Med12 cooperates with multiple differentiation signals to enhance embryonic stem cell plasticity

Cell differentiation results from coordinated changes in gene transcription in response to combinations of signals. FGF, Wnt, and mTOR signals regulate the differentiation of pluripotent mammalian cells towards embryonic and extraembryonic lineages, but how these signals cooperate with general transcriptional regulators is not fully resolved. Here, we report a genome-wide CRISPR screen that reveals both signaling components and general transcriptional regulators for differentiation-associated gene expression in mESCs. Focusing on the Mediator subunit Med12 as one of the strongest hits in the screen, we show that it regulates gene expression in parallel to FGF and mTOR signals. Loss of Med12 is compatible with differentiation along both the embryonic epiblast and the extraembryonic primitive endoderm lineage, but pluripotency transitions are slowed down, and the transcriptional separation between epiblast and primitive endoderm identities is enhanced in Med12-mutant cells. These cellular phenotypes correlate with reduced biological noise upon loss of Med12. These findings suggest that Med12 regulates cellular plasticity through the priming of transcriptional changes during differentiation, thereby modulating the effects of a broad range of signals.


Introduction
Cell differentiation during development manifests in expression changes of large gene modules that are triggered by extracellular signals and sequence-specific transcription factors.
In response to such stimuli, cells must both be able to faithfully mount complex transcriptional responses, as well as maintain plasticity to be able to adapt to changing environments.
Knowledge of the molecular regulators that balance faithful differentiation responses and plasticity is therefore of key importance for a mechanistic understanding of cell differentiation.
The earliest cell differentiation events of mammalian embryogenesis first segregate the extraembryonic trophoblast from the inner cell mass (ICM).In a second step, the ICM further differentiates into extraembryonic primitive endoderm (PrE) and the pluripotent embryonic epiblast (Epi), which ultimately forms the fetus (Chazaud & Yamanaka, 2016).Subsequently, epiblast cells transition from the naïve pluripotent state at pre-implantation to formative and then primed pluripotency as they prepare for germ layer differentiation (Kalkan & Smith, 2014;Nichols & Smith, 2009;Smith, 2017).Embryonic stem cells (ESCs) allow modeling both the differentiation towards an extraembryonic PrE identity, as well as transitions between different pluripotent states.Mouse ESCs (mESCs) can either be maintained in medium containing serum and the cytokine LIF, or in a ground state of pluripotency using serum-free N2B27 medium supplemented with LIF and two small molecule inhibitors that activate Wnt/beta-Catenin signaling and inhibit FGF/ERK signaling, respectively (2i + LIF; Ying et al., 2008).
Efficient PrE differentiation from mESCs can be achieved from ground state pluripotency by the forced expression of GATA transcription factors together with active FGF/ERK signaling (Fujikura et al., 2002;Schröter et al., 2015;Wamaitha et al., 2015).Transitions between pluripotency states in contrast can be triggered by the removal of small molecule inhibitors from the culture medium alone (Kalkan et al., 2017;Neagu et al., 2020).Together with experiments in the mouse embryo, these stem cell models have provided a comprehensive picture of the signaling control of the early lineage transitions in the mammalian embryo.Both PrE differentiation and exit of epiblast cells from naïve towards formative and primed pluripotency require FGF/ERK signaling as well as Wnt signaling inhibition (Athanasouli et al., 2023;Chazaud & Yamanaka, 2016;Kang et al., 2013;Nichols et al., 2009).PrE differentiation further benefits from LIF signaling (Morgani & Brickman, 2015), whereas the progression of epiblast cells is promoted by the Notch signaling effector RBPJ (Kalkan et al., 2019), and the mTOR signaling effector TFE3 (Betschinger et al., 2013;Villegas et al., 2019).
Developmental signaling systems often culminate in the activation or deactivation of sequence-specific transcription factors.In eukaryotes, transmission of such transcription factor activity changes into altered RNA polymerase activity at specific promoters requires large multiprotein assemblies such as the Mediator complex that physically bridges between transcription factors and the basal transcriptional machinery (Soutourina, 2018).The mammalian Mediator complex is formed by up to 30 subunits.It can be subdivided into a head, a core and a tail domain, and the transiently associated CDK8 module which consists of four subunits: the CDK8 kinase, CCNC, MED12, and MED13 (Luyties & Taatjes, 2022;Soutourina, 2018).Unperturbed Mediator function is required for expression of most protein-coding genes (Soutourina, 2018).Still, individual Mediator subunits have been linked to transcriptional changes in response to specific signaling systems.Deletion of Sur2, which encodes the MED23 subunit, for example abrogates transcriptional activation downstream of ERK-MAPK signaling in mESCs (Stevens et al., 2002).The CDK8 module in particular has been implicated in directing rapid changes in gene expression patterns in response to various stimuli, such as serum stimulation (Donner et al., 2010;Luyties & Taatjes, 2022).Furthermore, CDK8 inhibition in mouse and human ESCs impairs pluripotency exit, mirroring effects of MEK/ERK inhibition (Lynch et al., 2020).MED12, which activates CDK8 function in the kinase module (Knuesel et al., 2009;Park et al., 2018), is essential for axis elongation and the activation of Wnt target genes during mouse development (Rocha et al., 2010).Together, these works suggest that the transmission of specific developmental signals to RNA polymerase II activity can be mapped to specific Mediator subunits.How general these mappings are or whether they are context-dependent is however not clear.It is also not known how interference with Mediator activity in pluripotent cells affects the balance between faithful differentiation lineage plasticity.
Here, we aim at identifying factors that mediate transcriptional changes in response to signaling events during early mammalian cell differentiation, using the expression of a Spry4 H2B-Venus reporter allele as read-out in a genome-wide CRISPR screen.This reporter is an established indicator of developmental FGF/ERK signals, and its expression is switched on in both Epi and PrE cells during preimplantation development (Morgani et al., 2018).Our screen returns both known and new signaling inputs into Spry4 H2B-Venus reporter expression, as well as several components of the Mediator and Elongator complexes.Using epistasis analysis, we demonstrate that Med12, one of the strongest hits in the screen, functions independently of and in parallel to the FGF and mTOR signaling systems in pluripotent cells.
Functional assays showed that, while not strictly required for lineage transitions, loss of Med12 leads to impaired signal responsiveness during pluripotency transitions, as well as reduced mRNA levels and noise during extraembryonic cell differentiation.Collectively, these results point to new signal-independent functions of Med12 that promote cellular plasticity during lineage transitions in early development.

Results
A genome-wide CRISPR screen identifies signaling and transcriptional regulators of Spry4 expression The differentiation of pluripotent cells towards different lineages rests on transcriptional changes triggered by extracellular signals.To identify effectors of signal-regulated gene expression during cellular differentiation, we performed a genome-wide CRISPR screen using the expression of a Sprouty4 reporter as a read-out (Hanna & Doench, 2020;Morgani et al., 2018; Fig. 1A).We chose Sprouty4 because it is a known target gene of developmental FGF signals, and shows strong expression changes upon both Epi and PrE differentiation (Morgani et al., 2018;Fig. 1 Supp 1).In serum + LIF medium, the Spry4 reporter is expressed due to paracrine FGF signals.We generated Cas9-expressing Spry4:H2B-Venus reporter cells and transduced them with the Brie gRNA library targeting protein-coding genes (Doench et al., 2016).To identify positive and negative regulators of Spry4 reporter expression, we flow sorted cells with decreased and increased fluorescence at 6 and 9 days after transduction, and determined enriched guides in sorted fractions (Fig. 1A).We first analyzed perturbations leading to reduced reporter expression.Gene-targeting gRNAs were more strongly enriched in the sorted fractions compared to non-targeting controls (Fig. 1B, Fig. 1 Supp 2, Supp Table 1).We used the robust rank algorithm (RRA; W. Li et al., 2014) to combine information from multiple gRNAs and to rank genes in each of the four conditions (1% and 5% gate at both day 6 and day 9, Supp Table 1).This analysis revealed up to 17 individual genes with an FDR ≤ 0.05, and up to 26 genes with an FDR ≤ 0.2 (Fig. 1C, Fig. 1 Supp 2).We compiled a list of hits by selecting genes that were detected with an FDR of ≤ 0.05 in one condition, or with an FDR of ≤ 0.2 in at least two conditions (Fig. 1D).Protein-protein interaction network analysis with String-DB revealed that most of our hits fell into a small number of groups that were highly connected and associated with specific molecular functions (Szklarczyk et al., 2015; Fig. 1E).
One of these groups contained the FGF signaling genes Fgfr1, Grb2, Sos1, and Ptpn11, as would be expected given the strong regulation of Spry4 by FGF signaling.Another group contained several genes involved in protein glycosylation and specifically the synthesis of heparan sulfates, such as Slc35b2, Ext1, Ext2, and Extl3.Heparan sulfates are crucial cofactors for efficient FGF signaling (Ornitz & Itoh, 2015).Since pooled CRISPR screens detect mainly cell-autonomous functions of gene perturbations, the appearance of these hits indicates that surface-tethered heparan sulfates determine a cell's responsiveness to FGF signaling.Three further groups contained genes associated with ribosome biogenesis and translation (Rpl9, Rpl18, Eif3i and Dhx37), as well as genes of the Elongator (Elp2 -6, Ikbkap and Kti12) and Mediator complexes (Med10, Med12, Med16, Med24 and Med25, Fig. 1E), raising the possibility that these factors could have gene-or signal-specific functions in mESCs.
Next, to validate selected hits in an independent experimental setting, and to evaluate their effect size, we knocked out individual candidate genes using the most enriched gRNA from the screen.Since we were interested in mechanisms of transcriptional regulation, we focused on hits related to the Mediator and Elongator complexes, but also included Grb2 and Ptpn11 as a reference to evaluate the effects of perturbing FGF signal transduction, and Sox2 and Fam98b as candidates that could not clearly be linked to a functional group (Fig. 1E).Flow cytometry showed that knockout of all tested candidates led to a reduction of mean Spry4:H2B-Venus fluorescence levels, albeit to a different degree: Knockout of components of the Elongator complex as well as Fam98b and Sox2 affected Sprouty4:H2B-Venus expression only mildly, whereas knockout of the FGF signaling genes Grb2 and Ptpn11 had the strongest effect, although they did not reach the reduction achieved via pharmacological inhibition of FGF signal transduction with the MEK inhibitor PD0325901 (PD03) (Fig. 1F, G).

Knockout of Mediator components reduced Spry4:H2B-Venus levels to different degrees, with
Med12 having the strongest effect, reducing Spry4:H2B-Venus levels to 35.3% ± 1.6% compared to control (Fig. 1F, G).Thus, our screen and validation establish Med12 as a strong candidate regulator of signal-dependent gene expression in mESCs.A Schematic of the pooled CRISPR knockout screen.Cas9-expressing Sprouty4 H2B-Venus/+ reporter cells were transduced with a genome-wide gRNA library targeting protein-coding genes (Doench et al., 2016).Cells with in-or decreased fluorescence were flow sorted 6 or 9 days after transduction, and gRNAs enriched in sorted populations identified by sequencing.We next sought to use our screen to identify negative regulators of Spry4 expression, by looking at perturbations that led to increased reporter expression (Fig. 1A, right side of histogram).Also here, gene-targeting gRNAs were enriched over control guides in the sorted fractions (Fig. 2 Supp 1A-D, Supp Table 1).Ordering according to RRA-scores revealed up to 12 and 24 genes with FDR values of ≤ 0.05 and ≤ 0.2, respectively in each of the conditions (Fig. 2A, Fig. 2 Supp 1E-G, Supp Table 1).Using the same criteria as for the positive regulators, we compiled a list of 29 potential negative regulators of Spry4 transcription (Fig. 2B).Analysis with String-DB again showed that many of these hits were highly connected and associated with specific molecular functions (Fig. 2C).A large group of genes encoded proteins that localized to mitochondria, or were otherwise associated with mitochondrial functions.Several genes had signaling functions: Lztr1 is a negative regulator of RAS-MAPK and hence FGF signaling, in line with the strong representation of genes promoting FGF signaling amongst the positive regulators of Spry4 expression.We detected four genes that were related to mTOR signaling (Tsc1, Tsc2, Flcn, and Lamtor).Finally, we found a big group that contained genes involved in chromatin modification and transcription, amongst them three genes encoding SWI/SNF related proteins.Thus, similarly to the collection of positive hits identified above, our screen yields both signaling and transcriptional regulators that negatively control Spry4 expression.To select hits for validation, we focused on genes associated with mTOR signaling, because these genes have also been implicated in the maintenance of pluripotency (Betschinger et al., 2013;M. Li et al., 2018;Villegas et al., 2019).We also included Lztr1 as an FGF signaling gene, and Smarcc1 as a representative of the group of chromatin modifiers.Knockout of all six individual candidate genes with single gRNAs led to an increase of mean Spry4:H2B-Venus fluorescence levels in reporter cells.The effect of knocking out the mTOR signaling genes was stronger than that of knocking out Lztr1 or Smarcc1, and almost doubled reporter expression levels compared to mock-transfected cells (Fig. 2D, E).D, E H2B-Venus expression in Spry H2B-Venus/+ reporter cells upon knockout of selected candidate genes 6 d after transfection.(D) shows histograms of one representative experiment, (E) shows mean ± SEM of median H2B-Venus expression, N ≥ 3 independent experiments.p < 0.05 for mock-transfected wild type vs. Smarcc1 knockout; p < 0.01 for mock-transfected wild type vs. all other knockouts (Benjamin-Hochberg-adjusted, one-sided, paired t-test).

