted cells. It is an active effort in the field of cardiac regenerative therapy to mitigate the risk of arrhythmias upon transplantation by vastly increasing the maturation of iPSC-CMs.

Single-cell RNA sequencing (scRNA-seq) enables the characterization of cell populations' expression profiles at high resolution. scRNA-seq is widely used to explore iPSC derivation. As the technology has evolved, a vast amount of scRNA-seq data from human cardiac tissue at several developmental stages is publicly available. Access to virtually all scRNA-seq studies offers endless possibilities to raise novel questions and reuse data in new combinations to answer them.

This industrial PhD revolved around three studies aimed at identifying missing factors that halt iPSC-CM maturation, using publicly available scRNA-seq data in combination with in-house sequenced data from iPSC-CMs. The generated computational methodologies provide a generalized workflow for discovering targets to optimize stem-cell products across all regenerative therapeutic fields.

In the first study, we explored quality control (QC) measures to assess and quantify, in order to validate a proposed modified protocol for generating high-resolution CM data from scRNA-seq. These QC measures led to the development of an automated pipeline that has reduced the time required to analyze and assess scRNA-seq data, which is critical in an industrial setting for increasing customer satisfaction through reduced turnaround times.

In the second study, iPSC-CMs were derived in controlled environments with differing oxygen concentrations. By identifying a method to accurately represent iPSC-CMs and CMs isolated from the atrium or ventricle of adult donors in a combined space, we unexpectedly identified that iPSC-CMs derived at lower oxygen levels exhibited a transcriptomic profile resembling atrial CMs rather than vCMs.

In the third study, we characterized the cellular profile of iPSC-CMs derived using a modified protocol to enhance purity at single-cell resolution. Through the integration of iPSC-CMs with data from human donors at several developmental stages, we predicted the developmental “age” of iPSC-CMs to be at an early developmental stage and identified multiple transcription factors that were missing from the iPSC-CMs, which may have limited their maturity.

Acquired bioinformatic strategies led to the development of automated computational methodologies that provide a foundation for a commercialized assay to identify molecular targets that may enhance stem cell therapeutic protocols using scRNA-seq.