The differentiation of embryonic stem cells is initiated by a gradual loss of pluripotency-associated transcripts and induction of differentiation genes. Accordingly, the detection of differentially expressed genes at the early stages of differentiation could assist the identification of the causal genes that either promote or inhibit differentiation. The previous methods of identifying differentially expressed genes by comparing different cell types would inevitably include a large portion of genes that respond to, rather than regulate, the differentiation process. We demonstrate through the use of biological replicates and a novel statistical approach that the gene expression data obtained without prior separation of cell types are informative for detecting differentially expressed genes at the early stages of differentiation. Applying the proposed method to analyze the differentiation of murine embryonic stem cells, we identified and then experimentally verified Smarcad1 as a novel regulator of pluripotency and self-renewal. We formalized this statistical approach as a statistical test that is generally applicable to analyze other differentiation processes.
Cellular differentiation is the process by which a less specialized cell becomes a more specialized cell type, characterized by the expression pattern of a subset of genes (called marker genes hereafter) during the differentiation process. The search for marker genes is widely pursued in almost every differentiation process, although a principled approach is still missing. The current practice is to separate distinguishable cell types, measure gene expression from each cell type, and then identify differentially expressed genes (Table S1). Such methods require the expression data for both cell types to be available. A limitation of these methods is that by the time the cell types are distinguishable, for example by morphology, many genes have already shown differential expression. This set of differentially expressed genes may include the class of “early marker genes” that are enriched for markers of early differentiating cell lineages as well as genes whose down-regulation triggers differentiation. However, the set of differentially expressed genes will also include a second, larger class of genes in which gene expression is not important to the regulation of the differentiation process but in which genes are simply characteristic of the fully differentiated cell types. Traditional sample comparison procedures are not designed to separate the two classes differentially expressed genes and as a result, the large lists of differentially expressed genes usually do not provide direct guidance for dissecting underlining mechanisms of differentiation. Recognizing early marker genes enables separation of cell types at an early stage of differentiation; in turn, separating cell types at an early stage of differentiation enables identification of early marker genes. However, neither piece of the puzzle is currently available to a study of a new differentiation process. We demonstrate that, contrary to common belief, early marker genes can be detected by measuring the average expression of a mixture of cell types, provided that enough biological replicates have been measured and statistical test based on variance ratio has been used. We provide (1) the theoretical reasoning, (2) a statistical method, and (3) two validation experiments.
During the early stages of differentiation, a parental population of cells gives rise to at least one descendent cell type, generating a mixed population of both parent and descendent cells (Figure 1). In a general experimental design, the average expression of a gene in the cell mixture is measured, for example by microarrays, at a few time points () during the differentiation process. Biological replicates () are available for every time point. Our task is to identify the earliest group of genes that have differential expression patterns. For a toy example (Figure 1), this group of genes includes Gene 1 only, although all three genes have changed expression values over time. After time T1, the average expression level in a mixed cell population is measured for Gene 1 (dotted line, Figure 1B). After T1, the variance of measured expression of Gene 1 across biological replicates should inflate as compared to its variance before T1. The reason for this variance inflation is that the percentage of descendent cells is not identical across biological replicates (Figure S1, Text S1). For example, at t2, biological replicate 1 may have 50% parental cells and 50% descendent cells, whereas biological replicate 2 may have an 80%–20% split of parental and descendent cells in the mixture (see Fig 5B of [1] as an example). In contrast to a nearly 100% parental cell population at t0 for all biological replicates, the difference in percentage of sub-populations after differentiation is a signal that can be utilized in a statistical method, hereafter referred to as Differentiation-Test (Methods). Although the description of rationales above has various simplified assumptions, inflation of variance is intrinsic to unsynchronized differentiation events across biological replicates. Neither the model nor the applications assume the parental population is homogeneous (see Discussion). We used this approach to study the differentiation of mouse embryonic stem (mES) cells into embryoid bodies (EB). Very early in this differentiation process, different subsets of mES cells start to show different expression changes that then bias the development towards different lineages. These early marker genes are probably small in number, and the timing of their changes in early differentiating cells may be stochastic and exhibit large variation in replicate experiments. As differentiation continues, there will be further changes in the expression of these genes as well as in a larger number of other genes characteristic of the fully differentiated states of the various lineages (e. g. , ectoderm, mesoderm, visceral and definitive endoderm). Strictly speaking, a time dependent mixture of two or more cell populations, as formulated in the Methods section and the above titration experiment, is too simplistic to model the setting of mES to EB differentiation. However, the Differentiation-Test derived from such a model should still be applicable in this setting. At an early time