{"id":421,"date":"2023-06-15T07:01:00","date_gmt":"2023-06-15T05:01:00","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=421"},"modified":"2025-10-14T11:16:18","modified_gmt":"2025-10-14T09:16:18","slug":"mikaia-fl-colocalization-app","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-fl-colocalization-app\/","title":{"rendered":"MIKAIA FL Cell Analysis App"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-university\/\">MIKAIA<sup>\u00ae<\/sup> University<\/a> Application Note: Analyzing multiplexed immuno fluorescent (mIF) slides<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"http:\/\/www.mikaia.ai\">MIKAIA<sup>\u00ae<\/sup><\/a> has good support for viewing, annotating and analyzing multi-plexed (aka high-plex, aka hyper-plex) whole-slide images generated by instruments from various vendors such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Akoya Biosciences<\/strong> QPTIFF, e.g., PhenoCycler\u2122 (formerly CODEX<sup>\u00ae<\/sup>), PhenoImager Fusion<\/li>\n\n\n\n<li><strong>NanoString<\/strong> OME-TIFF, e.g., GeoMx DSP<\/li>\n\n\n\n<li><strong>Lunaphore<\/strong> Comet\u2122 OME-TIFF<\/li>\n\n\n\n<li><strong>Zeiss <\/strong>CZI, e.g,. AxioScan, AxioImager, &#8230;<\/li>\n\n\n\n<li><strong>Hamamatsu <\/strong>NDPIS, e.g., NanoZoomer S60<\/li>\n\n\n\n<li><strong>Olympus <\/strong>VSI, e.g., VS200 scanner<\/li>\n\n\n\n<li><strong>Leica <\/strong>Aperio SVS<\/li>\n\n\n\n<li><strong>Motic<\/strong> QPTIFF, from their new FL scanner<\/li>\n\n\n\n<li>any other that produces <strong>OME-TIFF<\/strong>.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">mIF scans can be rendered with <strong>16 bit fidelity<\/strong>. <br>Channels can be individually toggled on and off. <br>Their black and white levels, gamma, gain, and pseudo color can be individually changed.  <\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney.jpg\" alt=\"&quot;Human Kidney&quot; scanned with NanoString GeoMx DSP (slide copyright @ NanoString) \" class=\"wp-image-426\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney.jpg 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney-300x169.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney-1024x576.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney-768x432.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney-1536x864.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney-370x208.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney-270x152.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney-570x321.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Kidney-740x416.jpg 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">&#8220;Human Kidney&#8221; scanned with NanoString GeoMx DSP (slide copyright @ NanoString) <\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas.jpg\" alt=\"&quot;Human Pancreas&quot; scanned with NanoString GeoMx DSP (slide copyright @ NanoString) \" class=\"wp-image-422\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas.jpg 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas-300x169.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas-1024x576.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas-768x432.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas-1536x864.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas-370x208.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas-270x152.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas-570x321.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Human-Pancreas-740x416.jpg 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">&#8220;Human Pancreas&#8221; scanned with NanoString GeoMx DSP (slide copyright @ NanoString) <\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain.jpg\" alt=\"&quot;Mouse Brain&quot; scanned with NanoString GeoMx DSP (slide copyright @ NanoString) \" class=\"wp-image-423\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain.jpg 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain-300x169.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain-1024x576.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain-768x432.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain-1536x864.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain-370x208.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain-270x152.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain-570x321.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/NanoString-GeoMx-Mouse-Brain-740x416.jpg 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">&#8220;Mouse Brain&#8221; scanned with NanoString GeoMx DSP (slide copyright @ NanoString) <\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2.jpg\" alt=\"Only subset of markers enabled. (slide: &quot;Tonsil&quot; by Akoya PhenoCycler, copyright @ Akoya Biosciences)\" class=\"wp-image-425\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2.jpg 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2-300x169.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2-1024x576.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2-768x432.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2-1536x864.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2-370x208.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2-270x152.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2-570x321.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-2-740x416.jpg 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">&#8220;Tonsil&#8221;, only subset of markers enabled, created with Akoya PhenoCycler (slide copyright @ Akoya Biosciences)<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil.jpg\" alt=\"Akoya PhenoCycler - &quot;Tonsil&quot; (slide: copyright @ Akoya Biosciences)\" class=\"wp-image-427\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil.jpg 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-300x169.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-1024x576.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-768x432.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-1536x864.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-370x208.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-270x152.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-570x321.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-740x416.jpg 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">&#8220;Tonsil&#8221; created with Akoya PhenoCycler, (slide: copyright @ Akoya Biosciences)<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Get first insights within seconds: <br>Cross-channel correlation analysis<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Before diving into a cell-by-cell analysis, you may choose to start with a marker-pair-wise coexpression analysis that is computed from the entire slide at a proxy resolution. Open the &#8220;Colocalization&#8221; panel from the &#8220;Coloc&#8221; button in the main toolbar right next to the fluorescence channel buttons. It will compute the correlation between any two fluorescence channels using the Pearson Correlation Coefficient, (&#8220;PCC&#8221;, ranges from -1 to 1, higher absolute value indicates higher correlation) and display them in a channel-wise confusion matrix.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Each such combination is additionally visualized using an interactive scatter plot. The channel combinations are automatically sorted by descending correlation, facilitating fast inspection of the top N correlating combinations. Since the correlation is computed per pixel on a downscaled proxy of the whole-slide and does not involve segmentation of cells, this information is available within seconds.