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Cyclic microchip assay for measurement of hundreds of functional proteins in single neurons

Author

Listed:
  • Liwei Yang

    (Stony Brook University)

  • Avery Ball

    (Stony Brook University)

  • Jesse Liu

    (Stony Brook University)

  • Tanya Jain

    (Memorial Sloan Kettering Cancer Center
    Weill Graduate School of Medical Sciences of Cornell University)

  • Yue-Ming Li

    (Memorial Sloan Kettering Cancer Center
    Weill Graduate School of Medical Sciences of Cornell University
    Weill Graduate School of Medical Sciences of Cornell University)

  • Firoz Akhter

    (Stony Brook University)

  • Donghui Zhu

    (Stony Brook University)

  • Jun Wang

    (Stony Brook University)

Abstract

Despite the fact that proteins carry out nearly all cellular functions and mark the differences of cells, the existing single-cell tools can only analyze dozens of proteins, a scale far from full characterization of cells and tissue yet. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology that affords the comprehensive functional proteome profiling of single cells. We demonstrate the technology by detecting 182 proteins that include surface markers, neuron function proteins, neurodegeneration markers, signaling pathway proteins, and transcription factors. Further studies on cells derived from the 5XFAD mice, an Alzheimer’s Disease (AD) model, validate the utility of our technology and reveal the deep heterogeneity of brain cells. Through comparison with control mouse cells, we have identified differentially expressed proteins in AD pathology. Our technology could offer new insights into cell machinery and thus may advance many fields including drug discovery, molecular diagnostics, and clinical studies.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31336-x
    DOI: 10.1038/s41467-022-31336-x
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    References listed on IDEAS

    as
    1. Randy J. Giedt & Divya Pathania & Jonathan C. T. Carlson & Philip J. McFarland & Andres Fernandez Castillo & Dejan Juric & Ralph Weissleder, 2018. "Single-cell barcode analysis provides a rapid readout of cellular signaling pathways in clinical specimens," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    2. Hansruedi Mathys & Jose Davila-Velderrain & Zhuyu Peng & Fan Gao & Shahin Mohammadi & Jennie Z. Young & Madhvi Menon & Liang He & Fatema Abdurrob & Xueqiao Jiang & Anthony J. Martorell & Richard M. Ra, 2019. "Single-cell transcriptomic analysis of Alzheimer’s disease," Nature, Nature, vol. 570(7761), pages 332-337, June.
    3. Hansruedi Mathys & Jose Davila-Velderrain & Zhuyu Peng & Fan Gao & Shahin Mohammadi & Jennie Z. Young & Madhvi Menon & Liang He & Fatema Abdurrob & Xueqiao Jiang & Anthony J. Martorell & Richard M. Ra, 2019. "Author Correction: Single-cell transcriptomic analysis of Alzheimer’s disease," Nature, Nature, vol. 571(7763), pages 1-1, July.
    4. Pavel Zrazhevskiy & Xiaohu Gao, 2013. "Quantum dot imaging platform for single-cell molecular profiling," Nature Communications, Nature, vol. 4(1), pages 1-12, June.
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