1. Cancer Biology
  2. Computational and Systems Biology

Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes

  1. Jia-Ren Lin
  2. Benjamin Izar
  3. Shu Wang
  4. Clarence Yapp
  5. Shaolin Mei
  6. Parin M Shah
  7. Sandro Santagata
  8. Peter K Sorger  Is a corresponding author
  1. Harvard Medical School, United States
  2. Dana-Farber Cancer Institute, United States
  3. Broad Institute of MIT and Harvard, United States
  4. Harvard University, United States
  5. Brigham and Women’s Hospital, Harvard Medical School, United States
https://doi.org/10.7554/eLife.31657
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Cite this article
  1. Jia-Ren Lin
  2. Benjamin Izar
  3. Shu Wang
  4. Clarence Yapp
  5. Shaolin Mei
  6. Parin M Shah
  7. Sandro Santagata
  8. Peter K Sorger
(2018)
Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes
eLife 7:e31657.
https://doi.org/10.7554/eLife.31657
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12 figures, 4 tables and 7 additional files

Figures

Figure 1 with 2 supplements
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Steps in the t-CyCIF process.

(A) Schematic of the cyclic process whereby t-CyCIF images are assembled via multiple rounds of four-color imaging. (B) Image of human tonsil prior to pre-staining and then over the course of three rounds of t-CyCIF. The dashed circle highlights a region with auto-fluorescence in both green and red channels (used for Alexa-488 and Alexa-647, respectively) and corresponds to a strong background signal. With subsequent inactivation and staining cycles (three cycles shown here), this background signal becomes progressively less intense; the phenomenon of decreasing background signal and increasing signal-to-noise ratio as cycle number increases was observed in several staining settings (see also Figure 1—figure supplement 1).

https://doi.org/10.7554/eLife.31657.003
Figure 1—figure supplement 1
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Reduction in background signal intensity with repeated cycles of bleaching.

(A–C) Intensity distributions for three fluorescence channels (FITC/Alexa-488, Cy3/Alexa-555 and Cy5/Alexa-647) prior to pre-bleaching (blue), and after 1, 2 or 3 cycles of bleaching (black, red and green lines, respectively). With increasing number of bleaching cycles, the background signal is reduced 10- to 100-fold.

https://doi.org/10.7554/eLife.31657.004
Figure 1—figure supplement 2
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t-CyCIF using antibodies labelled with Zenon Alexa-555 Fab fragments.

A human tonsil specimen was stained with unconjugated anti-CD11b antibody and then with Alexa-488 conjugated anti-Rabbit secondary antibody (left) or with the same anti-CD11b antibody following incubation with Zenon Alexa-555 (ThermoFischer; right). The Zenon Fab fragments generate non-covalent immune complexes that ‘label’ the primary antibody in a manner that is stable to subsequent processing steps.

https://doi.org/10.7554/eLife.31657.005
Figure 2 with 2 supplements
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Multi-scale imaging of t-CyCIF specimens.

(A) Bright-field H&E image of a metastasectomy specimen that includes a large metastatic melanoma lesion and adjacent benign tissue. The H&E staining was performed after the same specimen had undergone t-CyCIF. (B) Representative t-CyCIF staining of the specimen shown in (A) stitched together using the Ashlar software from 165 successive CyteFinder fields using a 20X/0.8NA objective. (C) One field from (B) at the tumor-normal junction demonstrating staining for S100-postive malignant cells, α-SMA positive stroma, T lymphocytes (positive for CD3, CD4 and CD8), and the proliferation marker phospho-RB (pRB). (D) A melanoma tumor imaged on a GE INCell Analyzer 6000 confocal microscope to demonstrate sub-cellular and sub-organelle structures. This specimen was stained with phospho-Tyrosine (pTyr), Lamin A/C and p-Aurora A/B/C and imaged with a 60X/0.95NA objective. pTyr is localized in membrane in patches associated with receptor-tyrosine kinase, visible here as red punctate structures. Lamin A/C is a nuclear membrane protein that outlines the vicinity of the cell nucleus in this image. Aurora kinases A/B/C coordinate centromere and centrosome function and are visible in this image bound to chromosomes within a nucleus of a mitotic cell in prophase (yellow arrow). (E) Staining of a melanoma sample using the GE OMX Blaze structured illumination microscope with a 60X/1.42NA objective shows heterogeneity of structural proteins of the nucleus, including as Lamin B and Lamin A/C (indicated by yellow arrows) and part of the nuclear pore complex (NUP98) that measures ~120 nm in total size and indirectly allows the visualization of nuclear pores (indicated by non-continuous staining of NUP98). (F) Staining of a patient-derived mouse xenograft breast tumor using the OMX Blaze with a 60x/1.42NA objective shows a spindle in a mitotic cell (beta-tubulin in red) as well as vesicles staining positive for VEGFR2 (in cyan) and punctuate expression of the EGFR in the plasma membrane (in green).

https://doi.org/10.7554/eLife.31657.006
Figure 2—figure supplement 1
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Flat-field and shading correction for stitched images.

A large melanoma specimen (same as shown specimen as in Figure 2A–B) was imaged with a 20X/0.8NA objective on a CyteFinder, and a total 165 frame images were assembled to one image using the ASHLAR algorithm. Representative channels (Hoechst 33342 and S100-Alexa-488) of the stitched images before (left) and after (right) correction for uneven illumination using the BaSiC algorithm (Peng et al., 2017) (see Materials and methods).

https://doi.org/10.7554/eLife.31657.007
Figure 2—figure supplement 2
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OMX super-resolution t-CyCIF images.

(A) Single-channel images for the composite image shown in Figure 2E. This melanoma sample was imaged using the GE OMX Blaze structured illumination microscope with 60X/1.42NA objectives. (B). Single-channel images for the composite image shown in Figure 2F. This breast cancer xenograft sample was also imaged using the OMX Blaze.

https://doi.org/10.7554/eLife.31657.008
Figure 3
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t-CyCIF imaging of normal tissues.

(A) Selected images of a tonsil specimen subjected to 10-cycle t-CyCIF to demonstrate tissue, cellular, and subcellular localization of tissue and immune markers (see Supplementary file 1 for a list of antibodies). (B) Selected cycles from (A) demonstrating sub-nuclear features (Ki67 staining, cycle 1), immune cell distribution (cycle 2), structural proteins (E-Cadherin and Vimentin, cycle 5) and nuclear vs. cytosolic localization of transcription factors (NF-kB, cycle 6). (C) Five-cycle t-CyCIF of human skin to show the tight localization of some auto-fluorescence signals (Cycle 0), the elimination of these signals after pre-staining (Cycle 1), and the dispersal of rare cell types within a complex layered tissue (see Supplementary file 1 for a list of the antibodies).

https://doi.org/10.7554/eLife.31657.010
Figure 4 with 1 supplement
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Efficacy of fluorophore inactivation and preservation of tissue integrity.

