Name: area-based image analysis algorithm for quantification of macrophage-fibroblast cocultures
Uploaded: 2022-02-15T13:00:00
Description: Watch this Scientific Journal Video about Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures at JoVE.com

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...

<ol>
	<li>Young, D., Glasbey, C., Gray, A., Martin, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+and+sizing+of+cells+in+microscope+images+by+template+matching+and+edge+detection.">Identification and sizing of cells in microscope images by template matching and edge detection.</a> Fifth International Conference on Image Processing and its Applications, 1995. , 266-270 (1995).</li><li>Zhu, R., Sui, D., Qin, H., Hao, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+extended+type+cell+detection+and+counting+method+based+on+FCN.">An extended type cell detection and counting method based on FCN.</a> Proc. - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). , 51-56 (2018).</li><li>Choudhry, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+method+for+automated+colony+and+cell+counting+by+digital+image+analysis+based+on+edge+detection.">High-throughput method for automated colony and cell counting by digital image analysis based on edge detection.</a> PLoS One. 11 (2), 0148469 (2016).</li><li>Torre, V., Poggio, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+edge+detection.">On edge detection.</a> MIT Artificial Intelligence Lab Memo 768. , 1-9 (1984).</li><li>Fuller, M. 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=Development+of+a+vital+fluorescent+staining+method+for+monitoring+bacterial+transport+in+subsurface+environments.">Development of a vital fluorescent staining method for monitoring bacterial transport in subsurface environments.</a> Applied and Environmental Microbiology. 66 (10), 4486-4496 (2000).</li><li>Meyer, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Topographic+distance+and+watershed+lines.">Topographic distance and watershed lines.</a> Signal Processing. 38 (1), 113-125 (1994).</li><li>Image Processing Toolbox Documentation. MATLAB_Marker-controlled watershed segmentation. MathWorks Available from: <a target="_blank" href="https://www.mathworks.com/help/images/marker-controlled-watershed-segmentation.html">https://www.mathworks.com/help/images/marker-controlled-watershed-segmentation.html</a> (2022)</li><li>Bala, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+improved+watershed+image+segmentation+technique+using+MATLAB.">An improved watershed image segmentation technique using MATLAB.</a> International Journal of Scientific and Engineering Research. 3 (6), 1206-1209 (2012).</li><li>Glaros, T., Larsen, M., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Macrophages+and+fibroblasts+during+inflammation,+tissue+damage+and+organ+injury.">Macrophages and fibroblasts during inflammation, tissue damage and organ injury.</a> Frontiers in Bioscience (Landmark Edition). 14, 3988-3993 (2009).</li><li>Witherel, C., Abebayehu, D., Barker, T., Spiller, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Macrophage+and+fibroblast+interactions+in+biomaterial-mediated+fibrosis.">Macrophage and fibroblast interactions in biomaterial-mediated fibrosis.</a> Advanced Healthcare Materials. 8 (4), 1801451 (2019).