Single-molecule localization microscopy reveals the molecular organization of endogenous membrane receptors
Data files
Dec 05, 2025 version files 160.77 GB
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fig_S1.zip
31.44 GB
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fig_S10.zip
1.29 MB
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fig_S11.zip
45.50 KB
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fig_S12.zip
2.43 GB
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fig_S13.zip
4.97 GB
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fig_S14.zip
15.72 GB
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fig_S2.zip
23.25 GB
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fig_S3.zip
51.40 MB
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fig_S4.zip
902.39 KB
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fig_S5.zip
1.38 KB
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fig_S6.zip
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fig_S7.zip
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fig_S8.zip
8.14 GB
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fig_S9.zip
15.43 GB
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Fig1.zip
6.39 GB
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Fig2.zip
72.79 KB
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Fig3.zip
15.37 GB
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Fig4.zip
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README.md
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Abstract
Super-resolution microscopy in combination with genetic labeling methods allows imaging of single proteins in cells. However, visualizing endogenous proteins on primary cells remains challenging due to the use of sterically demanding antibodies for labeling. Here, we demonstrate how immunolabeling conditions and antibody crosslinking influence the quantification and identification of membrane receptor stoichiometry on cells using single molecule localization microscopy. We developed an optimized immunolabeling and analysis protocol and demonstrate the performance of the approach by resolving the molecular organization of endogenous CD45, CD69, and CD38 on Jurkat T cells. To demonstrate the usefulness of the method for immunotherapy applications, we investigated the interaction of primary multiple myeloma cells with the therapeutic monoclonal antibodies (mAbs) daratumumab and isatuximab, and a polyclonal anti-CD38 antibody. Our approach might lay the foundation for improved personalized diagnostics and treatment with therapeutic antibodies.
Dataset DOI: 10.5061/dryad.g4f4qrg3s
Description of the data and file structure
The provided .zip folders contain the data and plot variable values, arranged figure-wise for:
Patrick Eiring1, Maximilian J. Steinhardt2, Nele Bauer1, Cornelia Vogt2, Umair Munawar2, Seungbin Han2, Thomas Nerreter2, Hermann Einsele2, K. Martin Kortüm2, Sören Doose1, and Markus Sauer1,3. Single-molecule localization microscopy reveals the molecular organization of endogenous membrane receptors
Comments and requests should be addressed to Markus Sauer and Patrick Eiring: m.sauer@uni-wuerzburg.de ; patrick.eiring@uni-wuerzburg.de . All material is free of use, but we would appreciate being told, and this dataset and the paper cited where applicable.
General information:
Regarding .tif files ending with X2, X3 or X4: These files always belong to a single measurement and must be loaded as a file stack or concatenated, as the original data had to be split due to file size limitations. The files can be reconstructed with open source software such as rapidSTORM and ThunderSTORM.
All .csv files containing "cluster per µm²" contain data points from individually analyzed cell membranes.
All .csv files containing "localization per cluster" contain the number of detected localizations per grouped cluster.
All jupyter notebooks require a Locan environment (conda recommended).
As Input data .txt files are required: Convert .tif files to localization .txt files (e.g. ThunderSTORM, rapidSTORM). Set "directory=Path('.') / '.." in the notebooks to point to the folder containing your .txt files.
Abbreviations:
FA = Formaldehyde, PFA = Paraformaldehyde, GA = Glutharaldehyde, EtOH = Ethanol, MeOH = Methanol, FL = Fluoreszent widefield image, DL = Brightfield image, AF532, CF568 and AF647 are the dyes used for imaging. Antibodies used: DARA = Daratumumab, ISA = Isatuximab, CD38ME = CD38 multiepitope, HI30 = CD45 clone:HI30, 2D1 = CD45 clone 2D1, QA21A24 = CD45 clone QA21A24, CD69 = CD69 clone FN50
Files and variables
Fig1 A&B contain raw data of a CD45 (A) or CD69 (B) stained Jurkat T cell as .tif file and the respective reconstructed images of the dSTORM measurements as .png. Fig1 C-F contain the .csv files with the cluster per µm² for the different staining conditions in each row for CD45 (C) or CD69 (D) as well as all localizations per cluster for the staining conditions of CD45 (E) or CD69 (F).
Fig2 A-C contain the .csv files with the clusters per µm² for the tested dyes AF532, CF568 and AF647 (A) and the antibody clones HI30, 2D1 and QA21A24 of CD45 (B). Fig2 C contains the localizations per cluster for the different antibody clones shown in Fig2 B.
Fig3 A-C contain raw data of CD38ME (A), DARA (B) or ISA (C) stained Jurkat T cells as .tif files and the respective reconstructed images of the dSTORM measurements. Fig3 D&E contain the .csv files with all clusters per µm² (D) and localizations per cluster (E) for these three conditions.