point, such as 4 days after differentiation, the stochastic timing of the changes in an early marker gene will lead to increased variability of its measured expression level in biological replicates. The Differentiation-Test was designed to detect exactly this increased variability. To test this idea, we differentiated mES cells spontaneously into EBs (Figure S2). Gene expression of six biological replicates of undifferentiated mES cells (0-day), as well as 4-day, 8-day and 14-day EBs was measured by Affymetrix microarrays (Methods). We applied the Differentiation-Test to this dataset and identified the top 200 differentially expressed genes of 4-day and 8-day EBs (Text S2). These time points represented early stages of mES differentiation because after 8 days, numerous cystic structures were observed to become progressively larger over time. As a benchmark experiment, Zhou et al. used fluorescence activated cell sorting (FACS) to obtain the subset of differentiating mES cells that express a GFP under the control of an Oct4 promoter (Oct4+) and the subset of cells that do not express Oct4-promoter controlled GFP (Oct4−) [2]. Oct4 is master regulator of self-renewal of mES cells, and its expression level is used as the indicator of the differentiation state [3]. Differentially expressed genes between Oct4+ and Oct4− cells reported by Zhou et al. were used as a benchmark gene list. The statistical significance of the overlap between the Differentiation-Test reported gene lists and the benchmark genes was assessed by Fisher' s Exact Test, generating p-values of and for 4-day and 8-day EBs, respectively. These small p-values were not due to a particular cutoff of the number of top-ranking genes reported (Table S2). In contrast, in testing 10,000 random lists of 200 genes each against the benchmark list, none (0%) of these reached p-values as significant as or (Figure S3). In fact, the Differentiation-Test' s top-ranked transcription regulators in 4-day EBs (Table S3) included a number of markers of early differentiation, including Sox4, Egr1, Id2, and Pax6 (ranked as 6,9, 12, and 36, respectively), as well as known self-renewal regulators of mES cells, including Klf4 [4], and Oct4 [5], [6] (ranked 1 and 13, respectively). In contrast, a traditional T-test between 4-day EBs and undifferentiated mES cells failed to reveal any of these differentially expressed genes because 4-day EBs still had a similar mean expression of the marker genes as 0-day mES cells (Column H, Table S3). For example, T-test p-values for Klf4 and Oct4 are 0. 90 and 0. 95, respectively. These test results suggest that the Differentiation-Test detected differentially expressed genes in a very early stage of the differentiation process, generating consistent results to those obtained from a laborious experimental procedure of cell sorting. Cell sorting requires prior knowledge of a marker gene that is differentially expressed, which may not be available for every differentiation process in future studies. We hypothesized that the Differentiation-Test reported list would include uncharacterized critical regulators of pluripotency and self-renewal. Self-renewal regulators should have a lower expression in differentiated cells and therefore should be detectable in the cell mixture of 4-day EBs. We used short hairpin RNA (shRNA) to further study two transcription regulators detected by the Differentiation-Test, namely, Smarcad1 and Pias2. They ranked 10 and 99 respectively among all transcription regulators (Table S3). The other top-ranking regulators were not picked for experimental validation because they had known regulatory roles in ES cell differentiation. Upon 2 days of Smarcad1 shRNA induction, ES cells started to take on a flattened morphology; large percentages of cells lost Alkaline Phosphatase (AP) staining (Figure 2A). Quantitative real time polymerase chain reaction (qPCR) analysis showed that the knockdown of Smarcad1 induced the expression of Fgf5, a growth factor involved in multiple differentiation processes including differentiation to the neuronal lineage [7] (Figure 2B). At 4 days of shRNA induction, we observed further loss of AP staining (Figure S4A), reduction in pluripotency markers such as Oct4, Sox2, and Nanog, as well as induction of multiple differentiation marker genes including Fgf5, Cdx2, and Hand1, confirming that the cells depleted of Smarcad1 lost the ability to maintain their stemness state (Figure S4B). Multiple shRNA constructs targeting different regions of the target genes gave the same results. On the other hand, neither mock shRNA nor shRNA knockdown of Pias2 induced ES cell differentiation (Figure 2). These results demonstrate the ability of the Differentiation-Test to identify novel self-renewal regulators. A regulatory network of early differentiation genes might reveal the critical events that underlie the earliest differentiation of ES cells. Using the genes identified by the Differentiation-Test, we constructed a gene regulatory network (GRN) that demonstrates the transition of ES cells to 4-day EBs (see Methods). Nodes of this GRN were top-ranked transcription factors and signal transduction genes detected by the Differentiation-Test in 4-day EBs (Figure 3). Regulatory relationships among these nodes were taken from published results of ChIP-chip experiments [4], [8], [9], ChIP-seq experiments [10], and RNAi followed by microarray experiments [3], [4]. Comparing the mean expression value of a gene in Oct4 expressing cells (Oct4+) and Oct4 non-expressing cells (Oct4−) [2], we separated the differentiation regulators into two modules: the upregulated module during differentiation (termed the differentiation module, yellow nodes, Figure 3) and the downregulated module (termed the pluripotency module, blue and red nodes, Figure 3). The DNA binding motif of RBP-J, the canonical downstream transcription factor of the Notch signaling pathway, is strongly enriched in the upstream regions of the differentiation module as compared to those of the pluripotency module (Figures 4, S5, Text S3) [11], suggesting the Notch signaling pathway might trigger the early differentiation of ES cells. These data are consistent with recent reports that Notch signaling promotes neural lineage entry of mES cells [12] and that it is required for undifferentiated human ES cells to form the progeny of all three embryonic germ layers [13].
