{"id":126,"date":"2023-05-19T11:02:32","date_gmt":"2023-05-19T09:02:32","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=126"},"modified":"2026-04-10T22:00:41","modified_gmt":"2026-04-10T20:00:41","slug":"spatial-biology-101","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/spatial-biology-101\/","title":{"rendered":"Spatial Biology 101"},"content":{"rendered":"\n<p>Everyone is talking about spatial biology. But what is spatial biology really? It\u2019s an umbrella term for a new set of technologies, the most prominent ones being spatial transcriptomics and proteomics, but also spatial lipidomics, peptidomics, metabolomics (all enabled by mass spectrometry imaging), or spatial methylomics. In the past few years, many new devices were released to the market, which makes this amazing technology widely applicable to biologists, neurologists, pathologists, pharmacologists, etc. Read our Spatial Biology 101 to dive deeper into the field &#8230;<\/p>\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 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\/spatial-biology-101\/#Analysis_of_multi-omic_Spatial_Biology_datasets\" >Analysis of multi-omic Spatial Biology datasets<\/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\/spatial-biology-101\/#Spatial_Biology_introduction\" >Spatial Biology introduction<\/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\/spatial-biology-101\/#Spatial_Proteomics\" >Spatial Proteomics<\/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\/spatial-biology-101\/#Spatial_Transcriptomics\" >Spatial Transcriptomics<\/a><\/li><\/ul><\/nav><\/div>\n\n\n\n\n<ul class=\"wp-block-list has-gridlove-bg-color has-gridlove-highlight-bg-background-color has-text-color has-background\">\n<li>If you are not a molecular biologist, we recommend reading our <a data-type=\"post\" data-id=\"54\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=54\">Molecular Biology 101 for Techies<\/a> article first.<\/li>\n\n\n\n<li>If you are looking for commercial solutions, we recommend downloading our curated <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/spatial-biology-vendor-list\/\">List of commercial Spatial Biology instruments, assays and softwares<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Analysis_of_multi-omic_Spatial_Biology_datasets\"><\/span>Analysis of multi-omic Spatial Biology datasets<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>If you are a computer scientist, you will likely want to use one of these ecosystems: <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/scverse.org\/\">scverse<\/a> (python)<\/li>\n\n\n\n<li><a href=\"https:\/\/satijalab.org\/seurat\/\">Seurat<\/a> (R)<\/li>\n\n\n\n<li><a href=\"https:\/\/www.bioconductor.org\/\">bioconductor <\/a>(R)<\/li>\n<\/ul>\n\n\n\n<p>If you are a biologists or other type of life science researcher and prefer a no-code solution, take a look at these <a href=\"http:\/\/www.mikaia.ai\">MIKAIA<\/a>\u00ae app notes. MIKAIA can analyze scans from Akoya (CODEX), Lunaphore Comet, Miltenyi MACSima, Hamamatsu MoxiePlex, Zeiss AxioScan, Olympus, Xenium , MALDI or DESI (coming soon) and many other instruments.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/overview-of-spatial-analysis-apps-for-mif-slides\/\">Overview of MIKAIA\u2019s Spatial Analysis Apps for Multiplexed Immunofluorescence Slides<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/cycif-alignment\/\" type=\"post\" id=\"4879\">MIKAIA Slide Align for manual Spatial Proteomics: Fusing Cycles into OME-TIF was never easier<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-slide-alignment\/\" type=\"post\" id=\"4868\">MIKAIA\u2019s Slide Alignment for multi-omics slide registration (e.g. Xenium to HE, MALDI to HE, &#8230; ): An Overview<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/analyzing-macsima-scan\/\">Analyzing MACSima 47-plex mIF with MIKAIA: AI Cell Segmentation + Cell Typing + Cell-cell Connections + Cellular Neighborhoods<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-analysis-of-human-tonsil-15plex-imaged-with-akoya-phenocycler-fusion\/\">MIKAIA-Analysis of Human Tonsil 15plex Imaged with Akoya PhenoCycler-Fusion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/high-content-imaging-with-mikaia\/\">High Content Imaging: Analyzing Ex-Vivo Drug Response Assays with MIKAIA<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Spatial_Biology_introduction\"><\/span>Spatial Biology introduction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img decoding=\"async\" width=\"1024\" height=\"1023\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/07\/Spatial-Bio-word-cloud.jpg\" alt=\"Spatial Biology 101 visualized: word cloud for the term &quot;spatial biology&quot;\" class=\"wp-image-616\" style=\"width:516px;height:auto\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/07\/Spatial-Bio-word-cloud.