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PreprintCited by 1,255Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Chromatin Dynamics

Museum of Spatial Transcriptomics

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Moses, Lambda, Pachter, Lior·2021·Nature Methods
DOI 10.1101/2021.05.11.443152

Preprint — published as 10.1038/s41592-022-01409-2 · Paper page

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Abstract

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors depends on the spatial organization of their cells. In the past decade high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To assess the ability and potential of spatial gene expression technologies to drive biological discovery, we present a curated database of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field such as usage of experimental techniques, species, tissues studied and computational approaches used. Our analysis places current methods in historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .

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  • ST enables the investigation of biological patterns that might otherwise be hidden in other -omics technologies, such as scRNA-seq or bulk RNA-seq.

    “ST enables the investigation of biological patterns that might otherwise be hidden in other -omics technologies, such as scRNA-seq or bulk RNA-seq.26,27”

    From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [26]

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  1. Asp, M., Bergenstråhle, J. & Lundeberg, J. Spatially resolved transcriptomes-next generation tools for tissue exploration. Bioessays 42, e1900221 (2020).
  2. Gall, J. G. & Pardue, M. L. Formation and detection of RNA-DNA hybrid molecules in cytological preparations. Proc. Natl Acad. Sci. USA 63, 378-383 (1969).
  3. Langer-Safer, P. R., Levine, M. & Ward, D. C. Immunological method for mapping genes on Drosophila polytene chromosomes. Proc. Natl Acad. Sci. USA 79, 4381-4385 (1982).
  4. O'Kane, C. J. & Gehring, W. J. Detection in situ of genomic regulatory elements in Drosophila. Proc. Natl Acad. Sci. USA 84, 9123-9127 (1987). This is the oldest entry in our database. It also gives a glimpse into the early motivations behind profiling gene expression in space.
  5. Baker, D. et al. A cellular reference resource for the mouse urinary bladder. Preprint at bioRxiv https://doi.org/10.1101/2021.09.20.461121 (2021). · DOI
  6. Hufnagel, B. et al. High-quality genome sequence of white lupin provides insight into soil exploration and seed quality. Nat. Commun. 11, 492 (2020).
  7. Genshaft, A. S. et al. Live cell tagging tracking and isolation for spatial transcriptomics using photoactivatable cell dyes. Nat. Commun. 12, 4995 (2021).
  8. Roberts, K. et al. Transcriptome-wide spatial RNA profiling maps the cellular architecture of the developing human neocortex. Preprint at bioRxiv https://doi.org/10.1101/2021.03.20.436265 (2021). · DOI
  9. 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).
  10. Gyllborg, D. et al. Hybridization-based In Situ Sequencing (HybISS): spatial transcriptomic detection in human and mouse brain tissue. Nucleic Acids Res. 48, e112 (2020).
  11. Liu, S. et al. Barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses. Nucleic Acids Res. 49, e58 (2021).
  12. Lebrigand, K. et al. The spatial landscape of gene expression isoforms in tissue sections. Preprint at bioRxiv https://doi. org/10.1101/2020.08.24.252296 (2022). · DOI
  13. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays. Preprint at bioRxiv https://doi.org/ 10.1101/2021.01.17.427004 (2021). · DOI
  14. Fu, X. et al. Continuous polony gels for tissue mapping with high resolution and RNA capture efficiency. Preprint at bioRxiv https://doi.org/10.1101/ 2021.03.17.435795 (2021). · DOI · Paper page
  15. 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).
  16. Vickovic, S. et al. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun. 13, 795 (2022).
  17. Wang, G., Moffitt, J. R. & Zhuang, X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847 (2018).
