Single Molecule Tracking Nanoscopy Extended to Two Colors with MTT2col for the Analysis of Cell-Cell Interactions in Leukemia

Materials and Reagents

1. Micropipette tips (Thermo Fisher Scientific, catalog numbers: 11752584 [10 µL]; 10731194 [20 µL]; 16641953 [100 µL]; 11782584 [200 µL]; 11749855 [1,000 µL])

2. 1.5 mL tubes (Eppendorf, Dutscher, catalog number: 033305)

3. µ-Slide 8 Well Glass Bottom (Ibidi cells in focus, catalog number: 80827)

4. Trypan blue (Thermo Fisher Scientific, Invitrogen^TM, catalog number: T10282)

5. Rabbit serum (Thermo Fisher Scientific, Gibco^TM, catalog number: 16120099)

6. Cell lines

   1. MS5 (DSMZ ACC 441)

   2. KG1 (ATCC CCL-246)

7. IMDM (Thermo Fisher Scientific, Gibco^TM, catalog number: 12440053)

8. HBSS, with calcium and magnesium, no phenol red (Thermo Fisher Scientific, Gibco^TM, catalog number: 14025092)

9. L-glutamine (Thermo Fisher Scientific, Gibco^TM 25030149, catalog number: 15430614)

10. Penicillin and streptomycin (Thermo Fisher Scientific, Gibco^TM, catalog numbers: 15140122 and 15140148)

11. β-mercaptoethanol (PanReac AppliChem, catalog number: A1108)

12. HEPES (Thermo Fisher Scientific, Gibco^TM, catalog number: 15630080)

13. Fetal bovine serum (Thermo Fisher Scientific, Gibco^TM, catalog number: 10270106)

14. Sodium pyruvate (Thermo Fisher Scientific, Gibco^TM, catalog number: 11360070)

15. Antibodies:

    1. Anti-JAM-B: in-house rabbit polyclonal antibody, clone 829

    2. Anti-JAM-C, blocking: mouse IgG (R&D, catalog number: MAB1189, clone 208206)

    3. Anti-JAM-C, non-blocking: in-house rat IgG, clone 19H36

    4. Alexa Fluor 488 conjugated anti-rabbit-IgG (Thermo Fisher Scientific, catalog number: A-11008)

    5. Alexa Fluor 594 conjugated anti-mouse or anti-rat IgG (Thermo Fisher Scientific, catalog number: A-11001 or A-11006)

    6. Quantum-dot 655 conjugated anti-mouse IgG (Thermo Fisher Scientific, catalog number: Q-11021MP)

    7. Quantum-dot 655 conjugated anti-rat IgG (Thermo Fisher Scientific, catalog number: Q-11621MP)

16. Culture medium for MS5 cells (see Recipes)

17. Culture medium for KG1 cells (see Recipes)

Equipment

1. Micropipettes 20, 100 and 1000 (Eppendorf, Thermo Fisher Scientific, catalog number: 05-403-152, model: Research)

2. Centrifuge (Eppendorf, Thermo Fisher Scientific, catalog number: 5805 000.010)

3. CO₂ incubator Forma Scientific 3548 (Labexchange, Forma Scientific, catalog number: B00032422)

4. 4°C refrigerator

5. -80°C freezer

6. Hemocytometer (Ozyme, C-Chip, catalog number: DHC-M01)

7. Axio Observer Z1 inverted microscope (Zeiss), with thermostated incubator at 37°C

8. Yokogawa spinning disk device

9. 491- and 561-nm lasers (ILas2 or Cobolt Calypso 1358, 100 mW)

10. 100× α Plan Neofluar NA 1.45 oil-immersion objective (Zeiss) or equivalent, with high NA

11. Filter cubes n°15 and 44 (Zeiss), with 120-W metal halide lamp (X-cite) for visual inspection

12. 405/488/568/647 nm Yokogawa quadriband dichroic beamsplitter (Semrock, catalog number: Di01-T405/488/568/647-13x15x0.5)

