How AI is advancing neurodegenerative disease research

In this visitor editorial, discover how synthetic intelligence is turning into a robust instrument for drug discovery

As a part of the SelectScience Advances in Drug Discovery Special Feature, visitor editors Dr. Matthias Fassler, Genedata, and Daniel O’Connor, Molecular Devices, check out how the mixture of automated imaging and deep studying is addressing research challenges and spotlight how this method helps speed up drug growth.

The prevalence of neurological issues is on the rise, with Alzheimer’s and Parkinson’s circumstances estimated within the hundreds of thousands. To fight this development, many researchers are turning to synthetic intelligence (AI)1, which has proved helpful for figuring out advanced issues and aiding in optimized interventions. AI expertise predicts cognitive impairment and anticipates how severely motor abilities would possibly decline over time thereby serving to to speed up affected person diagnoses and enhance prognoses for neurodegenerative ailments2. Scientists may make use of AI to find potential drug and organic targets that would result in higher therapies.

In a current examine, researchers at Mount Sinai Hospital developed an AI platform to detect a variety of neurodegenerative ailments in human mind tissue samples. Their work is anticipated to assist scientists develop focused biomarkers and therapeutics3, resulting in extra environment friendly and correct diagnoses of advanced mind ailments. These thrilling developments are unleashing a capability to design therapies tailor-made for particular person sufferers, in the end bettering affected person outcomes.

AI and deep studying set new benchmarks for imaging evaluation

To perceive and determine tendencies and markers in neurobiology research, scientists should acquire, analyze, and characterize huge quantities of knowledge. This requires making ready advanced assays, performing high-content multi-parametric evaluation, and decoding giant, sophisticated datasets to find out the neurotoxicity ranges of varied affected person therapies.

In the biotech trade, organizations harnessing and analyzing large knowledge sooner and extra successfully can ship the correct diagnoses and therapeutics to sufferers shortly. New collaborations amongst revolutionary expertise suppliers are enabling extra biopharmaceutical laboratories and research leaders to extract data-rich insights whereas providing end-to-end automation with optimized picture evaluation capabilities. 

Molecular Devices and Genedata have come collectively to show machine studying strategies for analyzing morphological endpoints in advanced cell fashions by integrating deep learning-based software program (Genedata Imagence® 2.0) with an automatic, high-content imager (ImageXpress® Micro Confocal High-Content Imaging System). The mixture, which delivers incremental machine studying and data preservation, analyzes advanced phenotypic assay codecs with out in depth statistical fashions or professional enter. Now, biopharmaceutical corporations can shortly and effortlessly velocity growth and broaden the rollout of imaging assays of their R&D workflows. 


Visualizing Phenotypes. Genedata Imagence® Similarity Maps are generated by way of a NCS in a totally unsupervised style. In the maps, similar-looking cells are grouped carefully collectively. You can assign lessons by drawing a gate round carefully clustered factors, and labeling with the category identify. Metadata drives color-coded visible steering to simply interpret the Similarity Map, which allows annotating 1,000s of reference pictures inside a couple of minutes.

Advantages of an AI-based method 

To illustrate some great benefits of combining high-content imaging with an AI-based evaluation platform when quantifying advanced organic phenotypes, Molecular Devices and Genedata developed a cell-based take a look at for neurotoxicity analysis utilizing induced pluripotent stem cell (iPSC)-derived neuronal cells.

The work examined a set of neurotoxic compounds with suspected poisonous results on the nervous system, together with established medication to deal with most cancers, in addition to environmental substances. After iPSC-derived neurons had been handled with compounds for 72 hours, they had been imaged with a high-content imager and the info was then analyzed using convolutional neural networks (NCSs).

