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

Nature Protocols volume 14, pages 2900–2930 (2019)Cite this article

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Abstract

Multiplexed tissue imaging enables precise, spatially resolved enumeration and characterization of cell types and states in human resection specimens. A growing number of methods applicable to formalin-fixed, paraffin-embedded (FFPE) tissue sections have been described, the majority of which rely on antibodies for antigen detection and mapping. This protocol provides step-by-step procedures for confirming the selectivity and specificity of antibodies used in fluorescence-based tissue imaging and for the construction and validation of antibody panels. Although the protocol is implemented using tissue-based cyclic immunofluorescence (t-CyCIF) as an imaging platform, these antibody-testing methods are broadly applicable. We demonstrate assembly of a 16-antibody panel for enumerating and localizing T cells and B cells, macrophages, and cells expressing immune checkpoint regulators. The protocol is accessible to individuals with experience in microscopy and immunofluorescence; some experience in computation is required for data analysis. A typical 30-antibody dataset for 20 FFPE slides can be generated within 2 weeks.

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Fig. 1: Schematic of antibody validation approaches for multiplexed tissue imaging.
Fig. 2: Pixel-by-pixel validation of antibodies against PD-1 using t-CyCIF in human tonsil.
Fig. 3: Validation of antibodies for immune profiling by comparison to clinical-grade antibodies.
Fig. 4: Validation of antibodies for immune profiling by co-segregation of known markers and expected spatial localization.
Fig. 5: Spatially resolved cataloging of immune cells in lung cancer FFPE tissue and component analysis of the immune subpopulations by t-CyCIF.
Fig. 6: Illustration of low-frequency CD45+/CD3+/FOXP3+/CD4−/CD8a+ T cells detected and confirmed by t-CyCIF.
Fig. 7: Illustration of low-frequency CD45+/CD3+/PD-1+/LAG3+ T cells detected and confirmed by t-CyCIF.
Fig. 8: Systematic automated identification of rare subpopulations using t-CyCIF.

Data availability

The datasets generated and/or analyzed during the current study are available to view on cycif.org (https://www.cycif.org/featured-paper/du-and-lin-2019/figures/). The raw data are available from http://www.synapse.org/ (Synapse IDs: syn18684611 and syn17865732).

Code availability

Custom code used to analyze the data in this study is available on GitHub (https://github.com/sorgerlab/cycif).

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Acknowledgements

The authors are members of the Harvard Tissue Atlas Program and the Ludwig Tumor Atlas. The Dana-Farber/Harvard Cancer Center was supported in part by NCI Cancer Center Support grant P30-CA06516. This work was also funded by NIH grants U54-CA225088 and U2C-CA233262 to P.K.S. and S.S., U2C-CA233280 to P.K.S., and R41-CA224503 to P.K.S. as well as by the Ludwig Center at Harvard. S.W. was supported by Molecular Biophysics Training grant T32-GM008313. B.I. was supported by NIH grant K08-CA222663 and funding from the Claudia Adams Barr Program for Innovative Cancer Research. We thank Y.A. Chen, M. P. Wu, G. Baker, Y. Chen, J. Muhlich, S. Mei, and C. Yapp for their expert assistance.

Author information

Author notes

  1. These authors contributed equally: Ziming Du, Jia-Ren Lin, Rumana Rashid.

Authors and Affiliations

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

    Ziming Du, Rumana Rashid, Jon C. Aster & Sandro Santagata

  2. Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA

    Ziming Du, Jia-Ren Lin, Zoltan Maliga, Shu Wang, Jon C. Aster, Benjamin Izar, Peter K. Sorger & Sandro Santagata

  3. Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA

    Jia-Ren Lin, Zoltan Maliga, Shu Wang, Benjamin Izar, Peter K. Sorger & Sandro Santagata

  4. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Rumana Rashid

  5. Harvard Graduate Program in Biophysics, Harvard University, Boston, MA, USA

    Shu Wang

  6. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Benjamin Izar

  7. Broad Institute of MIT and Harvard, Boston, MA, USA

    Benjamin Izar

  8. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Peter K. Sorger

  9. Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA

    Sandro Santagata

  10. Department of Pathology, Boston Children’s Hospital, Boston, MA, USA

    Sandro Santagata

Authors
  1. Ziming Du
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  2. Jia-Ren Lin
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  3. Rumana Rashid
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  4. Zoltan Maliga
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  5. Shu Wang
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  6. Jon C. Aster
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  7. Benjamin Izar
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  8. Peter K. Sorger
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  9. Sandro Santagata
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Contributions

S.S. and P.K.S. supervised the project; S.S., J.-R.L., Z.D., B.I., J.C.A., and P.K.S. were involved in planning; Z.D. and J.-R.L. performed the experiments and data analysis; R.R., S.W., and Z.M. contributed to data collection and analysis. All authors wrote and reviewed the paper.

Corresponding authors

Correspondence to Peter K. Sorger or Sandro Santagata.

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Competing interests

P.K.S. is a member of the Scientific Advisory Board and a holder of equity in RareCyte, which manufactures one of the slide scanners used in this study; he is also a co-founder of Glencoe Software, which contributes to and supports the open-source OME/OMERO software used for image visualization in this work. S.S. has consulted for RareCyte. The remaining authors declare no competing interests.

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Related links

Key references using this protocol

Lin, J.-R. et al. Elife 7, e31657 (2018): https://elifesciences.org/articles/31657

Jerby-Arnon, L. et al. Cell 175, 984–997.e24 (2018): https://www.sciencedirect.com/science/article/pii/S0092867418311784

Dunn, I. F. et al. JCO Precis. Oncol. 2018, 10.1200/PO.18.00190 (2018): https://ascopubs.org/doi/full/10.1200/PO.18.00190

Coy, S. et al. Neuro Oncol. 20, 1101–1112 (2018): https://academic.oup.com/neuro-oncology/article-abstract/20/8/1101/4917565

Integrated supplementary information

Supplementary Fig. 1 Markers and major cell types identified by the t-CyCIF immune panel.

a) Canonical immune cell types and their markers. Seven major immune cell subtypes were assayed using a panel of 16 markers. Three additional markers (Ki-67, α-SMA and pan-keratin) were used to identify cell states (Ki-67 for proliferative cells) or separate immune cells from tumour cells (keratin-positive) or stromal cells (α-SMA-positive). b) Actual immune subpopulation identified from t-CyCIF immune profiling of LUNG-3-PR. 23,079 immune cells (keratin/α-SMA-negative cells) from the sample were used for binary gating of 15 different markers. A total of 1,356 different subpopulations were identified, of which 37 subpopulations represented >0.5% of total immune cells. The four subpopulations highlighted here are CD45+/IBA1+ (macrophage or dendritic cells), CD45+/CD20+ (B cells), CD45+/CD3+/CD8a+ (cytotoxic T cells) and CD45+/CD3+/CD4+ (helper T cells). Asterisks label 19 common immune cell subtypes.

Supplementary Fig. 2 Multi-antibody qualification of PD-L1 and FOXP3 antibodies by t-CyCIF in human FFPE tonsil tissue.

Representative images of immunofluorescence staining from human FFPE tonsil sections using a) three different antibodies for PD-L1: E1L3N, CST; 22C3, DAKO; and 28-8, Abcam (Scale bars: 100 µm). b) Plots of the pixel-by-pixel correlation of the signal intensity generated by the various PD-L1 antibodies. c) Immunofluorescence staining with two different antibodies for FOXP3: 23A/E7, eBioscience; and 206D, BioLegend (Scale bars: 100 µm). d) Plots of the pixel-by-pixel correlation of the signal intensity generated by the various FOXP3 antibodies. The plots in b and d display the correlation of a random sampling of 2,000 pixels. The plots on the lower left (with blue dots) show the original fluorescence intensity at each pixel, and the plots on the upper right (with cyan dots) show the log-transformed fluorescence intensity at each pixel. The Pearson correlation coefficients (R) are shown. DR, dynamic range. The dynamic range and correlation values from 5 different regions are presented in Supplementary Table 1.

Supplementary Fig. 3 Multi-antibody qualification of CD45 antibodies by t-CyCIF in human FFPE tonsil tissue.

