Primary cancer cell culture: mammary-optimized vs conditional reprogramming

原代癌细胞培养:乳腺优化与条件重编程

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作者:Ahmad M Alamri, Keunsoo Kang, Svenja Groeneveld, Weisheng Wang, Xiaogang Zhong, Bhaskar Kallakury, Lothar Hennighausen, Xuefeng Liu, Priscilla A Furth

Abstract

The impact of different culture conditions on biology of primary cancer cells is not always addressed. Here, conditional reprogramming (CRC) was compared with mammary-optimized EpiCult-B (EpiC) for primary mammary epithelial cell isolation and propagation, allograft generation, and genome-wide transcriptional consequences using cancer and non-cancer mammary tissue from mice with different dosages of Brca1 and p53 Selective comparison to DMEM was included. Primary cultures were established with all three media, but CRC was most efficient for initial isolation (P<0.05). Allograft development was faster using cells grown in EpiC compared with CRC (P<0.05). Transcriptome comparison of paired CRC and EpiC cultures revealed 1700 differentially expressed genes by passage 20. CRC promoted Trp53 gene family upregulation and increased expression of epithelial differentiation genes, whereas EpiC elevated expression of epithelial-mesenchymal transition genes. Differences did not persist in allografts where both methods yielded allografts with relatively similar transcriptomes. Restricting passage (<7) reduced numbers of differentially expressed genes below 50. In conclusion, CRC was most efficient for initial cell isolation but EpiC was quicker for allograft generation. The extensive culture-specific gene expression patterns that emerged with longer passage could be limited by reducing passage number when both culture transcriptomes were equally similar to that of the primary tissue. Defining impact of culture condition and passage on the transcriptome of primary cells could assist experimental design and interpretation. For example, differences that appear with passage and culture condition are potentially exploitable for comparative studies targeting specific biological networks in different transcriptional environments.

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