Dataset Information

A novel dual-color reporter for identifying insulin-producing beta-cells and classifying heterogeneity of insulinoma cell lines.

ABSTRACT: Many research studies use immortalized cell lines as surrogates for primary beta- cells. We describe the production and use of a novel "indirect" dual-fluorescent reporter system that leads to mutually exclusive expression of EGFP in insulin-producing (INS(+)) beta-cells or mCherry in non-beta-cells. Our system uses the human insulin promoter to initiate a Cre-mediated shift in reporter color within a single transgene construct and is useful for FACS selection of cells from single cultures for further analysis. Application of our reporter to presumably clonal HIT-T15 insulinoma cells, as well as other presumably clonal lines, indicates that these cultures are in fact heterogeneous with respect to INS(+) phenotype. Our strategy could be easily applied to other cell- or tissue-specific promoters. We anticipate its utility for FACS purification of INS(+) and glucose-responsive beta-like-cells from primary human islet cell isolates or in vitro differentiated pluripotent stem cells.

SUBMITTER: Lee NS

PROVIDER: S-EPMC3329476 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

A novel dual-color reporter for identifying insulin-producing beta-cells and classifying heterogeneity of insulinoma cell lines.

Lee Nan Sook NS Rohan Joyce G JG Zitting Madison M Kamath Sonia S Weitz Andrew A Sipos Arnold A Salvaterra Paul M PM Hasegawa Kouichi K Pera Martin M Chow Robert H RH

PloS one 20120418 4

Many research studies use immortalized cell lines as surrogates for primary beta- cells. We describe the production and use of a novel "indirect" dual-fluorescent reporter system that leads to mutually exclusive expression of EGFP in insulin-producing (INS(+)) beta-cells or mCherry in non-beta-cells. Our system uses the human insulin promoter to initiate a Cre-mediated shift in reporter color within a single transgene construct and is useful for FACS selection of cells from single cultures for f ...[more]

PMID: 22530041

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Project description:PURPOSE:The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP). METHODS:Primary data were derived from Kaiser Permanente Northwest (2008-2014), an integrated health care system (~n > 475 000 unique individuals per year). Data included International Classification of Diseases, Ninth Revision (ICD-9) codes for nonfatal diagnoses, International Classification of Diseases, Tenth Revision (ICD-10) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart review and conducted assessments of algorithm portability in Kaiser Permanente Washington, Tennessee State Medicaid, and Optum. RESULTS:Code-based algorithm performance was excellent for opioid-related overdoses (sensitivity = 97.2%, specificity = 84.6%) and classification of heroin-involved overdoses (sensitivity = 91.8%, specificity = 99.0%). Performance was acceptable for code-based suicide/suicide attempt classifications (sensitivity = 70.7%, specificity = 90.5%); sensitivity improved with NLP (sensitivity = 78.7%, specificity = 91.0%). Performance was acceptable for the code-based substance abuse-involved classification (sensitivity = 75.3%, specificity = 79.5%); sensitivity improved with the NLP-enhanced algorithm (sensitivity = 80.5%, specificity = 76.3%). The opioid-related overdose algorithm performed well across portability assessment sites, with sensitivity greater than 96% and specificity greater than 84%. Cross-site sensitivity for heroin-involved overdose was greater than 87%, specificity greater than or equal to 99%. CONCLUSIONS:Code-based algorithms developed to detect opioid-related overdoses and classify them according to heroin involvement perform well. Algorithms for classifying suicides/attempts and abuse-related opioid overdoses perform adequately for use for research, particularly given the complexity of classifying such overdoses. The NLP-enhanced algorithms for suicides/suicide attempts and abuse-related overdoses perform significantly better than code-based algorithms and are appropriate for use in settings that have data and capacity to use NLP.

Dataset Information

A novel dual-color reporter for identifying insulin-producing beta-cells and classifying heterogeneity of insulinoma cell lines.

Publications

A novel dual-color reporter for identifying insulin-producing beta-cells and classifying heterogeneity of insulinoma cell lines.

Similar Datasets

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