13. BS 573 dichroic beamsplitter (Semrock, catalog number: FF573-Di01-25x36), between the two cameras

14. 525/50 nm (green channel) and 641/75 nm (red channel) emission filters (Semrock, catalog numbers: FF03-525/50-25 and FF02-641/75-25)

15. Two Evolve 512 EMCCD cameras (Photometrics) or equivalent, such as the Prime 95B sCMOS camera (Photometrics)

Software

1. Metamorph software (Version 7.4.8, Molecular Devices)

2. MTT2col set of MATLAB functions and scripts, using the Image Processing Toolbox with Statistics and Machine Learning Toolbox (any version) (The Mathworks)

Procedure

Figure 1. Overview of the experiment. (A) Steps of the experimental procedure: preparation, acquisition, and data analysis. (B) Of note, labeling molecules of interest can be performed by a broad range of strategies.

1. Cell preparation 24 h or 48 h before videonanoscopy

   1. Pass MS5 cells (adherent): spread in a 1-cm² area Ibidi glass-bottom well at 30,000 cells in a 300-µL culture volume overnight. This cell density allows reaching about 50% confluence (about 50,000 cells) the day of the experiment, according to DSMZ guidelines. Of note, glass-bottom wells may need specific treatment, such as extracellular matrix coating, for proper adhesion of some cell lines.

   2. Pass KG1 cells (in suspension) at the appropriate culture density, between 100,000 and 1,000,000 cells/mL, according to ATCC guidelines.

   3. Incubate the cultures in a 37°C 5% CO₂ air humidified incubator.

      If using other types of chamber, they must have a glass bottom to allow fluorescence imaging, and cell numbers should be adjusted to the area.

      
      

2. Immunofluorescence staining of MS5 cells (Figure 1)

1. Dilute the primary and secondary antibodies in 100 µL of fresh cell culture medium, according to the determined optimal concentration. The optimal antibody concentration gives the best staining with minimum background. It is determined by using a series of dilutions in a titration experiment: 10 nM for anti-JAM-B 829 and for Alexa Fluor 488 conjugated anti-rabbit antibodies (or equivalent, such as quantum-dot conjugated antibodies) in IMDM supplemented with serum, which is necessary to saturate and limit non-specific binding.

2. Aspirate the culture medium from the well of the live cells to be stained and add 100 µL of the medium containing the diluted antibody directly to the well.

3. Incubate the cells at room temperature for 10 min.

4. Aspirate the supernatant and gently wash the cells twice with HBSS with 1% HEPES (or fresh culture medium—to limit autofluorescence, use culture medium without phenol red).

   
   

3. Immunofluorescence staining of KG1 cells

1. Dilute the primary antibody, blocking or not, and secondary antibody in 100 µL of fresh cell culture medium, according to the determined optimal concentration: 10 nM for anti-JAM-C (blocking: mouse RnD 208206, non-blocking: homemade rat 19H36), and for Alexa Fluor 594 conjugated anti-mouse (or equivalent, such as quantum-dot conjugated antibodies) in supplemented IMDM.

2. Determine the total number of cells and percent of cell viability using a hemocytometer: mix 0.4% trypan blue with the cell suspension, in a 1:1 proportion (or in other proportions, using the appropriate dilution factor for your calculations). Thoroughly mix the two solutions and pipette 20 µL of this mix into one hemocytometer chamber. Place the hemocytometer under the microscope. Count the unstained (live) and stained (dead) cells (according to the datasheet of the hemocytometer used). Multiply the total number of live cells by two (or by the appropriate dilution factor, if using a different dilution for trypan blue).

3. Pipette the appropriate volume to have either 50,000 cells (low density: as many as MS5 cells to image one KG1 cell per field of view, allowing cell by cell measurement) or 500,000 cells (high density: 10 times more than MS5 cells, to image several KG1 cells per field of view, enabling more statistics per video).

