Dataset Information

CellSpecks: A Software for Automated Detection and Analysis of Calcium Channels in Live Cells.

ABSTRACT: To couple the fidelity of patch-clamp recording with a more high-throughput screening capability, we pioneered a, to our knowledge, novel approach to single-channel recording that we named "optical patch clamp." By using highly sensitive fluorescent Ca²⁺ indicator dyes in conjunction with total internal fluorescence microscopy techniques, we monitor Ca²⁺ flux through individual Ca²⁺-permeable channels. This approach provides information about channel gating analogous to patch-clamp recording at a time resolution of ?2 ms with the additional advantage of being massively parallel, providing simultaneous and independent recording from thousands of channels in the native environment. However, manual analysis of the data generated by this technique presents severe challenges because a video recording can include many thousands of frames. To overcome this bottleneck, we developed an image processing and analysis framework called CellSpecks capable of detecting and fully analyzing the kinetics of ion channels within a video sequence. By using randomly generated synthetic data, we tested the ability of CellSpecks to rapidly and efficiently detect and analyze the activity of thousands of ion channels, including openings for a few milliseconds. Here, we report the use of CellSpecks for the analysis of experimental data acquired by imaging muscle nicotinic acetylcholine receptors and the Alzheimer's disease-associated amyloid ? pores with multiconductance levels in the plasma membrane of Xenopus laevis oocytes. We show that CellSpecks can accurately and efficiently generate location maps and create raw and processed fluorescence time traces; histograms of mean open times, mean close times, open probabilities, durations, and maximal amplitudes; and a "channel chip" showing the activity of all channels as a function of time. Although we specifically illustrate the application of CellSpecks for analyzing data from Ca²⁺ channels, it can be easily customized to analyze other spatially and temporally localized signals.

SUBMITTER: Shah SI

PROVIDER: S-EPMC6289074 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

CellSpecks: A Software for Automated Detection and Analysis of Calcium Channels in Live Cells.

Shah Syed Islamuddin SI Smith Martin M Swaminathan Divya D Parker Ian I Ullah Ghanim G Demuro Angelo A

Biophysical journal 20181025 11

To couple the fidelity of patch-clamp recording with a more high-throughput screening capability, we pioneered a, to our knowledge, novel approach to single-channel recording that we named "optical patch clamp." By using highly sensitive fluorescent Ca2+ indicator dyes in conjunction with total internal fluorescence microscopy techniques, we monitor Ca2+ flux through individual Ca2+-permeable channels. This approach provides information about channel gating analo ...[more]

PMID: 30447989

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Project description:BackgroundPrimary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. Typically, quantitative cilia analysis is done manually, which is very time-consuming. While some open-source and commercial image analysis software applications can segment input data, they still require the user to optimize many parameters, suffer from user bias, and often lack rigorous performance quality assessment (e.g., false positives and false negatives). Further, optimal parameter combinations vary in detection accuracy depending on cilia reporter, cell type, and imaging modality. A good automated solution would analyze images quickly, robustly, and adaptably-across different experimental data sets-without significantly compromising the accuracy of manual analysis.MethodsTo solve this problem, we developed a new software for automated cilia detection in cells (ACDC). The software operates through four main steps: image importation, pre-processing, detection auto-optimization, and analysis. From a data set, a representative image with manually selected cilia (i.e., Ground Truth) is used for detection auto-optimization based on four parameters: signal-to-noise ratio, length, directional score, and intensity standard deviation. Millions of parameter combinations are automatically evaluated and optimized according to an accuracy 'F1' score, based on the amount of false positives and false negatives. Afterwards, the optimized parameter combination is used for automated detection and analysis of the entire data set.ResultsThe ACDC software accurately and adaptably detected nuclei and primary cilia across different cell types (NIH3T3, RPE1), cilia reporters (AcTub, Smo-GFP, Arl13b), and image magnifications (60×, 40×). We found that false-positive and false-negative rates for Arl13b-stained cilia were 1-6%, yielding high F1 scores of 0.96-0.97 (max. = 1.00). The software detected significant differences in mean cilia length between control and cytochalasin D-treated cell populations and could monitor dynamic changes in cilia length from movie recordings. Automated analysis offered up to a 96-fold speed enhancement compared to manual analysis, requiring around 5 s/image, or nearly 18,000 cilia analyzed/hour.ConclusionThe ACDC software is a solution for robust automated analysis of microscopic images of ciliated cells. The software is extremely adaptable, accurate, and offers immense time-savings compared to traditional manual analysis.

Project description:ImportanceAmyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-β-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software.ObjectiveTo assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA.Design, setting, and participantsThis diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023.ExposuresUse of icobrain aria, an AI-based assistive software for ARIA detection and quantification.Main outcomes and measuresCoprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC).ResultsAmong the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types.Conclusions and relevanceThis diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-β-directed monoclonal antibody therapies.

Dataset Information

CellSpecks: A Software for Automated Detection and Analysis of Calcium Channels in Live Cells.

Publications

CellSpecks: A Software for Automated Detection and Analysis of Calcium Channels in Live Cells.

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