Med12 regulates gene expression in mESCs independently from pluripotency-related signaling pathways
We then wanted to know how the signaling and transcriptional regulators identified in the CRISPR-screen were functionally related.We focused on Med12, since of all transcriptional regulators, it had the strongest effect on Spry4 reporter expression.Furthermore, MED12 is a component of Mediator's kinase module which could couple the activities of specific signaling systems to transcriptional activity (Lynch et al., 2020;Rocha et al., 2010).To analyze Med12 functions in mESCs, we first generated monoclonal Med12-mutant cell lines.We removed part of exon 7, a region that was also targeted by sgRNAs used in the CRISPR-screen, and confirmed the loss of MED12 protein expression by immunoblotting (Fig. 3 Supp 1A, B).
Med12-mutant cells grew normally in both serum + LIF and 2i + LIF medium, and showed a reduced increase in Spry4 reporter expression upon switching from 2i + LIF to the N2B27 base medium (Fig. 3 Supp 1C, D). ppERK levels were indistinguishable between wild-type and Med12-mutant lines (Fig. 3 Supp 1E, F), indicating that reduced Spry4 reporter expression is not due to deregulated FGF/ERK signaling.
To assess global gene expression differences between wild-type and Med12-mutant cells, we performed bulk RNA sequencing of cells in 2i medium and upon 24 h of differentiation in N2B27 (Fig. 3A).Principal component analysis separated samples in pluripotency and differentiation medium along PC1 (32.1% of variance), and wild-type and Med12-mutant cells along PC2 (16.8% of variance, Fig. 3B).Fewer genes were differentially expressed between pluripotency conditions and 24 h in N2B27 in Med12-mutant compared to wild-type cells.Most genes that were differentially expressed in the mutant were also differentially expressed in the wild type (Fig. 3C, left, Supp Table 2).When comparing between wild-type and Med12-mutant cells within each culture condition, we found that more genes were differentially expressed between the two genotypes in N2B27 than in 2i (Fig. 3C, right, Supp Table 2).Thus, loss of Med12 impairs gene expression changes at the exit from pluripotency.Med12 has a closely related paralogue named Med12l.Med12l expression was upregulated in Med12-mutant cells (Fig. 3 Supp 2A), and Spry4 reporter expression in Med12-mutant lines could be further reduced by simultaneously knocking-out Med12l (Fig. 3 Supp 2B).Thus, it is possible that Med12l partially compensates for loss of Med12 function in the mutant lines.
Exit from pluripotency is regulated by the interplay of a set of signaling systems, such as mTOR, Notch, Wnt, and FGF (Betschinger et al., 2013;Kalkan et al., 2019;Ying et al., 2008).
We next asked if gene expression downstream of any of these signaling systems was affected by loss of Med12, using previously defined sets of target genes specific for each signaling system (Lackner et al., 2021).A strong expression change of such a set of target genes between wild type and mutant cells can be considered a footprint of the perturbation of the corresponding signaling system.In Med12 mutant cells, this gene expression footprint was strongest for mTOR target genes, less pronounced for Notch and Wnt target genes, and virtually absent for FGF target genes (Fig. 3D).We functionally tested the relationship between Med12 and mTOR signaling by knocking out the mTOR signaling genes Flcn, Lamtor4, Tsc1 and Tsc2 in Med12-mutant Spry4 H2B-Venus/+ cells.Similarly to the situation in the wild type, knockout of these genes led to increased reporter expression, albeit from a lower baseline level (Fig. 3E).Therefore, Med12 regulates Spry4 expression independently from mTOR signaling.In addition to mTOR signaling genes and Mediator subunits, our CRISPR screen revealed a large number of genes involved in FGF signal transduction, but the footprinting analysis suggested that FGF inputs into gene expression were independent from Med12.To further corroborate this result, we generated Med12 mutations in the background of an Fgf4mutant mESC line, which allowed us to specifically analyze the effects of FGF signaling upon the switch from pluripotency to differentiation medium.We wanted to focus on immediate gene expression changes triggered by FGF signaling, and therefore analyzed gene expression changes by bulk RNA-sequencing after 6 h of transfer into N2B27 medium with or without addition of exogenous FGF (Fig. 3F).Again, principal component analysis showed that cells in pluripotency and differentiation medium were separated along PC1 (33.2% of variance), and wild-type and Med12-mutant cells separated along PC3 (9.3% of variance), both when analyzing independent Med12-mutant clonal cell lines as well as experimental replicates (Fig. 3G).If expression of FGF target genes was generally dependent on Med12, we would expect that their fold-change is lower in Med12-mutant compared to wild-type cells.However, when plotting the fold expression-change for each gene upon 6 h of FGF stimulation for wildtype versus Med12-mutant cells, we found that the majority of genes were induced to a similar degree in both genotypes (2006 genes), while only 926 and 935 genes were differentially expressed in the wild-type or the Med12-mutant only (Fig. 3H, Supp Table 3).Furthermore, the ratios of fold-change values of FGF target genes between Med12-mutant and wild-type cells showed a unimodal distribution with a mode of 1.068 for downregulated and 0.927 for upregulated genes (Fig. 3J).While this slight deviation of the mode from 1 leaves open the possibility that Med12 influences their expression magnitude, overall these results argue against a strong and specific role of Med12 in the regulation of FGF target genes.D Expression footprint analysis using a set of 50 marker genes per pathway defined in (Lackner et al., 2021).Top row shows footprint of Med12-mutant cells.Lower rows show expression footprints of mutants from Lackner et al., 2021, for comparison.Gray: Mutants that were used to select marker genes.Green: Independent example mutants that show a strong and specific footprint of one of the I Ratio of fold changes for FGF target genes between wild-type and Med12-mutant cells, for up-(top) and downregulated genes (bottom).FGF target genes were defined as having a log2-fold change in wild-type cells upon FGF4 stimulation > |1| and an adjusted p-value < 0.05.

Med12 regulates naïve pluripotency
We then asked if altered gene expression in the Med12 mutants had any functional consequences.To probe exit from naïve pluripotency, we performed a colony formation assay (Kalkan et al., 2017), where we differentiated cells in N2B27 for 48 h, followed by seeding at clonal density in 2i + LIF medium (Fig. 4A).Because we grew cells in the presence of LIF before transfer into N2B27, wild-type cells still formed a large number of pluripotent colonies (Lackner et al., 2021).In Med12-mutant cells in contrast, the number of pluripotent colonies was significantly reduced.(Fig. 4B, left, Fig. 4 Supp 1A, Supp Table 4).To test how this phenotype related to FGF signaling, we carried out the same colony differentiation assay in an Fgf4 mutant background.When differentiated for 48 h in N2B27, Med12 wild-type and mutant cells formed a similar number of colonies upon transfer to pluripotency medium.Upon supplementation of N2B27 with FGF4, the number of pluripotent colonies formed by Fgf4; Med12 double mutant cells was much reduced, both compared to the Fgf4 single mutant supplemented with FGF4, and the Fgf4; Med12 double mutant in the absence of FGF4 (Fig. 4B, right, Fig. 4 Supp 1B).Thus, this experiment corroborates our conclusion from the transcriptomic analysis that Med12 cooperates with FGF signaling at the exit from pluripotency.
The ability to form pluripotent colonies in the colony formation assay depends on a cell's pluripotency state at the beginning of the differentiation period, and its ability to react to changing culture conditions.We used our bulk RNA sequencing data comparing wild-type and Med12-mutant cells after 24 h in N2B27 to evaluate which of these properties were affected upon loss of Med12.We first determined differentiation delays of the two genotypes using a published high time-resolution reference dataset (Lackner et al., 2021).Surprisingly, this analysis indicated that Med12-mutant cells differentiated more slowly than the wild-type (Fig. 4C).This could be a consequence of stronger expression of naïve genes in pluripotency conditions, or reflect slower downregulation of these genes in N2B27 in the Med12 mutant.To distinguish between these possibilities, we plotted the expression levels of 7 selected pluripotency marker genes in 2i and after 24 h in N2B27 in wild-type versus Med12-mutant cells.In this plot, data points above and below the unit line indicate higher and lower expression in the mutant relative to the wild type, respectively, and the slope of the connecting line between expression in 2i and N2B27 indicates the dynamics of down-regulation (Fig. 4D, left).We found that in 2i medium, all marker genes were less strongly expressed in the mutant compared to the wild type.Furthermore, their slopes were consistently smaller than one (Fig. 4D).Such a systematic reduction in the slope of downregulation was also observed when we analyzed the top 100 downregulated genes in wild-type or Med12-mutant cells (Fig. 4E).
These findings suggest that Med12-mutant cells both have impaired pluripotency gene expression in 2i, as well as a reduced ability to react to changing culture conditions.In the colony formation assay, cells need to re-establish pluripotency gene expression.We therefore surmise that the reduced colony formation capacity of Med12-mutant cells in the colony formation assay is dominated by their reduced ability to react to changing culture conditions.C Estimation of differentiation delay in wild-type and Med12-mutant cells, relative to a published time resolved gene expression dataset (Lackner et al., 2021).Plot shows the normalized Euclidean distance of the expression of naïve marker gene panel (Prdm14, Tfcp2l1, Klf4, Tbx3, Nanog, Zfp42, Esrrb) to the reference dataset.The negative delay values for our wild-type cells likely reflect small differences in experimental design compared to the study by (Lackner et al., 2021).

