CRISPR Gene Editing in Stem Cells

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the usefulness of this model in testing therapeutic interventions
in principle.
Despite the well-established use of iPSCs in disease
modeling, their potential to model cancer has barely been
explored (Papapetrou, 2016). We show here that integrated pa-
tient cell reprogramming and cancer genetics is a powerful way
to dissect cancer progression, deconstruct clonal hierarchies,
and mimic clonal evolution leveraging CRISPR technology.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:
d KEY RESOURCES TABLE
d CONTACT FOR REAGENTS AND RESOURCE SHARING
d EXPERIMENTAL MODEL AND SUBJECT DETAILS
B iPSC generation
B Mouse strain used for transplantation experiments
d METHOD DETAILS
B Mutational analysis
B Hematopoietic differentiation
B Globin Gene Expression Analysis
B Flow cytometry and cell sorting
B Clonogenic assays
B Cell growth assays
B Cytological analyses
B Transplantation into NSG mice
B RNA sequencing
B CRISPR/Cas9-mediated GATA2 inactivation
B CRISPR/Cas9-mediated ASXL1 mutation
B Engineering of chr7q deletions
B Treatment with 5-AzaC
d QUANTIFICATION AND STATISTICAL ANALYSIS
B Genome-wide gene expression analysis
B DNA methylation analysis by ERRBS
B Statistical analysis
d DATA AND SOFTWARE AVAILABILITY
SUPPLEMENTAL INFORMATION
Supplemental Information includes seven figures and six tables and can be
found with this article online at .
AUTHOR CONTRIBUTIONS
A.G.K. performed experiments, analyzed data, and assisted with manuscript
preparation. C.-J.C., A.C., T.-C.H, T.W., S.V., C H., and M.O. performed exper-
iments and analyzed data. J.T.-F. performed cytological analyses. H.Y., A.S.,
B.D.G., and C.S.L. performed bioinformatics analyses of RNA-seq data.
V.M.K., A.S., and L.S. provided patient samples. R.K.R. and E.P. analyzed
gene mutation data. D.P. and S.P. generated and analyzed ERRBS data.
E.P.R. provided materials. M.G.K. supervised experiments, analyzed data,
and prepared the manuscript. E.P.P. conceived, designed, and supervised
the study, analyzed data, and prepared the manuscript.
ACKNOWLEDGMENTS
This work was supported by NIH grants R00DK087923 and R01HL121570,
Damon Runyon-Rachleff Innovation Award 30-14 from the Damon Runyon
Cancer Research Foundation, the Edward P. Evans Foundation, New Scholar
in Aging Award AGS-NS-0984-13 from the Ellison Medical Foundation, and
research grants from the Henry and Marilyn Taub Foundation, the Babich Fam-
ily Foundation, and Alex’s Lemonade Stand Foundation (to E.P.P.). This work
was also supported by NIH grants R01DK101989, R01CA193842, and
P30CA008748, a Kimmel Scholar Award, and a V-Scholar Award (to
M.G.K.). H.Y. was supported by the Tri-Institutional Training Program in
Computational Biology and Medicine (NIH grant T32GM083937).
Received: June 28, 2016
Revised: December 18, 2016
Accepted: January 26, 2017
Published: February 16, 2017
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