Abstract
Advances in multiplexed imaging technologies have drastically improved our ability to characterize healthy and diseased tissues at the single-cell level. Co-detection by indexing (CODEX) relies on DNA-conjugated antibodies and the cyclic addition and removal of complementary fluorescently labeled DNA probes and has been used so far to simultaneously visualize up to 60 markers in situ. CODEX enables a deep view into the single-cell spatial relationships in tissues and is intended to spur discovery in developmental biology, disease and therapeutic design. Herein, we provide optimized protocols for conjugating purified antibodies to DNA oligonucleotides, validating the conjugation by CODEX staining and executing the CODEX multicycle imaging procedure for both formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen tissues. In addition, we describe basic image processing and data analysis procedures. We apply this approach to an FFPE human tonsil multicycle experiment. The hands-on experimental time for antibody conjugation is ~4.5 h, validation of DNA-conjugated antibodies with CODEX staining takes ~6.5 h and preparation for a CODEX multicycle experiment takes ~8 h. The multicycle imaging and data analysis time depends on the tissue size, number of markers in the panel and computational complexity.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
We have uploaded both the concatenated CODEX imaging montage of the tissue (https://doi.org/10.6084/m9.figshare.12986981) and single-cell segmented data (https://doi.org/10.6084/m9.figshare.12986099) used in this paper to figshare (https://figshare.com/). The size of the raw imaging data is too large to be stored in a public repository and will therefore be stored in a private cloud-based server. Access to these data will be provided by the corresponding authors upon request.
Code availability
The image processing and data analysis tools presented in this article are available at https://github.com/nolanlab. The code used in this protocol has been peer reviewed.
References
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).
Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359.e19 (2020).
Kennedy-Darling, J. et al. Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction. Eur. J. Immunol. 51, 1262–1277 (2021).
Huang, W., Hennrick, K. & Drew, S. A colorful future of quantitative pathology: validation of Vectra technology using chromogenic multiplexed immunohistochemistry and prostate tissue microarrays. Hum. Pathol. 44, 29–38 (2013).
Lin, J. R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7, 31657 (2018).
Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA 110, 11982–11987 (2013).
Schubert, W. et al. Analyzing proteome topology and function by automated multidimensional fluorescence microscopy. Nat. Biotechnol. 24, 1270–1278 (2006).
Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018).
Agasti, S. S. et al. DNA-barcoded labeling probes for highly multiplexed Exchange-PAINT imaging. Chem. Sci. 8, 3080–3091 (2017).
Wang, Y. et al. Rapid sequential in situ multiplexing with DNA exchange imaging in neuronal cells and tissues. Nano Lett. 17, 6131–6139 (2017).
Saka, S. K. et al. Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues. Nat. Biotechnol. 37, 1080–1090 (2019).
Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).
Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).
Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).
Moffitt, J. R. et al. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc. Natl Acad. Sci. USA 113, 14456–14461 (2016).
Wei, L. et al. Super-multiplex vibrational imaging. Nature 544, 465–470 (2017).
Zhou, W., Han, Y., Beliveau, B. J. & Gao, X. Combining Qdot nanotechnology and DNA nanotechnology for sensitive single-cell imaging. Adv. Mater. 32, e1908410 (2020).
Bendall, S. C., Nolan, G. P., Roederer, M. & Chattopadhyay, P. K. A deep profiler’s guide to cytometry. Trends Immunol. 33, 323–332 (2012).
Hartmann, F. J. et al. Comprehensive immune monitoring of clinical trials to advance human immunotherapy. Cell Rep. 28, 819–831.e4 (2019).
Gomariz, A. et al. Quantitative spatial analysis of haematopoiesis-regulating stromal cells in the bone marrow microenvironment by 3D microscopy. Nat. Commun. 9, 2532 (2018).
Phillips, D. et al. Highly multiplexed phenotyping of immunoregulatory proteins in the tumor microenvironment by CODEX tissue imaging. Front. Immunol. 12, 687673 (2021).
Newell, E. W. et al. Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization. Nat. Biotechnol. 31, 623–629 (2013).
Samusik, N., Good, Z., Spitzer, M. H., Davis, K. L. & Nolan, G. P. Automated mapping of phenotype space with single-cell data. Nat. Methods 13, 493–496 (2016).
Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).
Wu, D. et al. Comparison between UMAP and t-SNE for multiplex-immunofluorescence derived single-cell data from tissue sections. Preprint at bioRxiv https://doi.org/10.1101/549659 (2019).
Kishi, J. Y. et al. SABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues. Nat. Methods 16, 533–544 (2019).
Frei, A. P. et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nat. Methods 13, 269–275 (2016).
Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 25–36.e5 (2018).
Li, W., Germain, R. N. & Gerner, M. Y. Multiplex, quantitative cellular analysis in large tissue volumes with clearing-enhanced 3D microscopy (Ce3D). Proc. Natl Acad. Sci. USA 114, E7321–E7330 (2017).
Ueda, H. R. et al. Tissue clearing and its applications in neuroscience. Nat. Rev. Neurosci. 21, 61–79 (2020).
Wassie, A. T., Zhao, Y. & Boyden, E. S. Expansion microscopy: principles and uses in biological research. Nat. Methods 16, 33–41 (2019).
Winter, P. W. & Shroff, H. Faster fluorescence microscopy: advances in high speed biological imaging. Curr. Opin. Chem. Biol. 20, 46–53 (2014).
Leng, E. et al. Signature maps for automatic identification of prostate cancer from colorimetric analysis of H&E- and IHC-stained histopathological specimens. Sci. Rep. 9, 6992 (2019).
Wu, P. H. et al. Single-cell morphology encodes metastatic potential. Sci. Adv. 6, eaaw6938 (2020).
Goldblum, J. R., Lamps, L. W., McKenney, J. K. & Myers, J. L. Rosai and Ackerman’s Surgical Pathology 11th edn, Vol. 1 (Elsevier, 2018).
Uhlen, M. et al. Towards a knowledge-based Human Protein Atlas. Nat. Biotechnol. 28, 1248–1250 (2010).
Acknowledgements
We thank Angelica Trejo, Han Chen, Kenyi Donoso, Nilanjan Mukherjee and Gustavo Vazquez for excellent technical assistance and Dr. Xavier Rovira-Clavé for critical comments on the manuscript. This work was supported by the U.S. National Institutes of Health (2U19AI057229-16, 5P01HL10879707, 5R01GM10983604, 5R33CA18365403, 5U01AI101984-07, 5UH2AR06767604, 5R01CA19665703, 5U54CA20997103, 5F99CA212231-02, 1F32CA233203-01, 5U01AI140498-02, 1U54HG010426-01, 5U19AI100627-07, 1R01HL120724-01A1, R33CA183692, R01HL128173-04, 5P01AI131374-02, 5UG3DK114937-02, 1U19AI135976-01, IDIQ17X149, 1U2CCA233238-01 and 1U2CCA233195-01); the U.S. Department of Defense (W81XWH-14-1-0180 and W81XWH-12-1-0591); the U.S. Food and Drug Administration (HHSF223201610018C and DSTL/AGR/00980/01); Cancer Research UK (C27165/A29073); the Bill and Melinda Gates Foundation (OPP1113682); the Cancer Research Institute; the Parker Institute for Cancer Immunotherapy; the Kenneth Rainin Foundation (2018-575); the Silicon Valley Community Foundation (2017-175329 and 2017-177799-5022); the Beckman Center for Molecular and Genetic Medicine; Juno Therapeutics, Inc. (122401); Pfizer, Inc. (123214); Celgene, Inc. (133826 and 134073); Vaxart, Inc. (137364); and the Rachford & Carlotta A. Harris Endowed Chair (to G.P.N.). C.M.S. was supported by the Swiss National Science Foundation (P300PB_171189 and P400PM_183915) and the Lady Tata Memorial Trust, London, UK. D.P. was supported by an NIH T32 Fellowship (AR007422), an NIH F32 Fellowship (CA233203), a Stanford Dean’s Postdoctoral Fellowship, a Stanford Cancer Institute Fellowship and Stanford’s Dermatology Department. J.W.H. was supported by an NIH T32 Fellowship (T32CA196585) and an American Cancer Society—Roaring Fork Valley Postdoctoral Fellowship (PF-20-032-01-CSM). Parts of Fig. 2 were created with BioRender.com.
