DOI: 10.5061/dryad.g4f4qrg3s
title: Single-molecule localization microscopy reveals the molecular organization of endogenous membrane receptors

description: 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.
description: # Single-molecule localization microscopy reveals the molecular
 organization of endogenous membrane receptors Dataset DOI:
 [10.5061/dryad.g4f4qrg3s](https://doi.org/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 Eiring^1^*, Maximilian J. Steinhardt^2^, Nele Bauer^1^, Cornelia
 Vogt^2^, Umair Munawar^2^, Seungbin Han^2^, Thomas Nerreter^2^, Hermann
 Einsele^2^, K. Martin Kortüm^2^*, Sören Doose^1^*, and Markus Sauer^1,3^*.
 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](mailto:m.sauer@uni-wuerzburg.de) ;
 [patrick.eiring@uni-wuerzburg.de](mailto: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 *d*STORM
 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 *d*STORM 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 *d*STORM 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
 *d*STORM 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 *d*STORM 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
 *d*STORM 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
 *d*STORM 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 *d*STORM 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 *d*STORM 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
 *d*STORM 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 *d*STORM 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](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: * [https://github.com/super-resolution/Eiring-et-al-2025-supplement](https://github.com/super-resolution/Eiring-et-al-2025-supplement)