Individual and merged a) low (Scale bars: 100 µm) and b) high (Scale bars: 50 µm) magnification images of immunofluorescence from three CD45 antibodies: 2D1, R&D; HI30, BioLegend; and PD7/26, eBioscience. c) Plots of the pixel-by-pixel correlation of the signal intensity generated by these CD45 antibodies. The plots display the correlation of a random sampling of 2,000 pixels. The plots on the lower left (with blue dots) show the original fluorescence intensity at each pixel, and the plots on the upper right (with cyan dots) show the log-transformed fluorescence intensity at each pixel. The Pearson correlation coefficients (R) are shown. DR, dynamic range. The dynamic range and correlation values from 5 different regions are presented in Supplementary Table 1.

Supplementary Fig. 4 Multi-antibody qualification of LAG3 antibodies by t-CyCIF in human FFPE tonsil tissue.

Individual and merged a) low (Scale bars: 100 µm) and b) high (Scale bars: 50 µm) magnification images of immunofluorescence from five LAG3 antibodies: EPR4392, Abcam; Polyclonal, R&D; 17B4, Lifespan; 11E3, Abcam; and T47-530, BD Bioscience. c) Plots of the pixel-by-pixel correlation of the signal intensity generated by the various LAG3 antibodies. The plots display the correlation of a random sampling of 2,000 pixels. The plots on the lower left (with blue dots) are of the original fluorescence intensity at each pixel, and the plots on the upper right (with cyan dots) are of the log-transformed fluorescence intensity at each pixel. The Pearson correlation coefficients (R) are shown. DR, dynamic range. The dynamic range and correlation values from 5 different regions are presented in Supplementary Table 1.

Supplementary Fig. 5 Multi-antibody qualification of CD11b antibodies by t-CyCIF in human FFPE tonsil tissue.

Individual and merged a) low (Scale bars: 100 µm) and b) high (Scale bars: 50 µm) magnification images of immunofluorescence from three CD11b antibodies: EP1345Y, Abcam; C67F154, eBioscience; and EPR1344, Abcam. c) Plots of the pixel-by-pixel correlation of the signal intensity generated by the CD11b antibodies. The plots display the correlation of a random sampling of 2,000 pixels. The plots on the lower left (with blue dots) show the original fluorescence intensity at each pixel, and the plots on the upper right (with cyan dots) show the log-transformed fluorescence intensity at each pixel. The Pearson correlation coefficients (R) are shown. DR, dynamic range. The dynamic range and correlation values from 5 different regions are presented in Supplementary Table 1.

Supplementary Fig. 6 Multi-antibody qualification by t-CyCIF and IHC in human FFPE tonsil tissue.

Representative images of t-CyCIF (left) and IHC staining (right) using antibodies to a) CD8a, b) FOXP3, c) PD-L1, and d) CD68 (Scale bars: 500 µm). e) Bar plot of the percentage of total immune cells in the tonsil section that were positive for the specified antibodies. The estimated number of positive cells was determined by both manual gating and by GMM analysis. The plot includes the percentage of positive cells for each immune antibody by IHC, as counted using Aperio ImageScope software.

Supplementary Fig. 7 Immune profiling in human FFPE tonsil tissue by t-CyCIF.

Merged images of t-CyCIF data (left) and scatter plots (right) from a random sampling of 10,000 cells for a) CD3 and CD45RB, b) CD3 and FOXP3, c) LAG3 and PD-1, and d) IBA1 and CD163. e) Additional merged images for CD3 and CD20, CD4 and CD8a, CD19 and CD20, CD11b and CD14, CD163 and CD68, and IBA1 and CD14 (Scale bars: 50 µm).

Supplementary Fig. 8

Individual t-CyCIF images for 16 different immune markers in human FFPE tonsil tissue. Scale bars: 50 µm.

Supplementary Fig. 9 Evaluation of segmentation accuracy and error composition.

a) Overall error rates of segmentation. Several hundred segmented masks were validated by human review, and the errors versus total counts of segmented cells from four different samples (tonsil, LUNG-1-LN, LUNG-2-BR and LUNG-3-PR) are plotted here. The average error rate was ~20% (S.E. 2%) for each sample. b) Illustration of the types of errors encountered. An example single-channel grey-scale Hoechst image is shown, with segmented masks highlighted in yellow. Three major types of segmentation errors were found: fused (blue arrows), split (red arrows) and missed (green arrows) cells (Scale bar: 20 µm). c) The composition of the different types of segmentation errors from the four samples.

