ProjectAbstract Cellfateengineering,forexamplethedirecteddifferentiationofpluripotentstemcellsor thedirectconversionamongsomaticcelltypes,holdsgreatpromisetoimprovedisease modeling, drug screening, and to lead to regenerative medicine therapies. I recently developed a novel analytical tool, CellNet, which assesses how well engineered cells approachtheirinvivotargetcelltypesbasedoncellandtissuespecificgeneregulatory networks (GRNs). By applying CellNet to all cell fate engineering studies for which compatible data was available, I discovered several issues that were common to virtuallyallmethodsandtargetlineages.First,Ifoundthattheonlyrobustlyfaithfulfate engineeringwasthatofreprogrammingtopluripotency.Second,IfoundthattheGRNof the starting cell type (e.g. pluripotent stem cells in cases of directed differentiation or often fibroblasts in cases of direct conversion) is often partially retained in engineered cells.Third,IfoundthatGRNsofalternatelineages(i.e.thosenotassociatedwiththe starting cell type or the target lineage) are frequently established in engineered cells. Finally, I found that the complex signaling milieu of the mouse microenvironment to whichengineeredcellsaretransplantedpotentlyrepressesalternate/aberrantlineages and induces the target cell type GRNs in directly converted cells. These observations have revealed several fundamental barriers to faithful cell fate engineering and they define opportunities for progress. My current research program, which I seek to fund through this MIRA opportunity, is to develop novel theoretical and computational methodstodefinecelltypeidentityfromsinglecellRNA-Seq(scRNA-Seq)datawithan emphasisondevelopmentalcelltypesthatemergeduringmesodermdevelopmentand subsequent commitment to chondrocyte fate. As part of this work, we will generate scRNA-Seq data of the developing and adult synovial joint, we will harvest and incorporate publicly available and collaborator-provided scRNA-Seq data of other lineages to make a generally applicable platform for assessing cell type identity at the single cell level of resolution, and we will make the resulting methods, software, and datafreelyavailable.Finally,wewillusethesystemtodeterminetheextenttowhichthe cell types and compositions of directly differentiating mouse ESCs match their in vivo counterparts.

Public Health Relevance

Cellfateengineering,forexamplethedirecteddifferentiationofpluripotentstemcellsor thedirectconversionamongsomaticcelltypes,holdsgreatpromisetoimprovedisease modeling,drugscreening,andtoleadtoregenerativemedicinetherapies.However,our ability to engineering cell fate with fidelity has been impeded by the fact that our understandinganddefinitionofcelltypesderiveslargelyfrombulkpopulationsofcells or tissues, which are mixtures of cell types. In this work, I propose to address this problemthroughacombinationofalgorithmdevelopmentanddatageneration,withan emphasisoncelltypesrelatedtocartilage,bone,blood,andheart.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Unknown (R35)
Project #
5R35GM124725-04
Application #
9996716
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Brazhnik, Paul
Project Start
2017-08-01
Project End
2022-07-31
Budget Start
2020-08-01
Budget End
2021-07-31
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Anatomy/Cell Biology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
Velazquez, Jeremy J; Su, Emily; Cahan, Patrick et al. (2018) Programming Morphogenesis through Systems and Synthetic Biology. Trends Biotechnol 36:415-429
Spangler, Abby; Su, Emily Y; Craft, April M et al. (2018) A single cell transcriptional portrait of embryoid body differentiation and comparison to progenitors of the developing embryo. Stem Cell Res 31:201-215