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.

area-based-image-analysis-algorithm-for-quantification-macrophage

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 combinatorial library of materials to be screened in a parallel, high throughput format1. Herein is described the formation and characterization of a polymer microarray using an on-chip photopolymerization technique 2. This involves mixing monomers at varied ratios to produce a library of monomer solutions, transferring the solution to a glass slide format using a robotic printing device and curing with UV irradiation. This format is readily amenable to many biological assays, including stem cell attachment and proliferation, cell sorting and low bacterial adhesion, allowing the ready identification of 'hit' materials that fulfill a specific biological criterion3-5. Furthermore, the use of high throughput surface characterization (HTSC) allows the biological performance to be correlated with physio-chemical properties, hence elucidating the biological-material interaction6. HTSC makes use of water contact angle (WCA) measurements, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). In particular, ToF-SIMS provides a chemically rich analysis of the sample that can be used to correlate the cell response with a molecular moiety. In some cases, the biological performance can be predicted from the ToF-SIMS spectra, demonstrating the chemical dependence of a biological-material interaction, and informing the development of hit materials5,3.

A description of the formation of a polymer microarray using an on-chip photopolymerization technique. The high throughput surface characterization using atomic force microscopy, water contact angle measurements, X-ray photoelectron spectroscopy and time of flight secondary ion mass spectrometry and a cell attachment assay is also described.

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

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting airways, whereas adenocarcinomas are typically more peripheral in location. Lung malignancy detection early in the disease process may be difficult due to several limitations: radiological resolution, bronchoscopic limitations in evaluating tissue underlying the airway mucosa and identifying early pathologic changes, and small sample size and/or incomplete sampling in histology biopsies. High resolution imaging modalities, such as optical frequency domain imaging (OFDI), provide non-destructive, large area 3-dimensional views of tissue microstructure to depths approaching 2 mm in real time (Figure 1)2-6. OFDI has been utilized in a variety of applications, including evaluation of coronary artery atherosclerosis6,7 and esophageal intestinal metaplasia and dysplasia6,8-10.
Bronchoscopic OCT/OFDI has been demonstrated as a safe in vivo imaging tool for evaluating the pulmonary airways11-23 (Animation). OCT has been assessed in pulmonary airways16,23 and parenchyma17,22 of animal models and in vivo human airway14,15. OCT imaging of normal airway has demonstrated visualization of airway layering and alveolar attachments, and evaluation of dysplastic lesions has been found useful in distinguishing grades of dysplasia in the bronchial mucosa11,12,20,21. OFDI imaging of bronchial mucosa has been demonstrated in a short bronchial segment (0.8 cm)18. Additionally, volumetric OFDI spanning multiple airway generations in swine and human pulmonary airways in vivo has been described19. Endobronchial OCT/OFDI is typically performed using thin, flexible catheters, which are compatible with standard bronchoscopic access ports. Additionally, OCT and OFDI needle-based probes have recently been developed, which may be used to image regions of the lung beyond the airway wall or pleural surface17.
While OCT/OFDI has been utilized and demonstrated as feasible for in vivo pulmonary imaging, no studies with precisely matched one-to-one OFDI:histology have been performed. Therefore, specific imaging criteria for various pulmonary pathologies have yet to be developed. Histopathological counterparts obtained in vivo consist of only small biopsy fragments, which are difficult to correlate with large OFDI datasets. Additionally, they do not provide the comprehensive histology needed for registration with large volume OFDI. As a result, specific imaging features of pulmonary pathology cannot be developed in the in vivo setting. Precisely matched, one-to-one OFDI and histology correlation is vital to accurately evaluate features seen in OFDI against histology as a gold standard in order to derive specific image interpretation criteria for pulmonary neoplasms and other pulmonary pathologies. Once specific imaging criteria have been developed and validated ex vivo with matched one-to-one histology, the criteria may then be applied to in vivo imaging studies. Here, we present a method for precise, one to one correlation between high resolution optical imaging and histology in ex vivo lung resection specimens. Throughout this manuscript, we describe the techniques used to match OFDI images to histology. However, this method is not specific to OFDI and can be used to obtain histology-registered images for any optical imaging technique. We performed airway centered OFDI with a specialized custom built bronchoscopic 2.4 French (0.8 mm diameter) catheter. Tissue samples were marked with tissue dye, visible in both OFDI and histology. Careful orientation procedures were used to precisely correlate imaging and histological sampling locations. The techniques outlined in this manuscript were used to conduct the first demonstration of volumetric OFDI with precise correlation to tissue-based diagnosis for evaluating pulmonary pathology24. This straightforward, effective technique may be extended to other tissue types to provide precise imaging to histology correlation needed to determine fine imaging features of both normal and diseased tissues.