(A) Exemplary image of a human tonsil stained with PCNA-Alexa 488 that underwent 0, 15, 30 or 60 min of fluorophore inactivation. (B) Effect of bleaching duration on the distribution of anti-PCNA-Alexa 488 staining intensities for samples used in (A). The distribution is computed from mean values for the fluorescence intensities across all cells in the image that were successfully segmented. The gray band denotes the range of background florescence intensities (below 6.2 in log scale). (C) Effect of bleaching duration on mean intensity for nine antibodies conjugated to Alexa fluor 488, efluor 570 or Alexa fluor 647. Intensities were determined as in (B). The gray band denotes the range of background florescence intensities. (D) Impact of t-CyCIF cycle number on tissue integrity for four exemplary tissue cores. Nuclei present in the first cycle are labeled in red and those present after the 10th cycle are in green. The numbers at the bottom of the images represent nuclear counts in cycle 1 (red) and cycle 10 (green), respectively. (E) Impact of t-CyCIF cycle number on the integrity of a TMA containing 48 biopsies obtained from 16 different healthy and tumor tissues (see Materials and methods for TMA details) stained with 10 rounds of t-CyCIF. The number of nuclei remaining in each core was computed relative to the starting value; small fluctuations in cell count explain values > 1.0 and arise from errors in image segmentation. Data for six different breast cores is shown to the right. (F) Nuclear staining of a melanoma specimen subjected to 20 cycles of t-CyCIF emphasizes the preservation of tissue integrity (22 ± 4%). (G) Selected images of the specimen in (F) from cycles 0, 5, 15 and 20.

https://doi.org/10.7554/eLife.31657.012
Figure 4—source data 1

Mean intensity versus bleach time for multiple antibodies (Figure 4C).

https://doi.org/10.7554/eLife.31657.014
Figure 4—source data 2

Intensity distribution for single cells versus bleach time for one antibody (Figure 4B).

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Figure 4—source data 3

Cell counts dependent on number of staining cycles (Figure 4E).

https://doi.org/10.7554/eLife.31657.016
Figure 4—figure supplement 1
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Impact of bleaching time on fluorophore inactivation.

Intensity distributions for specimens stained with an antibody against Ki67 coupled to the following fluorophores: (A) Alexa-488, (B) Alexa-570 and (C) Alexa-647 prior to bleaching (blue curves) and after 15, 30 or 45 min of bleaching (black, red and green curves respectively). The distributions were calculated from the average fluorescence intensities of single cells as determined after image segmentation.

https://doi.org/10.7554/eLife.31657.013
Figure 5
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Design of a 16-cyle experiment used to assess the reliability of t-CyCIF data.

(A) t-CyCIF experiment involving two immediately adjacent tissue slices cut from the same block of tonsil tissue (Slide A and Slide B). The antibodies used in each cycle are shown (antibodies are described in Supplementary file 2). Highlighted in blue are cycles in which the same antibodies were used on slides A and B at the same time to assess reproducibility. Highlighted in yellow are cycles in which antibodies targeting PCNA, Vimentin and Tubulin were used repeatedly on both slides A and B to assess repeatability. Blue arrows connecting Slides A and B show how antibodies were swapped among cycles. (B) Representative images of Slide A (top panels) and Slide B specimens (bottom panels) after each t-CyCIF cycle. The color coding highlighting specific cycles is the same as in A.

https://doi.org/10.7554/eLife.31657.017
Figure 6 with 1 supplement
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Impact of cycle number on repeatability, reproducibility and strength of t-CyCIF immuno-staining.

(A) Plots on left: comparison of staining intensity for anti-PCNA Alexa 488 (top), anti-vimentin Alexa 555 (middle) and anti-tubulin Alexa 647 (bottom) in cycle 3 vs. 16 and cycle 7 vs. 12 of the 16-cycle t-CyCIF experiment show in Figure 5. Intensity values were integrated across whole cells and the comparison is made on a cell-by-cell basis. Spearman’s correlation coefficients are shown. Plots in middle: intensity distributions at cycles 3 (blue), 7 (yellow), 12 (red) and 16 (green); intensity values were integrated across whole cells to construct the distribution. Box plots to right: estimated dynamic range at four cycle numbers 3, 7, 12, 16. Red lines denote median intensity values (across 56 frames), boxes denote the upper and lower quartiles, whiskers indicate values outside the upper/lower quartile within 1.5 standard deviations, and red dots represent outliers. (B) Representative images showing anti-tubulin Alexa 647 staining at four t-CyCIF cycles; original images are shown on the left (representing the same exposure time and approximately the same illumination) and images scaled by histogram equalization to similar intensity ranges are shown on the right. (C) Image for anti-CD45RO-Alexa 555 at cycles 5 and 15 scaled to similar intensity ranges as described in (B); the dynamic range (DR) of the cycle 15 image is ~3.3 fold lower than that of the Cycle 5 image, but shows similar morphology. (D) Intensity distributions for selected antibodies that were used in different cycles on Slides A and B. Colors denote the degree of concordance between the slides ranging from high (overlap >0.8 in yellow; PCNA), slightly increased or decreased with increasing cycle (overlap 0.6 to 0.8 in light blue or light red; S100 and SMA) or substantially increased or decreased (overlap <0.6 in red or blue; VEGFR2 and CD45RO). (E) Summary of effects of cycle number on antibody staining based on the degree of overlap in intensity distributions (the overlap integral); color coding is the same as in (D). (F) Effect of cycle number and specimen identity on overlap integrals for all antibodies and all cycles assayed. The red line denotes the median intensity value, boxes denote the upper/lower quartiles, and whiskers indicate values outside the upper/lower quartile and within 1.5 standard deviations, and red dots represent outliers. All the numeric data in Figures 5 and 6 are available in a Jupyter notebook; see Code Availability section of Materials and methods for details.

https://doi.org/10.7554/eLife.31657.018
Figure 6—source data 1

Single-cell intensity data used in Figure 6.

https://doi.org/10.7554/eLife.31657.020
Figure 6—figure supplement 1
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Comparison of staining intensities across different cycles at a single-cell level.

Images come from the antibody-swap and repeat experiment showing in Figures 5 and 6. Comparison of single-cell level intensities for (A) PCNA Alexa-488, (B) Vimentin Alexa-555 and (C) Tubulin Alexa-647 stained in four different cycles (cycle 3, 7, 12 and16). For any given cycle pair, single-cell intensities density plots are shown. Spearman’s correlation coefficients (rho) and the mean intensity ratios between two cycles are shown.

https://doi.org/10.7554/eLife.31657.019
Figure 7 with 1 supplement
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t-CyCIF of a large resection specimen from a patient with pancreatic cancer.

(A) H&E staining of pancreatic ductal adenocarcinoma (PDAC) resection specimen that includes portions of cancer and non-malignant pancreatic tissue and small intestine. (B) The entire sample comprising 143 stitched 10X fields of view is shown. Fields that were used for downstream analysis are highlighted by yellow boxes. (C) A representative field of normal intestine across 8 t-CyCIF rounds; see Supplementary file 3 for a list of antibodies. (D) Segmentation data for four antibodies; the color indicates fluorescence intensity (blue = low, red = high). (E) Quantitative single-cell signal intensities of 24 proteins (rows) measured in ~4×103 cells (columns) from panel (C). The Pearson correlation coefficient for each measured protein with E-cadherin (at a single-cell level) is shown numerically. Known dichotomies are evident such as anti-correlated expression of epithelial (E-Cadherin) and mesenchymal (Vimentin) proteins. Proteins highlighted in red are further analyzed in Figure 8.

https://doi.org/10.7554/eLife.31657.021
Figure 7—source data 1

Single-cell intensity data used in Figure 7E.

https://doi.org/10.7554/eLife.31657.023
Figure 7—source data 2

Single-cell intensity data used in Figures 7 and 8.

https://doi.org/10.7554/eLife.31657.024
Figure 7—figure supplement 1
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t-CyCIF for examining large resection specimens of a human pancreatic cancer.

