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A guided interface and purpose-built tools make the process of acquiring, viewing, analyzing and sharing images of living cells easier than ever before using the Classic or new AI-driven Analysis. AI-driven Confluence Analysis provides a simple workflow for highly accurate segmentation of cells in phase contrast images, adapting to a wide range of cell types and morphologies with minimal user input.

By viewing images of all locations in your experiment at once, you can quickly assess treatment effects or identify outliers. You can also overlay metrics for rapid assessment and verification of image processing parameters. Then, generate presentation-ready images and movies with just a few clicks. Easily view images of all locations in an experiment at once to scan for trends and outliers, then zoom in on wells of interest.

By providing purpose-built tools to answer your scientific questions, image processing and analysis is uncomplicated yet extremely powerful. Create analysis definitions once, then reapply to subsequent experiments to generate real-time metrics that enable decision-making.

Guided analysis interface enables even first-time users to convert images into insights using powerful AI-driven or classic segmentation for confluence measurements. Graphing tools enable review of trends and rapid generation of publication- and presentation-ready graphs. Perform label-free cell counts and subsequent cell-by-cell classification based on shape, size or fluorescence intensity to quantify dynamics changes in cell subsets within heterogenous living cell cultures.

Characterize the differentiation and maturation of organoid cultures in or well plates and assess treatment effects on organoid growth in well microplates. Analyze growth, viability or invasion of single spheroids in round- bottom multi-well format or measure multiple spheroid in flat bottom plates to detect changes of growth or viability. Enables label-free or fluorescence analysis of neurite outgrowth, maturation or disruption in each well in or well plates. Track and quantify label-free and fluorescence labeled chemotaxis cell migration and invasion in microplate format.

Assess complex network development and disruption qualitatively to study therapeutic interventions of vascular formation.

Gain insights into biological processes of cells in real time via non-perturbing quantitative analysis. Ideal if you use your Incucyte intensively as it keeps data on-line and at your fingertips for longer. The content of our website is always available in English and partly in other languages. Choose your preferred language and we will show you the content in that language, if available. Stress-free Image Acquisition Guided interface enables even first-time users to set up long-term, live-cell experiments Flexible scheduling tool allows multiple users to run multiple applications in parallel.

Powerful, Purpose-built AI-driven Analysis Guided interface makes processing thousands of wells of data easier, driven by AI or Classic segmentation Graphing tools designed by biologists enable publication-ready graphs without the need for third-party software. Ordering Information. Software Module Description Cat. Add-on Software Modules. Classifier is trained using control wells. Literature and Documentation. Related Products.

Explore More. How can we assist you? What are you mainly interested in? What other areas are you interested in? Lab Water Purification. Pipetting and Dispensing. Cell Analysis - Live Cell Analysis. Cell Analysis - Advanced Flow Cytometry. Lab Filtration and Purification. Microbiological Testing.

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Language Preference. Please select your country so we can show you products that are available for you. Perform counts and track changes in adherent cell morphology via label-free image segmentation and multivariate analysis of cell shape.

     

Incucyte zoom 2016a download.Incucyte® ZOOM System Resources & Support

 

Representative images of NT processing definitions for six medically important fungi. NeuroTrack NT; pink processing definitions were optimized for each individual fungus. White arrowheads indicate examples of resting spores remaining unmasked by the NT module. In a second step, the reproducibility of NT and BA readouts was assessed using different inocula of selected fungi. For C. Only the two higher inocula resulted in a confluence plateau by the end of the observation period.

For all conditions, intraplate coefficients of variation CVs based on triplicate measurement were consistently below 0. Expectantly, higher fungal inocula tended to result in lower CVs Fig. Interplate CVs were slightly higher but remained consistently below 0.

Intra- and interplate variability of Basic Analyzer and NeuroTrack processing definitions depending on fungal inoculum and culture period. A to C Spores of C. Three plates, each containing triplicates of all tested concentrations, were prepared by different researchers to evaluate intra- and interplate reproducibility. Cell-free medium was used as background control Medium. A Microplate graphs generated by the IncuCyte software are provided, showing technical triplicates on one representative plate.

B Microplate graphs comparing intra- and interplate variability of NT readouts and confluence for 10 3 C. CVs were calculated based on h values. D The experimental setup described above A to C was applied to A. Results from one representative plate are shown. Intraplate CVs were calculated based on 8-h 10 4 , h 10 3 , and h 10 2 values.

