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Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method

Author

Listed:
  • Jia-Ren Lin

    (HMS LINCS Center & Laboratory of Systems Pharmacology, Harvard Medical School)

  • Mohammad Fallahi-Sichani

    (Department of Systems Biology Harvard Medical School 200 Longwood Avenue)

  • Peter K. Sorger

    (HMS LINCS Center & Laboratory of Systems Pharmacology, Harvard Medical School
    Department of Systems Biology Harvard Medical School 200 Longwood Avenue)

Abstract

Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications.

Suggested Citation

  • Jia-Ren Lin & Mohammad Fallahi-Sichani & Peter K. Sorger, 2015. "Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method," Nature Communications, Nature, vol. 6(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9390
    DOI: 10.1038/ncomms9390
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    Cited by:

    1. S. Vickovic & B. Lötstedt & J. Klughammer & S. Mages & Å Segerstolpe & O. Rozenblatt-Rosen & A. Regev, 2022. "SM-Omics is an automated platform for high-throughput spatial multi-omics," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Cemal Erdem & Sean M. Gross & Laura M. Heiser & Marc R. Birtwistle, 2023. "MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Yunhao Bai & Bokai Zhu & John-Paul Oliveria & Bryan J. Cannon & Dorien Feyaerts & Marc Bosse & Kausalia Vijayaragavan & Noah F. Greenwald & Darci Phillips & Christian M. Schürch & Samuel M. Naik & Edw, 2023. "Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    4. Timothy D Majarian & Robert F Murphy & Seema S Lakdawala, 2019. "Learning the sequence of influenza A genome assembly during viral replication using point process models and fluorescence in situ hybridization," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-20, January.
    5. Caitlin E. Mills & Kartik Subramanian & Marc Hafner & Mario Niepel & Luca Gerosa & Mirra Chung & Chiara Victor & Benjamin Gaudio & Clarence Yapp & Ajit J. Nirmal & Nicholas Clark & Peter K. Sorger, 2022. "Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    6. 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.
    7. Shu Wang & Jia-Ren Lin & Eduardo D Sontag & Peter K Sorger, 2019. "Inferring reaction network structure from single-cell, multiplex data, using toric systems theory," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-25, December.
    8. Seth Teague & Gillian Primavera & Bohan Chen & Zong-Yuan Liu & LiAng Yao & Emily Freeburne & Hina Khan & Kyoung Jo & Craig Johnson & Idse Heemskerk, 2024. "Time-integrated BMP signaling determines fate in a stem cell model for early human development," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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