{"id":5474,"date":"2026-09-24T22:33:58","date_gmt":"2026-09-24T20:33:58","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=5474"},"modified":"2026-09-25T11:27:21","modified_gmt":"2026-09-25T09:27:21","slug":"singular-genomics-g4x-with-mikaia","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/singular-genomics-g4x-with-mikaia\/","title":{"rendered":"Analyzing Singular Genomics G4X\u2122 Multi-Omics Datasets with MIKAIA"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">MIKAIA<sup>\u00ae<\/sup> studio v3.1 includes native support for viewing and analyzing multi-omics datasets created with Singular Genomics&#8217; G4X\u2122 instrument. G4X features 500plex transcriptomics, 18plex proteomics and same-slide morphology via fluorescent H&amp;E (FH&amp;E). Simply drag and drop the run_meta.jsonfile that is contained in any G4X dataset into MIKAIA<sup>\u00ae<\/sup> and it will open the multi-omics stacks. You can interactively select which image layer (H&amp;E, all or subset of protein layers, no image at all) and which overlays are displayed (cell annotations, Leiden-clustered cells, and\/or transcripts).<\/p>\n\n\n\n\n\n\n<div class=\"alignnormal\"><div id=\"metaslider-id-5478\" style=\"width: 100%;\" class=\"ml-slider-3-113-0 metaslider metaslider-flex metaslider-5478 ml-slider has-dots-nav ms-theme-default-base\" role=\"region\" aria-label=\"Singular Genomics G4X in MIKAIA\" data-height=\"300\" data-width=\"700\">\n    <div id=\"metaslider_container_5478\">\n        <div id=\"metaslider_5478\">\n            <ul aria-live='off' class='slides'>\n                <li style=\"display: block; width: 100%;\" class=\"slide-5489 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-24 07:42:16\" data-filename=\"Singular-Genomics-G4X-in-MIKAIA-protein-and-cell-overlays-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-cell-overlays.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\/Singular-Genomics-G4X-in-MIKAIA-protein-and-cell-overlays-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5478 slide-5489 msDefaultImage\" title=\"Singular Genomics G4X in MIKAIA - protein and cell overlays\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div>Singular Genomics G4X scan opened in MIKAIA<sup>\u00ae<\/sup> <span style=\"font-size:11pt;line-height:107%;font-family:Aptos, sans-serif;\">\u2013<\/span> protein and cell overlays are visible (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-cell-overlays.jpg\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/div><\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-5488 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-24 07:42:16\" data-filename=\"Singular-Genomics-G4X-in-MIKAIA-protein-only-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-only.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\/Singular-Genomics-G4X-in-MIKAIA-protein-only-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5478 slide-5488 msDefaultImage\" title=\"Singular Genomics G4X in MIKAIA - protein only\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div> <div>Singular Genomics G4X scan opened in MIKAIA<sup>\u00ae<\/sup> <span style=\"font-size:11pt;line-height:107%;font-family:Aptos, sans-serif;\">\u2013<\/span> only protein without any overlays is shown (<a href=\"Singular%20Genomics%20G4X%20scan%20opened%20in%20MIKAIA%20-%20protein%20and%20cell%20overlays%20are%20visible%20-%20open%20in%20new%20tab\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/div> <\/div><\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-5490 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-24 07:42:16\" data-filename=\"Singular-Genomics-G4X-in-MIKAIA-HE-only-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-HE-only.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\/Singular-Genomics-G4X-in-MIKAIA-HE-only-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5478 slide-5490 msDefaultImage\" title=\"Singular Genomics G4X in MIKAIA - HE only\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div>Singular Genomics G4X scan opened in MIKAIA<sup>\u00ae<\/sup> <span style=\"font-size:11pt;line-height:107%;font-family:Aptos, sans-serif;\">\u2013<\/span> same-slide H&amp;E image layer is shown, without any overlays (<a href=\"Singular%20Genomics%20G4X%20scan%20opened%20in%20MIKAIA%20-%20cell%20contours%20(extracted%20from%20protein)%20shown%20on%20top%20of%20same-slide%20H&amp;E%20image%20layer%20(open%20in%20new%20tab)\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/div><\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-5487 ms-image \" aria-roledescription=\"slide\" data-date=\"2026-09-24 07:42:16\" data-filename=\"Singular-Genomics-G4X-in-MIKAIA-HE-and-cell-overlays-700x300.jpg\" data-slide-type=\"image\"><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-HE-and-cell-overlays.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\/Singular-Genomics-G4X-in-MIKAIA-HE-and-cell-overlays-700x300.jpg\" height=\"300\" width=\"700\" alt=\"\" class=\"slider-5478 slide-5487 msDefaultImage\" title=\"Singular Genomics G4X in MIKAIA - HE and cell overlays\" \/><\/a><div class=\"caption-wrap\"><div class=\"caption\"><div>Singular Genomics G4X scan opened in MIKAIA<sup>\u00ae<\/sup> <span style=\"font-size:11pt;line-height:107%;font-family:Aptos, sans-serif;\">\u2013<\/span> cell contours (extracted from protein) shown on top of same-slide H&amp;E image layer (<a href=\"Singular%20Genomics%20G4X%20scan%20opened%20in%20MIKAIA%20-%20protein%20and%20cell%20overlays%20are%20visible%20-%20open%20in%20new%20tab\" target=\"_blank\" rel=\"noreferrer noopener\">open in new tab<\/a>)<\/div><\/div><\/div><\/li>\n            <\/ul>\n        <\/div>\n        \n    <\/div>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">When zooming in, the transcript spots become visible. G4X scans feature a very high transcript density. Each gene is available as an annotation class, and genes&#8217; visibility can be toggled on\/off in the &#8220;Annotations&#8221; side panel.