(A) Representative frame of small intestine from the PDAC resection shown in Figure 7 with images for PCNA, beta-catenin, Ki67 and pERk shown. These frames correspond to the segmented panels shown in 7D. (B) Creation of nuclear masks following identification of nuclei. Left panel: Hoechst image from t-CyCIF cycle one on the PDAC resection sample; middle panel: a binarized nuclear mask from cycle 8 (the final cycle in this t-CyCIF experiment); right panel: the overlay image of Hoechst stain (blue) and the cycle eight nuclear mask (yellow). The final cycle is used to create the mask used in this analysis so that the same cells can be tracked through all t-CyCIF cycles despite ~15% overall cell loss by cycle 8. Thus, regions in the overlay that show up in blue correspond in most cases to cells that are lost in the course of t-CyCIF and not to failure to identity and segment cells correctly.

https://doi.org/10.7554/eLife.31657.022
Figure 8
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High-dimensional single-cell analysis of human pancreatic cancer sample with t-CyCIF.

(A) t-SNE plots of cells derived from small intestine (left) or the PDAC region (right) of the specimen shown in Figure 7 with the fluorescence intensities for markers of proliferation (PCNA and Ki67) and signaling (pERK and β-catenin) overlaid on the plots as heat maps. In both tissue types, there exists substantial heterogeneity: circled areas indicate the relationship between pERK and β-catenin levels in cells and represent positive (‘a’), negative (‘b’) or no association (‘c’) between these markers. (B) Representative frames of normal pancreas and pancreatic ductal adenocarcinoma from the 8-cycle t-CyCIF staining of the same resection specimen from Figure 7. (C) t-SNE representation and clustering of single cells from normal pancreatic tissue (red), small intestine (blue) and pancreatic cancer (green). Projected onto the origin of each cell in t-SNE space are intensity measures for selected markers demonstrating distinct staining patterns. (D) Fluorescence intensity distributions for selected markers in small intestine, pancreas and PDAC.

https://doi.org/10.7554/eLife.31657.025
Figure 8—source data 1

Single-cell data in FCS format (Figure 8C–E).

https://doi.org/10.7554/eLife.31657.026
Figure 9 with 1 supplement
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Spatial distribution of immune infiltrates and checkpoint proteins.

(A) Low-magnification image of a clear cell renal cancer subjected to 12-cycle t-CyCIF (see Supplementary file 4 for a list of antibodies). Regions high in α-smooth muscle actin (α-SMA) correspond to stromal components of the tumor, those low in α-SMA represent regions enriched for malignant cells. (B) Representative images from selected t-CyCIF channels are shown. (C) Quantitative assessment of total lymphocytic cell infiltrates (CD3+ cells), CD8+ T lymphocytes, cells expressing PD-1 or its ligand PD-L1 or the VEGFR2 for the entire tumor or for α-SMAhigh and α-SMAlow regions. VEGFR2 is a protein primarily expressed in endothelial cells and is targeted in the treatment of renal cell cancer. The error bars represent the S.E.M. derived from 100 rounds of bootstrapping. (D) Density plot for CD3 and CD8 expression on single cells in the tumor (left) or stromal domains (right). (E) Centroids of CD3+ or CD3+CD8+ cells in blue or dark blue as well as cells staining as SMAhigh or SMAlow (gray and light-gray, respectively) used to define the stromal and tumor regions. (F) Centroids of PD-1+ and PD-L1+ cells are shown in red and green, respectively. (G) Results of a K-nearest neighbor algorithm used to compute areas in which PD-1+ and PD-L1+ cells lie within ~10 µm of each other and with high spatial density (in yellow) and thus, are potentially positioned to interact at a molecular level.

https://doi.org/10.7554/eLife.31657.027
Figure 9—source data 1

Immune cell counts from bootstrapping in tumor and stroma regions (Figure 9C).

https://doi.org/10.7554/eLife.31657.029
Figure 9—source data 2

Single-cell intensity data used in Figure 9.

https://doi.org/10.7554/eLife.31657.030
Figure 9—figure supplement 1
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Spatial analysis of PD-1 and PD-L1 expressing cells.

This figure is relevant to the t-CyCIF study on a clear cell cancer shown in Figure 9. To compare co-localization of PD-1 and PD-L1 in tumor vs stroma, we computed the probability that PD1+ and PDL1+ cells would co-occur within a radius of ~10 um, normalizing for the difference in the total number of PD1+ and PDL1+ cells in the two tissue regions. We interpret the spatial density of PD1+ or PDL1+ cells at each point in space as proportional to the probability of their occurring there (see Figure 9E–F). The co-occurrence density at a point (Figure 9G) is therefore the product of the spatial densities for PD1+ or PDL1 +cells at that point. For simplicity, regions corresponding to tumor and stroma regions were defined by a diagonal line that separated the upper-right and lower-left regions of the tissue; this corresponded closely to α-SMA-low (tumor) and α-SMA-high (tumor) domains (left panel). We found an We found an e^(0.98)=2.7 fold difference in the distributions of co-occurrence densities between the two regions, representing an effect size of 0.94 as measured by Hedge's g; this represents a significant and potentially meaningful fold-change (right panel).

https://doi.org/10.7554/eLife.31657.028
Figure 10 with 1 supplement
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Eight-cycle t-CyCIF of a tissue microarray (TMA) including 13 normal tissues and corresponding tumor types.

The TMA includes normal tissue types, and corresponding high- and low-grade tumors, for a total of 39 specimens (see Supplementary file 3 for antibodies and Supplementary file 5 for specifications of the TMA). (A) Selected images of different tissues illustrating the quality of t-CyCIF images (additional examples shown in Figure 9—figure supplement 1; full data available online at www.cycif.org). (B) t-SNE plot of single-cell intensities of all 39 cores; data were analyzed using the CYT package (see Materials and methods). Tissues of origin and corresponding malignant lesions were labeled as follows: BL, bladder cancer; BR, breast cancer CO, Colorectal adenocarcinoma, KI, clear cell renal cancer, LI, hepatocellular carcinoma, LU, lung adenocarcinoma, LY, lymphoma, OV, high-grade serous adenocarcinoma of the ovary, PA, pancreatic ductal adenocarcinoma, PR, prostate adenocarcinoma, UT, uterine cancer, SK, skin cancer (melanoma), ST, stomach (gastric) cancer. Numbers refer to sample type; ‘1’ to normal tissue, ‘2’ to -grade tumors and ‘3’ to high-grade tumors. (C) Detail from panel B of normal kidney tissue (KI1) a low-grade tumor (KI2) and a high-grade tumor (KI3) (D) Detail from panel B of normal ovary (OV1) low-grade tumor (OV2) and high-grade tumor (OV3). (E) t-SNE plot from Panel B coded to show the distributions of all normal, low-grade and high-grade tumors. (F) tSNE clustering of normal pancreas (PA1) and pancreatic cancers (low-grade, PA2, and high-grade, PA3) and normal stomach (ST1) and gastric cancers (ST2 and ST3, respectively) showing intermingling of high-grade cells.

https://doi.org/10.7554/eLife.31657.031
Figure 10—source data 1

Single-cell intensity data used in Figure 10.

https://doi.org/10.7554/eLife.31657.033
Figure 10—figure supplement 1
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Gallery of exemplary tissues imaged on the TMA described in Figure 10.

Gallery of exemplary tissues imaged on the TMA described in Figure 10.

https://doi.org/10.7554/eLife.31657.032
Figure 11
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Molecular heterogeneity in a single GBM tumor.

