A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes.
A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes.
复制标题
DOI:
10.1038/s41598-021-94732-1
复制
发表时间:
2021-08-04
影响因子:
4.6
通讯作者:
Gunawardane RN
中科院分区:
文献类型:
--
作者:
Grancharova T;Gerbin KA;Rosenberg AB;Roco CM;Arakaki JE;DeLizo CM;Dinh SQ;Donovan-Maiye RM;Hirano M;Nelson AM;Tang J;Theriot JA;Yan C;Menon V;Palecek SP;Seelig G;Gunawardane RN
We performed a comprehensive analysis of the transcriptional changes occurring during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using single cell RNA-seq, we sequenced > 20,000 single cells from 55 independent samples representing two differentiation protocols and multiple hiPSC lines. Samples included experimental replicates ranging from undifferentiated hiPSCs to mixed populations of cells at D90 post-differentiation. Differentiated cell populations clustered by time point, with differential expression analysis revealing markers of cardiomyocyte differentiation and maturation changing from D12 to D90. We next performed a complementary cluster-independent sparse regression analysis to identify and rank genes that best assigned cells to differentiation time points. The two highest ranked genes between D12 and D24 (MYH7 and MYH6) resulted in an accuracy of 0.84, and the three highest ranked genes between D24 and D90 (A2M, H19, IGF2) resulted in an accuracy of 0.94, revealing that low dimensional gene features can identify differentiation or maturation stages in differentiating cardiomyocytes. Expression levels of select genes were validated using RNA FISH. Finally, we interrogated differences in cardiac gene expression resulting from two differentiation protocols, experimental replicates, and three hiPSC lines in the WTC-11 background to identify sources of variation across these experimental variables.
登录
查看更多内容
影响因子:
11.8
作者:
DeLaughter, Daniel M.;Bick, Alexander G.;Wakimoto, Hiroko;McKean, David;Gorham, Joshua M.;Kathiriya, Irian S.;Hinson, John T.;Homsy, Jason;Gray, Jesse;Pu, William;Bruneau, Benoit G.;Seidman, J. G.;Seidman, Christine E.
通讯作者:
Seidman, Christine E.
影响因子:
9.3
作者:
Gerbin, Kaytlyn A.;Grancharova, Tanya;Gunawardane, Ruwanthi N.
通讯作者:
Gunawardane, Ruwanthi N.
影响因子:
8.8
作者:
Cui, Yueli;Zheng, Yuxuan;Tang, Fuchou
通讯作者:
Tang, Fuchou
影响因子:
1.2
作者:
Bizy, Alexandra;Guerrero-Serna, Guadalupe;Hu, Bin;Ponce-Balbuena, Daniela;Willis, B. Cicero;Zarzoso, Manuel;Ramirez, Rafael J.;Sener, Michelle F.;Mundada, Lakshmi V.;Klos, Matthew;Devaney, Eric J.;Vikstrom, Karen L.;Herron, Todd J.;Jalife, Jose
通讯作者:
Jalife, Jose
影响因子:
20.1
作者:
Goodyer, William R.;Beyersdorf, Benjamin M.;Wu, Sean M.
通讯作者:
Wu, Sean M.