We thank Andreas Radbruch for valuable discussions. We are grateful to Sabine Gruczek, Patrick Thiemann and Manuela Ohde for assistance with animal care and Robert G&#252;nther for excellent technical assistance. We thank our trained raters Laura Oehme, Jannike Bayat-Sarmadi, Karolin Pollok, Katrin Roth, Florence Pache and Katharina Horn for evaluation of the histology samples and Randy Lindquist for proofreading of the manuscript. We thank J. and N. Lee, Mayo Clinic, Scottsdale, Arizona, USA for MBP-specific antibodies.
This work was supported by DFG HA5354/4-1, by JIMI-a DFG core facility network grant for intravital microscopy and by TRR130/TP17,and DFG FOR 2165 (HA5354/6-1) to A.E.H. S.Z. was supported by the International Max Planck School for Infectious Diseases and Immunology (IMPRS-IDI), Berlin.

The animal experiments were approved by the appropriate state committees for animal welfare (Landesamt f&#252;r 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.
<ol>
	<li>Start treating Del-Cre x ROSA-tdRFP mice (mice expressing tandem red fluorescent protein (tdRFP) ubiquitously11-13) 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.</li>
	<li>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: &#160;For irradiation of mice, our Institute does not require anesthesia.&#160; Follow local Institutional policies regarding anesthesia for irradiation.&#160;Treat animals with 5 mg/kg of carprofen subcutaneously (s.c.) per day after the irradiation if there are signs of pain.</li>
	<li>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.</li>
	<li>Sacrifice mice&#160;and place them&#160;on a dissection board, sterilize the legs with 70% ethanol.&#160;Euthanize mice in accordance with local Institutional policies.&#160; Our Institute performs cervical dislocation.</li>
	<li>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.</li>
	<li>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).</li>
	<li>Fix whole femoral bones in 4% paraformaldehyde (PFA, electron microscopy-grade) for 4 - 6 hr.</li>
	<li>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.</li>
</ol>
2. Cryosectioning of Bones
NOTE: After 16 - 24 hr in 30% sucrose, freeze bones and cryosection them according to Kawamoto&#8217;s tape method14,15.
<ol>
	<li>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).</li>
	<li>Fill &#190; 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.
	<ol>
		<li>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 &#176;C until cryosectioning.</li>
	</ol>
	</li>
	<li>For cryosectioning of femoral bones use a standard microtome and microtome blades for hard tissues.</li>
	<li>Set the sample and blade temperature of the microtome to -24 &#176;C. Let the sample sit inside the microtome for about 15 min before cutting.
	<ol>
		<li>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 &#181;m (discard the first section).</li>
		<li>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.</li>
	</ol>
	</li>
	<li>Let sections dry for at least 30 min and store them at -80 &#176;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 &#176;C.</li>
</ol>
3. Image Collection
<ol>
	<li>Thaw and stain cryosections according to common immunofluorescence protocols9. Include a nuclear stain, e.g., 4&#8217;,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-&#954; light chain-fluorescein-iso-thiocyanate (FITC) and anti-&#955;1 light chain-FITC antibodies (details of the staining procedure are described in9).</li>
	<li>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.</li>
	<li>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:
	<ol>
		<li>Use a 20X objective lens and a field of view of 708.15 x 708.15 &#181;m. 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.</li>
		<li>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).</li>
		<li>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).</li>
	</ol>
	</li>
	<li>Save the images in a microscopy image file format. Check and, if necessary, adjust contrast for all channels in the image viewer/analysis software.</li>
	<li>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)).</li>
</ol>
4. Automated Image Analysis
<ol>
	<li>Use an image analysis tool to perform image segmentation, quantification of co-localization and neighborhood analysis (see Discussion).</li>
	<li>If using the Wimasis tools, proceed as follows:
	<ol>
		<li>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.</li>
		<li>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.</li>
		<li>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 &#181;m (which is measured from the cells&#8217; edges).</li>
		<li>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.</li>
	</ol>
	</li>
	<li>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.
	<ol>
		<li>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.</li>
	</ol>
	</li>
	<li>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).</li>
</ol>
5. Simulation of Random Bone Marrow Positioning
<ol>
	<li>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.</li>
	<li>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.</li>
	<li>Start the simulation tool, check the box &#8220;Auto-load image data&#8221;. 
	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 &#8220;Cell number&#8221; and &#8220;Cell size AVG&#8221; (Figure 5A).</li>
	<li>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.</li>
	<li>Load the .jpg file generated from the stroma channel (with filename ending _3).</li>
	<li>Enter the settings for generating the mask from the DAPI channel:
	<ol>
		<li>Check the box &#8220;Apply mask?&#8221; Set the threshold for converting the DAPI image into a binary mask to 10 (range 0 - 255).</li>
		<li>Check the box &#8220;8-bit?&#8221; Check the box &#8220;Dilate?&#8221; and set the grade of dilation to 5 px. Check the box &#8220;w/ erosion?&#8221;.</li>
		<li>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 &#181;m with a size of 2,048 x 2,048 px (pixel scaling xy: 0.346 &#181;m), 29 px correspond to 10 &#181;m.</li>
	</ol>
	</li>
	<li>Check box &#8220;Use Otsu?&#8221; to use Otsu&#8217;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.</li>
	<li>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).
	<ol>
		<li>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.</li>
	</ol>
	</li>
	<li>Measure the cell size distribution of the analyzed cell types with any image analysis software that includes object segmentation functions. Determine &#963; 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 &#963; and can be quite different for various cell types. (For details, see Figure 5B).
	<ol>
		<li>From these measurements, determine the &#8220;cell size cutoff&#8221;: the diameter in px that describes the smallest object still recognized as a complete cell by the image analysis tool.</li>
	</ol>
	</li>
	<li>Enter parameters in the respective boxes on the graphical user interface (Figure 5A).
	<ol>
		<li>Enter the cell size cut off, i.e., the minimum cell size allowed in the simulation, as a diameter in px.</li>
		<li>Enter &#963; in px for the simulation of the cell size distribution for red, green, and blue cells (from step 5.9).</li>
		<li>Check the &#8220;Delete&#8221; box in the subsection &#8220;Cells in Mask&#8221;. 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 &#8220;Avoid cell overlap?&#8221; in the subsection &#8220;Cell exclusion&#8221;.</li>
		<li>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.</li>
		<li>Set the maximum area of overlap to 100% if the overlap is defined by the minimum distance from center to center.</li>
		<li>Set the simulation tool to 1,000 repetitions.</li>
		<li>Check the box &#8220;Autosave cells?&#8221; 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 &#8220;Autosave images?&#8221; to save a .tiff file for each simulated image. Enter a common tag for the saved files. 
		NOTE: The &#8220;Autosave cells?&#8221; 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.</li>
	</ol>
	</li>
	<li>Start the simulation by clicking on &#8220;Run simulation&#8221;. 
	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.</li>
	<li>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.</li>
	<li>Apply appropriate statistical methods depending on the analysis19 (for Figure 7, we used a two-tailed Wilcoxon signed rank test).</li>
</ol>

