KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics

Zhenghui Wang; Ruoyan Dai; Mengqiu Wang; Lixin Lei; Zhiwei Zhang; Kaitai Han; Zijun Wang; Qianjin Guo

doi:10.1016/j.jgg.2024.11.009

Volume 52 Issue 5

May 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Genetics and Genomics > 2025 > 52(5): 689-705

PDF( 0 KB)

KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics

doi: 10.1016/j.jgg.2024.11.009

Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China

Funds:

This work was supported by the National Natural Science Foundation of China (52361145714, 21673252), the Beijing Municipal Education Commission (2019821001), Climbing Program Foundation from Beijing Institute of Petrochemical Technology (BIPTAAI-2021007), and the ZhiYuan Fund key Project from Beijing Institute of Petrochemical Technology (2024003).

Received Date: 2024-07-21
Accepted Date: 2024-11-10
Rev Recd Date: 2024-11-07

Available Online: 2025-07-11

Publish Date: 2024-11-21

Abstract

Abstract

KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics (ST) data. ST technologies provide insights into gene expression within tissue context, revealing cellular interactions and microenvironments. To fully leverage this potential, effective computational models are crucial. We evaluate KanCell on both simulated and real datasets from technologies such as STARmap, Slide-seq, Visium, and Spatial Transcriptomics. Our results demonstrate that KanCell outperforms existing methods across metrics like PCC, SSIM, COSSIM, RMSE, JSD, ARS, and ROC, with robust performance under varying cell numbers and background noise. Real-world applications on human lymph nodes, hearts, melanoma, breast cancer, dorsolateral prefrontal cortex, and mouse embryo brains confirmed its reliability. Compared with traditional approaches, KanCell effectively captures non-linear relationships and optimizes computational efficiency through KAN, providing an accurate and efficient tool for ST. By improving data accuracy and resolving cell type composition, KanCell reveals cellular heterogeneity, clarifies disease microenvironments, and identifies therapeutic targets, addressing complex biological challenges.
- Spatial transcriptomics,
- Cell type deconvolution,
- Single-cell RNA sequencing,
- Kolmogorov-Arnold networks,
- Cellular heterogeneity,
- Gene expression

FullText(HTML)

References(49)

References

Andersson, A., Bergenstrahle, J., Asp, M., Bergenstrahle, L., Jurek, A., Fernandez Navarro, J.,Lundeberg, J., 2020. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun Biol 3, 1-8.

Asp, M., Giacomello, S., Larsson, L., Wu, C., Furth, D., Qian, X., Wardell, E., Custodio, J., Reimegard, J., Salmen, F., et al., 2019. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647-1660.e1619.

Biancalani, T., Scalia, G., Buffoni, L., Avasthi, R., Lu, Z., Sanger, A., Tokcan, N., Vanderburg, C.R., Segerstolpe, A., Zhang, M., et al., 2021. Deep learning and alignment of spatially resolved single-cell transcriptomes with tangram. Nature Methods 18, 1352-1362.

Cable, D.M., Murray, E., Zou, L.S., Goeva, A., Macosko, E.Z., Chen, F.,Irizarry, R.A., 2022. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol 40, 517-526.

Cang, Z.,Nie, Q., 2020. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat Commun 11, 2084.

Casasent, A.K., Schalck, A., Gao, R., Sei, E., Long, A., Pangburn, W., Casasent, T., Meric-Bernstam, F., Edgerton, M.E.,Navin, N.E., 2018. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205-217.e212.

Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., et al., 2022. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777-1792.e1721.

Codeluppi, S., Borm, L.E., Zeisel, A., La Manno, G., van Lunteren, J.A., Svensson, C.I.,Linnarsson, S., 2018. Spatial organization of the somatosensory cortex revealed by osmfish. Nature Methods 15, 932-935.

Dong, R.,Yuan, G.-C., 2021. Spatialdwls: Accurate deconvolution of spatial transcriptomic data. Genome Biol 22, 145.

Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I.,Heyn, H., 2021. Spotlight: Seeded nmf regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res 49, e50.

Eng, C.-H.L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.-C., et al., 2019. Transcriptome-scale super-resolved imaging in tissues by rna seqfish+. Nature 568, 235-239.

Fridman, W.H., Pages, F., Sautes-Fridman, C.,Galon, J., 2012. The immune contexture in human tumours: Impact on clinical outcome. Nat Rev Cancer 12, 298-306.

Hanahan, D.,Coussens, L.M., 2012. Accessories to the crime: Functions of cells recruited to the tumor microenvironment. Cancer Cell 21, 309-322.

Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., Kocks, C., Rajewsky, N.,Zinzen, R.P., 2017. The drosophila embryo at single-cell transcriptome resolution. 358, 194-199.

Kingma, D.P.J.a.p.a., 2013. Auto-encoding variational bayes.

Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H.W., Li, T., Elmentaite, R., Lomakin, A., Kedlian, V., Gayoso, A., et al., 2022. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol 40, 661-671.

Lee, J.H., Protze, S.I., Laksman, Z., Backx, P.H.,Keller, G.M., 2017. Human pluripotent stem cell-derived atrial and ventricular cardiomyocytes develop from distinct mesoderm populations. Cell Stem Cell 21, 179-194.e174.

Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al., 2022. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nature Methods 19, 662-670.

Li, H., Zhou, J., Li, Z., Chen, S., Liao, X., Zhang, B., Zhang, R., Wang, Y., Sun, S.,Gao, X., 2023. A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics. Nat Commun 14, 1548.

Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., Li, B.,Liu, X.S., 2020. Timer2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Research 48, W509-W514.

Lin, G.L.,Hankenson, K.D., 2011. Integration of bmp, wnt, and notch signaling pathways in osteoblast differentiation.

Liu, C., Li, R., Li, Y., Lin, X., Zhao, K., Liu, Q., Wang, S., Yang, X., Shi, X., Ma, Y., et al., 2022. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Developmental Cell 57, 1284-1298.e1285.

Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljacic, M., Hou, T.Y.,Tegmark, M. 2024. Kan: Kolmogorov-arnold networks arXiv.

Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al., 2022. Destvi identifies continuums of cell types in spatial transcriptomics data. Nat Biotechnol 40, 1360-1369.

Ma, Y.,Zhou, X., 2022. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol 40, 1349-1359.

Markgraf, R., von Gaudecker, B., Muller-Hermelink, H.K.J.C.,research, t., 1982. The development of the human lymph node. 225, 387-413.

Miller, B.F., Huang, F., Atta, L., Sahoo, A.,Fan, J., 2022. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nat Commun 13, 2339.

Nolan, E., Lindeman, G.J.,Visvader, J.E., 2023. Deciphering breast cancer: From biology to the clinic. Cell 186, 1708-1728.

Paik, D.T., Cho, S., Tian, L., Chang, H.Y.,Wu, J.C.J.N.R.C., 2020. Single-cell rna sequencing in cardiovascular development, disease and medicine. 17, 457-473.

Qian, J., Olbrecht, S., Boeckx, B., Vos, H., Laoui, D., Etlioglu, E., Wauters, E., Pomella, V., Verbandt, S., Busschaert, P., et al., 2020. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res 30, 745-762.

Ren, Z., Yu, P., Li, D., Li, Z., Liao, Y., Wang, Y., Zhou, B.,Wang, L., 2020. Single-cell reconstruction of progression trajectory reveals intervention principles in pathological cardiac hypertrophy. Circulation 141, 1704-1719.

Schmidt-Hieber, J., 2021. The kolmogorov-arnold representation theorem revisited. Neural Networks 137, 119-126.

Song, Q.,Su, J., 2021. Dstg: Deconvoluting spatial transcriptomics data through graph-based artificial intelligence. Briefings in Bioinformatics 22, bbaa414.

