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Specifying Identity: Mechanisms of Developmental Patterning
Understanding the progression of cell specification from the zygote to the enormous diversity of cell types in the differentiated organism has been a fundamental pursuit of developmental biologists. It initially seemed impossible to catalog the changes that lead to differentiation in all cells of an embryo. Biologists have reasoned that if the genes expressed by a cell dictate its identity, then we could define this identity by determining which genes are being expressed in any cell
at any given time. Do we have the tools to do this?
It may sound far-fetched, but in 2018 the use of single-cell RNA sequencing paired with novel computational approaches provided those tools (reviewed in Harland 2018). Researchers have isolated all the cells from different embryonic stages and examined each individually to determine all the different RNAs the cell was making—they did this using single-cell RNA sequencing (FIGURE 1A; Briggs et al. 2018; Farrell et al. 2018; Wagner et al. 2018). The full complement of RNAs being produced by a cell is called its transcriptome, which represents all the genes being expressed in that cell. These researchers have now determined the transcriptomes for each individual cell of Xenopus and zebrafish embryos for various developmental times. This has generated an immense amount of data. For example, from just one zebrafish embryo at the 4-hour stage, the Wagner lab collected 2155 cells and identified an average of 1445 different transcripts per cell. Multiply that by seven different stages, with two to four replicates per stage (Wagner et al. 2018), and that’s a lot of data—approximately 90 million data points from this one study! How does one analyze so much data? And how can this kind of data—the transcriptomes for each cell of an embryo in space and time—inform us on cell specification?
To analyze such vast amounts of data, researchers use computational tools; in this case, the researchers used a “nearest-neighbor” computational approach to analyze the degree to which cells showed similarities and differences with their neighbors in the genes they expressed. This generated a spatial map of the embryos, showing where clusters of similar cells were (based on similar transcriptomes) and how gene expression in these cells changed over developmental time (FIGURE 1B). By cross-referencing these transcriptome relationships with what was already known about fate maps and differentiation in these embryos, the researchers were able to annotate these transcriptomes into cell types, and to construct treelike visualizations of the differentiation of cells over time. These “developmental trees” show the changes in gene expression of cells as they go through each step in their journey to the differentiated state (FIGURE 1C,D). This collection of temporal transcriptomic data has provided unique insight, not only for identifying the initial and terminal states of differentiation, but also for characterizing the experiential journey of transitional gene expression associated with cell specification between the two states (Briggs et. al. 2018; Farrell et al. 2018; Wagner et al. 2018).1
Figure 1 The developmental landscape of cell fate maturation. (A) Illustration of the experimental design from embryo to the visualization of cell maturation based on nearest-neighbor gene associations between cells over time. Individual cells from dissociated embryos are captured in droplets, together with “barcoded” reverse transcription reagents that attach unique sequence tags to the cDNAs generated from each cell. While still enclosed in the droplet, RNA is released from the cell and reverse transcribed. The resulting tagged cDNAs are then sequenced. scRNAseq, single-cell RNA sequencing. (B) T-distributed stochastic neighbor embedding (tSNE) plots for each developmental time point, with cells colored according to their expressed genes of known germ layer identity.
(C) Visualization of a full gene expression landscape with representative cell states over the course of the first 24 hours of zebrafish embryonic development. The earliest time points are at the image’s center, with more differentiated cells emanating outward to epidermal (blue), mesendodermal (green), and neural (red) lineages. (D) A developmental tree layout of zebrafish embryogenesis during the first 12 hours of development, showing the transcriptional trajectories of cell specification from undifferentiated cells at the tree’s base to 25 distinct cell lineages at the branch tips (colors denote different developmental stages). (A,B after D. E. Wagner et al. 2018. Science 360: 981–987 and J. A. Briggs et al. 2018. Science 360: eaar5780; D after J. A. Farrell et al. 2018. Science 360: eaar3131.)
Literature Cited
Briggs, J. A., C. Weinreb, D. E. Wagner, S. Megason, L. Peshkin, M. W. Kirschner and A. M. Klein. 2018. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360: eaar5780.
Farrell, J. A., Y. Wang, S. J. Riesenfeld, K. Shekhar, A. Regev and A. F. Schier. 2018. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science 360: eaar3131.
Harland, R. M. 2018. A new view of embryo development and regeneration. Science 360: 967–968.
Wagner, D. E., C. Weinreb, Z. M. Collins, J. A. Briggs, S. G. Megason and A. M. Klein. 2018. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360: 981–987.
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1. To stress the impact these studies have had on our understanding of development, they were collectively deemed the top scientific breakthrough of 2018 by the journal Science. https://vis.sciencemag.org/breakthrough2018/finalists/#cell-development.