Prospective isolation of chondroprogenitors from human iPSCs based on cell surface markers identified using a CRISPR-Cas9-generated reporter.
Articular cartilage shows little or no capacity for intrinsic repair, generating a critical need of regenerative therapies for joint injuries and diseases such as osteoarthritis. Human-induced pluripotent stem cells (hiPSCs) offer a promising cell source for cartilage tissue engineering and in vitro human disease modeling; however, off-target differentiation remains a challenge during hiPSC chondrogenesis. Therefore, the objective of this study was to identify cell surface markers that define the true chondroprogenitor population and use these markers to purify iPSCs as a means of improving the homogeneity and efficiency of hiPSC chondrogenic differentiation.
We used a CRISPR-Cas9-edited COL2A1-GFP knock-in reporter hiPSC line, coupled with a surface marker screen, to identify a novel chondroprogenitor population. Single-cell RNA sequencing was then used to analyze the distinct clusters within the population. An unpaired t test with Welch's correction or an unpaired Kolmogorov-Smirnov test was performed with significance reported at a 95% confidence interval.
Chondroprogenitors expressing CD146, CD166, and PDGFRβ, but not CD45, made up an average of 16.8% of the total population. Under chondrogenic culture conditions, these triple-positive chondroprogenitor cells demonstrated decreased heterogeneity as measured by single-cell RNA sequencing with fewer clusters (9 clusters in unsorted vs. 6 in sorted populations) closer together. Additionally, there was more robust and homogenous matrix production (unsorted: 1.5 ng/ng vs. sorted: 19.9 ng/ng sGAG/DNA; p < 0.001) with significantly higher chondrogenic gene expression (i.e., SOX9, COL2A1, ACAN; p < 0.05).
Overall, this study has identified a unique hiPSC-derived subpopulation of chondroprogenitors that are CD146+
and exhibit high chondrogenic potential, providing a purified cell source for cartilage tissue engineering or disease modeling studies.
Dicks, A; Wu, C-L; Steward, N; Adkar, SS; Gersbach, CA; Guilak, F
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