  18. Kim, M.-H. et al. Molecular and genetic approaches for assaying human cell type synaptic connectivity. Preprint at bioRxiv https://doi. org/10.1101/2020.10.16.343343 (2020). · DOI
  19. Li, Q. et al. In situ electro-sequencing in three-dimensional tissues. Preprint at bioRxiv https://doi.org/10.1101/2021.04.22.440941 (2021). · DOI
  20. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
  21. Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-seq. Genome Biol. 17, 77 (2016).
  22. Lee, H., Salas, S. M., Gyllborg, D. & Nilsson, M. Direct RNA targeted transcriptomic profiling in tissue using hybridization-based RNA in situ sequencing (HybRISS). Preprint at bioRxiv https://doi. org/10.1101/2020.12.02.408781 (2020). · DOI
  23. Zimmerman, S. M. et al. Spatially resolved whole transcriptome profiling in human and mouse tissue using digital spatial profiling. Preprint at bioRxiv https://doi.org/10.1101/2021.09.29.462442 (2021). · DOI
  24. Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22-29 (2012). 100. Villacampa, E. G. et al. Genome-wide spatial expression profiling in formalin-fixed tissues. Cell Genomics 1, 100065 (2021). 101. Liu, Y., Enninful, A., Deng, Y. & Fan, R. Spatial transcriptome sequencing of FFPE tissues at cellular level. Preprint at bioRxiv https://doi.org/10.1101/ 2020.10.13.338475 (2020). 102. · DOI · Paper page
  25. Carson, J. P., Thaller, C. & Eichele, G. A transcriptome atlas of the mouse brain at cellular resolution. Curr. Opin. Neurobiol. 12, 562-565 (2002). 145. Henrich, T. et al. MEPD: a Medaka gene expression pattern database. Nucleic Acids Res. 31, 72-74 (2003). 146. Luengo Hendriks, C. L. et al. Three-dimensional morphology and gene expression in the Drosophila blastoderm at cellular resolution I: data acquisition pipeline. Genome Biol. 7, R123 (2006). 147. Lécuyer, E. et al. Global analysis of mRNA localization reveals a prominent role in organizing cellular architecture and function. Cell 131, 174-187 (2007). 148.
  26. Bowes, J. B. et al. Xenbase: a Xenopus biology and genomics resource. Nucleic Acids Res. 36, D761-7 (2008). 149. Lovell, P. V. et al. ZEBrA: Zebra finch Expression Brain Atlas-a resource for comparative molecular neuroanatomy and brain evolution studies. J. Comp. Neurol. 528, 2099-2131 (2020). 150. Landegren, U., Kaiser, R., Sanders, J. & Hood, L. A ligase-mediated gene detection technique. Science 241, 1077-1080 (1988). 151. Belyavsky, A., Vinogradova, T. & Rajewsky, K. PCR-based cDNA library construction: general cDNA libraries at the level of a few cells. Nucleic Acids Res. 17, 5883-5883 (1989). 152. Van Gelder, R. N. et al. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc. Natl Acad. Sci. USA 87, 1663-1667 (1990). 153. Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467-470 (1995). 154. Luo, L. et al. Gene expression profiles of laser-captured adjacent neuronal subtypes. Nat. Med. 5, 117-122 (1999). 155. Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523-536 (2008). 156. Okamura-Oho, Y. et al. Transcriptome tomography for brain analysis in the web-accessible anatomical space. PLoS ONE 7, e45373 (2012).

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Authors
Moses, L., & Pachter, L.
Affiliations
California Institute of Technology (US)
Corresponding author
Pachter, Lior
ORCID
Venue
Nature Methods
Publisher
openRxiv
Published
2021-05-12
Volume
19
Issue
5
Pages
534-546
ISSN
1548-7091 (ISSN-L)
DOI
10.1101/2021.05.11.443152
OpenAlex
W4220765701
Cited by
1,255
References
0
Citations by year
2026: 319 · 2025: 330 · 2024: 304 · 2023: 217 · 2022: 79 · 2021: 5 · 2020: 1
FWCI
24.26
Type
journal article
Language
en
Open access
Locations
  • Nature Methods (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by-nc)
Funders
  • U.S. Department of Health and Human Services (U19MH114830)
  • National Institute of Mental Health (U19MH114830)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
spatial transcriptomics, spatial gene expression, research trends
MeSH terms
Liver, Museums, Transcriptome

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