The Regulatory Landscape of Lineage Differentiation in a Metazoan Embryo

Abstract

Elucidating the mechanism of cell lineage differentiation is critical for our understanding of development and fate manipulation. Here we combined systematic perturbation and direct lineaging to map the regulatory landscape of lineage differentiation in early C. elegans embryogenesis. High-dimensional phenotypic analysis of 204 essential genes in 1,368 embryos revealed that cell lineage differentiation follows a canalized landscape with barriers shaped by lineage distance and genetic robustness. We assigned function to 201 genes in regulating lineage differentiation, including 175 switches of binary fate choices. We generated a multiscale model that connects gene networks and cells to the experimentally mapped landscape. Simulations showed that the landscape topology determines the propensity of differentiation and regulatory complexity. Furthermore, the model allowed us to identify the chromatin assembly complex CAF-1 as a context-specific repressor of Notch signaling. Our study presents a systematic survey of the regulatory landscape of lineage differentiation of a metazoan embryo.

Figure 1. Systematic Perturbation and High-dimensional Phenotypic Analysis of Cell Lineage Differentiation in C. elegans

(A) Genome-wide RNAi screen and characterization of 1,061 essential genes for embryogenesis identified 204 conserved regulatory genes. Pie chart shows the functional distribution of the 204 selected developmental regulators. See also Figure S1 and Table S1. (B) 3D time-lapse imaging was used to record the development of RNAi treated embryos. (C) Phenotypic analysis of lineage differentiation. Lineage differentiation is assessed by analyzing tissue marker expression in the cell lineage. Color-coded tree branches represent the expression pattern of the three markers, with circles representing the clonal expression sites. Squares denote the 12 founder cells and colored bars below the tree indicate the germ layers that different sublineages belong to. (D) Embryos with the Emb phenotype were used for analysis. Left pie chart shows the frequency of imaged embryos with the Emb phenotype; right pie chart shows the number of analyzed embryos for each tissue marker. Color scheme is as Figure 1C. (E) Pie chart shows the total number of digitized cells and marker-expressing cells. (F) Heatmap shows progenitor cell fate changes induced by gene knockdown. Each row shows the progenitor cell fate changes (red) induced by a gene knockdown with names of progenitor cells indicated above. See also Figure S2 and Table S2. (G) Histograms of genes (left) and progenitor cells (right) in 1F. (H) Penetrance of phenotypes. (I) Estimated accuracy of lineaging. The fraction of cells that were correctly traced to different cell generations were estimated for uncurated (black, 10 embryos, ~3500 cell tracks) and curated lineages (red, 100 randomly picked cell tracks). See also Figure S2. (J) Cumulative histogram of the number of shared phenotypes between known interactors (n=68) and randomly selected gene pairs (n=68). P-value was calculated by two-tailed Mann-Whitney U Test. (K) Pie chart showing the accuracy of predicted gene action sites using the ABar spindle rotation phenotype (See Extended Experimental Procedure for details).

Figure 2. Canalized Landscape of Cell Fate

(A) Summary of cell fate changes. Bar plots show the numbers (red) and types (green) of cell fate changes observed for the 12 founder cells. (B) 3D plots show the phenotypic landscape of all detected progenitor cell fate changes. The X-Y plane is a phenotypic space with each type of cell fate being at a unique 2D coordinate. Z-axis shows the detected frequency of the fate. The first plot shows a random sampling from all 256 fate types. See also Figure S3. (C) Statistics of cell fate phenotypes. Left panel shows the fraction of detected (enriched and not enriched) and not detected fates out of the 256 types for each tissue marker. Error bars show S.D. based on 10,000 simulations with known error rates of lineaging. Right panel shows the fraction of the enriched and not enriched types among the detected fate changes. (D) Box plot shows the shortest distance of newly acquired fates to cell fates used in the wild type for different categories. Distance is quantified as the total number of clones whose marker expression status is different. In the box, horizontal lines and ‘+’ represent the median and mean respectively. P-value was calculated by two-tailed Mann-Whitney U Test. See also Figure S3. (E) Artistic rendering of the canalization of differentiation around the fates used in normal development. Green arrows indicate homeotic transformations. (F) Histogram of the observed occurrence of detected homeotic transformations. (G) Definition of lineage distance. (H) Fraction of detected types out of all possible types of homeotic transformations at different lineage distances. (I) Scatter plot shows the number of occurrences and the lineage distance of each observed transformation. (J) Scatter plot shows the correlation between the occurrence of X-to-Y and Y-to-X transformations for each detected fate pair.