</li><li>Urello, M., Kiick, K., Sullivan, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integration+of+growth+factor+gene+delivery+with+collagen-triggered+wound+repair+cascades+using+collagen-mimetic+peptides.">Integration of growth factor gene delivery with collagen-triggered wound repair cascades using collagen-mimetic peptides.</a> Bioengineering &amp; 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: <a target="_blank" href="https://www.mathworks.com/help/images/detect-and-measure-circular-objects-in-an-image.html">https://www.mathworks.com/help/images/detect-and-measure-circular-objects-in-an-image.html</a> (2022)</li><li>Atherton, T., Kerbyson, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Size+invariant+circle+detection.">Size invariant circle detection.</a> Image and Vision Computing. 17, 795-803 (1999).</li><li>Maitra, M., Kumar Gupta, R., Mukherjee, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+and+counting+of+red+blood+cells+in+blood+cell+images+using+Hough+transform.">Detection and counting of red blood cells in blood cell images using Hough transform.</a> International Journal of Computer Applications. 53 (16), 18-22 (2012).</li><li>Uslu, F., Icoz, K., Tasdemir, K., Do&#287;an, R. S., Yilmaz, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image-analysis+based+readout+method+for+biochip:+Automated+quantification+of+immunomagnetic+beads,+micropads+and+patient+leukemia+cell.">Image-analysis based readout method for biochip: Automated quantification of immunomagnetic beads, micropads and patient leukemia cell.</a> Micron. 133, 102863 (2020).</li><li>Uslu, F., Icoz, K., Tasdemir, K., Yilmaz, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+quantification+of+immunomagnetic+beads+and+leukemia+cells+from+optical+microscope+images.">Automated quantification of immunomagnetic beads and leukemia cells from optical microscope images.</a> Biomedical Signal Processing and Control. 49, 473-482 (2019).</li><li>Anderl, J., Redpath, S., Ball, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+neuronal+and+astrocyte+co-culture+assay+for+high+content+analysis+of+neurotoxicity.">A neuronal and astrocyte co-culture assay for high content analysis of neurotoxicity.</a> Journal of Visualized Experiments: JoVE. (27), e1173 (2009).</li><li>Holt, D., Chamberlain, L., Grainger, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-cell+signaling+in+co-cultures+of+macrophages+and+fibroblasts.">Cell-cell signaling in co-cultures of macrophages and fibroblasts.</a> Biomaterials. 31 (36), 9382-9394 (2010).</li><li>Boddupalli, A., Zhu, L., Bratlie, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+for+implant+acceptance+and+wound+healing:+material+selection+and+implant+location+modulate+macrophage+and+fibroblast+phenotypes.">Methods for implant acceptance and wound healing: material selection and implant location modulate macrophage and fibroblast phenotypes.</a> Advanced Healthcare Materials. 5 (20), 2575-2594 (2016).</li><li>Wang, Z., Li, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generalizing+cell+segmentation+and+quantification.">Generalizing cell segmentation and quantification.</a> BMC Bioinformatics. 18 (1), 189 (2017).</li><li>Zou, K. 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=Statistical+validation+of+image+segmentation+quality+based+on+a+spatial+overlap+index.">Statistical validation of image segmentation quality based on a spatial overlap index.</a> Academic Radiology. 11 (2), 178-189 (2004).</li></ol>