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/07\/Spatial-Bio-word-cloud-300x300.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/07\/Spatial-Bio-word-cloud-150x150.jpg 150w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/07\/Spatial-Bio-word-cloud-768x767.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/07\/Spatial-Bio-word-cloud-370x370.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/07\/Spatial-Bio-word-cloud-270x270.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/07\/Spatial-Bio-word-cloud-570x569.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/07\/Spatial-Bio-word-cloud-740x739.jpg 740w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Word cloud generated with www.wortwolken.com (\u00a9 Fraunhofer IIS)<\/figcaption><\/figure>\n<\/div>\n\n\n<p>&#8220;Spatially Resolved Transcriptomics\u201d was named the Method of the Year 2020 by Nature.<br>&#8220;Spatial Proteomics\u201d was named the Method of the Year 2024. <br><br>To understand what \u201cspatial\u201d means let us look at the preceding technologies, namely bulk sequencing and single-cell sequencing.<\/p>\n\n\n\n<p>In <strong>bulk sequencing<\/strong>, a collection of cells is collectively analyzed, e.g., with an Illumina sequencer. The output is the quantitative gene expression, but since many cells were mixed, the gene expression across all cells is measured. When it is for instance the goal to measure the expression of a tumor sample, it is important that the mix of cells is comprised predominantly of tumor cells, because intermingled stroma or immune cells will \u201cpollute\u201d the measurement.<\/p>\n\n\n\n<p>The evolution is <strong>single cell sequencing<\/strong> (scRNA-Seq), where a device first makes each single cell uniquely identifiable by inserting a unique molecular identifier (UMI) that serves as a barcode. All cells can then be bulk-sequenced, but in the digital output, individual genes that stem from the same cell can be identified thanks to the inserted barcodes.<\/p>\n\n\n\n<p>A drawback to both methods is that it is not known where in the tissue sample a particular cell originated from. Its morphology is lost and so is the information about which other cell lived in the cell\u2019s neighborhood. This is the unique advantage of <strong>spatial biology<\/strong> <strong>methods<\/strong>. They retain the position of each cell. This enables visualizing individual genes as a markup layer on top of the scanned tissue sample.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69df6eb75fc6a&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69df6eb75fc6a\" class=\"wp-block-image size-full wp-lightbox-container\"><img decoding=\"async\" width=\"1622\" height=\"718\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides.jpg\" alt=\"\" class=\"wp-image-202\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides.jpg 1622w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides-300x133.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides-1024x453.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides-768x340.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides-1536x680.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides-370x164.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides-270x120.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides-570x252.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/Spatial-Sequencing-3-glass-slides-740x328.jpg 740w\" sizes=\"(max-width: 1622px) 100vw, 1622px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">Sequencing paradigms (\u00a9 Fraunhofer IIS)<\/figcaption><\/figure>\n\n\n\n<p><span lang=\"EN-US\">To employ a metaphor, assume we want to find out about the individual food products and their ingredients in a supermarket. Bulk sequencing would yield the list of ingredients from all products in the market. It would inform us that the mix of all products contain sugar, carbon hydrates, vitamins, water. This is not very informative, however, since we want to find out about the ingredients of <u>individual<\/u> products. Single cell sequencing would do just that and enable us to group the measured ingredients by product. We would find out that some products contain a lot of sugar and hardly any vitamins (i.e., sweets) while others that do contain vitamins are otherwise comprised mainly of water (i.e., fruit). However, using this list, we would not