4. Transfer the cells to a 1.5-mL Eppendorf tube.

5. Briefly centrifuge at 88 × g for 3 min.

6. Resuspend in 1 mL of medium and saturate with 10% rabbit serum, in a 5% CO₂ incubator at 37°C for 60 min.

7. Briefly centrifuge at 88 × g for 3 min.

8. Resuspend in 100 µL of medium containing the diluted antibodies (see step 1).

9. Incubate the cells at room temperature for 10 min.

10. Briefly centrifuge at 88 × g for 3 min, aspirate the supernatant, and gently wash the cells twice with 1 mL of HBSS with 1% HEPES (or fresh culture medium without phenol red). Resuspend the cell pellet in 200 µL of HBSS with 1% HEPES.

11. Remove the supernatant from the MS5 cells, and add the 200 µL of KG1 cell suspension.

12. Examine the cells using a spinning disc fluorescence microscope with a thermostated incubator set to 37°C, and appropriate filters (see Equipment, items 7 to 15). Cells should be imaged immediately after staining, to limit endocytosis (Figure 2).

    
    

Figure 2. Representative videonanoscopy acquisition and MTT2col trajectory maps. (A) First image of a videonanoscopy acquisition, with the two colors combined into an RGB image. JAM-B staining on MS5 cells is in the green channel, and JAM-C staining on the KG1 cell is in the magenta channel. (B) Maximum projection of the video. (C) JAM-B and JAM-C single molecule trajectories are represented by green and magenta gradients, respectively, according to time, and superimposed on the transmission image of the cells (right). Inserts show magnifications of the framed areas around a leukemic KG1 cell. Spatiotemporal colocalizations are denoted by white circles, with a size proportional to duration. Several concentric circles correspond to successive colocalization events at nearby locations.

Representative videos can be seen from the supplementary data of the related article (Gorshkova et al., 2021).

4. Performing acquisitions on a confocal microscope

1. Create a folder, usually named with the date and a brief description of the experiment.

2. Select a field of view with KG1 cells on top of spread MS5 cells.

3. First, acquire a brightfield image that allows for further checking of cellular aspects and spatial limitations (as used, in combination with all trajectories, in Figure 2C).

4. Save this image using the same name as the video (time-stack), in a subfolder named “trans”. With this convention, the algorithm can automatically find the matching brightfield image for each stack.

5. Next, acquire one (or several) video (time-stack), typically at a 36-ms or 100-ms rate. Save the video file in the folder created for the experiment, as either tif or stk file format. For each video, 300 frames were usually acquired (see Figure 2A for an example). Video rate and length may be adapted for a given experiment. For instance, an experiment may require faster video rate or longer trajectories.

6. Last, optionally, acquire a z-stack ranging over the entire height of both MS5 and KG1 cells (typically more than 10 µm). Save the z-stack in a subfolder named ‘z’.

7. Repeat steps 2–5 as many times as needed. Cells should be kept less than an hour in HBSS with 1% HEPES.

Data analysis

1. Running MTT2col (Figure 1) (Gorshkova et al., 2021)

   1. Start Matlab (or Octave, alternatively, at least for MTT).

   2. Within the Matlab environment, navigate to the folder containing the data to be analyzed (either on a local drive or on the network). Evaluating a given dataset with Matlab or Octave essentially requires selecting the path to the directory containing the video files.

   3. Then, type the command MTT_2_colors in Matlab, which first displays a graphic interface listing all the parameters used, then runs the fully automated analysis (Figure 3).

   4. Of note, when analyzing a mix of one- and two-color datasets, the supplementary Matlab function MTT_1_or_2_colors can determine if the data correspond to one color (square images, such as 512 x 512 pixels), two colors (double square, meaning that the width is double the height, such as 1024 x 512 pixels) or any other possibility. The function automatically runs the analysis for one or two colors if it can be determined without ambiguity. Otherwise the file is skipped.

      
      

      
      Figure 3. MTT2col graphical user interface. The graphical user interface (GUI) lists all input parameters for molecule detection and estimation, trajectory reconnection, and further analyses, with their default values (as defined in the Matlab functions MTT23_dialog_box MTTparams_def and MTT_param).

