Imaging method measures cell mass using two beams of light

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A research team led by electrical and computer engineering professor Gabriel Popescu at Illinois University has developed spatial light interference microscopy (SLIM), an imaging method that can measure cell mass using two beams of light.

Described in the Proceedings of the National Academy of Science, the SLIM technique is claimed to offer new insight into the problem of whether cells grow at a constant rate or exponentially.

According to the university, SLIM is extremely sensitive, quantitatively measuring mass with femtogram accuracy. By comparison, a micron-sized droplet of water weighs 1,000 femtograms. It can measure the growth of a single cell and even mass transport within the cell. The technique is also broadly applicable.

‘A significant advantage over existing methods is that we can measure all types of cells — bacteria, mammalian cells, adherent cells, non-adherent cells, single cells and populations,’ said Mustafa Mir, a graduate student and a first author of the paper. ‘And all this while maintaining the sensitivity and the quantitative information that we get.’

Unlike most other cell imaging techniques, SLIM — a combination of phase contrast microscopy and holography — does not need staining or any other special preparation. As it is completely non invasive, the researchers can study cells as they go about their natural functions. It uses white light and can be combined with more traditional microscopy techniques, such as fluorescence, to monitor cells as they grow.

‘We were able to combine more traditional methods with our method because this is just an add-on module to a commercial microscope,’ Mir said. ‘Biologists can use all their old tricks and just add our module on top.’

As a result of SLIM’s sensitivity, the researchers could monitor cells’ growth through different phases of the cell cycle. They found that mammalian cells show clear exponential growth only during the G2 phase of the cell cycle, after the DNA replicates and before the cell divides. This information has great implications not only for basic biology, but also for diagnostics, drug development and tissue engineering.

The researchers hope to apply their new knowledge of cell growth to different disease models. For example, they plan to use SLIM to see how growth varies between normal cells and cancer cells, and the effects of treatments on the growth rate.