This work was funded in part by the National Institutes of Health (R01 AR067247) and in part by the Delaware INBRE program, supported by a grant from the National Institute of General Medical Sciences-NIGMS (P20 GM103446) from the National Institutes of Health and the State of Delaware. The contents of the manuscript do not necessarily reflect the views of the funding agencies.

1. Cell culture and image acquisition
<ol>
	<li>Culture RAW264.7 macrophages at 37 &#176;C and 5% CO2 in Dulbecco&#39;s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin, 1.5 g/L sodium bicarbonate, and 5 &#181;M &#946;-mercaptoethanol.
	<ol>
		<li>For monoculture imaging, culture RAW264.7 cells at a density of 25,000 cells/cm2 in a 5 mL cell culture flask with 1 mL of medium.</li>
	</ol>
	</li>
	<li>Culture NIH/3T3 cells at 37 &#176;C and 5% CO2 in DMEM supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin11.</li>
	<li>For coculture imaging, culture RAW264.7 macrophages and NIH/3T3 fibroblasts together at varied ratios, at a total density of 25,000 cells/cm2. Use coculture medium that is 1 part RAW264.7 medium (DMEM supplemented with 10% FBS, 1% penicillin-streptomycin, 1.5 g/L sodium bicarbonate, and 5 &#181;M &#946;-mercaptoethanol) and 1 part NIH/3T3 medium (DMEM supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin).</li>
	<li>Following seeding, incubate cells at 37 &#176;C and 5% CO2 to reach a viable cell density of 80% cell confluence.</li>
	<li>Image cells with an inverted microscope equipped with a 40x objective. Acquire all images in grayscale and export them in the raw &#39;.czi&#39; file.
	<ol>
		<li>Determine the capacity of the algorithm to accurately evaluate images with a range of image qualities. Acquire images with varying foci, producing both &#39;non-bulbous&#39; images (Figure 2) and &#39;bulbous&#39; images (Figure 3).</li>
		<li>Export and convert cell images to the 8-bit tiff (.tiff) image format using ImageJ using the &#39;Batch Convert&#39; function on the raw &#39;.czi&#39; files prior to MATLAB analysis. Store converted images in a local folder and manually transfer these image files to the relevant MATLAB bin.</li>
	</ol>
	</li>
</ol>
2. Image analysis-monoculture utilizing the &#34;monoculture.m&#34; file primarily
NOTE: The following steps were performed using MATLAB. Three files were used for the MATLAB protocol: &#34;process.m&#34; (Supplemental coding file 1), the file containing the algorithm, &#34;monoculture.m&#34; (Supplemental coding file 2), the file to run for analyzing monoculture images, and &#34;coculture_modified.m&#34; (Supplemental coding file 3), the file to run for analyzing coculture images.
<ol>
	<li>Use the area-based method to obtain images of cells showing relative height variations. Image outputs for each substep by utilizing the &#39;imshow()&#39; function for each subimage. Copy and paste the image file for analysis into the bin and enter the file name in the following command. Press run to start the program. 
	imread(&#39;filename.tiff&#39;)
	<ol>
		<li>Analyze images by &#39;opening by reconstruction&#39; followed by &#39;closing by reconstruction&#39; using source functions8 to magnify the foreground from the background. Use the first command for opening by reconstruction and the second and third sequences to close by reconstruction 
		&#39;imopen()&#39; 
		&#39;imerode()&#39; 
		&#39;imreconstruct()&#39;</li>
	</ol>
	</li>
	<li>Binarize the reconstructed images to pure black and pure white pixels utilizing a percentile-based identification system. Note that cells are converted to a pure white pixel value of &#39;255&#39; in this system, whereas background and extraneous (non-cellular) objects are converted to a pure black pixel value of &#39;0&#39;.
	<ol>
		<li>Distinguish cells from the background by using a percentile difference from the &#39;maximum relevant pixel,&#39; the largest pixel value comprising at least 0.5% of a given image.</li>
		<li>Analyze and evaluate the pixel values for RAW264.7 macrophages. If these values are within 4.5% of the maximum relevant pixel, then mark the pixel as cellular. 
		NOTE: This command can be issued using a simple if statement.</li>
		<li>For images containing bulbous cell profiles, such as those seen in Figure 2, implement an iterative procedure to correct for erroneous binarization at the centers of the cells (termed &#39;islands;&#39; see below), as follows.</li>
		<li>Determine an initial guess for the total cell coverage; 60% was utilized for this study. Note that the number of cells present within the image should be dependent on the cell coverage-the relationship between cell number and cell coverage can therefore be determined experimentally. Using this relationship, determine the variables &#39;Alpha&#39; and &#39;kappa.&#39; 
		NOTE: &#39;Alpha&#39; represents a 3 x 1 vector containing the following relative height percentiles: the height percentile at which cells were identified, the height percentile at which the background was identified, and the height percentile at which islands-regions in which intensity values were significantly lower than cell values-typically resided. &#39;Kappa&#39; represents the total area covered by cells in the image.</li>
