{"id":5419,"date":"2026-09-14T13:18:14","date_gmt":"2026-09-14T11:18:14","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=5419"},"modified":"2026-09-14T21:05:15","modified_gmt":"2026-09-14T19:05:15","slug":"mgi-visiomics-analysis-with-mikaia","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mgi-visiomics-analysis-with-mikaia\/","title":{"rendered":"Cell Typing and Neighborhood Analysis of mIF 20plex Tonsil Tissue Using MGI VisiOmics and MIKAIA studio"},"content":{"rendered":"\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-normal-font-size wp-block-paragraph\">The collaboration aims to show how researchers can use VisiOmics image data within MIKAIA<sup>\u00ae<\/sup> workflows for cell segmentation, phenotyping, and spatial analysis. This work supports greater flexibility in downstream analysis, helping laboratories connect automated tissue imaging with tools suited to their research questions.<br><br><em>Stockholm, Sweden, 14 September 2026<\/em>\u00a0: <a href=\"https:\/\/global-mgitech.com\/mgi-launches-visiomics-in-europe-and-unveils-ai-enabled-pathology-portfolio-at-ecp-2026\/\" data-type=\"link\" data-id=\"https:\/\/global-mgitech.com\/mgi-launches-visiomics-in-europe-and-unveils-ai-enabled-pathology-portfolio-at-ecp-2026\/\">MGI Launches VisiOmics in Europe and Unveils AI-Enabled Pathology Portfolio at ECP 2026<\/a><\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">MGI\u2019s <a href=\"https:\/\/global-mgitech.com\/multiomics\/visiomics-pmif20\/\">VisiOmics (PMIF-20RS)<\/a> integrates fully automated multiplex immunofluorescence staining and imaging, enabling the measurement of more than 20 protein markers within a single tissue section while preserving their spatial context. This application note demonstrates the analysis of a tonsil dataset generated using VisiOmics and analysed with MIKAIA<sup>\u00ae<\/sup> studio. The tissue was stained for 20 protein markers and one nuclear marker. The analysis included cell segmentation, marker-based phenotyping, and spatial neighbourhood analysis.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">MIKAIA<sup>\u00ae<\/sup> studio 3 natively supports datasets generated with MGI\u2019s fully automated multiplex immunofluorescence staining and imaging system, VisiOmics (PMIF-20RS). This example illustrates how VisiOmics image data can be incorporated into a third-party analysis workflow to investigate cellular composition and tissue organisation.<\/p>\n\n\n\n<div class=\"alignnormal\"><div id=\"metaslider-id-5425\" style=\"width: 100%;\" class=\"ml-slider-3-113-0 metaslider metaslider-flex metaslider-5425 ml-slider has-dots-nav ms-theme-default-base\" role=\"region\" aria-label=\"MGI Slideshow 1\" data-height=\"300\" data-width=\"700\">\n    <div id=\"metaslider_container_5425\">\n        <div id=\"metaslider_5425\">\n            <ul aria-live='off' class='slides'>\n                <li style=\"display: block; width: 100%;\" class=\"slide-5426 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-14 12:17:35\" data-filename=\"Screenshot-2026-09-10-165833-scaled-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-165833-scaled.jpg\" target=\"_blank\" aria-label=\"View Slide Details\" class=\"metaslider_image_link\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-165833-scaled-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5425 slide-5426 msDefaultImage\" title=\"Screenshot 2026-09-10 165833\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div> <p class=\"MsoCaption\"><span lang=\"en-us\" xml:lang=\"en-us\"><strong>Overview<\/strong>: MGI VisiOmics 20-plex protein markers + 1 nuclear marker scan of tonsil displayed in MIKAIA\u00ae studio (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-165833-scaled.jpg\">open in new tab<\/a>)<\/span><\/p> <\/div><\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-5427 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-14 12:17:35\" data-filename=\"Screenshot-2026-09-10-165938-scaled-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-165938-scaled.jpg\" target=\"_blank\" aria-label=\"View Slide Details\" class=\"metaslider_image_link\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-165938-scaled-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5425 slide-5427 msDefaultImage\" title=\"Screenshot 2026-09-10 165938\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div><span lang=\"en-us\" xml:lang=\"en-us\"><strong>Zoom<\/strong>: MGI VisiOmics 20-plex protein markers + 1 nuclear marker scan of tonsil displayed in MIKAIA\u00ae studio (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-165833-scaled.jpg\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/span><\/div><\/div><\/div><\/li>\n            <\/ul>\n        <\/div>\n        \n    <\/div>\n<\/div><\/div>\n\n\n\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_88 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mgi-visiomics-analysis-with-mikaia\/#AI_Cell_Segmentation_of_Nuclei_and_Cell_Contour\" >AI Cell Segmentation of Nuclei