New type of map connects the dots in cellular reprogramming

phys.org | 2/1/2019 | Staff
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A new use of an old mathematical method analyzes a massive single-cell RNA sequencing experiment to explore how cells move from one state to another.

Single-cell RNA sequencing (scRNA-seq) shows which genes an individual cell is expressing at a given moment, and can deliver an enormous amount of data on how cells develop over time.

Destroys - Cells - Scientists - Path - Cell

However, scRNA-seq destroys cells, so scientists cannot precisely trace the path a cell takes as it moves from one state to another. As a result, there is still a great deal we do not know about, for example, how cells transform during normal embryonic development, or when they are reprogrammed from a mature to a stem-cell-like state.

Seeking to fill these knowledge gaps, Broad scientists have leveraged a powerful mathematical method called "optimal transport" to create a framework dubbed Waddington-OT. They subsequently used this approach to predict how populations of cells transition from one state to another in a massive scRNA-seq time-course study of stem cell reprogramming.

Work - Capabilities - Cornucopia - Data - Biology

This work provides both new analytic capabilities and a vast cornucopia of developmental data to the biology community.

"In developmental biology, we want to be able to understand the origins and fates of cells at every stage of development, and recognize the regulatory programs that control those fates," said Geoffrey Schiebinger, a postdoctoral fellow with Broad core institute member and Klarman Cell Observatory director Aviv Regev. "By stitching snapshots of data together into movies, optimal transport helps reveal how fine details of developmental processes unfold."

Schiebinger - Authors - Paper - Work - Cell

Schiebinger is one of the co-first authors on a paper presenting the work in Cell, along with Jian Shu, a postdoctoral fellow with Broad Institute president and founding director Eric Lander; Regev lab postdoctoral fellow Marcin Tabaka; and Lander and Regev lab graduate student Brian Cleary.

"Previously, there was no large-scale scRNA-seq roadmap for the reprogramming process," said Shu, who...
(Excerpt) Read more at: phys.org
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