Researchers at Sungkyunkwan University and Massachusetts Institute of Technology (MIT) have developed a new way to achieve ultra-high resolution images of various tissue samples with a low-cost microscopy system.



The new technique uses a swellabel hydrogel to physically expand tissue samples. Two years ago, Prof. Ed Boyden's lab at MIT showed that is was possible to expand tissue samples after forming a swellabel hydrogel inside tissue samples and then washing the sample-hydrogel compites in water, resulting in 4.5-fold linear expansion (100-fold volume expansion). Now, the researchers at Sungkyunkwan University and MIT together have shown that it is possible to expand tissue samples multiple times, resulting in 20-fold or even larger linear expansion.



Using this technique, the researchers were able to image tissues with a resolution of 22 nanometers, which is similar to or even better than that achieved by state-of-the-art super-resolution imaging techniques, such as stimulated emission depletion (STED) microscopy or Stochastic optical reconstruction microscopy (STORM). However, this technique is much cheaper and simpler than those delicate, complicated, and expensive techniques. Also, this method enables large-scale 3-D volume super-resolution imaging.

Left: 100-um thick mouse brain slice. Small piece with a height of 0.17 cm was expanded 20-fold and the height after the expansion was 3.4 cm. Right: Confocal microscopy image of neurons after the 20-fold expansion.



The researchers showed that this technique works well with various tissue types, including mouse brain, lung, and liver. Prof. Jae-Byum CHANG, who is the first author of the paper, which apprears in the April 17 issue of Nature Methods, mentioned that this technique would be very useful in mapping neuron circuits of brain and also studying the heterogeneity of cancer or studying the detailed process of animal development. He also added that he would like to combine artificial intelligence (AI) and this technique to enable mass-data acquisition and mass-data analysis.