{"id":3341,"date":"2025-02-12T10:12:39","date_gmt":"2025-02-12T09:12:39","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=3341"},"modified":"2025-10-14T11:06:48","modified_gmt":"2025-10-14T09:06:48","slug":"proximity-analysis-imd","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/proximity-analysis-imd\/","title":{"rendered":"Proximity Analysis: Quantifying Cell Subpopulations in Vicinity of Intratumoral Microdevice (IMD)"},"content":{"rendered":"\n<p>The <strong>Laboratory for Bio-Micro Devices<\/strong> at<strong> Brigham and Women&#8217;s Hospital<\/strong> (<a href=\"https:\/\/jonaslab.bwh.harvard.edu\">https:\/\/jonaslab.bwh.harvard.edu<\/a>) develops drug releasing implantable intratumoral microdevices (IMD) <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/37672566\/\">[1]<\/a>. Using MIKAIA<sup>\u00ae<\/sup>, they want to examine in a quantitative fashion how the cell subpopulations change in the vicinity of the drug-releasing IMD.<\/p>\n\n\n\n<p>This app note presents two possible approaches<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/proximity-analysis-imd\/#proximity-analysis-app\"><strong>Option a)<\/strong> <strong>Proximity Analysis App<\/strong><\/a>. It computes, per cell, the distance to a target.<\/li>\n\n\n\n<li><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/proximity-analysis-imd\/#concentric-margins\"><strong>Option b)<\/strong> <strong>Concentric margins<\/strong><\/a>. Assign cells into concentric margins of fixed diameters.<\/li>\n<\/ul>\n\n\n\n<p>The following screenshot shows a fluorescent scan of the resected tumor tissue. The hole where the IMD was located is clearly visible in the center of the tissue.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1474\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/1.png\" alt=\"\" class=\"wp-image-3343\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/1.png 1474w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/1-300x217.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/1-1024x742.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/1-768x556.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/1-370x268.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/1-270x196.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/1-570x413.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/1-740x536.png 740w\" sizes=\"(max-width: 1474px) 100vw, 1474px\" \/><\/figure>\n\n\n\n<p>Using the magic brush, we first annotate the IMD location (yellow). We then mark the IMD\u2019s drug dispensing tip (violet marker) with the marker tool and finally annotate the region of interest (red) with the pen tool.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0.png\" alt=\"\" class=\"wp-image-3344\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/0-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"proximity-analysis-app\">Option a) Proximity Analysis App<\/h2>\n\n\n\n<p>In this option, we first run the FL Cell Analysis App to detect and phenotype cells. In this example, we provided no cell naming scheme (lineage map), and so default names are used. The following screenshot shows cells annotated by their co-expression profile, i.e., one annotation class per encountered combination of expressed cell markers is created.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2.png\" alt=\"\" class=\"wp-image-3345\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/2-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<p>This screenshot shows only the cells found positive for the green Alexa Fluor 488 dye. (The scan does not contain the metadata which antigens were targeted, but only the dye names.)<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3.png\" alt=\"\" class=\"wp-image-3346\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/3-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<p>Next, the Proximity Analysis App will be used to quantify distances of cells to our target point, the IMD\u2019s tip. As source classes (\u201csources\u201d list, top), here, the individual marker annotation classes are selected. Alternatively, the set of co-expression classes could also be selected to obtain distances per co-expression phenotype.<\/p>\n\n\n\n<p>The analysis can be started by zooming out so that the entire ROI is visible and then clicking the analyze-\u201cFoV\u201d button or by selecting the red \u201cROI\u201d annotation and then clicking the analyze-\u201cRoI\u201d button.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4.png\" alt=\"\" class=\"wp-image-3354\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/4-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<p>The analysis only takes a few seconds. The distance for each annotation in any of the selected source classes to the closest annotation in any of the selected target annotation classes is computed.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5.png\" alt=\"\" class=\"wp-image-3355\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/5-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<p>In this example, the violet \u201cIMD Tip\u201d marker annotation was selected as a target. Alternatively, the yellow \u201cIMD\u201d path annotation could have been selected, in which case the shortest path to any point on the target annotation is sought.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13.png\" alt=\"\" class=\"wp-image-3357\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/13-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<p>Since, in this example, we examined the distances per marker, we should view the \u201cshortest path\u201d classes one at a time, since the same cell could be positive for more than one marker and thus may have yielded multiple marker annotations. This screenshot shows the paths only for the AF488+ cells.<\/p>\n\n\n\n<p>The diagrams on the bottom left can be undocked and enlarged. They show the absolute abundances by cell type and by distance to the target (histogram with 10 bins). Further diagrams can easily be generated by clicking the \u201cExport to CSV\u201d button and opening the exported file in Microsoft Excel or importing it into Python, Matlab, or R. This spreadsheet includes the statistics presented in the UI as well as a row per cell.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6.png\" alt=\"\" class=\"wp-image-3356\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/6-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"concentric-margins\">Option b) Concentric margins<\/h2>\n\n\n\n<p>A similar alternative approach is to add concentric margins to the yellow IMD annotation. The \u201cAdd margins \u2026\u201d dialog can be opened via the main toolbar\u2019s \u201cActions\u201d menu.<\/p>\n\n\n\n<p>Here, we create five margins with a diameter of 200 \u00b5m each.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1583\" height=\"1066\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7.png\" alt=\"\" class=\"wp-image-3358\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7.png 1583w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7-1536x1034.