Mean values and interplate variation are provided in Fig. We used the Aspergillus fumigatus reference strain Af and clinical isolates of Rhizopus arrhizus and Fusarium solani to evaluate the reproducibility of NT and BA analysis in molds. While intra-assay CVs remained mostly below 0. At 10 2 spores per well, interplate CVs in early log phase went up to about 0. Taken together, these data suggest that NT monitoring is feasible and reproducible for a spectrum of medically important fungi, with both intra- and interplate variability meeting commonly applied acceptance criteria for cell-based bioassays 32 , As certain applications such as coculture studies with host immune cells require fluorescent labeling of pathogens 21 , we sought to evaluate the accuracy of fluorescence-based NT analysis.

In the example of GFP-expressing A. In both the germling and mycelium stages, excellent accuracy and high concordance with phase-based NT analysis [NT P ] were seen. Three plates, each containing triplicates of both concentrations, were prepared by different researchers to evaluate intra- and interplate reproducibility.

C Comparison of NT readouts depending on the processing module, Af spore inoculum, and culture period. Key parameters comparing the two modules are summarized in the statistical insets next to the diagram.

E Representative examples of fluorescence-based processing definitions applied to C. The only notable difference, seen in two independent runs with 3 plates each, was a higher plateau for total hyphal length in NT P than in NT G at the 10 3 -spore inoculum Fig.

Similar to phase image processing, the higher inoculum 10 4 spores tended to provide smaller intra- and interplate coefficients of variation in mid-log phase, whereas no difference was seen between NT P and NT G , indicating that phase- and fluorescence-based NT algorithms allow for comparably robust and reproducible analysis of fungal growth.

Even when immune cells accumulated in close proximity to fungal hyphae or fungal debris was encountered, near-optimal detection of hyphal filaments was achieved representative examples are shown in Fig. In quantitative analysis of hyphal length and branching in PBMC-fungus cocultures, a delayed increase in both readout parameters was seen depending on the PBMC concentration, with an approximately 3-h delay at a ratio Fig.

Though interwell variations were higher in the coculture setting than for fungus-only conditions, all CVs remained below 0. Monitoring of fungal growth and mycelial branching in mold-immune cell coculture experiments. Six wells were plated for each condition. Phase and green fluorescence ms acquisition time images were obtained hourly. A Representative images of mycelial growth in the presence or absence of PBMCs are provided, and mycelial recognition by a GFP-based processing definition yellow overlay is shown.

C Hyphal length and branch point numbers were determined using a green fluorescence-based processing definition. D Microplate graphs generated by the IncuCyte software are provided, derived from the same experiment and covering the entire h culture period.

To provide a proof of principle that NT analysis is able to recapitulate aberrant filamentation of a well-described C. Both hyphal length and branch point numbers concordantly revealed a 6- to 8-h delay in hyphal growth and branching and lower plateau Fig.

The interwell variability of both readout parameters was consistently below 0. Assessment of a filamentation-aberrant C. White arrowheads point to correctly nonmasked spores. B Hyphal length and branch point numbers were determined in a time course experiment. Mean values and standard deviations based on six wells per condition are provided.

As antifungal drug discovery may be another important potential field of application, we performed NT analysis to document the inhibition of R. NT analysis remained undisturbed by accumulation of fungal debris, whereas false-positive detection of debris by the BA module was observed e.

In addition, the BA module was susceptible to minor imprecision of the autofocus upon onset of vertical mycelial growth blue triangle in Fig. In contrast, both NT readouts showed a largely even plateau. In the example of isavuconazole, we confirmed that NT analysis is able to recapitulate a plausible dose-response correlation Fig. Impact of antifungal exposure on mycelial growth and fungal membrane integrity.

All conditions were assessed in triplicates. B Confluence, hyphal length, and branch point numbers were determined using BA confluence and NT hyphal length and branch points processing definitions. Mean values and standard deviations are provided depending on antifungal treatment and culture period.

Mean values and standard definitions are given. Additionally, we used cytotoxicity staining Cytotox Green; Sartorius to test the fungicidal activity of isavuconazole in parallel to NT analysis. The Cytotox probe is inert and does not enter viable cells. As fungal membrane integrity diminishes, the probe enters the cell and fluorescently labels DNA. The green-fluorescence-positive area and total fungal area were quantified by BA processing.

