Dataset Information

Defining the diversity of phenotypic respecification using multiple cell lines and reprogramming regimens.

ABSTRACT: To better understand the basis of variation in cellular reprogramming, we performed experiments with two primary objectives: first, to determine the degree of difference, if any, in reprogramming efficiency among cells lines of a similar type after accounting for technical variables, and second, to compare the efficiency of conversion of multiple similar cell lines to two separate reprogramming regimens-induced neurons and induced skeletal muscle. Using two reprogramming regimens, it could be determined whether converted cells are likely derived from a distinct subpopulation that is generally susceptible to reprogramming or are derived from cells with an independent capacity for respecification to a given phenotype. Our results indicated that when technical components of the reprogramming regimen were accounted for, reprogramming efficiency was reproducible within a given primary fibroblast line but varied dramatically between lines. The disparity in reprogramming efficiency between lines was of sufficient magnitude to account for some discrepancies in published results. We also found that the efficiency of conversion to one phenotype was not predictive of reprogramming to the alternate phenotype, suggesting that the capacity for reprogramming does not arise from a specific subpopulation with a generally "weak grip" on cellular identity. Our findings suggest that parallel testing of multiple cell lines from several sources may be needed to accurately assess the efficiency of direct reprogramming procedures, and that testing a larger number of fibroblast lines--even lines with similar origins--is likely the most direct means of improving reprogramming efficiency.

SUBMITTER: Alicea B

PROVIDER: S-EPMC3780423 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Defining the diversity of phenotypic respecification using multiple cell lines and reprogramming regimens.

Alicea Bradly B Murthy Shashanka S Keaton Sarah A SA Cobbett Peter P Cibelli Jose B JB Suhr Steven T ST

Stem cells and development 20130618 19

To better understand the basis of variation in cellular reprogramming, we performed experiments with two primary objectives: first, to determine the degree of difference, if any, in reprogramming efficiency among cells lines of a similar type after accounting for technical variables, and second, to compare the efficiency of conversion of multiple similar cell lines to two separate reprogramming regimens-induced neurons and induced skeletal muscle. Using two reprogramming regimens, it could be de ...[more]

PMID: 23672680

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Project description:BackgroundUnderstanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures.ResultsIn this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract features for cancer cell lines from the genetic feature vectors respectively. After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines. When the training set and the testing set are divided based on the interaction pairs between drugs and cell lines, tCNNS achieves 0.826, 0.831 for the mean and top quartile of the coefficient of determinant (R2) respectively and 0.909, 0.912 for the mean and top quartile of the Pearson correlation (Rp) respectively, which are significantly better than those of the previous works (Ammad-Ud-Din et al., J Chem Inf Model 54:2347-9, 2014), (Haider et al., PLoS ONE 10:0144490, 2015), (Menden et al., PLoS ONE 8:61318, 2013). However, when the training set and the testing set are divided exclusively based on drugs or cell lines, the performance of tCNNS decreases significantly and Rp and R2 drop to barely above 0.ConclusionsOur approach is able to predict the drug effects on cancer cell lines with high accuracy, and its performance remains stable with less but high-quality data, and with fewer features for the cancer cell lines. tCNNS can also solve the problem of outliers in other feature space. Besides achieving high scores in these statistical metrics, tCNNS also provides some insights into the phenotypic screening. However, the performance of tCNNS drops in the blind test.

Dataset Information

Defining the diversity of phenotypic respecification using multiple cell lines and reprogramming regimens.

Publications

Defining the diversity of phenotypic respecification using multiple cell lines and reprogramming regimens.

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