Moffitt Creates Open-Source Software for Viewing Multiplex Images

NuMedii will partner with Yale School of Medicine and Brigham and Women's Hospital on research designed to apply single-cell sequencing toward identifying novel precision therapies and biomarkers in idiopathic pulmonary fibrosis (pictured). [Pathology Education Informational Resource (PEIR) Digital Library]
NuMedii will partner with Yale School of Medicine and Brigham and Women’s Hospital on research designed to apply single-cell sequencing toward identifying novel precision therapies and biomarkers in idiopathic pulmonary fibrosis (pictured). [Pathology Education Informational Resource (PEIR) Digital Library]

Researchers at the Moffitt Cancer Center have created a new open-source software program that allows users to view multiple 2D images simultaneously. Their program, called Mistic, takes information from multidimensional images to create an abstract of each that can be viewed together. A paper in the journal patterns describes the program and several applications in cancer imaging.

“There are many commercial and open-source imaging technologies that one can use to visualize and process just one of those images,” explains lead author Sandhya Prabhakaran, PhD, lead author and applied research scientist at Moffitt. Users can open those programs to open an image to get an understanding or deeper insights of one image. “We wanted to create a software program where we did not have to click every picture or use additional software to view the images,” adds Prabhakaran. “Instead, we wanted to group multiple images together to see what pattern exists.” With Mistic, users can pull in multiples of these high dimensional images created via Vectra, CyCIF, t-CyCIF and CODEX, and then view them all together.

After looking at other imaging tools, the team realized they needed a better method to view both multiplex images and single images, and they needed to run on standard Mac computers. None of the available tools delivered all these essentials and many were not intended for biomedical use. “We realized we needed to build our own software so that we saw spatial patterns between the tumor and immune cells we were studying,” Prabhakaran explained.

Mistic builds on recent milestone improvements in imaging technologies for studying tissue samples. For example, machines can now be programmed to stain hundreds of slides simultaneously, or alternatively, up to 1,000 different tissue sample colors can be placed on a single slide and stained for biomarkers at the same time. With the advent of these approaches comes a wealth of possibilities to generate new data and information. Due to the magnitude of this information and the complex nature of cancer itself, computational modeling and software are needed to view and study the cancer biomarkers, tissue architecture, and cellular interactions among these samples.

Mistic relies on dimensionality reduction—a process of reducing high-dimensionality data into lower-dimensionality to create a bird’s eye view of each image simultaneously. “Then you can use Mistic to select a particular image you want to study further with sophisticated processing software, like Fiji or QuPath,” said Prabhakaran. “We didn’t want to reinvent the wheel with all these high-performing, well-coded software so we view Mistic as an initial pre-processing data visualization tool that gives the user that initial insight into what all of these images look like that they can then choose which images to choose for downstream processing.”

The researchers claim Mistic can be used for a variety of purposes, including identifying biomarkers and understanding tissue architecture and the spatial organization of different cell types. For example, the researchers demonstrated that the software could be used to view 92 images from patients with non-small cell lung cancer and deduce how biomarkers cluster across patients with different responses to treatment. In another example, the researchers used Mistic combined with statistical analysis to assess the spatial colocalization and coexpression of immune cell markers in 210 endometrial cancer samples.

“This open-source software now allows users to view all your images simultaneously – whether they are for individual biomarkers or multiplex images – and across multiple cancer types,” added senior author Alexander Anderson, PhD, chair of the Integrated Mathematical Oncology department at Moffitt . “And we definitely see that it can be applied all fields and domains, not necessarily across only for cancer images.”

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