Transition between embryonic and extraembryonic identities is buffered against loss of Med12
We next sought to further probe the role of Med12 in an experimental system where the doxycyclin-induced expression of GATA factors brings ground state pluripotent cells into an ICM-like state, from which they can differentiate towards the Epi identity marked by NANOG expression, or the PrE identity marked by the expression of endogenous GATA factors and SOX17 (Fig. 5A; Schröter et al., 2015).We used PiggyBac transgenesis to establish new cell lines carrying inducible GATA6-mCherry constructs, and mutated Med12 in this background to determine if it is required for PrE differentiation.After an 8 h doxycycline pulse followed by 20 h of differentiation in N2B27 medium, a mix of SOX17-positive PrE cells and NANOGpositive Epi cells differentiated irrespective of Med12 status, but the fraction of PrE cells was lower, and that of Epi cells was higher in mutant compared to wild-type cultures (Fig. 5B, C).This difference in ratios was maintained even upon supplementation of exogenous FGF during the differentiation phase, suggesting that the lowered PrE differentiation propensity upon loss of Med12 is not due to lowered FGF signaling in the mutant cultures (Fig. 5 Supp 1A).
PrE differentiation requires the expression of inducible GATA factors above a threshold level (Schröter et al., 2015).We therefore assessed transgene induction levels in Med12 mutant cells, and found that they were reduced in independent clonal inducible lines (Fig. 5D).To test if Med12 mutant cells had an altered GATA induction threshold for PrE differentiation, we performed time-lapse imaging of inducible GATA6-mCherry expression followed by fixation and staining for fate markers (Schröter et al., 2015).We attempted to equalize induction levels by reducing the induction time in wild-type cells to 4 h, while inducing Med12-mutant cells for 8 h in 2i + LIF medium.The mutant cells showed a longer delay between doxycycline addition and the appearance of GATA6-mCherry fluorescence, and GATA6-mCherry expression levels rose more slowly than in the wild type.Furthermore, GATA6-mCherry levels continued to rise for approximately 2 h after doxycycline removal in wild-type but not in Med12-mutant cells (Fig. 5E, Supp Movie 1).These altered induction dynamics point to a critical role of Med12 for efficient transgene expression.

Connecting inducible GATA expression with differentiation outcome in individual cells
revealed that low and high inducible GATA6-mCherry expression in Med12 mutants were associated with Epi and PrE differentiation, respectively, similar to the situation in wild-type cells (Fig. 5F, G; Schröter et al., 2015).We applied receiver operating characteristics to test how well differentiation could be predicted based on a GATA6-mCherry expression threshold, and to determine the value of an optimal threshold (Fawcett, 2006).GATA6-mCherry expression levels had similar predictive power for differentiation outcome in wild-type and Med12-mutant cells as judged by plotting the area under the curve (AUC) over time (Fig. 5H).
When comparing optimal threshold values, we found that these tracked overall GATA6-mCherry expression levels and were thus slightly lower in Med12-mutant cells (Fig. 5I).This indicates that altered proportions of Epi and PrE cells upon loss of Med12 are largely due to reduced transgene induction levels and not to a reduced ability for PrE differentiation.This conclusion was further supported by flow sorting wild-type and Med12-mutant cells with high GATA6-mCherry expression levels (Fig. 5 Supp 1 C, D).Disruption of cell-cell interactions upon sorting and reseeding resulted in low PrE differentiation in N2B27 medium (Raina et al., 2021), but supplementation of exogenous FGF4 rescued PrE differentiation to similarly high levels in both wild-type and Med12-mutant cells (Fig. 5 Supp 1 D, E).Taken together, these experiments suggest that although Med12 plays important roles for the efficient expression of individual genes, the transition between embryonic and extraembryonic identities is to a large degree buffered against loss of Med12.

Loss of Med12 reduces biological noise
Finally, we performed single-cell sequencing to determine how Med12 shapes the global transcriptional signature of PrE differentiation.Multiplexing and pooling of samples before mRNA capture and library preparation allowed us to minimize batch effects, and to generate sample replicates using independent Med12 wild type and mutant cell lines (Fig. 6A).As in previous experiments, we induced GATA transgenes for 4 h in wild-type and 8 h in Med12mutant cells, and started differentiation simultaneously in both genotypes by switching from 2i + LIF medium to N2B27.In a UMAP representation of transcriptomes from all ten samples, cells from pluripotency and differentiation conditions formed two coherent groups, respectively.Within each of the groups, wild type and Med12-mutant cells segregated from each other (Fig. 6B).Cells from differentiation conditions were negative for the Wnt/b-Catenin target Sp5 and positive for the differentiation marker Dnmt3l, while PrE markers Sox17, Dab2, and Cubn and the Epi markers Nanog and Fgf4 showed a mutually exclusive expression pattern, indicative of the split between the two lineages (Fig. 6C).To evaluate the consequences of loss of Med12 in this experiment, we next analyzed pluripotency and differentiation samples separately, and used principal component analysis as a means to graphically preserve quantitative differences in gene expression between cells.In pluripotency conditions, single cell transcriptomes from wild-type and Med12-mutant cells were clearly separated, demonstrating that loss of Med12 leads to a globally consistent shift of gene expression state (Fig. 6D).Consistent with our results from bulk RNA sequencing, expression levels of the pluripotency genes Klf4, Zfp42, and Tbx3 were reduced in the mutant cells, whereas levels of Prdm14, Nanog, Esrrb, and Tcf21l1 were similar between the two genotypes (Fig. 6E).Next, we exploited our multiplexed dataset to ask if loss of Med12 changes mRNA levels in cells.In pluripotency conditions, the number of mRNAs captured from wild-type and Med12-mutant cells was not significantly different (median number of UMIs 24437 and 23806, respectively).Differentiation resulted in an increased number of captured mRNAs in both genotypes, with wild-type cells expressing significantly more mRNAs than Med12 mutants (median number of UMIs 28227 and 25649, respectively, Fig. 6E).This suggests that Med12 enhances global transcriptional output during differentiation.
We then asked how loss of Med12 affected the separation between Epi and PrE identities.To consistently identify Epi and PrE cells across genotypes, we integrated the five differentiated samples and clustered them to separate two major groups across the integrated dataset (Fig. 6G).In principle component space, these two groups were separated along PC1 (Fig. 6G).
Cluster 0 was characterized by the expression of PrE marker genes such as Gata6, while cluster 1 expressed Epi markers such as Nanog (Fig. 6 H, Supp Table 5).The proportions of the two cell types captured by these clusters differed with Med12 genotype: In the two wildtype cell lines, 60% and 77% of all cells fell into the PrE cluster 0, whereas this cluster contained only between 35% and 42% of all cells in the Med12 mutants (Fig. 6I).We then used the cell type annotation based on this clustering approach to ask how similar the changes in gene expression profiles were between wild-type and Med12-mutant cells upon differentiation towards either of the two lineages.We first focused on genes that had a log2 fold change ≥ 0.5 between pluripotency and differentiation in wild-type cells.The fold-changes of these genes in the mutant showed a unimodal distribution in all four conditions (up-and downregulation, Epi and PrE differentiation), indicating that Med12 is not required for the regulation of specific gene modules during differentiation (Fig. 6J).This conclusion was supported by plotting the fold-change of all genes upon differentiation against each other for wild-type and mutant cells (Fig. 6 Supp 1 A, B).When comparing the top differentially expressed genes between wild type and mutant in pluripotency conditions as well as in the Epi and PrE clusters, we found that many of these genes were differentially expressed in all three states (Fig. 6 Supp 1C, Supp Table 6).This suggests that loss of Med12 does not prevent differentiation, but rather results in gene expression changes that are shared between cell states.
Despite not regulating specific differentiation-associated gene modules, loss of Med12 could still influence the efficiency of lineage separation.In the PCA plot in Fig. 6E, we noted that transcriptomes of Epi and PrE cells were separated more clearly in Med12-mutant compared to wild-type cultures.The Jaccard index is a quantitative measure for the separation of clusters in single cell sequencing data (Tang et al., 2021).We therefore computed the Jaccard index for clustering of 100 random subsets of each differentiated sample without integration (Materials and Methods).The average Jaccard index was higher in Med12-mutant samples compared to wild type, demonstrating that loss of Med12 indeed leads to a better transcriptional separation of clusters (Fig. 6K).This could be a consequence of different transgene induction dynamics between genotypes, or result from a reduction of cellular plasticity upon loss of Med12.Consistent with differences in plasticity, wild-type cells, but not Med12-mutant cells, showed a long tail of the Jaccard index distribution indicating the presence of a population of cells that explore transcriptional space beyond the main clusters.
As an alternative measure, we used VarID2 (Rosales-Alvarez et al., 2023) to test if biological noise levels were changed upon loss of Med12.This analysis revealed reduced noise levels in Med12 mutants in all three differentiation states, with the strongest changes in pluripotency and in Epi cells (Fig. 6L).Taken together, these results indicate that loss of Med12 results in overall lower transcriptional output and decreased noise.These effects may result in reduced cellular plasticity during lineage transitions, which can enhance separation between lineages.

