Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data

Overview

Journal Genomics Proteomics Bioinformatics

Specialty Biology

Date 2024 Aug 7

PMID 39110523

Authors

Xiuying Liu

Xianwen Ren

Affiliations

Soon will be listed here.

Abstract

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses in estimating the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.

References

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

Zhao E, Stone M, Ren X, Guenthoer J, Smythe K, Pulliam T . Spatial transcriptomics at subspot resolution with BayesSpace. Nat Biotechnol. 2021; 39(11):1375-1384. PMC: 8763026. DOI: 10.1038/s41587-021-00935-2. View

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

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

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

Edsgard D, Johnsson P, Sandberg R . Identification of spatial expression trends in single-cell gene expression data. Nat Methods. 2018; 15(5):339-342. PMC: 6314435. DOI: 10.1038/nmeth.4634. View

Stringer C, Wang T, Michaelos M, Pachitariu M . Cellpose: a generalist algorithm for cellular segmentation. Nat Methods. 2020; 18(1):100-106. DOI: 10.1038/s41592-020-01018-x. View

Lopez R, Li B, Keren-Shaul H, Boyeau P, Kedmi M, Pilzer D . DestVI identifies continuums of cell types in spatial transcriptomics data. Nat Biotechnol. 2022; 40(9):1360-1369. PMC: 9756396. DOI: 10.1038/s41587-022-01272-8. View

Geras A, Darvish Shafighi S, Domzal K, Filipiuk I, Raczkowska A, Szymczak P . Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data. Genome Biol. 2023; 24(1):120. PMC: 10190053. DOI: 10.1186/s13059-023-02951-8. View

10.

Kulkarni A, Anderson A, Merullo D, Konopka G . Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr Opin Biotechnol. 2019; 58:129-136. PMC: 6710112. DOI: 10.1016/j.copbio.2019.03.001. View

11.

Cannoodt R, Saelens W, Saeys Y . Computational methods for trajectory inference from single-cell transcriptomics. Eur J Immunol. 2016; 46(11):2496-2506. DOI: 10.1002/eji.201646347. View

12.

Hu C, Li T, Xu Y, Zhang X, Li F, Bai J . CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2022; 51(D1):D870-D876. PMC: 9825416. DOI: 10.1093/nar/gkac947. View

13.

Heydari A, Sindi S . Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing. Biophys Rev (Melville). 2024; 4(1):011306. PMC: 10903438. DOI: 10.1063/5.0091135. View

14.

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

15.

Elosua-Bayes M, Nieto P, Mereu E, Gut I, Heyn H . SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 2021; 49(9):e50. PMC: 8136778. DOI: 10.1093/nar/gkab043. View

16.

Chen J, Liu W, Luo T, Yu Z, Jiang M, Wen J . A comprehensive comparison on cell-type composition inference for spatial transcriptomics data. Brief Bioinform. 2022; 23(4). PMC: 9294426. DOI: 10.1093/bib/bbac245. View

17.

Cheng A, Hu G, Li W . Benchmarking cell-type clustering methods for spatially resolved transcriptomics data. Brief Bioinform. 2022; 24(1). PMC: 9851325. DOI: 10.1093/bib/bbac475. View

18.

Chang T, Han W, Jiang M, Li J, Liao Z, Tang M . Rapid and signal crowdedness-robust in situ sequencing through hybrid block coding. Proc Natl Acad Sci U S A. 2023; 120(47):e2309227120. PMC: 10666108. DOI: 10.1073/pnas.2309227120. View

19.

Stahl P, Salmen F, Vickovic S, Lundmark A, Fernandez Navarro J, Magnusson J . Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016; 353(6294):78-82. DOI: 10.1126/science.aaf2403. View

20.

Longo S, Guo M, Ji A, Khavari P . Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet. 2021; 22(10):627-644. PMC: 9888017. DOI: 10.1038/s41576-021-00370-8. View