Abstract
Observing the spatial characteristics of gene expression by image-based spatial transcriptomics technology allows studying gene activity across different cells and intracellular structures. We present a framework for the registration and analysis of transcriptome images and immunostaining images. The method is based on particle filters and jointly exploits intensity information and image features. We applied our approach to synthetic data as well as real transcriptome images and immunostaining microscopy images of the mouse brain. It turns out that our approach accurately registers the multi-modal images and yields better results than a state-of-the-art method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rao, A., Barkley, D., França, G.S., Yanai, I.: Exploring tissue architecture using spatial transcriptomics. Nature 596(7871), 211–220 (2021)
Chen, A., et al.: Large field of view-spatially resolved transcriptomics at nanoscale resolution. bioRxiv (2021)
Cho, C.-S., et al.: Microscopic examination of spatial transcriptome using SEQ-scope. Cell 184(13), 3559–3572 (2021)
Yoosuf, N., Navarro, J.F., Salmén, F., Ståhl, P.L., Daub, C.O.: Identification and transfer of spatial transcriptomics signatures for cancer diagnosis. Breast Cancer Res. 22(1), 1–10 (2020)
Saiselet, M.: Transcriptional output, cell-type densities, and normalization in spatial transcriptomics. J. Mol. Cell Biol. 12(11), 906–908 (2020)
Chen, W.-T., et al.: Spatial transcriptomics and in situ sequencing to study alzheimer’s disease. Cell 182(4), 976–991 (2020)
Rohr, K.: Landmark-Based Image Analysis. Springer, Dordrecht (2001). https://doi.org/10.1007/978-94-015-9787-6
Goshtasby, A.A.: 2-D and 3-D Image Registration: For Medical Remote Sensing, and Industrial Applications. Wiley (2005)
Islam, K.T., Wijewickrema, S., O’Leary, S.: A deep learning based framework for the registration of three dimensional multi-modal medical images of the head. Sci. Rep. 11(1), 1860 (2021)
Chen, R., Das, A.B., Varshney, L.R.: Registration for image-based transcriptomics: parametric signal features and multivariate information measures. In: Proceedings of the 2019 53rd Annual Conference on Information Sciences and Systems (CISS), pp. 1–6. IEEE (2019)
Sage, D., Neumann, F.R., Hediger, F., Gasser, S.M., Unser, M.: Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics. IEEE Trans. Image Process. 14(9), 1372–1383 (2005)
Qiang, Y., Lee, J.Y., Bartenschlager, R., Rohr, K.: Colocalization analysis and particle tracking in multi-channel fluorescence microscopy images. In: Proceedings of the ISBI 2017, pp. 646–649. IEEE (2017)
Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2262–2275 (2010)
Bock, M., Tyagi, A.K., Kreft, J.-U., Alt, W.: Generalized Voronoi tessellation as a model of two-dimensional cell tissue dynamics. Bull. Math. Biol. 72(7), 1696–1731 (2010)
Rajaram, S., Pavie, B., Hac, N.E., Altschuler, S.J., Wu, L.F.: SimuCell: a flexible framework for creating synthetic microscopy images. Nat. Meth. 9(7), 634–635 (2012)
Acknowledgements
This work was supported by BGI Research and the Institute of Neuroscience (ION) of the Chinese Academy of Sciences. The authors gratefully acknowledge Prof. Mu-Ming Poo, Qing Xie, and Ao Chen for providing the mouse brain spatial transcriptomics data and many helpful discussions during the development.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Qiang, Y. et al. (2022). A Framework for Registration of Multi-modal Spatial Transcriptomics Data. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-09037-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09036-3
Online ISBN: 978-3-031-09037-0
eBook Packages: Computer ScienceComputer Science (R0)