Discussion
Here we use a Spry4 H2B-Venus reporter to screen for regulators of developmental gene expression in pluripotent stem cells.This screen returned components of the FGF/ERK and the mTOR signaling systems that positively and negatively regulate reporter expression, respectively, as well as several members of the Mediator and Elongator complexes.Focusing on Med12 we show that it cooperates with multiple signaling systems to regulate gene expression in pluripotent cells.Functional assays reveal that loss of Med12 both reduces the ability to re-establish a naïve pluripotency gene expression program in colony forming assays, as well as the propensity to populate transition states in an epiblast-to-primitive endoderm differentiation paradigm.These phenotypes correlate with reduced noise in Med12-mutant cells.Together, these results suggest that Med12 amplifies transcriptional changes in pluripotent cells, and thereby contributes to the maintenance of cellular plasticity during differentiation and lineage transitions.
Previous genome-wide screens have used retention of clonogenicity or the continued expression of pluripotency-associated reporter alleles in differentiation conditions as read-outs to identify regulators of pluripotency and lineage transitions (Betschinger et al., 2013;Kagey et al., 2010;M. Li et al., 2018;Villegas et al., 2019).In our study, the combination of a Spry4 H2B- Venus allele with flow sorting constitutes a highly sensitive read-out that is focused on the activity of specific signaling systems in pluripotent cells, and reliably detects new regulators even if they have only small effect sizes.The screen's specificity shows in the strong representation of genes involved in the FGF/ERK signaling cascade, from genes that encode synthetases for FGF co-factors, over FGF receptors, to intracellular signaling genes.Surprisingly however, we did not find any sequence-specific transcription factors downstream of FGF/ERK signaling in our screen.This could be explained by the expression of multiple functionally redundant FGF/ERK signaling effectors in pluripotent cells, or through previously proposed transcription factor-independent regulation of RNA polymerase activity by ERK (Tee et al., 2014).
In addition to components of FGF and mTOR signaling systems, the screen returned several members of the SWI/SNF and the Mediator complexes.The core Mediator complex is thought to be required for the expression of most genes in eukaryotic genomes, but individual subunits have been suggested to regulate gene expression downstream of specific signaling systems such as the serum response network (Donner et al., 2010;Stevens et al., 2002), or Wnt (Rocha et al., 2010).However, when we tested this idea for Med12, we found that its loss did not phenocopy the effects of specific signaling perturbations.This finding suggests that previously reported functional connections, such as the link between Med12 and Wnt signaling (Rocha et al., 2010), are strongly context-dependent.
Med12 is a critical component of the Mediator-associated CDK8-module.In contrast to pharmacological inhibition of CDK8 which has been reported to boost pluripotency similarly to ERK signaling inhibition (Lynch et al., 2020), we find that loss of Med12 leads to reduced pluripotency.These opposing phenotypes indicate that MED12 has functions that are independent from the CDK8-module (Aranda-Orgilles et al., 2016).This would also explain why we did not detect any other CDK8-module components in our screen.Lynch et al. found that maintenance of pluripotency required the presence of CDK8, but absence of its kinase activity.Another possible explanation for the opposing phenotypes of CDK8 inhibition and Med12 loss-of-function therefore is that MED12 participates in assembling kinase-inactive CDK8 complexes that support pluripotency.
Med12's function to maintain pluripotency reported in the present work is supported by earlier studies which found that MED12 and NANOG proteins interact, that MED12 and NANOG have similar DNA-binding profiles, and that MED12 promotes Nanog expression (Apostolou et al., 2013;Tutter et al., 2009).Several Mediator subunits, including Med12, have been identified in a screen for pluripotency maintenance that focused on transcription and chromatin regulators (Kagey et al., 2010).This study furthermore suggested that interactions between Mediator and cohesin contribute to genome folding and efficient enhancer-promoter interactions.When we probe Med12 functions in lineage transitions, we find that Med12mutant cells show decreased transcriptional plasticity, which manifests in a slower downregulation of pluripotency genes, a decreased ability to revert to naïve pluripotency in a colony-forming assay, and a reduced tendency to populate transition states in the binary decision between an epiblast and a primitive endoderm identity.We speculate that these cellular phenotypes are a reflection of a reduced ability of Med12-mutant cells to reconfigure the chromatin upon changing signaling environments.It is likely that the expression of individual genes is differentially sensitive to the loss of Med12.This may be the reason why expression from the Sprouty4 locus, which is the most strongly upregulated gene upon acute FGF stimulation, shows a particularly high sensitivity to loss of Med12.In line with this idea, requirements of Med12 for efficient induction are not exclusive to endogenous genes, but extend to exogenous transgenes such as the inducible GATA6-mCherry construct used to trigger primitive endoderm differentiation in our study.Surprisingly, such differential quantitative defects in the regulation of single genes upon loss of Med12 do not lead to strong defects in acquiring early differentiated fates, such that transcriptomes of individual differentiated cells are not systematically different from each other in Med12-mutant and wildtype cells.This suggests that intracellular regulatory networks can buffer the composition of cellular transcriptomes against variable transcription efficiencies.

Cell lines
All cell lines generated in this study were derived from the E14tg2a wild-type line (Hooper et al., 1987).The GATA4-mCherry inducible line used for single cell RNA sequencing has been described previously (Raina et al., 2021).The Spry4 H2B-Venus/+ -reporter line was generated with a previously described targeting construct (Morgani et al., 2018) using lipofectamine 2000 according manufacturer's instructions (Thermo Fisher Scientific).Correctly targeted clones were identified via long-range PCR as described in (Morgani et al., 2018).GATA6-mCherry inducible lines were established as described for GATA4-mCherry inducible lines in (Raina et al., 2021), but replacing the Gata4 with a Gata6 coding sequence in the PiggyBac vector for inducible gene expression.We established multiple clonal lines and tested them for GATA6-mCherry induction levels upon dox-treatment by flow cytometry.Three independent clones with induction levels similar or slightly higher than the previously established GATA4-mCherry inducible lines were selected for the experiment shown in Fig. 5D, and a single clonal line was chosen for all other experiments.Newly generated Spry4 H2B-Venus/+ -reporter and GATA6-mCherry inducible cell lines were checked for karyotypic abnormalities.To label nuclei for time lapse imaging cells were transfected with pCX-H2B-Cerulean-IRES-puro (Schumacher et al., 2023).Cell lines carrying PiggyBac transgenes were kept under appropriate selection to prevent transgene repression over passaging.

