sam mouse embryonic stem cells escs  (Addgene inc)

Xue et al. (2013) . (C) Number of cells expressing a unique sgRNA (blue), two sgRNAs (dark gray), more than two sgRNAs (light gray), or none (pink) in each of the three transduction replicates. The number of cells assigned to a unique sgRNA in each replicate is displayed. (D) Genes ranked by the loadings of PC1 (left) and PC2 (right), highlighting in red previously know ZGA genes (as described in ; see also for gene loading values). (E) PC analysis displaying a scatterplot of the first two PCs (PC1 versus PC2) with cells colored by the expression of the ZGA markers Zscan4c , Zscan4d , Gm8300, and Tmem92 . Marginal distributions of PC1 and PC2 values are displayed as rug plots along the respective axis. (F) Box-whisker plots showing normalized expression levels (log 2 reads per million; RPM) for the top 50 loadings for PC1 (gray) and PC2 (light blue) during preimplantation development (data analyzed from Deng et al., 2014 ) (see for gene loadings). As expected in serum-grown ESCs, PC1 loadings peak at blastocyst stages whereas PC2 loadings peak at mid-to-late two-cell embryo stages, identifying this component as ZGA-like. " title="... (see for gene loadings). As expected in serum-grown ESCs, PC1 loadings peak at blastocyst stages whereas PC2 ..." property="contentUrl" width="100%" height="100%"/>
Figure Legend Snippet: A CRISPRa Screen for ZGA-like Regulators at Single-Cell Resolution (A) Schematic overview of the single-cell CRISPRa screen, highlighting the selection of candidates, lentiviral transduction strategy, and generation of 10x Genomics 3′ scRNA-seq libraries and barcoded sgRNA amplicon libraries. (B) Dot-plot showing normalized expression levels (log 2 reads per million; RPM) of the screening candidates in oocytes, zygotes, two-cell, and four-cell embryos. Data analyzed from Xue et al. (2013) . (C) Number of cells expressing a unique sgRNA (blue), two sgRNAs (dark gray), more than two sgRNAs (light gray), or none (pink) in each of the three transduction replicates. The number of cells assigned to a unique sgRNA in each replicate is displayed. (D) Genes ranked by the loadings of PC1 (left) and PC2 (right), highlighting in red previously know ZGA genes (as described in ; see also for gene loading values). (E) PC analysis displaying a scatterplot of the first two PCs (PC1 versus PC2) with cells colored by the expression of the ZGA markers Zscan4c , Zscan4d , Gm8300, and Tmem92 . Marginal distributions of PC1 and PC2 values are displayed as rug plots along the respective axis. (F) Box-whisker plots showing normalized expression levels (log 2 reads per million; RPM) for the top 50 loadings for PC1 (gray) and PC2 (light blue) during preimplantation development (data analyzed from Deng et al., 2014 ) (see for gene loadings). As expected in serum-grown ESCs, PC1 loadings peak at blastocyst stages whereas PC2 loadings peak at mid-to-late two-cell embryo stages, identifying this component as ZGA-like.

Techniques Used: Selection, Transduction, Amplification, Expressing, Whisker Assay

Deng et al., 2014 ). Differential gene expression was calculated with EdgeR (FDR < 0.05). The number of analyzed genes in each case is depicted in brackets. (F) Representative single optical slices of zygotes immunostained for PATZ1, DPPA2, and SMARCA5, showing single channels and composites with DAPI. Scale bars represent 25 μm. " title="... control, consisting of cDNA-eGFP transient transfections into mouse ESCs followed by eGFP + fluorescence-activated cell sorting (FACS) ..." property="contentUrl" width="100%" height="100%"/>
Figure Legend Snippet: Patz1 , Dppa2 , and Smarca5 Are Potent Inducers of ZGA-like Transcription (A) Schematic representation of a complementary validation approach for Patz1 , Dppa2 , and Smarca5 , using Carhsp1 as a negative control, consisting of cDNA-eGFP transient transfections into mouse ESCs followed by eGFP + fluorescence-activated cell sorting (FACS) and bulk polyA-capture RNA-seq. (B) Heatmap showing normalized gene expression, scaled per gene, of Patz1 , Dppa2 , Smarca5 , and Carhsp1 in bulk RNA-seq libraries after cDNA overexpression of these genes, compared with an eGFP-only transfection. (C) Heatmap showing normalized gene expression, scaled per gene, of the top 50 gene loadings for MOFA+ factor 3 (ZGA-like factor) in bulk RNA-seq libraries for Patz1 , Dppa2 , Smarca5, and Carhsp1 cDNA overexpression. The control is a eGFP-only transfection. (D) Box-whisker plots showing expression of the MERVL repeat family in percentage of total reads measured by bulk RNA-seq after cDNA overexpression of Patz1 (green), Dppa2 (orange), Smarca5 (purple), and Carhsp1 (gray). The control is an eGFP-only transfection (gray). Each dot represents a biological replicate. Statistically significant differences to eGFP-only control are reported as ∗∗∗∗ p value < 0.0001, ∗∗∗ p value < 0.001, ns (non-significant): p value > 0.05; Mann-Whitney two-tailed test. (E) Box-whisker plots showing normalized expression levels (log 2 reads per million; RPM) of differentially upregulated genes by both arrayed CRISPRa and cDNA overexpression of Patz1 (green), Dppa2 (orange), and Smarca5 (purple) as well as a random set of expressed genes (gray) during preimplantation development (data analyzed from Deng et al., 2014 ). Differential gene expression was calculated with EdgeR (FDR < 0.05). The number of analyzed genes in each case is depicted in brackets. (F) Representative single optical slices of zygotes immunostained for PATZ1, DPPA2, and SMARCA5, showing single channels and composites with DAPI. Scale bars represent 25 μm.

