Name: SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments (Video) | JoVE Journal
Uploaded: 2025-08-08T13:30:00.000+00:00
Duration: 6 min 34 s
Description: Learn semi-automated skin cell analysis using SCAnED for immune and non-immune cell quantification in human epidermis and dermis. This open-source ImageJ macro enhances accuracy in studying inflammation and cancer without requiring coding experience.

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The development of this image analysis pipeline was funded by the Land Nieder&#246;sterreich as part of the Danube Allergy Research Cluster and by the Fellinger Krebsforschung.

<ol><li>Lichtenberger, B. M., Kasper, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cellular+heterogeneity+and+microenvironmental+control+of+skin+cancer%5BTitle%5D)+AND+%22J+Intern+Med%22%5BJournal%5D)">Cellular heterogeneity and microenvironmental control of skin cancer.</a> J Intern Med. 289 (5), 614-628 (2021).</li>
<li>Watt, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mammalian+skin+cell+biology%3A+at+the+interface+between+laboratory+and+clinic%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Mammalian skin cell biology: at the interface between laboratory and clinic.</a> Science. 346 (6212), 937-940 (2014).</li>
<li>Bergstresser, P. R., Pariser, R. J., Taylor, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Counting+and+sizing+of+epidermal+cells+in+normal+human+skin%5BTitle%5D)+AND+%22J+Invest+Dermatol%22%5BJournal%5D)">Counting and sizing of epidermal cells in normal human skin.</a> J Invest Dermatol. 70 (5), 280-284 (1978).</li>
<li>Régnier, M., Patwardhan, A., Scheynius, A., Schmidt, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Reconstructed+human+epidermis+composed+of+keratinocytes%2C+melanocytes+and+Langerhans+cells%5BTitle%5D)+AND+%22Med+Biol+Eng+Comput%22%5BJournal%5D)">Reconstructed human epidermis composed of keratinocytes, melanocytes and Langerhans cells.</a> Med Biol Eng Comput. 36 (6), 821-824 (1998).</li>
<li>Régnier, M., Staquet, M. J., Schmitt, D., Schmidt, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integration+of+Langerhans+cells+into+a+pigmented+reconstructed+human+epidermis%5BTitle%5D)+AND+%22J+Invest+Dermatol%22%5BJournal%5D)">Integration of Langerhans cells into a pigmented reconstructed human epidermis.</a> J Invest Dermatol. 109 (4), 510-512 (1997).</li>
<li>Bankhead, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((QuPath%3A+Open+source+software+for+digital+pathology+image+analysis%5BTitle%5D)+AND+%22Sci+Rep%22%5BJournal%5D)">QuPath: Open source software for digital pathology image analysis.</a> Sci Rep. 7 (1), 16878(2017).</li>
<li>McQuin, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CellProfiler+3.0%3A+Next-generation+image+processing+for+biology%5BTitle%5D)+AND+%22PLoS+Biol%22%5BJournal%5D)">CellProfiler 3.0: Next-generation image processing for biology.</a> PLoS Biol. 16 (7), e2005970(2018).</li>
<li>Berg, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ilastik%3A+interactive+machine+learning+for+%28bio%29image+analysis%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">ilastik: interactive machine learning for (bio)image analysis.</a> Nat Methods. 16 (12), 1226-1232 (2019).</li>
