Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Summary

A strategy to quantitatively analyze histological data in the bone marrow is presented. Confocal microscopy of fluorescently labeled cells in tissue sections results in 2-dimensional images, which are automatically analyzed. Co-localization analyses of different cell types are compared to data from simulated images, giving quantitative information about cellular interactions.

Abstract

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. However, the analysis and quantification of cellular localization is difficult, as in many cases it relies on manual counting, thus bearing the risk of introducing a rater-dependent bias and reducing interrater reliability. Moreover, it is often difficult to judge whether the co-localization between two cells results from random positioning, especially when cell types differ strongly in the frequency of their occurrence. Here, a method for unbiased quantification of cellular co-localization in the bone marrow is introduced. The protocol describes the sample preparation used to obtain histological sections of whole murine long bones including the bone marrow, as well as the staining protocol and the acquisition of high-resolution images. An analysis workflow spanning from the recognition of hematopoietic and non-hematopoietic cell types in 2-dimensional (2D) bone marrow images to the quantification of the direct contacts between those cells is presented. This also includes a neighborhood analysis, to obtain information about the cellular microenvironment surrounding a certain cell type. In order to evaluate whether co-localization of two cell types is the mere result of random cell positioning or reflects preferential associations between the cells, a simulation tool which is suitable for testing this hypothesis in the case of hematopoietic as well as stromal cells, is used. This approach is not limited to the bone marrow, and can be extended to other tissues to permit reproducible, quantitative analysis of histological data.

Introduction

Due to recent rapid technological developments in microscopy, including optical imaging, the analysis of cells within the context of the whole tissue has become increasingly accessible for immunologists. The characterization of single cells in suspension represents a valuable and indispensable method to understand cellular and molecular function. However, the analysis of the cells within their (micro)-anatomical environment is essential for understanding the interactions between various cell types that collaborate in complex processes such as the development of immune responses.

While it is relatively easy for microscopists to obtain qualitative information from images, it remains a challenge to quantify these data, partly due to the fact that analysis methods in this field are lagging behind compared to what is possible in image acquisition. Many researchers still rely on time-consuming manual cell counting in their histology images, thus introducing a bias amongst different raters and hindering replication by other groups. Oftentimes, one representative image is chosen to underline a statement on cellular position or co-localization in a publication, making it hard for the reader to judge the statistical relevance of such an event.

Together with the fact that the full information content of image data is rarely exploited, this emphasizes the need for a more unbiased, faster and comprehensive approach to analyze histological images.

The bone marrow is a complex tissue, which takes on important vital functions as the organ of hematopoiesis in adult vertebrates. Besides being the birthplace for hematopoietic cells^1,2 and playing an important role in B lymphocyte development³, it also acts as a site where immune reactions are initiated⁴ and supports mature, recirculating B cells⁵. In addition, its role in maintaining immunological memory has become increasingly appreciated in the last decade, as several types of cells constituting immune memory have been found to reside there^6-9.

The relation between the complex tissue architecture of the bone marrow and its functions still remain elusive. Unlike secondary lymphoid organs, which are organized in macro-compartments such as T and B cell zones, the bone marrow lacks a clear macro-compartmentalization. So far distinct compartments in bone marrow are defined by their proximity to the bone cortex or to vasculature. The importance of the various resident stromal cell populations in the bone marrow for a number of processes such as supporting stem cells, development of B cells or maintenance of immune memory cell populations (such as long-lived plasma cells (PCs), CD4⁺ and CD8⁺ memory T cells) clearly indicates that there is a certain degree of micro-compartmentalization in the bone marrow.

These observations have led to the concept of distinct microanatomical niches, which are specialized in certain functionalities (stem cell maintenance, B cell development at various stages, and maintenance of immunological memory) in the bone marrow. Although there seems to be a certain degree of heterogeneity among the niches that serve different functions, some of the factors produced by stromal cells, such as CXC-chemokine ligand 12 (CXCL12) or interleukin 7 (IL-7), are crucial components for several of these niches¹⁰. The visualization and characterization of stromal cells in the bone marrow is difficult due to their morphological features with long, thin dendritic extensions forming a network throughout the bone marrow, and the lack of appropriate markers to discriminate stromal subpopulations.