Supplementary Fig. 10 Illustration of manual inspection and gating of intensity data.

a) Histogram of single-cell intensity from CD8a staining. Human inspection of this signal profile was used to set the gate/threshold at 8.5 log. The digital representation of the same intensity data was projected into physical maps of b) the original intensity data in log scale, c) data with all positive cells coloured in red, and d) the relative density of positive cells.

Supplementary Fig. 11 Analysis of effect of t-CyCIF cycle number on antigenicity in human FFPE tonsil tissue.

Histograms of signal intensity (upper left) and representative images of t-CyCIF from eight sequential sections of human FFPE tonsil tissue across different staining cycles for a) CD3, b) CD4, c) CD8a, d) CD20, and e) FOXP3. The same antibody concentration and exposure time for imaging were used for each cycle (see Supplementary Tables 5, 6 for additional details). The histogram plots were made from single cell data to show the intensity distribution from the different populations from different cycles. For the images shown, the same threshold was used for each marker for visualization and comparison. In e, the black arrow indicates the FOXP3+ population (Scale bars: 50 µm).

Supplementary Fig. 12 Analysis of effect of t-CyCIF cycle number on antigenicity in human FFPE tonsil tissue.

Histograms of signal intensity (upper left) and representative images of t-CyCIF from eight sequential sections of human FFPE tonsil tissue across different staining cycles for a) IBA1, b) CD14, c) CD68, d) CD163, and e) keratin. The same antibody concentration and exposure time for imaging were used in each cycle (see Supplementary Table 5, 6 for details). The histogram plots were made from single cell data to show the intensity distribution from the different populations from different cycles. For the images shown, the same threshold was used for each marker for visualization and comparison. In e, the black arrow indicates the keratin+ population (Scale bars: 50 µm).

Supplementary Fig. 13 H&E images of lung cancer specimens.

Whole slide scans of H&E-stained slides (left) and t-CyCIF images (right) from a) lung adenocarcinoma metastasis to the lymph node (LUNG-1-LN), b) lung squamous cell carcinoma metastasis to the brain (LUNG-2-BR), and c) primary lung squamous cell carcinoma (LUNG-3-PR) (Scale bars: 15 mm in H&E images; 1 mm in t-CyCIF images).

Supplementary Fig. 14 Geographic visualization of t-CyCIF data in LUNG-1-LN and LUNG-2-BR.

a) Montage of t-CyCIF images and b) corresponding dot plot for tumour cells (keratin+, blue), fibroblasts (α-SMA+, green) and immune cells (CD45+ or IBA1+, red) in LUNG-1-LN. c) Representative merged images of t-CyCIF data for α-SMA, keratin, CD45RB, IBA1, CD20, and CD3 from LUNG-1-LN (Scale bars: 50 µm). d) Montage of t-CyCIF images and e) corresponding dot plot for tumour cells (keratin+, blue), fibroblasts (α-SMA+, green) and immune cells (CD45+ or IBA1+, red) in LUNG-2-BR. f) Representative merged images of t-CyCIF data for α-SMA, keratin, CD45RB, IBA1, CD20, and CD3 from LUNG-2-BR (Scale bars: 50 µm).

Supplementary Fig. 15 t-SNE analysis of immune cells from lung cancer samples.

t-SNE plots of immune cell markers CD4, CD8a, FOXP3, IBA1, CD68, CD14, PD-1, PD-L1 and LAG3 from a random sampling of 2,000 immune cells for LUNG-1-LN, LUNG-2-BR and LUNG-3-PR. The staining for each of the indicated markers is mapped by colour (red = high, blue = low).

Supplementary Fig. 16 Low-frequency immune cell types detected and confirmed by t-CyCIF in LUNG-1-LN and LUNG-2-BR.