and Cell Contour<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mgi-visiomics-analysis-with-mikaia\/#LLM-aided_Cell_Typing_based_on_Protein_Co-expression\" >LLM-aided Cell Typing based on Protein Co-expression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mgi-visiomics-analysis-with-mikaia\/#Export_of_cell_coordinates_and_protein_expression_for_downstream_analysis\" >Export of cell coordinates and protein expression for downstream analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mgi-visiomics-analysis-with-mikaia\/#Cellular_Neighborhood_Analysis_in_MGI_VisiOmics_Scans\" >Cellular Neighborhood Analysis in MGI VisiOmics Scans<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mgi-visiomics-analysis-with-mikaia\/#Cell_Type_Proximity_Analysis\" >Cell Type Proximity Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mgi-visiomics-analysis-with-mikaia\/#Interrogate_Spatial_Heterogeneity_of_Cell_Types\" >Interrogate Spatial Heterogeneity of Cell Types<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mgi-visiomics-analysis-with-mikaia\/#Investigate_Spatial_Clustering_of_specific_Cell_Types\" >Investigate Spatial Clustering of specific Cell Types<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"AI_Cell_Segmentation_of_Nuclei_and_Cell_Contour\"><\/span><a>AI Cell Segmentation of Nuclei and Cell Contour<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">After tissue detection (see green outline in screenshot above), the next step is to run cell segmentation and cell typing using <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-fl-colocalization-app\/\">MIKAIA<sup>\u00ae<\/sup> FL Cell AI \/ FISH App<\/a>.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"487\" height=\"197\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-093020.png\" alt=\"\" class=\"wp-image-5454\" style=\"aspect-ratio:2.4721979852462916;width:397px;height:auto\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-093020.png 487w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-093020-300x121.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-093020-370x150.png 370w\" sizes=\"auto, (max-width: 487px) 100vw, 487px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">For cell segmentation, MIKAIA<sup>\u00ae<\/sup> offers two AI cell segmentation models (CellPose 2 or CellPose-SAM) that can be used out of the box. Since here multiple cytoplasmic and membranous markers are available, the \u201c<strong>membrane+nuclear<\/strong>\u201d option can be used so that the AI delineates true cell outlines. We select a few non-nuclear markers for membrane segmentation; as soon as one of them is present in a cell, it will be picked up by the AI. (Alternatively, it is also possible to segment nuclei and then dilate nuclei contours by a fixed radius, e.g., 3\u00b5m).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"LLM-aided_Cell_Typing_based_on_Protein_Co-expression\"><\/span><a>LLM-aided Cell Typing based on Protein Co-expression<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Cell typing can be done either by unsupervised clustering or based on co-expression. The slide\u2019s high plexity requires that explicit rules\u00a0\u2013 called a <em>Cell Type Mapping<\/em> in MIKAIA<sup>\u00ae<\/sup> \u2013 are configured for the used panel. The mapping is stored locally and can be re-used for further slides stained in the same panel.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"944\" height=\"678\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-1.png\" alt=\"\" class=\"wp-image-5429\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-1.png 944w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-1-300x215.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-1-767x551.png 767w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-1-570x409.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-1-740x531.png 740w\" sizes=\"auto, (max-width: 944px) 100vw, 944px\" \/><figcaption class=\"wp-element-caption\">MIKAIA<sup>\u00ae<\/sup> studio FL Cell AI \/ FISH App: LLM-generated cell type mapping<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Cell types can be manually configured, but MIKAIA<sup>\u00ae<\/sup> 3.1 includes a new option: \u201c<strong>Create new scheme with LLM<\/strong>\u201d. A dialog shows up, where the user only enters the tissue type (here: \u201ctonsil\u201d) and then an LLM prompt is automatically generated that instructs the LLM to create a cell type mapping based on the available markers. Simply copy and paste the prompt into any LLM, copy and paste the LLM\u2019s answer back into the dialog, and press \u201cProcess\u201d. We tested this with <strong>ChatGPT <\/strong>and <strong>Gemini<\/strong>. The created cell type mapping can then still be manually altered, so this option serves as a good starting point.