Cell clustering for spatial transcriptomics data with graph neural networks

doi:10.1038/s43588-022-00266-5

. 2022 Jun;2(6):399-408.

doi: 10.1038/s43588-022-00266-5. Epub 2022 Jun 27.

Cell clustering for spatial transcriptomics data with graph neural networks

Jiachen Li ^{1

2}, Siheng Chen ^{3

4}, Xiaoyong Pan ^{1

2}, Ye Yuan ^{5

6}, Hong-Bin Shen ^{7

8}

Affiliations

¹ Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.
² Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.
³ Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China.
⁴ Shanghai Artificial Intelligence Laboratory, Shanghai, China.
⁵ Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China. yuanye_auto@sjtu.edu.cn.
⁶ Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China. yuanye_auto@sjtu.edu.cn.
⁷ Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China. hbshen@sjtu.edu.cn.
⁸ Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China. hbshen@sjtu.edu.cn.

PMID: 38177586
DOI: 10.1038/s43588-022-00266-5

Cell clustering for spatial transcriptomics data with graph neural networks

Jiachen Li et al. Nat Comput Sci. 2022 Jun.

. 2022 Jun;2(6):399-408.

doi: 10.1038/s43588-022-00266-5. Epub 2022 Jun 27.

Authors

Jiachen Li ^{1

2}, Siheng Chen ^{3

4}, Xiaoyong Pan ^{1

2}, Ye Yuan ^{5

6}, Hong-Bin Shen ^{7

8}

Affiliations

¹ Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.
² Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.
³ Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China.
⁴ Shanghai Artificial Intelligence Laboratory, Shanghai, China.
⁵ Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China. yuanye_auto@sjtu.edu.cn.
⁶ Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China. yuanye_auto@sjtu.edu.cn.
⁷ Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China. hbshen@sjtu.edu.cn.
⁸ Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China. hbshen@sjtu.edu.cn.

PMID: 38177586
DOI: 10.1038/s43588-022-00266-5

Abstract

Spatial transcriptomics data can provide high-throughput gene expression profiling and the spatial structure of tissues simultaneously. Most studies have relied on only the gene expression information but cannot utilize the spatial information efficiently. Taking advantage of spatial transcriptomics and graph neural networks, we introduce cell clustering for spatial transcriptomics data with graph neural networks, an unsupervised cell clustering method based on graph convolutional networks to improve ab initio cell clustering and discovery of cell subtypes based on curated cell category annotation. On the basis of its application to five in vitro and in vivo spatial datasets, we show that cell clustering for spatial transcriptomics outperforms other spatial clustering approaches on spatial transcriptomics datasets and can clearly identify all four cell cycle phases from multiplexed error-robust fluorescence in situ hybridization data of cultured cells. From enhanced sequential fluorescence in situ hybridization data of brain, cell clustering for spatial transcriptomics finds functional cell subtypes with different micro-environments, which are all validated experimentally, inspiring biological hypotheses about the underlying interactions among the cell state, cell type and micro-environment.

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References

1. Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018). - DOI
1. Moffitt, J. R. & Zhuang, X. RNA imaging with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Methods Enzymol. 572, 1–49 (2016). - DOI
1. Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl. Acad. Sci. U.S.A. 113, 11046–11051 (2016). - DOI
1. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015). - DOI
1. Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl. Acad. Sci. U.S.A. 116, 19490–19499 (2019). - DOI

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[1] Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018). - DOI

[2] Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018). - DOI

[3] Moffitt, J. R. & Zhuang, X. RNA imaging with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Methods Enzymol. 572, 1–49 (2016). - DOI

[4] Moffitt, J. R. & Zhuang, X. RNA imaging with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Methods Enzymol. 572, 1–49 (2016). - DOI

[5] Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl. Acad. Sci. U.S.A. 113, 11046–11051 (2016). - DOI

[6] Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl. Acad. Sci. U.S.A. 113, 11046–11051 (2016). - DOI

[7] Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015). - DOI

[8] Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015). - DOI

[9] Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl. Acad. Sci. U.S.A. 116, 19490–19499 (2019). - DOI

[10] Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl. Acad. Sci. U.S.A. 116, 19490–19499 (2019). - DOI

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Cell clustering for spatial transcriptomics data with graph neural networks

Affiliations

Cell clustering for spatial transcriptomics data with graph neural networks

Authors

Affiliations

Abstract

References

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources