From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture
By Xi Chen, Sarah A. Teichmann, Kerstin B. Meyer. Annual Review of Biomedical Data Science, 2018
88 citations recorded by the source corpus.
With the recent transformative developments in single-cell genomics and, in particular, single-cell gene expression analysis, it is now possible to study tissues at the single-cell level, rather than having to rely on data from bulk measurements. Here we review the rapid developments in single-cell RNA sequencing (scRNA-seq) protocols that have the potential for unbiased identification and profiling of all cell types within a tissue or organism. In addition, novel approaches for spatial profiling of gene expression allow us to map individual cells and cell types back into the three-dimensional context of organs. The combination of in-depth single-cell and spatial gene expression data will reveal tissue architecture in unprecedented detail, generating a wealth of biological knowledge and a better understanding of many diseases.
Subfields: Biophysics, Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/d24751bfe9a1dc53758c662e1c299ff43260d2b3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
In Immunology
In Molecular Biology
Citations in the corpus
- Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data (2022, unknown): “Consensus (1) Robust Rank Aggregate65—preferentially highly-ranked interactions are obtained from a distribution”
- Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, background): “expression at single-cell resolution (Kolodziejczyk et al., 2015; Haque et al., 2017; Picelli, 2017; Chen et al., 2018).”