Stickels, R.R., Murray, E., Kumar, P., Li, J., Marshall, J.L., Di Bella, D.J., Arlotta, P., Macosko, E.Z.,Chen, F., 2021. Highly sensitive spatial transcriptomics at near-cellular resolution with slide-seqv2. Nat Biotechnol 39, 313-319.

Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W.M., 3rd, Hao, Y., Stoeckius, M., Smibert, P.,Satija, R., 2019. Comprehensive integration of single-cell data. Cell 177, 1888-1902 e1821.

Sun, D., Liu, Z., Li, T., Wu, Q.,Wang, C., 2022. Stride: Accurately decomposing and integrating spatial transcriptomics using single-cell rna sequencing. Nucleic Acids Research 50, e42-e42.

Tang, F., Li, J., Qi, L., Liu, D., Bo, Y., Qin, S., Miao, Y., Yu, K., Hou, W., Li, J., et al., 2023. A pan-cancer single-cell panorama of human natural killer cells. Cell 186, 4235-4251.e4220.

Vaca-Rubio, C.J., Blanco, L., Pereira, R.,Caus, M. 2024. Kolmogorov-arnold networks (kans) for time series analysis arXiv.

Vahid, M.R., Brown, E.L., Steen, C.B., Zhang, W., Jeon, H.S., Kang, M., Gentles, A.J.,Newman, A.M., 2023. High-resolution alignment of single-cell and spatial transcriptomes with cytospace. Nat Biotechnol 41, 1543-1548.

Kanemaru, K., Cranley, J., Muraro, D., Miranda, A.M., Ho, S.Y., Wilbrey-Clark, A., Patrick Pett, J., Polanski, K., Richardson, L.,Litvinukova, M.J.N., 2023. Spatially resolved multiomics of human cardiac niches. 619, 801-810.

Wei, R., He, S., Bai, S., Sei, E., Hu, M., Thompson, A., Chen, K., Krishnamurthy, S.,Navin, N.E., 2022. Spatial charting of single-cell transcriptomes in tissues. Nat Biotechnol 40, 1190-1199.

Xia, C., Fan, J., Emanuel, G., Hao, J.,Zhuang, X., 2019. Spatial transcriptome profiling by merfish reveals subcellular rna compartmentalization and cell cycle-dependent gene expression. Proceedings of the National Academy of Sciences 116, 19490-19499.

Xu, H., Wang, S., Fang, M., Luo, S., Chen, C., Wan, S., Wang, R., Tang, M., Xue, T., Li, B., et al., 2023. Spacel: Deep learning-based characterization of spatial transcriptome architectures. Nat Commun 14, 7603.

Xun, Z., Ding, X., Zhang, Y., Zhang, B., Lai, S., Zou, D., Zheng, J., Chen, G., Su, B., Han, L., et al., 2023. Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis. Nat Commun 14, 933.

Yan, L.,Sun, X., 2023. Benchmarking and integration of methods for deconvoluting spatial transcriptomic data. Bioinformatics 39, btac805.

Yang, W., Wang, P., Xu, S., Wang, T., Luo, M., Cai, Y., Xu, C., Xue, G., Que, J., Ding, Q., et al., 2024. Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network. Nat Commun 15, 7101.

Yin, W., Wan, Y.,Zhou, Y., 2024. Spatialcogcn: Deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding. Briefings in Bioinformatics 25, bbae130.

Zhou, Y., Yin, W., Wu, X., Chen, L.,Wan, Y. 2023a. Accurate and flexible single cell to spatial transcriptome mapping with celloc.

Zhou, Z., Zhong, Y., Zhang, Z.,Ren, X., 2023b. Spatial transcriptomics deconvolution at single-cell resolution using redeconve. Nat Commun 14, 7930.

Relative Articles

Supplements(0)

Cited By

Proportional views