be able to locate the sweets or fruits department in the market. With spatially resolved methods, we would be able to measure the ingredients for individual products and on top of that know where in the supermarket the products are located. This would enable us to pick a comparatively healthy sweet from the sweets section for our kids and pick a fruit that does not contain too many acids for our sensitive stomachs. <\/span><\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69df6eb7606b0&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69df6eb7606b0\" class=\"wp-block-image size-full wp-lightbox-container\"><img decoding=\"async\" width=\"1789\" height=\"990\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage.jpg\" alt=\"\" class=\"wp-image-2196\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage.jpg 1789w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage-300x166.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage-1024x567.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage-768x425.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage-1536x850.jpg 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage-370x205.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage-270x149.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage-570x315.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-transcriptomics-advantage-740x410.jpg 740w\" sizes=\"(max-width: 1789px) 100vw, 1789px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">Comparison of outputs that the different types of sequencing produce (\u00a9 Fraunhofer IIS)<\/figcaption><\/figure>\n\n\n\n<p>The figure shows the quantitative results that can be obtained by the sequencing methods. Bulk sequencing reports only the frequency of genes. Single cell sequencing allows clustering by gene expression. Combined with a dimension reduction algorithm such as PCA, UMAP, or T-SNE, cells can be visualized in a dimensionless 2D or 3D plot. The results from spatial sequencing can be visualized as an overlay on top of the scanned tissue sample. Depending on the spatial resolution, RNA data points either cover multiple cells (e.g., <em>Visium Spatial<\/em> by <em>10x Genomics<\/em> has a resolution of 55&#215;55 \u00b5m) or transcripts can be measured with subcellular resolution (e.g., single molecule FISH \/ smFISH, or <em>Xenium in Situ<\/em> by <em>10x Genomics<\/em>).<\/p>\n\n\n\n<p>Similar to Spatial Transcriptomics, <strong>proteomics<\/strong> also retain the spatial origin. The difference is that these methods visualize proteins instead of RNA transcripts. Proteomics scans are plexed images that contain one fluorescence channel per protein plus extra channels with DAPI, which shows the DNA in nuclei, and possibly an auto-fluorescence channel. These multi-channel scans are sometimes also called multi-plex, high-plex or hyper-plex scans.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69df6eb761c7e&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69df6eb761c7e\" class=\"wp-block-image size-full wp-lightbox-container\"><img decoding=\"async\" width=\"1050\" height=\"398\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-3.png\" alt=\"\" class=\"wp-image-152\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-3.png 1050w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-3-300x114.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-3-1024x388.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-3-768x291.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-3-370x140.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-3-270x102.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-3-570x216.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-3-740x280.png 740w\" sizes=\"(max-width: 1050px) 100vw, 1050px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">MIKAIA\u00ae\u2019s marker-wise correlation module (left), FL Colocalization App identifies cells (right). (\u00a9 Fraunhofer IIS)<\/figcaption><\/figure>\n\n\n\n<p>MIKAIA\u00ae\u2019s marker-wise correlation module which computes within seconds on a pixel-level the correlation between any two markers. The <strong><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-fl-colocalization-app\/\">FL Cell AI \/ FISH App<\/a><\/strong> segments nuclei based on the DAPI channel and then classifies cells based on the protein markers\u2019 expression levels.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69df6eb762dc9&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69df6eb762dc9\" class=\"wp-block-image size-full is-resized wp-lightbox-container\"><img decoding=\"async\" width=\"1026\" height=\"414\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-5.png\" alt=\"\" class=\"wp-image-154\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-5.png 1026w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-5-300x121.