      
      

2. Overview of the MTT2col algorithm (Sergé et al., 2008; Rouger et al., 2012)

   1. Inputs for each field of view

      The program expects to run the analysis using the following files and subfolders in the local path:

   1. Dual-color time-stacks (i.e. left: green / right: magenta)

   2. Dual-color ‘DIC’ image (transmitted light, in a subfolder named 'dic', optional but used for registration)

   3. Dual-color z-stacks (in a subfolder named 'z', optional)
      
      

   2. Analysis

      The main function of the MTT2col algorithm is called MTT_2_colors. Below is a list of the main Matlab functions and sub-functions in MTT2col. Key steps are in bold, variables are in purple, and comments are in green. This fully automated procedure allows to build trajectories from dual-color video acquisitions (Figure 2), and to extract several descriptors (Figure 4), as explained in the main text. See the original articles (Sergé et al., 2008; Rouger et al., 2012; Gorshkova et al., 2021) for an extensive description of the algorithmic processes and input/output parameters.

      MTT_2_colors % MTT dedicated to 2 colors

      MTT23i % with method = 'coloc' by default

      MTT23_dialog_box, MTTparams_def, MTT_param % User interface listing input parameters

      detect_reconnex_23 % trajectories (Sergé et al., 2008; Rouger et al., 2012)

      sort_traces % select long & fast enough trajectories

      ct4_by_file, ct5_by_file % histograms of peak & trajectory descriptors such as number, length, diffusion coefficient

      fit_directed_by_file % sort trajectories for D & γ

      cartobyf % maps for all files

      reg_2_colors % compute registration from DIC images, save mean_tform.mat

      traj_xy_coloc % map with colocalizations, registering magenta trajectories

      coloc3 % compute distance from green to magenta SM

      merge_colors % magenta/green stacks & max. projection

      detect_cell_contact % statistics at cell contact only

      cd(zdir) % analyze z-stack data

      MTT23i % run MTT on z-stack

      gradient_z2 % z profile of z-stack

      merge_colors % for z-stack
      
      

   3. Outputs

      MTT2col generates several files for each video (time-stack or z-stack), placed in different folders:

      1. 'output23': this folder contains the raw data, as a matrix of trajectory parameters. Each column corresponds to a trajectory, and lines correspond to parameters: frame number, x and y position, signal, radius, offset, blink, and noise. Each set of 8 lines corresponds to a given frame. Thus, the matrix has as many columns as trajectories, and 8 times more lines than frames in the video. The dat file contains all trajectories, and the mat file is filtered for trajectories with length and diffusion coefficient above the thresholds defined in the list of input parameters. See code from detect_reconnex_23 for more details (Sergé et al., 2008).

      2. 'ct4_by_file', 'ct5_by_file': these folders contain histograms of peak & trajectory descriptors (ct4) and trajectory shape (ct5), as png images for each file and for all files pulled together, as well as a text file containing a table of the mean value for each parameter and file.

      3. 'carto': this folder contains a map, i.e., an image of the trajectories superimposed over the image of the cells, as saved in the 'dic' folder (or the first frame of the video otherwise). The color code corresponds to the method selected in the input parameters: colocalization (for 2 colors), linearity (SCI), intensity, time, relative or absolute confinement level, or speed.

         
         

   
   Figure 4. Representative results from MTT2col. (A) Diagram of localization (top left), lateral view of a z-stack with each cell delimited by a white dotted line (bottom left), and z-profile of JAM axial positions, with the expected theoretical distribution for a uniform repartition on a sphere (right). (B) Representative trajectories, illustrating the variety of either Brownian, linear, or confined motions. (C) Dot plot of the anomalous exponent γ vs. the anomalous diffusion coefficient D_γ, which was computed from the mean squared displacement of each trajectory for simulations (top), JAM-C (middle), and JAM-B (bottom).

   
   

3. Data and code availability

   The source code for both MTT and MTT2col is available for download as open-source software on GitHub at https://github.com/arnauldserge1/MTT2col.

   The data that support the findings of this study are accessible for academic and non-profit research, upon reasonable request to the corresponding author, A.S. (arnauld.serge@univ-amu.fr).