Minimal green positivity Fig. Amphotericin B treatment was used as a positive control, causing a rapid peak in absolute and relative green fluorescent area. Finally, we applied NT and Cytotox analysis to A. For Af , CAS concentrations of 0. In contrast, the hyphal branch point endpoint largely failed to discriminate the wild-type and FKS1 mutant strains Fig.

The phenomenon of dissociated hyphal length and branch point kinetics of CAS-exposed A. Evaluating cytotoxicity patterns at different fluorescence sensitivity settings, the highest affinity of the Cytotox probe was found at hyphal tips and recently developed subapical branch points Fig. NeuroTrack analysis and cytotoxicity staining of caspofungin-exposed A. A to C Spores of A. A Hyphal length and branch point numbers were determined using a previously established NeuroTrack NT processing definition.

The Cytotox-positive area was determined using a green fluorescence-based Basic Analyzer processing definition. All conditions were assessed in triplicate. Mean values are shown. Triangles indicate that analysis was terminated due to extensive vertical growth diminishing precision of the autofocus. B Representative images of A.

C Representative phase and green fluorescence images of Cytotox Green-stained A. Different fluorescence sensitivity settings were applied to document the degree of Cytotox positivity, with increasing RFU relative fluorescence units thresholds resulting in lower detection sensitivity. Taken together, these proof-of-concept experiments suggest that NT analysis provides a reliable tool for monitoring filamentation-aberrant morphotypes and inhibition of mycelial expansion by exposure to immune effector cells and antifungal drugs.

Further refinement by combining cytotoxicity staining and NT analysis allows for the parallel detection of inhibitory and fungicidal effects. Microscopic techniques are pivotal to our understanding of fungal morphology and physiology. Various image analysis systems have been developed to quantitatively assess mycelial characteristics 12 , and the refinement of algorithms led to the establishment of fully automated classification schemes based on color micrographs 15 or fluorescence microscopy 2.

While substantial progress in the field of microscopic imaging and image analysis algorithms significantly contributed to an improved understanding of fungal diversity, cell surface structures, regulation of fungal cell homeostasis, and host-pathogen interplay, there has been an unmet need for high-throughput real-time capture of cellular growth kinetics and morphological features The recently introduced IncuCyte live-cell imaging system satisfies this need, integrating multiple imaging modes with customizable image processing algorithms.

To perform quantitative live-cell analysis in the IncuCyte software, processing definitions need to be tailored to the cell type or pathogen of interest through a user interface to account for specific morphologic features of the studied cell types or pathogens Both the NT module and the more widely used BA are applicable to phase-contrast or fluorescent images, and analysis may be further refined by addition of viability, membrane integrity, and nuclear dyes 18 , Importantly, the NT module distinguishes cell bodies and neurites, allowing for the recognition and analysis of distinct morphological features As we sought to provide a method for precise and reliable detection and quantitative analysis of mycelial networks, we adapted our processing definitions to ensure complete and accurate detection of neurites hyphae.

We believe that these settings reflect an important difference between neuronal and mycelial morphology, the absence of prominent fungal cell bodies equivalent to the neuronal soma 29 , Reassuringly, formation of spherical pellets due to aggregation of mycelial biomass in liquid culture e.

However, if needed, simple adaptation of our processing definitions can be achieved by setting the cell body segmentation adjustment to 0. We thoroughly validated the NT approach for a range of medically important fungi. Despite major morphological differences, e. In our experience, an important advantage of the NT processing module is the ability to adjust the preferably detected hyphal width neurite width , further optimizing the accuracy of image analysis in the context of heterogeneous mycelial morphology.

Hyphal length and branching kinetics seen in our experiments were in line with earlier studies 40 , 41 which reported an initially exponential increase in total mycelial length and active tip formation branch points. As described in a mathematical model for the hyphal growth unit by Steele and Trinci 42 , a nearly linear correlation between total length and branch points per mm 2 was found in the absence of immune cells or antifungals.

Unlike previous efforts employing the oCelloScope platform for real-time monitoring of fungal inhibition and morphological changes 44 , the IncuCyte technology allows for parallel fluorescence-based cytotoxicity readouts to evaluate both inhibitory and fungicidal effects in a single assay. To validate Cytotox staining in the context of antifungal drug exposure, we documented dose-response kinetics for different classes of antifungal agents and confirmed, in the example of CAS-treated A.