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-5-1024x413.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-5-768x310.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-5-370x149.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-5-270x109.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-5-570x230.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-5-740x299.png 740w\" sizes=\"(max-width: 1026px) 100vw, 1026px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">The MIKAIA\u00ae Cell-Cell Connection App: cells + connections + density heatmap (left); no image + cells + connections (right). (\u00a9Fraunhofer IIS)<\/figcaption><\/figure>\n\n\n\n<p>The MIKAIA\u00ae <strong><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/overview-of-spatial-analysis-apps-for-mif-slides\/\">Cell-Cell Connection App<\/a><\/strong> quantifies cells\u2019 neighborhoods by connecting each cell to their direct neighbors. This yields the basis for a bystander analysis (\u201con average cell type A has 2.6 neighbors of type B\u201d) and a proximity analysis (\u201cthe average distance between cells of type A and B is 8,9 \u00b1 2.3 \u00b5m\u201d). The <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-cellular-neighborhood-app\/\">Cellular Neighborhood App<\/a> investigates not only direct cell neighbors, but quantifies a wider neighborhood and identifies Cellular Neighborhoods based on the local cell type composition.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69df6eb7635e1&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69df6eb7635e1\" class=\"wp-block-image size-full wp-lightbox-container\"><img decoding=\"async\" width=\"946\" height=\"384\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-6.png\" alt=\"Spatial biology instruments landscape\" class=\"wp-image-155\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-6.png 946w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-6-300x122.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-6-768x312.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-6-370x150.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-6-270x110.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-6-570x231.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-6-740x300.png 740w\" sizes=\"(max-width: 946px) 100vw, 946px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">Spatial biology instruments landscape (\u00a9 Fraunhofer IIS)<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69df6eb763d0c&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69df6eb763d0c\" class=\"wp-block-image size-full wp-lightbox-container\"><img decoding=\"async\" width=\"1529\" height=\"955\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-biology-tree.jpg\" alt=\"\" class=\"wp-image-2197\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-biology-tree.jpg 1529w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-biology-tree-300x187.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-biology-tree-1024x640.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-biology-tree-768x480.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-biology-tree-370x231.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-biology-tree-270x169.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-biology-tree-570x356.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/07\/spatial-biology-tree-740x462.jpg 740w\" sizes=\"(max-width: 1529px) 100vw, 1529px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">spatial biology technologies tree<\/figcaption><\/figure>\n\n\n\n<p>The figure above shows how the instruments from both spatial transcriptomics and proteomics can be categorized by the technological principles they use.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Spatial_Proteomics\"><\/span>Spatial Proteomics<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>For <strong>proteomics<\/strong>, only 2 main technological categories are predominant:<\/p>\n\n\n\n<ol style=\"list-style-type:1\" class=\"wp-block-list\">\n<li>Cyclic Restaining and<\/li>\n\n\n\n<li>Time of Flight (ToF) Mass Spectrometry.