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egfp coding sequence  (Addgene inc)


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    Addgene inc egfp coding sequence
    Egfp Coding Sequence, supplied by Addgene inc, used in various techniques. Bioz Stars score: 93/100, based on 6 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Azenta egfp coding sequences
    a , HEK293T cells cotransfected with ChREBPα, MLX, ChoRE-luciferase and the indicated genetic constructs were assessed for ChoRE-luciferase activity. The data show that Lb NOX depresses, Ec STH increases and GPD1 greatly increases ChoRE-luciferase activity. Significant differences between were calculated by applying one-way analysis of variance and Dunnett’s multiple comparisons test in which *** P < 0.0005 and **** P < 0.0001. Numbers of biological replicates were: <t>eGFP</t> ( n = 11); GK ( n = 4); GPD1 ( n = 3); GAPDH ( n = 5); Lb NOX ( n = 3); and Ec STH ( n = 3). b – d , Relative levels of 137 metabolites were determined by LC–MS; by plotting ChoRE-luciferase activity against each metabolite, we show that G3P is highly correlated (CC of 0.96) with ChREBPα-dependent ChoRE-luciferase activity ( b ), whereas G6P ( c ) and GA3P ( d ) are not. P values tested the hypothesis of a non-zero slope. e , HEK293T cells cotransfected <t>with</t> <t>ChREBPβ</t> or eGFP plus ChoRE-luciferase were assessed for ChoRE-luciferase activity. Numbers of biological replicates were: eGFP ( n = 3); ChREBPβ ( n = 3); unpaired t -test (two-tailed), P = 0.0248. f , The HEK293T cells cotransfected with ChREBPβ with MLX or eGFP plus ChoRE-luciferase were assessed for ChoRE-luciferase activity. The numbers of biological replicates were: eGFP ( n = 3); MLX ( n = 3); unpaired t -test (two-tailed), P = 0.0009. g , HEK293T cells cotransfected with MLX plus ChoRE-luciferase plus either ChREBPα or ChREBPβ were assessed for ChoRE-luciferase activity. Numbers of biological replicates were: ChREBPα ( n = 3): ChREBP β ( n = 3); unpaired t -test (two-tailed), P = 0.0178. h , i , Finally, HEK293T cells cotransfected with ChREBPβ, MLX and ChoRE-luciferase plus were assayed for ChoRE-luciferase activity with either eGFP or Lb NOX ( h ) or Ec STH ( i ). There were three biological replicates for each sample. For h , the unpaired t -test (two-tailed): P = 0.7680. For i , the unpaired t -test (two-tailed): P = 0.5023. The data in e and i show that ChREBPβ has significant ChoRE-luciferase activity in HEK293T cells, e , that is, further boosted by MLX transfection, f . g , The data show that the ChoRE-luciferase activity of ChREBPβ-MLX exceeds that of ChREBPα-MLX. However, in contrast to the ability of Lb NOX to depress and Ec STH to increase ChoRE-luciferase activity of ChREBPα-MLX, a , ChREBPβ-MLX was not regulated by either Lb NOX, h , or Ec STH, i , thereby mapping the modulation of ChREBPα to the N-terminal GSM domain. Significant differences were calculated with unpaired t -tests in which * P < 0.05 and *** P < 0.0005. The error bars represent means ± s.e.m. n.s., not significant.
    Egfp Coding Sequences, supplied by Azenta, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc egfp coding sequence
    a , HEK293T cells cotransfected with ChREBPα, MLX, ChoRE-luciferase and the indicated genetic constructs were assessed for ChoRE-luciferase activity. The data show that Lb NOX depresses, Ec STH increases and GPD1 greatly increases ChoRE-luciferase activity. Significant differences between were calculated by applying one-way analysis of variance and Dunnett’s multiple comparisons test in which *** P < 0.0005 and **** P < 0.0001. Numbers of biological replicates were: <t>eGFP</t> ( n = 11); GK ( n = 4); GPD1 ( n = 3); GAPDH ( n = 5); Lb NOX ( n = 3); and Ec STH ( n = 3). b – d , Relative levels of 137 metabolites were determined by LC–MS; by plotting ChoRE-luciferase activity against each metabolite, we show that G3P is highly correlated (CC of 0.96) with ChREBPα-dependent ChoRE-luciferase activity ( b ), whereas G6P ( c ) and GA3P ( d ) are not. P values tested the hypothesis of a non-zero slope. e , HEK293T cells cotransfected <t>with</t> <t>ChREBPβ</t> or eGFP plus ChoRE-luciferase were assessed for ChoRE-luciferase activity. Numbers of biological replicates were: eGFP ( n = 3); ChREBPβ ( n = 3); unpaired t -test (two-tailed), P = 0.0248. f , The HEK293T cells cotransfected with ChREBPβ with MLX or eGFP plus ChoRE-luciferase were assessed for ChoRE-luciferase activity. The numbers of biological replicates were: eGFP ( n = 3); MLX ( n = 3); unpaired t -test (two-tailed), P = 0.0009. g , HEK293T cells cotransfected with MLX plus ChoRE-luciferase plus either ChREBPα or ChREBPβ were assessed for ChoRE-luciferase activity. Numbers of biological replicates were: ChREBPα ( n = 3): ChREBP β ( n = 3); unpaired t -test (two-tailed), P = 0.0178. h , i , Finally, HEK293T cells cotransfected with ChREBPβ, MLX and ChoRE-luciferase plus were assayed for ChoRE-luciferase activity with either eGFP or Lb NOX ( h ) or Ec STH ( i ). There were three biological replicates for each sample. For h , the unpaired t -test (two-tailed): P = 0.7680. For i , the unpaired t -test (two-tailed): P = 0.5023. The data in e and i show that ChREBPβ has significant ChoRE-luciferase activity in HEK293T cells, e , that is, further boosted by MLX transfection, f . g , The data show that the ChoRE-luciferase activity of ChREBPβ-MLX exceeds that of ChREBPα-MLX. However, in contrast to the ability of Lb NOX to depress and Ec STH to increase ChoRE-luciferase activity of ChREBPα-MLX, a , ChREBPβ-MLX was not regulated by either Lb NOX, h , or Ec STH, i , thereby mapping the modulation of ChREBPα to the N-terminal GSM domain. Significant differences were calculated with unpaired t -tests in which * P < 0.05 and *** P < 0.0005. The error bars represent means ± s.e.m. n.s., not significant.
    Egfp Coding Sequence, supplied by Addgene inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc egfp coding sequence cds
    ( a ) Schematic of the model at the cellular level. In a confluent tissue, cells rearrange by trading places with their neighbors. To trade places with their neighbors, cells must let go of their adhesions with their initial neighbors, migrate, and then form new adhesions with their new neighbors. ( b ) Schematic of the model’s physical interpretation of tissue fluidity. Tissue fluidity is defined as the neighbor exchange rate in the tissue. The adhesions between cells resist cell movement and act to reduce tissue fluidity. The forces of random cell migration provide the energy for cells to move and therefore increase tissue fluidity. Overall, it is the competition between motility and adhesion which determines the tissue fluidity. ( c ) Schematic of the model’s mathematical interpretation of tissue fluidity. The tissue fluidity, or the rate at which any cell pair swap places in the tissue, is determined by an Arrhenius relationship. The adhesion energy is represented as the energy barrier needed to be overcome to swap places. The random motility is represented as an effective temperature (more specifically, an effective thermal energy), which provides the energy to overcome this energy barrier. The amplitude A is defined as the fluidity of the tissue in the absence of adhesion, which is modeled as the diffusion-limited rate of random motility A = kBTM / γD 2 . A increases with the motility energy, kBTM , and decreases with both the coefficient of viscosity experienced by cells migrating through the tissue, γ , and the distance between cell centers, D , which must be traversed in order for cells to exchange places. Tissue fluidity decreases with the adhesion strength and increases with the motility energy. ( d ) Time-lapse montage of an example simulation initialized with two cell types randomly mixed in equal proportion, under the case of equal homotypic adhesion between cells of the same cell type and no heterotypic adhesion. Top: Color represents a unique identifier for each cell, determined by that cell’s initial position. The mixing of cells in space is indicated by the increasing rearrangement of colors over time. Bottom: Color represents each cell’s cell type, where sorting can be visualized by the formation of spatial domains of the same cell type. ( e ) For the simulation shown in (d), the degree of sorting is plotted as a function of time, using two different metrics of sorting. The left-hand y-axis represents the fraction of each cell’s four nearest neighbors that are the same cell type as that cell, averaged across all cells in the tissue. The right-hand axis represents the average width of domains of the same cell type in the tissue (see Methods ). ( f ) The tissue fluidity (i.e., the rate of neighbor exchange averaged over the tissue) over time (see Methods ). ( g ) Time-lapse montage of the experimental cell-sorting assay ( top ) and its associated best-fit simulation ( bottom ). Top: L929 cells co-expressing either Cdh3 (P-cadherin) and <t>EGFP</t> (green) or Cdh1 (E-cadherin) and mRFP (magenta) were mixed in equal proportions and imaged by confocal time-lapse microscopy. Images represent maximum intensity projections. Bottom: Best-fit simulations displayed as a heat map of cell type. Best fit parameters are listed in the inset legend. ( h-i ) Mean (h) and standard deviation (i) of the same-cell-type domain size over time for the experiments (red dots) and for 5 replicate best-fit simulations (grey lines).