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E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Euclidian+distance+mapping.">Euclidian distance mapping.</a> Computer Graphics and Image Processing. 14, 21 (1980).</li><li>Tellier, J., Kallies, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Finding+a+home+for+plasma+cells+-+a+niche+to+survive.">Finding a home for plasma cells - a niche to survive.</a> Eur J Immunol. , (2014).</li><li>Winter, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Megakaryocytes+constitute+a+functional+component+of+a+plasma+cell+niche+in+the+bone+marrow.">Megakaryocytes constitute a functional component of a plasma cell niche in the bone marrow.</a> Blood. 116, 1867-1875 (2010).</li><li>Rozanski, C. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sustained+antibody+responses+depend+on+CD28+function+in+bone+marrow-resident+plasma+cells.">Sustained antibody responses depend on CD28 function in bone marrow-resident plasma cells.</a> J Exp Med. 208, 1435-1446 (2011).</li><li>Rodriguez Gomez, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Basophils+support+the+survival+of+plasma+cells+in+mice.">Basophils support the survival of plasma cells in mice.</a> J Immunol. 185, 7180-7185 (2010).</li><li>Belnoue, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Homing+and+adhesion+patterns+determine+the+cellular+composition+of+the+bone+marrow+plasma+cell+niche.">Homing and adhesion patterns determine the cellular composition of the bone marrow plasma cell niche.</a> J Immunol. 188, 1283-1291 (2012).</li><li>Kohler, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=G-CSF-mediated+thrombopoietin+release+triggers+neutrophil+motility+and+mobilization+from+bone+marrow+via+induction+of+Cxcr2+ligands.">G-CSF-mediated thrombopoietin release triggers neutrophil motility and mobilization from bone marrow via induction of Cxcr2 ligands.</a> Blood. 117, 4349-4357 (2011).</li></ol>

Juan Escribano Navarro is affiliated with Wimasis GmbH, Munich, Germany. The other authors have no conflicts of interest to declare.