<li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fiji%3A+an+open-source+platform+for+biological-image+analysis%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Fiji: an open-source platform for biological-image analysis.</a> Nat Methods. 9 (7), 676-682 (2012).</li>
<li>Schmidt, U., Weigert, M., Broaddus, C., Myers, G. Cell detection with star-convex polygons. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aSchmidt%2C+U+%22Medical+Image+Computing+and+Computer+Assisted+Intervention+-+MICCAI+2018%22">Medical Image Computing and Computer Assisted Intervention - MICCAI 2018</a>. Frangi, A., Schnabel, J., Davatzikos, C., Alberola-Lopez, C., Fichtinger, G. 11071, Springer. Cham. Lecture notes in Computer Science 265-273 (2018).</li>
<li>Google Colab. , Google. 
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<li>Balsini, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Stiffness-dependent+lysyl+oxidase+regulation+through+hypoxia-inducing+factor+1+drives+extracellular+matrix+modifications+in+psoriasis%5BTitle%5D)+AND+%22J+Invest+Dermatol%22%5BJournal%5D)">Stiffness-dependent lysyl oxidase regulation through hypoxia-inducing factor 1 drives extracellular matrix modifications in psoriasis.</a> J Invest Dermatol. 1145 (7), 1653-1669.e10 (2024).</li>
<li>Mahrle, G., Bolling, R., Osborn, M., Weber, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Intermediate+filaments+of+the+vimentin+and+prekeratin+type+in+human+epidermis%5BTitle%5D)+AND+%22J+Invest+Dermatol%22%5BJournal%5D)">Intermediate filaments of the vimentin and prekeratin type in human epidermis.</a> J Invest Dermatol. 81 (1), 46-48 (1983).</li>
<li>de Waal, R. M., Semeijn, J. T., Cornelissen, M. H., Ramaekers, F. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Epidermal+Langerhans+cells+contain+intermediate-sized+filaments+of+the+vimentin+type%3A+an+immunocytologic+study%5BTitle%5D)+AND+%22J+Invest+Dermatol%22%5BJournal%5D)">Epidermal Langerhans cells contain intermediate-sized filaments of the vimentin type: an immunocytologic study.</a> J Invest Dermatol. 82 (6), 602-604 (1984).</li>
<li>Bata-Csorgo, Z., Hammerberg, C., Voorhees, J. J., Cooper, K. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Flow+cytometric+identification+of+proliferative+subpopulations+within+normal+human+epidermis+and+the+localization+of+the+primary+hyperproliferative+population+in+psoriasis%5BTitle%5D)+AND+%22J+Exp+Med%22%5BJournal%5D)">Flow cytometric identification of proliferative subpopulations within normal human epidermis and the localization of the primary hyperproliferative population in psoriasis.</a> J Exp Med. 178 (4), 1271-1281 (1993).</li>
<li>Nakamura, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Expression+of+CD90+on+keratinocyte+stem%2Fprogenitor+cells%5BTitle%5D)+AND+%22British+Journal+of+Dermatology%22%5BJournal%5D)">Expression of CD90 on keratinocyte stem/progenitor cells.</a> British Journal of Dermatology. 154, 1062-1070 (2006).</li>
<li>Wetzel, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Increased+neutrophil+adherence+in+psoriasis%3A+role+of+the+human+endothelial+cell+receptor+Thy-1+%28CD90%29%5BTitle%5D)+AND+%22J+Invest+Dermatol%22%5BJournal%5D)">Increased neutrophil adherence in psoriasis: role of the human endothelial cell receptor Thy-1 (CD90).</a> J Invest Dermatol. 126 (2), 441-452 (2006).</li>
<li>RRID:SCR_018350. , Indica Labs. 
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The authors declare no competing financial interests.