<\/li>\n<\/ol>\n\n\n\n<p>The major difference is that <strong>cyclic restaining<\/strong> instruments mark and image the set of proteins serially, i.e., in a time-multiplexed fashion. In each cycle, a small amount of proteins are stained, imaged, and then the dyes are bleached away:<\/p>\n\n\n\n<p>stain, image, erase  \u2013  stain, image, erase  \u2013  stain, image, erase \u2013 \u2026<\/p>\n\n\n\n<p>By running multiple cycles back to back, the obtainable set of markers is virtually unlimited. The advantage is that in each cycle, the same fluorescent dyes can be re-used \u2013 let\u2019s say 4 dyes per cycle \u2013 but they bind to different targets. The light spectrum only has to be divided into 4 sub spectra in this case, which allows minimizing the overlap between individual dyes. A drawback is that scans take very long. Commercial instruments of this category are <em>PhenoCycler<\/em> (formerly <em>CODEX<\/em>) by <em>Akoya Biosciences<\/em>, <em>Comet<\/em> by <em>Lunaphore<\/em>, <em>MACSima<\/em> by <em>Miltenyi Biotec<\/em> or <em>CellScape<\/em> by <em>Canopy Biosciences<\/em>.<\/p>\n\n\n\n<p>Cyclic immunofluorescence (CycIF) can also be done manually with a regular low-plex fluorescence scanner, e.g. a Zeiss Axioscan, Olympus VS200 or similar. The MIKAIA Slide Align module can then be used to align (register) and merge the cycles into a high-plex OME-TIFF (app note: <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/cycif-alignment\/\" type=\"post\" id=\"4879\">Manual Spatial Proteomics: Fusing Cycles into OME-TIF was never easier<\/a> )<\/p>\n\n\n\n<p>Instruments based on <strong>ToF mass spectrometry<\/strong>, on the other hand, image all proteins in a single shot. This is possible by staining the samples with metal antibodies. These metals are then decoupled from their target protein using a very precisely focused laser beam and then measured with a mass spectrometer. In contrast to the emission wavelengths of fluorescence dyes, which form broad curves in the spectrum, these metal antibodies appear as narrow peaks in the spectrum without overlap. A larger set of up to ~40 metal antibodies can be differentiated in this way. Commercially available instruments that use this technology are for instance <em>Hyperion CyToF<\/em> by <em>Standard Biotools<\/em> (formerly by <em>Fluidigm<\/em>) or the <em>MIBIscope<\/em> by <em>IONPATH<\/em>.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Spatial_Transcriptomics\"><\/span>Spatial Transcriptomics<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69df6eb765327&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69df6eb765327\" class=\"wp-block-image size-full wp-lightbox-container\"><img decoding=\"async\" width=\"738\" height=\"570\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-11.png\" alt=\"Comparison of spatial transcriptomics methods\" class=\"wp-image-160\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-11.png 738w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-11-300x232.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-11-370x286.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-11-270x209.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2023\/06\/grafik-11-570x440.png 570w\" sizes=\"(max-width: 738px) 100vw, 738px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">Comparison of spatial transcriptomics methods (Source: William et al., Genome Medicine, 2022, Fig.1 <a href=\"https:\/\/genomemedicine.biomedcentral.com\/articles\/10.1186\/s13073-022-01075-1\">https:\/\/genomemedicine.biomedcentral.com\/articles\/10.1186\/s13073-022-01075-1<\/a> )<\/figcaption><\/figure>\n\n\n\n<p>The newest instrument in the <em>10x Genomics<\/em> spatial biology portfolio, <em>Xenium in Situ<\/em>, released in December 2022, represents the <strong>In-situ sequencing (ISS)<\/strong> method. In contrast to ISH, where probes hybridize to an entire target gene, in ISS bases are profiled individually, 1-2 bases at a time. Fluorophores linked to the individual bases enable imaging of the bases and make the sequence readable using imaging.<\/p>\n\n\n\n<p>The advantage of imaging based methods is that they allow for measuring a large number of RNAs at a high spatial resolution, going all the way up to subcellular resolution. A disadvantage is that high resolution imaging is required, sometimes in combination with sequential imaging and multiple focus layers. smFISH scans, for instance, are typically 5D &#8211; when cyclic staining is used, even a 6<sup>th<\/sup> dimension is added. Scan protocols for 6D look as follows:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>For each staining cycle\n<ul class=\"wp-block-list\">\n<li>For each field of view\n<ul class=\"wp-block-list\">\n<li>For each fluorophore\n<ul class=\"wp-block-list\">\n<li>At each focus position (z-position)\n<ul class=\"wp-block-list\">\n<li>Capture 2D image with high exposure and 16 bit dynamic range.