Notes

1. The signal-to-noise ratio of the fluorescence images may be enhanced by:

   1. Increasing laser power, but avoiding photobleaching and photodamage.

   2. Using bright (as well as stable) dyes: high cross section and quantum yield.

   3. Increasing the acquisition time, but this is limited by the dynamics of the molecules: displacement (increasing with time, according to r² = 4Dt for Brownian motion) should remain smaller than the pixel size to generate diffraction limited signals.

   4. Increase the gain, while avoiding saturation, when using an EMCCD camera.

2. The pixel size in the images is determined by the pixel size of the camera, divided by the magnifications of the objective and the tube lens (optovar) and multiplied by the binning. We use a camera with a pixel size of 16 µm, a 100× lens, no optovar, and no binning, resulting in a pixel size of 160 nm in the images. Ideally, according to the Shannon-Nyquist theorem, the pixel size should be equal to (or less than) half the diffraction limit, i.e., 0.3λ/NA (λ: fluorescence emission wavelength, NA: numerical aperture of the lens).

   1. Too large a size leads to under-sampling of the single molecule signal (i.e., the point-spread function of the microscope, with a size set by diffraction).

   2. Too small a size dilutes the signal over neighboring pixels, leading to a weaker signal (lower number of collected photons per pixel).

3. Scientific cameras may generate 8, 12, 14, 16, or 32-bit images (as well as 24, 36, or 48-bit for color cameras, which is not appropriate here). MTT is intended for 16-bit images by default. It might be used for other formats, but with careful checking of the results.

4. A typical experiment generates about tens of videos, with about hundreds of molecules per frame, and hundreds of frames per video. There is thus a large variability in the total size of data to analyze. Similarly, computer processing capability can change over at least an order of magnitude. Of note, computing time scales linearly with the number of videos, as well as the number and size of frames, but exponentially with the density of targets (labeled molecules), which leads to the increasing computation of connectivity possibilities, when molecules are getting close to each other. Running MTT2col analysis on a standard computer (e.g., 6–8 cores, 3–3.5 GHz processor, and 16–32 Gb RAM) for a few tens of video files typically takes a few hours (e.g., 0.5–6 h). Therefore, an experiment performed during one day can be analyzed overnight, with results available the next day.

5. Statistical tests can be automatically performed using an appropriate Matlab script.

Recipes

1. Culture medium for MS5 cells

   Iscove's Modified Dulbecco's Medium (IMDM)

   10% Fetal Calf Serum

   1% HEPES, essential amino acids

   1% sodium pyruvate

   25 µM β-mercaptoethanol

   1% L-glutamine

2. Culture medium for KG1 cells

   Iscove's Modified Dulbecco's Medium (IMDM)

   10% Fetal Calf Serum

   1% L-glutamine

Acknowledgments

We thank Jessica Chevallier (TAGC, CIML, LAI, Aix-Marseille Univ) for critical reading of the manuscript. We thank Martine Biarnes-Pelicot (LAI, Aix-Marseille Univ) for providing technical help for cell culture. We thank Dr. Dalia El Arawi and Dr. Laurent Limozin (LAI, Aix-Marseille Univ) for providing technical help for the microscopy setup. We thank Rémi Torro and Dr. Pierre-Henri Puech (LAI, Aix-Marseille Univ) for providing technical help for Github. We also thank the microscopy, cytometry, and statistics research facilities at CRCM, as well as Thi Tien Nguyen for the cell culture facility at LAI, for excellent technical support.

This work was supported by the Fondation ARC pour la Recherche sur le Cancer (PJA 20131200238 to A.S.), the Institute Marseille Imaging, and institutional grants from INSERM and Aix-Marseille Univ. L.M. was supported by grants from the GdR Imabio (Bourse Accueil Master Interdisciplinaire), and from the Ligue contre le Cancer (IP/SC/SK - 17250). This protocol was originally developed in Gorshkova et al. J Cell Sci. 2021.

Competing interests

We declare no competing interest.