The SCAnED protocol is a semi-automated method for compartment-specific analysis of both immune and non-immune cells in human skin tissue. The workflow integrates an E-cadherin-based staining protocol to enable epidermis and dermis segmentation, DAPI-based nuclei and cell detection, followed by the quantification of marker expression in nuclei and cells using our ImageJ-based macro. Further, we guide users through the analysis of our Python workflow to visualize extracted single-cell data. The protocol is flexible and su...

Understanding skin tissues on a cellular level and discerning cell phenotypes, morphology, and their localization within both the epidermal and dermal compartments are pivotal in dermatological research and histopathology1,2. The cellular composition within the epidermis, encompassing keratinocytes, melanocytes, and Langerhans cells under healthy conditions, becomes greatly imbalanced in inflammatory skin diseases and cancer. Moreover, the phenotype and localization of cells within the dermis, situated beneath the epidermis and harboring fibroblasts and the majority of immune cells, can change significantly in disease. Hence, cell and nuclei segmentation are imperative for assessing cellular attributes such as cell number, protein expression, morphology and spatial organization, and nucleus characteristics such as size, shape, and transcription factor expression, thereby aiding in the comprehension of skin homeostasis and diseases3,4,5.
Despite the availability of general-purpose image analysis platforms such as QuPath6, CellProfiler7, and ilastik8, these tools often require extensive customization and scripting to adapt to the structural complexity and heterogeneous cell morphology of skin tissue. Few tools have been specifically tailored for automated segmentation of epidermal and dermal compartments, and many available solutions are commercial and not freely accessible. These limitations make it challenging for non-experts to implement efficient pipelines for skin-specific analysis.
Here, we present a comprehensive protocol for the analysis of cells and nuclei within skin tissue and its compartments, covering all vital steps from tissue staining to image analysis using our semi-automated image analysis pipeline. We start with detailed information on immunofluorescence staining of skin tissue, including staining with E-cadherin for epidermis/dermis segmentation and 4&#8242;,6-diamidino-2-phenylindole (DAPI) for cell/nuclei detection. Two markers of interest, Vimentin (VIM) and CD90, were included for validation purposes. We recommend using antibodies at optimized concentrations (see Table 1) and capturing images using confocal microscopy with consistent settings for laser intensity, exposure time, and resolution to ensure reproducibility.
Further, we introduce our custom-designed ImageJ9 macro SCAnED, which stands for Skin Compartment Analysis of Epidermis and Dermis, and we provide all necessary steps for in silico segmentation and detection of nuclei and cells within both the epidermis and dermis. Users can choose between two different nuclei detection methods, either using simple and fast thresholding or the deep learning tool StarDist10 and our trained skin model, before proceeding with the SCAnED protocol. Unlike general tools, SCAnED was developed specifically for skin histology and outperforms them by enabling automatic compartmentalization of epidermal and dermal regions and integrated quantification of multiple markers across both regions. This is particularly useful for researchers who lack expertise in programming or deep learning, offering a ready-to-use tool tailored for skin image analysis.
We further show how mean intensity levels are extracted per cell from immunofluorescence images of up to five distinct markers of interest. Finally, we provide a Python code for the statistical analysis of the obtained single-cell data written and executed in Google Colab11 and shared as a Jupyter notebook. Our protocol and the SCAnED macro aim to provide skin researchers without advanced immunofluorescence and computing expertise with a practical tool to label and semi-automatically quantify cells and marker expression in human skin, focusing on the epidermal and dermal compartments.