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Axio Observer 7 Inverted Microscope</td>
			<td>Zeiss</td>
			<td>1028290770</td>
			<td></td>
		</tr>
		<tr>
			<td>&#946;-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&#39;s Modified Eagle Medium</td>
			<td>Fisher Scientific</td>
			<td>12430047</td>
			<td></td>
		</tr>
		<tr>
			<td>Dublecco&#39;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&#8211;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 ...

This method can be used to help researchers analyze and count co-cultures of cells using a simple and effective area-based analysis. This technique is relatively easy to implement using widely available software and offers a reliable and accurate means of identifying various cell types within a co-culture setting. This method can provide insight into how specific co-cultures of cells synergize to aid in tissue regeneration.This method can also be employed to validate monoculture biomarker studies. To begin, culture raw 264.7 macrophages at 37 degrees Celsius and 5%carbon dioxide for monoculture imaging in one milliliter of DMEM supplemented with FBS, penicillin-streptomycin, sodium bicarbonate, and beta-mercaptoethanol in a five milliliter cell culture flask at a density of 25, 000 cells per centimeter square. Culture NIH/3T3 cells in DMEM supplemented with 10%FBS and 1%penicillin-streptomycin.For co-culture imaging, culture raw 264.7 macrophages and NIH/3T3 fibroblasts using co-culture medium containing one part raw 264.7 medium and one part NIH/3T3 medium together at varied ratios and total density of 25, 000 cells per centimeter square. Following seeding, incubate the cells at 37 degrees Celsius and 5%carbon dioxide to reach a viable cell density of 80%cell confluence. To acquire cell images using an inverted microscope equipped with the 40X objective, determine the capacity of the algorithm to accurately evaluate images with a range of image qualities.Acquire images in gray scale with varying foci producing both non-bulbus and bulbus images and export them in the raw czi file. To obtain images of cells using the area-based method, copy and paste the image file for analysis into the bin and enter the filename by executing the command. Press Run to start the program.Analyze the images by opening by reconstruction, followed by closing by reconstruction using source functions to magnify the foreground from the background by executing the respective command. To binarize the reconstructed images utilizing a percentile-based identification system, distinguish cells from the background by using a percentile difference from the maximum relevant pixel with the largest pixel value comprising at least 0.5%of a given image. Analyze and evaluate the pixel values for raw 264.7 macrophages, ensuring that the values are within 4.5%of the maximum relevant pixel.Then mark the pixel as cellular. For images containing bulbus cell profiles, implement an iterative procedure to correct for erroneous binarization at the centers of the cells. Determine an initial guess for the total cell coverage.Run the algorithm and analyze images using initial estimates of alpha and kappa to fill a portion of the islands. Then use the post-analysis cell counts and coverage to recalculate kappa. To determine the average cell area after binarization of the image, obtain a vector of all center locations and radii of circles found within the image by executing the command.Use the radii outputs to calculate the average cell area by averaging and analyze at least 10 cells to ensure accurate area identification. Perform the image analysis using the commands as described previously. Then conduct an analysis of co-cultures containing raw 264.7 and NIH/3T3 cells by experimentally determining a parameter phi using images of raw 264.7 and NIH/3T3 cells.Guess an initial phi value and iterate until cell counts and coverage match closely with the manual counts specific to the raw 264.7 and NIH/3T3 co-culture. Determine raw 264.7 cell counts in total cell coverage as described previously for the monoculture images. Analyze the watershed transformed image again without the phi parameter, detecting both macrophages and fibroblasts.Acquire NIH/3T3 fibroblast data by selectively subtracting raw 264.7 cell pixels obtained using the standard thresholding and area-based quantification methods described previously. Analysis of non-bulbus raw 264.7 macrophages was performed and algorithm outputs were recorded. Average area calculations of the image using algorithm counted 226 cells, while a manual count identified 241 cells with an approximate error value of 6%Analysis of bulbus raw 264.7 macrophages was also carried out.The average area calculations of the image using algorithm counted 221 cells, verified by a manual count of 252 cells with an approximate error value of 12%Analysis of co-cultures containing both raw 264.7 macrophages and NIH/3T3 fibroblasts was conducted. Algorithm outputs for the raw 264.7 macrophage count were 137, while a manual count identified 155 cells with an approximate error value of 11%To determine the robustness of the cell counting algorithm, five raw 264.7 macrophages images were counted by automatic cell identification and manual user counts. The Dice coefficient was obtained with an average parameter of 0.85 across five images.The critical step within this protocol is the use of percentile-based parameters which allows for robust cell detection in both monoculture and co-culture images. This protocol can be used to identify cells of interest for further analysis using molecular biology techniques to assess biomarkers that define specific cell populations and biomarker development in co-culture systems.

Research

JoVE Journal

Bioengineering

Polymer Microarrays for High Throughput Discovery of Biomaterials

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a ...

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting ...

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows. The images are cross-correlated to ...

Self-reporting Scaffolds for 3-Dimensional Cell Culture

Culturing cells in 3D on appropriate scaffolds is thought to better mimic the in vivo microenvironment and increase cell-cell interactions. The resulting ...

Electric Cell-substrate Impedance Sensing for the Quantification of Endothelial Proliferation, Barrier Function, and Motility

Electric Cell-substrate Impedance Sensing (ECIS) is an in vitro impedance measuring system to quantify the behavior of cells within adherent cell layers. ...

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Quantitative cardiac function assessment remains a challenge for physiologists and clinicians.&#160;Although historically invasive methods have comprised ...

Hybrid &#181;CT-FMT imaging and image analysis

Hybrid  &#181;CT-FMT imaging and image analysis

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to ...

Quantifying Intermembrane Distances with Serial Image Dilations

A recently-described extracellular nanodomain, termed the perinexus, has been implicated in ephaptic coupling, which is an alternative mechanism for ...

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in ...

Simultaneous Quantification of Selected Kynurenines Analyzed by Liquid Chromatography-Mass Spectrometry in Medium Collected from Cancer Cell Cultures

The kynurenine pathway and the tryptophan catabolites called kynurenines have received increased attention for their involvement in immune regulation and ...