(A) Representative low-magnification image of a GBM specimen generated from 221 stitched 10X frames; the sample was subjected to 10 rounds of t-CyCIF using antibodies listed in Supplementary file 6. (B) Magnification of frame 152 (whose position is marked with a white box in panel A) showing staining of pERK, pRB and EGFR; lower panel shows a further magnification to allow single cells to be identified. (C) Normalized Shannon entropy of each of 221 fields of view to determine the extent of variability in signal intensity for 1000 cells randomly selected from that field for each of the antibodies shown. The size of the circles denotes the number of cells in the field and the color represents the value of the normalized Shannon entropy (data are shown only for those fields with more than 1000 cells; see Materials and methods for details).

https://doi.org/10.7554/eLife.31657.034
Figure 11—source data 1

Normalized entropy data shown in Figure 11C.

https://doi.org/10.7554/eLife.31657.035
Figure 11—source data 2

Single-cell intensity data used in Figure 11 and 12.

https://doi.org/10.7554/eLife.31657.036
Figure 12 with 2 supplements
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Spatial distribution of molecular phenotypes in a single GBM.

(A) Clustering of intensity values for 30 antibodies in a 10-cycle t-CyCIF analysis integrated over each whole cell based on images shown in Figure 11. Intensity values were clustered using expected-maximization with Gaussian mixtures (EMGM), yielding eight clusters, of which four clusters accounted for the majority of cells. The intensity scale shows the average level for each intensity feature in that cluster. The number of cells in the cluster is shown as a percentage of all cells in the tumor (bottom of panel). An analogous analysis is shown for 12 clusters in Figure 12—figure supplement 2. (B) EMGM clusters (in color code) mapped back to the positions of individual cells in the tumor. The coordinate system is the same as in Figure 11A. The positions of seven macroscopic regions (R1-R7) representing distinct lobes of the tumor are also shown. (C) Magnified view of Frame 147 from region R5 with EMGM cluster assignment for each cell in the frame; dots represent the centroids of single cells. (D) The proportional representation of EMGM clusters in each tumor region as defined in panel (B).

https://doi.org/10.7554/eLife.31657.037
Figure 12—source data 1

Ratios of EMGM clusters in different regions of a GBM (Figure 12D).

https://doi.org/10.7554/eLife.31657.040
Figure 12—figure supplement 1
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Determination of cluster number for semi-supervised clustering using expectation–maximization Gaussian mixture (EMGM) modeling.

To determine an appropriate number of clusters (k) for analysis of the GBM tumor shown in Figure 12 we determined negative log-likelihood-ratio for various values of k. Due to the large sample size, likelihood-ratio tests were not helpful in choosing k. Thus, for each choice of cluster number n, the likelihood-ratio was calculated for a Gaussian mixture model with n = k-1 and with n = k and the ratio then plotted relative to k. The EMGM algorithm was initialized 30 times for each value of k and it converged in all instances. The inflection at k = 8 (red arrow) suggested that inclusion of additional clusters (k > 8) explains a smaller, distinct source of variation in the data. The plot is shown on a logarithmic scale to better visualize the range of the log-likelihood ratios, and should not be confused with the logarithm already applied to the likelihood ratios themselves.

https://doi.org/10.7554/eLife.31657.038
Figure 12—figure supplement 2
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Spatial distribution of molecular phenotypes in a single GBM.

This analysis is directly analogous to the analysis shown in Figure 11, but uses 12 clusters for EMGM analysis rather than 8. (A) Intensity values from the tumor in Figure 10 were clustered using expected-maximization with Gaussian mixtures (EMGM) with k = 12. The number of cells in each cluster is shown as a percentage of all cells in the tumor. (B) EMGM clusters (in color code) mapped back to singles cells and their positions in the tumor. The coordinate system is the same as in Figure 10. The positions of seven macroscopic regions (R1-R7) representing distinct lobes of the tumour are shown. (C) Magnified view of Frame 147 from region R5 with EMGM cluster assignment for each cell in the frame shown as a dot. (D) The proportional representation of EMGM clusters in each tumor region as defined in Panel B.

https://doi.org/10.7554/eLife.31657.039

Tables

Table 1
Microscopes used in this study and their properties.
https://doi.org/10.7554/eLife.31657.009
InstrumentTypeObjectiveField of viewNominal
Resolution*
RareCyte CytefinderSlide Scanner10X/0.3 NA1.6 × 1.4 mm1.06 µm
20X/0.8NA0.8 × 0.7 mm0.40 µm
40X/0.6 NA0.42 × 0.35 mm0.53 µm
GE INCell Analyzer 6000Confocal60X/0.95 NA0.22 × 0.22 mm0.21 µm
GE OMX BlazeStructured
Illumination Microscope		60 × 1.42 NA0.08 × 0.08 mm0.11 µm

  1. *Except in the case of the OMX Blaze, nominal resolution was calculated using the formula (r) = 0.61λ/NA for widefield and (r) = 0.4λ/NA for confocal microscopy with λ = 520 nm. Actual resolution depends on optical properties and thickness of sample, alignment and quality of the optical components in the light path. For structured illumination microscopy, actual resolution depends on accurate matching of immersion oil refractive index with sample in the Cy3 channel and use of an optimal point spread function during reconstruction process. The resolution in other channels will be sub-nominal.