sgRNA cloning and generation of single-gene mutants
For mutagenesis of individual genes via CRISPR/Cas9, gene targeting sgRNAs (Supp Table 7) were cloned into pX459 (Addgene plasmid #48139) using BbsI (NEB) overhangs following Ran et al., 2013(Ran et al., 2013).Clonal mutant lines were generated using a combination of sgRNAs with targeting sequences 100 to 200 bp apart in the genome.Single sgRNAs were used when generating polyclonal lines.For validation experiments of the CRISPR screen (Figs 1 and 2), we selected the most enriched sgRNA in sorted cells.A total of 1 µg of sgRNA containing px459 vectors was mixed with a final concentration of 0.04 µg/ml Lipofectamine 2000 (Thermo Fisher Scientific) in Opti-MEM (Gibco) according to the manufacturer's protocol.For the generation of clonal lines, cells were seeded at clonal density into 10 cm dishes after transfection, for polyclonal experiments approximately 50k cells/cm 2 were seeded.To enrich for successfully transfected cells, selection with 1.5 μg/ml puromycin was started 24 h after transfection for 48 h.To establish clonal lines, single-cell derived colonies were picked 4 to 6 d after transfection and expanded.For molecular characterization of genetic lesions, genomic DNA was purified with Terra™ PCR Direct Genotyping Kit (Takara), followed by PCR amplification and Sanger sequencing of specific genomic regions encompassing the target site.

Genome-wide CRISPR Screen
To generate stably CAS9-expressing Spry4 H2B-Venus/+ reporter cells, cells were transduced with lentiCas9-Blast lentiviral particles (Addgene #52962-LV) at a multiplicity of infection of approximately 0.1.Transduction was performed with attached cells, 20 h after seeding at 20 000 cells/cm 2 , in presence of 5 µg/ml Polybrene in ESL.Continuous blasticidin (15 µg/ml, Gibco) selection was started 24 h after transduction.Lentiviral particles of the genome-wide gRNA library Brie (Addgene #73633) were generated according to standard protocols (Doench et al., 2016).For library transduction, 150 * 10 6 CAS9-expressing Spry4 H2B-Venus/+ reporter cells were detached and mixed with the virus library in ESL with 5 µg/ml Polybrene.The following day, the same number of cells was reseeded and put under selection with puromycin (1.5 µg/ml, Sigma-Aldrich).Comparing cell counts with and without selection indicated a transduction efficiency of 25%, resulting in a >400-fold coverage of transduced cells per gRNA.
In all subsequent steps, at least 31 * 10 6 cells were processed to maintain gRNA coverage.
To identify gRNAs enriched in cell populations with high and low Spry4:H2B-Venus expression, at least 0.5 * 10 6 cells with the lowest or highest 1% of Spry4:H2B-Venus fluorescence, or 3 * 10 6 cells with the lowest or highest 5% of Spry4:H2B-Venus fluorescence were FAC sorted and their DNA isolated by column-based genomic DNA purification (Monarch Genomic DNA Purification Kit, NEB).For reference, the genomic DNA of 31 * 10 6 non-sorted control cells was purified in parallel.The integrated gRNA was PCR amplified using Pfu polymerase (prepared in house) with a sample specific, sequencing adapter and index containing primers (Supp Table 7; Carlini et al., 2021) using the complete purified genomic DNA as template.PCR-samples were purified with the SPRIselect reagent (Beckman Coulter) with double-sided size selection.Briefly, 0.5x SPRIselect was added to each sample, incubated for 5 min at RT and the SPRIselect removed with a magnet.This supernatant was again mixed with 1.2x SPRIselect, incubated and then discarded.After washing the beads, the DNA library was eluted from the beads and used for sequencing.
Paired-End Illumina Sequencing with a read length of 150 bp pairs was performed with at least 10 * 10 6 reads per sorted sample and 30 * 10 6 reads for the unsorted library controls.The raw reads were trimmed using Cutadapt (Martin, 2011) to remove the vector binding sequence.
The reads were mapped to individual gRNAs, counted using norm-method total and statistically tested on the targeted gene levels using gene-lfc-method alphamean with Mageck (W.Li et al., 2014).Hits were selected based on the false discovery rate.

Immunoblotting
For western blot analysis of MED12 and ppERK, cells were washed twice with ice-cold PBS, supplemented with 1 mM activated orthovanadate in case of ppERK detection.Cells were mechanically detached in lysis buffer, based on commercially available lysis buffer (Cell Signaling) supplemented with benzonase (Sigma-Aldrich), cOmplete EDTA-free protease inhibitor cocktail (Roche), phosphate inhibitors P1 and P2 (Sigma).The lysates were snap frozen in liquid nitrogen twice and centrifuged.Protein concentration in the supernatant was measured with a micro BCA assay (Thermo Scientific).For western analysis, 20 µg of protein per sample were denatured by adding 5x Laemelli buffer and incubation at 95 °C for 5 min.

Imaging
Tilescans of immunostainings were imaged with a Leica SP8 confocal microscope (Leica Microsystems) with a 63x 1.4 NA oil immersion objective.Images were analyzed in Fiji (Schindelin et al., 2013).For segmentation, StarDist 2D (Schmidt et al., 2018) was used using the versatile (fluorescent nuclei) model and default post processing parameters.Mean fluorescence intensity was measured in segmented cells in all acquired channels.Cells with a nuclear area smaller than 40 µm 2 were filtered out.To determine fluorescence intensity threshold values for the classification of cell types, we manually selected thresholds that best bisected the bimodal expression profiles of the lineage markers.The same thresholds were applied to different samples in a single experiment.
Hardware was controlled by Olympus CellSens Software.Time lapse imaging was performed with a 40x 0.9 NA objective on an Olympus IX81 widefield microscope, equipped with an LEDbased illumination system (pE4000, CoolLED) and an iXon 888 EM-CCD camera (Andor).
MicroManager (Edelstein et al., 2010) was used to control the hardware.Images were taken every 10 min.Tracking was performed with the manual tracking function in Trackmate v7 (Ershov et al., 2022) and fluorescence intensity was measured as the mean intensity in a spot with a 4 µm radius within the nucleus.In R, tracks were smoothed with a rolling average over 7 frames.For ROC analysis the R package pROC (Robin et al., 2011) was applied and the optimal threshold was defined by the Youden's J statistic (Youden, 1950).

Flow cytometry
Analysis of Spry4:H2B-Venus reporter expression in live or fixed cells was performed on a LSRII flow cytometer (BD Biosciences).Cell sorting and analysis of GATA6-mCherry expression was carried out using a FACS Aria Fusion (BD Biosciences).Primary data analysis including gating single cells based on SSC and FSC was done with FlowJo version 9 (BD Biosciences).

Clonogenicity Assay
Clonogenicity assays were performed according to (Kalkan et al., 2017).Briefly, 1 * 10 4 cells/cm 2 were seeded in 2i + LIF for 24 h, followed by differentiation in N2B27 for 48 h.Control wells for each parental cell line were kept in 2i + LIF.Cells were then detached with Accutase to single cells, and 500 cells were reseeded into 6-well plates with 2i + LIF + 10% FBS.10% FBS were included to support survival of Fgf4-mutant cells.
After 5 d, the colonies formed were fixed and stained with an alkaline phosphatase assay kit (Sigma-Aldrich) to distinguish pluripotent and differentiated colonies.Tile scans of the wells were acquired with an Olympus IX81 widefield microscope with a 4x 0.16 NA objective.We applied background subtraction, gaussian blurring, Otsu-thresholding, and conversion of images to a binary mask in ImageJ, and then used the AnalyzeParticles function to set thresholds for size and circularity and to determine the number of colonies.Colony numbers were normalized to the number of colonies obtained in the control.