a) Scatter plots of CD4 and FOXP3 expression in LUNG-1-LN, with CD3 and CD8a expression mapped by colour (red = high, blue = low). 2.31% of the immune cells were CD45+/CD3+/FOXP3+/CD4+/CD8a−, while 0.031% of the immune cells were CD45+/CD3+/FOXP3+/CD4−/CD8a+. b, Representative image of t-CyCIF data for CD4, CD8a and FOXP3 in LUNG-1-LN (Scale bar: 50 µm). c. Scatter plots for CD4 and FOXP3, with CD3 and CD8a mapped as in panel a in LUNG-2-BR. 2.65% of the immune cells were CD45+/CD3+/FOXP3+/CD4+/CD8a− while 0.006% of the immune cells were CD45+/CD3+/FOXP3+/CD4−/CD8a+. d) Merged image of t-CyCIF data for CD4, CD8a and FOXP3 in LUNG-2-BR (Scale bar: 50 µm). e) Scatter plots for LAG3 and PD-1 expression in LUNG-1-LN, with CD3 and CD8a expression mapped as in panel a. 3.6% of immune cells were CD45+/CD3+/PD-1+, 0.87% were CD45+/CD3+/PD-1+/LAG3+ cells, 0.67% were CD45+/CD3+/ PD-1+/LAG3+/ CD4−/CD8a+, and 0.07% were CD45+/CD3+/PD-1+/LAG3+/ CD4+/CD8a−. See Supplementary Table 8 for additional details on these data.

Supplementary Fig. 17 Galleries of images of immune markers expressed in rare cell clusters 1 and 2.

Galleries of images of immune markers (LAG3, PD-1, CD45RB, CD3, PD-L1, CD4, CD45, CD8a, CD163, CD68, CD14, CD11b, FOXP3, IBA1, CD20) and DNA stain for 12 individual rare cells identified in cluster 1 using automated methods that putatively express CD45+/CD45RB+/CD3+/CD8a+/PD-1+ (top) or 11 individual rare cells in cluster 2 that putatively express CD45+/CD45RB+/CD3+/CD4+/PD-1+ (bottom) (Scale bar: 25 µm). Supplementary Table 10 indicates whether visual review of these images by a trained pathologist was deemed to be consistent with the automated calls.

Supplementary Fig. 18 Galleries of images of immune markers expressed in rare cell clusters 3 and 4.

Galleries of images of immune markers (LAG3, PD-1, CD45RB, CD3, PD-L1, CD4, CD45, CD8a, CD163, CD68, CD14, CD11b, FOXP3, IBA1, CD20) and DNA stain for 10 individual rare cells in cluster 3 identified using automated methods that putatively express CD45+/CD45RB+/CD3+/CD8a+/PD-1+/LAG3+ (top), or 10 individual rare cells in cluster 4 that putatively express CD45+/CD45RB+/CD3+/CD8a+/PD-1+/LAG3+/PD-L1+ (bottom) (Scale bar: 25 µm). Supplementary Table 10 indicates whether visual review of these images by a trained pathologist was deemed to be consistent with the automated calls.

Supplementary Fig. 19 Galleries of images of immune markers expressed in rare cell clusters 5, 6, and 7.

Galleries of images of immune markers (LAG3, PD-1, CD45RB, CD3, PD-L1, CD4, CD45, CD8a, CD163, CD68, CD14, CD11b, FOXP3, IBA1, CD20) and DNA stain for 10 individual rare cells in cluster 5 identified using automated methods that putatively express CD45+/CD45RB+/CD3+/CD4+/CD20+/PD-1+ (top), 6 individual rare cells in cluster 6 that putatively express CD45+/CD45RB+/IBA1+/CD163+/CD14+/CD68+/CD11b+/PD-1+/LAG3+/PD-L1+ (middle), or 6 individual rare cells in cluster 7 that putatively express CD45+/CD45RB+/CD3+/PD-1+ (Scale bar: 25 µm). Supplementary Table 10 indicates whether visual review of these images by a trained pathologist was deemed to be consistent with the automated calls.

Supplementary Fig. 20 Galleries of images of immune markers expressed in rare cell clusters 8, 9, 10, and 11.

Galleries of images of immune markers (LAG3, PD-1, CD45RB, CD3, PD-L1, CD4, CD45, CD8a, CD163, CD68, CD14, CD11b, FOXP3, IBA1, CD20) and DNA stain for 6 individual rare cells in cluster 8 identified using automated methods that putatively express CD45+/CD45RB+/CD3+/CD8a+/IBA1+/PD-1+, 6 individual rare cells in cluster 9 that putatively express CD45+/CD45RB+/CD3+/CD8a+/CD20+/PD-1+/LAG3+, 5 individual rare cells in cluster 10 that putatively express CD45+/CD45RB+/IBA1+/CD163+/CD14+/CD68+/PD-1+/LAG3+/PD-L1+, or 5 individual rare cells in cluster 11 that putatively express CD45+/CD45RB+/CD3+/CD8a+/FOXP3+/PD-1+/LAG3+ (Scale bar: 25 µm). Supplementary Table 10 indicates whether visual review of these images by a trained pathologist was deemed to be consistent with the automated calls.