		<li>Run the algorithm, which will (i) analyze images using initial estimates of alpha and kappa to fill a portion of the islands, and (ii) use the postanalysis cell counts and coverage to recalculate kappa. If the kappa value is within 10% of the initial guess, proceed to the next step. 
		NOTE: For images containing RAW264.7 and NIH/3T3 cells, alpha was found to be [4.3, 5.5, 10]. In other words, a difference of 4.3% from the maximum relevant pixel determined the height percentile at which cells were identified, a difference of 5.5% from the maximum relevant pixel determined the height percentile at which the background was identified. A difference of 10% from the maximum relevant pixel determined the height percentile at which islands typically resided.</li>
	</ol>
	</li>
	<li>Following binarization of the image, determine the average cell area automatically using the open-source circle finding algorithm, which performs a Hough transform on the binarized image12,13,14. Use the following command to obtain a vector of all center locations and radii of circles found within the image. 
	&#39;[centers, radii] = imfindcircle(A, [minradius, maxradius])&#39; 
	&#39;A&#39; in this command is the image of choice, and [minradius, maxradius] is the range of radii that the algorithm will attempt to detect. 
	NOTE: This procedure to determine the average cell area assumes that the morphology of macrophage cells was both circular and consistent among cells10. From experimental data, the radii of macrophages are observed to be most commonly between 30 and 50 pixels at 40x magnification. This pixel range is defined as the acceptable range for the circle finding algorithm. The radii might be significantly different for other cell types and would require determination using experimental analysis.
	<ol>
		<li>Use the radii outputs to calculate the average cell area by averaging. Analyze at least 10 cells to ensure accurate area identification.</li>
	</ol>
	</li>
	<li>Determine the total number of cells within the image using the average cell area.
	<ol>
		<li>Loop through the image matrix and count the total number of cell pixels. Determine the total cell coverage by dividing the number of cell pixels by the total number of pixels within the image. Determine the total number of cells within the image by dividing the number of cell pixels by the average area of a cell.</li>
	</ol>
	</li>
</ol>
3. Image analysis-coculture utilizing the &#34;coculture_modified.m&#34; file primarily
NOTE: The following steps were performed using MATLAB.
<ol>
	<li>Use the area-based method to obtain images of cells showing relative height variations. Image outputs for each substep by utilizing the &#39;imshow()&#39; function for each subimage. Copy and paste the image file for analysis into the bin and enter the file name in the following command. Press run to start the program. 
	&#39;imread(&#39;filename.tiff&#39;)&#39;
	<ol>
		<li>Analyze images by &#39;opening by reconstruction&#39; followed by &#39;closing by reconstruction&#39; using source functions10 to magnify the foreground from the background. Use the first command for opening by reconstruction and the second and third sequences to close by reconstruction. 
		&#39;imopen ()&#39; 
		&#39;imerode()&#39; 
		&#39;imreconstruct()&#39;</li>
	</ol>
	</li>
	<li>Conduct an analysis of cocultures containing RAW264.7 and NIH/3T3 cells by using two selective binarizations, obtained by exploiting the height differences between the two cell types.
	<ol>
		<li>Experimentally determine a parameter &#39;phi&#39; using images of RAW264.7 and NIH/3T3 cells, with phi representing the proportional height difference between the two cell types. Guess an initial phi value and iterate until cell counts and coverage match closely with manual counts, specific to the RAW264.7 and NIH/3T3 coculture. 
		NOTE: The value for phi used in these studies was found to be 3.2. Phi is used to selectively filter an image to appear to contain only RAW264.7 cells, as seen in Figure 4C.</li>
		<li>Determine RAW264.7 cell counts and total cell coverage in a similar fashion as for monoculture images.</li>
	</ol>
	</li>
	<li>Analyze the watershed-transformed image again without the phi parameter, detecting both macrophages and fibroblasts. Acquire NIH/3T3 fibroblast data by selectively subtracting RAW264.7 cell pixels, obtained using the standard thresholding and area-based quantification methods described in step 2.
	<ol>
		<li>Once the entire image has been analyzed, determine the total number of NIH/3T3 pixels by subtracting the total number of cell pixels from the number of RAW264.7 pixels. Calculate the coverage of NIH/3T3 and RAW264.7 cells by dividing by the number of cell pixels for each cell line by the total number of image pixels.</li>
	</ol>
	</li>
</ol>