Bulk RNA sequencing
For bulk RNA sequencing, cells were seeded at a density between 3.5 and 5.5 * 10 4 cells/cm 2 in 2i or N2B27 + Chiron + LIF, followed by stimulation under indicated conditions.Replicates were obtained either from independent biological experiments (Fig. 3 A, B) or from both independent biological experiments and independent Med12-mutant lines (Fig. 3F, G).RNA isolation was performed with TRIzol (ambion) according to the manufacturer's instructions.
Sequencing libraries were prepared on polyA-enriched RNAs, followed by paired-end sequencing at a read-length of 150 bp and depth of approximately 30 * 10 6 reads per sample.
Strand-specific libraries were generated only for the FGF-titration experiment (Fig. 1 Supp 1) and the differentiation of the Med12 wild-type and mutant cells (Fig. 3A, B).Raw reads were mapped to the mouse genome (GRCm39, release 108 (both Med12 mutant experiments) or release 97 (FGF titration experiment) with hisat2 (v2.1.0;Kim et al., 2019).SeqMonk was used to quantify counts per gene, either as TPM or as raw counts as input for downstream DESeq2 analysis (Love et al., 2014) for identification of differentially expressed genes.
Differentiation delay in Med12 mutants was estimated according to (Lackner et al., 2021).We This study defined a specific set of target genes for each pluripotency associated signaling system based on gene expression changes in knockouts of signaling genes.A signaling footprint for a knockout line can then be determined from the difference in the expression of pathway footprint genes to the wild-type line after 24 h of differentiation.Measures for the signaling footprint are the Spearman correlation between each knockout line and the respective pathway defining knockout, and the ratio between the sum of expression fold changes between a knockout line and the respective pathway defining knockout, defined as pathway activity.To compare the Med12 mutant data from this study, the wild type conditions were used for batch correction.

Cell multiplexing and scRNA sequencing
Cells for scRNAseq were seeded at a density of 3.5 * 10 4 cells/cm 2 in 6-well plates in 2i + LIF and grown overnight.The next morning, induction in 2i + LIF + dox was first started in the mutant clones, and 4 h later in the wild-type lines.After 8h and 4 h, respectively, induction was stopped by washing once with N2B27, followed by 20 h of differentiation in N2B27.Controls for each cell line were continuously kept in 2i + LIF.For sequencing, cells were washed three times with PBS and detached with Accutase.Accutase was removed by centrifugation and 1 * 10 6 cells per sample were resuspended in PBS + 0.04 % BSA and immediately used for multiplexing labeling following the protocol of 10x Genomics for samples with a viability above 80 % (Cell Multiplexing Oligo Labeling for Single Cell RNA Sequencing Protocols with Feature Barcode technology, CG000391).Briefly, cells were spun down once again, resuspended with individual cell multiplexing oligos (CMO no.301 to 310) and incubated for 5 min at RT. Cells were washed twice with PBS + 1 % BSA and passed through a cell strainer (FALCON, mesh size 35 µm).A total of 1.2 * 10 5 single cells from all samples were pooled at equal ratios, and 4 * 10 4 were used for droplet generation, corresponding to a target number of 2.4 * 10 4 cellcontaining droplets.Droplet generation, lysis, mRNA and cell barcode capture, and generation of both the gene expression library as well as the cell multiplexing library was performed following the instructions by 10x genomics (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 (Dual Index) with Feature Barcode technology for Cell Multiplexing, CG000388).Specifically, we chose 11 PCR cycles for cDNA amplification and 10 cycles for the sample index PCR.Concentration and insert size distribution for both the gene expression library and the cell multiplexing library were determined with a BioAnalyzer High Sensitivity DNA Assay (Agilent).Sequencing was performed on a NovaSeq 6000 on multiple flowcells with a pairedend 150 bp configuration.In total 1.2 * 10 9 and 2.3 * 10 8 read pairs were obtained for the gene expression and multiplexing library, respectively.
Demultiplexing to the individual samples, based on the cell multiplexing barcode and alignment to the mouse genome mm10 (GENCODE vM23/Ensembl 98, obtained from 10x Genomics) was performed with CellRanger (version 7.1.0,10x Genomics).Downstream analysis was performed in R with Seurat v5 (Hao et al., 2023).We first filtered cells by removing barcodes with ≤ 2500 detected genes and ≥15 % of reads aligned to mitochondrial genes, retaining between 1100 and 1700 cells per sample with median mRNA counts per cell between 23233 and 27890 in the different samples.mRNA counts for each gene were normalized by dividing its counts by the total number of counts per cell, multiplied by 10000.
Log1p transformation was applied before plotting expression data as violin plots.For downstream analysis and representation of gene expression as heatmaps, centering counts for each feature and scaling to its standard deviation was applied.Principal component analysis was performed on the 2000 most variable features in the relevant subset of cells.The resolution of the Louvain clustering algorithm was set to 0.05 when clustering multiple samples.In case of Jaccard-Index estimation the clustering resolution was set to 0.15 and the clustering was performed on each sample separately.100-fold repetition of this clustering approach with a random subset of the data with 70 % of the cells allowed the calculation of a Jaccard-index, as previously described (Tang et al., 2021).For annotation of the Epi-and PrEfate, the cells of the differentiated samples were integrated with Seurat integration based on the rpca reduction.Differentially expressed genes between cell states and genotypes were identified with the FindMarkers function in Seurat with a minimal expression difference in the log1p transformed expression values of 0.5.Biological noise was quantitated and distinguished from modeled technical noise in local neighborhoods of each cell with VarID2 (Rosales-Alvarez et al., 2023).

Data and code availability
Sequencing data from this paper has been deposited at GEO with accession number GSE253609.All code used for analysis and visualization, together with a list of the R packages used, is available from the authors upon request.lowermost 1% on day 9 after transduction (B), or sorted for the the lowermost 5% on day 9 after transduction (C).D -F RRA scores for genes corresponding to enriched gRNAs identified in A -C.
Enrichment of gene-targeting (dark blue) and control gRNAs (light blue) in cells sorted for the lowermost 1% of H2B-Venus signal 6 days after gRNA transduction, displayed as log2-fold change (B) or RRA score of corresponding genes (C). Green and gray background in (C) indicates FDR < 0.05 and < 0.2, respectively.D Hierarchical clustering of gene perturbations leading to reduced Spry4 H2B-Venus expression (FDR ≤ 0.05 in at least one condition or FDR ≤ 0.2 in at least two conditions).E Protein interaction network of genes shown in (D) based on String-DB.Background colors of genes and gene clusters were manually assigned based on classification by functional similarity.F, G H2B-Venus expression in Spry4 H2B-Venus/+ reporter cells upon knockout of selected candidate genes 6 d after transfection.(F) shows histograms of one representative experiment, (G) shows mean ± SEM of median H2B-Venus expression from N = 3 independent experiments.p < 0.05 for wild type vs. Elp3, Elp5, Fam98b, Ikbkap, Kti12, Med25 or Tada1 knockouts; p < 0.01 for wild type vs. Grb2, Med12, Med24, Ptpn11, Sox2 knockouts or PD03-treated cells (Benjamin-Hochberg-adjusted, one-sided, paired t-test).

Fig. 2 :
Fig. 2: Genome-wide CRISPR knockout screen reveals negative regulators of Sprouty4 expression.A RRA scores of genes corresponding to gRNAs enriched in cells sorted for the topmost 1% of H2B-Venus signal on day 6 after gRNA transduction.B Hierarchical clustering of gene perturbations leading to increased Spry4 H2B-Venus expression (FDR ≤ 0.05 in at least one condition or FDR ≤ 0.2 in at least two conditions).C Protein interaction network of genes shown in (B) based on String-DB.Background colors of genes and gene clusters were manually assigned based on classification by functional similarity.

