Scientific Publications by FDA Staff

Genomics 2006 Apr;87(4):552-9

Quality prediction of cell substrate using gene expression profiling.

Han J, Farnsworth RL, Tiwari JL, Tian J, Lee H, Ikonomi P, Byrnes AP, Goodman JL, Puri RK

Puri RK, US FDA, Lab Mol Tumor Biol, Div Cellular & Gene Therapies, Ctr Biol Evaluat & Res, Bldg 29B,Room 2NN20,29 Lincoln Dr, Bethesda, MD 20892 USA US FDA, Lab Mol Tumor Biol, Div Cellular & Gene Therapies, Ctr Biol Evaluat & Res, Bethesda, MD 20892 USA Amer Type Culture Collect, Manassas, VA 20110 USA US FDA, Div Biostat, Ctr Biol Evaluat & Res, Bethesda, MD 20892 USA US FDA, Lab Immunol & Virol, Div Cellular & Gene Therapies, Ctr Biol Evaluat & Res, Bethesda, MD 20892 USA

Changes in cell culture conditions influence the metabolism of cells, which consequently affects the quality of the products that they produce, such as viral vectors, recombinant proteins, or vaccines. Currently there is no effective technique available to monitor global quality of cells in cell culture. Here we describe a new method using gene expression profiling by microarray to predict the quality of cell substrates. Human embryonic kidney 293 cells are a commonly used cell substrate in the production of biological products. We demonstrate that the yield of adenoviral vectors was lower in over-confluent 293 cells, compared to 40 or 90% confluent cells. Total RNA derived from these cells of different confluence states was reverse transcribed, labeled, and used to hybridize 10K cDNA arrays to determine biomarkers for confluence states. Phenotype scatter-plot analysis and cluster analysis were used for class discovery. Based on this approach, we identified genes that were either up-regulated or down-modulated in response to different cell confluence states. By multivariate predictive models we identified a set of 37 genes that were either down-regulated or up-regulated compared to 90% confluent cells as a predictor of cell confluence and quality of 293 cell cultures. The predictive accuracy of these models was assessed by the leave-one-out cross-validation method. The expression of selected gene predictors was validated by quantitative PCR analysis. Our results demonstrate that gene expression profiling can assess the quality of cell substrates prior to large-scale production of a biological product.