Biology subjects

Spanier, K. I.

Publications and source records attributed to Spanier, K. I..

3 recordsLinked to original sources

bioRxiv2022

Cell type directed design of synthetic enhancers

Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating "dual-code" enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

genomics↗

bioRxiv2022

Cell type diversity in a developing octopus brain

Octopuses are mollusks that have evolved intricate neural systems comparable with vertebrates in terms of cell number, complexity and size. The cell types within the octopus brain that control their amazingly rich behavioral repertoire are still unknown. Here we profile cell diversity of the paralarval Octopus vulgaris brain to build a comprehensive cell type atlas that comprises mostly neural cells, as well as multiple glial subtypes, endothelial cells and fibroblasts. Moreover, we spatially map cell types within the octopus brain, including vertical and optic lobe cell types. Investigation of cell type conservation reveals a shared gene signature between glial cells of mice, fly and octopus. Genes related to learning and memory are enriched in vertical lobe cells, which show molecular similarities with Kenyon cells in Drosophila. Taken together, our data sheds light on cell type diversity and evolution of the complex octopus brain. Highlights & Key findingsO_LICharacterization of different cell types present in the early paralarval brain C_LIO_LICross-species comparisons reveal a conserved glial gene expression signature C_LIO_LIVertical lobe amacrine cells in octopus have molecular similarities to fly Kenyon cells C_LIO_LIHomeobox genes are defining transcription factors for cell type identity C_LIO_LIRecently expanded gene families may underlie cellular diversification C_LI

developmental biology↗

bioRxiv2021

Decoding gene regulation in the fly brain

The Drosophila brain is a work horse in neuroscience. Single-cell transcriptome analysis 1-5, 3D morphological classification 6, and detailed EM mapping of the connectome 7-10 have revealed an immense diversity of neuronal and glial cell types that underlie the wide array of functional and behavioral traits in the fruit fly. The identities of these cell types are controlled by - still unknown - gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. To characterize the GRN for each cell type in the Drosophila brain, we profiled chromatin accessibility of 240,919 single cells spanning nine developmental timepoints, and integrated this data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which around 70,000 are linked to specific developmental trajectories, involving neurogenesis, reprogramming and maturation. For 40 cell types, their uniquely accessible regions could be associated with their expressed transcription factors and downstream target genes, through a combination of motif discovery, network inference techniques, and deep learning. We illustrate how these "enhancer-GRNs" can be used to reveal enhancer architectures leading to a better understanding of neuronal regulatory diversity. Finally, our atlas of regulatory elements can be used to design genetic driver lines for specific cell types at specific timepoints, facilitating the characterization of brain cell types and the manipulation of brain function.

genomics↗