Multiplex digital spatial profiling of proteins and RNA in fixed tissue

By Chris Merritt, Giang T Ong, S. Church, K. Barker, P. Danaher, G. Geiss, M. Hoang, Jaemyeong Jung, Yan Liang, Jill McKay-Fleisch and 12 more. Nature Biotechnology, 2020

864 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e4b3862e0e64a3e158e5f24a840a4dde925df319

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Roser Vento-Tormo
2. Oliver Stegle
3. Artem Shmatko
4. Moritz Gerstung

In Molecular Biology

1. Roser Vento-Tormo
2. A. Haque
3. Oliver Stegle
4. Cameron G. Williams
5. E. Dann
6. H. J. Lee
7. Takahiro Asatsuma
8. Rob Knight

Citations in the corpus

• Cell2location maps fine-grained cell types in spatial transcriptomics (2022, methodology): “This includes support for higher-resolution spatial transcriptomics assays, for example by adapting the model to account for differences in the noise characteristics such as background probe binding or variable capture areas (for example, Nanostring 48,49 and LCM-RNA-seq 50 ).”
• An introduction to spatial transcriptomics for biomedical research (2022, methodology): “Some sequencing-based methods require instruments for tailored mRNA capture—e.g. GeoMx and CosMx— whereas Visium requires no specific instrumentation at all except access to NGS.”
• The microbiome and human cancer (2021, background): “In-depth functional analyses at community and per-microbe scales are likely necessary to elucidatemicrobialimmune-cancer cell mechanistic interactions, and emerging spatial multi-omic tools may prove invaluable here (169, 170).”