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1. Transcriptomics reveals complex kinetics of dorsal–ventral patterning gene expression in the mandibular arch

   https://doi.org/10.1002/dvg.23275  

   Sharma, Praveer P.; MacLean, Adam L.; Meinecke, Lina; Clouthier, David E.; Nie, Qing; Schilling, Thomas F. (December 2018, genesis)

   Summary The mandibular or first pharyngeal arch forms the upper and lower jaws in all gnathostomes. A gene regulatory network that defines ventral, intermediate, and dorsal domains along the dorsal–ventral (D–V) axis of the arch has emerged from studies in zebrafish and mice, but the temporal dynamics of this process remain unclear. To define cell fate trajectories in the arches we have performed quantitative gene expression analyses of D–V patterning genes in pharyngeal arch primordia in zebrafish and mice. Using NanoString technology to measure transcript numbers per cell directly we show that, in many cases, genes expressed in similar D–V domains and induced by similar signals vary dramatically in their temporal profiles. This suggests that cellular responses to D‐V patterning signals are likely shaped by the baseline kinetics of target gene expression. Furthermore, similarities in the temporal dynamics of genes that occupy distinct pathways suggest novel shared modes of regulation. Incorporating these gene expression kinetics into our computational models for the mandibular arch improves the accuracy of patterning, and facilitates temporal comparisons between species. These data suggest that the magnitude and timing of target gene expression help diversify responses to patterning signals during craniofacial development. 

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2. RVAgene: generative modeling of gene expression time series data

   https://doi.org/10.1093/bioinformatics/btab260  

   Mitra, Raktim; MacLean, Adam L (May 2021, Bioinformatics)

   Mathelier, Anthony (Ed.)

   Abstract Motivation Methods to model dynamic changes in gene expression at a genome-wide level are not currently sufficient for large (temporally rich or single-cell) datasets. Variational autoencoders offer means to characterize large datasets and have been used effectively to characterize features of single-cell datasets. Here, we extend these methods for use with gene expression time series data. Results We present RVAgene: a recurrent variational autoencoder to model gene expression dynamics. RVAgene learns to accurately and efficiently reconstruct temporal gene profiles. It also learns a low dimensional representation of the data via a recurrent encoder network that can be used for biological feature discovery, and from which we can generate new gene expression data by sampling the latent space. We test RVAgene on simulated and real biological datasets, including embryonic stem cell differentiation and kidney injury response dynamics. In all cases, RVAgene accurately reconstructed complex gene expression temporal profiles. Via cross validation, we show that a low-error latent space representation can be learnt using only a fraction of the data. Through clustering and gene ontology term enrichment analysis on the latent space, we demonstrate the potential of RVAgene for unsupervised discovery. In particular, RVAgene identifies new programs of shared gene regulation of Lox family genes in response to kidney injury. Availability and implementation All datasets analyzed in this manuscript are publicly available and have been published previously. RVAgene is available in Python, at GitHub: https://github.com/maclean-lab/RVAgene; Zenodo archive: http://doi.org/10.5281/zenodo.4271097. Supplementary information Supplementary data are available at Bioinformatics online. 

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3. A multiscale model via single-cell transcriptomics reveals robust patterning mechanisms during early mammalian embryo development

   https://doi.org/10.1371/journal.pcbi.1008571  

   Cang, Zixuan; Wang, Yangyang; Wang, Qixuan; Cho, Ken W.; Holmes, William; Nie, Qing (March 2021, PLOS Computational Biology)

   Umulis, David (Ed.)

