Research update: the datasets you've helped analyze so far in Stall Catchers 🔎🐁

Can you believe it?! So far in Stall Catchers you've helped us analyze six datasets (and that's not counting the validation ones!), and we're currently on our seventh!!

Each dataset could take up to a year to analyze in the lab without your help. So just imagine how much time we've already saved, getting closer to understanding and treating Alzheimer's with every new result! Even if it's a negative result! Our biomedical collaborators at Cornell University need to know all about this space we're exploring, what is and is not related with regards to stalls in the brain in Alzheimer's disease, and which research paths are worth pursuing.

So here's a short report on what you've helped us find out so far...

Datasets completed:

• Amyloid proximity
• High fat diet
• NOX
• Tau
• Hypertension
• Long-term

Current dataset:

• Anti-VEGF

Amyloid proximity dataset

Question: do stalls co-occur with amyloid plaques in the brain of mice with Alzheimer's?
Result: stalls do not appear co-located with amyloid plaques.
Outcome: results published in Nature Neuroscience; no more planned investigations on this topic.

The Amyloid proximity study looked into the location of stalls in the brain of mice affected by Alzheimer's disease, with respect to the location of amyloid plaques. The short video below summarizes the research question:

Interestingly, this was a "negative" finding: in other words, stalls were not found to be close to amyloid plaque aggregates. This result was actually expected by the lab because the plaques are inert, and simply reveal the prior existence of their toxic precursors.

The results of Amyloid proximity study have been published in a recent paper by the Schaffer-Nishimura Lab in Nature Neuroscience. You can read more about it here. Unfortunately, the paper did not yet include the results from Stall Catchers -- the finding was based on the lab's own in-house analysis. Together with other initial research it helped to show (to the world, for the first time!), that stalls are involved in blood flow reduction in Alzheimer's disease.

Meanwhile, the Stall Catchers analysis that you helped carry out reproduced the same findings with a slightly different set of data. That gave us confidence in the findings of the amyloid proximity study, AND in the reliability of crowd analysis!

There will be no further investigations about this topic due to the negative finding. That is very important, because now we know this path is not worth pursuing, and can focus on more promising research questions!

High-fat diet dataset

Question: does a high fat diet increase the incidence of stalls in mice?
Result: a high fat diet does increase the incidence of stalls.
Outcome: results to be published in the first ever paper using Stall Catchers data; preliminary results announced at the British Science Festival 2017.

Our next dataset was concerned with the role of a high fat diet in the formation of stalls. You can read more about the dataset here. This short video summarizes the question well:

The preliminary results of the Stall Catchers analysis showed that a high fat diet does indeed increase the incidence of stalls. We announced these initial findings at the British Science Festival in September 2017.

Since then, the lab has looked more deeply into the data, and there is now a draft of a paper by Oliver Bracko in the Schaffer-Nishimura Lab, incorporating these results. Besides stalls, the paper will also cover more aspects of Alzheimer's disease that are affected by a high fat diet, such as broad inflammation of brain tissue, cell signalling pathways, as well as behavioral changes in mice.

But the stalls part will rely entirely on Stall Catchers data, thus it will likely be the first paper that uses data that YOU helped generate! 🎉

NOX dataset

Question: does blocking NOX reduce the incidence of stalls in mice?
Result: blocking NOX does reduce the incidence of stalls: in other words, NOX seems to be part of the molecular processes leading to stalls.
Outcome: results to be reported in a paper which is now in early stages of development; Stall Catchers data needs more attention due to an issue with movie preprocessing.

Next: NOX! The NOX dataset was the very first dataset checking a particular molecular pathway that may be involved in stall formation in Alzheimer's. NOX is a molecule involved in the generation of free radicals, which are involved in inflammation and could therefore lead to stalls. In this dataset we looked at whether blocking NOX helps to reduce the incidence of stalls. You can read more about the dataset here.

Questions looking into the molecular mechanisms behind stalls are very important, because that helps to understand the processes in depth and find an appropriate step or molecule to intervene with to disrupt this process (instead of shooting blind!). This short video explains it well:

We finished analyzing this dataset in February this year, and the lab are now in early stages of writing a manuscript reporting the results. Interestingly though, there is some disparity between the blood flow analysis done in-house in the lab (measuring the rate of blood flow instead of observing actual stalls), and stall analysis done by us! Blood flow analysis shows (with high significance) that inhibiting NOX is indeed improving blood flow.

However, Stall Catchers analysis does not show an apparent difference in the frequency of stalls in untreated and treated groups. This is very interesting and slightly concerning - it lead the lab to believe that the preprocessing algorithms that prepare movies to be analyzed in Stall Catchers might be a little out-of-whack, and not handling all data appropriately. Seems like they might be removing the potential stalled blood vessels from the analysis before we've even had a chance to look at them! Which is why the lab are now working on patching up these preprocessors, and that should hopefully help us rehabilitate the Stall Catchers data and achieve more accurate results. More about that soon, we hope!

Tau dataset

Question: do stalls occur when there are Tau tangles induced genetically in mice?
Result: TBA!
Outcome: TBA; results will be useful for a multi-disease path of research related to protein aggregation and inflammation.

The Tau dataset, which came after NOX, was looking into whether stalls occur when there are Tau tangles induced genetically in mice. Interestingly, in humans with Alzheimer's disease we see both amyloid plaques and Tau tangles, but in mice they are induced separately and do not tend to co-occur. This gives us an opportunity to isolate potential stalling effects of Tau - in other words are there increased incidence of stalls with Tau aggregation, just as there are with amyloid aggregation?

One of the reasons for studying this was the fact that different protein aggregation seems implicated in various diseases: Huntington's, ALS, Lewy Body Dementia, and others, besides Alzheimer's. One of the members of the Schaffer-Nishimura Lab, Oliver Bracko, is interested in exploring how various protein aggregations may drive inflammation in the brain and blood vessels, and how they might be contributing to symptoms of all these diseases.

We haven't yet received any more feedback on this dataset from the lab, but hopefully more results are coming soon!

Hypertension dataset

Started: 2019-04-13 Finished: 2019-05-30

Question: do stalls occur more in the brain of mice that have high blood pressure, and can that stalling can be reversed?
Result: TBA!
Outcome: TBA!

Next came the Megathon, and the Megathon dataset, also known as the Hypertension dataset, which we completed in record time! The research question was whether stalls occur more in the brain of mice that have high blood pressure, and whether that stalling can be reversed. You can read the full dataset report here.

Cornell's preliminary blood flow studies (using a different and less precise technique than visualizing stalls) seemed to confirm those hypotheses. The lab are yet to fully examine and evaluate the Stall Catchers data though, so more news on that coming soon!

Long-term dataset

Started: 2019-03-19 Finished: 2019-06-20

Question: how late into disease development an increase in brain blood flow, due to a decrease in capillary stalling, still leads to an improvement in performance on short-term memory tasks?
Result: TBA!
Outcome: TBA!

Next we had the "Long Term" dataset, named so since it was looking into disease development an increase in brain blood flow, due to a decrease in capillary stalling, still leads to an improvement in performance on short-term memory tasks in mice that have Alzheimer's disease. Read more about the dataset here.

It will be fascinating to know how far into disease development can an intervention targeting stalls still have a positive effect. The dataset is now complete, and we await for further news about the results from the lab!

Anti-VEGF dataset

Question: how late into disease development an increase in brain blood flow, due to a decrease in capillary stalling, still leads to an improvement in performance on short-term memory tasks?
Result: TBA!
Outcome: TBA!

Currently in Stall Catchers we have the "Anti-VEGF" dataset, which is looking at whether blocking the VEGF molecule, involved in free radical formation and inflammation in tissues, reduces the incidence of stalls. Read more about the dataset and the science behind here.

Interestingly, the blood flow studies in the lab already showed a decrease in stalling after anti-VEGF treatment in mice, so now we need to confirm it with stall analysis. Knowing the answer to this question would help fill another piece of the puzzle concerning the molecular mechanisms involved in stall formation, and help identify treatment targets. In fact, anti-VEGF are actually already popular cancer drugs, and it would be very interesting to see if we could apply the same already approved and widely used drugs in Alzheimer's too. But there's a lot we need to understand first!

Speaking of which, we could also do existing drug screening in Stall Catchers, if that proves a reasonable path of research. We're not there yet, but this short animation explains how that would work:

So that's that so far - we've already done an impressive amount of work with your help!! 💜

There's still a big reserve of data waiting to be analyzed, and plenty of questions to crack before we get closer to an effective Alzheimer's treatment!
Read more about what we mean by "effective treatment" here.

So let's get cracking!! The more we all play, and the more friends & families we bring in, the faster we'll get there! 😄 👇