IAMSAM: Image-based Analysis of Molecular Signatures Using the Segment Anything Model

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2024 Nov 11

PMID 39529142

Authors

Dongjoo Lee

Jeongbin Park

Seungho Cook

Seongjin Yoo

Daeseung Lee

Hongyoon Choi

Affiliations

Soon will be listed here.

Abstract

Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.

Citing Articles

Generation of super-resolution images from barcode-based spatial transcriptomics by deep image prior.

Park J, Cook S, Lee D, Choi J, Yoo S, Bae S Cell Rep Methods. 2024; 5(1):100937.

PMID: 39729996 PMC: 11840945. DOI: 10.1016/j.crmeth.2024.100937.

References

Asp M, Bergenstrahle J, Lundeberg J . Spatially Resolved Transcriptomes-Next Generation Tools for Tissue Exploration. Bioessays. 2020; 42(10):e1900221. DOI: 10.1002/bies.201900221. View

Wolf F, Angerer P, Theis F . SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018; 19(1):15. PMC: 5802054. DOI: 10.1186/s13059-017-1382-0. View

Wu M . Endothelial focal adhesions and barrier function. J Physiol. 2005; 569(Pt 2):359-66. PMC: 1464245. DOI: 10.1113/jphysiol.2005.096537. View

Chang Y, He F, Wang J, Chen S, Li J, Liu J . Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning. Comput Struct Biotechnol J. 2022; 20:4600-4617. PMC: 9440291. DOI: 10.1016/j.csbj.2022.08.029. View

Yu Y, He Y, Xie Z . Accurate Identification of Spatial Domain by Incorporating Global Spatial Proximity and Local Expression Proximity. Biomolecules. 2024; 14(6). PMC: 11201407. DOI: 10.3390/biom14060674. View

Pham D, Tan X, Balderson B, Xu J, Grice L, Yoon S . Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues. Nat Commun. 2023; 14(1):7739. PMC: 10676408. DOI: 10.1038/s41467-023-43120-6. View

Mages S, Moriel N, Avraham-Davidi I, Murray E, Watter J, Chen F . TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics. Nat Biotechnol. 2023; 41(10):1465-1473. PMC: 10513360. DOI: 10.1038/s41587-023-01657-3. View

He B, Bergenstrahle L, Stenbeck L, Abid A, Andersson A, Borg A . Integrating spatial gene expression and breast tumour morphology via deep learning. Nat Biomed Eng. 2020; 4(8):827-834. DOI: 10.1038/s41551-020-0578-x. View

Bae S, Choi H, Lee D . Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images. Nucleic Acids Res. 2021; 49(10):e55. PMC: 8191797. DOI: 10.1093/nar/gkab095. View

10.

Long Y, Ang K, Li M, Chong K, Sethi R, Zhong C . Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat Commun. 2023; 14(1):1155. PMC: 9977836. DOI: 10.1038/s41467-023-36796-3. View

11.

Chan J . The wonderful colors of the hematoxylin-eosin stain in diagnostic surgical pathology. Int J Surg Pathol. 2014; 22(1):12-32. DOI: 10.1177/1066896913517939. View

12.

Park J, Choi J, Lee J, Choi H, Im H . Spatial Transcriptomics-Based Identification of Molecular Markers for Nanomedicine Distribution in Tumor Tissue. Small Methods. 2022; 6(11):e2201091. DOI: 10.1002/smtd.202201091. View

13.

Wang J, Li B, Luo M, Huang J, Zhang K, Zheng S . Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance. Signal Transduct Target Ther. 2024; 9(1):83. PMC: 10991592. DOI: 10.1038/s41392-024-01779-3. View

14.

Fang Z, Liu X, Peltz G . GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics. 2022; 39(1). PMC: 9805564. DOI: 10.1093/bioinformatics/btac757. View

15.

Williams C, Lee H, Asatsuma T, Vento-Tormo R, Haque A . An introduction to spatial transcriptomics for biomedical research. Genome Med. 2022; 14(1):68. PMC: 9238181. DOI: 10.1186/s13073-022-01075-1. View

16.

Bae S, Na K, Koh J, Lee D, Choi H, Kim Y . CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data. Nucleic Acids Res. 2022; 50(10):e57. PMC: 9177989. DOI: 10.1093/nar/gkac084. View

17.

Dries R, Zhu Q, Dong R, Eng C, Li H, Liu K . Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 2021; 22(1):78. PMC: 7938609. DOI: 10.1186/s13059-021-02286-2. View

18.

Trinh A, Gil Del Alcazar C, Shukla S, Chin K, Chang Y, Thibault G . Genomic Alterations during the to Invasive Ductal Breast Carcinoma Transition Shaped by the Immune System. Mol Cancer Res. 2021; 19(4):623-635. PMC: 8026652. DOI: 10.1158/1541-7786.MCR-20-0949. View