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"1392\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots.jpg\" alt=\"\" class=\"wp-image-5491\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots.jpg 2560w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-300x163.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-767x417.jpg 767w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-1024x557.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-1536x835.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-2048x1114.jpg 2048w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-370x201.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-270x147.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-570x310.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-740x402.jpg 740w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><figcaption class=\"wp-element-caption\">Singular Genomics G4X dataset opened in MIKAIA<sup>\u00ae<\/sup>. Zoom-in on transcritps. Genes can be independently toggled on\/off (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-and-RNA-spots-1024x557.jpg\">open in new tab<\/a>)<\/figcaption><\/figure>\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\/singular-genomics-g4x-with-mikaia\/#Investigate_transcript_layer_in_Singular_Genomics_G4X_scans\" >Investigate transcript layer in Singular Genomics G4X scans<\/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\/singular-genomics-g4x-with-mikaia\/#Cell_typing_in_Singular_Genomics_G4X_scans\" >Cell typing in Singular Genomics G4X scans<\/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\/singular-genomics-g4x-with-mikaia\/#Relating_transcriptome_to_proteome\" >Relating transcriptome to proteome<\/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\/singular-genomics-g4x-with-mikaia\/#Automatically_find_ROI_Train_custom_tumor_detection_AI_with_AI_Author\" >Automatically find ROI: Train custom tumor detection AI with AI Author<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Investigate_transcript_layer_in_Singular_Genomics_G4X_scans\"><\/span>Investigate transcript layer in Singular Genomics G4X scans <span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The transcriptome can be analyzed with the <strong>MIKAIA<sup>\u00ae<\/sup> Cell x Gene App<\/strong>. In the below screenshot of a lung carcinoma, the tumor was manually outlined (class &#8220;ROI&#8221;). The Cell x Gene App was then run on the whole-slide in &#8220;Only overview, no cell query&#8221; mode. In the &#8220;Divide by ROI&#8221; section, the created &#8220;ROI&#8221; class was selected. The app now assigns cell and transcripts contained within the outlined tumor to the tumor ROI. All remaining cells and transcripts are automatically assigned to the &#8220;other&#8221; ROI. Various statistics are now collected per ROI and can be compared in order to investigate biological differences. The diagrams in the results panel illustrate statistics per ROI. All statistics can also be exported to CSV and\/or JSON.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"1392\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs.jpg\" alt=\"\" class=\"wp-image-5493\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs.jpg 2560w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-300x163.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-767x417.jpg 767w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-1024x557.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-1536x835.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-2048x1114.jpg 2048w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-370x201.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-270x147.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-570x310.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-740x402.jpg 740w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><figcaption class=\"wp-element-caption\">Singular Genomics G4X dataset opened in MIKAIA<sup>\u00ae<\/sup>. Cell x Gene app compares transcriptome inside vs outside ROI (green outline) (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-Compare-transcriptome-of-ROIs-1024x557.jpg\">open in new tab<\/a>)<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Cell_typing_in_Singular_Genomics_G4X_scans\"><\/span>Cell typing in Singular Genomics G4X scans <span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The transcriptome can be evaluated with the untyped or Leiden-clustered cell annotations already available in the dataset. Alternatively, the <strong>MIKAIA<sup>\u00ae<\/sup> FL Cell AI \/ FISH App<\/strong> can be used for single cell analysis. Two options are available:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Phenotyping only<\/strong>: Skip AI cell segmentation \u2013 use existing annotations and only do phenotyping.<\/li>\n\n\n\n<li><strong>Segmentation + phenotyping<\/strong>: Run AI cell segmentation from scratch using MIKAIA&#8217;s cell segmentation AI (fast regular mode or foundation-model based &#8220;XXL AI&#8221;) and then do phenotyping.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">For the phenotyping-only option, use the app&#8217;s new &#8220;Existing cells&#8221; mode and select &#8220;Analyze &amp; phenotype cells&#8221;:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"484\" height=\"520\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-6.png\" alt=\"\" class=\"wp-image-5495\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-6.png 484w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-6-279x300.png 279w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-6-370x398.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/image-6-270x290.png 270w\" sizes=\"auto, (max-width: 484px) 100vw, 484px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">For phenotyping, the app will measure the protein expression for each protein marker in each cell. The measured expression is then compared against a threshold to decide if the protein is expressed in a cell or not. It is most convenient to set the threshold to &#8220;Auto&#8221;, in which case a threshold will automatically be picked per marker at the end of the analysis, when all cells&#8217; expressions are known. Subsequently, the picked thresholds can be manually adapted on a marker-by-marker basis, if required.