If high-throughput measurements of gene expression at the single-cell level were available, currently available statistical tools (Table S1) would be applicable to the search for differentially expressed genes during differentiation. However, microarrays typically cannot measure gene expression from a single cell but can only measure the average signal from a bulk of cells. Such data demand new gene expression models from the single-cell level to the cell-mixture level. The Differentiation-Test method makes a number of abstractions to the differentiation process. Most remarkably, the method assumes that the differentiation process starts from a relatively homogeneous initial cell mixture and progresses into a more heterogeneous cell mixture with identifiable events of divergence of expression levels of certain genes during the process. There are at least two sources contributing to the heterogeneity of gene expression in a cell mixture, including the unsynchronized cell-cycle stages and the cell type difference. The first source of heterogeneity is assumed to persist over time, and therefore it is adjusted for by the ratio of variances across time points. Statistically, when the initial cell mixture is not purely homogeneous, Equation (5) would have a non-zero first term in the summation. In such a scenario, the DT statistic still reflects the contrast of variation across time and the null distribution can be approximated by an F distribution with the same degrees of freedom. Therefore, the Differentiation-Test does not require the initial cell mixture to be absolutely homogenous but does require the heterogeneity of the cell mixture to increase over time. The same set of core regulatory proteins and protein complexes interact and regulate the genes in both the pluripotency module and the differentiation module (Figure 3). The complex interactions of these regulatory proteins suggest that their pivotal roles in ES cells may not be sufficiently reflected in a binary description as “activators” or “repressors, ” whereas they may serve to strike a balance between the multiple extrinsic signals that the cells receive, filter intrinsic noise of the system, and collectively predispose the ES cells to pro- or anti-differentiation states. The implications of such complex interactions to data modeling and interpretation are twofold. First, a predictive model for cell fate decision might require modeling the regulators as continuous rather than Boolean variables. A case in point is the observation that the feedback loop of Oct4-Sox2-Nanog is capable of translating continuous differentiation signals into an irreversible bistable switch [14]. Second, gene knockout data should be interpreted with caution given that a regulator may not merely activate or repress gene expression but may also buffer variability in transcription by minimizing stochastic extrinsic and intrinsic signals that create noise in gene expression [15]. A case in point is the deletion experiment of the Polycomb complex protein Suz12 [16]. Suz12 (−/−) ES cells are viable and exhibit defective differentiation, which seems to contradict the role of the Polycomb group as a repressor complex that suppresses the expression of lineage-specific differentiation genes in ES cells [8]. However Suz12 (−/−) ES cells exhibit a global loss of H3K27 trimethylation (H3K27me3) [16], which may have lost a buffering mechanism that renders the intrinsic signal for pluripotency unrestrictedly amplified. More experiments, such as a series of knockdowns of Suz12 into different concentrations, may produce data to further investigate such questions. The new gene expression and RNA knockdown data suggest that Smarcad1 is a chromatin modeling factor that contributes to maintaining the pluripotency of ES cells. Smarcad1 is structurally classified into the SWI2/SNF2 superfamily of DNA-dependent ATPases that are catalytic subunits of chromatin-remodeling complexes. Although the importance of other members of the SWR1-like subfamily in chromatin remodeling (EP400, INOC1, and SRCAP) has already been elucidated, little was known about the biological function of Smarcad1 in transcriptional regulation. Homozygous mutation of Smarcad1 gives rise to a number of phenotypes including prenatal-perinatal lethality [17], confirming Smarcad1' s importance in regulating early development. Smarcad1 preferentially binds to transcription start sites in embryonal carcinoma cells [18], which suggests that Smarcad1 is a gene specific transcription regulator rather than a ubiquitous chromatin modeling factor. These data and our observations collectively suggest that Smarcad1 might be an overlooked sequence-specific transcription regulator important for both ES cells and early development.
Cellular differentiation is the process by which a less specialized cell becomes a more specialized cell type, characterized by the expression pattern of a subset of genes during the differentiation process. Identifying the subset of genes that initiate the differentiation process is critical to study differentiation mechanisms. The current practice for finding such genes is to separate distinguishable cell types, measure gene expression from each cell type, and then identify differentially expressed genes. These methods would inevitably include a large portion of genes that respond to, rather than regulate, the differentiation process. We demonstrate through the use of biological replicates and a novel statistical approach that the gene expression data obtained without prior separation of cell types are informative for detecting genes that may regulate differentiation. Applying this method to analyze the differentiation of murine embryonic stem cells, we identified and then experimentally verified a novel regulatory gene. We formalized this statistical approach as a statistical test that is generally applicable to analyze other differentiation processes.
developmental biology/cell differentiation
computational biology/transcriptional regulation
Dissecting Early Differentially Expressed Genes in a Mixture of Differentiating Embryonic Stem Cells