@article {1401907,
	title = {Methods and tools for spatial mapping of single-cell RNAseq clusters in Drosophila},
	journal = {Genetics},
	volume = {217},
	year = {2021},
	month = {2021 Apr 15},
	abstract = {Single-cell RNA sequencing (scRNAseq) experiments provide a powerful means to identify clusters of cells that share common gene expression signatures. A major challenge in scRNAseq studies is to map the clusters to specific anatomical regions along the body and within tissues. Existing data, such as information obtained from large-scale in situ RNA hybridization studies, cell type specific transcriptomics, gene expression reporters, antibody stainings, and fluorescent tagged proteins, can help to map clusters to anatomy. However, in many cases, additional validation is needed to precisely map the spatial location of cells in clusters. Several approaches are available for spatial resolution in Drosophila, including mining of existing datasets, and use of existing or new tools for direct or indirect detection of RNA, or direct detection of proteins. Here, we review available resources and emerging technologies that will facilitate spatial mapping of scRNAseq clusters at high resolution in Drosophila. Importantly, we discuss the need, available approaches, and reagents for multiplexing gene expression detection in situ, as in most cases scRNAseq clusters are defined by the unique coexpression of sets of genes.},
	issn = {1943-2631},
	doi = {10.1093/genetics/iyab019},
	author = {Mohr, Stephanie E and Tattikota, Sudhir Gopal and Xu, Jun and Zirin, Jonathan and Hu, Yanhui and Perrimon, Norbert}
}