References

1. Achimovich, A. M., Ai, H. and Gahlmann, A. (2019). Enabling technologies in super-resolution fluorescence microscopy: reporters, labeling, and methods of measurement. Curr Opin Struct Biol 58: 224-232.
2. Balzarotti, F., Eilers, Y., Gwosch, K. C., Gynna, A. H., Westphal, V., Stefani, F. D., Elf, J. and Hell, S. W. (2017). Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes. Science 355(6325): 606-612.
3. De Grandis, M., Bardin, F., Fauriat, C., Zemmour, C., El-Kaoutari, A., Serge, A., Granjeaud, S., Pouyet, L., Montersino, C., Chretien, A. S., et al. (2017). JAM-C Identifies Src Family Kinase-Activated Leukemia-Initiating Cells and Predicts Poor Prognosis in Acute Myeloid Leukemia. Cancer Res 77(23): 6627-6640.
4. Gorshkova, O., Cappai, J., Maillot, L. and Serge, A. (2021). Analyzing normal and disrupted leukemic stem cell adhesion to bone marrow stromal cells by single-molecule tracking nanoscopy. J Cell Sci 134(18): jcs258736.
5. Jacquemet, G., Carisey, A. F., Hamidi, H., Henriques, R. and Leterrier, C. (2020). The cell biologist's guide to super-resolution microscopy. J Cell Sci 133(11): jcs240713.
6. Lemon, W. C. and McDole, K. (2020). Live-cell imaging in the era of too many microscopes. Curr Opin Cell Biol 66: 34-42.
7. Li, H. and Vaughan, J. C. (2018). Switchable Fluorophores for Single-Molecule Localization Microscopy. Chem Rev 118(18): 9412-9454.
8. De Los Santos, C., Chang, C. W., Mycek, M. A. and Cardullo, R. A. (2015). FRAP, FLIM, and FRET: Detection and analysis of cellular dynamics on a molecular scale using fluorescence microscopy. Mol Reprod Dev 82(7-8): 587-604.
9. Manley, S., Gillette, J. M., Patterson, G. H., Shroff, H., Hess, H. F., Betzig, E. and Lippincott-Schwartz, J. (2008). High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat Methods 5(2): 155-157.
10. Manzo, C. and Garcia-Parajo, M. F. (2015). A review of progress in single particle tracking: from methods to biophysical insights. Rep Prog Phys 78(12): 124601.
11. Owen, D. M., Williamson, D., Rentero, C. and Gaus, K. (2009). Quantitative Microscopy: Protein Dynamics and Membrane Organisation. Traffic 10: 962-971.
12. Petazzi, R. A., Aji, A. K. and Chiantia, S. (2020). Fluorescence microscopy methods for the study of protein oligomerization. Prog Mol Biol Transl Sci 169: 1-41.
13. Rouger, V., Bertaux, N., Trombik, T., Mailfert, S., Billaudeau, C., Marguet, D. and Serge, A. (2012). Mapping molecular diffusion in the plasma membrane by Multiple-Target Tracing (MTT). J Vis Exp (63): e3599.
14. Salles, A., Billaudeau, C., Serge, A., Bernard, A. M., Phelipot, M. C., Bertaux, N., Fallet, M., Grenot, P., Marguet, D., He, H. T., et al. (2013). Barcoding T cell calcium response diversity with methods for automated and accurate analysis of cell signals (MAAACS). PLoS Comput Biol 9(9): e1003245.
15. Schmidt, T., Schütz, G. J., Gruber, H. J. and Schindler, H. (1996). Local Stoichiometries Determined by Counting Individual Molecules. Analytical Chemistry 68(24): 4397-4401.
16. Sergé, A., Bertaux, N., Rigneault, H. and Marguet, D. (2008). Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes. Nat Methods 5(8): 687-694.
17. Sergé, A. (2016). The Molecular Architecture of Cell Adhesion: Dynamic Remodeling Revealed by Videonanoscopy. Front Cell Dev Biol 4: 36.
18. Sharonov, A. and Hochstrasser, R. M. (2006). Wide-field subdiffraction imaging by accumulated binding of diffusing probes. Proc Natl Acad Sci U S A 103(50): 18911-18916.