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<li>Urello, M., Kiick, K., Sullivan, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integration+of+growth+factor+gene+delivery+with+collagen-triggered+wound+repair+cascades+using+collagen-mimetic+peptides%5BTitle%5D)+AND+%22Bioengineering+%26+Translational+Medicine%22%5BJournal%5D)">Integration of growth factor gene delivery with collagen-triggered wound repair cascades using collagen-mimetic peptides.</a> Bioengineering & Translational Medicine. 1 (2), 207-219 (2016).</li>
<li>Image Processing Toolbox Documentation. MATLAB_Detect and measure circular objects in an image. MathWorks. , Available from: 
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<li>Atherton, T., Kerbyson, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Size+invariant+circle+detection%5BTitle%5D)+AND+%22Image+and+Vision+Computing%22%5BJournal%5D)">Size invariant circle detection.</a> Image and Vision Computing. 17, 795-803 (1999).</li>
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The authors declare that they have no conflicts of interest.

We designed a general area-based procedure that accurately and efficiently counted cells on the basis of cell height, allowing for stain-free quantitation of cells even in coculture systems. Critical steps for this procedure included the implementation of a relative intensity system by which cells could be differentiated. The use of a relative height analysis served two purposes: the need for external parameters was rendered unnecessary, as relative parameters were constant for the given cell type and parameter, and abso...

