Photonics tools begin to clarify astrocyte calcium transients

Astrocytic Calcium Signaling Toolkit (astroCaST): efficient analysis of dynamic astrocytic calcium events

The Astrocytic Calcium Signaling Toolkit (astroCaST) is a novel solution to a longstanding challenge in neuroscience research: the specialized analysis of astrocytic calcium events within fluorescence time-series imaging. Distinct from existing neuron-centric tools, astroCaST is adept at detecting and clustering astrocytic calcium events based on their unique spatiotemporal characteristics, thus filling a gap in astrocytic research methodologies. This toolkit not only facilitates the detection of such events but also extends its utility to provide comprehensive end-to-end analysis. This feature is absent in most tools targeting astrocytic activity. AstroCaST's development was motivated by the critical need for dedicated software that supports researchers in transitioning from raw video data to insightful experimental conclusions, efficiently managing large-scale datasets without compromising computational speed. It offers a user-friendly interface that caters to both novice and expert users, incorporating both a graphical user interface (GUI) for detailed explorations and a command-line interface (CLI) for extensive analyses. Expected outcomes from utilizing astroCaST include the ability to process and analyze a significantly larger volume of data. This enables a more profound and comprehensive analysis than previously possible, addressing the demands of large-scale astrocytic studies. In summary, astroCaST aims to advance astrocytic calcium imaging analysis, offering a tailored, efficient, and comprehensive toolset that enhances our understanding of astrocytic functions and their implications in neuroscience.

deCLUTTER2+ - a pipeline to analyze calcium traces in a stem cell model for ventral midbrain patterned astrocytes

Astrocytes are the most populous cell type of the human central nervous system and are essential for physiological brain function. Increasing evidence suggests multiple roles for astrocytes in Parkinson's disease, nudging a shift in the research focus, which historically pivoted around ventral midbrain dopaminergic neurons (vmDANs). Studying human astrocytes and other cell types in vivo remains challenging. However, in vitro-reprogrammed human stem cell-based models provide a promising alternative. Here, we describe a novel protocol for astrocyte differentiation from human stem cell-derived vmDAN-generating progenitors. This protocol simulates the regionalization, gliogenic switch, radial migration and final differentiation that occur in the developing human brain. We characterized the morphological, molecular and functional features of these ventral midbrain patterned astrocytes with a broad palette of techniques and identified novel candidate midbrain-astrocyte specific markers. In addition, we developed a new pipeline for calcium imaging data analysis called deCLUTTER2+ (deconvolution of Ca2+ fluorescent patterns) that can be used to discover spontaneous or cue-dependent patterns of Ca2+ transients. Altogether, our protocol enables the characterization of the functional properties of human ventral midbrain patterned astrocytes under physiological conditions and in disease.

Shining the Light on Astrocytic Ensembles

While neurons have traditionally been considered the primary players in information processing, the role of astrocytes in this mechanism has largely been overlooked due to experimental constraints. In this review, we propose that astrocytic ensembles are active working groups that contribute significantly to animal conduct and suggest that studying the maps of these ensembles in conjunction with neurons is crucial for a more comprehensive understanding of behavior. We also discuss available methods for studying astrocytes and argue that these ensembles, complementarily with neurons, code and integrate complex behaviors, potentially specializing in concrete functions.

Combined segmentation and classification-based approach to automated analysis of biomedical signals obtained from calcium imaging

Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand-crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy.

Applications of Induced Pluripotent Stem Cell-Derived Glia in Brain Disease Research and Treatment

Glia are integral components of neural networks and are crucial in both physiological functions and pathological processes of the brain. Many brain diseases involve glial abnormalities, including inflammatory changes, mitochondrial damage, calcium signaling disturbance, hemichannel opening, and loss of glutamate transporters. Induced pluripotent stem cell (iPSC)-derived glia provide opportunities to study the contributions of glia in human brain diseases. These cells have been used for human disease modeling as well as generating new therapies. This chapter introduces glial involvement in brain diseases, then summarizes different methods of generating iPSC-derived glia disease models of these cells. Finally, strategies for treating disease using iPSC-derived glia are discussed. The goal of this chapter is to provide an overview and shed light on the applications of iPSC-derived glia in brain disease research and treatment.

Looking to the stars for answers: Strategies for determining how astrocytes influence neuronal activity

Astrocytes are critical components of neural circuits positioned in close proximity to the synapse, allowing them to rapidly sense and respond to neuronal activity. One repeatedly observed biomarker of astroglial activation is an increase in intracellular Ca²⁺ levels. These astroglial Ca²⁺ signals are often observed spreading throughout various cellular compartments from perisynaptic astroglial processes, to major astrocytic branches and on to the soma or cell body. Here we review recent evidence demonstrating that astrocytic Ca²⁺ events are remarkably heterogeneous in both form and function, propagate through the astroglial syncytia, and are directly linked to the ability of astroglia to influence local neuronal activity. As many of the cellular functions of astroglia can be linked to intracellular Ca²⁺ signaling, and the diversity and heterogeneity of these events becomes more apparent, there is an increasing need for novel experimental strategies designed to better understand the how these signals evolve in parallel with neuronal activity. Here we review the recent advances that enable the characterization of both subcellular and population-wide astrocytic Ca²⁺ dynamics. Additionally, we also outline the experimental design required for simultaneous in vivo Ca²⁺ imaging in the context of neuronal or astroglial manipulation, highlighting new experimental strategies made possible by recent advances in viral vector, imaging, and quantification technologies. Through combined usage of these reagents and methodologies, we provide a conceptual framework to study how astrocytes functionally integrate into neural circuits and to what extent they influence and direct the synaptic activity underlying behavioral responses.

Special Section Guest Editorial: Imaging Neuroimmune, Neuroglial, and Neurovascular Interfaces

The guest editorial provides an introduction to Parts 1 and 2 of the Neurophotonics Special Section on Imaging Neuroimmune, Neuroglial, and Neurovascular Interfaces.