Most importantly, the NT remained unbiased by fungal debris due to exposure to either antifungal drugs or immune cells Fig. Additionally, the NT was less susceptible to minor inaccuracies of the autofocus upon onset of vertical growth than the BA and therefore provided an accurate reading for an extended period of time. Nonetheless, the BA has its place in the monitoring of fungal viability by cytotoxicity staining. As drugs eliciting early fungicidal activity prevent or at least significantly suppress germination and hyphal growth Fig.

While the kinetics of the studied NT parameters of mycelial expansion were highly similar in untreated fungi, antifungal drug exposure led to a dissociation of hyphal length and branch point kinetics. In particular, A. On the other hand, branch point quantification resulted in greater differences between untreated and triazole- or amphotericin B-exposed R. Therefore, it is crucial to elucidate the most relevant endpoints e. In addition, several reports highlight the impact of different culture periods on antifungal efficacy endpoints 47 , — Unlike most conventional microbiological methods, such as broth microdilution, XTT [2,3-bis- 2-methoxynitrosulfophenyl -2H-tetrazoliumcaroxanilide salt] assays, or DiBAC staining, IncuCyte imaging allows for continuous monitoring of fungistatic and fungicidal parameters and thus streamlines the establishment of protocols to determine antifungal efficacy kinetics with excellent temporal resolution and a high level of objectivity.

It is important to recognize that there are further differences between IncuCyte image analysis and existing methods. Most conventional microbiological methods, such as visual MIC determination or XTT assays, rely on fungal biomass or global metabolic endpoints. Instead, IncuCyte image analysis could provide a rapid and convenient method to widen our understanding of how antifungals affect fungal morphogenetic programs.

Further research and direct correlation with in vivo outcomes in animal models will be needed to define the comparative merits of the IncuCyte platform versus conventional microbiological methods to assess and predict the efficacy of antifungal drugs.

As IncuCyte time-lapse microscopy is compatible with a broad selection of fungal growth media, our approach could also aid future studies focusing on the impact of therapeutic interventions on fungal growth and morphology in the setting of interkingdom infections or altered culture environments such as iron depletion 50 or changes in pH or O 2 content. Another major field that may benefit from NT analysis is the assessment of fungal cocultures with host immune cells, particularly in the context of increasing efforts to utilize primary or engineered e.

Thereby, our approach may aid the refinement and in vitro fungicidal efficacy validation of engineered cell therapeutics P.

Kumaresan et al. Although IncuCyte imaging may help to explore these areas, limitations to NT studies of pathogenic fungi need to be considered. As the NT processing module operates strictly in two dimensions, NT analysis is poorly suited to track yeast biofilms, which are often subject to substantial vertical extension 11 , Similarly, although it provides reliable quantification of mycelial networks in the focal plane, IncuCyte imaging is not able to accurately represent mycelial biomass in advanced growth stages and thus may underestimate late-onset alterations to growth patterns, e.

This is particularly relevant when studying Mucorales, characterized by early, sometimes explosive, formation of abundant mycelium Therefore, it is crucial to assay a spectrum of spore inocula including seeding densities not resulting in rapid confluent growth plateau stage in order to monitor both early and protracted effects.

Whereas our study documents acceptable intra- and interplate CVs for inocula as low as spores per well, lowering spore inocula results in increased stochastic imprecision. Acquisition and analysis of multiple images per well, the use of the IncuCyte whole-well imaging module, or the use of larger well diameters e. As another limitation, the NT method is not suitable to study yeasts that do not form hyphae or pseudohyphae under physiological conditions.

For example, in the assessment of the emerging yeast pathogen Candida auris 57 , IncuCyte analysis poorly correlated with established assays such as optical density or growth curves determined by manual counting data not shown. In summary, despite some limitations, the IncuCyte imaging system and its NT processing module provide an innovative and reliable tool to track growth patterns in the context of a vastly heterogeneous microscopic appearance and the existence of disparate morphotypes in human-pathogenic fungi.

Our findings recapitulated key characteristics of mycelial growth, and a high degree of accuracy and reproducibility of NT readouts was documented for both phase-contrast and fluorescence imaging of several medically important fungi. Preparing the ground for translational applications, proof-of-concept experiments demonstrated the feasibility of NT analysis in the context of fungal mutant assessment, antifungal drug exposure, and immune cell encounters. Clinical isolates of Candida albicans strain Y , Rhizopus arrhizus strain , Rhizomucor pusillus strain , Fusarium solani strain , and Lomentospora prolificans strain were obtained from patients at The University of Texas M.