Techniques Used: Biomarker Discovery, Negative Control, Transfection, Fluorescence, FACS, RNA Sequencing, Gene Expression, Over Expression, Control, Whisker Assay, Expressing, MANN-WHITNEY, Two Tailed Test

Xue et al., 2013 ). Data are shown as mean plus standard deviation of biological replicates. (B) Representative single optical slices of zygotes (top row) and two-cell stage embryos (bottom row) immunostained for DPPA2 and SMARCA5, showing single channels and composites. Scale bars represent 20 μm. (C) Box-plots showing Pearson correlation coefficients calculated for co-localization of DPPA2 and SMARCA5 in the pronuclei of 10 zygotes and in the nuclei of 10 two-cell stage embryos. Co-localization values in the two pronuclei in zygotes and nuclei of each blastomere in two-cell embryos were measured separately. DPPA2 and SMARCA5 co-localize in two-cell embryos but not in zygotes ( ∗∗∗∗ p value < 0.0001, Mann-Whitney two-tailed test). (D) Heatmap showing normalized expression, scaled per gene, of downregulated ZGA genes in Smarca5 KO mouse ESCs compared to WT (EdgeR, FDR < 0.05), in WT ESCs, Smarca5 KO ESCs, and Smarca5 KO ESCs expressing a Smarca5 WT protein or a Smarca5 catalytically dead mutant protein (Mut) (data analyzed from Barisic et al., 2019 ). (E and F) Analysis of relative expression levels of ZGA-like transcripts by quantitative reverse transcription PCR in (E) WT and Smarca5 KO mouse ESCs after 48-h transient transfection of eGFP or Dppa2 -eGFP and (F) WT and Dppa2 KO mouse ESCs after 48-h transient transfection of eGFP or Smarca5 -eGFP. eGFP + cells were FACS-sorted before gene expression analysis. Relative expression levels are normalized to WT cells transfected with eGFP and sorted for eGFP + . Data are shown as mean plus standard deviation of three biological replicates. Statistically significant differences to WT GFP + control are reported ( ∗∗ p value < 0.01, ∗∗∗ p value < 0.001, ∗∗∗∗ p value < 0.0001; absence of stars (non-significant): p value > 0.05; homoscedastic two-tailed t test). " title="... of downregulated ZGA genes in Smarca5 KO mouse ESCs compared to WT (EdgeR, FDR < 0.05), in ..." property="contentUrl" width="100%" height="100%"/>
Figure Legend Snippet: Smarca5 Requires Dppa2 to Induce ZGA-like Transcription (A) Normalized expression levels (log 2 reads per kilobase per million; RPKM) of Dppa2 (orange, triangles) and Smarca5 (purple, squares) in oocytes and preimplantation development (data analyzed from Xue et al., 2013 ). Data are shown as mean plus standard deviation of biological replicates. (B) Representative single optical slices of zygotes (top row) and two-cell stage embryos (bottom row) immunostained for DPPA2 and SMARCA5, showing single channels and composites. Scale bars represent 20 μm. (C) Box-plots showing Pearson correlation coefficients calculated for co-localization of DPPA2 and SMARCA5 in the pronuclei of 10 zygotes and in the nuclei of 10 two-cell stage embryos. Co-localization values in the two pronuclei in zygotes and nuclei of each blastomere in two-cell embryos were measured separately. DPPA2 and SMARCA5 co-localize in two-cell embryos but not in zygotes ( ∗∗∗∗ p value < 0.0001, Mann-Whitney two-tailed test). (D) Heatmap showing normalized expression, scaled per gene, of downregulated ZGA genes in Smarca5 KO mouse ESCs compared to WT (EdgeR, FDR < 0.05), in WT ESCs, Smarca5 KO ESCs, and Smarca5 KO ESCs expressing a Smarca5 WT protein or a Smarca5 catalytically dead mutant protein (Mut) (data analyzed from Barisic et al., 2019 ). (E and F) Analysis of relative expression levels of ZGA-like transcripts by quantitative reverse transcription PCR in (E) WT and Smarca5 KO mouse ESCs after 48-h transient transfection of eGFP or Dppa2 -eGFP and (F) WT and Dppa2 KO mouse ESCs after 48-h transient transfection of eGFP or Smarca5 -eGFP. eGFP + cells were FACS-sorted before gene expression analysis. Relative expression levels are normalized to WT cells transfected with eGFP and sorted for eGFP + . Data are shown as mean plus standard deviation of three biological replicates. Statistically significant differences to WT GFP + control are reported ( ∗∗ p value < 0.01, ∗∗∗ p value < 0.001, ∗∗∗∗ p value < 0.0001; absence of stars (non-significant): p value > 0.05; homoscedastic two-tailed t test).

Techniques Used: Expressing, Standard Deviation, MANN-WHITNEY, Two Tailed Test, Mutagenesis, Reverse Transcription, Transfection, Gene Expression, Control

Figure Legend Snippet:

Techniques Used: Virus, Clone Assay, Recombinant, Transfection, Multiplex Assay, Sequencing, Over Expression, Amplification, Plasmid Preparation, Software