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"525\" height=\"551\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-2.png\" alt=\"\" class=\"wp-image-5431\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-2.png 525w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-2-286x300.png 286w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-2-285x300.png 285w\" sizes=\"auto, (max-width: 525px) 100vw, 525px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">In order to decide for each cell which markers are positive and which are negative, their expression is measured and compared against a marker-specific threshold. Here, we used automatic thresholding. The whole-slide analysis takes between a few minutes up to an hour (depending on available GPU, selected AI model and number, and plexity). Here, ca 65k cells were detected, the nucleus and contour delineated, and all markers\u2019 expression measured for both cell compartments. Cells are classified based on the above cell type mapping.\u00a0<\/p>\n\n\n\n<div class=\"alignnormal\"><div id=\"metaslider-id-5432\" style=\"width: 100%;\" class=\"ml-slider-3-113-0 metaslider metaslider-flex metaslider-5432 ml-slider has-dots-nav ms-theme-default-base\" role=\"region\" aria-label=\"MGI Slideshow 2\" data-height=\"300\" data-width=\"700\">\n    <div id=\"metaslider_container_5432\">\n        <div id=\"metaslider_5432\">\n            <ul aria-live='off' class='slides'>\n                <li style=\"display: block; width: 100%;\" class=\"slide-5436 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-14 12:25:45\" data-filename=\"Screenshot-2026-09-10-170536-scaled-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-170536-scaled.jpg\" target=\"_blank\" aria-label=\"View Slide Details\" class=\"metaslider_image_link\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-170536-scaled-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5432 slide-5436 msDefaultImage\" title=\"VisiOmics analysis in MIKAIA - Heatmap\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div>Interactive density heatmap tool illustrates hotspots of cells of type \"B cell germinal center\" (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-170536-scaled.jpg\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/div><\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-5438 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-14 12:25:45\" data-filename=\"Screenshot-2026-09-10-170346-scaled-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-170346-scaled.jpg\" target=\"_blank\" aria-label=\"View Slide Details\" class=\"metaslider_image_link\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-170346-scaled-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5432 slide-5438 msDefaultImage\" title=\"Screenshot 2026-09-10 170346\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div> <div>ROI with cell annotation overlay (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-170346-scaled.jpg\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/div> <\/div><\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-5437 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-14 12:25:45\" data-filename=\"Screenshot-2026-09-10-172213-scaled-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-170346-scaled.jpg\" target=\"_blank\" aria-label=\"View Slide Details\" class=\"metaslider_image_link\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-172213-scaled-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5432 slide-5437 msDefaultImage\" title=\"Screenshot 2026-09-10 172213\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div>Alternative visualization: cell annotations shown in filled style. Image is hidden (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-170346-scaled.jpg\">open in new tab<\/a>)<\/div><\/div><\/div><\/li>\n            <\/ul>\n        <\/div>\n        \n    <\/div>\n<\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Export_of_cell_coordinates_and_protein_expression_for_downstream_analysis\"><\/span><a>Export of cell coordinates and protein expression for downstream analysis<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">MIKAIA<sup>\u00ae<\/sup> by default generates a CSV spreadsheet that contains statistics per cell type, but also marker expressions per cell. Cell coordinates can be exported to geojson, XML, CSV, or, alternatively, a label map can be generated using the Tile Export App. In an upcoming update, a html report will be generated additionally and cell coordinates including marker expressions will always be stored in hdf5.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These various convenient ways of exporting analysis results enable downstream tasks, such as more advanced neighbor analyses or <strong>Deep Visual Proteomics<\/strong>, where cells or ROIs are physically collected using laser-capture-microdissection and then undergo high-resolution mass spectroscopy.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Cellular_Neighborhood_Analysis_in_MGI_VisiOmics_Scans\"><\/span><a>Cellular Neighborhood Analysis in MGI VisiOmics Scans<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"1036\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-scaled.jpg\" alt=\"\" class=\"wp-image-5441\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-scaled.jpg 2560w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-300x121.