In addition, the following reference strains and mutants were used: C. All C. Fungal suspensions were washed twice with sterile saline, and spore concentrations were determined using a hemocytometer. One hundred microliters of the fungal suspension was added to each well. Each condition was assessed in triplicate.

Imaging periods and intervals for each individual experiment are specified in the figure legends. Phase images were acquired for every experiment. Representative images capturing different morphotypes and fungal cell densities were used for training image collections Fig. Ranges of key processing parameters are summarized in Table 1 phase and Table 2 fluorescence. The optimized processing definitions were subsequently used for real-time image analysis Fig. Raw data for confluence, neurite hyphal length, branch point numbers, and fluorescence-positive area if applicable were exported to Microsoft Excel and GraphPad Prism to calculate mean values, measures of variability, coefficients of correlation and concordance, and area under the curve AUC.

Coefficients of variation CVs were calculated by dividing standard deviations by arithmetic means. FDA recommendations for bioanalytical method validation suggest a CV of 0.

Anderson Cancer Center. The technology described in this paper was used to guide the optimization of engineered cell therapeutics for which a patent application has been filed by P. The authors are not affiliated with Sartorius or the IncuCyte brand and have not received any support with the exception of general consumer services. Live monitoring and analysis of fungal growth, viability, and mycelial morphology using the IncuCyte NeuroTrack processing module. Published online May Sebastian Wurster , a Pappanaicken R.

Kumaresan , b Nathaniel D. Albert , a Paul J. Hauser , b Russell E. Lewis , c and Dimitrios P. Kontoyiannis a. Pappanaicken R. Nathaniel D. Paul J. Russell E. Dimitrios P. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Address correspondence to Sebastian Wurster, gro. Kontoyiannis, gro. Received Mar 29; Accepted Apr This is an open-access article distributed under the terms of the Creative Commons Attribution 4.

This article has been cited by other articles in PMC. This content is distributed under the terms of the Creative Commons Attribution 4. FIG S2. FIG S3. ABSTRACT Efficient live-imaging methods are pivotal to understand fungal morphogenesis, especially as it relates to interactions with host immune cells and mechanisms of antifungal drugs.

RESULTS NeuroTrack image processing provides a reliable and reproducible tool to quantify growth and mycelial branching in a spectrum of human-pathogenic fungi.

Open in a separate window. FIG 1. Processing parameter C. FIG S1 Establishment of processing definitions. FIG 2. FIG 3. NeuroTrack analysis of GFP-expressing or fluorescently labeled fungi facilitates reliable tracking of fungal growth patterns in coculture studies.

FIG 4. FIG 5. NeuroTrack processing facilitates rapid and reliable assessment of antifungal efficacy and filamentation-defective fungal mutants. FIG 6. FIG 7. FIG 8. Parameter C. Antifungal exposure and cytotoxicity assay. Isolation of PBMCs. Fungus-immune cell coculture experiments. Imaging and image analysis. Generation of time-lapse videos. Li Z, Nielsen K. Screenshots are included for further clarity, and definitions of all the IncuCyte ZOOM terms can be found in blue boxes.

Mean Intensity CU : The image average of the objects mean fluorescent intensity. Eccentricity: The average of how round or compact the objects are. Ranges from 0 to 1 with a perfect circle having a value of 0.

Image wells containing cells that express the green signal ONLY or wells containing cells that express the red signal ONLY depending upon the reagent being used. These images MUST be acquired using both the red and green channel to evaluate if the fluorophore produces signal in both channels.

NOTE: It is rare that green fluorescence is detected by the red channel. Once a few images have been collected, the User can visualize how much of the red fluorescence is detected by the green channel and vice-versa by toggling between Image Channels Screenshot 1.

Too high a percentage of Red removal from Green may result in overcorrection and the appearance of holes within the image.

Once established, the percentage can be applied at time of vessel scheduling in future assays containing that fluorophore. The User must be aware that overcorrection using the spectral unmixing tool may affect assay metrics as well as the loss of detection of true green objects. Click Save to apply the spectral unmixing to the current vessel. Step 2. Creating an Image Collection 1.