It is not yet clear as to what extent these niches share common features with respect to their cellular and molecular composition, and which elements render a certain niche unique. In addition to stromal cells, hematopoietic cell types have been shown to play a crucial role by providing certain signals at least for some of the niches. Clearly, the complexity of the niche composition requires their analysis in situ, and it has become increasingly important for immunologists and hematologists to zoom in on bone marrow microarchitecture, e.g., by analyzing the spatial relationships between its cellular components.

Here, a strategy to quantify cellular co-localization and neighborhood relationships in the bone marrow in an automated and unbiased way is presented. A detailed workflow including the generation of chimeric mice, harboring fluorescent stromal cells and non-fluorescent hematopoietic cells, preparation of histological sections from undecalcified bones, acquisition of confocal images covering the whole bone, as well as the automated image analysis of cellular co-localization and its validation/discrimination from random positioning by a simulation tool is provided (Figure 8).

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Protocol

The animal experiments were approved by the appropriate state committees for animal welfare (Landesamt für Gesundheit und Soziales, Berlin) and were performed in accordance with current guidelines and regulations (animal experiment license G0194/11).

1. Generation of Fluorescent Bone Marrow Chimeric Mice

NOTE: The generation of fluorescent bone marrow chimeric mice to visualize bone marrow stromal cells is performed as described before⁹.

1. Start treating Del-Cre x ROSA-tdRFP mice (mice expressing tandem red fluorescent protein (tdRFP) ubiquitously^11-13) to prepare them for irradiation. Alternatively, use any other strain with ubiquitous expression of fluorescent protein. Administer 1 mg/ml of Neomycin and 1 mg/ml of vitamins (A, D3, E, C) via the drinking water two days before irradiation.
2. Irradiate mice twice with 3.8 Gray with a Cesium-137 gamma-irradiator within an interval of 3 hr. For this, place mice in an irradiation pie cage suitable for the respective irradiator.
   NOTE:  For irradiation of mice, our Institute does not require anesthesia.  Follow local Institutional policies regarding anesthesia for irradiation. Treat animals with 5 mg/kg of carprofen subcutaneously (s.c.) per day after the irradiation if there are signs of pain.
3. The next day, reconstitute mice by an intravenous injection of 3 x 10⁶ bone marrow cells prepared from long bones of C57BL/6 donor mice in transfer buffer⁹. Keep the mice on Neomycin and vitamins for up to 2 weeks and monitor their well-being and weight during this time. Wait at least 4 weeks to allow for reconstitution of the immune system before starting the specific experimental treatments (e.g., immunization)⁹.
4. Sacrifice mice and place them on a dissection board, sterilize the legs with 70% ethanol. Euthanize mice in accordance with local Institutional policies.  Our Institute performs cervical dislocation.
5. Use forceps and scissors to remove the skin from the thighs. Remove muscle tissue to expose the femoral bone. Luxate the femoral bone from the hip and knee joint using forceps and scissors. Be careful not to break or cut the bone.
6. Carefully remove remaining large pieces of muscle tissue and cartilage from the bone using scissors. Remove remaining muscle tissue by rubbing the bone with laboratory tissue paper. Collect the cleaned bones in a petri dish with phosphate buffered saline (PBS).
7. Fix whole femoral bones in 4% paraformaldehyde (PFA, electron microscopy-grade) for 4 - 6 hr.
8. Discard PFA and incubate bones in 10% sucrose in PBS O/N. The next day, incubate the bones in 20% sucrose O/N. The day after, incubate the bones in 30% sucrose O/N.

2. Cryosectioning of Bones

NOTE: After 16 - 24 hr in 30% sucrose, freeze bones and cryosection them according to Kawamoto’s tape method^14,15.