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-766x310.jpg 766w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-1024x414.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-1536x622.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-370x150.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-2048x829.jpg 2048w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-570x231.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-740x299.jpg 740w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><figcaption class=\"wp-element-caption\">MIKAIA<sup>\u00ae<\/sup> Cellular Neighborhood App: cell types grouped into 4 cellular neighborhoods. Cells with uncertain cell type are excluded and hidden (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-10-173603-scaled.jpg\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-cellular-neighborhood-app\/\">MIKAIA<sup>\u00ae<\/sup> Cellular Neighborhood App<\/a> collects the cell composition in the neighborhood of each cell. The neighborhood can be defined using a radius (e.g., 100 \u00b5m), the k-nearest neighbors, or a combination of both. The app then computes neighborhood statistics per cell type and also breaks them down into a distance histogram. Furthermore, it uses the neighborhood compositions for identifying cellular neighborhoods (CNs) and then assigns each cell to a one such CN. The \u201cCluster Explorer\u201d 2D heatmap informs per CN type, which cell types have a high or low occurrence.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Cell_Type_Proximity_Analysis\"><\/span><a>Cell Type Proximity Analysis<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/proximity-analysis-imd\/\">MIKAIA<sup>\u00ae<\/sup> Proximity Analysis App<\/a> can be used to measure proximities between two sets of cell types. In the screenshot below, the cell type \u201cEpithelial cells general\u201d was selected as the source type, and \u201cBasal epithelium\u201d as the target type. The viewer is configured to show only the two cell types as well as the \u201cshortest paths\u201d annotations that were generated by the Proximity Analysis App to visualize the results. A distance histogram shows in bins of 20 \u00b5m (configurable) what percentage of source cells have a target cell within the distance represented by the histogram bin.<\/p>\n\n\n\n<div class=\"alignnormal\"><div id=\"metaslider-id-5442\" style=\"width: 100%;\" class=\"ml-slider-3-113-0 metaslider metaslider-flex metaslider-5442 ml-slider has-dots-nav ms-theme-default-base\" role=\"region\" aria-label=\"MGI Slideshow 3\" data-height=\"300\" data-width=\"700\">\n    <div id=\"metaslider_container_5442\">\n        <div id=\"metaslider_5442\">\n            <ul aria-live='off' class='slides'>\n                <li style=\"display: block; width: 100%;\" class=\"slide-5446 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-14 12:31:19\" data-filename=\"Screenshot-2026-09-11-074407-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074407.jpg\" target=\"_blank\" aria-label=\"View Slide Details\" class=\"metaslider_image_link\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074407-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5442 slide-5446 msDefaultImage\" title=\"MIKAIA Proximity Analysis on MGI VisiOmics Data\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div> <p class=\"MsoCaption\"><span lang=\"en-us\" xml:lang=\"en-us\">MIKAIA\u00ae Proximity Analysis App illustrated proximity of two cell types: \"Epithelial cells general\" and \"Basal epithelium\". Image layer is hidden (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074407.jpg\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/span><\/p> <\/div><\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-5445 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-14 12:31:19\" data-filename=\"Screenshot-2026-09-11-074415-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074415.jpg\" target=\"_blank\" aria-label=\"View Slide Details\" class=\"metaslider_image_link\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074415-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5442 slide-5445 msDefaultImage\" title=\"Screenshot 2026-09-11 074415\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div> <p class=\"MsoCaption\"><span lang=\"en-us\" xml:lang=\"en-us\">MIKAIA\u00ae Proximity Analysis App illustrated proximity of two cell types: \"Epithelial cells general\" and \"Basal epithelium\". (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074407.jpg\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/span><\/p> <\/div><\/div><\/div><\/li>\n            <\/ul>\n        <\/div>\n        \n    <\/div>\n<\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Interrogate_Spatial_Heterogeneity_of_Cell_Types\"><\/span><a>Interrogate Spatial Heterogeneity of Cell Types<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The MIKAIA<sup>\u00ae<\/sup> Grid Analysis App allows to overlay a virtual grid, collect statistics per grid tile, and then grade grid tiles. Here, we use the absolute abundance of a selected cell type (\u201cB cell germinal center\u201d) as a metric and configure a grading scheme of 6 grades ranging from none\/very few (grade 1) to many (grade 6) cells per tile. The exact cutoffs can be interactively configured. Additionally, a histogram illustrates number of tiles by target cell type abundance. The screenshot nicely shows that the \u201cB cell germinal center\u201d cell type has high occurrence inside germinal centers and very low \/ no occurrence outside.