   During early mammalian embryo development, a small number of cells make robust fate decisions at particular spatial locations in a tight time window to form inner cell mass (ICM), and later epiblast (Epi) and primitive endoderm (PE). While recent single-cell transcriptomics data allows scrutinization of heterogeneity of individual cells, consistent spatial and temporal mechanisms the early embryo utilize to robustly form the Epi/PE layers from ICM remain elusive. Here we build a multiscale three-dimensional model for mammalian embryo to recapitulate the observed patterning process from zygote to late blastocyst. By integrating the spatiotemporal information reconstructed from multiple single-cell transcriptomic datasets, the data-informed modeling analysis suggests two major processes critical to the formation of Epi/PE layers: a selective cell-cell adhesion mechanism (via EphA4/EphrinB2) for fate-location coordination and a temporal attenuation mechanism of cell signaling (via Fgf). Spatial imaging data and distinct subsets of single-cell gene expression data are then used to validate the predictions. Together, our study provides a multiscale framework that incorporates single-cell gene expression datasets to analyze gene regulations, cell-cell communications, and physical interactions among cells in complex geometries at single-cell resolution, with direct application to late-stage development of embryogenesis. 

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4. Lighting Up the Central Dogma for Predictive Developmental Biology

   https://doi.org/10.1016/bs.ctdb.2019.10.010  

   Garcia, Hernan G; Berrocal, Augusto; Kim, Yang Joon; Martini, Gabriella; Zhao, Jiaxi (November 2019, Current topics in developmental biology)

   Although the last 30 years have witnessed the mapping of the wiring diagrams of the gene regulatory networks that dictate cell fate and animal body plans, specific understanding building on such network diagrams that shows how DNA regulatory regions control gene expression lags far behind. These networks have yet to yield the predictive power necessary to, for example, calculate how the concentration dynamics of input transcription factors and DNA regulatory sequence prescribes output patterns of gene expression that, in turn, determine body plans themselves. Here, we argue that reaching a predictive understanding of developmental decision-making calls for an interplay between theory and experiment aimed at revealing how the regulation of the processes of the central dogma dictate network connections and how network topology guides cells toward their ultimate developmental fate. To make this possible, it is crucial to break free from the snapshot-based understanding of embryonic development facilitated by fixed-tissue approaches and embrace new technologies that capture the dynamics of developmental decision-making at the single cell level, in living embryos. 

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5. Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential hematopoietic progenitors

   https://doi.org/10.1371/journal.pcbi.1009779  

   Handzlik, Joanna E.; Manu (January 2022, PLOS Computational Biology)

   Newman, Stuart A (Ed.)

   Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) comprising transcription factors (TFs) and the effectors of cytokine signaling. Based largely on analyses conducted at steady state, these GRNs are thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. Here, we utilize transient gene expression patterns to infer the genetic architecture—the type and strength of regulatory interconnections—and dynamics of a twelve-gene GRN including key TFs and cytokine receptors. We trained gene circuits, dynamical models that learn genetic architecture, on high temporal-resolution gene-expression data from the differentiation of an inducible cell line into erythrocytes and neutrophils. The model is able to predict the consequences of gene knockout, knockdown, and overexpression experiments and the inferred interconnections are largely consistent with prior empirical evidence. The inferred genetic architecture is densely interconnected rather than hierarchical, featuring extensive cross-antagonism between genes from alternative lineages and positive feedback from cytokine receptors. The analysis of the dynamics of gene regulation in the model reveals that PU.1 is one of the last genes to be upregulated in neutrophil conditions and that the upregulation of PU.1 and other neutrophil genes is driven by Cebpa and Gfi1 instead. This model inference is confirmed in an independent single-cell RNA-Seq dataset from mouse bone marrow in which Cebpa and Gfi1 expression precedes the neutrophil-specific upregulation of PU.1 during differentiation. These results demonstrate that full PU.1 upregulation during neutrophil development involves regulatory influences extrinsic to the Gata1-PU.1 bistable switch. Furthermore, although there is extensive cross-antagonism between erythroid and neutrophil genes, it does not have a hierarchical structure. More generally, we show that the combination of high-resolution time series data and data-driven dynamical modeling can uncover the dynamics and causality of developmental events that might otherwise be obscured. 

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