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

A method to image ex vivo pulmonary resection specimens with optical frequency domain imaging (OFDI) and obtain precise correlation to histology is described, which is essential to developing specific OFDI interpretation criteria for pulmonary pathology. This method is applicable to other tissue types and imaging techniques to obtain precise imaging to histology correlation for accurate image interpretation and assessment. Imaging criteria established with this technique would then be applicable to image assessment in future in vivo studies.

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 give an accurate velocity profile. A protocol is presented for &mu;PIV measurements of blood flows in microchannels. At the scale of the microcirculation, blood cannot be considered a homogeneous fluid, as it is a suspension of flexible particles suspended in plasma, a Newtonian fluid. Shear rate, maximum velocity, velocity profile shape, and flow rate can be derived from these measurements. Several key parameters such as focal depth, particle concentration, and system compliance, are presented in order to ensure accurate, useful data along with examples and representative results for various hematocrits and flow conditions.

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows which are cross-correlated to give an accurate velocity profile. Shear rate, maximum velocity, velocity profile shape, and flow rate, each of which has clinical applications, can be derived from these measurements.

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 3D cellular construct can often be more relevant to studying the molecular events and cell-cell interactions than similar experiments studied in 2D. To create effective 3D cultures with high cell viability throughout the scaffold the culture conditions such as oxygen and pH need to be carefully controlled as gradients in analyte concentration can exist throughout the 3D construct. Here we describe the methods of preparing biocompatible pH responsive sol-gel nanosensors and their incorporation into poly(lactic-co-glycolic acid) (PLGA) electrospun scaffolds along with their subsequent preparation for the culture of mammalian cells. The pH responsive scaffolds can be used as tools to determine microenvironmental pH within a 3D cellular construct. Furthermore, we detail the delivery of pH responsive nanosensors to the intracellular environment of mammalian cells whose growth was supported by electrospun PLGA scaffolds. The cytoplasmic location of the pH responsive nanosensors can be utilized to monitor intracellular pH (pHi) during ongoing experimentation.

Biocompatible pH responsive sol-gel nanosensors can be incorporated into poly(lactic-co-glycolic acid) (PLGA) electrospun scaffolds. The produced self-reporting scaffolds can be used for in&#160;situ monitoring of microenvironmental conditions whilst culturing cells upon the scaffold. This is beneficial as the 3D cellular construct can be monitored in real-time without disturbing the experiment.

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. To this end, cells are grown in special culture chambers on top of opposing, circular gold electrodes. A constant small alternating current is applied between the electrodes and the potential across is measured. The insulating properties of the cell membrane create a resistance towards the electrical current flow resulting in an increased electrical potential between the electrodes. Measuring cellular impedance in this manner allows the automated study of cell attachment, growth, morphology, function, and motility. Although the ECIS measurement itself is straightforward and easy to learn, the underlying theory is complex and selection of the right settings and correct analysis and interpretation of the data is not self-evident. Yet, a clear protocol describing the individual steps from the experimental design to preparation, realization, and analysis of the experiment is not available. In this article the basic measurement principle as well as possible applications, experimental considerations, advantages and limitations of the ECIS system are discussed. A guide is provided for the study of cell attachment, spreading and proliferation; quantification of cell behavior in a confluent layer, with regard to barrier function, cell motility, quality of cell-cell and cell-substrate adhesions; and quantification of wound healing and cellular responses to vasoactive stimuli. Representative results are discussed based on human microvascular (MVEC) and human umbilical vein endothelial cells (HUVEC), but are applicable to all adherent growing cells.