<table><tbody><tr><td>0.3% Triton X&amp;nbsp;</td><td>Sigma-Aldrich</td><td>T9284</td><td></td></tr><tr><td>BSA (Bovine serum albumin)</td><td>Sigma Aldrich</td><td></td><td></td></tr><tr><td>CD90</td><td>cell signaling</td><td>D3V8A</td><td></td></tr><tr><td>DAPI&amp;nbsp;</td><td>Roche</td><td>10236276001</td><td></td></tr><tr><td>Deionized water</td><td></td><td></td><td></td></tr><tr><td>E-cadherin&amp;nbsp;</td><td>Abcam</td><td>ab1416</td><td></td></tr><tr><td>Fluorescence mounting medium&amp;nbsp;</td><td>Dako</td><td>S3023</td><td></td></tr><tr><td>Goat anti-Mouse IgG1 Cross-Adsorbed Secondary Antibody, Alexa Fluor 488&amp;nbsp; (AF 488 anti-mouse)</td><td>Thermo Fisher Scientific</td><td>A-21121</td><td></td></tr><tr><td>Goat anti-Mouse IgG2a Cross-Adsorbed Secondary Antibody, Alexa Fluor 647 (AF 647 anti-mouse)</td><td>Thermo Fisher Scientific</td><td>A-21241</td><td></td></tr><tr><td>Goat anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 568 ( AF 568 anti-rabbit)</td><td>Thermo Fisher Scientific</td><td>A-11011</td><td></td></tr><tr><td>Goat serum&amp;nbsp;</td><td>Agilent Technologies &amp;Ouml;sterreich GmbH, Dako</td><td>X0907</td><td></td></tr><tr><td>PBS&amp;nbsp;</td><td>Gibco</td><td>14190-094</td><td></td></tr><tr><td>Vimentin&amp;nbsp;</td><td>Dako</td><td>M7020</td><td></td></tr><tr><td>&lt;strong&gt;Materials&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Chamber box (wet box)</td><td></td><td></td><td></td></tr><tr><td>Coverslips&amp;nbsp;</td><td>Thermo Fisher</td><td></td><td></td></tr><tr><td>Liquid-repelling slide marker pen for staining (hydrophobic)&amp;nbsp;</td><td>Daido Sangyo</td><td>Z377821</td><td>24x50mm</td></tr><tr><td>&lt;strong&gt;Solutions&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Permeabilization buffer (0.3% Triton X in PBS)&amp;nbsp;</td><td></td><td></td><td>Refer to Table 2</td></tr><tr><td>Staining buffer (2% BSA in PBS)&amp;nbsp;</td><td></td><td></td><td>Refer to Table 2</td></tr><tr><td>&lt;strong&gt;Software and Datasets&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Confocal laser scanning microscope&amp;nbsp;</td><td>Olympus</td><td>FV3000 based on inverted IX83</td><td>equipped with a 10x objective (NA 0.4), 4 laser lines (405 nm, 488 nm, 561 nm, 640 nm, Coherent)</td></tr><tr><td>Fiji (ImageJ)&amp;nbsp;</td><td></td><td>V1.54h</td><td></td></tr><tr><td>Google Colab</td><td>Google</td><td></td><td></td></tr><tr><td>GraphPad Prism&amp;nbsp;</td><td>GraphPad Software Inc.</td><td>Prism 10</td><td>for further statistical analysis</td></tr><tr><td>HALO</td><td>Indica Labs</td><td></td><td></td></tr><tr><td>Python program</td><td></td><td>Python 3.11.12</td><td></td></tr><tr><td>QuPath</td><td>open software ( Bankhead, P. et al, https://doi.org/10.1038/s41598-017-17204-5)</td><td>v0.5.1</td><td></td></tr><tr><td>StarDist</td><td></td><td></td><td>Install TF1.15.0 CPU version</td></tr></tbody></table>

scaned - an open-source skin segmentation macro for semi-automated cell and nuclei detection in epidermal and dermal skin compartments

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

The spatial distribution of immune and non-immune cells within tissues and the expression of cell-specific markers provides essential information on cell function in situ. Human skin is a highly complex organ with defined compartments comprising different cells with diverse phenotypes. By using multi-fluorescence labeling, distinct skin cell populations can be determined and further characterized. However, currently, the availability of non-commercial tools for image analysis of skin samples that allow in silico segmentation of cells specifically within the epidermis or the dermis at a single-cell resolution is limited. We provide here a step-by-step protocol for immunofluorescence staining of skin sections, confocal microscopy, and image analysis using our freely available tool SCAnED (Skin Compartment Analysis of Epidermis and Dermis). The SCAnED macro allows the precise identification and classification of cells and nuclei in both the epidermal and dermal compartments of human skin. We provide guidelines on how to determine average intensity levels of maker expression in different cells and cellular compartments, such as the cytoplasm and the nucleus, and how to quantify cells expressing varying amounts of these markers with an accompanying Python pipeline provided as a ready-to-use Jupyter notebook executed in Google Colab. This protocol will allow inexperienced users to determine cell-specific expression profiles within skin tissue and provide insights into the spatial distribution of cells within the epidermal and dermal compartments, allowing a deeper understanding of the intricate tissue structure and cell composition of human skin.

In a previous study, we successfully used SCAnED to detect and quantify cells and expression levels of lysyl oxidase (LOX), a cytoplasmic protein, and the transcription factor hypoxia-inducible factor 1 (HIF-1) in cells within the dermis of human skin12 highlighting the applicability of this tool for skin research.
To further assess the precision of our skin segmentation macro in identifying cells and nuclei in the epidermal and dermal compartments, we stained paraffin-...

Here, we present a semi-automated protocol for identifying and quantifying immune and non-immune cells in skin sections using SCAnED, a free ImageJ-based macro for skin segmentation.

