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Quantum dot imaging platform for single-cell molecular profiling

Author

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  • Pavel Zrazhevskiy

    (University of Washington, 3720 15th Avenue NE, Seattle, Washington 98195, USA)

  • Xiaohu Gao

    (University of Washington, 3720 15th Avenue NE, Seattle, Washington 98195, USA)

Abstract

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. Here we report the development of a multicolour multicycle in situ imaging technology capable of creating detailed quantitative molecular profiles for individual cells at the resolution of optical imaging. A library of stoichiometric fluorescent probes is prepared by linking target-specific antibodies to a universal quantum dot-based platform via protein A in a quick and simple procedure. Surprisingly, despite the potential for multivalent binding between protein A and antibody and the intermediate affinity of this non-covalent bond, fully assembled probes do not aggregate or exchange antibodies, facilitating highly multiplexed parallel staining. This single-cell molecular profiling technology is expected to open new opportunities in systems biology, gene expression studies, signalling pathway analysis and molecular diagnostics.

Suggested Citation

  • Pavel Zrazhevskiy & Xiaohu Gao, 2013. "Quantum dot imaging platform for single-cell molecular profiling," Nature Communications, Nature, vol. 4(1), pages 1-12, June.
    • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms2635
    DOI: 10.1038/ncomms2635
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    Cited by:

    1. Liwei Yang & Avery Ball & Jesse Liu & Tanya Jain & Yue-Ming Li & Firoz Akhter & Donghui Zhu & Jun Wang, 2022. "Cyclic microchip assay for measurement of hundreds of functional proteins in single neurons," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Daniel P Riordan & Sushama Varma & Robert B West & Patrick O Brown, 2015. "Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-18, July.
    3. Junyoung Seo & Yeonbo Sim & Jeewon Kim & Hyunwoo Kim & In Cho & Hoyeon Nam & Young-Gyu Yoon & Jae-Byum Chang, 2022. "PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurements," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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