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The automation of repetitive jobs has triggered some of the biggest recent scientific discoveries and like other lab tools that leverage computer power, the robotic microscope opens up new worlds of possibility through its capability to simplify painstaking, repetitive tasks.
In biological sciences, scientists have long had the capacity to track individual cells through a microscope over time. However, the task had been a manual one, making it mind-numbing and slow. The tedium of looking at the same cells over and over, watching them long enough to generate meaningful results, taxed human patience and likely led to errors caused by the strain.
Robotic microscopes tackle this issue head-on by automatically scanning and recording specimens, in a manner much faster than humanly possible. Another benefit to a robotic system is that specimen imagery and data can be automatically digitized, meaning they can be used years later without any decrease in quality.
As cells can be individually observed en masse, robotic microscopes give scientists the ability to develop new types of trials, such as following responses of trial drugs for populations of cells with different genetics. Scientists could get rid of selected genes in some of the cells and quickly figure out if that action made them more or less responsive to a treatment.
Robotic microscopes also let scientists image the exact same location on a sample numerous times over the course of days, weeks or months, a technique of observation known as a longitudinal study. These types of analyses let scientists pick apart certain risk factors of diseases and ascertain which of those are significant contributors.
The robotic microscope can track the path of disease in detail, 24 hours a day; thereby boosting the statistical power of the experiment and enabling scientists to determine if particular factors of the disease are harmful or not.
Investigating Huntington’s Disease
Huntington’s disease research is one of many areas where the robotic microscope has already had a transformative effect. The disease is brought on by a mutation in the gene for a protein known as huntingtin (htt).
In Huntington's patients, the htt protein is not properly folded and sheds its functionality, triggering the disease. The misfolded protein often leads to clumps or inclusion bodies, which are large, sticky and insoluble groupings of htt protein.
For quite some time, inclusion bodies were thought to be the principal reason for the neurodegeneration in patients with Huntington's disease. Using a robotic microscope, researchers were able to track cells over long periods of time to see the sequence of neurodegeneration in Huntington's as it happened and keep an eye on the effects of medications and other treatment methods.
Importantly, the robotic microscope allowed for the team to treat each cell like an individual patient in a clinical trial. The team said their approach is up to 1,000 times more sensitive than standard approaches.
To investigate Huntington’s disease, robotic microscopes operated 24/7, producing terabytes of information each day. The team used automated software to tag each cell with its own distinctive number and watch it over time.
Hundreds of biosensors created by the team gave them the chance to see even more structural and functional features. The automated programs extracted and quantified key features from each cell and examined the behavior of individual cells to find new insights.
To get the most from their immense amount of information, the team worked with statisticians to develop new models and partnered with engineers at Google to leverage machine-learning tools. The team said they were successful in training neural networks to accurately forecast features and label cells from an image that a human can’t typically see without special methods.
The team discovered that inclusion bodies are actually a protective mechanism. With a more comprehensive picture, the team discovered the neurons that produced inclusion bodies lasted longer than neurons with other varieties of the htt proteins.
These findings were possible because the robotic microscope allowed for the observation of thousands of individual cells over time. In leveraging the power of the robotic microscope and machine-learning investigation software, the scientists carried out “hypothesis-free science”: They simply viewed behaviors and spotted trends related to the cause and effect of the disease.
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