MICROSCOPY/MACHINE LEARNING: Open-source software automates complex microscopy tasks

A novel computer program can quickly learn what a scientist is looking for and then automatically perform complex microscopy experiments when it detects corresponding cell features.

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A novel computer program can quickly learn what a scientist is looking for and then automatically perform complex microscopy experiments when it detects corresponding cell features.

Called Micropilot, the open-source software analyzes low-resolution images taken by a microscope and, once it has identified a cell or structure of interest, automatically instructs the microscope to start an experiment—which can be as simple as recording high-resolution time-lapse videos, or as complex as using lasers to probe fluorescently tagged proteins and record the results.

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Micropilot analyzes low-res images, identifies cells or structures of interest, and then automatically instructs the microscope to start an experiment.

Jan Ellenberg and Rainer Pepperkok, whose teams at the European Molecular Biology Laboratory (EMBL; Heidelberg, Germany) designed Micropilot, have used the software to deploy several different experiments investigating aspects of cell division. They determined the formation of structures known as endoplasmic reticulum exit sites, and uncovered the roles of two proteins, CBX1 and CENP-E, in condensing genetic material into tightly wound chromosomes and in forming the spindle that helps align those chromosomes. And, in four nights of unattended microscope operation, Micropilot detected 232 cells in two particular stages of cell division and performed a complex imaging experiment on them; an experienced microscopist would have to work full-time for at least a month just to find those cells among the many thousands in the sample.

The software and experiments are detailed in Nature Methods (2011), doi:10.1038/nmeth.1558.

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