Challenges and Insights into Long-Term functional GCaMP imaging

I'm absolutely fascinated by brains. The way neurons wire together with each other to create intricate networks without a clear blueprint is nothing short of astonishing. This awe turns my attention to GCaMP - a ground-breaking protein that allows me to peek into these circuits as they emerge from chaos.

But let's go back to the beginning. Since its debut in 2001 in mice, GCaMP proved itself by transforming Neuroscientists into part-time telepaths. It allowed us to monitor the activity of living neurons without harming the animals these neurons belonged to. GCaMP especially shined at the supervision of many neurons superseding the established method of electrophysiology. Combining this strength with the accessible and small brains of Drosophila, C. Elegans, and Zebrafish, GCaMP paved the way to a new era of circuit neuroscience which investigates how brain-wide neural circuits process information and influence behaviour.

For instance, the work of Green et al. (2017) et al. used GCaMP in Drosophila to unravel a neural circuit mechanism for angular integration, empowering little fruit flies to maintain and update their heading direction while walking. Meanwhile, Ngyuen et al. (2016) bridged the connection to behaviour by expressing GCaMP in freely moving C. Elegans. They identified clusters with neural activity correlated with locomotion behaviours including turning and forward/backward motion. But it doesn't stop there. Finally through the expression of GCaMP in larval zebrafish, Bahl and Engert (2020) illuminated a decision-making circuit, showcasing GCaMP's prowess in identifying circuit mechanisms responsible for higher cognitive functions.

In essence, GCaMP has paved a path to a ground-breaking era of neuroscience, one that meticulously investigates how single cells together process information and produce relevant behaviours. We now have a good overview of GCaMP and how it is currently used to identify circuits based on neuronal calcium dynamics. However, these approaches often look at circuits at very defined time points and overlook their part in a dynamic animal that constantly changes. Thus, I want to introduce you to the idea of long-term GCaMP imaging which allows us to observe how the circuits develop and how network-defining dynamics form over time. I will talk about the characteristics of GCaMP and some technical aspects to think about when you want to imagine an animal for 24h+. Moreover, I aspire to inspire you to observe your favourite circuit for an absurdly long time. So, prepare your lasers, secure your specimens, and ready your stimuli since we are about to jump right in.

Long-term imaging generates a lot of data that needs to be stored. This raises the question of whether to store raw data or pre-processed data. What format should the data be stored in? And finally, where to store the data, whether on a local hard drive or on a server system that allows access from the cloud. To give an example of how large a long-term imaging dataset would be, let's say we were imaging at 1Hz with a region of interest of 500 x 500 pixels for 24 hours at 16-bit integer bit depth. This would result in an uncompressed dataset of 43.2 gigabytes.

The Hierarchical Data Format (HDF) is an ideal data format for storing such large image datasets. Its accessibility to most programming languages, its ability to efficiently compress data, and its flexibility to store hierarchical data in custom organization make HDF an exceptionally versatile and scalable solution for the complex needs of neuroimaging data and its analysis.

A major problem we have to consider, not only when imaging for long periods, but in general, is how our experimental animal moves while we are imaging it. In the ideal case, the animal does not move in its x, y, or z axis, but if we want to observe circuits, this is almost unavoidable. Of course, we can't anesthetize the experimental animal either, because that would introduce artifacts that would cause unnatural responses in the network dynamics.

Movements in x, y, and z can be caused by the animal responding with motor outputs to the displayed stimuli. Here, x and y shifts are easily corrected with motion correction algorithms (Giovannucci et al., 2019), allowing voxel/cell-based analysis. Z-shifts are more critical because they change the focal plane, meaning that we no longer observe the same cells. We can counteract these Z shifts either by including contingency plans in our imaging software that counteract Z shifts or by using wide-field imaging methods such as light sheet microscopy (Stelzer, 2015), which scans the whole brain several times per second.

Now that we have overcome the many challenges of long-term imaging, I would like to ask the question: What do we learn when we look at a network for a long time? I'm most excited about observing the dynamic evolution of neural networks in real-time. How do they emerge and mature? Is there a recognizable precursor stage, or do functional circuits spontaneously emerge from previously dormant cell assemblies? These questions lie at the heart of neurodevelopment and neural plasticity and are not only of academic interest.

The observation of circuit development is highly relevant when applied to the study of neurological disorders. For example, long-term imaging of circuit formation in zebrafish models of autism provides a unique opportunity to determine when and how developmental trajectories diverge from wild-type patterns. Such insights could help elucidate the network pathology of autism and other disorders, and ultimately pave the way for new therapeutic strategies.

In summary, long-term functional GCaMP imaging holds great promise for neuroscience. Despite the challenges and complexities involved, the potential to gain deep insights into circuit formation and function justifies the effort. I look forward to future studies that focus more on neuron-based brain development.