Fig4 contain the raw data of patient 455 or 677 stained with either CD38ME, DARA or ISA as .tif files and the respective reconstructed images of the dSTORM measurements. Fig4 G&H contains the .csv files with the clusters per µm² (G) and localizations per cluster (H) for these three conditions.
fig_S1 contains the raw data as .tif files and the reconstructed image of dSTORM imaging for the CD45 receptor stained with CD45-HI30 after prefixation and staining. Prefixation was done with 4%PFA(A), 4%FA(B), 4%FA+0.25%(C), 2%GA(D), EtOH(E) & MeOH(F).
fig_S2 contains the raw data as .tif files and the reconstructed image of dSTORM imaging for the CD69 receptor stained with CD69-FN50 after prefixation and staining. Prefixation was done with 4%PFA(A), 4%FA(B), 4%FA+0.25%GA(C), 2%GA(D), EtOH(E) & MeOH(F).
fig_S3 contains the raw data as .czi files of Jurkat-ZAP70 GFP cells incubated on either PDL coated 8 well chambers after 2h as a z-stack or a timeseries (30min - 2h) of the cells on CD3 (UCHT1) coated 8 well chambers.
fig_S4 contains the jupyter notebooks with the ripley analysis for CD45 (A) & CD69 (B).
fig_S5 contains the .csv files with all clusters per µm² and localizations per cluster for the titration of anti-CD45 (A) and anti-CD69 (B).
fig_S6 contains the respective jupyter notebook with the Probability density function (PDF) of the data shown in fig_S4.
fig_S7 contains the respective jupyter notebook with the Probability density function (PDF) of Jurkat T cells of low passage number stained for CD45 with 5 µg/mL HI30 or 2D1.
fig_S8 A-C contains the raw data of .tif files and the reconstructed images of dSTORM measurements of CD45-stained cell membranes additionally stained with a secondary gam antibody (sAb) after 15 min(A), 30 min(B) and 60 min(C) as well as the respective elastic transformation matrices (Direct_transf) used for merging both channels. This transformation can be used to align both channels with the plugin "bUnwarpJ" implemented in Fiji/ImageJ. An already combined image is included in the folder named "MERGE.png". An additional colocalization Image is provided for FigS8A ("Colocalized Pixel Map RGB Image")
fig_S9 A-F contain the raw data as .tif files and the reconstructed image of dSTORM imaging of membrane receptor CD38 stained with mAb (HIT2) (A), CD38ME(B) & DARA(C) at 4°C or at 37°C (D-F) on OPM-2 cells. All cells were stained according to the live cell staining protocol, with fixation as a last step.
fig_S10 A-E contains the respective jupyter notebook with the ripley analysis of the mAb used at 4°C (A) or 37°C (B) as well as the polyclonal CD38ME used at 4°C (C) or at 37°C (D&E) OPM-2 cells.
fig_S11 contains the .csv file with the localizations per cluster for all conditions shown in Fig.S9.
fig_S12 contain the raw data as .tif files and the reconstructed images of dSTORM imaging of membrane receptor CD45 stained with two different CD45 antibodies (HI30 and 2D1) sequentially as well as the respective elastic transformation matrices (Direct_transf) used for merging both channels. This transformation can be used to align both channels with the plugin "bUnwarpJ" implemented in Fiji/ImageJ. An already combined image is included in the folder named "MERGE.png".
Fig_S13 A contain the raw data as .tif files and the reconstructed images of dSTORM imaging of membrane receptor CD69 stained with anti-CD69-AF647 and anti-CD69-CF568 simultaneously as well as the respective elastic transformation matrice (Direct_transf) used for merging both channels. This transformation can be used to align both channels with the plugin "bUnwarpJ" implemented in Fiji/ImageJ. An already combined image is included in the folder named "MERGE.png". An additional colocalization Image is provided ("Colocalized Pixel Map RGB Image").
fig_S13 B contain the raw data as .tif files and the reconstructed images of dSTORM imaging of membrane receptor CD69 &CD45 stained with anti-CD69-AF647 and anti-CD45-CF568 simultaneously as well as the respective elastic transformation matrice (Direct_transf) used for merging both channels. This transformation can be used to align both channels with the plugin "bUnwarpJ" implemented in Fiji/ImageJ. An already combined image is included in the folder named "MERGE.png". An additional colocalization Image is provided ("Colocalized Pixel Map RGB Image").
fig_S14 A-C contain the raw data as .tif files and the reconstructed image of dSTORM imaging of membrane receptor CD38 stained with CD38ME (A), DARA(B) and ISA(C) on cells of patients 824. The files can be reconstructed with open source software such as rapidSTORM and ThunderSTORM. Fig3 D&E contains the .csv files with all clusters per µm² (D) and localizations per cluster (E) for these three conditions.
Code/software
All raw data (.tif) files can be reconstructed using rapidSTORM. Data analysis was performed using Locan (https://github.com/super-resolution/Locan). Data can be viewed using ImageJ (for images and movies) and using Excel or OriginPro for .txt or .csv files.
Access information
Other publicly accessible locations of exemplary datasets and jupyter notebooks used for analysis: