Dataset Information

Cross-Platform DNA Encoding for Single-Cell Imaging of Gene Expression.

ABSTRACT: Integration of imaging data across different molecular target types can provide in-depth insight into cell physiology and pathology, but remains challenging owing to poor compatibility between target-type-specific labeling methods. We show that cross-platform imaging analysis can be readily achieved through DNA encoding of molecular targets, which translates the molecular identity of various target types into a uniform in?situ array of ssDNA tags for subsequent labeling with complementary imaging probes. The concept was demonstrated through multiplexed imaging of mRNAs and their corresponding proteins with multicolor quantum dots. The results reveal heterogeneity of cell transfection with siRNA and outline disparity in RNA interference (RNAi) kinetics at the level of both the mRNA and the encoded protein.

SUBMITTER: Zrazhevskiy P

PROVIDER: S-EPMC5036947 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Cross-Platform DNA Encoding for Single-Cell Imaging of Gene Expression.

Zrazhevskiy Pavel P Akilesh Shreeram S Tai Wanyi W Queitsch Konstantin K True Lawrence D LD Fromm Jonathan J Wu David D Nelson Peter P Stamatoyannopoulos John A JA Gao Xiaohu X

Angewandte Chemie (International ed. in English) 20160607 31

Integration of imaging data across different molecular target types can provide in-depth insight into cell physiology and pathology, but remains challenging owing to poor compatibility between target-type-specific labeling methods. We show that cross-platform imaging analysis can be readily achieved through DNA encoding of molecular targets, which translates the molecular identity of various target types into a uniform in situ array of ssDNA tags for subsequent labeling with complementary imagin ...[more]

PMID: 27273345

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Project description:MotivationCross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.ResultsWe present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study.Availability and implementationCuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Cross-Platform DNA Encoding for Single-Cell Imaging of Gene Expression.

Publications

Cross-Platform DNA Encoding for Single-Cell Imaging of Gene Expression.

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