The spatial distribution of immune and non-immune cells within tissues and the expression of cell-specific markers provides essential information on cell ...

In our work, we investigate regulatory mechanisms that maintain tissue homeostasis at molecular, cellular, structural, and mechanical levels. Our goal is to understand pathological conditions, such as skin inflammation or cancer. With new high resolution imaging and multiomics tools, we can now map cellular and gene expression changes in tissues in 2D and 3D.This is really pushing our understanding of how diseases develop and progress. One of the big challenges right now is the massive amount of data generated by advanced microscopy. And while there are more tools available to analyze images, they're often not user-friendly for people without a lot of experience, or are quite expensive.Our protocol offers a free user-friendly tool for analyzing human skin cells in both epidermis and dermis. It improves accuracy over general tools, and doesn't require coding or prior image analysis experience. To begin, open the immunofluorescence images in Fiji or Image J, using bio formats with the default settings.Do not split the channels during this step. Download the SCAnED macro and run it by selecting plugins. Then macros and clicking run.Select the SCAnED macro script file when prompted. Follow the onscreen instructions provided by the macro. Select the E-cadherin channel when prompted by the macro.Next, navigate to image, choose adjust, then threshold to manually adjust the threshold for the E-cadherin channel. Click OK once the epidermal region is fully captured. Then select the DAP-E channel when prompted to proceed with segmentation.Auto define the intensity levels for nuclei segmentation when using the threshold function. For StarDist, open the plugin in Image J.Load the nuclei detection model into the StarDist graphical interface, and click on the nuclei channel to select it for analysis. Now, adjust the tile settings in StarDist to match the expected patch size of 256 by 256 pixels.When prompted, select yes to measure marker intensity within the nuclei. Then repeat the segmentation and analysis process for each fluorescence channel. After all channels are processed, save the output data as comma separated value files as instructed by the macro.For cytoplasmic region, when nuclei segmentation has been performed using the threshold function, apply binary dilation to expand the nuclear region of interest. When StarDist has been used for nuclei segmentation, apply enlargement expansion to the nuclear region of interest. Select yes when prompted to measure marker intensity within the whole cell region.Then save the resulting data files as comma separated value format according to macro instructions. Establish marker specific intensity thresholds using isotype control samples. Download the appropriate Jupyter notebook, and navigate to Google Collab to select file, followed by open notebook.Choose the file and run the code cells sequentially. When prompted with the choose files option, click the button and upload the comma separated value file. For each antibody, calculate the mean fluorescence intensity using the isotype control files.To determine the cutoff thresholds, open the dot plot notebook, upload the isotype control data, and run the code. Based on the distribution of dots in the plots for both isotype control and specific antibody data, select an intensity threshold to distinguish background signal from true marker expression. Use this threshold to classify each cell as positive or negative for the given marker.Finally, create dot plots to visualize co-expression patterns of markers by running the dot plot code in the Google Collab notebook. In manually segmented images, vimentin positive cells showed a strong cytoplasmic signal with clear ROI boundaries covering the full cell body. The SCAnED semi-automated segmentation captured vimentin positive cells with similar boundaries using a threshold and dilation approach.Quantitative comparison of vimentin intensity in 21 cells showed no significant difference between manual and SCAnED segmentation. Histogram analysis revealed a higher frequency of vimentin positive cells in psoriasis dermis compared to healthy skin. Dot plot analysis of dermal cells showed increased CD-90 positive vimentin positive cells in psoriasis compared to healthy skin.In the epidermis, psoriasis samples showed an increase in CD-90 positive vimentin negative cells, possibly keratinocytes, compared to healthy skin. Vimentin positive cell counts in psoriatic and healthy epidermis were comparable between SCAnED and QuPath. visual segmentation showed SCAnED more accurately assigned border cells to the epidermis than QuPath, which misassigned several.

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Learn semi-automated skin cell analysis using SCAnED for immune and non-immune cell quantification in human epidermis and dermis. This open-source ImageJ macro enhances accuracy in studying inflammation and cancer without requiring coding experience.

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments (Video) | JoVE Journal

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

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