Mapping Billions of Synapses with Microscopy and Mathematics


Microscopy and mathematics

We had been using an upright epifluorescence microscope, acquiring dual channel, three micron-thick z-sections in order to reconstruct 3D immunofluorescent images. The challenge for us was to maintain our optical sections at varying depths in the tissue section to match the optimal plane of labeling. To do this we needed an autofocus that could focus on the micron-sized, individual spheres that are our labeled synapses. For this we found Metamorph’s algorithms to be perfectly suited. Additionally this software provided the flexibility to go from adjacent locations and find the optimally labeled plane. We tried a number of programs, and we even tried to write our own software for this before we found Metamorph. 

Now we are collecting images around the clock 24 hours a day in order to collect enough data to generate large scale maps. We found that using the internal filter turret slowed us down and caused too much wear and tear on the microscope, so we invested in fast external filter wheels. We now have assembled a system that has very low mechanical wear and tear and can run robustly and reliably for days on end.

Using this new system we developed a workflow where Metamorph performs the acquisition and as soon as the images are acquired we use custom-written software to send it to our cloud analytical system, which begins the analysis immediately. The images are deconvolved and then they are analyzed by image segmentation, which is all in-house custom designed software that we’ve built over the past three or four years. In short we’ve gone from a study that originally took about six months to image and analyze to one that we can do in about a week. The increased speed makes it possible for us to map large brain regions.

In our image segmentation analysis we’re looking for objects that are approximately 200 nm in diameter. We perform the image segmentation separately on each channel. We then identify colocalization, better termed coplacement, by comparing the boundaries that are identified for each object. We have found that the numbers of excitatory synapses containing dense phosphoprotein labeling is in the order or 2–5 %; it is a very sparse signal as we would expect from a high-capacity memory system.

We used confocal microscopy in our first publication, but we have since elected to use widefield microscopy coupled with deconvolution because the acquisition is much faster. We know that the resolution we are able to obtain is not as good as what we could get with a laser scanning system, but we are willing to accept that trade-off in order to have the speed and efficiency that our current system permits.

We have also found that the elements we are looking at, dimly labeled elements that photobleach quickly, are not identifiable with confocal microscopy. Deconvolution, on the other hand, preserves the light making it easier to identify the synapses. 



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