Software is routinely implemented during image analysis techniques to ensure that the results are accurate, efficient, and unbiased. For cell-based assays, a common problem is the misidentification of cells. Images with improper focal and contrast settings may lead to cell blurring, in which the boundary of individual cells becomes hard to identify1. The presence of extraneous image features such as pores, bubbles, or other undesired objects can hamper counting procedures by slowing the counting process and leading to misidentification. Furthermore, cell counting can be onerous, and counting hundreds of replicates can be extremely time-consuming. Moreover, an inherent subjective bias exists during manual counting, and therefore decision-making regarding cell identification is often inaccurate2. Automated software offers exciting potential to bypass all these issues by rapidly and precisely differentiating cells from extraneous objects, including objects far beyond human capacity for precise detection, based on well-defined identification criteria that reduce the influence of investigator bias. Common techniques to identify cells using automated software involve two main methods: segmentation and thresholding3. Herein, we demonstrate a generalizable area-based protocol that enables rapid, accurate, and inexpensive cell counting within a widely accessible software framework.
Segmentation techniques, such as edge detection, seek to isolate individual cells by utilizing intensity differences within an image. Intensity changes that distinguish a cell from the rest of the image most often consist of sharp changes in brightness4. Edge detection involves a regularizing filtering step, followed by a differentiation step in which intensity changes are detected. The differentiation process identifies edges and contours within the image of high-intensity changes, and these edges and contours are correlated with cell presence.&#160;Although images with noise can be run through denoising algorithms4, edge detection techniques are ideally used for analyzing images with low background noise. The process functions optimally when cell boundaries are clearly and easily distinguishable and are not impeded by brightness contours unrelated to cell presence, cell blur, extraneous objects, or defined internal cell structures1,2. If an image is particularly noisy, cells may be further distinguished through fluorescent staining or transfection with fluorescent proteins2,5. Although this significantly improves the accuracy of segmentation techniques, it requires added costs and additional time investments to prepare cell cultures for imaging.
Thresholding techniques involve the division of an image into two categories: the foreground and the background, with cells assigned to the foreground3. These techniques utilize color/contrast changes to define the apparent height of an object; objects that are routinely &#39;taller&#39; than the background can be easily identified as cells. The watershed-transform functions in this way by associating surfaces with light pixels as the foreground and those with dark pixels as the background6,7. Through height-based identification, thresholding techniques can routinely distinguish noise from desired objects, provided they exist within the same focal plane. When paired with an area-based quantification, a watershed-transform can accurately identify groups of objects in environments where typical segmentation techniques such as edge detection would be inaccurate.
Watershed-transforms are commonly coupled with segmentation techniques to prepare images for a cleaner analysis, resulting in higher accuracy of cell counting. For this process, the watershed-transform is used to highlight potential regions of interest prior to segmentation. A watershed-transform provides unique benefits by identifying cells in the foreground of images, which can improve the accuracy of segmentation analysis by removing potential false positives for cells, such as uneven patches of background. However, difficulties can arise when attempting to adapt cell-based images to a watershed-transform. Images with high cell density can be plagued with undersegmentation, in which aggregates of cells are identified as a singular group rather than as individual components. The presence of noise or sharp intensity changes can also result in oversegmentation, in which the algorithm overisolates cells, resulting in excessive and inaccurate cell counts8.
Herein, we detail a method to minimize the primary drawbacks of the watershed-transform by incorporating components of a thresholding analysis within an area-based quantification algorithm, as depicted in Figure 1. Notably, this algorithm was implemented with open-source and/or widely available software, and application of this cell-counting framework was possible without expensive reagents or complex cell preparation techniques. RAW264.7 macrophages were used to demonstrate the method due to their critical role in regulating connective tissue maintenance and wound healing processes9. Additionally, NIH/3T3 fibroblasts were analyzed due to their key role in tissue maintenance and repair. Fibroblast cells often coexist with and support macrophages, generating the need to distinguish these phenotypically distinct cell types in coculture studies.
Cell counts from images with high viable cell density (VCD) could be quantified reliably and efficiently by calculating the area covered by the cells, and the average area occupied by a singular cell. The use of thresholding as opposed to segmentation for cell identification also enabled more complex analyses, such as experiments in which different cell types in cocultures were analyzed concurrently. NIH/3T3 fibroblasts, which are often found to colocalize with RAW264.7 macrophages within a wound healing site, were found to grow at a focal plane that was distinct from the focal plane of macrophages10. Accordingly, multiple thresholding algorithms were run to define the background and foreground depending on the cell type being analyzed, enabling accurate counting of two different cell types within the same image.

<table><tbody><tr><td>Axio Observer 7 Inverted Microscope</td><td>Zeiss</td><td>1028290770</td><td></td></tr><tr><td>&amp;beta;-mercaptoethanol</td><td>Life Technologies</td><td>21985023</td><td></td></tr><tr><td>Cell Scrapers</td><td>CellTreat</td><td>229310</td><td></td></tr><tr><td>Dublecco&#039;s Modified Eagle Medium</td><td>Fisher Scientific</td><td>12430047</td><td></td></tr><tr><td>Dublecco&#039;s PBS</td><td>Fisher Scientific</td><td>14190144</td><td></td></tr><tr><td>MATLAB Software</td><td>MathWorks</td><td>2021A</td><td></td></tr><tr><td>NIH/3T3 Cells</td><td>ATCC</td><td>ATCC CRL - 1658</td><td></td></tr><tr><td>Penicillin&amp;ndash;Streptomycin</td><td>Sigma Aldrich</td><td>P4333-20ML</td><td></td></tr><tr><td>RAW264.7 Cells</td><td>ATCC</td><td>ATCC TIB - 71</td><td></td></tr><tr><td>Sodium Bicarbonate</td><td>Sigma Aldrich</td><td>S6014-25G</td><td></td></tr><tr><td>T75 Cell Culture Flask</td><td>Corning</td><td>CLS3814-24EA</td><td></td></tr></tbody></table>

area-based image analysis algorithm for quantification of macrophage-fibroblast cocultures

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. 
We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. 
Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

Analysis of non-bulbous RAW264.7 macrophages was conducted in a monoculture setting at 25,000 cells/cm2. Representative images were taken of the cell culture and processed in MATLAB following conversion to 8-bit tiff in ImageJ. Algorithm outputs throughout the process were recorded and documented in Figure 2 for the representative image. In this image, the algorithm counted 226 cells, and this image count was verified by comparison with a manual count that identified 241 cells (6....

Watch this Scientific Journal Video about Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures at JoVE.com

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either ...

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2.5K Views.  University of Delaware. Deze methode kan worden gebruikt om onderzoekers te helpen bij het analyseren en tellen van co-culturen van cellen met behulp van een eenvoudige en effectieve gebiedsgebaseerde analyse. Deze techniek is relatief eenvoudig te implementeren met behulp van algemeen beschikbare software en biedt een betrouwbare en nauwkeurige manier om verschillende celtypen binnen een co-cultuuromgeving te identificeren. Deze methode kan inzicht geven in hoe specifieke co-culturen van cellen synergetisch samenwe...

Video: Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

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.

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.

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

Developmental Biology

A Simple and Efficient Method to Isolate Macrophages from Mixed Primary Cultures of Adult Liver Cells

Kupffer cells are liver-specific resident macrophages and play an important role in the physiological and pathological functions of the liver1-3. Although the isolation methods of liver macrophages have been well-described4-6, most of these methods require sophisticated equipment, such as a centrifugal elutriator and technical skills. Here, we provide a novel method to obtain liver macrophages in sufficient number and purity from mixed primary cultures of adult rat liver cells, as schematically illustrated in Figure 1.
After dissociation of the liver cells by two-step perfusion method7,8,a fraction mostly composed of parenchymal hepatocytes is prepared and seeded into T75 tissue culture flasks with culture medium composed of DMEM and 10% FCS.Parenchymal hepatocytes lose the epithelial cell morphology within a few days in culture, degenerate or transform into fibroblast-like cells (Figure 2). As the culture proceeds, around day 6, phase contrast-bright, round macrophage-like cells start to proliferate on the fibroblastic cell sheet (Figure 2). The growth of the macrophage-like cells continue and reach to maximum levels around day 12, covering the cell sheet on the flask surface. By shaking of the culture flasks, macrophages are readily suspended into the culture medium. Subsequent transfer and short incubation in plastic dishes result in selective adhesion of macrophages(Figure 3), where as other contaminating cells remain suspended. After several rinses with PBS, attached macrophages are harvested. More than 106 cells can be harvested repeatedly from the same T75 tissue culture flask at two to three day intervals for more than two weeks(Figure 3).The purities of the isolated macrophages were 95 to 99%, as evaluated by flow cytometry or immunocytochemistry with rat macrophage-specific antibodies (Figure 4).The isolated cells show active phagocytosis of polystylene beads (Figure 5), proliferative response to recombinant GM-CSF, secretion of inflammatory/anti-inflammatory cytokines upon stimulation with LPS, and formation of multinucleated giant cells9.
In conclusion, we provide a simple and efficient method to obtain liver macrophages in sufficient number and purity without complex equipment and skills.This method might be applicable to other mammalian species.

A novel method to obtain macrophages from primary culture of rat liver cells is described. This method utilizes the proliferation of macrophages in the culture, followed by shaking of culture flasks and purification by selective attachment to plastic dishes. This technique efficiently provides liver macrophages without complex equipment and skills.

Kupffer cells are liver-specific resident macrophages and play an important role in the physiological and pathological functions of the liver1-3. Although ...

