Study of normal cell physiology and disease pathogenesis heavily relies on untangling the complexity of intracellular molecular mechanisms and pathways. To achieve this goal, comprehensive molecular profiling of individual cells within the context of microenvironment is required. New research from the University of Washington offers a more comprehensive way of analyzing a single cell’s unique behavior and could reveal patterns that indicate why a cell will or will not become malignant.
Xiaohu Gua and graduate student Pavel Zrazhevskiy have used an array of distinctly colored quantum dots to illuminate 100 biomarkers, a ten-fold increase from the current research standard, to help analyze individual cells from cultures or tissue biopsies. The investigators published their findings in the journal Nature Communications (« Quantum dot imaging platform for single-cell molecular profiling »). While other approaches have measured multiple biomarkers in a single cell, what makes this technique so promising is that it reuses the same precious tissue sample in a cyclical process to measure 100 biomolecules in groups of ten. The process starts by pairing a commercially available antibody that is known to bind with specific biomolecule with a quantum dot of distinct size and therefore color. The investigators then inject a solution of ten of these antibody-quantum dot pairs onto a tissue sample and use a fluorescence microscope to quantify which of the constructs bind at the single cell level.Once the measurement is complete, they then wash the tissue sample with a fluid of detergents at low pH to get rid of the antibodies and quantum dots without degrading the tissue sample. The two investigators have shown that they can repeat this process at least ten times without producing any signs of tissue damage.The researchers note that because this methodology uses commercially available enzymes and standard fluorescence microscopes, it is relatively low cost. They also plan to automate the procedure using microfluidics and automated image processing technologies.
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