Project description:Correct gene expression levels in space and time are crucial for normal development. Advances in genomics enable the inference of gene regulatory programs that are active during development. However, this approach cannot capture the complex multicellular interactions that occur in embryogenesis. Compared to model organisms such as fruit flies and zebrafish, the growth of mammalian embryos in utero further complicates the analysis of cell-cell communication during development. However, in vitro models of mammalian development such as gastruloids allow to overcome this limitation. Using time-resolved single-cell chromatin accessibility analysis, we have delineated the regulatory landscape during gastruloid development and thereby identified the critical drivers of developmental transitions. We observed that gastruloids develop from pluripotent cells driven by the transcription factor (TF) dimer OCT4-SOX2 and differentiate along two main branches. A mesoderm branch driven by the TF MSGN1 and a spinal cord branch driven by CDX1, 2, 4 (CDX). Consistent with our lineage reconstruction, ΔCDX gastruloids fail to form spinal cord. Conversely, Msgn1 ablation inhibits the development of paraxial mesoderm, as expected. However, this also abolished spinal cord cells, which is surprising given that MSGN1 is not associated with differentiation along this branch. Therefore, formation of paraxial mesoderm is required for spinal cord development. To validate this, we generated chimeric gastruloids using ΔMSGN1 and wildtype cells, which formed both spinal cord and paraxial mesoderm. Strikingly, ΔMsgn1 cells specifically contributed to spinal cord, suggesting that cell-cell interactions between paraxial mesoderm and spinal cord are necessary for the formation the latter. Our work has important implications for the study of cell-cell communication in development and how the bridge can be made between gene regulatory programs and complex multicellular developmental structures.

Project description:Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA-based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type-specific developmental gene expression patterns.

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Identifying cross-lineage dependencies of cell-type specific regulators in gastruloids

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