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots.jpg\" alt=\"Channel-wise Correlation Scatter plots for &quot;Tonsil&quot; (Akoya PhenoCycler) (slide: @ Akoya Biosciences)\" class=\"wp-image-424\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots.jpg 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots-300x169.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots-1024x576.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots-768x432.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots-1536x864.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots-370x208.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots-270x152.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots-570x321.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Akoya-Phenocycler-Tonsil-Scatter-Plots-740x416.jpg 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">Channel-wise Correlation Scatter plots for &#8220;Tonsil&#8221; (Akoya PhenoCycler) (slide: @ Akoya Biosciences)<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">A single scatter plot allows defining quadrants or even a custom rectangular value range and will compute the correlation of each such value range. The axes can be interactively constrained and the heatmap sensitivity adjusted. <\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"580\" height=\"281\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-11.png\" alt=\"\" class=\"wp-image-437\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-11.png 580w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-11-300x145.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-11-370x179.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-11-270x131.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-11-570x276.png 570w\" sizes=\"(max-width: 580px) 100vw, 580px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Cell-based colocalization analysis<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><span lang=\"EN-US\">For a cell-based colocalization analysis, open the app center, and select the <strong>FL Colocalization App<\/strong>. In the app configuration pane, select first which fluorescent channel marks all cells, e.g., typically the DAPI marker (in the below screenshot it is the &#8220;DNA&#8221; channel). This channel is then analyzed first and cells (nuclei) are identified. Each detected nucleus is then dilated by a user-defined margin (e.g., 5 \u00b5m), since the cell marker channel typically represents the nucleus and the remaining markers may includes ones that are expressed only in a cell&#8217;s cytoplasm or membrane. MIKAIA<sup>\u00ae<\/sup> will then compute the mean fluorescence intensity (MFI) for each cell per marker separately in the nucleus and cytoplasm. <\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span lang=\"EN-US\">The user selects manually per marker which signal level (MFI) is considered positive, because sometimes a weak signal does not mean the marker is really expressed. Additionally, this evaluation (&#8220;MFI exceeds threshold?&#8221;) can be configured to be carried out only in the nucleus (&#8220;N&#8221;), only in the cytoplasm (&#8220;C&#8221;) or in both compartments of the cell (&#8220;N+C&#8221;). As an optional 2nd constraint, you can require that also a certain ratio of this area exceeds the threshold. This is useful when you want to require that a certain signal level is expressed throughout the entire nucleus or cytoplasm.<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bottom line, MIKAIA<sup>\u00ae<\/sup> collects for each cell the MFIs per marker separately for the nucleus and cytoplasm. Based on the thresholds you configured, it will then decide per cell which markers are expressed. Each encountered marker combination is represented by an annotation class, e.g., in the below screenshot class &#8220;E (GFAP, NeuN)&#8221; contains all cells where &#8220;GFAP&#8221; and &#8220;NeuN&#8221; are expressed, but the remaining marker &#8220;Iba-1&#8221; is not. Since some combinations may not exist, the classes are not strictly alphabetic (&#8220;A,B,C &#8230;&#8221;), but some letters may be missing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1040\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12.png\" alt=\"FL Colocalization App analysis result\" class=\"wp-image-440\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12.png 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12-300x163.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12-1024x555.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12-768x416.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12-1536x832.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12-370x200.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12-270x146.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12-570x309.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-12-740x401.png 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">FL Colocalization App analysis result (slide: &#8220;mouse brain&#8221; by NanoString) <\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Now, the amount, location, and density (in #\/\u00b5m<sup>2<\/sup>) is known. Individual cell classes as well as the whole-slide-image channels can be hidden or shown. Additionally, generated annotation classes (one per encountered marker combination) are &#8220;tagged&#8221; with marker-tags such as &#8220;GFAP&#8221; or &#8220;NeuN&#8221; in this example. By clicking on a blue tag button, all classes that include this marker are toggled on\/off. The markup appearances can be configured individually per class, e.g., the outline and fill color can be changed. Also, all classes&#8217; appearances can be globally changed via the &#8220;Markup&#8221; button in the main toolbar. It allows to force outlined or filled cell annotations and the markup&#8217;s opacity can be changed.  <\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1040\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger.jpg\" alt=\"FL Colocalization App analysis result\" class=\"wp-image-441\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger.jpg 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger-300x163.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger-1024x555.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger-768x416.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger-1536x832.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger-370x200.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger-270x146.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger-570x309.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/mouse-Brain-cell-analysis-larger-740x401.jpg 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">Zoom in on detected cells,(slide: &#8220;mouse brain&#8221; by NanoString) <\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The &#8220;Results&#8221; pane shows two bar diagrams of the absolute amounts of cells by (1) marker combinations and (2) marker as well two tables containing the same absolute and additionally the relative counts. By clicking &#8220;Export&#8221; these values plus many more values, in summary but additinoally per cell can  be easily exported into a CSV file that can be opened by Microsoft Excel or imported into R or Matlab for further statistical processing. <\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"571\" height=\"402\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-13.png\" alt=\"\" class=\"wp-image-442\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-13.png 571w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-13-300x211.