Gedeon Richter, a multinational pharmaceutical and biotechnology company headquartered in Hungary, operates the largest pharmaceutical research center in the region. As a company dedicated to innovation, they are constantly seeking ways to improve the quality of their solutions.
Part of Richter’s R&D work includes finding solutions to quantify the properties of the mitochondrial network within neurons to enable more effective analysis of medications for various neurological diseases (mitochondria serve as the combustion engine of the cell, and their malfunctioning can cause network-level problems leading to dementia and other related issues). There is a well-established methodology for quantifying the properties of the mitochondrial network in two dimensions, but because the neuronal network is elaborate and thick, 2D imaging is not sufficient for capturing all necessary information. To get past the limitations of 2D imaging, Richter initiated a project with Starschema to use data science to build a solution that captures information at greater levels of specificity.
Richter identified three key deliverables for the project that would enable them to gain access to the information needed to optimize their products. First, the team had to develop a methodology for the automatic and reliable detection of mitochondria in high-resolution 3D confocal microscopy images – a process commonly known as segmentation. Second, they had to find a way to quantify the properties of a mitochondrial network based on the segmentation. Finally, the solution had to enable researchers to tell if the cells are healthy or damaged based on the properties of the mitochondrial network.
The project represented a relatively new field, with very little established technology to rely on. A major reason for this is that 3D image capturing is not very widespread. Since 3D image capture is used predominantly for medical purposes, the range of tools, packages and methodologies suitable for this use case are narrow. Even transforming a 3D image from one format to another is a complicated process. This made it necessary to develop completely new toolsets to reach the desired goals, and the project took shape as an R&D proof of concept program involving a lot of experimentation.
The Richter-Starschema team used Richter’s IT environment as a reference to develop a methodology that biologists can leverage without data science and programming knowledge. With this requirement in mind, the team designed a solution that would remain flexible enough to serve future needs by allowing biologists to make minor modifications. The team focused primarily on the task of quantifying the properties of a mitochondrial network and using it to assess the health of the cells. Given that there is no currently available toolset for this purpose, successful development would open up unprecedented abilities for researchers and likely lead to great advancements in the field.