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Abstract A promising new field of genetically encoded ultrasound contrast agents in the form of gas vesicles has recently emerged, which could extend the specificity of medical ultrasound imaging. However, given the delicate genetic nature of how these genes are integrated and expressed, current methods of producing gas vesicle‐expressing mammalian cell lines requires significant cell processing time to establish a clonal/polyclonal line that robustly expresses the gas vesicles sufficiently enough for ultrasound contrast. Here, we describe an inducible and drug‐selectable acoustic reporter gene system that can enable gas vesicle expression in mammalian cell lines, which we demonstrate using HEK293T cells. Our drug‐selectable construct design increases the stability and proportion of cells that successfully integrate all plasmids into their genome, thus reducing the amount of cell processing time required. Additionally, we demonstrate that our drug‐selectable strategy forgoes the need for single‐cell cloning and fluorescence‐activated cell sorting, and that a drug‐selected mixed population is sufficient to generate robust ultrasound contrast. Successful gas vesicle expression was optically and ultrasonically verified, with cells expressing gas vesicles exhibiting an 80% greater signal‐to‐noise ratio compared to negative controls and a 500% greater signal‐to‐noise ratio compared to wild‐type HEK293T cells. This technology presents a new reporter gene paradigm by which ultrasound can be harnessed to visualize specific cell types for applications including cellular reporting and cell therapies.
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Cell Line Classification Using Electric Cell-Substrate Impedance Sensing (ECIS)
Abstract We present new methods for cell line classification using multivariate time series bioimpedance data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS technology, which monitors the attachment and spreading of mammalian cells in real time through the collection of electrical impedance data, has historically been used to study one cell line at a time. However, we show that if applied to data from multiple cell lines, ECIS can be used to classify unknown or potentially mislabeled cells, factors which have previously been associated with the reproducibility crisis in the biological literature. We assess a range of approaches to this new problem, testing different classification methods and deriving a dictionary of 29 features to characterize ECIS data. Most notably, our analysis enriches the current field by making use of simultaneous multi-frequency ECIS data, where previous studies have focused on only one frequency; using classification methods to distinguish multiple cell lines, rather than simple statistical tests that compare only two cell lines; and assessing a range of features derived from ECIS data based on their classification performance. In classification tests on fifteen mammalian cell lines, we obtain very high out-of-sample predictive accuracy. These preliminary findings provide a baseline for future large-scale studies in this field.
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- PAR ID:
- 10580859
- Publisher / Repository:
- De Gruyter
- Date Published:
- Journal Name:
- The International Journal of Biostatistics
- Volume:
- 16
- Issue:
- 1
- ISSN:
- 2194-573X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1515/ijb-2018-0083