Immunology and Infection

Time-Lapse 2D Imaging of Phagocytic Activity in M1 Macrophage-4T1 Mouse Mammary Carcinoma Cells in Co-cultures

Tumor-associated macrophages (TAMs) have been identified as an important component for tumor growth, invasion, metastasis, and resistance to cancer therapies. However, tumor-associated macrophages can be harmful to the tumor depending on the tumor microenvironment and can reversibly alter their phenotypic characteristics by either antagonizing the cytotoxic activity of immune cells or enhancing anti-tumor response. The molecular actions of macrophages and their interactions with tumor cells (e.g., phagocytosis) have not been extensively studied. Therefore, the interaction between immune cells (M1/M2-subtype TAM) and cancer cells in the tumor microenvironment is now a focus of cancer immunotherapy research. In the present study, a live cell coculture model of induced M1 macrophages and mouse mammary 4T1 carcinoma cells was developed to assess the phagocytic activity of macrophages using a time-lapse video feature using phase-contrast, fluorescent, and differential interference contrast (DIC) microscopy. The present method can observe and document multipoint live-cell imaging of phagocytosis. Phagocytosis of 4T1 cells by M1 macrophages can be observed using fluorescent microscopy before staining 4T1 cells with carboxyfluorescein succinimidyl ester (CFSE). The current publication describes how to coculture macrophages and tumor cells in a single imaging dish, polarize M1 macrophages, and record multipoint events of macrophages engulfing 4T1 cells during 13 h of coculture.

Macrophage phagocytic activity against cancer cells, specifically 4T1 mouse mammary carcinoma cells, was imaged in this study. The live cell coculture model was established and observed using a combination of fluorescent and differential interference contrast microscopy. This assessment was imaged using imaging software to develop multipoint time-lapse video.

Tumor-associated macrophages (TAMs) have been identified as an important component for tumor growth, invasion, metastasis, and resistance to cancer ...

Cancer Research

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Microfabricated cellular microarrays, which consist of contact-printed combinations of biomolecules on an elastic hydrogel surface, provide a tightly controlled, high-throughput engineered system for measuring the impact of arrayed biochemical signals on cell differentiation. Recent efforts using cell microarrays have demonstrated their utility for combinatorial studies in which many microenvironmental factors are presented in parallel. However, these efforts have focused primarily on investigating the effects of biochemical cues on cell responses. Here, we present a cell microarray platform with tunable material properties for evaluating both cell differentiation by immunofluorescence and biomechanical cell&#8211;substrate interactions by traction force microscopy. To do so, we have developed two different formats utilizing polyacrylamide hydrogels of varying Young&#39;s modulus fabricated on either microscope slides or glass-bottom Petri dishes. We provide best practices and troubleshooting for the fabrication of microarrays on these hydrogel substrates, the subsequent cell culture on microarrays, and the acquisition of data. This platform is well-suited for use in investigations of biological processes for which both biochemical (e.g., extracellular matrix composition) and biophysical (e.g., substrate stiffness) cues may play significant, intersecting roles.

Cell differentiation is regulated by a host of microenvironmental factors, including both matrix composition and substrate material properties. We describe here a technique utilizing cell microarrays in conjunction with traction force microscopy to evaluate both cell differentiation and biomechanical cell&#8211;substrate interactions as a function of microenvironmental context.

Microfabricated cellular microarrays, which consist of contact-printed combinations of biomolecules on an elastic hydrogel surface, provide a tightly ...

Examining Macrophage-Tumor Spheroid Interactions in a 3D Collagen Matrix Setup

A Macrophage-Tumor Spheroid Co-Invasion Assay

The interaction of immune and cancer cells and their respective impact on metastasis represents an important aspect of cancer research. So far, only a few protocols are available that allow an in vitro approximation of the in vivo situation. Here, we present a novel approach to observing the impact of human macrophages on the invasiveness of cancer cells, using tumor spheroids of H1299 non-small cell lung carcinoma cells embedded in a three-dimensional (3D) collagen I matrix. With this co-cultivation setup, we tested the impact of small interfering RNA (siRNA)-based depletion of regulatory factors in macrophages on the 3D invasion of cancer cells from the tumor spheroid compared to controls. This method allows the determination of different parameters, such as spheroid area or the number of invading cancer cells, and thus, to detect differences in cancer cell invasion. In this article, we present the respective setup, discuss the subsequent analysis, as well as the advantages and potential pitfalls of this method.

Here, we present a protocol to investigate the interaction of primary human monocyte-derived macrophages with a tumor spheroid in a three-dimensional (3D) collagen I matrix, with the possibility to compare the impact of soluble and physical properties of the microenvironment on cell invasion.

The interaction of immune and cancer cells and their respective impact on metastasis represents an important aspect of cancer research. So far, only a few ...

