Abstract
Techniques for calcium imaging were first demonstrated in the mid-1970s, whilst tools to analyse these markers of cellular activity are still being developed and improved today. For image analysis, custom tools were developed within labs and until relatively recently, software packages were not widely available between researchers. We will discuss some of the most popular methods for calcium imaging analysis that are now widely available and describe why these protocols are so effective. We will also describe some of the newest innovations in the field that are likely to benefit researchers, particularly as calcium imaging is often an inherently low signal-to-noise method. Although calcium imaging analysis has seen recent advances, particularly following the rise of machine learning, we will end by highlighting the outstanding requirements and questions that hinder further progress and pose the question of how far we have come in the past sixty years and what can be expected for future development in the field.
Highlights
Techniques for calcium imaging were first demonstrated in the mid1970s, whilst tools to analyse these markers of cellular activity are still being developed and improved today
In vitro imaging, or in vivo invertebrate imaging, may use exogenous or genetically-encoded calcium indicators (GECIs), imaged using light-sheet microscopy (LSM), epifluorescence, 2-photon microscopy (2PM) or other fluorescence microscopes depending on the temporal and spatial resolution, timescale of imaging, and thickness of the sample being taken into consideration
A great number of analysis advancements have been made since calcium imaging was first developed
Summary
Techniques for calcium imaging were first demonstrated in the mid1970s, whilst tools to analyse these markers of cellular activity are still being developed and improved today. Calcium imaging is an inherently noisy method due to the high spatiotemporal information desired from a sample often showing low signal-to-noise alongside drift or cell movement, for living organisms. This processing pipeline includes image denoising, motion correction, classification for cell identification, and quantification of calcium signals.
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