Table 2
List of antibodies tested and validated for t-CyCIF.
https://doi.org/10.7554/eLife.31657.011
Antibody nameTarget proteinPerformanceVendorCatalog no.CloneFluorophoreResearch resource
Identifier
Bax-488Bax*BioLegend6336032D2Alexa Fluor 488AB_2562171
CD11b-488CD11b*AbcamAB204271EPR1344Alexa Fluor 488
CD4-488CD4*R and D SystemsFAB8165GPolyclonalAlexa Fluor 488
CD8a-488CD8*eBioscience53-0008-80AMC908Alexa Fluor 488AB_2574412
cJUN-488cJUN*AbcamAB193780E254Alexa Fluor 488
CK18-488Cytokeratin 18*eBioscience53-9815-80LDK18Alexa Fluor 488AB_2574480
CK8-FITCCytokeratin 8*eBioscience11-9938-80LP3KFITCAB_10548518
CycD1-488CycD1*AbcamAB190194EPR2241Alexa Fluor 488
Ecad-488E-Cadherin*CST319924E10Alexa Fluor 488AB_10691457
EGFR-488EGFR*CST5616D38B1Alexa Fluor 488AB_10691853
EpCAM-488EpCAM*CST5198VU1D9Alexa Fluor 488AB_10692105
HES1-488HES1*AbcamAB196328EPR4226Alexa Fluor 488
Ki67-488Ki67*CST11882D3B5Alexa Fluor 488AB_2687824
LaminA/C-488Lamin A/C*CST86174C11Alexa Fluor 488AB_10997529
LaminB1-488Lamin B1*AbcamAB194106EPR8985(B)Alexa Fluor 488
mCD3E-FITCms_CD3E*BioLegend100306145–2 C11FITCAB_312671
mCD4-488ms_CD4*BioLegend100532RM4-5Alexa Fluor 488AB_493373
MET-488c-MET*CST8494D1C2Alexa Fluor 488AB_10999405
mF4/80-488ms_F4/80*BioLegend123120BM8Alexa Fluor 488AB_893479
MITF-488MITF*AbcamAB201675D5Alexa Fluor 488
Ncad-488N-Cadherin*BioLegend3508098C11Alexa Fluor 488AB_11218797
p53-488p53*CST54297F5Alexa Fluor 488AB_10695458
PCNA-488PCNA*CST8580PC10Alexa Fluor 488AB_11178664
PD1-488PD1*CST15131D3W4UAlexa Fluor 488
PDI-488PDI*CST5051C81H6Alexa Fluor 488AB_10950503
pERK-488pERK(T202/Y204)*CST4344D13.14.4EAlexa Fluor 488AB_10695876
pNDG1-488pNDG1(T346)*CST6992D98G11Alexa Fluor 488AB_10827648
POL2A-488POL2A*Novus BiologicalsNB200-598AF4884H8Alexa Fluor 488AB_2167465
pS6(S240/244)−488pS6(240/244)*CST5018D68F8Alexa Fluor 488AB_10695861
S100a-488S100alpha*AbcamAB207367EPR5251Alexa Fluor 488
SQSTM1-488SQSTM1/p62*CST8833D1D9E3Alexa Fluor 488
STAT3-488STAT3*CST14047B3Z2GAlexa Fluor 488
Survivin-488Survivin*CST281071G4B7Alexa Fluor 488AB_10691462
Catenin-488β-Catenin*CST2849L54E2Alexa Fluor 488AB_10693296
Actin-555Actin*CST804613E5Alexa Fluor 555AB_11179208
CD11c-570CD11c*eBioscience41-9761-80118/A5eFluor 570AB_2573632
CD3D-555CD3D*AbcamAB208514EP4426Alexa Fluor 555
CD4-570CD4*eBioscience41-2444-80N1UG0eFluor 570AB_2573601
CD45-PECD45*R and D SystemsFAB1430P-1002D1PEAB_2237898
CK7-555Cytokeratin 7*AbcamAB209601EPR17078Alexa Fluor 555
cMYC-555cMYC*AbcamAB201780Y69Alexa Fluor 555
E2F1-555E2F1*AbcamAB208078EPR3818(3)Alexa Fluor 555
Ecad-555E-Cadherin*CST429524E10Alexa Fluor 555
EpCAM-PEEpCAM*BioLegend3242059C4PEAB_756079
FOXO1a-555FOXO1a*AbcamAB207244EP927YAlexa Fluor 555
FOXP3-570FOXP3*eBioscience41-4777-80236A/E7eFluor 570AB_2573608
GFAP-570GFAP*eBioscience41-9892-80GA5eFluor 570AB_2573655
HSP90-PEHSP90b*AbcamAB115641PolyclonalPEAB_10936222
KAP1-594KAP1*BioLegend61930420A1Alexa Fluor 594AB_2563298
Keratin-555pan-Keratin*CST3478C11Alexa Fluor 555AB_10829040
Keratin-570pan-Keratin*eBioscience41-9003-80AE1/AE3eFluor 570AB_11217482
Ki67-570Ki67*eBioscience41-5699-8020Raj1eFluor 570AB_11220088
LC3-555LC3*CST13173D3U4CAlexa Fluor 555
MAP2-570MAP2*eBioscience41-9763-80AP20eFluor 570AB_2573634
pAUR-555pAUR1/2/3(T288/T2*CST13464D13A11Alexa Fluor 555
pCHK2-PEpChk2(T68)*CST12812C13C1PE
PDL1-555PD-L1/CD274*AbcamAB21335828–8Alexa Fluor 555
pH3-555pH3(S10)*CST3475D2C8Alexa Fluor 555AB_10694639
pRB-555pRB(S807/811)*CST8957D20B12Alexa Fluor 555
pS6(235/236)–555pS6(235/236)*CST3985D57.2.2EAlexa Fluor 555AB_10693792
pSRC-PEpSRC(Y418)*eBioscience12-9034-41SC1T2M3PEAB_2572680
S6-555S6*CST698954D2Alexa Fluor 555AB_10828226
SQSTM1-555SQSTM1/p62*AbcamAB203430EPR4844Alexa Fluor 555
VEGFR2-555VEGFR2*CST12872D5B1Alexa Fluor 555
VEGFR2-PEVEGFR2*CST12634D5B1PE
Vimentin-555Vimentin*CST9855D21H3Alexa Fluor 555AB_10859896
Vinculin-570Vinculin*eBioscience41-9777-807F9eFluor 570AB_2573646
gH2ax-PEgH2ax*BioLegend6134122F3PEAB_2616871
AKT-647AKT*CST5186C67E7Alexa Fluor 647AB_10695877
aSMA-660aSMA*eBioscience50-9760-801A4eFluor 660AB_2574361
B220-647CD45R/B220*BioLegend103226RA3-6B2Alexa Fluor 647AB_389330
Bcl2-647Bcl2*BioLegend658705100Alexa Fluor 647AB_2563279
Catenin-647Beta-Catenin*CST4627L54E2Alexa Fluor 647AB_10691326
CD20-660CD20*eBioscience50-0202-80L26eFluor 660AB_11151691
CD45-647CD45*BioLegend304020HI30Alexa Fluor 647AB_493034
CD8a-660CD8*eBioscience50-0008-80AMC908eFluor 660AB_2574148
CK5-647Cytokeratin 5*AbcamAB193895EP1601YAlexa Fluor 647
CoIIV-647Collagen IV*eBioscience51-9871-801042Alexa Fluor 647AB_10854267
COXIV-647COXIV*CST75613E11Alexa Fluor 647AB_10994876
cPARP-647cPARP*CST6987D64E10Alexa Fluor 647AB_10858215
FOXA2-660FOXA2*eBioscience50-4778-823C10eFluor 660AB_2574221
FOXP3-647FOXP3*BioLegend320113206DAlexa Fluor 647AB_439753
gH2ax-647H2ax(S139)*CST972020E3Alexa Fluor 647AB_10692910
gH2ax-647H2ax(S139)*BioLegend6134072F3Alexa Fluor 647AB_2114994
HES1-647HES1*AbcamAB196577EPR4226Alexa Fluor 647
Ki67-647Ki67*CST12075D3B5Alexa Fluor 647
Ki67-647Ki67*BioLegend350509Ki-67Alexa Fluor 647AB_10900810
mCD45-647ms_CD45*BioLegend10312430-F11Alexa Fluor 647AB_493533
mCD4-647ms_CD4*BioLegend100426GK1.5Alexa Fluor 647AB_493519
mEPCAM-647ms_EPCAM*BioLegend118211G8.8Alexa Fluor 647AB_1134104
MHCI-647MHCI/HLAA*AbcamAB199837EP1395YAlexa Fluor 647
MHCII-647MHCII*AbcamAB201347EPR11226Alexa Fluor 647
mLy6C-647ms_Ly6C*BioLegend128009HK1.4Alexa Fluor 647AB_1236551
mTOR-647mTOR*CST50487C10Alexa Fluor 647AB_10828101
NFkB-647NFkB (p65)*AbcamAB190589E379Alexa Fluor 647