This protocol reviews Electric Cell-substrate Impedance Sensing, a method to record and analyze the impedance spectrum of adherent cells for the quantification of cell attachment, proliferation, motility, and cellular responses to pharmacological and toxic stimuli. Detection of endothelial permeability and assessment of cell-cell and cell-substrate contacts are emphasized.

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 the only means available, the development of noninvasive imaging modalities (echocardiography, MRI, CT) having high temporal and spatial resolution provide a new window for quantitative diastolic function assessment. Echocardiography is the agreed upon standard for diastolic function assessment, but indexes in current clinical use merely utilize selected features of chamber dimension (M-mode) or blood/tissue motion (Doppler) waveforms without incorporating the physiologic causal determinants of the motion itself.&#160;The recognition that all left ventricles (LV) initiate filling by serving as mechanical suction pumps allows global diastolic function to be assessed based on laws of motion that apply to all chambers. What differentiates one heart from another are the parameters of the equation of motion that governs filling. Accordingly, development of the Parametrized Diastolic Filling (PDF) formalism&#160;has shown that the entire range of clinically observed early transmitral flow (Doppler E-wave) patterns are extremely well fit by the laws of damped oscillatory motion.&#160;This permits analysis of individual E-waves in accordance with a causal mechanism (recoil-initiated suction) that yields three (numerically) unique lumped parameters whose physiologic analogues are chamber stiffness (k), viscoelasticity/relaxation (c), and load (xo).&#160;The recording of transmitral flow (Doppler E-waves) is standard practice in clinical cardiology and, therefore, the echocardiographic recording method is only briefly reviewed. Our focus is on determination of the PDF parameters from routinely recorded E-wave data.&#160;As the highlighted results indicate, once the PDF parameters have been obtained from a suitable number of load varying E-waves, the investigator is free to use the parameters or construct indexes from the parameters (such as stored energy 1/2kxo2, maximum A-V pressure gradient kxo, load independent index of diastolic function, etc.) and select the aspect of physiology or pathophysiology to be quantified.

Accurate, causality-based quantification of global diastolic function has been achieved by kinematic modeling-based analysis of transmitral flow via the Parametrized Diastolic Filling (PDF) formalism. PDF generates unique stiffness, relaxation,&#160;and load parameters and elucidates &#39;new&#39; physiology while providing sensitive and specific indexes of dysfunction.

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

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to assess the biodistribution of novel probes and to assess disease progression using established molecular probes or reporter genes. The combination with an anatomical modality, e.g., micro computed tomography (&#181;CT), is beneficial for image analysis and for fluorescence reconstruction. We describe a protocol for multimodal &#181;CT-FMT imaging including the image processing steps necessary to extract quantitative measurements. After preparing the mice and performing the imaging, the multimodal data sets are registered. Subsequently, an improved fluorescence reconstruction is performed, which takes into account the shape of the mouse. For quantitative analysis, organ segmentations are generated based on the anatomical data using our interactive segmentation tool. Finally, the biodistribution curves are generated using a batch-processing feature. We show the applicability of the method by assessing the biodistribution of a well-known probe that binds to bones and joints.

