Involvement of cyclic nucleotide-gated channels in spontaneous activity generated in isolated interstitial cells of Cajal from the rabbit urethra

Automated denoising software for calcium imaging signals using deep learning

Dynamic Ca2+ signaling is crucial for cell survival and death, and Ca2+ imaging approaches are commonly used to study and measure cellular Ca2+ patterns within cells. However, the presence of image noise from instrumentation and experimentation protocols can impede the accurate extraction of Ca2+ signals. Removing noise from Ca2+ Spatio-Temporal Maps (STMaps) is essential for precisely analyzing Ca2+ datasets. Current methods for denoising STMaps can be time-consuming and subjective and rely mainly on image processing protocols. To address this, we developed CalDenoise, an automated software that employs robust image processing and deep learning models to remove noise and enhance Ca2+ signals in STMaps effectively. CalDenoise integrates four pipelines capable of efficiently removing salt-and-pepper, impulsive, and periodic noise and detecting and removing background noise. Comprising both an image-processing-based pipeline and three generative-adversarial-network-based (GAN) deep learning models, CalDenoise proficiently removes complex noise patterns. The software features adjustable parameters to enhance accuracy and is integrated into a user-friendly graphical interface for easy access and streamlined usage. CalDenoise can serve as a robust platform for denoising complex dynamic fluorescence signal images across diverse cell types, including Ca2+, voltage, ions, and pH signals.

New open-source software for subcellular segmentation and analysis of spatiotemporal fluorescence signals using deep learning

Cellular imaging instrumentation advancements as well as readily available optogenetic and fluorescence sensors have yielded a profound need for fast, accurate, and standardized analysis. Deep-learning architectures have revolutionized the field of biomedical image analysis and have achieved state-of-the-art accuracy. Despite these advancements, deep learning architectures for the segmentation of subcellular fluorescence signals is lacking. Cellular dynamic fluorescence signals can be plotted and visualized using spatiotemporal maps (STMaps), and currently their segmentation and quantification are hindered by slow workflow speed and lack of accuracy, especially for large datasets. In this study, we provide a software tool that utilizes a deep-learning methodology to fundamentally overcome signal segmentation challenges. The software framework demonstrates highly optimized and accurate calcium signal segmentation and provides a fast analysis pipeline that can accommodate different patterns of signals across multiple cell types. The software allows seamless data accessibility, quantification, and graphical visualization and enables large dataset analysis throughput.

Role of Ano1 Ca2+-activated Cl- channels in generating urethral tone

Urinary continence is maintained in the lower urinary tract by the contracture of urethral sphincters, including smooth muscle of the internal urethral sphincter. These contractions occlude the urethral lumen, preventing urine leakage from the bladder to the exterior. Over the past 20 years, research on the ionic conductances that contribute to urethral smooth muscle contractility has greatly accelerated. A debate has emerged over the role of interstitial cell of Cajal (ICC)-like cells in the urethra and their expression of Ca²⁺-activated Cl^- channels encoded by anoctamin-1 [Ano1; transmembrane member 16 A (Tmem16a) gene]. It has been proposed that Ano1 channels expressed in urethral ICC serve as a source of depolarization for smooth muscle cells, increasing their excitability and contributing to tone. Although a clear role for Ano1 channels expressed in ICC is evident in other smooth muscle organs, such as the gastrointestinal tract, the role of these channels in the urethra is unclear, owing to differences in the species (rabbit, rat, guinea pig, sheep, and mouse) examined and experimental approaches by different groups. The importance of clarifying this situation is evident as effective targeting of Ano1 channels may lead to new treatments for urinary incontinence. In this review, we summarize the key findings from different species on the role of ICC and Ano1 channels in urethral contractility. Finally, we outline proposals for clarifying this controversial and important topic by addressing how cell-specific optogenetic and inducible cell-specific genetic deletion strategies coupled with advances in Ano1 channel pharmacology may clarify this area in future studies.NEW & NOTEWORTHY Studies from the rabbit have shown that anoctamin-1 (Ano1) channels expressed in urethral interstitial cells of Cajal (ICC) serve as a source of depolarization for smooth muscle cells, increasing excitability and tone. However, the role of urethral Ano1 channels is unclear, owing to differences in the species examined and experimental approaches. We summarize findings from different species on the role of urethral ICC and Ano1 channels in urethral contractility and outline proposals for clarifying this topic using cell-specific optogenetic approaches.

A high throughput machine-learning driven analysis of Ca2+ spatio-temporal maps

High-resolution Ca2+ imaging to study cellular Ca2+ behaviors has led to the creation of large datasets with a profound need for standardized and accurate analysis. To analyze these datasets, spatio-temporal maps (STMaps) that allow for 2D visualization of Ca2+ signals as a function of time and space are often used. Methods of STMap analysis rely on a highly arduous process of user defined segmentation and event-based data retrieval. These methods are often time consuming, lack accuracy, and are extremely variable between users. We designed a novel automated machine-learning based plugin for the analysis of Ca2+ STMaps (STMapAuto). The plugin includes optimized tools for Ca2+ signal preprocessing, automated segmentation, and automated extraction of key Ca2+ event information such as duration, spatial spread, frequency, propagation angle, and intensity in a variety of cell types including the Interstitial cells of Cajal (ICC). The plugin is fully implemented in Fiji and able to accurately detect and expeditiously quantify Ca2+ transient parameters from ICC. The plugin's speed of analysis of large-datasets was 197-fold faster than the commonly used single pixel-line method of analysis. The automated machine-learning based plugin described dramatically reduces opportunities for user error and provides a consistent method to allow high-throughput analysis of STMap datasets.