NGFR-647NGFR/CD271*AbcamAB195180EP1039YAlexa Fluor 647
NUP98-647NUP98*CST13393C39A3Alexa Fluor 647
p21-647p21*CST858712D1Alexa Fluor 647AB_10892861
p27-647p27*AbcamAB194234Y236Alexa Fluor 647
pATM-660pATM(S1981)*eBioscience50-9046-4110H11.E12eFluor 660AB_2574312
PAX8-647PAX8*AbcamAB215953EPR18715Alexa Fluor 647
PDL1-647PD-L1/CD274*CST15005E1L3NAlexa Fluor 647
pMK2-647pMK2(T334)*CST432027B7Alexa Fluor 647AB_10695401
pmTOR-660pmTOR(S2448)*eBioscience50-9718-41MRRBYeFluor 660AB_2574351
pS6_235–647pS6(S235/S236)*CST4851D57.2.2EAlexa Fluor 647AB_10695457
pSTAT3-647pSTAT3(Y705)*CST4324D3A7Alexa Fluor 647AB_10694637
pTyr-647p-Tyrosine*CST9415p-Tyr-100Alexa Fluor 647AB_10693160
S100A4-647S100A4*AbcamAB196168EPR2761(2)Alexa Fluor 647
Survivin-647Survivin*CST286671G4B7Alexa Fluor 647AB_10698609
TUBB3-647TUBB3*BioLegend657405AA10Alexa Fluor 647AB_2563609
Tubulin-647beta-Tubulin*CST36249F3Alexa Fluor 647AB_10694204
Vimentin-647Vimentin*BioLegend677807O91D3Alexa Fluor 647AB_2616801
anti-14-3-314-3-3*Santa CruzSC-629-GPolyclonalN/DAB_630820
anti-53BP153BP1*BethylA303-906APolyclonalN/DAB_2620256
anti-5HMC5HMC*Active Motif39769PolyclonalN/DAB_10013602
anti-CD11bCD11b*AbcamAB133357EPR1344N/DAB_2650514
anti-CD2CD2*AbcamAB37212PolyclonalN/DAB_726228
anti-CD20CD20*DakoM0755L26N/DAB_2282030
anti-CD3CD3*DakoA0452PolyclonalN/DAB_2335677
anti-CD4CD4*DakoM73104B12N/D
anti-CD45ROCD45RO*DakoM0742UCHL1N/DAB_2237910
anti-CD8CD8*DakoM7103C8/144BN/DAB_2075537
anti-CycA2CycA2*AbcamAB38E23.1N/DAB_304084
anti-ET1ET-1*AbcamAB2786TR.ET.48.5N/DAB_303299
anti-FAPFAP*eBioscienceBMS168F11-24N/DAB_10597443
anti-FOXP3FOXP3*BioLegend320102206DN/DAB_430881
anti-LAMP2LAMP2*AbcamAB25631H4B4N/DAB_470709
anti-MCM6MCM6*Santa CruzSC-9843PolyclonalN/DAB_2142543
anti-PAX8PAX8*AbcamAB191870EPR18715N/D
anti-PD1PD1*CST86163D4W2JN/D
anti-pEGFRpEGFR(Y1068)*CST3777D7A5N/DAB_2096270
anti-pERKpERK(T202/Y204)*CST4370D13.14.4EN/DAB_2315112
anti-pRBpRB(S807/811)*Santa CruzSC-16670PolyclonalN/DAB_655250
anti-pRPA32pRPA32 (S4/S8)*BethylIHC-00422PolyclonalN/DAB_1659840
anti-pSTAT3pSTAT3**CST9145D3A7N/DAB_2491009
anti-pTyrpTyr*CST9411p-Tyr-100N/DAB_331228
anti-RPA32RPA32*BethylIHC-00417PolyclonalN/DAB_1659838
anti-TPCN2TPCN2*NOVUSBIONBP1-86923PolyclonalN/DAB_11021735
anti-VEGFR1VEGFR1/FLT1*Santa CruzSC-31173PolyclonalN/DAB_2106885
Abeta-488Beta-Amyloid (1-16)†BioLegend8030136E10Alexa Fluor 488AB_2564765
BRAF-FITCB-RAF†Abcamab175637K21-FFITC
BrdU-488BrdU†BioLegend3641053D4Alexa Fluor 488AB_2564499
cCasp3-488cCasp3†R and D SystemsIC835G-025269518Alexa Fluor 488
CD11b-488CD11b†BioLegend101219M1/70Alexa Fluor 488AB_493545
CD123-488CD123†BioLegend3060356H6Alexa Fluor 488AB_2629569
CD49b-FITCCD49b†BioLegend359305P1E6-C5FITCAB_2562530
CD69-FITCCD69†BioLegend310904FN50FITCAB_314839
CD71-FITCCD71†BioLegend334103CY1G4FITCAB_1236432
CD80-FITCCD80†R and D SystemsFAB140F37711FITCAB_357027
CD8a-488CD8a†eBioscience53-0086-41OKT8Alexa Fluor 488AB_10547060
CDC2-FITCCDC2/p34†Santa CruzSC-54 FITC17FITCAB_627224
CycB1-FITCCycB1†Santa CruzSC-752 FITCPolyclonalFITCAB_2072134
FN-488Fibronection†AbcamAB198933F1Alexa Fluor 488
IFNG-488Interferron-Gamma†BioLegend5025174S.B3Alexa Fluor 488AB_493030
IL1-FITCIL1†BioLegend511705H1b-98FITCAB_1236434
IL6-FITCIL6†BioLegend501103MQ2-13A5FITCAB_315151
mCD31-FITCms_CD31†eBioscience11-0311-82390FITCAB_465012
mCD8a-488ms_CD8a†BioLegend10072653–6.7Alexa Fluor 488AB_493423
Nestin-488Nestin†eBioscience53-9843-8010C2Alexa Fluor 488AB_1834347
NeuN-488NeuN†MilliporeMAB377XA60Alexa Fluor 488AB_2149209
PR-488PR/PGR†AbcamAB199224YR85Alexa Fluor 488
Snail1-488Snail1†eBioscience53-9859-8020C8Alexa Fluor 488AB_2574482
TGFB-FITCTGFB1†BioLegend349605TW4-2F8FITCAB_10679043
TNFa-488TNFa†BioLegend502917MAb11Alexa Fluor 488AB_493122
AR-555AR†CST8956D6F11Alexa Fluor 555AB_11129223
CD11a-PECD11a†BioLegend301207HI111PEAB_314145
CD11b-555CD11b†AbcamAB206616EPR1344Alexa Fluor 555
CD131-PECD131†BD559920JORO50PEAB_397374
CD14-PECD14†eBioscience12–014961D3PEAB_10597598
CD1a-PECD1a†BioLegend300105HI149PEAB_314019
CD1c-PECD1c†BioLegend331505L161PEAB_1089000
CD20-PECD20†BioLegend3023052H7PEAB_314253
CD23-PECD23†eBioscience12-0232-81B3B4PEAB_465592
CD31-PECD31†eBioscience12-0319-41WM-59PEAB_10670623
CD31-PECD31†R and D SystemsFAB3567P-0259G11PEAB_2279388
CD34-PECD34†AbcamAB30377QBEND/10PEAB_726407
CD45R-e570CD45R/B220†eBioscience41-0452-80RA3-6B2eFluor 570AB_2573598
CD71-PECD71†eBioscience12-0711-81R17217PEAB_465739
CD86-PECD86†BioLegend305405IT2.2PEAB_314525
CK19-570Cytokeratin 19†eBioscience41-9898-80BA17eFluor 570AB_11218678
HER2-570HER2†eBioscience41-9757-80MJD2eFluor 570AB_2573628
IL3-PEIL3†BD554383MP2-8F8PEAB_395358
NFATc1-PENFATc1†BioLegend6496057A6PEAB_2562546
PDL1-PEPD-L1/CD274†BioLegend32970529E.2A3PEAB_940366
pMAPK (T202/Y204)pERK1/2(T202/Y20†CST14095197G2PE
pMAPK (Y204/Y187)pERK1/2(Y204/Y18†CST75165D1H6GPE
pSTAT1-PEpSTAT1(Y705)†BioLegend686403A15158BPEAB_2616938
ABCC1-647ABCC1†BioLegend370203QCRL-2Alexa Fluor 647AB_2566664
AnnexinV-674N/D†BioLegend640911NAAlexa Fluor 647AB_2561293
CD103-647CD103†BioLegend350209Ber-ACT8Alexa Fluor 647AB_10640870
CD25-647CD25†BioLegend302617BC96Alexa Fluor 647AB_493046
CD31-APCCD31†eBioscience17-0319-41WM-59APCAB_10853188
CD68-APCCD68†BioLegend333809Y1/82AAPCAB_10567107
CD8a-647CD8a†BioLegend344725SK1Alexa Fluor 647AB_2563451
CD8a-647CD8a†R and D SystemsFAB1509R-02537006Alexa Fluor 647
CycE-660CycE†eBioscience50-9714-80HE12eFluor 660AB_2574350
HIF1-647HIF1†BioLegend359705546–16Alexa Fluor 647AB_2563331
HP1-647HP1†AbcamAB198391EPR5777Alexa Fluor 647