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/7-740x498.png 740w\" sizes=\"(max-width: 1583px) 100vw, 1583px\" \/><\/figure>\n\n\n\n<p>Next, we can clip the margins with our red ROI annotation. To do that, click \u201cClip annotations \u2026\u201d in the main toolbar\u2019s \u201cActions\u201d menu. Select the green margin annotations as subjects by first multi-selecting them in the viewer (click on them while pressing CTRL key) and then clicking the &#8220;take from viewer&#8221; button in the dialog. Next, select the red &#8220;ROI&#8221; annotation as the &#8220;Clip annotation&#8221; in the analog way. Click &#8220;Clip&#8221;.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1584\" height=\"1066\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8.png\" alt=\"\" class=\"wp-image-3359\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8.png 1584w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8-1024x689.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8-1536x1034.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/8-740x498.png 740w\" sizes=\"(max-width: 1584px) 100vw, 1584px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9.png\" alt=\"\" class=\"wp-image-3360\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/9-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<p>The margins are now clipped to the ROI. Just in case, the original unclipped margin annotations still exist, but have been moved into the &#8220;Backup&#8221; group and hidden. <\/p>\n\n\n\n<p>Now, the <strong>FL Cell Analysis<\/strong> <strong>App <\/strong>is used to detect and phenotype cells in the ROI. In the app&#8217;s configuration panel&#8217;s \u201cDivide by ROIs\u201d section, select the 5 margins classes. This way, the app will group all detected cells by the ROI in which they are contained.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10.png\" alt=\"\" class=\"wp-image-3361\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/10-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<p>As before, the FL Cell Analysis App has identified the same set of cells in the red ROI annotation, but now grouped them into individual annotation classes. In the screenshot, only the negative (i.e., not positive for any of the four markers) cells in margin #4 are shown.<\/p>\n\n\n\n<p>The results table on the bottom left lists the abundances and cell densities (cells per mm\u00b2) per margin. In the below screenshot, only cells in the 4<sup>th<\/sup> margin that are not positive for any of the four markers are shown, all other cell classes are hidden.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1585\" height=\"1068\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11.png\" alt=\"\" class=\"wp-image-3362\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11.png 1585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11-300x202.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11-1024x690.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11-768x517.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11-1536x1035.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11-370x249.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11-270x182.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11-570x384.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/11-740x499.png 740w\" sizes=\"(max-width: 1585px) 100vw, 1585px\" \/><\/figure>\n\n\n\n<p>As will all MIKAIA<sup>\u00ae<\/sup> analyses, quantitative results, including information on each individual cell, can be readily exported by clicking the \u201cExport to CSV\u201d button. Here is a (low-magnification) screenshot of the exported spreadsheet to illustrate how all numbers are organized in a column oriented fashion that can be easily processed by Microsoft Excel, Python, Matlab, or R.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"3440\" height=\"1408\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12.png\" alt=\"\" class=\"wp-image-3363\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12.png 3440w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12-300x123.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12-1024x419.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12-768x314.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12-1536x629.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12-2048x838.png 2048w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12-370x151.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12-270x111.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12-570x233.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/02\/12-740x303.png 740w\" sizes=\"(max-width: 3440px) 100vw, 3440px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">References<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>[1] Peruzzi P, Dominas C, Fell G, Bernstock JD, Blitz S, Mazzetti D, Zdioruk M, Dawood HY, Triggs DV, Ahn SW, Bhagavatula SK, Davidson SM, Tatarova Z, Pannell M, Truman K, Ball A, Gold MP, Pister V, Fraenkel E, Chiocca EA, Ligon KL, Wen PY, Jonas O. Intratumoral drug-releasing microdevices allow in situ high-throughput pharmaco phenotyping in patients with gliomas. Sci Transl Med. 2023 Sep 6;15(712):eadi0069. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/37672566\/\">doi: 10.1126\/scitranslmed.adi0069. Epub 2023 Sep 6. PMID: 37672566; PMCID: PMC10754230.<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Laboratory for Bio-Micro Devices at Brigham and Women&#8217;s Hospital (https:\/\/jonaslab.bwh.harvard.edu) develops drug releasing implantable intratumoral microdevices (IMD) [1]. Using MIKAIA\u00ae, they want to examine in a quantitative fashion how the cell subpopulations change in the vicinity of the drug-releasing IMD. This app note presents two possible approaches The following screenshot shows a fluorescent scan [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":3357,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,35,28],"tags":[37,7,29,109],"coauthors":[56],"class_list":["post-3341","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-pathology","category-life-science","category-mikaia-university","tag-ai","tag-mikaia","tag-mikaia-app-note","tag-use-case"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Proximity Analysis: Quantifying Cell Subpopulations in Vicinity of Intratumoral Microdevice (IMD) - SMART SENSING insights<\/title>\n<meta name=\"description\" content=\"A lab at Brigham and Women&#039;s Hospital develops drug releasing implantable intratumoral microdevices (IMD) and uses MIKAIA\u00ae to examine cell subpopulations vicinity of the drug-releasing IMD, including proximity analysis \/ distance analysis.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/proximity-analysis-imd\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Proximity Analysis: Quantifying Cell Subpopulations in Vicinity of Intratumoral Microdevice (IMD) - 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