Figure 3. Regulatory Switches of Cell Fate

(A) Categories of cell fate regulators. Genes were classified as three categories based on their knockdown phenotypes: without phenotypes, induced homeotic transformations and induced abnormal fates. Venn diagram shows the overlap between genes that induced fate transformations and abnormal fates. See also Table S3. (B) The wild-type cell lineage. Circles represent cells with lines connecting mother and daughter cells. Cell names are indicated as text near cells and germline precursors are highlighted in black. (C) Regulatory switches of progenitor cell fate. Figure shows all identified homeotic transformations, their associated developmental processes and the regulatory genes. Numbers in parentheses indicate the number of new genes found in this study. See also Table S3. (D) Functional distribution of regulatory switch of cell fate. Bar plot shows the expected (orange) and observed (green) number of genes in each functional category. (E) Heatmap shows the regulation of fate choice by genes in different biological processes. (F) Status of functional annotation of genes analyzed in this study. See also Table S1.

Figure 4. Extensive Temporal Flexibility of Cell Fate Progression

(A) Regulation of temporal identity. Black arrows represent progression of normal development. Germline cells are shown above with numbers indicating different generations. Somatic cells are shown below. Red arrows indicate the fate transformations and boxes below show genes that induced the corresponding phenotype. Black indicates known genes and red indicates new genes found in this study. (B) Regulation of cell fate restriction. Boxes show detected cases of fate renewal (delayed restriction, left) and skipping of cell fate (precocious restriction, right). (C) Differentiation of the AB lineage in normal development. In the left panel, colored tree represents expression patterns of tissue markers used to assay cell fate. Purple bar below the tree highlights the ABp fate. Right panel summarizes cell fate progression from P₀ to the ABx generation in the wild type. Texts denote cell name and different colors denote cell fates. (D) cdc-25.1(RNAi) induces precocious differentiation. Left panel shows lineage differentiation and right panel summarizes the fate progression. Tissue marker expression pattern for the AB cell is identical to that of normal ABp. The pattern highlighted by cyan box-3 is different from that of box 1 in Figure 4C, but identical to that of box-2. This is presumably a secondary phenotype caused by fate change of AB cell in cdc-25.1(RNAi), which causes the loss of the third Notch signaling that distinguishes ABpla and ABpra fates. In the absence of third Notch, both ABpla and ABpra cell adopt the ABpra fate (box 2) (Hutter and Schnabel, 1995). (E) Schematic representation of ABp fate induction caused by Notch signaling (arrow) from the neighbor cell. Upper panel shows the process in the wild type that occurs at the 4-cell stage and lower panel shows a possible scenario of premature induction of ABp fate in the AB cell in cdc-25.1(RNAi). Text and color indicate cell and fates respectively. (F) AB lineage adopts two “ABa” fates in glp-1(e2141). The hypodermis marker is used to assay differentiation. Bars below the tree highlight the “ABa fate”. The difference between the “ABa” fate and normal ABa fate (shown in Figure 4C) is due to additional function of Notch in ABalp and ABara sublineages (Hutter and Schnabel, 1994). Number shown above indicates penetrance. (G) AB lineage adopts the “ABa” fate in glp-1(e2141); cdc-25.1(RNAi) suggesting AB skipping is not caused by to premature Notch induction. Hypodermis marker is used to assay differentiation and number shown above indicates penetrance. In cdc-25.1(RNAi) embryos, the position of ABal and ABar cells is not conventional.

