A transcription factor atlas of directed differentiation

Joung, Julia; Ma, Sai; Tay, Tristan; Geiger-Schuller, Kathryn; Kirchgatterer, Paul C.; Verdine, Vanessa K.; Guo, Baolin; Arias-García, Mario A.; Allen, William E; Singh, Ankita; Kuksenko, Olena; Abudayyeh, Omar O.; Gootenberg, Jonathan S.; Fu, Zhanyan; Macrae, Rhiannon K.; Buenrostro, Jason D.; Regev, Aviv; Zhang, Feng

doi:10.1016/j.cell.2022.11.026

Cited by 63 publications

(67 citation statements)

References 156 publications

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“…The comprehensive GRN and the PageRank algorithm effectively identify key transcription factors with high (Legend on next page) cell-type specificity in diverse brain regions. This approach generates numerous predictions about transcription factor functions in determining cell identity, paving the way for future perturbation experiments 54 .…”

Section: Weighted Grns Identify Key Transcription Factorsmentioning

confidence: 99%

Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain

Liu

Zeng

Zhou

et al. 2023

Preprint

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Cytosine DNA methylation is essential in brain development and has been implicated in various neurological disorders. A comprehensive understanding of DNA methylation diversity across the entire brain in the context of the brain's 3D spatial organization is essential for building a complete molecular atlas of brain cell types and understanding their gene regulatory landscapes. To this end, we employed optimized single-nucleus methylome (snmC-seq3) and multi-omic (snm3C-seq) sequencing technologies to generate 301,626 methylomes and 176,003 chromatin conformation/methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell type taxonomy that contains 4,673 cell groups and 261 cross-modality-annotated subclasses. We identified millions of differentially methylated regions (DMRs) across the genome, representing potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide multiplexed error-robust fluorescence in situ hybridization (MERFISH) data validated the association of this spatial epigenetic diversity with transcription and allowed the mapping of the DNA methylation and topology information into anatomical structures more precisely than our dissections. Furthermore, multi-scale chromatin conformation diversities occur in important neuronal genes, highly associated with DNA methylation and transcription changes. Brain-wide cell type comparison allowed us to build a regulatory model for each gene, linking transcription factors, DMRs, chromatin contacts, and downstream genes to establish regulatory networks. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a companion whole-brain SMART-seq dataset. Our study establishes the first brain-wide, single-cell resolution DNA methylome and 3D multi-omic atlas, providing an unparalleled resource for comprehending the mouse brain's cellular-spatial and regulatory genome diversity.

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Section: Weighted Grns Identify Key Transcription Factorsmentioning

confidence: 99%

Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain

Liu

Zeng

Zhou

et al. 2023

Preprint

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“…While reporter assays can produce rich data, it will also be useful to measure the biochemical intermediates between sequence and expression, for instance detecting TF binding using enzymatic footprinting 138 . In addition, combining reporter assays with perturbations of regulators [139][140][141] will allow us to connect the identities of the regulators with their cognate functions in the models (Fig. 4B).…”

Section: Current and Needed Technologies To Facilitate Large-scale Sy...mentioning

confidence: 99%

Hold out the genome: A roadmap to solving the cis-regulatory code

Boer

Taipale

2023

Preprint

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Gene expression is regulated by transcription factors that work together to read cis-regulatory DNA sequences. The “cis-regulatory code” - the rules that cells use to determine when, where, and how much genes should be expressed - has proven to be exceedingly complex, but recent advances in the scale and resolution of functional genomics assays and Machine Learning have enabled significant progress towards deciphering this code. However, we will likely never solve the cis-regulatory code if we restrict ourselves to models trained only on genomic sequences; regions of homology can easily lead to overestimation of predictive performance, and there is insufficient sequence diversity in our genomes to learn all relevant parameters. Fortunately, randomly synthesized DNA sequences enable us to test a far larger sequence space than exists in our genomes in each experiment, and designed DNA sequences enable a targeted query of the sequence space to maximally improve the models. Since cells use the same biochemical principles to interpret DNA regardless of its source, models that are trained on these synthetic data can predict genomic activity, often better than genome-trained models. Here, we provide an outlook on the field, and propose a roadmap towards solving the cis-regulatory code by training models exclusively on non-genomic DNA sequences, and using genomic sequences solely for evaluating the resulting models.

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“…Most human TFs are expressed as multiple isoforms, expanding the number of functionally distinct TFs 34,35 . We used pY1H assays to determine whether alternative isoforms of a given TF differ in their functional relationships with other TFs.…”

Section: Alternative Isoform Usage Alters Tf-tf Relationshipsmentioning

confidence: 99%

Paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism across transcription factors

Berenson

Lane

Soto

et al. 2023

Preprint

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Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA sequence-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage, and identify cooperativity and antagonism between human TFs and viral proteins. pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.

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A transcription factor atlas of directed differentiation

Cited by 63 publications

References 156 publications

Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain

Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain

Hold out the genome: A roadmap to solving the cis-regulatory code

Paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism across transcription factors

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