A NCS is primarily a machine studying mannequin that takes uncooked pictures as enter, assigns significance to numerous features inside the pictures, and learns to distinguish one from one other. NCSs are extra environment friendly than the traditional phenotypic evaluation method the place readouts can embody characterization of advanced neurite constructions – similar to neurite outgrowth, branching, variety of processes, and cell viability – making it time consuming and difficult to investigate knowledge at scale. Instead, they outlined a set of consultant pictures for reference phenotypes similar to these induced by management therapies. This so-called coaching set enabled the NCS to mechanically acknowledge standards separating the phenotypes, eliminating the necessity for analytic software program specialists who’re required in typical picture evaluation.

The take a look at outlined 4 reference phenotypes within the NCS that mirrored completely different states of stem cell differentiation and cell injury. With AI, 1,000 cells per phenotype had been sampled in only a few minutes versus the hours usually required with typical phenotypic evaluation. Instead of presenting a number of readouts that needed to be additional analyzed in a multi-parametric style, the deep learning-based method offered a extra built-in evaluation of neurotoxicity.

The educated NCS predicted neurotoxicity with the identical sensitivity as a traditional answer, discovering some substances precipitated extreme disintegration of neural networks and cell demise whereas chosen medication precipitated principally average perturbations. With dose-response data, we had been additionally capable of shortly and simply rank chemical compounds in keeping with their toxicity or security. 

From starting to finish, AI-assisted picture evaluation was a sport changer for the research crew.

The way forward for automated picture evaluation 

Deploying Genedata Imagence® 2.0 software program with the ImageXpress® Micro Confocal High-Content Imaging System and unleashing the flexibility to automate NCS coaching for such neurotoxicity assays is a double bonus: laboratory leaders can free workers from onerous evaluation and even scale back or remove resourcing calls for for extra coaching or imaging specialists.  

The skill to automate NCSs coaching for such assays allows biologists to make use of these highly effective instruments with none professional data in picture evaluation, thus resulting in super time financial savings throughout assay growth. As improved high-content imaging generates extra knowledge, deep studying can be essential to R&D by enabling sooner, cheaper, and extra efficacious drug discovery. While research in neurodegenerative ailments has made monumental strides in current many years, AI and deep studying will proceed to offer extra alternatives to know the unknowns of degenerative ailments.


Visit the SelectScience Advances in Drug Discovery Special Feature to study extra in regards to the applied sciences and strategies advancing the sphere.


Matthias Fassler, Ph.D., product supervisor at Genedata, is a cell biologist captivated with bridging the hole between biologists and software program builders and transferring new applied sciences into sensible purposes. Having authored and co-authored many scientific papers, Fassler leads the event of deep learning-based options at Genedata. ([email protected]

Daniel O’Connor is the Director of BioPharma and Drug Discovery enterprise items at Molecular Devices specializing in revolutionary high-content imaging applied sciences for screening 2D and 3D mobile disease fashions. With over 20 years of trade expertise, he has developed an revolutionary application-focused method to buyer options. Daniel sits on the Strategic Innovation Review Board at Molecular Devices and has gained quite a few industrial and innovation awards all through his profession. He earned a BSc in neuroscience from University of Minnesota-Twin Cities. ([email protected])  


  1. Brasil S, Pascoal C, Francisco R, Dos Reis Ferreira V, Videira PA, Valadão AG. Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?. Genes (Basel). 2019
  2. Monika A. Myszczynska MA, Ojamies PN, Lacoste AMB, Neil D, Saffari A, Mead R, Hautbergue GM, Holbrook JD, Ferraiuolo L. Applications of machine studying to analysis and therapy of neurodegenerative ailments. Nat Rev Neurol. 2020
  3. Signaevsky M, Prastawa M, Farrell Ok, Tabish N, Baldwin E, Han N, Iida MA, Koll J, Bryce C, Purohit D, Haroutunian V, McKee AC, Stein TD, White CL third, Walker J, Richardson TE, Hanson R, Donovan MJ, Cordon-Cardo C, Zeineh J, Fernandez G, Crary JF. Artificial intelligence in neuropathology: deep learning-based evaluation of tauopathy. Lab Invest. 2019


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