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Related Experiment Video

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Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging.

Ziming Du1,2, Jia-Ren Lin2,3, Rumana Rashid1,4

  • 1Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Nature Protocols
|September 20, 2019
PubMed
Summary

This study presents a protocol for validating antibodies used in multiplexed tissue imaging of formalin-fixed, paraffin-embedded (FFPE) tissues. It enables accurate cell type enumeration and characterization for research and diagnostics.

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Area of Science:

  • Biomedical Imaging
  • Immunohistochemistry
  • Cell Biology

Background:

  • Multiplexed tissue imaging offers spatial resolution for cell analysis in human tissues.
  • Current methods often rely on antibodies for antigen mapping in formalin-fixed, paraffin-embedded (FFPE) sections.
  • Ensuring antibody specificity is crucial for accurate results.

Purpose of the Study:

  • To provide a protocol for validating antibody selectivity and specificity in fluorescence-based tissue imaging.
  • To detail the construction and validation of antibody panels for multiplexed imaging.
  • To demonstrate the application of these methods using tissue-based cyclic immunofluorescence (t-CyCIF).

Main Methods:

  • Step-by-step procedures for antibody testing and panel construction.
  • Implementation on a fluorescence-based tissue imaging platform (t-CyCIF).
  • Validation using a 16-antibody panel for immune cell enumeration.

Main Results:

  • Demonstrated assembly of a 16-antibody panel for T cells, B cells, macrophages, and immune checkpoint regulators.
  • The protocol is broadly applicable beyond the t-CyCIF platform.
  • A 30-antibody dataset for 20 FFPE slides can be generated within 2 weeks.

Conclusions:

  • The protocol facilitates reliable antibody validation for multiplexed FFPE tissue imaging.
  • Accurate cell enumeration and spatial characterization are achievable.
  • The methods are accessible to researchers with microscopy, immunofluorescence, and computational experience.