Choosing imaging settings

Choosing the correct imaging parameters is important. For example, if you under-sample you risk losing important information but if you oversample you can quickly generate very large datasets that are unnecessary for the question at hand. The following information discusses imaging brains.

Resolution

There are two resolution numbers: X/Y resolution and Z resolution. X/Y resolution is determined by the number of pixels in the image. Z resolution is determined by the spacing between optical planes. You should choose these according to what you are trying to quantify. Often you will be extracting features with an automated analysis pipeline. The goal is to generate data that are adequate for the pipeline, not data that "look nice". Doing the latter tends to result in over-sampling.

The following table is a general guide. If you are unsure: ask.

Experiment	X/Y voxel size	Z spacing
Probe tracks	8 to 4 microns	20 microns
Mapping bulk labeling	8 to 2 microns	20 microns
Cell counting	2 to 2.5 microns	5 to 7 micron optical planes

The Z resolution numbers above assume you want to register data to the atlas. If you know you don't care about this, then it's totally reasonable to take one physical section every 50 microns and no optical planes.

It is very bad practice to acquire one "pretty" sample at higher quality for use in a figure or presentation whilst the rest of the data are acquired and quantified at lower -- but of course adequate --quality. Figures should be representative of the whole dataset. Consider imaging a small number of discarded slices on a confocal if you need to verify that BrainSaw is capturing all relevant detail.

Mapping axonal bulk projections

A common misconception is that mapping bulk projections requires very high resolution to resolve the axons. However, we are not tracing single axons here. Instead, we are simply seeking to map to which regions of the brain do the axons go. In other words: which voxels in the 25 or 10 micron Allen Atlas space contain fluorescence? This is why it usually adequate to do this at 4 by 4 by 20 microns. It is possible you might need finer resolution in X/Y, but this is unlikely.

If you think you need a highly non-isotropic resolution like 2x2x20 microns or 1x1x20 microns, please ask. Usually these resolutions are a mistake.

Counting neurons

Currently cellfinder requires high resolution in Z as it is designed to find all cells in the brain. In other words, it is not currently designed to cope with the one plane every 50 microns scenario. A suitable resolution for cellfinder is 2x2x5 microns and we have no reason think finer resolution is needed.

Background channels

Two photon images of fixed tissue contain rich autofluorescence, especially at shorter excitation wavelengths. You must always acquire an autofluorescence background channel to complement your signal channel. This provides contrast so it is possible to tell which features are labelled with your fluorophore of interest vs which are just bright objects in that channel. Background channels are particularly important for sparser signals, fainter signals, or when you wish to run cellfinder. You should overlay the background channel even when visually assessing the data for signal. Faint signals will be more prominent when overlaid on top of an autofluorescence background and there will be no ambiguity regarding whether bright objects are signal or bright background labeling.

Field of view containing sparse GFP-labelled axons. Left image is a composite that includes a background channel. Right image is the green (GFP) channel alone. Yellow features in the left image are autofluorescence, possibly from lipofuscin. Green features are GFP. These two sets of features can not be unambiguously discriminated using the GFP channel alone (right).

Averaging to improve image quality

You may choose to average frames to improve image quality. For resonant scanning, this is achieved by altering the Frame Average number in the ScanImage Image Controls window. You may alter this number once the acquisition started but you have to wait until the sample is being sliced to do so. Averaging will not increase the data size but it will slow down the acquisition: a little bit for 4 micron x/y and a lot for 2 micron x/y. Think carefully, therefore, if this is worth it. For example, cellfinder generally copes well without any averaging. The following image shows sequential physical sections imaged at n=4, n=2, and n=1 frame averaging with an 8 kHz scanner. Pixel size is 2 microns. Neurons are expressing tdTomato. The goal of the experiment is to count somata. The n=4 averaging looks a little smoother but is obviously not needed to count somata. You should not enable averaging for datasets such as this: it will hugely slow down the acquisition and provide no improvement for downstream analyses.

Three sequential sections from a double transgenic mouse (cFos-creERT2 x Ai14) showing averaging of n=4 frames, n=2 frames, and no averaging. Laser is at 920 nm and 150 mW at the sample. The goal of the experiment is count cells with cellfinder. Averaging frames makes the background autofluorescence look smoother but is obviously not going to affect the quality of the cell segmentation. Do not enable averaging for samples such as this.

You might want to average signal if you are trying to resolve faint sparse fibres. Below are two fields of view showing such data. Each panel shows different values of frame averaging. Data acquired with an 8 kHz resonant scanner.

Two views of faint sparse fibres at different levels of frame averaging

From this we learn that n=16 provides no advantage over n=8. We see that a lot more fine detail is visible compared to n=1 or n=2. However, you would need a mechanism of automatically extracting meaning from the higher level of averaging if it is to be of practical use. Lacking that, you might be better off at n=1 or n=2.

In summary, whether to average and by how much will depend on your question and how you are quantifying features extracted from the images.

Laser power

Up to a point you can get more signal by increasing laser power. However, beyond a certain point fluorophores can "saturate". Saturation occurs at higher powers when all of the available fluorophores are constantly in the excited state, so further increases in laser power does not produce a corresponding increase in signal. In 2-photon microscopy, going beyond the saturation limit will decrease Z resolution (an effect in some ways analogous to increasing the pinhole aperture in confocal microscopy). Signals can not be quantitatively compared if saturation is taking place, however binary comparisons (signal/no signal) are valid.

The image below shows how faint sparse GFP-labelled fibres are not substantially more visible at 90 mW compared to 175 mW at 920 nm. The main difference between the two images is lower noise in the background autoflourescence. This is either because of increased out of plane fluorescence or because the background fluorescence has a lower quantum yield and so a higher saturation threshold.

Faint sparse fibres at 920 nm, averaging 4 frames, at 90 mW (left) and 175 mW (right).

Choosing cut thickness

Say you want to image a sample taking planes every 20 microns. That's a little too thin to cut reliably with a steel blade, so you must take thicker sections with multiple optical planes within each section. Cutting thick sections (e.g. 80 microns) with many optical planes will be a little faster but in many tissues the increased scattering from deeper planes will need to a pronounced decrease in resolution and signal strength. As general rule, try to cut as thin as possible to avoid this problem. For PFA-fixed uncleared brains you should be able to cut 40 micron sections at a speed of about 0.35 to 0.5 mm/s.

There is no compelling reason to match your z step size to the resolution of the atlas to which you are registering. i.e. there is no harm acquiring data at a higher z resolution then using a lower resolution atlas since the registration will almost certainly end up tilting the brain in the coronal plane. For instance, you might cut 40 micron sections with a 20 micron spacing between optical planes then register to the 25 micron atlas.

The following image shows optical planes spaced 10 microns apart. Images show autofluorescence from a mouse brain imaged at 920 nm, starting from the very top of the sample (denoted as -30 microns). Here the field of view is large (about 1.6 mm) and the objective exhibits a good deal of field curvature so it takes 40 microns for the whole FOV to fill with sample. We would start imaging about 35 microns below the surface (about where is shown by 0 microns in this image series). Typically we aim for 40 micron sections, which means we'd acquire the images shown at 0, 10, 20, and 30 microns. The last 5 images look substantially fuzzier: the resolution is worse. For this reason we want to cut thin and start imaging as near to the surface as possible. A smaller FOV or an objective with a flatter field would allow us to start imaging higher up and so the whole stack would look better.