The Drosophila embryo at single-cell transcriptome resolution

By Nikos Karaiskos, Philipp Wahle, Jonathan Alles, Anastasiya Boltengagen, Salah Ayoub, Claudia Kipar, C. Kocks, Nikolaus Rajewsky, R. Zinzen. Science, 2017

368 citations recorded by the source corpus.

Subfields: Cell Biology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ac2a8e0e0860bed66336c6cfa163fbc5e43e3b27

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In Molecular Biology

Citations in the corpus

Integrative single-cell analysis (2019, background): “By building spatial gene expression maps (discussed further below), the authors were able to systematically compare expression profiles of orthologous genes across species and identify evolutionary changes 90 .”
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. (2018, positive): “…for the molecular profiling of single cells are diversifying, but recent organism-scale cell atlases (Cao et al., 2017; Fincher et al., 2018; Han et al., 2018; Karaiskos et al., 2017; Plass et al., 2018; The Tabula Muris Consortium et al., 2017) all use single-cell RNA sequencing (scRNA-seq).”
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics (2018, background): “As studies have progressed to profiling complex human tissues [3, 4] and even entire organisms [5, 6], there is a growing appreciation of the need for massively parallel technologies and datasets to uncover rare and subtle cell states [7–9].”
Inferring spatial and signaling relationships between cells from single cell transcriptomic data (2020, methodology): “At an individual cell level, similarity measurements based on correlation coef ﬁ cients 10,11 or correspondence scores 12 between commonly examined genes in both spatial imaging data and scRNA-seq data were used to reconstruct spatial gene expression or map cells in scRNA-seq data to their…”