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...

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 cells1,2 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 there6-9.
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).

<table border="0" cellpadding="0" cellspacing="0" width="1864">
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			<td height="21" style="height:21px;width:365px;">Name of Reagent/ Equipment</td>
			<td style="width:145px;">Company</td>
			<td style="width:155px;">Catalog Number</td>
			<td style="width:1200px;">Comments/Description</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Neomycin</td>
			<td style="width:145px;">Sigma</td>
			<td style="width:155px;">N6386 SIGMA</td>
			<td>Neomycin trisulfate salt hydrate, EU hazard code: GHS08; www.sigmaaldrich.com</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:365px;">Ursovit AD3EC</td>
			<td style="width:145px;">Serumwerke Bernburg</td>
			<td style="width:155px;"></td>
			<td>1 ml contains: 50.000 I.E retinyl palmitate,&#160; 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</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:365px;">Transfer buffer (100ml PBS, 1ml 1M HEPES, 50U/ml Penicillin/Streptomycin)</td>
			<td style="width:145px;">Sigma</td>
			<td style="width:155px;">P4333&#160;&#160; Sigma&#160;&#160;&#160;&#160; H3375&#160;&#160; Sigma&#160;&#160;</td>
			<td>www.sigmaaldrich.com</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:365px;">4-Hydroxy-3-nitrophenylacetyl hapten conjugated to chicken gamma globulin)</td>
			<td style="width:145px;"></td>
			<td style="width:155px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Chicken gamma globulin (CGG) 100 mg</td>
			<td style="width:145px;">Rockland</td>
			<td style="width:155px;">D602-0100</td>
			<td>www.rockland-inc.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">20 % Paraformaldehyde solution (EM-grade)</td>
			<td style="width:145px;">Science Services</td>
			<td style="width:155px;">15713</td>
			<td>EU hazard codes: GHS02, GHS05, GHS07, GHS08</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">D(+)-Sucrose</td>
			<td style="width:145px;">Carl Roth</td>
			<td style="width:155px;">4621.1</td>
			<td>www.carlroth.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Dry ice</td>
			<td style="width:145px;"></td>
			<td style="width:155px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:365px;">Acetone</td>
			<td style="width:145px;">Sigma.Aldrich</td>
			<td style="width:155px;">179124 SIGMA-ALDRICH</td>
			<td>EU hazard codes: GHS02, GHS07; www.sigmaaldrich.com</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:365px;">Hexane</td>
			<td style="width:145px;">Sigma-Aldrich</td>
			<td style="width:155px;">208752 SIGMA-ALDRICH</td>
			<td>EU hazard codes: GHS02, GHS07, GHS08, GHS09; www.sigmaaldrich.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Tissue-Tek cryomolds (standard)</td>
			<td style="width:145px;">Sakura&#160;</td>
			<td style="width:155px;">4557</td>
			<td>25 x 20 x 5 mm; www.sakuraus.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Tissue-Tek O.C.T.</td>
			<td style="width:145px;">Sakura&#160;</td>
			<td style="width:155px;">4583</td>
			<td>&#160;www.sakuraus.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Kawamoto&#39;s SCEM embedding medium</td>
			<td style="width:145px;">Section-Lab, JP</td>
			<td style="width:155px;"></td>
			<td>http://section-lab.jp/index.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Kawamoto&#39;s cryosection preparation kit&#160;</td>
			<td style="width:145px;">Section-Lab, JP</td>
			<td style="width:155px;"></td>
			<td>http://section-lab.jp/index.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Kawamoto&#39;s cryofilm type 2C(9)</td>
			<td style="width:145px;">Section-Lab, JP</td>
			<td style="width:155px;"></td>
			<td>http://section-lab.jp/index.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Microtome blade MX35 Premier Plus, Low Profile</td>
			<td style="width:145px;">Thermo Scientific</td>
			<td style="width:155px;">3052835</td>
			<td>L X W: 80 x 8 mm (31.5 x 3.13&#34;), thickness: 0.25 mm (0.01&#34;);www.thermoscientific.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">polyclonal rabbit anti-RFP antibody, biotinylated</td>
			<td style="width:145px;">Rockland</td>
			<td style="width:155px;">600-406-379</td>
