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| Status |
Public on Nov 24, 2024 |
| Title |
SPARROW reveals microenvironment zone-specific cell states in healthy and diseased tissues. |
| Platform organism |
synthetic construct |
| Sample organism |
Homo sapiens |
| Experiment type |
Other
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| Summary |
Spatially resolved transcriptomics technologies have significantly enhanced our ability to understand cellular characteristics within tissue contexts. However, current analytical tools often treat cell type inference and cellular neighbourhood identification as separate and hard clustering processes, resulting in models that are not comparable across tissue feature scales and samples, thus hindering a unified understanding of tissue features. Our computational framework, SPARROW, addresses these challenges by representing cell types and cellular organization patterns as latent embeddings learned through an interconnected neural network architecture. SPARROW integrates clustering directly into the learning of these latent embeddings, enabling feature extraction specific to clustering while ensuring comparability across samples through shared latent spaces. When applied to diverse datasets, SPARROW outperformed state-of-the-art methods in cell type inference and microenvironment zone delineation and uncovered microenvironment zone-specific fine cell states that reveal underlying biology. Furthermore, SPARROW algorithmically achieves single cell spatial resolution and whole transcriptome coverage---an experimental challenge---by integrating spatially resolved transcriptomics and scRNA-seq data in a shared latent space. This formulation enabled SPARROW to uncover both established and novel microenvironment zone-specific ligand-receptor interactions in human tonsils---discoveries not possible with either data modality alone. Overall, SPARROW provides a comprehensive characterization of tissue features across scales, samples and conditions.
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| Overall design |
Spatially resolved transcriptomics merFISH (Vizgen MERSCOPE) of human tonsil and lymph nodes
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| Contributor(s) |
Zhao PA |
| Citation missing |
Has this study been published? Please login to update or notify GEO. |
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| Submission date |
Nov 23, 2024 |
| Last update date |
Nov 25, 2024 |
| Contact name |
Peiyao A Zhao |
| E-mail(s) |
peiyao.zhao@alleninstitute.org
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| Organization name |
Allen Institute
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| Street address |
615 Westlake Ave N
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| City |
Seattle |
| State/province |
WA |
| ZIP/Postal code |
98109 |
| Country |
USA |
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| Platforms (1) |
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| Samples (6)
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| Relations |
| BioProject |
PRJNA1189845 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE282714_RAW.tar |
1.5 Gb |
(http)(custom) |
TAR (of CSV, TAR) |
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