Collect Fluorescent images of your cells every hours until assay is complete. Open the Vessel View. If the appropriate spectral unmixing was not applied at the time of Vessel Scheduling, use the Spectral Unmixing tool in the Vessel View and click Save. Review images and select an image you want to add to the Image Collection by clicking on the Create or Add to Image Collection link Screenshot 2. When adding the first image of a collection, select New and assign a unique name.

Choose the Required Image Channels. For a 2-color assay, both green and red channels are required. Select only those channels that you intend to process. Screenshot 2. Adding Images to an Image Collection 7.

Continue to add images to the image collection by clicking on the Create or Add to Image Collection link Screenshot 2. The image collection named in Step 5 will now be listed as an Existing image collection. Choose images that represent the objects at their dimmest, brightest, smallest, and largest. The image collection should be limited to representative images. An Image Collection may contain images from multiple tissue culture vessels.

The collection should not contain too many images as it will prolong the development of the Processing Definition. Creating a Processing Definition 1. Select the proper Image Collection and click Continue Screenshot 3. This will default to the image collection selected in the previous step Screenshot 3 , but it is advised to check before changing parameters. NOTE: You may preview a processing definition on multiple image collections, if desired.

Check the boxes of the channels which you wish to analyze and create masks. Masks are binary overlays that tell the software which pixels are of interest for analysis and which are not for a given channel. If you are only analyzing Green and Red data, for instance, it is unnecessary to check the Phase box. This will save time in previewing and running jobs. This overlap mask can be used to count objects made up of overlapping areas of green and red label, as well as measure total overlap area.

Assign phenotypic Object Names for example, if your green objects represent nuclei, you will label them Nuclei, and the metric would appear as Nuclei per image. Choose a Preview Image Collection on which to test your processing definitions. This will default to the image collection selected in step 2, but it is advised to check before changing parameters.

Parameters can be adjusted to mask objects in 3 different ways: Adaptive Segmentation A local background level LBL across each processed image is automatically determined and the user inputs a Threshold Adjustment value this far above the LBL. It is advised to preview the default threshold adjustment of 2. Fixed Threshold A single threshold level in calibrated fluorescence units is used across the image.

This number can be set as a number near or in between the dimmest positive object and the brightest background area. Top Hat A background trend across the image is estimated and then subtracted. The radius should be measured slightly larger than the smallest radius of the biggest object Screenshot 5. Use the measuring tool to estimate that distance. A radius that is set too small may result in a loss in object detection.

A radius that is set too large can cause incorrect background estimation. This method works best for low-density objects. After determining the Parameters, click Preview Current" to assess the changes on the current image only or Preview all to apply the changes on all the images in your collection. NOTE: If using Top-Hat, once the image is previewed, a background subtracted image is formed and displayed in a new tab under the available color channels.

Use the Original and Background Subtracted tabs to compare between the two images. Only the Background Subtracted image will be used for segmentation. Make sure that both the correct fluorescent image channel box and the Mask box are checked and evaluate your mask. Changing these will not affect the processing definition. To clearly determine the mask, you can zoom in on the network using the tool slider followed by the weight slider located under the mask section.

If necessary, increase the threshold to eliminate masking of background or decrease the threshold to include dimmer objects. Click Preview to assess the changes. Further refine the mask by modifying the Edge split, Cleanup, and Filters. Screenshot 6: Previewing Object Masks a. Select Edge split off for Edge Split if objects are not closely spaced. The user also has the option to finely tune the Edge split by moving the edge sensitivity bar to the left to minimize the number of splits and to the right to maximize the number of splits.

Decrease the size of the mask by entering a negative Adjust size value or increase the size of the mask by entering a positive value. Fill the holes in the mask by entering a value in Hole Fill option.

Apply filters for area, eccentricity, mean intensity, and integrated intensity to eliminate dead cells, debris, or background from being masked as objects See Fluorescent Processing Parameters on next page for definitions. If you need to modify your processing definition for a new assay e. Adjust the necessary parameters, filters, and cleanup, then Preview the changes. A threshold is set this far above the LBL in calibrated fluorescence units. Any object that is higher than the background by this value will be included in the mask.

A single threshold level in calibrated fluorescence units is used across the image, so all objects below this level will be excluded. A background trend across the image is estimated and then subtracted. In case of noncircular objects, the smaller distance is measured.

