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
The ability to image calcium ion (Ca2+) dynamics in cells has long been of interest, in the neurosciences, where it can be used as a marker for neuronal excitability
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
A number of software packages have been written for individual aspects of the commonly used pipeline in calcium imaging analysis (Figure 1)
Summary
The ability to image calcium ion (Ca2+) dynamics in cells has long been of interest, in the neurosciences, where it can be used as a marker for neuronal excitability. Two correction methods have been produced for 2-photon in vivo imaging in awake rodents, one based on the Lucas– Kanade (gradient descent) image registration algorithm using MathWorks® MATLAB platform (Greenberg & Kerr, 2009), the other using a Hidden Markov Model (Dombeck et al, 2007) Effective, these methods have not been packaged for easy implementation and are reliant in cells remaining in the x- and y- dimensions as it cannot track following movement between z-axes. Using limited CaImAn function in EZcalcium does not allow for segmentation of more complex structures or large organelles or clusters of cells and is better for somas or smaller, less complex areas Cellpose is another generalist, deep learningbased segmentation method that uses entirely open source packages in Python with a GUI to aid implementation. EZcalcium directly shows the raw fluorescence, inferred activity and deconvolved neural ‘spiking’, whereby the data can be exported into file formats for proprietary (.mat, .xlsx) or open (.csv) software programmes for further analysis (Cantu et al, 2020; Giovannucci et al, 2019)
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