Apr 21, 2017 01:15 PM EDT
Columbia University researchers were able to break the "color barrier" of light microscopy for biological systems. This is expected to lead to more comprehensive, system-wide labeling and imaging of a larger number of biomolecules in living cells and tissues.
This was previously unattainable and the progress has the potential to for several future applications. This may help guide the development of therapies to treat and cure disease.
The study has been published in the journal "Nature." The team of researchers was led by Wei Min, an associate professor of Chemistry at Columbia University.
Phys.org reported that the team has developed a new optical microscopy platform with enhanced detection sensitivity. The study also detailed the creation of new molecules that allow for simultaneous labeling and imaging of up to 24 specific biomolecules when paired with the new instrumentation.
Min described their work as "new and unique" because it has two synergistic parts: instrumentation and molecules. These pieces work together to combat the color barrier.
Their platform can help bring more understanding of complex biological systems such as the human cell map, metabolic pathways, the functions of several structures within the brain and the macromolecule assembly, among others.
One example is fluorescence microscopy, which allows scientists to monitor cellular processes in living systems through proteins, is hindered by the color barrier. This limits researchers to seeing only five structures at a time.
There are also a variety of Raman microscopy techniques used for observing living cell and tissue structures that make the vibrations visible stemming from characteristic chemical bonds in structures. Traditional Raman microscopy is able to produce highly-defined colors that fluorescence microscopy.
The researchers were able to develop a new platform called electronic pre-resonance stimulated Raman scattering (epr-SRS) microscopy that brings a high level of sensitivity and selectivity. The technique specifically identifies structures with lower concentration instead of millions of the same structure.
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