Author information
Authors and Affiliations
Contributions
S.B., D.P., J.K.-D., N.S., Y.G. and C.M.S. developed the original experimental protocol in the G.P.N. laboratory. S.B., D.P., J.W.H. and C.M.S. performed experiments and analyzed data. V.G.V., N.S. and Y.G. developed the software used. S.B., D.P. and J.W.H. wrote the manuscript and created figures, with input from C.M.S. and G.P.N. All authors revised the manuscript and accepted its final version.
Corresponding authors
Ethics declarations
Competing interests
J.K.-D. is an employee of Akoya Biosciences, Inc. N.S. is an employee of Becton Dickinson, Inc. G.P.N. received research grants from Pfizer, Inc.; Vaxart, Inc.; Celgene, Inc.; and Juno Therapeutics, Inc. during the course of this work. N.S., Y.G. and G.P.N. are inventors on US patent 9909167, granted to Stanford University, that covers some aspects of the technology described in this paper. J.K.-D., N.S., Y.G. and G.P.N. have equity in and/or are scientific advisory board members of Akoya Biosciences, Inc. C.M.S. is a scientific advisor to Enable Medicine, Inc. The other authors declare no competing interests.
Additional information
Peer review information Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Related links
Key reference using this protocol
Schürch, C. M. et al. Cell 182, 1341–1359.e19 (2020): https://doi.org/10.1016/j.cell.2020.07.005
Extended data
Extended Data Fig. 1 Tissue morphology conserved over multicycle imaging.
One tile of the CODEX multicycle for the human tonsil was segmented by using the CODEXSegm software and the nuclear stain for cycle 1 or cycle 26 (nuclear images on the top; segmentation masks on the bottom). For both instances, 2,464 cells were identified. Scale bar, 100 µm.
Extended Data Fig. 2 Analysis of healthy and cancerous tissue morphology during multicycle imaging.
a, H&E image of a healthy spleen. Scale bar, 200 µm. b, Corresponding nuclear (Hoechst) stained image and cellular segmentation (Seg) mask. Scale bar, 200 µm. c, Zoomed-in nuclear stained image and cellular segmentation mask from cycle 2. Scale bar, 20 µm (Extended Data Applied Sciences 28 April 2020). d, Zoomed-in nuclear stained image and cellular segmentation mask from cycle 22. Scale bar, 200 µm. e, Line plot of total cell count per tissue microarray core, measured at cycles 1, 2, 8, 14 and 22, for five healthy tissues. Lines are colored according to the corresponding legend. f, H&E image of stomach cancer. Scale bar, 200 µm. g, Corresponding nuclear (Hoechst) stained image and cellular segmentation mask. Scale bar, 200 µm. h, Zoomed-in nuclear stained image and cellular segmentation mask from cycle 2. Scale bar, 20 µm. i, Zoomed-in nuclear stained image and cellular segmentation mask from cycle 22. Scale bar, 200 µm. j, Line plot of total cell count per tissue microarray core, measured at cycles 1, 2, 8, 14 and 22, for five cancer tissues. Lines are colored according to the corresponding legend.
Supplementary information
Supplementary Information
Supplementary Notes 1–4 and Supplementary Figs. 1–3.
Supplementary Tables
Supplementary Table 1 (tab 1): oligonucleotide sequences; Supplementary Table 2 (tab 2): antibodies, clones and manufacturers; Supplemental Table 3 (tab 3): CODEX multicycle antibody panel
Rights and permissions
About this article
Cite this article
Black, S., Phillips, D., Hickey, J.W. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc 16, 3802–3835 (2021). https://doi.org/10.1038/s41596-021-00556-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41596-021-00556-8
This article is cited by
-
Spatial insights into immunotherapy response in non-small cell lung cancer (NSCLC) by multiplexed tissue imaging
Journal of Translational Medicine (2024)
-
Mapping cancer biology in space: applications and perspectives on spatial omics for oncology
Molecular Cancer (2024)
-
Tunable PhenoCycler imaging of the murine pre-clinical tumour microenvironments
Cell & Bioscience (2024)
-
Multiplex protein imaging in tumour biology
Nature Reviews Cancer (2024)
-
MAPS: pathologist-level cell type annotation from tissue images through machine learning
Nature Communications (2024)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.