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

We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (&#181;CT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

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

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Particle image velocimetry (PIV) is used in a wide variety of fields, due to the opportunity it provides for precisely visualizing and quantifying flows across a large spatiotemporal range. However, its implementation typically requires the use of expensive and specialized instrumentation, which limits its broader utility. Moreover, within the field of bioengineering, in vitro flow visualization studies are also often further limited by the high cost of commercially sourced tissue phantoms that recapitulate desired anatomical structures, particularly for those that span the mesoscale regime (i.e., submillimeter to millimeter length scales). Herein, we present a simplified experimental protocol developed to address these limitations, the key elements of which include 1) a relatively low-cost method for fabricating mesoscale tissue phantoms using 3-D printing and silicone casting, and 2) an open-source image analysis and processing framework that reduces the demand upon the instrumentation for measuring mesoscale flows (i.e., velocities up to tens of millimeters/second). Collectively, this lowers the barrier to entry for nonexperts, by leveraging resources already at the disposal of many bioengineering researchers. We demonstratethe applicability of this protocol within the context of neurovascular flow characterization; however, it is expected to be relevant to a broader range of mesoscale applications in bioengineering and beyond.

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Here we present simplified methods for fabricating transparent neurovascular phantoms and characterizing the flow therein. We highlight several important parameters and demonstrate their relationship to field accuracy.

Particle image velocimetry (PIV) is used in a wide variety of fields, due to the opportunity it provides for precisely visualizing and quantifying flows ...

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 live-cell imaging and high-throughput microscopy techniques. This led to a constant increase in the amount and complexity of the microscopy data for a single experiment. Because manual analysis of microscopy data is very time consuming, subjective, and prohibits quantitative analyses, automation of bioimage analysis is becoming almost unavoidable. We built an informatics workflow called Substructure Analyzer to fully automate signal analysis in bioimages from fluorescent microscopy. This workflow is developed on the user-friendly open-source platform Icy and is completed by functionalities from ImageJ. It includes the pre-processing of images to improve the signal to noise ratio, the individual segmentation of cells (detection of cell boundaries) and the detection/quantification of cell bodies enriched in specific cell compartments. The main advantage of this workflow is to propose complex bio-imaging functionalities to users without image analysis expertise through a user-friendly interface. Moreover, it is highly modular and adapted to several issues from the characterization of nuclear/cytoplasmic translocation to the comparative analysis of different cell bodies in different cellular sub-structures. The functionality of this workflow is illustrated through the study of the Cajal (coiled) Bodies under oxidative stress (OS) conditions. Data from fluorescence microscopy show that their integrity in human cells is impacted a few hours after the induction of OS. This effect is characterized by a decrease of coilin nucleation into characteristic Cajal Bodies, associated with a nucleoplasmic redistribution of coilin into an increased number of smaller foci. The central role of coilin in the exchange between CB components and the surrounding nucleoplasm suggests that OS induced redistribution of coilin could affect the composition and the functionality of Cajal Bodies.

We present a freely available workflow built for rapid exploration and accurate analysis of cellular bodies in specific cell compartments in fluorescence microscopy images. This user-friendly workflow is designed on the open-source software Icy and also uses ImageJ functionalities. The pipeline is affordable without knowledge in image analysis.

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

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental technique and image analysis protocol presented here can also be applied for imaging microscopic bubbles in other fields of research; therefore, it has a wide range of applications. We apply this to image cavitation around dental ultrasonic scalers. It is important to image cavitation to characterize it and to understand how it can be exploited for various applications. Cavitation occurring around dental ultrasonic scalers can be used as a novel method of dental plaque removal, which would be more effective and cause less damage than current periodontal therapy techniques. We present a method for imaging the cavitation bubble clouds occurring around dental ultrasonic scaler tips using a high-speed camera and a zoom lens. We also calculate the area of cavitation using machine learning image analysis. Open source software is used for image analysis. The image analysis presented is easy to replicate, does not require programming experience, and can be modified easily to suit the application of the user.

Cavitation microbubbles are imaged using a high-speed camera attached to a zoom lens. The experimental setup is explained, and image analysis is used to calculate the area of the cavitation. Image analysis is done using ImageJ.

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental ...