Stem cell and machine learning approaches for understanding heart field development
Abstract/Contents
- Abstract
- During embryogenesis, the heart is derived from two major progenitor populations known as the first and second heart fields that give rise to the left and right ventricles, respectively. While these cell lineages have been extensively studied in mice, the lack of access to early human embryonic tissue has largely limited the study of these cell populations to animal models. Here, we present a novel TBX5/MYL2 lineage tracing reporter system and machine learning prediction pipeline for elucidating the identity of heart field lineages during a human induced pluripotent stem cell (hiPSC) cardiac development. Using our lineage tracing reporter system, we reveal the unexpected predominance of FHF differentiation using a well published cardiac differentiation protocol. We conduct a detailed single cell RNA sequencing time course where we confirm the FHF differentiation trajectory of our hiPSC cardiac differentiations and establish an atlas of human left ventricular development. Moreover, we developed a machine learning algorithm, devCellPy, to allow for the automated annotation of single cell RNA-seq datasets across a complex hierarchy of annotation layers. Using our algorithm, we trained the algorithm on a large murine cardiac developmental cell atlas and apply the cardiac prediction algorithm to conduct a cross-species identification of hiPSC-derived cardiomyocytes. Concordant with our lineage tracing data, devCellPy predicted a predominance of left ventricular differentiation, effectively demonstrating the power of the algorithm at identifying human cell types using a murine embryonic reference dataset. Lastly, we applied our devCellPy cardiac prediction algorithm to hiPSC cardiomyocytes derived from patients with a single ventricle congenital heart disease known as Hypoplastic Left Heart Syndrome. Using our algorithm, we discovered a predominance of left ventricular differentiation and reveal impairments in contractile force generation and metabolic activity within the disease lines. In summary, our work provides two powerful new tools for the study heart field development and provides a transcriptional reference of human first heart field development.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Galdos, Francisco |
|---|---|
| Degree supervisor | Wu, Sean F |
| Thesis advisor | Wu, Sean F |
| Thesis advisor | Mercola, Mark |
| Thesis advisor | Rabinovitch, Marlene |
| Degree committee member | Mercola, Mark |
| Degree committee member | Rabinovitch, Marlene |
| Associated with | Stanford University, Program of Stem Cell Biology and Regenerative Medicine |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Francisco Xavier Galdos. |
|---|---|
| Note | Submitted to the Program of Stem Cell Biology and Regenerative Medicine. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/gr467st1453 |
Access conditions
- Copyright
- © 2022 by Francisco Galdos
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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