1. Prepare a large beaker (2,000 ml volume) with dry ice and acetone (approximately 2:1 volume ratio, e.g., 400 ml of dry ice and 200 ml of acetone) under a fume hood. Place a small beaker (150 - 250 ml volume) with hexane inside (30 - 50 ml approximately). Wait for the mix to cool down (approximately 10 min, until frost appears on the outside of the large beaker).
2. Fill ¾ of the labeled cryomold with Super Cryoembedding Medium (SCEM); carefully place the bones inside until they are fully immersed, taking care that they do not touch the edges of the mold. With large forceps hold the cryomold into the beaker with the bottom of the mold just touching the surface of the hexane.
   1. Let the outer edges of the SCEM freeze (indicated by opacity, this takes approximately 15 sec). Then fully drop the mold into the hexane and let it freeze for 1 - 2 min. Take out the frozen sample and wrap in cellophane and then aluminum foil (to protect the sample from drying out and to avoid exposure to light). Store at -80 °C until cryosectioning.
3. For cryosectioning of femoral bones use a standard microtome and microtome blades for hard tissues.
4. Set the sample and blade temperature of the microtome to -24 °C. Let the sample sit inside the microtome for about 15 min before cutting.
   1. Fix the sample block to the metal sample holder with SCEM or optimal cutting temperature (OCT) medium. Adjust the orientation of the block if necessary. Trim the sample until the bone is fully opened and the marrow is visible. Adjust the section thickness to 7 µm (discard the first section).
   2. Fix a piece of Kawamoto tape with the sticky side on the top of the sample block using a deer leather-covered wooden spatula. Subsequently, cut the sample and turn the tape so that the section is positioned on the top side. Transfer the tape to a slide glass using forceps. Fix the tape to the slide glass with Scotch tape.
5. Let sections dry for at least 30 min and store them at -80 °C until usage. Store unstained and unmounted slides in plastic slide boxes with spacers between single slides in order to avoid that they stick together. Stained and mounted slides can be stored for up to a week in cardboard slide folders at 4 °C.

3. Image Collection

1. Thaw and stain cryosections according to common immunofluorescence protocols⁹. Include a nuclear stain, e.g., 4’,6-diamidino-2-phenylindole dihydrochloride (DAPI) to visualize tissue integrity.
   NOTE: Stain, for example, for RFP to visualize stromal cells (anti-RFP-biotin antibody and streptavidin-Alexa Fluor 555), stain eosinophils with rat anti-major basic protein (MBP) antibody and anti-rat-Alexa Fluor 647 antibody, B cells with rat anti-B220-Alexa Fluor 594 antibody and PCs with anti-κ light chain-fluorescein-iso-thiocyanate (FITC) and anti-λ1 light chain-FITC antibodies (details of the staining procedure are described in⁹).
2. Mount stained sections: put one drop of fluoromount on the section and then cover with a #1 glass cover slip, whilst carefully avoiding the formation of air bubbles. Subsequently, perform laser scanning confocal microscopy with an instrument equipped with laser lines suitable for the staining.
3. In order to record images for automated co-localization analysis of bone marrow hematopoietic cells with stromal cells using the Wimasis tool, apply the following settings:
   1. Use a 20X objective lens and a field of view of 708.15 x 708.15 µm. For automated analysis, keep the size of all images consistent at 2,048 x 2,048 pixel (px) in order to keep the results comparable.
   2. Record one channel for stromal structures (e.g., 561 nm laser line), one channel for nuclei (e.g., DAPI, 405 nm) and additional channels for the hematopoietic cells of interest (e.g., 3 channels, 488/594/633 nm for eosinophils, B cells and PCs).
   3. Record images using line averaging 4 ¹⁶. Ideally, cover the whole femoral section by taking single adjacent images. Alternatively, take non-adjacent pictures from various regions of the bone marrow (diaphyseal as well as epiphyseal).
      NOTE: Be careful not to produce overlaps between adjacent images (to avoid repeated analysis of cells in the overlapping areas).
4. Save the images in a microscopy image file format. Check and, if necessary, adjust contrast for all channels in the image viewer/analysis software.
5. Export 3 .jpg files per image file: one .jpg file for the DAPI channel (red/green/blue (RGB) format, false color coded in yellow), one .jpg file for the stroma channel (greyscale) and one .jpg file containing the channels for hematopoietic cells (3 channels maximum, RGB format, e.g., FITC (PCs, false color: green)/ Alexa Fluor 594 (B cells, false color: blue) / Alexa Fluor 647 (eosinophils, false color: red)).

