Drosophila melanogaster is a powerful, long-standing model for metazoan development and gene regulation. We profiled chromatin accessibility in almost one million, and gene expression in half a million, nuclei from overlapping windows spanning the entirety of embryogenesis. Leveraging developmental asynchronicity within embryo collections, we applied deep neural networks to infer the age of each nucleus, resulting in continuous, multimodal views of molecular and cellular transitions in absolute time. We identify cell lineages, infer their developmental relationships, and link dynamic changes in enhancer usage, transcription factor (TF) expression and the accessibility of TFs’ cognate motifs. With these data, the dynamics of enhancer usage and gene expression can be explored within and across lineages at the scale of minutes, including for precise transitions like zygotic genome activation.
The raw sci-ATAC-seq and sci-RNA-seq fastqs generated by this study are all available from GEO organized under the superseries with accession no. GSE190149, with sci-ATAC-seq in the subseries GSE190130 and sci-RNA-seq in the subseries GSE190147.
For visualization of chromatin accessibility as genome browser tracks use the following (link) to add tracks to a UCSC browser. Alternatively click the following (link) to open the UCSC browser with the tracks already added. There's a multiwig container per time window that holds bigwigs cells pseudobulked by leiden-based clustering. Additionally, one can individually visualize each cluster per time window (the putative annotation should be labeled once a track is made visible). Alternatively, raw bigwig tracks are available for direct download (link).
Please try our newly developed web app (link) to interactively visualize RNA and ATAC UMAPs for each time window.
All supplementary tables are aggregated in the following excel file with one table per spreadsheet: SupplementaryTables.xlsx.
We generated many additional intermediate processed files that might be helpful for further analyses:
Note: Files with the .Rds or .rds suffix can be read in R with:
df <- readRDS('some_file.Rds')
We have prepared a folder that includes the scripts used to perform these analyses (link). Generally, they're separated by code used for RNA in the RNA folder, and ATAC in the ATAC folder and a folder for the NNLS code. Note that we provide these mostly as a guidelines to see how we performed the analyses and to serve as an example for your own analyses. Most will require the installation of several tools that these scripts depend on and minor edits (e.g., adjusting local paths, folder structure).
If you use this resource in your research, please cite:
Calderon, Diego+, Ronnie Blecher-Gonen+, Xingfan Huang+, Stefano Secchia+, James Kentro, Riza M. Daza, Beth Martin, Alessandro Dulja, Christoph Schaub, Cole Trapnell, Erica Larschan, Kate M. O’Connor-Giles, Eileen E. M. Furlong*, Jay Shendure*. "The continuum of Drosophila embryonic development at single-cell resolution." Science 377, 620 (2022). DOI: https://doi.org/10.1126/science.abn5800 +These authors contributed equally to this work. *Corresponding author.
For questions please contact Diego Calderon ().