Characterizing cell-type spatial relationships across length scales in spatially resolved omics data
Abstract Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enha...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55700-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559252334804992 |
|---|---|
| author | Rafael dos Santos Peixoto Brendan F. Miller Maigan A. Brusko Gohta Aihara Lyla Atta Manjari Anant Mark A. Atkinson Todd M. Brusko Clive H. Wasserfall Jean Fan |
| author_facet | Rafael dos Santos Peixoto Brendan F. Miller Maigan A. Brusko Gohta Aihara Lyla Atta Manjari Anant Mark A. Atkinson Todd M. Brusko Clive H. Wasserfall Jean Fan |
| author_sort | Rafael dos Santos Peixoto |
| collection | DOAJ |
| description | Abstract Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we present CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we apply CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we apply CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest. |
| format | Article |
| id | doaj-art-e742b0d2c57b4301a591ae535aed15da |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e742b0d2c57b4301a591ae535aed15da2025-01-05T12:39:03ZengNature PortfolioNature Communications2041-17232025-01-0116111410.1038/s41467-024-55700-1Characterizing cell-type spatial relationships across length scales in spatially resolved omics dataRafael dos Santos Peixoto0Brendan F. Miller1Maigan A. Brusko2Gohta Aihara3Lyla Atta4Manjari Anant5Mark A. Atkinson6Todd M. Brusko7Clive H. Wasserfall8Jean Fan9Center for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityDepartment of Pathology, Immunology, and Laboratory Medicine, University of FloridaCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityDepartment of Pathology, Immunology, and Laboratory Medicine, University of FloridaDepartment of Pathology, Immunology, and Laboratory Medicine, University of FloridaDepartment of Pathology, Immunology, and Laboratory Medicine, University of FloridaCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityAbstract Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we present CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we apply CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we apply CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest.https://doi.org/10.1038/s41467-024-55700-1 |
| spellingShingle | Rafael dos Santos Peixoto Brendan F. Miller Maigan A. Brusko Gohta Aihara Lyla Atta Manjari Anant Mark A. Atkinson Todd M. Brusko Clive H. Wasserfall Jean Fan Characterizing cell-type spatial relationships across length scales in spatially resolved omics data Nature Communications |
| title | Characterizing cell-type spatial relationships across length scales in spatially resolved omics data |
| title_full | Characterizing cell-type spatial relationships across length scales in spatially resolved omics data |
| title_fullStr | Characterizing cell-type spatial relationships across length scales in spatially resolved omics data |
| title_full_unstemmed | Characterizing cell-type spatial relationships across length scales in spatially resolved omics data |
| title_short | Characterizing cell-type spatial relationships across length scales in spatially resolved omics data |
| title_sort | characterizing cell type spatial relationships across length scales in spatially resolved omics data |
| url | https://doi.org/10.1038/s41467-024-55700-1 |
| work_keys_str_mv | AT rafaeldossantospeixoto characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata AT brendanfmiller characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata AT maiganabrusko characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata AT gohtaaihara characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata AT lylaatta characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata AT manjarianant characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata AT markaatkinson characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata AT toddmbrusko characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata AT clivehwasserfall characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata AT jeanfan characterizingcelltypespatialrelationshipsacrosslengthscalesinspatiallyresolvedomicsdata |