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1920\" height=\"1032\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729.jpg\" alt=\"\" class=\"wp-image-5448\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729.jpg 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729-300x161.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729-767x412.jpg 767w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729-1024x550.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729-1536x826.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729-570x306.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729-740x398.jpg 740w\" sizes=\"auto, (max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">MIKAIA<sup>\u00ae<\/sup> Grid Analysis app shows spatial heterogeneity of \u201cB cell germinal center\u201d cell type (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729.jpg\" data-type=\"link\" data-id=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-074729.jpg\">open in new tab<\/a>)<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Investigate_Spatial_Clustering_of_specific_Cell_Types\"><\/span><a>Investigate Spatial Clustering of specific Cell Types<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-spatial-clustering-app\/\" data-type=\"post\" data-id=\"961\">Spatial Clustering App<\/a> can group selected cell types into spatial clusters. When two target cell types are located within a user-defined distance (e.g., 30\u00b5m), they are grouped into the same cluster. The cluster is grown until no more target cell is within the given distance of the cluster\u2019s outermost cells. Additionally, a minimum cell count per cluster can be defined in order to avoid that many small clusters are generated. Cluster outlines are generated as yellow annotations, and a histogram informs whether the cell type builds few large clusters (\u201ccells tend to occur in the same place\u201d) or many small clusters or now clusters at all (\u201ccells tend to stay away from each other\u201d)<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1920\" height=\"1032\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017.jpg\" alt=\"\" class=\"wp-image-5449\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017.jpg 1920w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017-300x161.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017-767x412.jpg 767w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017-1024x550.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017-1536x826.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017-570x306.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017-740x398.jpg 740w\" sizes=\"auto, (max-width: 1920px) 100vw, 1920px\" \/><figcaption class=\"wp-element-caption\">MIKAIA<sup>\u00ae<\/sup> Spatial Clustering app groups cells of type \u201cB cell germinal center\u201d into spatial clusters. (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017.jpg\" data-type=\"link\" data-id=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-11-075017.jpg\">open in new tab<\/a>)<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The collaboration aims to show how researchers can use VisiOmics image data within MIKAIA\u00ae workflows for cell segmentation, phenotyping, and spatial analysis. This work supports greater flexibility in downstream analysis, helping laboratories connect automated tissue imaging with tools suited to their research questions. Stockholm, Sweden, 14 September 2026\u00a0: MGI Launches VisiOmics in Europe and Unveils [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":5457,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[35,24],"tags":[37,7,29,6,111],"coauthors":[56],"class_list":["post-5419","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-life-science","category-spatial-biology","tag-ai","tag-mikaia","tag-mikaia-app-note","tag-partner","tag-workflow"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Neighborhood analysis of mIF 20plex using MGI VisiOmics and MIKAIA studio<\/title>\n<meta name=\"description\" content=\"Use MIKAIA 3 to analyze datasets generated with MGI\u2019s multiplex immunofluorescence staining and imaging system, VisiOmics (PMIF-20RS)\" \/>\n<meta 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