Scalable spatial transcriptomics through computational array reconstruction – Nature Biotechnology

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).
Google Scholar
Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).
Google Scholar
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Google Scholar
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
Google Scholar
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Google Scholar
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).
Google Scholar
Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681 (2020).
Google Scholar
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
Google Scholar
Singer, A. A remark on global positioning from local distances. Proc. Natl Acad. Sci. USA 105, 9507–9511 (2008).
Google Scholar
Novembre, J. et al. Genes mirror geography within Europe. Nature 456, 98–101 (2008).
Google Scholar
Glaser, J. I., Zamft, B. M., Church, G. M. & Kording, K. P. Puzzle imaging: using large-scale dimensionality reduction algorithms for localization. PLoS One 10, e0131593 (2015).
Google Scholar
Boulgakov, A. A., Ellington, A. D. & Marcotte, E. M. Bringing microscopy-by-sequencing into view. Trends Biotechnol. 38, 154–162 (2020).
Google Scholar
Weinstein, J. A., Regev, A. & Zhang, F. DNA microscopy: optics-free spatio-genetic imaging by a stand-alone chemical reaction. Cell 178, 229–241.e16 (2019).
Google Scholar
Weinstein, J. A. & Qian, N. Volumetric imaging of an intact organism by a distributed molecular network. Preprint at bioRxiv https://doi.org/10.1101/2023.08.11.553025 (2023).
Hoffecker, I. T., Yang, Y., Bernardinelli, G., Orponen, P. & Högberg, B. A computational framework for DNA sequencing microscopy. Proc. Natl Acad. Sci. USA 116, 19282–19287 (2019).
Google Scholar
Greenstreet, L. et al. DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods. Cell Syst. 14, 844–859.e4 (2023).
Google Scholar
McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for dimension reduction. Preprint at arXiv https://doi.org/10.48550/arxiv.1802.03426 (2018).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
Google Scholar
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).
Google Scholar
Russell, A. J. C. et al. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 625, 101–109 (2024).
Google Scholar
Yu, T. et al. Differentially expressed transcripts from phenotypically identified olfactory sensory neurons. J. Comp. Neurol. 483, 251–262 (2005).
Google Scholar
Cable, D. M. et al. Cell type-specific inference of differential expression in spatial transcriptomics. Nat. Methods 19, 1076–1087 (2022).
Google Scholar
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).
Google Scholar
Kajihara, T. et al. Non-rigid registration of serial section images by blending transforms for 3D reconstruction. Pattern Recognit. 96, 106956 (2019).
Lee, B. C., Tward, D. J., Mitra, P. P. & Miller, M. I. On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model. PLoS Comput. Biol. 14, e1006610 (2018).
Google Scholar
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Google Scholar
Nolet, C. J. et al. Bringing UMAP closer to the speed of light with GPU acceleration. In Proc. AAAI Conf. Artif. Intell. Vol. 35, 418–426 (AAAI Press, 2021).
Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).
Google Scholar
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Google Scholar
Fleming, S. J. et al. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat. Methods 20, 1323–1335 (2023).
Google Scholar
Qiu, C. et al. A single-cell time-lapse of mouse prenatal development from gastrula to birth. Nature 626, 1084–1093 (2024).
Google Scholar
Hu, C. et al. Scalable spatial transcriptomics through computational array reconstruction. Datasets. NCBI SRA. https://www.ncbi.nlm.nih.gov/sra/PRJNA1221542 (2025).
Hu, C. et al. Scalable spatial transcriptomics through computational array reconstruction. Source code. Github https://github.com/Chenlei-Hu/Slide_recon (2024).