4. Automated Image Analysis

1. Use an image analysis tool to perform image segmentation, quantification of co-localization and neighborhood analysis (see Discussion).
2. If using the Wimasis tools, proceed as follows:
   1. Upload the sets of 3 .jpg files per image with the same file name followed by an underline and a number indicating the type of the image.
   2. Use _1 for the .jpg file with hematopoietic cells, _2 for the .jpg file for the DAPI channel, _3 for the .jpg file for the stroma channel. For example: Image1_1.jpg (hematopoietic cells), Image1_2.jpg (DAPI channel), and Image1_3.jpg (stroma channel). Upload the images via the customer account.
   3. For cell contact quantification choose the cell contact tool by clicking on the respective field. For cell vicinity quantification, choose the cell vicinity tool and enter the preferred vicinity radius in µm (which is measured from the cells’ edges).
   4. Download the results.
      NOTE: The results are provided as .jpg files showing the hematopoietic cells and the stroma channel with the boundaries of the detected objects highlighted, as well as single .csv files containing the measurements for every image, in addition to a summary .csv file that contains the data for all uploaded images.
3. From the contact measurements determine the frequency of hematopoietic cells (red, green, or blue cells) in contact with stromal cells, or the frequencies of red, green, or blue cells contacting the other hematopoietic cell types (an example is shown in Representative Results, Figure 4). In order to determine the frequencies, divide the given contact counts per image by the total cell counts per image.
   1. For experiments with individual mice, sum up the total contact counts of all images for the single mice and divide them by the sum of total cell counts of all images.
4. From the vicinity measurements, determine the frequency of red, green or blue cells within the selected distance from stromal cells and/or the frequencies of red, green or blue cells in the preferred proximity to the other hematopoietic cell types as described in step 4.3 (see also Representative Results, Figure 4).