Fig. 3 :
Fig. 3: Med12 affects gene expression independently of pluripotency related signaling systems.A Schematic of experiment to identify Med12-regulated genes by bulk RNA sequencing.B Principal component analysis transcriptomes from (A).C Euler-diagram showing the number differentially expressed genes (log2-fold change > |1|, adjusted p-value < 0.01) in bulk transcriptomes.Left panel compares genes differentially expressed upon 24 h of differentiation between Med12-mutant and wild-type cells, right panel compares genes differentially expressed upon loss of Med12 between N2B27 and 2i.

F
pathways.Tile color indicates relative pathway activity, tile size indicates spearman correlation of footprint genes with pathway-defining mutants.E Median H2B-Venus fluorescence upon mutation of mTOR related genes in Med12-mutant Spry4 H2B- Venus/+ cells, normalized to H2B-Venus expression in Med12 wild-type cells.Median H2B-Venus fluorescence upon mutation of mTOR related genes in Med12 wild-type cells is reproduced from Fig. 2E for comparison (light blue).Error-bars indicate SEM, points individual replicates, N ≥ 3 independent experiments.Schematic of experiment to test Med12-dependency of FGF target genes by RNA sequencing.G Principal component analysis transcriptomes from (F).PC2 (not shown; 12.8% of variance) separated experimental replicates from each other.H Gene expression changes and number of significantly differentially expressed genes (adjusted pvalue < 0.01) upon FGF4 stimulation in wild-type versus Med12-mutant cells.Dotted orange line indicates the unity line.

Fig. 4 :
Fig. 4: Mutation of Med12 affects the exit of pluripotency.A Experimental approach to determine clonogenicity of Med12-mutant and wild-type cells.2i + LIF was supplemented with FBS to support growth of Fgf4-mutant cells after reseeding.B Number of colonies after treatment as indicated in (A) for both wild-type and Med12-mutant cells in an Fgf4 wild-type (left) and Fgf4 mutant background with and without supplementation with 10 ng/µL FGF4 (right).N ≥ 4 independent experiments, same symbols in different colors indicate technical replicates within an independent experiment.ns indicates p ≥ 0.05, *** indicates p ≤ 0.001, paired Wilcoxon signed rank test.
N2B27 (dots) in wild-type versus Med12-mutant cells.Relative expression values shown as log2(TPM), error bars indicate standard deviation.E Distribution of downregulation slopes determined as in D for the 100 genes with the strongest negative fold-change in wild-type (left) and Med12-mutant cells (right).Bar indicates median, boxes indicate 25 th and 75 th percentile.

Fig. 5 :AF
Fig. 5: Transition between embryonic and extraembryonic identities is buffered against loss of Med12.A Schematic of experimental approach to model differentiation of mESCs towards epiblast and primitive endoderm via GATA induction.B Immunostaining for the Epi-marker NANOG (green) and the PrE marker SOX17 (magenta) after 8 h of GATA6 induction followed by 20 h of differentiation in wild-type and Med12-mutant cells.Scale bar: 50 µm.C Quantification of cell type proportions after differentiating wild-type and Med12-mutant cells as in (B).N = 3, n > 1100 cells per replicate, error bars indicate SEM.D Median Gata6-mCherry fluorescence upon 8h dox induction in three independent clonal GATA6-mCherry inducible cell lines 7 days after transfection with control or Med12-targeting gRNAs.* indicates p ≤ 0.05, paired student's t-test.E Quantification of Gata6-mCherry expression dynamics from time-lapse movies during induction and differentiation.Boxes indicate induction times (8 h for Med12 mutant, 4 h for wild type).Error bars indicate SD.One out of N = 5 replicates shown, n > 300 cells per time point.F Same experiment as in (E), but showing Gata6-mCherry fluorescence in single cells.Trace color indicates differentiation outcome determined by immunostaining (Epi: green; PrE: magenta).G Gata6-mCherry fluorescence in single cells 2 h after the end of induction, plotted separately for prospective Epi and PrE cells.H Predictive power of GATA6-mCherry expression determined as Area Under the Curve (AUC) from ROC-analysis.

Fig. 6 :F
Fig. 6: Role of Med12 in PrE differentiation.A Schematic of the single cell RNA sequencing experiment to compare single cell transcriptional signatures between wild-type and Med12-mutant cells in pluripotency and upon PrE differentiation.Replicates were generated by including two Med12 wild-type lines carrying inducible GATA4-or first determined the expression change of the naïve marker genes Nanog, Esrrb, Tbx3, Tfcp2l1, Klf4, Prdm14 and Zfp4 in Med12-mutant and wild-type cells, and then plotted the Euclidean distance of this expression change to that of the time-resolved dataset from Lackner et al., 2021.Signaling footprint analysis in Med12 mutants was performed similarly toLacker et al., 2021.

Fig. 2
Fig. 2 Supp 1: Robust enrichment of gRNAs and corresponding genes that negatively regulate Spry4:H2B-Venus expression.A -D Log 2-fold enrichment of gene-targeting (dark blue) and control gRNAs (light blue) in cells sorted for high H2B-Venus expression on different days as indicated.E -G RRA scores for genes corresponding to enriched gRNAs identified in B -D. RRA scores for genes corresponding to gRNAs enriched in the 1% of the cells with highest fluorescence after 6 days are shown in Fig. 2A.

Fig. 3 F
Fig. 3 Supp1: Generation of Med12 mutant cell lines.A Schematic of the Med12 gene locus and the gRNAs used to create a Med12 loss-of-function by deleting part of exon7.B Immunoblotting of cell lysates from several monoclonal Med12 mutant lines generated in different genetic genetic backgrounds, stained for MED12 and Tubulin.C Spry4 H2B-Venus/+ Expression upon release from 2i + LIF to N2B27 in wild-type and Med12-mutant cells measured by flow cytometry.Data points show median fluorescence in each experiment.N = 3. D H2B-Venus expression in live wild-type and Med12-mutant Spry4 H2B-Venus/+ cells after 24 h of growth in N2B27 following release from 2i + LIF.E Immunoblotting of cell lysates from Med12 wild type and Med12-mutant Spry4 H2B-Venus/+ and iGata6 mESCs, stained for Tubulin, total-and phopsho-ERK.F Quantification of phospho-ERK signals from immunoblots, normalized to total-ERK.N=3.

Fig. 5 A
Fig. 5 Supp 1: Lower GATA6-mCherry induction levels limit PrE differentiation in Med12 mutant cells.A Immunostaining of the Epi-marker NANOG (green) and the PrE marker SOX17 (magenta) after 8 h of GATA6 induction and 20 h of differentiation with and without exogenous FGF4 in wild-type and Med12-mutant cells.Scale bar: 100 µm.B Quantification of cell type proportions after differentiating wild-type and Med12-mutant cells as in (A).N=3, n > 1100 cells per replicate, error bars indicate SEM.C Gata-mCherry fluorescence after 8 h of dox induction.Left shows distribution of expression levels in the whole population, right shows expression levels after flow sorting of cells with similar fluorescence intensity.Dashed lines indicate sorting gate.D Immunostaining of the Epi-marker NANOG (green) and the PrE marker SOX17 (magenta) after 8 h of GATA6 induction, flow sorting as described in (C), reseeding and 20 h of differentiation with and without exogenous FGF4 in wild-type and Med12-mutant cells.Scale bar: 100 µm.E Quantification of cell type proportions after differentiating wild-type and Med12-mutant cells as in (D).N=2, n > 500 cells per replicate.

Fig. 6
Fig. 6 Supp 1: A and B Expression change of each gene upon differentiation from pluripotency (2iL) to Epi (A) and PrE (B) in wild-type versus Med12-mutant cells.Dotted, orange line indicates the unity line.C Differentially expressed genes between Med12 wild type and mutant cells for the three different cell states.Tile color shows scaled average gene expression, colored boxes indicate the 10 genes with the largest fold-change between Med12 wild type and mutant cells in each cell state.