mCD123-APCms_CD123†eBioscience17-1231-815B11APCAB_891363
NGFR-647NGFR/CD271†BD560326C40-1457Alexa Fluor 647AB_1645403
pBTK-660pBTK(Y551/Y511)†eBioscience50-9015-80M4G3LNeFluor 660AB_2574306
PD1-647PD1†AbcamAB201825EPR4877 (2)Alexa Fluor 647
PR-660PR/PGR†eBioscience50-9764-80KMC912eFluor 660AB_2574363
RUNX3-660RUNX3†eBioscience50-9817-80R3-5G4eFluor 660AB_2574383
SOX2-647SOX2†AbcamAB192075PolyclonalAlexa Fluor 647
anti-53BP153BP1†MilliporeMAB3802BP13N/DAB_2206767
anti-AxlAxl†R and DAF154PolyclonalN/DAB_354852
anti-CD11bCD11b†AbcamAB52478EP1345YN/DAB_868788
anti-CD8aCD8†eBioscience14-0085-80C8/144BN/DAB_11151339
anti-CEP170CEP170†AbcamAB72505PolyclonalN/DAB_1268101
anti-cMYCcMYC†BioLegend6268019E10N/DAB_2235686
anti-CPS1CPS1†AbcamAB129076EPR7493-3N/DAB_11156290
anti-E2F1E2F1†ThermoFisherMS-879-P1KH95N/DAB_143934
anti-eEF2KeEF2K†Santa CruzSC-21642K-19N/DAB_640043
anti-Emil1Emil1†AbcamAB212397EMIL/1176N/D
anti-FKHRL1FKHRL1†Santa CruzSC-9812PolyclonalN/DAB_640608
anti-FLAGFLAG†SigmaF1804M2N/DAB_262044
anti-GranBGranzyme_B†DakoM7235M7235N/DAB_2114697
anti-HMB45HMB45†AbcamAB732HMB45 + M2- 7C10 + M2-
9E3		N/DAB_305844
anti-HSP90bHSP90b†Santa CruzSC-1057D-19N/DAB_2121392
anti-IL2RaIL2Ra†AbcamAB128955EPR6452N/DAB_11141054
anti-LAMP2LAMP2†R and DAF6228PolyclonalN/DAB_10971818
anti-MITFMITF†AbcamAB12039C5N/DAB_298801
anti-NcadN-Cadherin†AbcamAB18203PolyclonalN/DAB_444317
anti-NCAMNCAM†AbcamAB6123ERIC-1N/DAB_2149537
anti-NF1NF1†AbcamAB178323McNFn27bN/D
anti-pCTDPol II CTD(S2)†Active Motif610833E10N/DAB_2687450
anti-PD1PD1†CST43248EH33N/D
anti-pTuberinpTuberin(S664)†AbcamAB133465EPR8202N/DAB_11157389
anti-S100S100†DakoZ0311PolyclonalN/DAB_10013383
anti-SIRT3SIRT3†CST2627C73E3N/DAB_2188622
anti-TIA1TIA1†Santa CruzSC-1751PolyclonalN/DAB_2201433
anti-TLR3TLR3†Santa CruzSC-8691PolyclonalN/DAB_2240700
anti-TNFaTNFa†AbcamAB11564MP6-XT3N/DAB_298170
anti-TPCN2TPCN2†AbcamAB119915PolyclonalN/DAB_10903692
CD11a-FITCCD11a‡eBioscience11-0119-41HI111FITCAB_10597888
CD20-FITCCD20‡BioLegend3023032H7FITCAB_314251
CD2-FITCCD2‡BioLegend300206RPA-2.10FITCAB_314030
CD45RO-488CD45RO‡BioLegend304212UCHL1Alexa Fluor 488AB_528823
CD8a-488CD8‡BioLegend301024RPA-T8Alexa Fluor 488AB_2561282
cJUN-FITCcJUN‡Santa CruzSC-1694 FITCPolyclonalFITCAB_631263
CXCR5-FITCCXCR5‡BioLegend356913J252D4FITCAB_2561895
Ecad-FITCEcad‡BioLegend32410367A4FITCAB_756065
FOXP3-488FOXP3‡BioLegend320011150DAlexa Fluor 488AB_439747
MITF-488MITF‡Novus BiologicalsNB100-56561AF48821D1418Alexa Fluor 488AB_838580
NCAM-488NCAM/CD56‡AbcamAB200333EPR2566Alexa Fluor 488
NCAM-FITCNCAM/CD56‡ThermoFisher11-0566-41TULY56FITCAB_2572458
NGFR-FITCNGFR/CD271‡BioLegend345103ME20.4FITCAB_1937226
PD1-488PD-1‡BioLegend367407NAT105Alexa Fluor 488AB_2566677
PD1-488PD-1‡BioLegend329935EH12.2H7Alexa Fluor 488AB_2563593
pERK-488pERK(T202/Y204)‡CST4374E10Alexa Fluor 488AB_10705598
pERK-488pERK(T202/Y204)‡CST4780137F5Alexa Fluor 488AB_10705598
S100A4-FITCS100A4‡BioLegend370007NJ-4F3-D1FITCAB_2572073
SOX2-488SOX2‡BioLegend65610914A6A34Alexa Fluor 488AB_2563956
CD133-PECD133‡eBioscience12-1338-41TMP4PEAB_1582258
cMyc-TRITCcMYC‡Santa CruzSC-40 TRITC9E10TRITCAB_627268
cPARP-555cPARP‡CST6894D64E10Alexa Fluor 555AB_10830735
CTLA4-PECTLA4‡BioLegend369603BNI3PEAB_2566796
GATA3-594GATA3‡BioLegend65381616E10A23Alexa Fluor 594AB_2563353
GFAP-Cy3GFAP‡MilliporeMAB3402C3NACy3AB_11213580
Oct4-555OCT_4‡CST4439C30A3Alexa Fluor 555AB_10922586
p21-555p21‡CST849312D1Alexa Fluor 555AB_10860074
PD1-PEPD1‡BioLegend329905EH12.2H7PEAB_940481
PDGFRb-555PDGFRb‡AbcamAB206874Y92Alexa Fluor 555
pSTAT1-555pSTAT1‡CST818358D6Alexa Fluor 555AB_10860600
TIM1-PETIM1‡BioLegend3539031D12PEAB_11125165
cCasp3-647cCasp3‡CST9602D3E9Alexa Fluor 647AB_2687881
CD103-APCCD103‡eBioscience17-1038-41B-Ly7APCAB_10669816
CD3-647CD3‡BioLegend300422UCHT1Alexa Fluor 647AB_493092
CD3-660CD3‡eBioscience50-0037-41OKT3eFluor 660AB_2574150
CD3-APCCD3‡eBioscience17-0038-41UCHT1APCAB_10804761
CD45RO-APCCD45RO‡BioLegend304210UCHL1APCAB_314426
ER-647ER‡AbcamAB205851EPR4097Alexa Fluor 647
FOXO3a-647FOXO3a‡AbcamAB196539EP1949YAlexa Fluor 647
GZMA-e660Granzyme A‡ThermoFisher50-9177-41CB9eFluor 660AB_2574330
GZMB-647Granzyme_B‡BioLegend515405GB11Alexa Fluor 647AB_2294995
GZMB-APCGranzyme_B‡R and D SystemsIC29051A356412APCAB_894691
HER2-647HER2‡BioLegend32441224D2Alexa Fluor 647AB_2262300
mCD49b-647ms_CD49b‡BioLegend103511HMα2Alexa Fluor 647AB_528830
NCAM-647NCAM/CD56‡BioLegend3625135.1H11Alexa Fluor 647AB_2564086
NCAM-e660NCAM/CD56‡ThermoFisher50-0565-805tukon56eFluor 660AB_2574160
pAKT-647pAKT‡CST4075D9EAlexa Fluor 647AB_10691856
pERK-647pERK (T202/Y204)‡CST4375E10Alexa Fluor 647AB_10706777
pERK-647pERK (T202/Y204)‡BioLegend3695036B8B69Alexa Fluor 647AB_2571895
pIKBa-660pIKBa‡eBioscience50-9035-41RILYB3ReFluor 660AB_2574310
YAP-647YAP‡CST38707SD8H1XAlexa Fluor 647
anit-FANCD2FANCD2‡BethylIHC-00624PolyclonalN/DAB_10752755
anit-pcJUNp-cJUN‡Santa CruzSC-822KM-1N/DAB_627262
anti-AXLAXL‡CST8661C89E7N/DAB_11217435
anti-CXCR5CXCR5‡GeneTexGTX100351PolyclonalN/DAB_1240668
anti-CXCR5CXCR5‡R and DMAB-190-SP51505N/DAB_2292654
anti-FOXO3aFOXO3a‡CST249775D8N/DAB_836876
anti-GZMBGranzyme B‡AbcamAB4059PolyclonalN/DAB_304251
anti-PD1PD-1‡AbcamAB63477PolyclonalN/DAB_2159165
anti-PD1PD-1‡ThermoFisher14-9985-81J43N/DAB_468663
anti-PD1PD-1‡R and DAF1021PolyclonalN/DAB_354541
anti-RFPRFP‡ThermoFisherR10367PolyclonalN/DAB_2315269
CD11C-BV570CD11C‡BioLegend117331N418BV570AB_10900261
CD45-BV785CD45‡BioLegend304047HI30BV785AB_2563128
LY6G-BV570LY6G‡BioLegend1276291A8BV570AB_10899738