Fluorescence-mediated Tomography for the Detection and Quantification of Macrophage-related Murine Intestinal Inflammation

Murine models of disease are indispensable to scientific research. However, many diagnostic tools such as endoscopy or tomographic imaging are not routinely employed in animal models. Conventional experimental readouts often rely on post mortem and ex vivo analyses, which prevent intra-individual follow-up examinations and increase the number of study animals needed. Fluorescence-mediated tomography enables the non-invasive, repetitive, quantitative, three-dimensional assessment of fluorescent probes. It is highly sensitive and permits the use of molecular makers, which allows for the specific detection and characterization of distinct molecular targets. In particular, targeted probes represent an innovative tool for analyzing gene activation and protein expression in inflammation, autoimmune disease, infection, vascular disease, cell migration, tumorigenesis, etc. In this article, we provide step-by-step instructions on this sophisticated imaging technology for the in vivo detection and characterization of inflammation (i.e., F4/80-positive macrophage infiltration) in a widely used murine model of intestinal inflammation. This technique might also be used in other research areas, such as immune cell or stem cell tracking.

Target-specific probes represent an innovative tool for analyzing molecular mechanisms, such as protein expression in various types of disease (e.g., inflammation, infection, and tumorigenesis). In this study, we describe a quantitative three-dimensional tomographic assessment of intestinal macrophage infiltration in a murine model of colitis using F4/80-specific fluorescence-mediated tomography.

Murine models of disease are indispensable to scientific research. However, many diagnostic tools such as endoscopy or tomographic imaging are not ...

Medicine

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Microglia are brain phagocytes that participate in brain homeostasis and continuously survey their environment for dysfunction, injury, and disease. As the first responders, microglia have important functions to mitigate neuron and glia dysfunction, and in this process, they undergo a broad range of morphologic changes. Microglia morphologies can be categorized descriptively or, alternatively, can be quantified as a continuous variable for parameters such as cell ramification, complexity, and shape. While methods for quantifying microglia are applied to single cells, few techniques apply to multiple microglia in an entire photomicrograph. The purpose of this method is to quantify multiple and single cells using readily available ImageJ protocols. This protocol is a summary of the steps and ImageJ plugins recommended to convert fluorescence and bright-field photomicrographs into representative binary and skeletonized images and to analyze them using software plugins AnalyzeSkeleton (2D/3D) and FracLac for morphology data collection. The outputs of these plugins summarize cell morphology in terms of process endpoints, junctions, and length as well as complexity, cell shape, and size descriptors. The skeleton analysis protocol described herein is well suited for a regional analysis of multiple microglia within an entire photomicrograph or region of interest (ROI) whereas FracLac provides a complementary individual cell analysis. Combined, the protocol provides an objective, sensitive, and comprehensive assessment tool that can be used to stratify between diverse microglia morphologies present in the healthy and injured brain.

Microglia are brain immune cells that survey and react to altered brain physiology through morphologic changes which may be evaluated quantitatively. This protocol outlines an ImageJ based analysis protocol to represent microglia morphology as continuous data according to metrics such as cell ramification, complexity, and shape.

Microglia are brain phagocytes that participate in brain homeostasis and continuously survey their environment for dysfunction, injury, and disease. As ...

Neuroscience

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density and the location of immune cells in the TME have important diagnostic and prognostic values. Multiomic profiling of the TME has exponentially increased our understanding of the numerous cellular and molecular networks regulating tumor initiation and progression. However, these techniques do not provide information about the spatial organization of cells or cell-cell interactions. Affordable, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to complement single cell-based high-throughput technologies. Here, we describe a strategy that integrates serial imaging, sequential labeling, and image alignment to generate virtual multiparameter slides of whole tissue sections. Virtual slides are subsequently analyzed in an automated fashion using user-defined protocols that enable identification, quantification, and mapping of cell populations of interest. The image analysis is done, in this case using the analysis modules Tissuealign, Author, and HISTOmap. We present an example where we applied this strategy successfully to one clinical specimen, maximizing the information that can be obtained from limited tissue samples and providing an unbiased view of the TME in the entire tissue section.