Supplementary Fig. 21 Galleries of images of immune markers expressed in rare cell clusters 12, 13, 14, 15, and 16.

Galleries of images of immune markers (LAG3, PD-1, CD45RB, CD3, PD-L1, CD4, CD45, CD8a, CD163, CD68, CD14, CD11b, FOXP3, IBA1, CD20) and DNA stain for 5 individual rare cells in cluster 12 identified using automated methods that putatively express CD45+/CD45RB+/CD3+/CD4+/CD8a+/PD-1+, 4 individual rare cells in cluster 13 that putatively express CD45+/CD45RB+/IBA1+/CD14+/CD11b+/PD-1+/LAG3+/PD-L1+, 4 individual rare cells in cluster 14 that putatively express CD45+/CD45RB+/CD3+/CD20+/PD-1+, 4 individual rare cells in cluster 15 that putatively express CD45+/CD45RB+/CD3+/IBA1+/CD14+/CD11b+/PD-1+/LAG3+/PD-L1+, or 4 individual rare cells in cluster 16 that putatively express CD45+/CD45RB+/CD3+/CD8a+/CD20+/PD-1+ (Scale bar: 25 µm). Supplementary Table 10 indicates whether visual review of these images by a trained pathologist was deemed to be consistent with the automated calls.

Supplementary Fig. 22 Galleries of images of immune markers expressed in rare cell clusters 17, 18, 19, 20, and 21.

Galleries of images of immune markers (LAG3, PD-1, CD45RB, CD3, PD-L1, CD4, CD45, CD8a, CD163, CD68, CD14, CD11b, FOXP3, IBA1, CD20) and DNA stain for 4 individual rare cells in cluster 17 identified using automated methods that putatively express CD45+/CD45RB+/CD3+/CD8a+/FOXP3+/PD-1+, 4 individual rare cells in cluster 18 that putatively express CD45+/CD45RB+/CD3+/CD4+/IBA1+/CD163+/CD14+/PD-1+, 3 individual rare cells in cluster 19 that putatively express CD45+/CD45RB+/CD3+/CD8a+/CD11b+/PD-1+, 3 individual rare cells in cluster 20 that putatively express CD45+/CD45RB+/CD3+/CD8a+/IBA1+/PD1+/PD-L1+/LAG3+, or 3 individual rare cells in cluster 21 that putatively express CD45+/CD45RB+/CD3+/CD8a+/IBA1+/CD14+/CD68+/CD11b+/PD-1+/PD-L1+/LAG3+ (Scale bar: 25 µm). Supplementary Table 10 indicates whether visual review of these images by a trained pathologist was deemed to be consistent with the automated calls.

Supplementary Fig. 23 Galleries of images of immune markers expressed in rare cell cluster 1.

Image galleries of cells identified by automated systematic cell calling that express CD45, CD45RB, CD3, CD8a, and PD-1 (rare cell cluster 1, Fig. 8) (Scale bar: 25 µm) were reviewed by a trained pathologist who then annotated whether the automated calls were consistent with visual review (notes are shown in the right-most column; visual review confirmation for all rare cell populations is presented in Supplementary Table 10). Pseudo-colour images and merged images of the markers are shown here; monochromatic images for this rare cell cluster are shown in Supplementary Fig. 17.

Supplementary information

Supplementary Information

Supplementary Figs. 1–23 and Supplementary Tables 1–10

Reporting Summary

Supplementary Movie 1

t-CyCIF images from FFPE human tonsil tissue.

Supplementary Movie 2

t-CyCIF images from an FFPE human primary lung cancer resection sample, LUNG-3-PR.

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Du, Z., Lin, JR., Rashid, R. et al. Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging. Nat Protoc 14, 2900–2930 (2019). https://doi.org/10.1038/s41596-019-0206-y

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