Spotiphy Enables Single-cell Spatial Whole Transcriptomics Across an Entire Section

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2025 Mar 13

PMID 40074951

Authors

Jiyuan Yang

Ziqian Zheng

Yun Jiao

Kaiwen Yu

Sheetal Bhatara

Xu Yang

Sivaraman Natarajan

Jiahui Zhang

Qingfei Pan

John Easton

Koon-Kiu Yan

Junmin Peng

Kaibo Liu

Jiyang Yu

Affiliations

Soon will be listed here.

Abstract

Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole-tissue sections, but current approaches remain plagued by the challenge of achieving single-cell resolution without sacrificing whole-genome coverage. Here we present Spotiphy (spot imager with pseudo-single-cell-resolution histology), a computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. Spotiphy delivers the most precise cellular proportions in extensive benchmarking evaluations. Spotiphy-derived inferred single-cell profiles reveal astrocyte and disease-associated microglia regional specifications in Alzheimer's disease and healthy mouse brains. Spotiphy identifies multiple spatial domains and alterations in tumor-tumor microenvironment interactions in human breast ST data. Spotiphy bridges the information gap and enables visualization of cell localization and transcriptomic profiles throughout entire sections, offering highly informative outputs and an innovative spatial analysis pipeline for exploring complex biological systems.

References

Buckley C, Ospelt C, Gay S, Midwood K . Location, location, location: how the tissue microenvironment affects inflammation in RA. Nat Rev Rheumatol. 2021; 17(4):195-212. DOI: 10.1038/s41584-020-00570-2. View

Armingol E, Officer A, Harismendy O, Lewis N . Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet. 2020; 22(2):71-88. PMC: 7649713. DOI: 10.1038/s41576-020-00292-x. View

Marx V . Method of the Year: spatially resolved transcriptomics. Nat Methods. 2021; 18(1):9-14. DOI: 10.1038/s41592-020-01033-y. View

Bressan D, Battistoni G, Hannon G . The dawn of spatial omics. Science. 2023; 381(6657):eabq4964. PMC: 7614974. DOI: 10.1126/science.abq4964. View

Rao A, Barkley D, Franca G, Yanai I . Exploring tissue architecture using spatial transcriptomics. Nature. 2021; 596(7871):211-220. PMC: 8475179. DOI: 10.1038/s41586-021-03634-9. View

Liu Y, Yang M, Deng Y, Su G, Enninful A, Guo C . High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue. Cell. 2020; 183(6):1665-1681.e18. PMC: 7736559. DOI: 10.1016/j.cell.2020.10.026. View

Rodriques S, Stickels R, Goeva A, Martin C, Murray E, Vanderburg C . Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019; 363(6434):1463-1467. PMC: 6927209. DOI: 10.1126/science.aaw1219. View

Stickels R, Murray E, Kumar P, Li J, Marshall J, Di Bella D . Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol. 2020; 39(3):313-319. PMC: 8606189. DOI: 10.1038/s41587-020-0739-1. View

Chen A, Liao S, Cheng M, Ma K, Wu L, Lai Y . Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022; 185(10):1777-1792.e21. DOI: 10.1016/j.cell.2022.04.003. View

10.

Lubeck E, Coskun A, Zhiyentayev T, Ahmad M, Cai L . Single-cell in situ RNA profiling by sequential hybridization. Nat Methods. 2014; 11(4):360-1. PMC: 4085791. DOI: 10.1038/nmeth.2892. View

11.

Eng C, Lawson M, Zhu Q, Dries R, Koulena N, Takei Y . Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature. 2019; 568(7751):235-239. PMC: 6544023. DOI: 10.1038/s41586-019-1049-y. View

12.

Wang F, Flanagan J, Su N, Wang L, Bui S, Nielson A . RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J Mol Diagn. 2011; 14(1):22-9. PMC: 3338343. DOI: 10.1016/j.jmoldx.2011.08.002. View

13.

Chen K, Boettiger A, Moffitt J, Wang S, Zhuang X . RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015; 348(6233):aaa6090. PMC: 4662681. DOI: 10.1126/science.aaa6090. View

14.

Moffitt J, Zhuang X . RNA Imaging with Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH). Methods Enzymol. 2016; 572:1-49. PMC: 5023431. DOI: 10.1016/bs.mie.2016.03.020. View

15.

He S, Bhatt R, Brown C, Brown E, Buhr D, Chantranuvatana K . High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat Biotechnol. 2022; 40(12):1794-1806. DOI: 10.1038/s41587-022-01483-z. View

16.

Kleshchevnikov V, Shmatko A, Dann E, Aivazidis A, King H, Li T . Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol. 2022; 40(5):661-671. DOI: 10.1038/s41587-021-01139-4. View

17.

Cable D, Murray E, Zou L, Goeva A, Macosko E, Chen F . Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol. 2021; 40(4):517-526. PMC: 8606190. DOI: 10.1038/s41587-021-00830-w. View

18.

Vahid M, Brown E, Steen C, Zhang W, Jeon H, Kang M . High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nat Biotechnol. 2023; 41(11):1543-1548. PMC: 10635828. DOI: 10.1038/s41587-023-01697-9. View

19.

Ma Y, Zhou X . Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol. 2022; 40(9):1349-1359. PMC: 9464662. DOI: 10.1038/s41587-022-01273-7. View

20.

Biancalani T, Scalia G, Buffoni L, Avasthi R, Lu Z, Sanger A . Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods. 2021; 18(11):1352-1362. PMC: 8566243. DOI: 10.1038/s41592-021-01264-7. View