NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport

Noa Moriel, Enes Senel, Nir Friedman, Nikolaus Rajewsky, Nikos Karaiskos, Mor Nitzan

Published: 2021-08-03 DOI: 10.1038/s41596-021-00573-7

single-cell RNA-sequencing

spatial reconstruction

novoSpaRc

optimal transport

tissue organization

AI 解读

A nanobody-based strategy for rapid and scalable purification of human protein complexes

A general protocol for precise syntheses of ordered mesoporous intermetallic nanoparticles

Mutate-and-chemical-shift-fingerprint (MCSF) to characterize excited states in RNA using NMR spectroscopy

The use of methanol as a C1 building block

A surgical mouse model of neonatal pressure overload by transverse aortic constriction

Long-term culture, genetic manipulation and xenotransplantation of human normal and breast cancer organoids

A clinically relevant model of focal embolic cerebral ischemia by thrombus and thrombolysis in rhesus monkeys

Nacre-inspired underwater superoleophobic films with high transparency and mechanical robustness

Mapping mechanical properties of biological materials via an add-on Brillouin module to confocal microscopes

Probing low-copy-number proteins in single living cells using single-cell plasmonic immunosandwich assays

查看全部

Sections

Figures

References

Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences. A fundamental challenge is to incorporate spatial information to study tissue organization and spatial gene expression patterns. Here, we describe a detail
Introduction
Materials
Procedure
Troubleshooting
Timing
Anticipated results
- References
- References
References
ACKNOWLEDGMENTS

Aldridge, S. & Teichmann, S. A. Single cell transcriptomics comes of age. Nat. Commun. 11, 4307 (2020).
Kulkarni, A., Anderson, A. G., Merullo, D. P. & Konopka, G. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr. Opin. Biotechnol. 58, 129–136 (2019).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).
Birnbaum, K. D. Power in numbers: single-cell RNA-seq strategies to dissect complex tissues. Annu. Rev. Genet. 52, 203–221 (2018).
Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).
Plasschaert, L. W. et al. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature 560, 377–381 (2018).
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030.e16 (2018).
Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015).
Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21, 410–427 (2020).
Cannoodt, R., Saelens, W. & Saeys, Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur. J. Immunol. 46, 2496–2506 (2016).
Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167.e15 (2018).
Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624.e24 (2017).
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. USA 116, 19490–19499 (2019).
Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).
Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, (2018).
Gumbleton, M. et al. Spatial expression and functionality of drug transporters in the intact lung: objectives for further research. Adv. Drug Deliv. Rev. 63, 110–118 (2011).
Arnol, D., Schapiro, D., Bodenmiller, B., Saez-Rodriguez, J. & Stegle, O. Modeling cell-cell interactions from spatial molecular data with spatial variance component analysis. Cell Rep. 29, 202–211.e6 (2019).
Teves, J. M. & Won, K. J. Mapping cellular coordinates through advances in spatial transcriptomics technology. Mol. Cells 43, 591–599 (2020).
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).
Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2020).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0739-1 (2020).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).
Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).
Okochi, Y., Sakaguchi, S., Nakae, K., Kondo, T. & Naoki, H. Model-based prediction of spatial gene expression via generative linear mapping. Nat. Commun. 12, 3731 (2021).
Biancalani, T. et al. Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram. Preprint at bioRxiv https://doi.org/10.1101/2020.08.29.272831 (2020).
Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019).
Peyré, G. & Cuturi, M. Computational optimal transport: with applications to data science. Found. Trends Mach. Learn. 11, 355–607 (2019).
Villani, C. Topics in Optimal Transportation (American Mathematical Society, 2003).
Durruthy-Durruthy, R. et al. Reconstruction of the mouse otocyst and early neuroblast lineage at single-cell resolution. Cell 157, 964–978 (2014).
Stickels, R. R. et al. Sensitive spatial genome wide expression profiling at cellular resolution. Preprint at bioRxiv https://doi.org/10.1101/2020.03.12.989806 (2020).
Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).
Espina, V. et al. Laser-capture microdissection. Nat. Protoc. 1, 586–603 (2006).
Combs, P. A. & Eisen, M. B. Sequencing mRNA from cryo-sliced Drosophila embryos to determine genome-wide spatial patterns of gene expression. PLoS ONE 8, e71820 (2013).
Junker, J. P. et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662–675 (2014).
Mémoli, F. On the use of Gromov-Hausdorff distances for shape comparison. in Eurographics Symposium on Point Based Graphics (eds Botsch, M. et al) https://doi.org/10.2312/SPBG/SPBG07/081-090 (2007).
Peyre, G., Cuturi, M. & Solomon, J. Gromov-Wasserstein averaging of kernel and distance matrices. in Proceedings of the 33rd International Conference on International Conference on Machine Learning https://dspace.mit.edu/handle/1721.1/112918 (2016).
Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).
Waldhaus, J., Durruthy-Durruthy, R. & Heller, S. Quantitative high-resolution cellular map of the organ of corti. Cell Rep. 11, 1385–1399 (2015).
Habib, N. et al. Div-Seq: single-nucleus RNA-seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016).
Ren, X. et al. Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly. Cell Res. 30, 763–778 (2020).
Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020).
Larkin, A. et al. FlyBase: updates to the Drosophila melanogaster knowledge base. Nucleic Acids Res. 49, D899–D907 (2021).
Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Moriel, N. et al. NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport. Zenodo https://doi.org/10.5281/zenodo.4661199 (2021).

NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport

推荐阅读