  1. *Show positive/correct signals in multiple samples/tissues.

    †Show positive/correct signals in some but not all samples tested.

  2. ‡Show no signal or incorrect signals in most samples tested.

Key resources table
Reagent type
(species) or resource		DesignationSource or referenceIdentifiersAdditional information
Biological sample
(human tissue specimen)		TMA:TMA-1207Protein BiotechnologiesCat: TMA-1207http://www.proteinbiotechnologies.com/pdf/TMA-1207.pdf
Biological sample
(human tissue specimen)		TMA:MTU481BiomaxCat: MTU-481https://www.biomax.us/tissue-arrays/Multiple_Organ/MTU481
AntibodyAlexa-488 anti-Rabbit
antibodies (Fab)		ThermoFisher ScientificCat: A-11034
(RRID:AB_2576217)		Dilution 1:2000
AntibodyAlexa-555 anti-Rat
antibodies		ThermoFisher ScientificCat: A-21434
(RRID:AB_141733)		Dilution 1:2000
AntibodyAlexa-647 anti-Mouse
antibodies (Fab)		ThermoFisher ScientificCat: A-21236
(RRID:AB_141725)		Dilution 1:2000
Chemical compound,
drug		Hoechst 33342ThermoFisher ScientificCat: H3570https://www.thermofisher.com/order/catalog/product/H3570
Software, algorithmImageJPMID:22930834RRID: SCR_003070https://imagej.nih.gov/ij/
Software, algorithmMatlabMathWorks, Inc.RRID:SCR_001622
Software, algorithmAshlarLaboratory of Systems
Pharmacology, Harvard
Medical School		RRID:SCR_016266https://github.com/sorgerlab/ashlar (copy archived at
https://github.com/elifesciences-publications/ashlar)
Software, algorithmBaSiCHelmholtz Zentrum
München		RRID: SCR_016371https://www.nature.com/articles/ncomms14836
Otherwww.cycif.orgLaboratory of Systems
Pharmacology, Harvard
Medical School		RRID:SCR_016267Online resource for
cyclic immunofluorescence
Otherlincs.hms.harvard.eduHMS LINCS CenterRRID:SCR_016370Additional data/image
resource for t-CyCIF
Table 3
Breakdown of individual steps performed for dewaxing and antigen retrieval on a Leica BOND.
https://doi.org/10.7554/eLife.31657.041
StepReagentSupplierIncubation (min)Temp. (°C)
1*No ReagentN/D3060
2BOND Dewax SolutionLeica060
3BOND Dewax SolutionLeica0R.T.
4BOND Dewax SolutionLeica0R.T.
5200 proof ethanolUser*0R.T.
6200 proof ethanolUser*0R.T.
7200 proof ethanolUser*0R.T.
8Bond Wash SolutionLeica0R.T.
9Bond Wash SolutionLeica0R.T.
10Bond Wash SolutionLeica0R.T.
11Bond ER1 solutionLeica099
12Bond ER1 solutionLeica099
13Bond ER1 solutionLeica2099
14Bond ER1 solutionLeica0R.T.
15Bond Wash SolutionLeica0R.T.
16Bond Wash SolutionLeica0R.T.
17Bond Wash SolutionLeica0R.T.
18Bond Wash SolutionLeica0R.T.
19Bond Wash SolutionLeica0R.T.
20IF BlockUser*30R.T.
21Antibody MixUser*60R.T.
22Bond Wash SolutionLeica0R.T.
23Bond Wash SolutionLeica0R.T.
24Bond Wash SolutionLeica0R.T.
25Hoechst SolutionUser*30R.T.
26Bond Wash SolutionLeica0R.T.
27Bond Wash SolutionLeica0R.T.
28Bond Wash SolutionLeica0R.T.

Additional files

Supplementary file 1

List of antibodies used for staining in Figure 3.

https://doi.org/10.7554/eLife.31657.042
Supplementary file 2

List of antibodies used for staining in Figures 5 and 6.

https://doi.org/10.7554/eLife.31657.043
Supplementary file 3

List of antibodies used for staining in Figures 7, 8 and 10.

https://doi.org/10.7554/eLife.31657.044
Supplementary file 4

List of antibodies used for staining in Figure 9.

https://doi.org/10.7554/eLife.31657.045
Supplementary file 5

Descriptions of TMA shown in Figure 10.

https://doi.org/10.7554/eLife.31657.046
Supplementary file 6

List of antibodies used for staining in Figures 11 and 12.

https://doi.org/10.7554/eLife.31657.047
Transparent reporting form
https://doi.org/10.7554/eLife.31657.048

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  1. Jia-Ren Lin
  2. Benjamin Izar
  3. Shu Wang
  4. Clarence Yapp
  5. Shaolin Mei
  6. Parin M Shah
  7. Sandro Santagata
  8. Peter K Sorger
(2018)
Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes
eLife 7:e31657.
https://doi.org/10.7554/eLife.31657