5. Simulation of Random Bone Marrow Positioning

1. Before executing the simulations of random cell positioning on the analyzed bone marrow histological images, prepare the following files in advance: the single .csv files provided by the cell contact tool, the original .jpg files for the DAPI channel and the original .jpg files for the stromal channel.
2. In order to perform batch simulations on a series of images (e.g., all images from one femoral section), collect all original .jpg files (_1, _2 and _3) and the corresponding single .csv files in one folder.
   NOTE: The simulation tool for random bone marrow cell positioning is available upon request.
3. Start the simulation tool, check the box “Auto-load image data”.
   NOTE: The program will read the cell counts and average cell size from the .csv file automatically. These values are displayed in the boxes for “Cell number” and “Cell size AVG” (Figure 5A).
4. Enter the common tag for the .csv files, i.e., the common factor in their names. Enter the number of image sets that should be used for batch-mode simulation.
5. Load the .jpg file generated from the stroma channel (with filename ending _3).
6. Enter the settings for generating the mask from the DAPI channel:
   1. Check the box “Apply mask?” Set the threshold for converting the DAPI image into a binary mask to 10 (range 0 - 255).
   2. Check the box “8-bit?” Check the box “Dilate?” and set the grade of dilation to 5 px. Check the box “w/ erosion?”.
   3. Enter the desired value for the vicinity radius (neighborhood radius) used for the analysis of the simulated images. For an image of 708.15 x 708.15 µm with a size of 2,048 x 2,048 px (pixel scaling xy: 0.346 µm), 29 px correspond to 10 µm.
7. Check box “Use Otsu?” to use Otsu’s algorithm¹⁷ for automatic detection of stromal structures.
   NOTE: This is the same algorithm that is used by the contact and vicinity tool. Using the same image segmentation algorithm in the automated analysis of the recorded image and the simulated image is crucial in order to keep the data comparable.
8. Enter the settings for hematopoietic cells, which are simulated as circular shapes.
   NOTE: The cell numbers for red, green, and blue cells are directly loaded from the .csv file (see also step 5.6).
   1. Use the simulation tool to calculate the average diameter in px of red, green and blue cells. Determine the average area of a single cell as the total area of each cell type in the image in px divided by the total cell count per image. Use the average area to calculate the radius of a disc using the formula. Double the radius to determine the average diameter.
9. Measure the cell size distribution of the analyzed cell types with any image analysis software that includes object segmentation functions. Determine σ for all hematopoietic cell types (¹⁸ and Figure 5B).
   NOTE: Since the cell size distributions of the recorded cells are not determined by the automated image analysis, use a Gaussian distribution as an approximation of the real distributions. The width of the distribution curve is described by σ and can be quite different for various cell types. (For details, see Figure 5B).
   1. From these measurements, determine the “cell size cutoff”: the diameter in px that describes the smallest object still recognized as a complete cell by the image analysis tool.
10. Enter parameters in the respective boxes on the graphical user interface (Figure 5A).
    1. Enter the cell size cut off, i.e., the minimum cell size allowed in the simulation, as a diameter in px.
    2. Enter σ in px for the simulation of the cell size distribution for red, green, and blue cells (from step 5.9).
    3. Check the “Delete” box in the subsection “Cells in Mask”. With this setting, the program will chose a new position for a cell if it overlaps with at least one pixel with a no-go area of the mask. Check the box “Avoid cell overlap?” in the subsection “Cell exclusion”.
    4. Enter the allowed minimum distance between the centers of two cells in px.
       NOTE: The minimum distance needs to be determined from the recorded images. Wrongly measured minimum distances will lead to biased/artificial results for the comparison of recorded and simulated images.
    5. Set the maximum area of overlap to 100% if the overlap is defined by the minimum distance from center to center.
    6. Set the simulation tool to 1,000 repetitions.
    7. Check the box “Autosave cells?” to save the coordinates of the simulated objects for all repetitions of each image of the batch as .rect files (text format). Check the box “Autosave images?” to save a .tiff file for each simulated image. Enter a common tag for the saved files.
       NOTE: The “Autosave cells?” option reduces simulation running time and overall data size, compared to when saving the actual simulated images. The .rect files save the coordinates of the simulated cells as rectangles and can thus be converted into simulated images if needed.
11. Start the simulation by clicking on “Run simulation”.
    NOTE: The simulation tool automatically generates a .csv file for the 1,000 simulations of every image, including the average contact counts and vicinity counts for red, green and blue cells with red, green, blue, and stromal cells for every set of repeated simulations. Additionally, for all these values, averages of the 1,000 simulations with standard deviation (STD) and standard error of mean (SEM) are provided.
12. Determine the average contact frequencies and vicinity frequencies of one set of 1,000 simulated images. For this, divide the average contact and vicinity counts by the recorded cell count per image. Compare the frequencies to the results of the automated co-localization analysis of the recorded image.
13. Apply appropriate statistical methods depending on the analysis¹⁹ (for Figure 7, we used a two-tailed Wilcoxon signed rank test).

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Results

Cutting cryosections of undecalcified bone with the Kawamoto tape method allows the whole bone to be cut as an intact section, with the bone marrow of the endosteal region still attached to the mineralized bone, both in the diaphysis as well as in the epiphyseal areas with their high density of trabecular bone (Figure 1). Nuclear staining of the sections reveals that although small cracks in the preparation cannot be fully avoided, the structure of the sinusoids and arteries as well as the reticular netw...

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Discussion

Despite the progress in modern optical imaging methods, the analysis of histological data is still often hindered by the lack of proper quantification tools and methods, or by biased analyses that focus on a small area of interest. The synergistic approach presented here combines image analysis covering the entire bone marrow region, automated segmentation and object recognition of various hematopoietic and stromal cell types, co-localization analysis, and finally a validation tool of non-randomly occurring contacts prov...

