Altered β-Cell Calcium Dynamics via Electric Field Exposure

Liebman, Caleb; Vu, Thao-Mi; Phillips, A. O.; Chen, Bin; Cho, Michael

doi:10.1007/s10439-020-02517-w

Cited by 4 publications

(6 citation statements)

References 40 publications

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“…The images used in our automated pipeline were sourced from previous experimental images that monitored and recorded Ca 2+ dynamics in real-time [46]. Mouse derived βTC-6 insulinoma cells were cultured and allowed to grow for several days.…”

Section: Calcium Imaging and Deep Learning-based Segmentationmentioning

confidence: 99%

“…A calcium indicator dye (Fluo-8) was used to measure changes in intracellular calcium levels. The cells were stained with 0.8 μM Fluo-8, and 1 drop/mL Nucblue to visualize the nuclei, for 30 min at 37˚C and loaded onto a custom-designed electric field exposure chamber [46]. Non-invasive electric field stimulation (EFS; 15 min exposure) was chosen to manipulate the voltage-gated calcium channels and therefore modulate intracellular Ca 2+ dynamics.…”

Section: Calcium Imaging and Deep Learning-based Segmentationmentioning

confidence: 99%

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BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells

Alsup,

Fowlds,

Cho

et al. 2024

PLoS ONE

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Insulin secretion from pancreatic β-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of β-cells, involve multiple software packages, and necessitate multiple researchers—all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in β-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions.

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Section: Calcium Imaging and Deep Learning-based Segmentationmentioning

confidence: 99%

Section: Calcium Imaging and Deep Learning-based Segmentationmentioning

confidence: 99%

BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells

Alsup,

Fowlds,

Cho

et al. 2024

PLoS ONE

Self Cite

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“…The Na v -dependent "galvanotaxis" alongside this physiological transepithelial voltage gradient that corresponds to an electric field intensity of 5 V/cm has been hypothesized to promote invasion of prostate cancer cells into the gland ducts and metastasis (Djamgoz et al, 2001). Moreover, DC-EF (1-3 V/cm for 15 min) reportedly stimulates Ca 2+ entry, firing rate, and insulin secretion in mouse insulinoma (bTC-6) β cells (Liebman et al, 2021). This DC-EF effect was sensitive to verapamil, a blocker of L-and T-type voltage-gated Ca 2+ Ca v channels (Bergson et al, 2011) suggesting an involvement of Ca v channels in the transduction of the static electric field into Ca 2+ signaling.…”

Section: Ion Channels As Targets Of Low Frequency (<1 Khz) Electrothe...mentioning

confidence: 99%

Ion channels as molecular targets of glioblastoma electrotherapy

Abed

Ganser

Eckert

et al. 2023

Front. Cell. Neurosci.

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Therapies with weak, non-ionizing electromagnetic fields comprise FDA-approved treatments such as Tumor Treating Fields (TTFields) that are used for adjuvant therapy of glioblastoma. In vitro data and animal models suggest a variety of biological TTFields effects. In particular, effects ranging from direct tumoricidal, radio- or chemotherapy-sensitizing, metastatic spread-inhibiting, up to immunostimulation have been described. Diverse underlying molecular mechanisms, such as dielectrophoresis of cellular compounds during cytokinesis, disturbing the formation of the spindle apparatus during mitosis, and perforating the plasma membrane have been proposed. Little attention, however, has been paid to molecular structures that are predestinated to percept electromagnetic fields—the voltage sensors of voltage-gated ion channels. The present review article briefly summarizes the mode of action of voltage sensing by ion channels. Moreover, it introduces into the perception of ultra-weak electric fields by specific organs of fishes with voltage-gated ion channels as key functional units therein. Finally, this article provides an overview of the published data on modulation of ion channel function by diverse external electromagnetic field protocols. Combined, these data strongly point to a function of voltage-gated ion channels as transducers between electricity and biology and, hence, to voltage-gated ion channels as primary targets of electrotherapy.

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“…Next, to establish individual baseline activity, mean intensities were normalized specific to each individual cell. In keeping with the original experiment's methodology, a change in fluorescent intensity >=10% was notated as spiking activity [46]. We kept this 10% threshold for the purpose of comparing the accuracy of either manual or automated data analysis of calcium dynamics in b-cells.…”

Section: Normalizationmentioning

confidence: 99%

BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells

Fowlds

Cho

et al. 2023

Preprint

Self Cite

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Insulin secretion from pancreatic β-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of β-cells, involve multiple software packages, and necessitate multiple researchers - all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in β-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions in a cluster of β-cells.

show abstract

Altered β-Cell Calcium Dynamics via Electric Field Exposure

Cited by 4 publications

References 40 publications

BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells

BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells

Ion channels as molecular targets of glioblastoma electrotherapy

BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells

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