			<td>www.rockland-inc.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Alexa Fluor 555 streptavidin</td>
			<td style="width:145px;">Life Technologies</td>
			<td style="width:155px;">S-32355</td>
			<td>www.lifetechnologies.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">rat anti-MBP</td>
			<td style="width:145px;">J. and N. Lee</td>
			<td style="width:155px;"></td>
			<td>available from: J. and N. Lee, Mayo Clinic, Scottsdale, AZ , U.S.A., clone MT-14.7</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">goat anti-rat-Alexa Fluor 647</td>
			<td style="width:145px;">Life Technologies</td>
			<td style="width:155px;">A-21247</td>
			<td>www.lifetechnologies.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">rat anti-B220 - Alexa Fluor 594</td>
			<td style="width:145px;"></td>
			<td style="width:155px;"></td>
			<td>produced and coupled in-house (DRFZ), clone RA3.6B2, Alexa Fluor 594 from Life Technologies</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">mouse anti-l1 light chain -FITC</td>
			<td style="width:145px;"></td>
			<td style="width:155px;"></td>
			<td>produced and coupled in-house (DRFZ), clone LS136</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">rat anti-k light chain - FITC</td>
			<td style="width:145px;"></td>
			<td style="width:155px;"></td>
			<td>produced and coupled in-house (DRFZ), clone 187.1</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">DAPI (4&#8242;,6-diamidino-2-phenylindole dihydrochloride)</td>
			<td style="width:145px;">Sigma</td>
			<td style="width:155px;">D9542 SIGMA</td>
			<td>www.sigmaaldrich.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Fluorescent mounting medium</td>
			<td style="width:145px;">DAKO</td>
			<td style="width:155px;">S3023</td>
			<td>www.dako.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Cover slips (24mm x 24mm x 0.13-0.16 mm)</td>
			<td style="width:145px;">Carl Roth</td>
			<td style="width:155px;">H875.2</td>
			<td>www.carlroth.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Superfrost slides glasses (75 mm x 25 mm)</td>
			<td style="width:145px;">VWR</td>
			<td style="width:155px;">48311-703</td>
			<td>www.vwr.com</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Laser scanning confocal microscope&#160;</td>
			<td style="width:145px;"></td>
			<td style="width:155px;"></td>
			<td>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.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Automated image analysis tools for bone marrow</td>
			<td style="width:145px;">Wimasis, Munich</td>
			<td style="width:155px;"></td>
			<td>The cell contact tool and cell vicinity tool will be made available by Wimasis upon request: www.wimasis.de</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">VC2012 Runtime</td>
			<td style="width:145px;">Microsoft</td>
			<td style="width:155px;"></td>
			<td>free download: http://www.microsoft.com/en-us/download/details.aspx?id=30679</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Simulation tool for random cell positioning</td>
			<td style="width:145px;"></td>
			<td style="width:155px;"></td>
			<td>available from us, upon request</td>
		</tr>
		<tr height="43">
			<td height="43" style="height:43px;width:365px;">Image analysis software with image segmentation functions</td>
			<td style="width:145px;"></td>
			<td style="width:155px;"></td>
			<td>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: http://www.cellprofiler.org)&#160;&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:365px;">Fiji image analysis software&#160;</td>
			<td style="width:145px;"></td>
			<td style="width:155px;"></td>
			<td>free download: http://fiji.sc/Fiji. Fiji was used by trained raters for manual cell count (Figure 3).</td>
		</tr>
	</tbody>
</table>

automated quantification of hematopoietic cell &#8211; stromal cell interactions in histological images of undecalcified bone

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.

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...

Watch this Scientific Journal Video about Automated Quantification of Hematopoietic Cell â€“ Stromal Cell Interactions in Histological Images of Undecalcified Bone at JoVE.com

Automated Quantification of Hematopoietic Cell &amp;#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone

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.

