Troy, NJ, USA. Researchers at Rensselaer Polytechnic Institute discovered a new method for predicting the fate of stem cells with up to 99 percent accuracy. Using advanced computer vision technology to detect subtle cell movements that are impossible to discern with the human eye, Professor Badri Roysam and his former student Andrew Cohen can successfully forecast how a stem cell will split and key characteristics the daughter cells.

By allowing the isolation of cells with specific capabilities, this discovery could one day lead to effective methods for growing stem cells on a large scale for therapeutic use. The research appears in Nature Methods.

"If you have many cells in a culture, they all look the same. But our new method senses all sorts of tiny differences in the shapes and movements of the cells, and uses these cues to predict what kind of cells it will divide into," said Roysam, professor of electrical, computer, and systems engineering at Rensselaer.

Cell Movements. Advanced computer vision technology detects subtle cell movements that are impossible to discern with the human eye. Roysam and Cohen tracked the development of rat retinal progenitor cells cultured in their collaborator's laboratory at McGill University. The computer system they developed took images of the cells every five minutes, and employed algorithmic information theoretic prediction (AITP) to observe the behavior of the cells, analyze the behavior, and discern whether each individual cell is fated to split into self-replicating or terminal daughter cells. This process occurs in real time, so researchers know the fate of cells before they actually divide. Image courtesy of Rensselaer Polytechnic Institute.

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In order to achieve successful stem cell-based therapies, researchers require access to large amounts of specific cells. This has proven difficult, as there are currently no methods for controlling or manipulating the division of bulk quantities of cells. When stem cells or progenitor cells divide via mitosis, the resulting daughter cells can be self-renewing or terminal.

A self-renewing cell will go on to split into two daughter cells, while a terminally differentiated cell is fated to be a specific, specialized cell type. Researchers want the ability to influence this division in order to produce large volumes of the correct type of cells.

Roysam and Cohen tracked the development of rat retinal progenitor cells cultured in their collaborator's laboratory at McGill University. The computer system they developed took images of the cells every five minutes, and employed algorithmic information theoretic prediction (AITP) to observe the behavior of the cells, analyze the behavior, and discern whether each individual cell is fated to split into self-replicating or terminal daughter cells. This process occurs in real time, so researchers know the fate of cells before they actually divide.

The researchers predicted with 99 percent accuracy if the rat retinal progenitor cells would split into self-renewing or specialized cells, and predicted with 87 percent accuracy certain characteristics of the specialized cells.

"Our results suggest that stem cells display subtle dynamic patterns that can be sensed computationally to predict the outcome of their next division using AITP," Roysam said. "In theory, AITP can be used to analyze nearly any type of cell, and could lead to advances in many different fields."

Roysam said prototyping and development of the system leveraged the processing power of Rensselaer's supercomputer, the Computational Center for Nanotechnology Innovations (CCNI).

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