Spatial transcriptomics is undergoing rapid advancements and iterations. It is a beneficial tool to significantly enhance our understanding of tissue organization and relationships between cells. Recent technological advancements have achieved subcellular resolution, providing much denser spot placement for downstream analysis. A key challenge for this following analysis is accurate cell segmentation and the assignment of spots to individual cells. The primary objective of this study was to evaluate the effectiveness of a new cell segmentation approach based on subcellular level spatial transcriptomic data by confirming nuclei positions and using Voronoi diagrams, compared to direct clustering with cellbin data. Our findings demonstrate that the Voronoi method not only outperforms traditional methods in providing clearer boundaries and better separation of cell types, but also excels in preserving the most transcripts, addressing the issue of low capture efficiency. This integrative methodology presents a substantial advancement in spatial transcriptomics, offering improved cell type classification and spatial pattern recognition.
Hu, W., Zhang, Y., Mei, J. and Fang, X. (2023) Spatial Transcriptomics in Human Biomedical Research and Clinical Application. Current Medicine , 2, Article No. 6. https://doi.org/10.1007/s44194-023-00023-4
Efremova, M., Vento-Tormo, M., Teichmann, S.A. and Vento-Tormo, R. (2020) Cellphonedb: Inferring Cell-Cell Communication from Combined Expression of Multi-Subunit Ligand–Receptor Complexes. Nature Protocols , 15, 1484-1506. https://doi.org/10.1038/s41596-020-0292-x
Larsson, L., Frisén, J. and Lundeberg, J. (2021) Spatially Resolved Transcriptomics Adds a New Dimension to Genomics. Nature Methods , 18, 15-18. https://doi.org/10.1038/s41592-020-01038-7
Jin, Y., Zuo, Y., Li, G., Liu, W., Pan, Y., Fan, T., et al . (2024) Advances in Spatial Transcriptomics and Its Applications in Cancer Research. Molecular Cancer , 23, Article No. 129. https://doi.org/10.1186/s12943-024-02040-9
Hu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D.J., et al . (2021) SpaGCN: Integrating Gene Expression, Spatial Location and Histology to Identify Spatial Domains and Spatially Variable Genes by Graph Convolutional Network. Nature Methods , 18, 1342-1351. https://doi.org/10.1038/s41592-021-01255-8
Cao, J., Li, C., Cui, Z., Deng, S., Lei, T., Liu, W., et al . (2024) Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery. Theranostics , 14, 2946-2968. https://doi.org/10.7150/thno.95908
Williams, C.G., Lee, H.J., Asatsuma, T., Vento-Tormo, R. and Haque, A. (2022) An Introduction to Spatial Transcriptomics for Biomedical Research. Genome Medicine , 14, Article No. 68. https://doi.org/10.1186/s13073-022-01075-1
Chen, K.H., Boettiger, A.N., Moffitt, J.R., Wang, S. and Zhuang, X. (2015) Spatially Resolved, Highly Multiplexed RNA Profiling in Single Cells. Science , 348, aaa6090. https://doi.org/10.1126/science.aaa6090
Eng, C.L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., et al . (2019) Transcriptome-scale Super-Resolved Imaging in Tissues by RNA SeqFISH. Nature , 568, 235-239. https://doi.org/10.1038/s41586-019-1049-y
Ståhl, P.L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J.F., Magnusson, J., et al . (2016) Visualization and Analysis of Gene Expression in Tissue Sections by Spatial Transcriptomics. Science , 353, 78-82. https://doi.org/10.1126/science.aaf2403
Chen, T., You, L., Hardillo, J.A.U. and Chien, M. (2023) Spatial Transcriptomic Technologies. Cells , 12, Article 2042. https://doi.org/10.3390/cells12162042
Xia, K., Sun, H., Li, J., Li, J., Zhao, Y., Chen, L., et al . (2022) The Single-Cell Stereo-Seq Reveals Region-Specific Cell Subtypes and Transcriptome Profiling in Arabidopsis Leaves. Developmental Cell , 57, 1299-1310. https://doi.org/10.1016/j.devcel.2022.04.011
You, Y., Fu, Y.T., Li, L.X., Zhang, Z.M., et al . (2024) Systematic Comparison of Sequencing-Based Spatial Transcriptomic Methods. Nature Methods , 21, 1743-1754.
Chen, A., Liao, S., Cheng, M.N., et al . (2022) Spatiotemporal Transcriptomic Atlas of Mouse Organogenesis Using DNA Nanoball-Patterned Arrays. Cell , 185, 1777-1792.