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. ...

automated-quantification-hematopoietic-cell-stromal-cell-interactions

Research

JoVE Journal

Developmental Biology

11.5K vues.  German Rheumatism Research Center, a Leibniz Institute. 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.

Vidéo: Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone

Quantification of  Colonic Stem Cell Mutations

The ability to measure stem cell mutations is a powerful tool to quantify in a critical cell population if, and to what extent, a chemical can induce mutations that potentially lead to cancer. The use of an enzymatic assay to quantify stem cell mutations in the X-linked glucose-6-phosphate dehydrogenase gene has been previously reported.1 This method requires the preparation of frozen sections and incubation of the sectioned tissue with a reaction mixture that yields a blue color if the cells produce functional glucose-6-phosphate dehydrogenase (G6PD) enzyme. If not, the cells appear whitish. We have modified the reaction mixture using Optimal Cutting Temperature Compound (OCT) medium in place of polyvinyl alcohol. This facilitates pH measurement, increases solubilization of the G6PD staining components and restricts diffusion of the G6PD enzyme. To demonstrate that a mutation occurred in a stem cell, the entire crypt must lack G6PD enzymatic activity. Only if a stem cell harbors a phenotypic G6PD mutation will all of the progeny in the crypt lack G6PD enzymatic activity. To identify crypts with a stem cell mutation, four consecutive adjacent frozen sections (a level) were cut at 7 &#181;m thicknesses. This approach of making adjacent cuts provides conformation that a crypt was fully mutated since the same mutated crypt will be observed in adjacent sections. Slides with tissue samples that were more than 50 &#181;m apart were prepared to assess a total of &#62;104 crypts per mouse. The mutation frequency is the number of observed mutated (white) crypts &#247; by the number of wild type (blue) crypts in a treatment group.

We report significant improvements for the reproducible measurement of somatic colonic stem cell mutations after exposure of mice to potential DNA damaging agents.

The ability to measure stem cell mutations is a powerful tool to quantify in a critical cell population if, and to what extent, a chemical can induce ...

Competitive Transplants to Evaluate Hematopoietic Stem Cell Fitness

The gold standard definition of a hematopoietic stem cell (HSC) is a cell that when transferred into an irradiated recipient will have the ability to reestablish blood cell production for the lifespan of the recipient. This protocol explains how to set up a functional assay to compare the HSC capacities of two different populations of cells, such as bone marrow from mice of two different genotypes, and how to analyze the recipient mice by flow cytometry. The protocol uses HSC equivalents rather than cell sorting for standardization and discusses the advantages and disadvantages of both approaches. We further discuss different variations to the basic protocol, including serial transplants, limiting dilution assays, homing assays and non-competitive transplants, including the advantages and preferred uses of these varied approaches. These assays are central for the study of HSC function and could be used not only for the investigation of fundamental HSC intrinsic aspects of biology but also for the development of preclinical assays for bone marrow transplant and HSC expansion in culture.

This protocol provides step-by-step guidelines for setting up competitive mouse bone marrow transplant experiments to study hematopoietic stem/progenitor cell function without prior purification of stem cells by cell sorting.

The gold standard definition of a hematopoietic stem cell (HSC) is a cell that when transferred into an irradiated recipient will have the ability to ...

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

During somitogenesis, pairs of epithelial somites form in a progressive manner, budding off from the anterior end of the pre-somitic mesoderm (PSM) with a strict species-specific periodicity. The periodicity of the process is regulated by a molecular oscillator, known as the "segmentation clock," acting in the PSM cells. This clock drives the oscillatory patterns of gene expression across the PSM in a posterior-anterior direction. These so-called clock genes are key components of three signaling pathways: Wnt, Notch, and fibroblast growth factor (FGF). In addition, Notch signaling is essential for synchronizing intracellular oscillations in neighboring cells. We recently gained insight into how this may be mechanistically regulated. Upon ligand activation, the Notch receptor is cleaved, releasing the intracellular domain (NICD), which moves to the nucleus and regulates gene expression. NICD is highly labile, and its phosphorylation-dependent turnover acts to restrict Notch signaling. The profile of NICD production (and degradation) in the PSM is known to be oscillatory and to resemble that of a clock gene. We recently reported that both the Notch receptor and the Delta ligand, which mediate intercellular coupling, themselves exhibit dynamic expression at both the mRNA and protein levels. In this article, we describe the sensitive detection methods and detailed image analysis tools that we used, in combination with the computational modeling that we designed, to extract and overlay expression data from distinct points in the expression cycle. This allowed us to construct a spatio-temporal picture of the dynamic expression profile for the receptor, the ligand, and the Notch target clock genes throughout an oscillation cycle. Here, we describe the protocols used to generate and culture the PSM explants, as well as the procedure to stain for the mRNA or protein. We also explain how the confocal images were subsequently analyzed and temporally ordered computationally to generate ordered sequences of clock expression snapshots, hereafter defined as "kymographs," for the visualization of the spatiotemporal expression of Delta-like1 (Dll1) and Notch1 throughout the PSM.

The segmentation clock drives oscillatory gene expression across the pre-somitic mesoderm (PSM). Dynamic Notch activity is key to this process. We use imaging and computational analyses to extract temporal dynamics from spatial expression data to demonstrate that Delta ligand and Notch receptor expression oscillate in the vertebrate PSM.

During somitogenesis, pairs of epithelial somites form in a progressive manner, budding off from the anterior end of the pre-somitic mesoderm (PSM) with a ...

A Novel Mammary Fat Pad Transplantation Technique to Visualize the Vessel Generation of Vascular Endothelial Stem Cells

Endothelial cells (ECs) are the fundamental building blocks of the vascular architecture and mediate vascular growth and remodeling to ensure proper vessel development and homeostasis. However, studies on endothelial lineage hierarchy remain elusive due to the lack of tools to gain access as well as to directly evaluate their behavior in vivo. To address this shortcoming, a new tissue model to study angiogenesis using the mammary fat pad has been developed. The mammary gland develops mostly in the postnatal stages, including puberty and pregnancy, during which robust epithelium proliferation is accompanied by extensive vascular remodeling. Mammary fat pads provide space, matrix, and rich angiogenic stimuli from the growing mammary epithelium. Furthermore, mammary fat pads are located outside the peritoneal cavity, making them an easily accessible grafting site for assessing the angiogenic potential of exogenous cells. This work also describes an efficient tracing approach using fluorescent reporter mice to specifically label the targeted population of vascular endothelial stem cells (VESCs) in vivo. This lineage tracing method, coupled with subsequent tissue whole-mount microscopy, enable the direct visualization of targeted cells and their descendants, through which the proliferation capability can be quantified and the differentiation commitment can be fate-mapped. Using these methods, a population of bipotent protein C receptor (Procr) expressing VESCs has recently been identified in multiple vascular systems. Procr+ VESCs, giving rise to both new ECs and pericytes, actively contribute to angiogenesis during development, homeostasis, and injury repair. Overall, this manuscript describes a new mammary fat pad transplantation and in vivo lineage tracing techniques that can be used to evaluate the stem cell properties of VESCs.

This work demonstrates a novel approach to assess the proliferation, differentiation, and vessel-forming potential of vascular endothelial stem cells (VESCs) through mammary fat pad transplantation followed by whole-mount tissue preparation for microscopic observation. A lineage tracing strategy to investigate the behavior of VESCs in vivo is also presented.

Endothelial cells (ECs) are the fundamental building blocks of the vascular architecture and mediate vascular growth and remodeling to ensure proper ...

Bioengineering of Humanized Bone Marrow Microenvironments in Mouse and Their Visualization by Live Imaging

Human hematopoietic stem cells (HSCs) reside in the bone marrow (BM) niche, an intricate, multifactorial network of components producing cytokines, growth factors, and extracellular matrix. The ability of HSCs to remain quiescent, self-renew or differentiate, and acquire mutations and become malignant depends upon the complex interactions they establish with different stromal components. To observe the crosstalk between human HSCs and the human BM niche in physiological and pathological conditions, we designed a protocol to ectopically model and image a humanized BM niche in immunodeficient mice. We show that the use of different cellular components allows for the formation of humanized structures and the opportunity to sustain long-term human hematopoietic engraftment. Using two-photon microscopy, we can live-image these structures in situ at the single-cell resolution, providing a powerful new tool for the functional characterization of the human BM microenvironment and its role in regulating normal and malignant hematopoiesis.