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Materials

Name	Company	Catalog Number	Comments
Neomycin	Sigma	N6386 SIGMA	Neomycin trisulfate salt hydrate, EU hazard code: GHS08
Ursovit AD3EC	Serumwerke Bernburg		1 ml contains: 50.000 I.E. retinyl palmitate, 5.000 I.E. cholecalciferol, 30 mg tocopheryl acetate, 100 mg ascorbic acid, 1 mg sorbic acid, 200 mg polyoxyl 35 castor oil, 0,5 mg propyl gallate
Transfer buffer (100 ml PBS, 1 ml 1 M HEPES, 50 U/ml penicillin/streptomycin)	Sigma	P4333, H3375
4-Hydroxy-3-nitrophenylacetyl hapten conjugated to chicken gamma globulin
Chicken gamma globulin (CGG) 100 mg	Rockland	D602-0100
20% Paraformaldehyde solution (EM-grade)	Science Services	15713	EU hazard codes: GHS02, GHS05, GHS07, GHS08
D(+)-sucrose	Carl Roth	4621.1
Dry ice
Acetone	Sigma-Aldrich	179124 SIGMA-ALDRICH	EU hazard codes: GHS02, GHS07
Hexane	Sigma-Aldrich	208752 SIGMA-ALDRICH	EU hazard codes: GHS02, GHS07, GHS08, GHS09
Tissue-Tek cryomolds (standard)	Sakura	4557	25 x 20 x 5 mm
Tissue-Tek O.C.T.	Sakura	4583
Kawamoto's SCEM embedding medium	Section-Lab, JP
Kawamoto's cryosection preparation kit	Section-Lab, JP
Kawamoto's cryofilm type 2C(9)	Section-Lab, JP
Microtome blade MX35 premier plus, low profile	Thermo Scientific	3052835	L X W: 80 x 8 mm (31.5 x 3.13"), thickness: 0.25 mm (0.01")
Polyclonal rabbit anti-RFP antibody, biotinylated	Rockland	600-406-379
Alexa Fluor 555 streptavidin	Life Technologies	S-32355
Rat anti-MBP	J. and N. Lee		available from: J. and N. Lee, Mayo Clinic, Scottsdale, AZ , U.S.A., clone MT-14.7
Goat anti-rat-Alexa Fluor 647	Life Technologies	A-21247
Rat anti-B220 - Alexa Fluor 594			produced and coupled in-house (DRFZ), clone RA3.6B2, Alexa Fluor 594 from Life Technologies
Mouse anti-l1 light chain -FITC			produced and coupled in-house (DRFZ), clone LS136
Rat anti-k light chain - FITC			produced and coupled in-house (DRFZ), clone 187.1
DAPI (4′,6-diamidino-2-phenylindole dihydrochloride)	Sigma	D9542 SIGMA
Fluorescent mounting medium	DAKO	S3023
Cover slips (24 x 24 x 0.13-0.16 mm)	Carl Roth	H875.2
Superfrost slides glasses (75 x 25 mm)	VWR	48311-703
Laser scanning confocal microscope			equipped with laser lines of 405, 488, 561, 594, 633 nm and a 20X/0.8 NA air objective lens. We used a Zeiss LSM710 and Zen 2010 Version 6.0 software.
Automated image analysis tools for bone marrow	Wimasis, Munich		The cell contact tool and cell vicinity tool will be made available by Wimasis upon request.
VC2012 runtime	Microsoft		free download
Simulation tool for random cell positioning			available from us, upon request
Image analysis software with image segmentation functions			We used Volocity (Perkin Elmer) for measuring cell size distributions of hematopoietic cell types in bone marrow (Figure 5). Alternatives are Definiens Image Analysis (Definiens) or Cell Profiler (free download)
Fiji image analysis software			free download. Fiji was used by trained raters for manual cell count (Figure 3).