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-13-370x260.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-13-270x190.png 270w\" sizes=\"(max-width: 571px) 100vw, 571px\" \/><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img decoding=\"async\" width=\"360\" height=\"507\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-14.png\" alt=\"\" class=\"wp-image-443\" style=\"width:180px;height:254px\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-14.png 360w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-14-213x300.png 213w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-14-270x380.png 270w\" sizes=\"(max-width: 360px) 100vw, 360px\" \/><\/figure>\n<\/div><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Density Heatmap<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">You could also use the density heatmap function available via the &#8220;Heatmap&#8221; button in the main toolbar to add a heatmap layer. In the dialog, you can select which cell classes should be considered by the density heatmap. Areas with a higher density of these cells will then be indicated by a warmer color. The heatmap&#8217;s internal 2D bin size, sensitivity and cutoff, smoothness, and opacity can be interactively configured.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1040\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17.png\" alt=\"FL Colocalization App analysis result: density heat map\" class=\"wp-image-446\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17.png 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17-300x163.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17-1024x555.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17-768x416.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17-1536x832.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17-370x200.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17-270x146.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17-570x309.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-17-740x401.png 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Cell-cell-interactions<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In a next step, it may be worthwhile to gain insights on possible interactions between cell types. The <strong>Cell-Cell Connections App<\/strong> interprets cells as nodes in a graph and connects each cell with its nearest neighbors. A connection (edge) between a cell of type A with a cell of type B is denoted an A-B connection. Each occurring combination is again visualized as a markup class in the UI.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1040\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15.png\" alt=\"FL Colocalization App analysis result: cell-cell interactions\" class=\"wp-image-444\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15.png 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15-300x163.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15-1024x555.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15-768x416.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15-1536x832.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15-370x200.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15-270x146.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15-570x309.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-15-740x401.png 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">By default, connections between cells of the same phenotype will not be created, though this behavior can be changed. Optionally, long connections can also be omitted, either based on the standard deviation (default: edges with length outside of the 1\u03c3 interval are omitted) or by defining an absolute threshold in \u00b5m.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bar charts display, which connections occur most frequently, both between markers and marker-combinations. The tables contain additional statistics such as the average distance (in \u00b5m) between cell types or a bystander analysis (&#8220;on average, a cell of type A contains 2.3 neighbors of type B&#8221;).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You can again change the appearance, e.g., in the below screenshot. All cell annotations are hidden by clicking the &#8220;cell&#8221; class tag, the image is hidden (via toolbar, markup, &#8220;hide image&#8221;), and the viewer background color is changed to white. You may want to include a screenshot in your publication or study report. This can be done simply using the Windows Snipping Tool or, if a higher DPI is required, by using the &#8220;File|Save As&#8221; option which will show a dialog where you can select to export the current FoV, burn in annotations, and choose a DPI resolution.  <\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"1040\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16.png\" alt=\"FL Colocalization App analysis result: cell-cell interactions\" class=\"wp-image-445\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16.png 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16-300x163.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16-1024x555.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16-768x416.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16-1536x832.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16-370x200.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16-270x146.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16-570x309.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/image-16-740x401.png 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Exporting annotations and quantitative results<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Each of the above apps allows exporting the markup into GeoJson or Aperio XML, which can be opened by QuPath or Leica ImageScope. Additionally, they support exporting quantitative results to CSV. E.g., the <strong>Cell Segmentation App<\/strong> will create one row per cell and include metrics such as the location, area, compactness, elongation, or mean intensity.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>MIKAIA\u00ae University Application Note: Analyzing multiplexed immuno fluorescent (mIF) slides MIKAIA\u00ae has good support for viewing, annotating and analyzing multi-plexed (aka high-plex, aka hyper-plex) whole-slide images generated by instruments from various vendors such as: mIF scans can be rendered with 16 bit fidelity. Channels can be individually toggled on and off. Their black and white [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":444,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,28,24],"tags":[7,29,111],"coauthors":[56],"class_list":["post-421","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-pathology","category-mikaia-university","category-spatial-biology","tag-mikaia","tag-mikaia-app-note","tag-workflow"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>MIKAIA FL Cell Analysis App - SMART SENSING insights<\/title>\n<meta name=\"description\" content=\"Introducing the MIKAIA FL Colocalization App, a versatile solution for viewing, annotating, and analyzing multi-plexed whole-slide images.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-fl-colocalization-app\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MIKAIA FL Cell Analysis App - 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