Figure 5. Gene Network Controlling Lineage Differentiation

(A) Gene regulatory network based on phenotype similarity. Nodes represents genes, edges represent genes that predicted to have similar function. Strong and weak edges are shown in different thickness. See also Figure S4 and Table S4. (B) Box plot showing phenotypic similarity scores for genes within a protein complex/pathway (red) compared to that of background (gray). Background similarity is calculated as the average similarity between members of a complex/pathway to all other genes. P-value was calculated by two-tailed Mann-Whitney U Test. See also Table S4. (C) Venn diagrams show the number of shared genes among three studies. (D) Frequency of shared and distinct edges among three studies. For Green’s network, the automatically clustered network at medium resolution (CSI≥0.95) was used although different resolutions showed similar results.

Figure 6. Multiscale Model of Lineage Differentiation

(A) Depiction of a canalized landscape as a directed graph. Each homeotic transformation (dashed box) is interpreted as an alternative trajectory of fate in a landscape (orange) (left panel). Canalized trajectories of fate progression are depicted as arrows. Genes causing a homeotic transformation are interpreted as repressors of the alternative trajectory (middle panel). To simplify the view, an alternative trajectory is not linked to the major node for the corresponding fate but to a small red node denoting the destination (right panel). (B) Concept of multiscale model. (C) Visualization of the multiscale model. Progenitor cell fates (green boxes) are organized based on the wild-type lineage. A gene network repressing each alternative path (light blue boxes) is placed on the corresponding trajectories. Gene networks regulating the execution of cell fate differentiation are placed inside the corresponding fates. See also Figure S5 and Table S5.

Figure 7. Effect of Landscape Topology

(A) Directed graph shows the landscape contained in the multiscale model in Figure 6. (B) Fate trajectories leading to the MS fate. Black arrows show the trajectories in normal embryogenesis. Red arrows show the alternative trajectories revealed by homeotic transformations. Solid arrows show the observed trajectories to MS. Dashed ones show possible trajectories by combining phenotypes of multiple genes. (C) Distribution of the in- and out-degree of the directed graph. (D) Top: frequency of each terminal cell in (A) being generated by the zygote (P₀) following random fate choices (n=1,000) based on the detected landscape (histogram) and randomized landscapes (line, 1,000 randomized landscapes with 1,000 runs each). Bottom: degree of bias among the terminal cells in (A) across random landscapes. Vertical line marks the experimentally mapped landscape. (E) Curves show the success rate of differentiation in a multicellular system with a given number of cells randomly differentiating into a given number of cell types (n=10,000 for each multicellular system).

Figure 8. Context-specific Regulation of Notch Signaling

(A–B) Multiscale model of a Notch-mediated fate choice between ABala and ABara. (A) Notch signaling (N) induces the ABara fate. Color-coded trees below represent tissue marker expression patterns in the corresponding sublineages. (B) Gene regulatory networks that regulate cell fate choices between ABala and ABara. The network required for the ABara fate is shown in the red box, which contains known genes in the Notch pathway (stars). The network required for the ABala fate is shown in the blue box. It is further divided into 3 modules based on network connectivity. See also Figure S6. (C) Differentiation of the ABala lineage for different genotypes. Expression of PHA-4 (red) was used to assay lineage differentiation. For each genotype a micrograph of embryo at the terminal stage is shown on the left (green labels all cells and red labels PHA-4 expressing cells) and PHA-4 expression pattern in the ABala lineage is shown on the right. Number shows the penetrance of each phenotype. (D) Differentiation of the ABara lineage for different genotypes. (E) Schematic representation of the CAF-1 complex. (F) Quantification of ref-1::mCherry expression in ABala and ABarp lineages in the wild type and rba-1(RNAi). Each bar represents an embryo assayed. Expression level was averaged for ABala8 and ABarp8 cells in each embryo. P-value was calculated by T-test. (G) Summary of genetic epistasis.