A method to create and live-image different humanized bone-marrow niches in mice is presented. Based on the supportive niche created by human mesenchymal cells, the addition of human endothelial cells induces the formation of human vessels, while the addition of rhBMP-2 induces the formation of human-mouse chimeric mature bone tissue.

Human hematopoietic stem cells (HSCs) reside in the bone marrow (BM) niche, an intricate, multifactorial network of components producing cytokines, growth ...

Corneal Tissue Engineering: An In Vitro Model of the Stromal-nerve Interactions of the Human Cornea

Tissue engineering has gained substantial recognition due to the high demand for human cornea replacements with an estimated 10 million people worldwide suffering from corneal vision loss1. To address the demand for viable human corneas, significant progress in three-dimensional (3D) tissue engineering has been made2,3,4. These cornea models range from simple monolayer systems to multilayered models, leading to 3D full-thickness corneal equivalents2.
However, the use of a 3D tissue-engineered cornea in the context of in vitro disease models studied to date lacks resemblance to the multilayered 3D corneal tissue structure, function, and the networking of different cell types (i.e., nerve, epithelium, stroma, and endothelium)2,3. In addition, the demand for in vitro cornea tissue models has increased in an attempt to reduce animal testing for pharmaceutical products. Thus, more sophisticated models are required to better match systems to human physiological requirements, and the development of a model that is more relevant to the patient population is absolutely necessary. Given that multiple cell types in the cornea are affected by diseases and dystrophies, such as Keratoconus, Diabetic Keratopathy, and Fuchs, this model includes a 3D co-culture model of primary human corneal fibroblasts (HCFs) from healthy donors and neurons from the SH-SY5Y cell line. This allows us for the first time to investigate the interactions between the two cell types within the human corneal tissue. We believe that this model could potentially dissect the underlying mechanisms associated with the stromal-nerve interactions of corneal diseases that exhibit nerve damages. This 3D model mirrors the basic anatomical and physiological nature of the corneal tissue in vivo and can be used in the future as a tool for investigating corneal defects as well as screening the efficacy of various agents before animal testing.

Corneal Tissue Engineering: An In Vitro Model of the Stromal-nerve Interactions of the Human Cornea

This protocol describes a novel three-dimensional in vitro model, where corneal stromal cells and differentiated neuronal cells are cultured together to assist in the examination and understanding of the interactions of the two cell types.

Tissue engineering has gained substantial recognition due to the high demand for human cornea replacements with an estimated 10 million people worldwide ...

A RANKL-based Osteoclast Culture Assay of Mouse Bone Marrow to Investigate the Role of mTORC1 in Osteoclast Formation

Osteoclasts are unique bone-resorbing cells that differentiate from the monocyte/macrophage lineage of bone marrow. Dysfunction of osteoclasts may result in a series of bone metabolic diseases, including osteoporosis. To develop pharmaceutical targets for the prevention of pathological bone mass loss, the mechanisms by which osteoclasts differentiate from precursors must be understood. The ability to isolate and culture a large number of osteoclasts in vitro is critical in order to determine the role of specific genes in osteoclast differentiation. Inactivation of the mammalian/mechanistic target of rapamycin complex 1 (TORC1) in osteoclasts can decrease osteoclast number and increase bone mass; however, the underlying mechanisms require further study. In the present study, a RANKL-based protocol to isolate and culture osteoclasts from mouse bone marrow and to study the influence of mTORC1 inactivation on osteoclast formation is described. This protocol successfully resulted in a large number of giant osteoclasts, typically within one week. Deletion of Raptor impaired osteoclast formation and decreased the activity of secretory tartrate-resistant acid phosphatase, indicating that mTORC1 is critical for osteoclast formation.

This manuscript describes a protocol to isolate and culture osteoclasts in vitro from mouse bone marrow, and to study the role of the mammalian/mechanistic target of rapamycin complex 1 in osteoclast formation.

Osteoclasts are unique bone-resorbing cells that differentiate from the monocyte/macrophage lineage of bone marrow. Dysfunction of osteoclasts may result ...