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Additionally, it is recommended to select whether the expression in the <strong>nucleus <\/strong>or <strong>cytoplasm+membrane<\/strong> (the app uses a 2-cell-compartments model at the moment) or in the entire cell should be used. The choice depends on the marker. Click &#8220;<strong>Auto-select marker type &amp; analysis with LLM<\/strong>&#8221; to generate an LLM prompt that asks for the recommended choice for each marker. Copy and paste it into your favorite LLM, copy and paste its response back into the dialog, and MIKAIA<sup>\u00ae<\/sup> will then process the response and automatically set each marker&#8217;s type and analysis. In the example below, most markers are set to &#8220;Protein \/ cytoplasmic or membranous&#8221;, but, e.g., FoxP3 and Ki67 are both set to &#8220;Protein \/ nuclear&#8221; as expected.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"498\" height=\"900\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-setup.png\" alt=\"\" class=\"wp-image-5492\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-setup.png 498w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-setup-166x300.png 166w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-setup-370x669.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-protein-setup-270x488.png 270w\" sizes=\"auto, (max-width: 498px) 100vw, 498px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Relating_transcriptome_to_proteome\"><\/span>Relating transcriptome to proteome<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Now that cell types have been determined based on the proteome, the Cell x Gene App can be re-run to obtain transcriptomic statistics broken down by proteome-defined cell phenotypes: <\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"550\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-1024x550.jpg\" alt=\"\" class=\"wp-image-5499\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-1024x550.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-300x161.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-767x412.jpg 767w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-1536x826.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-370x199.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-270x145.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-570x306.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-740x398.jpg 740w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells.jpg 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Singular Genomics G4X dataset opened in MIKAIA<sup>\u00ae<\/sup>. Cell x Gene app compares transcriptome in preoteome-defined cell phenotypes (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-RNA-analysis-in-cells-1024x550.jpg\">open in new tab<\/a>)<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Automatically_find_ROI_Train_custom_tumor_detection_AI_with_AI_Author\"><\/span>Automatically find ROI: Train custom tumor detection AI with AI Author <span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of circling the ROI manually, as above, the same-slide H&amp;E layer available in G4X datasets can be used to segment tissue types. Here, we keep it simple and train a small &#8220;Lung&#8221; AI model using the Classification AI Author. A single tumor annotation (blue) and non-tumor annotation (pink) is drawn and the model trained. Subsequently, we run the AI on the entire slide to obtain a tumor and non-tumor mask. Then, in the Cell x Gene App&#8217;s &#8220;Divide by ROI&#8221; section, we select the &#8220;Tumor &#8220;class as the ROI in order to obtain separate transcriptome statistics for inside vs outside the tumor.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"550\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-1024x550.jpg\" alt=\"\" class=\"wp-image-5500\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-1024x550.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-300x161.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-767x412.jpg 767w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-1536x826.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-370x199.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-270x145.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-570x306.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-740x398.jpg 740w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis.jpg 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Result of user-trained tumor detection AI analysis, trained with MIKAIA<sup>\u00ae<\/sup> AI author, run on same.slide H&amp;E layer in G4X dataset (<a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2026\/09\/Singular-Genomics-G4X-in-MIKAIA-morphology-analysis-1024x550.jpg\">open in new tab<\/a>)<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>MIKAIA\u00ae studio v3.1 includes native support for viewing and analyzing multi-omics datasets created with Singular Genomics&#8217; G4X\u2122 instrument. G4X features 500plex transcriptomics, 18plex proteomics and same-slide morphology via fluorescent H&amp;E (FH&amp;E). Simply drag and drop the run_meta.jsonfile that is contained in any G4X dataset into MIKAIA\u00ae and it will open the multi-omics stacks. You can [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":5504,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[35,24],"tags":[37,7,29,111],"coauthors":[56],"class_list":["post-5474","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-workflow"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Analyzing Singular Genomics G4X\u2122 Multi-Omics Datasets with MIKAIA<\/title>\n<meta name=\"description\" content=\"MIKAIA\u00ae now natively supports analyzing Singular Genomics G4x multi-omics datasets, incl. 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