Dataset Information

Cell Surface Capture of mouse PSC-derived Cardiac Progenitor Cells

ABSTRACT: The identification of cell surface proteins on stem cells or stem cell derivatives is a key strategy for the functional characterization, isolation, and understanding of stem cell population dynamics. Here, using an integrated mass spectrometry and microarray based approach, we analyzed the surface proteome and transcriptome of cardiac progenitor cells (CPCs) generated from the stage-specific differentiation of mouse and human pluripotent stem cells. Through bioinformatic analysis, we have identified and characterized FZD4 as a new marker for lateral plate mesoderm. Additionally, we utilized FZD4, in conjunction with FLK1 and PDGFRA, to further purify CPCs and increase cardiomyocyte (CM) enrichment in both mouse and human systems. Moreover, we have shown that NORRIN presented to FZD4 further increases CM output via proliferation through the canonical WNT pathway. Taken together, these findings demonstrate a role for FZD4 in mammalian cardiac development.

OTHER RELATED OMICS DATASETS IN: PRJNA401864

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Cell Culture, Embryonic Stem Cell

SUBMITTER: Peter Zandstra

LAB HEAD: Peter Zandstra

PROVIDER: PXD007684 | Pride | 2018-01-03

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	OT1_2011-07-05_CY106_d0.pepxml	Pepxml
	OT1_2011-07-05_CY106_d0.protxml	Other
	OT1_2011-07-05_CY106_d0.raw	Raw
	OT1_2011-07-05_CY109_d375.pepxml	Pepxml
	OT1_2011-07-05_CY109_d375.protxml	Other

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Publications

FZD4 Marks Lateral Plate Mesoderm and Signals with NORRIN to Increase Cardiomyocyte Induction from Pluripotent Stem Cell-Derived Cardiac Progenitors.

Yoon Charles C Song Hannah H Yin Ting T Bausch-Fluck Damaris D Frei Andreas P AP Kattman Steven S Dubois Nicole N Witty Alec D AD Hewel Johannes A JA Guo Hongbo H Emili Andrew A Wollscheid Bernd B Keller Gordon G Zandstra Peter W PW

Stem cell reports 20171214 1

The identification of cell surface proteins on stem cells or stem cell derivatives is a key strategy for the functional characterization, isolation, and understanding of stem cell population dynamics. Here, using an integrated mass spectrometry- and microarray-based approach, we analyzed the surface proteome and transcriptome of cardiac progenitor cells (CPCs) generated from the stage-specific differentiation of mouse and human pluripotent stem cells. Through bioinformatics analysis, we have ide ...[more]

PMID: 29249665

Similar Datasets

Project description:The differentiation of induced pluripotent stem cells (iPSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variability, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, iPSC-to-cardiomyocyte (CM) differentiation is vulnerable with great sensitivity to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), iPSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial iPSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor to further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize iPSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications. With the assistance of RNA sequencing, we transcriptomically characterize the image-recognized CPCs (IR-CPCs), the cells treated with different CHIR doses at stage I, iPSC-CM treated with or without BI-1347, and cells treated with or without BI-1347 at stage I.