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>It becomes clear that scans not only take long (hours to days), but also produce super vast amounts of data that need to be analyzed.<\/p>\n\n\n\n<p><strong>Sequencing methods<\/strong> do not measure transcripts in place, but instead can be regarded as preprocessing steps that are followed by a regular RNA-sequencing. Commercially available array-based solutions are <em>STOmics<\/em> by <em>BGI<\/em> or <em>Visium Spatial<\/em> by <em>10x Genomics<\/em>. Their spatial resolution varies widely with BGI\u2019s product based on the Stereo-SEQ technology achieving sub-cellular resolution by employing micro-wells with a diameter of only 0.5 \u00b5m and <em>Visium Spatial<\/em> achieving only a near-single-cell resolution of 55&#215;55\u00b5m per spot. A personal experience based on attending the European Spatial Biology Congress 2022 in The Hague is that despite this lower resolution, <em>Visium Spatial<\/em> is still the most widely used Spatial Biology solution to date. Reasons might well be that no expensive instrument has to be purchased &#8212; a scanner and sequencer are available in many labs anyway \u2013 and that the solution is well established, having been released already in Q4\u20192019.&nbsp;<\/p>\n\n\n\n<p>The other sequencing method, <strong>microdissection<\/strong>, is based on the principle that probes are first hybridized in situ to the target tissue, and then only the areas of interest are analyzed. Which area is of interest, is defined by the user on the screen based on a brightfield or fluorescent scan. In the case of the <em>nanoString GeoMx Digital Spatial Profiler (DSP)<\/em>, photo-cleavable probes are used, which can be detached from their targets using a precisely focused UV-laser. The detection oligos are then collected and can subsequently be analyzed with an <em>Illumina<\/em> sequencer or <em>nanoString\u2019s nCounter<\/em> sequencer.<\/p>\n\n\n\n<p>The presented solutions differ in the number of targets they can distinguish, scanning time, price, compatibility with FFPE and other attributes. For more in-depth information, we recommend reading one of these reviews:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Moses, L., Pachter, L. Museum of spatial transcriptomics. Nat Methods 19, 534\u2013546 (2022). <a href=\"https:\/\/doi.org\/10.1038\/s41592-022-01409-2\">https:\/\/doi.org\/10.1038\/s41592-022-01409-2<\/a><\/li>\n\n\n\n<li>Kleino I, Frolovait\u0117 P, Suomi T, Elo LL. Computational solutions for spatial transcriptomics. Comput Struct Biotechnol J. 2022 Sep 1;20:4870-4884. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.csbj.2022.08.043\">https:\/\/doi.org\/10.1016\/j.csbj.2022.08.043<\/a> . PMID: 36147664; PMCID: PMC9464853.<\/li>\n\n\n\n<li>Liao J, Lu X, Shao X, Zhu L, Fan X. Uncovering an Organ&#8217;s Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics. Trends Biotechnol. 2021 Jan;39(1):43-58. doi: 10.1016\/j.tibtech.2020.05.006. Epub 2020 Jun 3. PMID: 32505359.<\/li>\n\n\n\n<li>Strell, C., Hilscher, M.M., Laxman, N., Svedlund, J., Wu, C., Yokota, C. and Nilsson, M. (2019), Placing RNA in context and space \u2013 methods for spatially resolved transcriptomics. FEBS J, 286: 1468-1481. <a href=\"https:\/\/doi.org\/10.1111\/febs.14435\">https:\/\/doi.org\/10.1111\/febs.14435<\/a><\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center has-gridlove-bg-color has-vivid-cyan-blue-to-vivid-purple-gradient-background has-text-color has-background\">download MIKAIA\u00ae lite for free from<b> <\/b><a style=\"font-weight: bold;\" href=\"https:\/\/www.mikaia.ai\">www.mik<\/a><strong><a href=\"https:\/\/www.mikaia.ai\">aia.ai<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Everyone is talking about spatial biology. But what is spatial biology really? It\u2019s an umbrella term for a new set of technologies, the most prominent ones being spatial transcriptomics and proteomics, but also spatial lipidomics, peptidomics, metabolomics (all enabled by mass spectrometry imaging), or spatial methylomics. In the past few years, many new devices were [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":167,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,35,24],"tags":[62],"coauthors":[56],"class_list":["post-126","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-pathology","category-life-science","category-spatial-biology","tag-science-101"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Spatial Biology 101<\/title>\n<meta name=\"description\" content=\"Discover spatial proteomics \/ transcriptomics \/ mass spec. technologies and explore their applications. 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