Here, we describe a simple and accessible strategy for visualizing, quantifying, and mapping immune cells in formalin-fixed paraffin-embedded tumor tissue sections. This methodology combines existing imaging and digital analysis techniques with the purpose of expanding the multiplexing capability and multiparameter analysis of imaging assays.

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density ...

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Complex disease models demand cutting-edge tools able to deliver physiologically and pathologically relevant, actionable insights, and unveil otherwise invisible processes. Advanced cell assays closely mimicking in vivo scenery are establishing themselves as novel ways to visualize and measure the bidirectional tumor-host interplay influencing the progression of cancer. Here we describe two versatile protocols to recreate highly controllable 2D and 3D co-cultures in microdevices, mimicking the complexity of the tumor microenvironment (TME), under natural and therapy-induced immunosurveillance. In section 1, an experimental setting is provided to monitor crosstalk between adherent tumor cells and floating immune populations, by bright field time-lapse microscopy. As an applicative scenario, we analyze the effects of anti-cancer treatments, such as the so-called immunogenic cancer cell death inducers on the recruitment and activation of immune cells. In section 2, 3D tumor-immune microenvironments are assembled in a competitive layout. Differential immune infiltration is monitored by fluorescence snapshots up to 72 h, to evaluate combination therapeutic strategies. In both settings, image processing steps are illustrated to extract a plethora of immune cell parameters (e.g., immune cell migration and interaction, response to therapeutic agents). These simple and powerful methods can be further tailored to simulate the complexity of the TME encompassing the heterogeneity and plasticity of cancer, stromal and immune cells subtypes, as well as their reciprocal interactions as drivers of cancer evolution. The compliance of these rapidly evolving technologies with live-cell high-content imaging can lead to the generation of large informative datasets, bringing forth new challenges. Indeed, the triangle &#39;&#39;co-cultures/microscopy/advanced data analysis&#34; sets the path towards a precise problem parametrization that may assist tailor-made therapeutic protocols. We expect that future integration of cancer-immune on-a-chip with artificial intelligence for high-throughput processing will synergize a large step forward in leveraging the capabilities as predictive and preclinical tools for precision and personalized oncology.

In the age of immunotherapy and single-cell genomic profiling, cancer biology requires novel in vitro and computational tools for investigating the tumor-immune interface in a proper spatiotemporal context. We describe protocols to exploit tumor-immune microfluidic co-cultures in 2D and 3D settings, compatible with dynamic, multiparametric monitoring of cellular functions.

Complex disease models demand cutting-edge tools able to deliver physiologically and pathologically relevant, actionable insights, and unveil otherwise ...

In vitro Quantitative Imaging Assay for Phagocytosis of Dead Neuroblastoma Cells by iPSC-Macrophages

Microglia orchestrate neuroimmune responses in several neurodegenerative diseases, including Parkinson&#39;s disease and Alzheimer&#39;s disease. Microglia clear up dead and dying neurons through the process of efferocytosis, a specialized form of phagocytosis. The phagocytosis function can be disrupted by environmental or genetic risk factors that affect microglia. This paper presents a rapid and simple in vitro&#160;microscopy protocol for studying microglial efferocytosis in an induced pluripotent stem cell (iPSC) model of microglia, using a human neuroblastoma cell line (SH-SY5Y) labeled with a pH-sensitive dye for the phagocytic cargo. The procedure results in a high yield of dead neuroblastoma cells, which display surface phosphatidylserine, recognized as an &#34;eat-me&#34; signal by phagocytes. The 96-well plate assay is suitable for live-cell time-lapse imaging, or the plate can be successfully fixed prior to further processing and quantified by high-content microscopy. Fixed-cell high-content microscopy enables the assay to be scaled up for screening of small molecule inhibitors or assessing the phagocytic function of genetic variant iPSC lines. While this assay was developed to study phagocytosis of whole dead neuroblastoma cells by iPSC-macrophages, the assay can be easily adapted for other cargoes relevant to neurodegenerative diseases, such as synaptosomes and myelin, and other phagocytic cell types.

Neurodegenerative diseases are associated with dysregulated microglia functions. This article outlines an in vitro assay of phagocytosis of neuroblastoma cells by iPSC-macrophages. Quantitative microscopy readouts are described for both live-cell time-lapse imaging and fixed-cell high-content imaging.

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

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