Single-Cell Quantification of Protein Degradation Rates by Time-Lapse Fluorescence Microscopy in Adherent Cell Culture

Proteins are in a dynamic state of synthesis and degradation and their half-lives can be adjusted under various circumstances. However, most commonly used approaches to determine protein half-life are either limited to population averages from lysed cells or require the use of protein synthesis inhibitors. This protocol describes a method to measure protein half-lives in single living adherent cells, using SNAP-tag fusion proteins in combination with fluorescence time-lapse microscopy. Any protein of interest fused to a SNAP-tag can be covalently bound by a fluorescent, cell permeable dye that is coupled to a benzylguanine derivative, and the decay of the labeled protein population can be monitored after washout of the residual dye. Subsequent cell tracking and quantification of the integrated fluorescence intensity over time results in an exponential decay curve for each tracked cell, allowing for determining protein degradation rates in single cells by curve fitting. This method provides an estimate for the heterogeneity of half-lives in a population of cultured cells, which cannot easily be assessed by other methods. The approach presented here is applicable to any type of cultured adherent cells expressing a protein of interest fused to a SNAP-tag. Here we use mouse embryonic stem (ES) cells grown on E-cadherin-coated cell culture plates to illustrate how single cell degradation rates of proteins with a broad range of half-lives can be determined.

This protocol describes a method to determine protein half-lives in single living adherent cells, using pulse labeling and fluorescence time-lapse imaging of SNAP-tag fusion proteins.

Proteins are in a dynamic state of synthesis and degradation and their half-lives can be adjusted under various circumstances. However, most commonly used ...

Clonal Analysis of Embryonic Hematopoietic Stem Cell Precursors Using Single Cell Index Sorting Combined with Endothelial Cell Niche Co-culture

The ability to study hematopoietic stem cell (HSC) genesis during embryonic development has been limited by the rarity of HSC precursors in the early embryo and the lack of assays that functionally identify the long-term multilineage engraftment potential of individual putative HSC precursors. Here, we describe methodology that enables the isolation and characterization of functionally validated HSC precursors at the single cell level. First, we utilize index sorting to catalog the precise phenotypic parameter of each individually sorted cell, using a combination of phenotypic markers to enrich for HSC precursors with additional markers for experimental analysis. Second, each index-sorted cell is co-cultured with vascular niche stroma from the aorta-gonad-mesonephros (AGM) region, which supports the maturation of non-engrafting HSC precursors to functional HSC with multilineage, long-term engraftment potential in transplantation assays. This methodology enables correlation of phenotypic properties of clonal hemogenic precursors with their functional engraftment potential or other properties such as transcriptional profile, providing a means for the detailed analysis of HSC precursor development at the single cell level.

Here we present methodology for the clonal analysis of hematopoietic stem cell precursors during murine embryonic development. We combine index sorting of single cells from the embryonic aorta-gonad-mesonephros region with endothelial cell co-culture and transplantation to characterize the phenotypic properties and engraftment potential of single hematopoietic precursors.

The ability to study hematopoietic stem cell (HSC) genesis during embryonic development has been limited by the rarity of HSC precursors in the early ...

Use of Hematopoietic Stem Cell Transplantation to Assess the Origin of Myelodysplastic Syndrome

Myelodysplastic syndromes (MDS) are a diverse group of hematopoietic stem cell disorders that are defined by ineffective hematopoiesis, peripheral blood cytopenias, dysplasia, and a propensity for transformation to acute leukemia. NUP98-HOXD13 (NHD13) transgenic mice recapitulate human MDS in terms of peripheral blood cytopenias, dysplasia, and transformation to acute leukemia. We previously demonstrated that MDS could be transferred from a genetically engineered mouse with MDS to wild-type recipients by transplanting MDS bone marrow nucleated cells (BMNC). To more clearly understand the MDS cell of origin, we have developed approaches to transplant specific, immunophenotypically defined hematopoietic subsets. In this article, we describe the process of isolating and transplanting specific populations of hematopoietic stem and progenitor cells. Following transplantation, we describe approaches to assess the efficiency of transplantation and persistence of the donor MDS cells.

We describe the use of hematopoietic stem cell transplantation (HSCT) to assess the malignant potential of genetically engineered hematopoietic cells. HSCT is useful for evaluating various malignant hematopoietic cells in vivo as well as generating a large cohort of mice with myelodysplastic syndromes (MDS) or leukemia to evaluate novel therapies.

Myelodysplastic syndromes (MDS) are a diverse group of hematopoietic stem cell disorders that are defined by ineffective hematopoiesis, peripheral blood ...