Li, Y. and Luo, Y. (2024) STdGCN: Spatial Transcriptomic Cell-Type Deconvolution Using Graph Convolutional Networks. Genome Biology , 25, Article No. 206. https://doi.org/10.1186/s13059-024-03353-0
Fang, S., Chen, B., Zhang, Y., Sun, H., Liu, L., Liu, S., et al . (2023) Computational Approaches and Challenges in Spatial Transcriptomics. Genomics , Proteomics & Bioinformatics , 21, 24-47. https://doi.org/10.1016/j.gpb.2022.10.001
Zormpas, E., Queen, R., Comber, A. and Cockell, S.J. (2023) Mapping the Transcriptome: Realizing the Full Potential of Spatial Data Analysis. Cell , 186, 5677-5689. https://doi.org/10.1016/j.cell.2023.11.003
Ma, J., Xie, R., Ayyadhury, S., Ge, C., Gupta, A., Gupta, R., et al . (2024) The Multimodality Cell Segmentation Challenge: Toward Universal Solutions. Nature Methods , 21, 1103-1113. https://doi.org/10.1038/s41592-024-02233-6
Gamarra, M., Zurek, E., Escalante, H.J., Hurtado, L. and San-Juan-Vergara, H. (2019) Split and Merge Watershed: A Two-Step Method for Cell Segmentation in Fluorescence Microscopy Images. Biomedical Signal Processing and Control , 53, Article 101575. https://doi.org/10.1016/j.bspc.2019.101575
Greenwald, N.F., Miller, G., Moen, E., Kong, A., Kagel, A., Dougherty, T., et al . (2022) Whole-cell Segmentation of Tissue Images with Human-Level Performance Using Large-Scale Data Annotation and Deep Learning. Nature Biotechnology , 40, 555-565. https://doi.org/10.1038/s41587-021-01094-0
Wang, Y.X., Wang, W.G., Liu, D.F., et al . (2023) GeneSegNet: A Deep Learning Framework for Cell Segmentation by Integrating Gene Expression and Imaging. Genome Biology , 24, Article No. 235. https://doi.org/10.1186/s13059-023-03054-0
Stringer, C., Wang, T., Michaelos, M., et al . (2020) Cellpose: A Generalist Algorithm for Cellular Segmentation. Nature Methods , 18, 100-106.
Chen, H., Li, D. and Bar-Joseph, Z. (2023) SCS: Cell Segmentation for High-Resolution Spatial Transcriptomics. Nature Methods , 20, 1237-1243. https://doi.org/10.1038/s41592-023-01939-3
Xue, S., Zhu, F., Wang, C. and Min, W. (2024) StEnTrans: Transformer-Based Deep Learning for Spatial Transcriptomics Enhancement. In: Lecture Notes in Computer Science , Springer, 63-75. https://doi.org/10.1007/978-981-97-5128-0_6
Fu, X.H., Lin, Y.X., Lin, D.M., et al . (2024) BIDCell: Biologically-Informed Self-Supervised Learning for Segmentation of Subcellular Spatial Transcriptomics Data. Nature Communications , 15, Article No. 509.
Aurenhammer, F. (1991) Voronoi Diagrams—A Survey of a Fundamental Geometric Data Structure. ACM Computing Surveys , 23, 345-405. https://doi.org/10.1145/116873.116880
Senechal, M., Okabe, A., Boots, B. and Sugihara, K. (1995) Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. The College Mathematics Journal , 26, 79-81. https://doi.org/10.2307/2687299
Fang, S.S., Xu, M.Y., Cao, L., et al . (2023) Stereopy: Modeling Comparative and Spatiotemporal Cellular Heterogeneity via Multi-Sample Spatial Transcriptomics. Nature Communications , 16, Article No. 3741.
Yao, Z., van Velthoven, C.T.J., Kunst, M., Zhang, M., McMillen, D., Lee, C., et al . (2023) A High-Resolution Transcriptomic and Spatial Atlas of Cell Types in the Whole Mouse Brain. Nature , 624, 317-332. https://doi.org/10.1038/s41586-023-06812-z
Hafemeister, C. and Satija, R. (2019) Normalization and Variance Stabilization of Single-Cell RNA-Seq Data Using Regularized Negative Binomial Regression. Genome Biology , 20, Article 296.
Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. and Regev, A. (2015) Spatial Reconstruction of Single-Cell Gene Expression Data. Nature Biotechnology , 33, 495-502. https://doi.org/10.1038/nbt.3192
Booeshaghi, A.S. and Pachter, L. (2020) Normalization of Single-Cell RNA-Seq Counts by Log(x+1)* or log(1+x)*. Bioinformatics , 37, 2223-2224.
Marshall, J.L., Noel, T., Wang, Q.S., Chen, H., Murray, E., Subramanian, A., et al . (2022) High-Resolution Slide-Seqv2 Spatial Transcriptomics Enables Discovery of Disease-Specific Cell Neighborhoods and Pathways. iScience , 25, Article 104097. https://doi.org/10.1016/j.isci.2022.104097
McInnes, L., Healy, J., Saul, N. and Großberger, L. (2018) UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software , 3, Article 861. https://doi.org/10.21105/joss.00861
Traag, V.A., Waltman, L. and van Eck, N.J. (2019) From Louvain to Leiden: Guaranteeing Well-Connected Communities. Scientific Reports , 9, Article No. 5233. https://doi.org/10.1038/s41598-019-41695-z
Rousseeuw, P.J. (1987) Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics , 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
Wang, X. and Xu, Y.S. (2019) An Improved Index for Clustering Validation Based on Silhouette Index and Calinski-Harabasz Index. IOP Conference Series : Materials Science and Engineering , 569, Article 052024. https://doi.org/10.1088/1757-899x/569/5/052024
Wang, Q., Ding, S., Li, Y., Royall, J., Feng, D., Lesnar, P., et al . (2020) The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell , 181, 936-953.e20. https://doi.org/10.1016/j.cell.2020.04.007
BICCN: The First Complete Cell Census and Atlas of a Mammalian Brain. https://www.nature.com/immersive/d42859-023-00069-2/index.html
Harris, K.D. and Shepherd, G.M.G. (2015) The Neocortical Circuit: Themes and Variations. Nature Neuroscience , 18, 170-181. https://doi.org/10.1038/nn.3917