MIC-Drop-seq: Scalable single-cell phenotyping of mutant vertebrate embryos
Carey, C. M.; Parvez, S.; Brandt, Z. J.; Bisgrove, B. W.; Yates, C. J.; Peterson, R. T.; Gagnon, J. A.
Show abstract
Advances in genome engineering and single-cell RNA sequencing (scRNAseq) have revolutionized the ability to precisely map gene functions, yet scaling these techniques for large-scale genetic screens in animals remains challenging. We combined high-throughput gene disruption in zebrafish embryos via Multiplexed Intermixed CRISPR Droplets with phenotyping by multiplexed scRNAseq (MIC-Drop-seq). In one MIC-Drop-seq experiment, we intermixed and injected droplets targeting 50 transcriptional regulators into 1,000 zebrafish embryos, followed by pooled scRNAseq. Tissue-specific gene expression and cell abundance analysis of demultiplexed mutant cells recapitulated many known phenotypes, while also uncovering novel functions in brain and mesoderm development. We observed pervasive cell-extrinsic effects among these phenotypes, highlighting how whole-embryo sequencing captures complex developmental interactions. Thus, MIC-Drop-seq provides a powerful and scalable platform for mapping gene functions in vertebrate development with cellular resolution.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Cross-species transcriptomic and epigenomic analysis reveals key regulators of injury response and neuronal regeneration in vertebrate retinas. 97%
- High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer 96%
- Imaging translation dynamics in live embryos reveals spatial heterogeneities 96%
Similar papers in this journal
- Methylome inheritance and enhancer dememorization reset an epigenetic gate safeguarding embryonic programs 97%
- Gastrulation-stage gene expression in Nipbl+/- mouse embryos foreshadows the development of syndromic birth defects 97%
- Decoding sexual dimorphism of the sex-shared nervous system at single-neuron resolution 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.