Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering
Abstract While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-40068-5 |
| _version_ | 1797752736538689536 |
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| author | Candace C. Liu Noah F. Greenwald Alex Kong Erin F. McCaffrey Ke Xuan Leow Dunja Mrdjen Bryan J. Cannon Josef Lorenz Rumberger Sricharan Reddy Varra Michael Angelo |
| author_facet | Candace C. Liu Noah F. Greenwald Alex Kong Erin F. McCaffrey Ke Xuan Leow Dunja Mrdjen Bryan J. Cannon Josef Lorenz Rumberger Sricharan Reddy Varra Michael Angelo |
| author_sort | Candace C. Liu |
| collection | DOAJ |
| description | Abstract While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface. |
| first_indexed | 2024-03-12T17:07:46Z |
| format | Article |
| id | doaj.art-fc52063a5a024bd2bb29a5024a6c9e72 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-12T17:07:46Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-fc52063a5a024bd2bb29a5024a6c9e722023-08-06T11:19:50ZengNature PortfolioNature Communications2041-17232023-08-0114111610.1038/s41467-023-40068-5Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clusteringCandace C. Liu0Noah F. Greenwald1Alex Kong2Erin F. McCaffrey3Ke Xuan Leow4Dunja Mrdjen5Bryan J. Cannon6Josef Lorenz Rumberger7Sricharan Reddy Varra8Michael Angelo9Department of Pathology, Stanford UniversityDepartment of Pathology, Stanford UniversityDepartment of Pathology, Stanford UniversityDepartment of Pathology, Stanford UniversityDepartment of Pathology, Stanford UniversityDepartment of Pathology, Stanford UniversityDepartment of Pathology, Stanford UniversityMax-Delbrueck-Center for Molecular MedicineDepartment of Pathology, Stanford UniversityDepartment of Pathology, Stanford UniversityAbstract While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.https://doi.org/10.1038/s41467-023-40068-5 |
| spellingShingle | Candace C. Liu Noah F. Greenwald Alex Kong Erin F. McCaffrey Ke Xuan Leow Dunja Mrdjen Bryan J. Cannon Josef Lorenz Rumberger Sricharan Reddy Varra Michael Angelo Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering Nature Communications |
| title | Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering |
| title_full | Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering |
| title_fullStr | Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering |
| title_full_unstemmed | Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering |
| title_short | Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering |
| title_sort | robust phenotyping of highly multiplexed tissue imaging data using pixel level clustering |
| url | https://doi.org/10.1038/s41467-023-40068-5 |
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