Edge split is recommended for separating closely-spaced objects at the weak signal points between. Making the Edge Sensitivity number larger will result in more splits. Removes any holes in the segmentation mask that are smaller than the area specified Adjusts the size of your mask in pixels by either shrinking the mask if negative or growing the mask if positive.

Eccentricity ranges from 0 to 1 with a perfect circle having a value of 0. Defines the limits of mean intensity of an object, the average pixel intensity in calibrated units , and eliminates objects that fall outside this range. Defines the limits of integrated intensity of an object, the summed pixel intensity in calibrated units , and eliminates objects that fall outside this range.

Launch an Analysis Job for an existing vessel 1. Open the Vessel View for the vessel you wish to analyze. Select Basic Analyzer for Job Type 4. Choose the Processing Definition you wish to use from the drop down menu. Assign a unique name to the Job. Select the wells you wish to analyze and click OK.

Screenshot 7: Launching an Analysis Job for an existing vessel Option 2. Add a New Vessel and select the appropriate channels required for imaging Screenshot 8. Under the same heading, select your Processing Definition. Name your vessel, add notes, determine the frequency of scans, and click Apply. Data will be processed following each scan to provide fluorescent object metrics in real-time. Screenshot 8. Launching an Analysis Job at the time of Vessel Scheduling.

A: Adaptive Segmentation algorithm breaks each image into small, square segments and estimates the background in each square individually. When the Threshold Adjustment value is set too close to the value of the background, the algorithm is not able to differentiate between the background and fluorescent objects.

Top-Hat Background Subtraction may be more suitable for handling the background in this case. This issue could also be solved by using brighter cells or low-fluorescence media see Background Fluorescence Tech Note. A: Most people use trial-and-error approach. Fixed threshold works best for images with consistent background which is usually not the case if you have cells in cell culture media.

Top-Hat is a filtering algorithm that helps to remove uneven illumination and isolate positive objects by estimating and subtracting the background from an image. The main advantage of the Top-Hat Subtraction algorithm is the ability to generate a background subtracted image and segment that image, thus enabling the computation of raw fluorescent intensities of objects.

Adaptive Segmentation method differentiates the positive objects from the background by detecting the lowest background level and segmenting objects at a user defined threshold above that level. It does not subtract the background from fluorescence intensity metrics and does not provide the user with a background subtracted image.

How easy is it to make a bad mistake? A: As a general rule, it is better to overestimate you radius value than to underestimate it. Slightly overestimating the radius is likely to still give you correct background subtraction. Overestimating the radius by a large value may cause loss of sensitivity for background trend, meaning that the background subtraction will be constant throughout the image.

Underestimating the radius may cause objects to be undetected and should be avoided. If your results are good, use that! A: The Top-Hat method is equally effective for circular and non-circular objects. I have always used Adaptive method for my cell counts, should I switch to Top-Hat now? A: Not necessarily. If you have been using Adaptive and it worked for you, there is no reason to switch.

Adaptive method works great for counting objects. However, you may want to switch to Top-Hat if: 1 You want to have raw values of your fluorescent objects and would like to perform comparative analysis between objects within an image or multiple images.

Can I re-analyze them now? A: Yes, you can! Open your previous Processing Definition or create a new one and choose Top-Hat under the Parameters tab. Preview images then adjust parameters, cleanup and filters as necessary in order to correctly mask the objects of interest.

If Top-Hat more appropriately masks the fluorescent objects, re-analyze your data using this saved Processing Definition. Should I choose the object surrounded by the highest background or lowest background? A: The brightness of the object or the brightness of the background around the object does not matter when measuring radius.

Choose a largest object you want to be included in you masking and measure the radius with the measuring tool. User Manual for HoloStudio M4 2. Information on MetroPro is provided in Getting. A For research use only. Not for use in diagnostic procedures.

Trademarks Affymetrix and, and QuantiGene are trademarks. Switch on the Main switch labeled 1 and 2 mounted on the wall. Turn the Laser Key labeled 3 90 clockwise for power. Definiens Tissue Studio 4. All rights reserved. This document may be. Switch on Main power switch 2. Switch on Components power button 4. The image features a little.

Not intended for any animal or human therapeutic or diagnostic use. Information in this document. Sign on log sheet according to Actual start time 2. Check Compressed Air supply for the air table 3. How to blend, feather, and smooth Quite often, you need to select part of an image to modify it. When you select uniform geometric areas squares, circles, ovals, rectangles you don t need to worry too.

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