    Egfp Coding Sequence Cds, supplied by Addgene inc, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    VectorBuilder GmbH egfp coding sequences
    ( a ) Schematic of the model at the cellular level. In a confluent tissue, cells rearrange by trading places with their neighbors. To trade places with their neighbors, cells must let go of their adhesions with their initial neighbors, migrate, and then form new adhesions with their new neighbors. ( b ) Schematic of the model’s physical interpretation of tissue fluidity. Tissue fluidity is defined as the neighbor exchange rate in the tissue. The adhesions between cells resist cell movement and act to reduce tissue fluidity. The forces of random cell migration provide the energy for cells to move and therefore increase tissue fluidity. Overall, it is the competition between motility and adhesion which determines the tissue fluidity. ( c ) Schematic of the model’s mathematical interpretation of tissue fluidity. The tissue fluidity, or the rate at which any cell pair swap places in the tissue, is determined by an Arrhenius relationship. The adhesion energy is represented as the energy barrier needed to be overcome to swap places. The random motility is represented as an effective temperature (more specifically, an effective thermal energy), which provides the energy to overcome this energy barrier. The amplitude A is defined as the fluidity of the tissue in the absence of adhesion, which is modeled as the diffusion-limited rate of random motility A = kBTM / γD 2 . A increases with the motility energy, kBTM , and decreases with both the coefficient of viscosity experienced by cells migrating through the tissue, γ , and the distance between cell centers, D , which must be traversed in order for cells to exchange places. Tissue fluidity decreases with the adhesion strength and increases with the motility energy. ( d ) Time-lapse montage of an example simulation initialized with two cell types randomly mixed in equal proportion, under the case of equal homotypic adhesion between cells of the same cell type and no heterotypic adhesion. Top: Color represents a unique identifier for each cell, determined by that cell’s initial position. The mixing of cells in space is indicated by the increasing rearrangement of colors over time. Bottom: Color represents each cell’s cell type, where sorting can be visualized by the formation of spatial domains of the same cell type. ( e ) For the simulation shown in (d), the degree of sorting is plotted as a function of time, using two different metrics of sorting. The left-hand y-axis represents the fraction of each cell’s four nearest neighbors that are the same cell type as that cell, averaged across all cells in the tissue. The right-hand axis represents the average width of domains of the same cell type in the tissue (see Methods ). ( f ) The tissue fluidity (i.e., the rate of neighbor exchange averaged over the tissue) over time (see Methods ). ( g ) Time-lapse montage of the experimental cell-sorting assay ( top ) and its associated best-fit simulation ( bottom ). Top: L929 cells co-expressing either Cdh3 (P-cadherin) and <t>EGFP</t> (green) or Cdh1 (E-cadherin) and mRFP (magenta) were mixed in equal proportions and imaged by confocal time-lapse microscopy. Images represent maximum intensity projections. Bottom: Best-fit simulations displayed as a heat map of cell type. Best fit parameters are listed in the inset legend. ( h-i ) Mean (h) and standard deviation (i) of the same-cell-type domain size over time for the experiments (red dots) and for 5 replicate best-fit simulations (grey lines).
    Egfp Coding Sequences, supplied by VectorBuilder GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    92
    Addgene inc mouse znf263 coding sequence
    (A) Venn diagram showing RAD21, CTCF, and PATZ1 binding in HEK293 cells. (B) Heat maps of RAD21, CTCF, PATZ1, MAZ, and other zinc finger proteins, <t>ZNF263,</t> ZNF341 and ZNF467 in HEK293 cells. ChIP-seq read density was grouped as Cluster 1, Cluster 2, and Cluster 3 based on the indicated overlaps with RAD21 signal within a 4 kb window. (C) Western blot analysis of RAD21, FLAG, and CTCF upon FLAG-PATZ1 immunoprecipitation from mESCs (n=2, see for biological replicate). (D) Visualization of Hi-C contact matrices for a zoomed-in region around the TBC1D1 locus in HepG2 cells. Shown below are loops with PATZ1 at both anchors in HepG2 cells, ChIP-seq read densities for RAD21, CTCF, PATZ1, and MAZ, and gene annotations. ChIP-seq data in HepG2 cells is from two combined biological replicates. (E) Percentage of Hi-C loops in HepG2 cells overlapping with RAD21, CTCF, and PATZ1 ChIP-seq peaks. (F) Heat maps of RAD21, PATZ1, ZNF263, ZNF341, and ZNF467 in HEK293 cells. ChIP-seq read density was grouped as Cluster 1, Cluster 2, Cluster 3, and Cluster 4 based on the combinatorial overlaps of zinc finger proteins with RAD21 within a 4 kb window in HEK293 cells. The model on the right side indicates combinatorial binding of the indicated factors in each cluster (see ). ChIP-seq data in HEK293 cells is from one replicate for RAD21 and one representative of two biological replicates for others (see for datasets).
    Mouse Znf263 Coding Sequence, supplied by Addgene inc, used in various techniques. Bioz Stars score: 92/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    90
    Addgene inc cas13d coding sequence
    ( A ) Schematic of the one-vector CRISPR/Cas13b/d system construct (top) and the EGFP reporter construct (bottom). ( B ) Schematic of type VI-d (left) and VI-b (right) crRNA structures with the target RNA. The crRNAs carry a direct repeat sequence (blue) to facilitate the binding with their corresponding Cas13 enzyme, and a spacer sequence (red) specific for the target RNA, r(GGGGCC) n (purple). ( C and D ) The knockdown efficiency test in HEK293 cells via cotransfection of the <t>CRISPR/Cas13d</t> vector ( C ), or the CRISPR/Cas13b vector ( D ), and the reporter construct. Immunoblotting of EGFP showed the knockdown efficiency for different guide RNAs (gRNAs). Data are presented as means ± SD of 3 independent experiments and were analyzed with ordinary 1-way ANOVA with Dunnett’s multiple-comparison test. * P < 0.05, ** P < 0.01, *** P < 0.001.
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    a , HEK293T cells cotransfected with ChREBPα, MLX, ChoRE-luciferase and the indicated genetic constructs were assessed for ChoRE-luciferase activity. The data show that Lb NOX depresses, Ec STH increases and GPD1 greatly increases ChoRE-luciferase activity. Significant differences between were calculated by applying one-way analysis of variance and Dunnett’s multiple comparisons test in which *** P < 0.0005 and **** P < 0.0001. Numbers of biological replicates were: eGFP ( n = 11); GK ( n = 4); GPD1 ( n = 3); GAPDH ( n = 5); Lb NOX ( n = 3); and Ec STH ( n = 3). b – d , Relative levels of 137 metabolites were determined by LC–MS; by plotting ChoRE-luciferase activity against each metabolite, we show that G3P is highly correlated (CC of 0.96) with ChREBPα-dependent ChoRE-luciferase activity ( b ), whereas G6P ( c ) and GA3P ( d ) are not. P values tested the hypothesis of a non-zero slope. e , HEK293T cells cotransfected with ChREBPβ or eGFP plus ChoRE-luciferase were assessed for ChoRE-luciferase activity. Numbers of biological replicates were: eGFP ( n = 3); ChREBPβ ( n = 3); unpaired t -test (two-tailed), P = 0.0248. f , The HEK293T cells cotransfected with ChREBPβ with MLX or eGFP plus ChoRE-luciferase were assessed for ChoRE-luciferase activity. The numbers of biological replicates were: eGFP ( n = 3); MLX ( n = 3); unpaired t -test (two-tailed), P = 0.0009. g , HEK293T cells cotransfected with MLX plus ChoRE-luciferase plus either ChREBPα or ChREBPβ were assessed for ChoRE-luciferase activity. Numbers of biological replicates were: ChREBPα ( n = 3): ChREBP β ( n = 3); unpaired t -test (two-tailed), P = 0.0178. h , i , Finally, HEK293T cells cotransfected with ChREBPβ, MLX and ChoRE-luciferase plus were assayed for ChoRE-luciferase activity with either eGFP or Lb NOX ( h ) or Ec STH ( i ). There were three biological replicates for each sample. For h , the unpaired t -test (two-tailed): P = 0.7680. For i , the unpaired t -test (two-tailed): P = 0.5023. The data in e and i show that ChREBPβ has significant ChoRE-luciferase activity in HEK293T cells, e , that is, further boosted by MLX transfection, f . g , The data show that the ChoRE-luciferase activity of ChREBPβ-MLX exceeds that of ChREBPα-MLX. However, in contrast to the ability of Lb NOX to depress and Ec STH to increase ChoRE-luciferase activity of ChREBPα-MLX, a , ChREBPβ-MLX was not regulated by either Lb NOX, h , or Ec STH, i , thereby mapping the modulation of ChREBPα to the N-terminal GSM domain. Significant differences were calculated with unpaired t -tests in which * P < 0.05 and *** P < 0.0005. The error bars represent means ± s.e.m. n.s., not significant.

    Journal: Nature Metabolism

    Article Title: Glycerol-3-phosphate activates ChREBP, FGF21 transcription and lipogenesis in citrin deficiency

    doi: 10.1038/s42255-025-01399-3

    Figure Lengend Snippet: a , HEK293T cells cotransfected with ChREBPα, MLX, ChoRE-luciferase and the indicated genetic constructs were assessed for ChoRE-luciferase activity. The data show that Lb NOX depresses, Ec STH increases and GPD1 greatly increases ChoRE-luciferase activity. Significant differences between were calculated by applying one-way analysis of variance and Dunnett’s multiple comparisons test in which *** P < 0.0005 and **** P < 0.0001. Numbers of biological replicates were: eGFP ( n = 11); GK ( n = 4); GPD1 ( n = 3); GAPDH ( n = 5); Lb NOX ( n = 3); and Ec STH ( n = 3). b – d , Relative levels of 137 metabolites were determined by LC–MS; by plotting ChoRE-luciferase activity against each metabolite, we show that G3P is highly correlated (CC of 0.96) with ChREBPα-dependent ChoRE-luciferase activity ( b ), whereas G6P ( c ) and GA3P ( d ) are not. P values tested the hypothesis of a non-zero slope. e , HEK293T cells cotransfected with ChREBPβ or eGFP plus ChoRE-luciferase were assessed for ChoRE-luciferase activity. Numbers of biological replicates were: eGFP ( n = 3); ChREBPβ ( n = 3); unpaired t -test (two-tailed), P = 0.0248. f , The HEK293T cells cotransfected with ChREBPβ with MLX or eGFP plus ChoRE-luciferase were assessed for ChoRE-luciferase activity. The numbers of biological replicates were: eGFP ( n = 3); MLX ( n = 3); unpaired t -test (two-tailed), P = 0.0009. g , HEK293T cells cotransfected with MLX plus ChoRE-luciferase plus either ChREBPα or ChREBPβ were assessed for ChoRE-luciferase activity. Numbers of biological replicates were: ChREBPα ( n = 3): ChREBP β ( n = 3); unpaired t -test (two-tailed), P = 0.0178. h , i , Finally, HEK293T cells cotransfected with ChREBPβ, MLX and ChoRE-luciferase plus were assayed for ChoRE-luciferase activity with either eGFP or Lb NOX ( h ) or Ec STH ( i ). There were three biological replicates for each sample. For h , the unpaired t -test (two-tailed): P = 0.7680. For i , the unpaired t -test (two-tailed): P = 0.5023. The data in e and i show that ChREBPβ has significant ChoRE-luciferase activity in HEK293T cells, e , that is, further boosted by MLX transfection, f . g , The data show that the ChoRE-luciferase activity of ChREBPβ-MLX exceeds that of ChREBPα-MLX. However, in contrast to the ability of Lb NOX to depress and Ec STH to increase ChoRE-luciferase activity of ChREBPα-MLX, a , ChREBPβ-MLX was not regulated by either Lb NOX, h , or Ec STH, i , thereby mapping the modulation of ChREBPα to the N-terminal GSM domain. Significant differences were calculated with unpaired t -tests in which * P < 0.05 and *** P < 0.0005. The error bars represent means ± s.e.m. n.s., not significant.

    Article Snippet: Human ChREBPα (accession number NM_032951.3 ), human ChREBPβ (accession number XM_047420437.1 ), human MLX (accession number NM_170607.3 ) and eGFP coding sequences were synthesized by Genewiz and cloned into pcDNA3.1 vectors (Invitrogen).

    Techniques: Luciferase, Construct, Activity Assay, Liquid Chromatography with Mass Spectroscopy, Two Tailed Test, Transfection

    ( a ) Schematic of the model at the cellular level. In a confluent tissue, cells rearrange by trading places with their neighbors. To trade places with their neighbors, cells must let go of their adhesions with their initial neighbors, migrate, and then form new adhesions with their new neighbors. ( b ) Schematic of the model’s physical interpretation of tissue fluidity. Tissue fluidity is defined as the neighbor exchange rate in the tissue. The adhesions between cells resist cell movement and act to reduce tissue fluidity. The forces of random cell migration provide the energy for cells to move and therefore increase tissue fluidity. Overall, it is the competition between motility and adhesion which determines the tissue fluidity. ( c ) Schematic of the model’s mathematical interpretation of tissue fluidity. The tissue fluidity, or the rate at which any cell pair swap places in the tissue, is determined by an Arrhenius relationship. The adhesion energy is represented as the energy barrier needed to be overcome to swap places. The random motility is represented as an effective temperature (more specifically, an effective thermal energy), which provides the energy to overcome this energy barrier. The amplitude A is defined as the fluidity of the tissue in the absence of adhesion, which is modeled as the diffusion-limited rate of random motility A = kBTM / γD 2 . A increases with the motility energy, kBTM , and decreases with both the coefficient of viscosity experienced by cells migrating through the tissue, γ , and the distance between cell centers, D , which must be traversed in order for cells to exchange places. Tissue fluidity decreases with the adhesion strength and increases with the motility energy. ( d ) Time-lapse montage of an example simulation initialized with two cell types randomly mixed in equal proportion, under the case of equal homotypic adhesion between cells of the same cell type and no heterotypic adhesion. Top: Color represents a unique identifier for each cell, determined by that cell’s initial position. The mixing of cells in space is indicated by the increasing rearrangement of colors over time. Bottom: Color represents each cell’s cell type, where sorting can be visualized by the formation of spatial domains of the same cell type. ( e ) For the simulation shown in (d), the degree of sorting is plotted as a function of time, using two different metrics of sorting. The left-hand y-axis represents the fraction of each cell’s four nearest neighbors that are the same cell type as that cell, averaged across all cells in the tissue. The right-hand axis represents the average width of domains of the same cell type in the tissue (see Methods ). ( f ) The tissue fluidity (i.e., the rate of neighbor exchange averaged over the tissue) over time (see Methods ). ( g ) Time-lapse montage of the experimental cell-sorting assay ( top ) and its associated best-fit simulation ( bottom ). Top: L929 cells co-expressing either Cdh3 (P-cadherin) and EGFP (green) or Cdh1 (E-cadherin) and mRFP (magenta) were mixed in equal proportions and imaged by confocal time-lapse microscopy. Images represent maximum intensity projections. Bottom: Best-fit simulations displayed as a heat map of cell type. Best fit parameters are listed in the inset legend. ( h-i ) Mean (h) and standard deviation (i) of the same-cell-type domain size over time for the experiments (red dots) and for 5 replicate best-fit simulations (grey lines).

    Journal: bioRxiv

    Article Title: Tissue Fluidity: A Double-Edged Sword for Multicellular Patterning

    doi: 10.1101/2025.03.01.640992

    Figure Lengend Snippet: ( a ) Schematic of the model at the cellular level. In a confluent tissue, cells rearrange by trading places with their neighbors. To trade places with their neighbors, cells must let go of their adhesions with their initial neighbors, migrate, and then form new adhesions with their new neighbors. ( b ) Schematic of the model’s physical interpretation of tissue fluidity. Tissue fluidity is defined as the neighbor exchange rate in the tissue. The adhesions between cells resist cell movement and act to reduce tissue fluidity. The forces of random cell migration provide the energy for cells to move and therefore increase tissue fluidity. Overall, it is the competition between motility and adhesion which determines the tissue fluidity. ( c ) Schematic of the model’s mathematical interpretation of tissue fluidity. The tissue fluidity, or the rate at which any cell pair swap places in the tissue, is determined by an Arrhenius relationship. The adhesion energy is represented as the energy barrier needed to be overcome to swap places. The random motility is represented as an effective temperature (more specifically, an effective thermal energy), which provides the energy to overcome this energy barrier. The amplitude A is defined as the fluidity of the tissue in the absence of adhesion, which is modeled as the diffusion-limited rate of random motility A = kBTM / γD 2 . A increases with the motility energy, kBTM , and decreases with both the coefficient of viscosity experienced by cells migrating through the tissue, γ , and the distance between cell centers, D , which must be traversed in order for cells to exchange places. Tissue fluidity decreases with the adhesion strength and increases with the motility energy. ( d ) Time-lapse montage of an example simulation initialized with two cell types randomly mixed in equal proportion, under the case of equal homotypic adhesion between cells of the same cell type and no heterotypic adhesion. Top: Color represents a unique identifier for each cell, determined by that cell’s initial position. The mixing of cells in space is indicated by the increasing rearrangement of colors over time. Bottom: Color represents each cell’s cell type, where sorting can be visualized by the formation of spatial domains of the same cell type. ( e ) For the simulation shown in (d), the degree of sorting is plotted as a function of time, using two different metrics of sorting. The left-hand y-axis represents the fraction of each cell’s four nearest neighbors that are the same cell type as that cell, averaged across all cells in the tissue. The right-hand axis represents the average width of domains of the same cell type in the tissue (see Methods ). ( f ) The tissue fluidity (i.e., the rate of neighbor exchange averaged over the tissue) over time (see Methods ). ( g ) Time-lapse montage of the experimental cell-sorting assay ( top ) and its associated best-fit simulation ( bottom ). Top: L929 cells co-expressing either Cdh3 (P-cadherin) and EGFP (green) or Cdh1 (E-cadherin) and mRFP (magenta) were mixed in equal proportions and imaged by confocal time-lapse microscopy. Images represent maximum intensity projections. Bottom: Best-fit simulations displayed as a heat map of cell type. Best fit parameters are listed in the inset legend. ( h-i ) Mean (h) and standard deviation (i) of the same-cell-type domain size over time for the experiments (red dots) and for 5 replicate best-fit simulations (grey lines).

    Article Snippet: This resultant plasmid (pLJM-EGFP_v2) was then used to generate a pLJM1-mRFP plasmid by replacing the EGFP coding sequence (CDS) with the mRFP CDS, obtained by PCR-amplification from the pcDNA-mRFP plasmid (Addgene; plasmid #13032).

    Techniques: Migration, Diffusion-based Assay, Viscosity, FACS, Expressing, Time-lapse Microscopy, Standard Deviation

    ( a ) L929 cells co-expressing either high or low levels of Cdh2 (N-cadherin) or Cdh3 (P-cadherin) and EGFP (green) were mixed in equal proportions with cells co-expressing either high or low levels of Cdh1 (E-cadherin) and mRFP (magenta) and imaged by confocal time-lapse microscopy. From the maximum intensity projection of the first timepoint for each condition, the median fluorescence intensity across the entire image was calculated for both the EGFP (open circles) and mRFP (closed circles) channels. The color indicates the experimental condition, and the two cell populations mixed in a single condition are grouped together along the x-axis. The x-label indicates which cell population corresponds to each channel within a given condition. The markers indicate different experimental replicates of a given condition. There were four replicates in each condition.

    Journal: bioRxiv

    Article Title: Tissue Fluidity: A Double-Edged Sword for Multicellular Patterning

    doi: 10.1101/2025.03.01.640992

    Figure Lengend Snippet: ( a ) L929 cells co-expressing either high or low levels of Cdh2 (N-cadherin) or Cdh3 (P-cadherin) and EGFP (green) were mixed in equal proportions with cells co-expressing either high or low levels of Cdh1 (E-cadherin) and mRFP (magenta) and imaged by confocal time-lapse microscopy. From the maximum intensity projection of the first timepoint for each condition, the median fluorescence intensity across the entire image was calculated for both the EGFP (open circles) and mRFP (closed circles) channels. The color indicates the experimental condition, and the two cell populations mixed in a single condition are grouped together along the x-axis. The x-label indicates which cell population corresponds to each channel within a given condition. The markers indicate different experimental replicates of a given condition. There were four replicates in each condition.

    Article Snippet: This resultant plasmid (pLJM-EGFP_v2) was then used to generate a pLJM1-mRFP plasmid by replacing the EGFP coding sequence (CDS) with the mRFP CDS, obtained by PCR-amplification from the pcDNA-mRFP plasmid (Addgene; plasmid #13032).

    Techniques: Expressing, Time-lapse Microscopy, Fluorescence

    ( a-b ) The average (a) and coefficient of variation (b) of the domain size are plotted as a function of time for each of the 4 different experimental conditions. Light markers: Replicates. Dark Markers: Average across 4 replicates. Sorting can only occur in a very narrow window where the adhesion and motility energies are nearly equal. ( c ) Representative images of each experimental cell-sorting assay ( top ) and its associated representative best-fit simulation ( bottom ) after 18 hours. Top: L929 cells co-expressing either high or low levels of Cdh2 (N-cadherin) or Cdh3 (P-cadherin) and EGFP (green) were mixed in equal proportions with cells co-expressing either high or low levels of Cdh1 (E-cadherin) and mRFP (magenta) and imaged by confocal time-lapse microscopy. Images represent maximum intensity projections. Bottom: Best-fit simulations with displayed as a heat map of cell type. Best fit parameters are listed in the inset legend. ( d ) The best fit simulation parameters for each experimental dataset are plotted on top of a heatmap of the average domain size for all simulation parameters tested. Markers with a white outline represent the mean across the 4 replicates for each condition. ( e ) The best fit adhesion energy is plotted as a function of the best fit motility energy for each experimental dataset. The narrow sorting window is plotted exactly as in .

    Journal: bioRxiv

    Article Title: Tissue Fluidity: A Double-Edged Sword for Multicellular Patterning

    doi: 10.1101/2025.03.01.640992

    Figure Lengend Snippet: ( a-b ) The average (a) and coefficient of variation (b) of the domain size are plotted as a function of time for each of the 4 different experimental conditions. Light markers: Replicates. Dark Markers: Average across 4 replicates. Sorting can only occur in a very narrow window where the adhesion and motility energies are nearly equal. ( c ) Representative images of each experimental cell-sorting assay ( top ) and its associated representative best-fit simulation ( bottom ) after 18 hours. Top: L929 cells co-expressing either high or low levels of Cdh2 (N-cadherin) or Cdh3 (P-cadherin) and EGFP (green) were mixed in equal proportions with cells co-expressing either high or low levels of Cdh1 (E-cadherin) and mRFP (magenta) and imaged by confocal time-lapse microscopy. Images represent maximum intensity projections. Bottom: Best-fit simulations with displayed as a heat map of cell type. Best fit parameters are listed in the inset legend. ( d ) The best fit simulation parameters for each experimental dataset are plotted on top of a heatmap of the average domain size for all simulation parameters tested. Markers with a white outline represent the mean across the 4 replicates for each condition. ( e ) The best fit adhesion energy is plotted as a function of the best fit motility energy for each experimental dataset. The narrow sorting window is plotted exactly as in .

    Article Snippet: This resultant plasmid (pLJM-EGFP_v2) was then used to generate a pLJM1-mRFP plasmid by replacing the EGFP coding sequence (CDS) with the mRFP CDS, obtained by PCR-amplification from the pcDNA-mRFP plasmid (Addgene; plasmid #13032).

    Techniques: FACS, Expressing, Time-lapse Microscopy

    ( a,d,h,j ) Time-lapse montage of the experimental cell-sorting assay ( top ) and its associated best-fit simulation ( bottom ). Top: L929 cells co-expressing either high or low levels of Cdh2 (N-cadherin) or Cdh3 (P-cadherin) and EGFP (green) were mixed in equal proportions with cells co-expressing either high or low levels of Cdh1 (E-cadherin) and mRFP (magenta) and imaged by confocal time-lapse microscopy. Images represent maximum intensity projections. Bottom: Best-fit simulations with displayed as a heat map of cell type. Best fit parameters are listed in the inset legend. ( b,c,e,f,h,i,k,l ) Mean ( b,e,h,k ) and standard deviation ( c,f,i,l ) of the same-cell-type domain size over time for the experiments (red dots) and for 5 replicate best-fit simulations (grey lines). ( a-c ) Cdh2–EGFP high cells mixed with Cdh1–mRFP high cells, ( d-f ) Cdh2–EGFP low cells mixed with Cdh1 – mRFP low cells, (g -i ) Cdh3–EGFP high cells mixed with Cdh1 - mRFP high cells, (j -l ) Cdh3 – EGFP low cells mixed with Cdh1 – mRFP low cells

    Journal: bioRxiv

    Article Title: Tissue Fluidity: A Double-Edged Sword for Multicellular Patterning

    doi: 10.1101/2025.03.01.640992

    Figure Lengend Snippet: ( a,d,h,j ) Time-lapse montage of the experimental cell-sorting assay ( top ) and its associated best-fit simulation ( bottom ). Top: L929 cells co-expressing either high or low levels of Cdh2 (N-cadherin) or Cdh3 (P-cadherin) and EGFP (green) were mixed in equal proportions with cells co-expressing either high or low levels of Cdh1 (E-cadherin) and mRFP (magenta) and imaged by confocal time-lapse microscopy. Images represent maximum intensity projections. Bottom: Best-fit simulations with displayed as a heat map of cell type. Best fit parameters are listed in the inset legend. ( b,c,e,f,h,i,k,l ) Mean ( b,e,h,k ) and standard deviation ( c,f,i,l ) of the same-cell-type domain size over time for the experiments (red dots) and for 5 replicate best-fit simulations (grey lines). ( a-c ) Cdh2–EGFP high cells mixed with Cdh1–mRFP high cells, ( d-f ) Cdh2–EGFP low cells mixed with Cdh1 – mRFP low cells, (g -i ) Cdh3–EGFP high cells mixed with Cdh1 - mRFP high cells, (j -l ) Cdh3 – EGFP low cells mixed with Cdh1 – mRFP low cells

    Article Snippet: This resultant plasmid (pLJM-EGFP_v2) was then used to generate a pLJM1-mRFP plasmid by replacing the EGFP coding sequence (CDS) with the mRFP CDS, obtained by PCR-amplification from the pcDNA-mRFP plasmid (Addgene; plasmid #13032).

    Techniques: FACS, Expressing, Time-lapse Microscopy, Standard Deviation

    (A) Venn diagram showing RAD21, CTCF, and PATZ1 binding in HEK293 cells. (B) Heat maps of RAD21, CTCF, PATZ1, MAZ, and other zinc finger proteins, ZNF263, ZNF341 and ZNF467 in HEK293 cells. ChIP-seq read density was grouped as Cluster 1, Cluster 2, and Cluster 3 based on the indicated overlaps with RAD21 signal within a 4 kb window. (C) Western blot analysis of RAD21, FLAG, and CTCF upon FLAG-PATZ1 immunoprecipitation from mESCs (n=2, see for biological replicate). (D) Visualization of Hi-C contact matrices for a zoomed-in region around the TBC1D1 locus in HepG2 cells. Shown below are loops with PATZ1 at both anchors in HepG2 cells, ChIP-seq read densities for RAD21, CTCF, PATZ1, and MAZ, and gene annotations. ChIP-seq data in HepG2 cells is from two combined biological replicates. (E) Percentage of Hi-C loops in HepG2 cells overlapping with RAD21, CTCF, and PATZ1 ChIP-seq peaks. (F) Heat maps of RAD21, PATZ1, ZNF263, ZNF341, and ZNF467 in HEK293 cells. ChIP-seq read density was grouped as Cluster 1, Cluster 2, Cluster 3, and Cluster 4 based on the combinatorial overlaps of zinc finger proteins with RAD21 within a 4 kb window in HEK293 cells. The model on the right side indicates combinatorial binding of the indicated factors in each cluster (see ). ChIP-seq data in HEK293 cells is from one replicate for RAD21 and one representative of two biological replicates for others (see for datasets).

    Journal: Molecular cell

    Article Title: Members of an array of zinc finger proteins specify distinct Hox chromatin boundaries

    doi: 10.1016/j.molcel.2024.08.007

    Figure Lengend Snippet: (A) Venn diagram showing RAD21, CTCF, and PATZ1 binding in HEK293 cells. (B) Heat maps of RAD21, CTCF, PATZ1, MAZ, and other zinc finger proteins, ZNF263, ZNF341 and ZNF467 in HEK293 cells. ChIP-seq read density was grouped as Cluster 1, Cluster 2, and Cluster 3 based on the indicated overlaps with RAD21 signal within a 4 kb window. (C) Western blot analysis of RAD21, FLAG, and CTCF upon FLAG-PATZ1 immunoprecipitation from mESCs (n=2, see for biological replicate). (D) Visualization of Hi-C contact matrices for a zoomed-in region around the TBC1D1 locus in HepG2 cells. Shown below are loops with PATZ1 at both anchors in HepG2 cells, ChIP-seq read densities for RAD21, CTCF, PATZ1, and MAZ, and gene annotations. ChIP-seq data in HepG2 cells is from two combined biological replicates. (E) Percentage of Hi-C loops in HepG2 cells overlapping with RAD21, CTCF, and PATZ1 ChIP-seq peaks. (F) Heat maps of RAD21, PATZ1, ZNF263, ZNF341, and ZNF467 in HEK293 cells. ChIP-seq read density was grouped as Cluster 1, Cluster 2, Cluster 3, and Cluster 4 based on the combinatorial overlaps of zinc finger proteins with RAD21 within a 4 kb window in HEK293 cells. The model on the right side indicates combinatorial binding of the indicated factors in each cluster (see ). ChIP-seq data in HEK293 cells is from one replicate for RAD21 and one representative of two biological replicates for others (see for datasets).

    Article Snippet: The mouse Znf263 coding sequence (NM_148924.3) with an HA-tag sequence fused to its 5’ end was cloned into a PiggyBac vector (pPB-CAG-FLAG-HA, a modified version of pPB-CAG-3xFLAG-empty-pgk-hph from Addgene, #48754) to create pPB-CAG-FLAG-HA-ZNF263 plasmid via Gibson assembly (NEB, #E2611).

    Techniques: Binding Assay, ChIP-sequencing, Western Blot, Immunoprecipitation, Hi-C

    (A) Normalized ChIP-seq densities for RAD21, CTCF, and PATZ1, and ZNF263 in WT, Patz1 KO, and Znf263 KO mESCs at the indicated regions in the HoxA cluster. ChIP-seq data represents one representative replicate of two biological replicates for RAD21, CTCF, and one replicate for FH-PATZ1 and FH-ZNF263. (B-D) RT-qPCR analysis for the indicated Hox genes in (B) the HoxA , (C) the HoxC , and (D) the HoxD clusters in WT and Patz1 KO cervical MNs. RT-qPCR signal was normalized to Atp5f1 and ActB levels. The fold-change in expression was calculated relative to WT MNs. All RT-qPCR results are represented as mean values and error bars indicating log 2 (SE) across three biological replicates (two-sided Student’s t -test without multiple testing correction; *** P ≤ 0.001, ** P ≤ 0.01, * P < 0.05). (E) Differentially expressed genes by RNA-seq upon Patz1 KO in MNs from two biological replicates (see all in ). (F) GO analysis showing the top biological processes enriched in the differentially expressed genes in Patz1 KO versus WT MNs. PANTHER overrepresentation test tools were used for GO analysis and top 15 categories having a fold enrichment > 2.5 were plotted (see all in ). (G-I) RT-qPCR analysis for the indicated Hox genes in (G) the HoxA , (H) the HoxC , and (I) the HoxD clusters in WT and Znf263 KO cervical MNs. RT-qPCR signal was normalized to Atp5f1 and Gapdh levels. The fold-change in expression was calculated relative to WT MNs. All RT-qPCR results are represented as mean values and error bars indicating log 2 (SE) across three technical replicates. Two independent Znf263 KO clones are shown (see ).

    Journal: Molecular cell

    Article Title: Members of an array of zinc finger proteins specify distinct Hox chromatin boundaries

    doi: 10.1016/j.molcel.2024.08.007

    Figure Lengend Snippet: (A) Normalized ChIP-seq densities for RAD21, CTCF, and PATZ1, and ZNF263 in WT, Patz1 KO, and Znf263 KO mESCs at the indicated regions in the HoxA cluster. ChIP-seq data represents one representative replicate of two biological replicates for RAD21, CTCF, and one replicate for FH-PATZ1 and FH-ZNF263. (B-D) RT-qPCR analysis for the indicated Hox genes in (B) the HoxA , (C) the HoxC , and (D) the HoxD clusters in WT and Patz1 KO cervical MNs. RT-qPCR signal was normalized to Atp5f1 and ActB levels. The fold-change in expression was calculated relative to WT MNs. All RT-qPCR results are represented as mean values and error bars indicating log 2 (SE) across three biological replicates (two-sided Student’s t -test without multiple testing correction; *** P ≤ 0.001, ** P ≤ 0.01, * P < 0.05). (E) Differentially expressed genes by RNA-seq upon Patz1 KO in MNs from two biological replicates (see all in ). (F) GO analysis showing the top biological processes enriched in the differentially expressed genes in Patz1 KO versus WT MNs. PANTHER overrepresentation test tools were used for GO analysis and top 15 categories having a fold enrichment > 2.5 were plotted (see all in ). (G-I) RT-qPCR analysis for the indicated Hox genes in (G) the HoxA , (H) the HoxC , and (I) the HoxD clusters in WT and Znf263 KO cervical MNs. RT-qPCR signal was normalized to Atp5f1 and Gapdh levels. The fold-change in expression was calculated relative to WT MNs. All RT-qPCR results are represented as mean values and error bars indicating log 2 (SE) across three technical replicates. Two independent Znf263 KO clones are shown (see ).

    Article Snippet: The mouse Znf263 coding sequence (NM_148924.3) with an HA-tag sequence fused to its 5’ end was cloned into a PiggyBac vector (pPB-CAG-FLAG-HA, a modified version of pPB-CAG-3xFLAG-empty-pgk-hph from Addgene, #48754) to create pPB-CAG-FLAG-HA-ZNF263 plasmid via Gibson assembly (NEB, #E2611).

    Techniques: ChIP-sequencing, Quantitative RT-PCR, Expressing, RNA Sequencing, Clone Assay

    Key Resources Table

    Journal: Molecular cell

    Article Title: Members of an array of zinc finger proteins specify distinct Hox chromatin boundaries

    doi: 10.1016/j.molcel.2024.08.007

    Figure Lengend Snippet: Key Resources Table

    Article Snippet: The mouse Znf263 coding sequence (NM_148924.3) with an HA-tag sequence fused to its 5’ end was cloned into a PiggyBac vector (pPB-CAG-FLAG-HA, a modified version of pPB-CAG-3xFLAG-empty-pgk-hph from Addgene, #48754) to create pPB-CAG-FLAG-HA-ZNF263 plasmid via Gibson assembly (NEB, #E2611).

    Techniques: Virus, Recombinant, Magnetic Beads, SYBR Green Assay, Western Blot, Cloning, Plasmid Preparation, Software, Injection

    ( A ) Schematic of the one-vector CRISPR/Cas13b/d system construct (top) and the EGFP reporter construct (bottom). ( B ) Schematic of type VI-d (left) and VI-b (right) crRNA structures with the target RNA. The crRNAs carry a direct repeat sequence (blue) to facilitate the binding with their corresponding Cas13 enzyme, and a spacer sequence (red) specific for the target RNA, r(GGGGCC) n (purple). ( C and D ) The knockdown efficiency test in HEK293 cells via cotransfection of the CRISPR/Cas13d vector ( C ), or the CRISPR/Cas13b vector ( D ), and the reporter construct. Immunoblotting of EGFP showed the knockdown efficiency for different guide RNAs (gRNAs). Data are presented as means ± SD of 3 independent experiments and were analyzed with ordinary 1-way ANOVA with Dunnett’s multiple-comparison test. * P < 0.05, ** P < 0.01, *** P < 0.001.

    Journal: The Journal of Clinical Investigation

    Article Title: CRISPR/Cas13d targeting suppresses repeat-associated non-AUG translation of C9orf72 hexanucleotide repeat RNA

    doi: 10.1172/JCI179016

    Figure Lengend Snippet: ( A ) Schematic of the one-vector CRISPR/Cas13b/d system construct (top) and the EGFP reporter construct (bottom). ( B ) Schematic of type VI-d (left) and VI-b (right) crRNA structures with the target RNA. The crRNAs carry a direct repeat sequence (blue) to facilitate the binding with their corresponding Cas13 enzyme, and a spacer sequence (red) specific for the target RNA, r(GGGGCC) n (purple). ( C and D ) The knockdown efficiency test in HEK293 cells via cotransfection of the CRISPR/Cas13d vector ( C ), or the CRISPR/Cas13b vector ( D ), and the reporter construct. Immunoblotting of EGFP showed the knockdown efficiency for different guide RNAs (gRNAs). Data are presented as means ± SD of 3 independent experiments and were analyzed with ordinary 1-way ANOVA with Dunnett’s multiple-comparison test. * P < 0.05, ** P < 0.01, *** P < 0.001.

    Article Snippet: Cas13d coding sequence was amplified from a plasmid (Addgene, 109049) using a forward primer introducing an NdeI site (TACCACATATGATCGAAAAAAAAAAGTCCTTCGCCAA) and a reverse primer introducing a BamHI site (TTGCAGGATCCTTAGGAATTGCCGGACACCTTCTTTTTCTC).

    Techniques: Plasmid Preparation, CRISPR, Construct, Sequencing, Binding Assay, Knockdown, Cotransfection, Western Blot, Comparison

    ( A ) Schematic of the inducible luciferase-based C9orf72 RAN translation reporter system in HeLa Flp-In cells. ( B ) The reporter cells stably expressing Cas13d and gRNA S24 and S30 showed lower signals of NanoLuc and firefly luciferase signal from the (GGGGCC)70-containing reporter transcripts. The significant reduction of the NanoLuc luciferase signal relative to the firefly luciferase signal demonstrates that the repeat-associated RAN translation is inhibited by Cas13d-mediated S24 or S30 treatment. ( C ) The control cells harboring the reporter without the G4C2 repeat showed no effect of the Cas13d gRNAs on the translation of the reporters. ( D ) Immunoblot analysis of C9orf72 protein showed that the C9orf72 protein level was unaffected in HeLa RAN translation reporter cell lines stably expressing Cas13d-NT30, Cas13d-S24, and Cs13d-S30. ( E ) Transient cotransfection of CRISPR/Cas13d constructs with either the GA-frame, GP-frame, or GR-frame or the No-G4C2-repeat control construct in HEK293 cells showed that Cas13d-S24 and Cas13d-S30 significantly reduced both NanoLuc and firefly luciferase signals from the GA-, GP-, and GR-frame but not the negative No-G4C2-repeat control reporter compared with the non-targeting control Cas13d-NT30. ( F ) Immunoblot analysis of C9orf72 protein showed that the C9orf72 protein level was unaffected in HEK293 cells cotransfected with CRISPR/Cas13d and either the GA-frame, GP-frame, or GR-frame or the No-G4C2-repeat control construct. Data are presented as means ± SD of 3 or 4 biological replicates as indicated by the number of dots in each graph, and were analyzed with ordinary 1-way ANOVA with Dunnett’s multiple-comparison test. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

    Journal: The Journal of Clinical Investigation

    Article Title: CRISPR/Cas13d targeting suppresses repeat-associated non-AUG translation of C9orf72 hexanucleotide repeat RNA

    doi: 10.1172/JCI179016

    Figure Lengend Snippet: ( A ) Schematic of the inducible luciferase-based C9orf72 RAN translation reporter system in HeLa Flp-In cells. ( B ) The reporter cells stably expressing Cas13d and gRNA S24 and S30 showed lower signals of NanoLuc and firefly luciferase signal from the (GGGGCC)70-containing reporter transcripts. The significant reduction of the NanoLuc luciferase signal relative to the firefly luciferase signal demonstrates that the repeat-associated RAN translation is inhibited by Cas13d-mediated S24 or S30 treatment. ( C ) The control cells harboring the reporter without the G4C2 repeat showed no effect of the Cas13d gRNAs on the translation of the reporters. ( D ) Immunoblot analysis of C9orf72 protein showed that the C9orf72 protein level was unaffected in HeLa RAN translation reporter cell lines stably expressing Cas13d-NT30, Cas13d-S24, and Cs13d-S30. ( E ) Transient cotransfection of CRISPR/Cas13d constructs with either the GA-frame, GP-frame, or GR-frame or the No-G4C2-repeat control construct in HEK293 cells showed that Cas13d-S24 and Cas13d-S30 significantly reduced both NanoLuc and firefly luciferase signals from the GA-, GP-, and GR-frame but not the negative No-G4C2-repeat control reporter compared with the non-targeting control Cas13d-NT30. ( F ) Immunoblot analysis of C9orf72 protein showed that the C9orf72 protein level was unaffected in HEK293 cells cotransfected with CRISPR/Cas13d and either the GA-frame, GP-frame, or GR-frame or the No-G4C2-repeat control construct. Data are presented as means ± SD of 3 or 4 biological replicates as indicated by the number of dots in each graph, and were analyzed with ordinary 1-way ANOVA with Dunnett’s multiple-comparison test. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

    Article Snippet: Cas13d coding sequence was amplified from a plasmid (Addgene, 109049) using a forward primer introducing an NdeI site (TACCACATATGATCGAAAAAAAAAAGTCCTTCGCCAA) and a reverse primer introducing a BamHI site (TTGCAGGATCCTTAGGAATTGCCGGACACCTTCTTTTTCTC).

    Techniques: Luciferase, Stable Transfection, Expressing, Control, Western Blot, Cotransfection, CRISPR, Construct, Comparison

    ( A ) Schematic of RAN translation product detection in human iPSCs stably expressing Cas13d and gRNA via lentivirus transduction. ( B ) ELISA quantification in multiple C9-ALS patient iPSC cell lines showed significant reduction of poly-GP and poly-GA levels by Cas13d-S24 and CRISPR/S30 compared with the non-targeting control Cas13d-NT30. ( C ) Quantification of relative RNA levels of Cas13d in the C9-ALS patient iPSC cell lines showed variable Cas13d levels among lines, while in each line there were no significant differences among the S24, S30, and non-targeting NT30 groups. ( D and E ) Linear regression and correlation analyses showed a strong positive correlation between Cas13d expression level and poly-GP ( D ) and poly-GA ( E ) knockdown efficiency among C9-ALS patient iPSC lines. Pearson’s correlation coefficients and 2-tailed P value were computed. ( F ) Schematic of poly-GP and poly-GA detection in iMNs derived from human C9-ALS patient iPSCs. ( G ) ELISA quantification in iMN lines derived from multiple iPSC cell lines showed significant reduction in poly-GP and poly-GA levels by Cas13d-S24 and CRISPR/S30 compared with the non-targeting control Cas13d-NT30. ( H ) Quantification of relative RNA levels of Cas13d in the C9-ALS patient iMN lines showed variable Cas13d levels among lines, while in each line there were no significant differences among the S24, S30, and non-targeting NT30 groups. ( I and J ) Linear regression and correlation analyses showed a strong positive correlation between Cas13d expression level and poly-GP ( I ) and poly-GA ( J ) knockdown efficiency among C9-ALS patient iMN lines. Pearson’s correlation coefficients and 2-tailed P value were computed. Data are presented as means ± SD of 2–4 biological replicates as indicated by the number of dots in each graph, and were analyzed with ordinary 1-way ANOVA with Dunnett’s multiple-comparison test. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

    Journal: The Journal of Clinical Investigation

    Article Title: CRISPR/Cas13d targeting suppresses repeat-associated non-AUG translation of C9orf72 hexanucleotide repeat RNA

    doi: 10.1172/JCI179016

    Figure Lengend Snippet: ( A ) Schematic of RAN translation product detection in human iPSCs stably expressing Cas13d and gRNA via lentivirus transduction. ( B ) ELISA quantification in multiple C9-ALS patient iPSC cell lines showed significant reduction of poly-GP and poly-GA levels by Cas13d-S24 and CRISPR/S30 compared with the non-targeting control Cas13d-NT30. ( C ) Quantification of relative RNA levels of Cas13d in the C9-ALS patient iPSC cell lines showed variable Cas13d levels among lines, while in each line there were no significant differences among the S24, S30, and non-targeting NT30 groups. ( D and E ) Linear regression and correlation analyses showed a strong positive correlation between Cas13d expression level and poly-GP ( D ) and poly-GA ( E ) knockdown efficiency among C9-ALS patient iPSC lines. Pearson’s correlation coefficients and 2-tailed P value were computed. ( F ) Schematic of poly-GP and poly-GA detection in iMNs derived from human C9-ALS patient iPSCs. ( G ) ELISA quantification in iMN lines derived from multiple iPSC cell lines showed significant reduction in poly-GP and poly-GA levels by Cas13d-S24 and CRISPR/S30 compared with the non-targeting control Cas13d-NT30. ( H ) Quantification of relative RNA levels of Cas13d in the C9-ALS patient iMN lines showed variable Cas13d levels among lines, while in each line there were no significant differences among the S24, S30, and non-targeting NT30 groups. ( I and J ) Linear regression and correlation analyses showed a strong positive correlation between Cas13d expression level and poly-GP ( I ) and poly-GA ( J ) knockdown efficiency among C9-ALS patient iMN lines. Pearson’s correlation coefficients and 2-tailed P value were computed. Data are presented as means ± SD of 2–4 biological replicates as indicated by the number of dots in each graph, and were analyzed with ordinary 1-way ANOVA with Dunnett’s multiple-comparison test. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

    Article Snippet: Cas13d coding sequence was amplified from a plasmid (Addgene, 109049) using a forward primer introducing an NdeI site (TACCACATATGATCGAAAAAAAAAAGTCCTTCGCCAA) and a reverse primer introducing a BamHI site (TTGCAGGATCCTTAGGAATTGCCGGACACCTTCTTTTTCTC).

    Techniques: Stable Transfection, Expressing, Transduction, Enzyme-linked Immunosorbent Assay, CRISPR, Control, Knockdown, Derivative Assay, Comparison

    ( A ) Purified Cas13d showed a nearly 100% degradation efficiency of the target RNA r(NT24) with gRNA NT24 but not the other gRNAs, confirming the specificity and high cleavage activity of the Cas13d system. ( B ) The target RNA r(GGGGCC)2 was too short to be degraded by Cas13d. ( C – E ) The purified Cas13d showed partial degradation of the target RNAs r(GGGGCC)5 ( C ), r(GGGGCC)8 ( D ), and r(GGGGCC)12 ( E ) with gRNA S24 or S30 but not the other gRNAs, indicating a compromised cleavage activity of Cas13d targeting GGGGCC repeat RNAs and a trend of decreased cleavage efficiency with increased repeat lengths. ( F ) Cas13d was unable to degrade r(GGGGCC)28, demonstrating the limited activity of Cas13d to target or cleave longer GGGGCC repeat RNAs. The cleavage assay was performed in a buffer containing 0.3 μM of gRNA, 0.6 μM of Cas13d protein, and 40 ng/μL of target RNA. Data are presented as means ± SD of 3 independent experiments and were analyzed with unpaired 2-tailed Student’s t test ( A ) and ordinary 1-way ANOVA with Dunnett’s multiple-comparison test ( B – F ). * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.001.

    Journal: The Journal of Clinical Investigation

    Article Title: CRISPR/Cas13d targeting suppresses repeat-associated non-AUG translation of C9orf72 hexanucleotide repeat RNA

    doi: 10.1172/JCI179016

    Figure Lengend Snippet: ( A ) Purified Cas13d showed a nearly 100% degradation efficiency of the target RNA r(NT24) with gRNA NT24 but not the other gRNAs, confirming the specificity and high cleavage activity of the Cas13d system. ( B ) The target RNA r(GGGGCC)2 was too short to be degraded by Cas13d. ( C – E ) The purified Cas13d showed partial degradation of the target RNAs r(GGGGCC)5 ( C ), r(GGGGCC)8 ( D ), and r(GGGGCC)12 ( E ) with gRNA S24 or S30 but not the other gRNAs, indicating a compromised cleavage activity of Cas13d targeting GGGGCC repeat RNAs and a trend of decreased cleavage efficiency with increased repeat lengths. ( F ) Cas13d was unable to degrade r(GGGGCC)28, demonstrating the limited activity of Cas13d to target or cleave longer GGGGCC repeat RNAs. The cleavage assay was performed in a buffer containing 0.3 μM of gRNA, 0.6 μM of Cas13d protein, and 40 ng/μL of target RNA. Data are presented as means ± SD of 3 independent experiments and were analyzed with unpaired 2-tailed Student’s t test ( A ) and ordinary 1-way ANOVA with Dunnett’s multiple-comparison test ( B – F ). * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.001.

    Article Snippet: Cas13d coding sequence was amplified from a plasmid (Addgene, 109049) using a forward primer introducing an NdeI site (TACCACATATGATCGAAAAAAAAAAGTCCTTCGCCAA) and a reverse primer introducing a BamHI site (TTGCAGGATCCTTAGGAATTGCCGGACACCTTCTTTTTCTC).

    Techniques: Purification, Activity Assay, Cleavage Assay, Comparison

    ( A ) Schematic of poly-GP and poly-GA detection in mice treated with AAV9 expressing Cas13d and gRNA. ( B and C ) Quantification showed decreased levels of poly-GP ( B ) or poly-GA ( C ) in C9-500 BAC mice but not in the WT mice, when the mice were treated with AAV9 expressing Cas13d-S24 or Cas13d-S30, compared with those treated with control AAV9 expressing the non-targeting Cas13d-NT30. The number of dots in each group indicates the number of mice in the corresponding group. Data are presented as means ± SD and were analyzed with unpaired 1-tailed Student’s t test. * P < 0.05, **** P < 0.0001.

    Journal: The Journal of Clinical Investigation

    Article Title: CRISPR/Cas13d targeting suppresses repeat-associated non-AUG translation of C9orf72 hexanucleotide repeat RNA

    doi: 10.1172/JCI179016

    Figure Lengend Snippet: ( A ) Schematic of poly-GP and poly-GA detection in mice treated with AAV9 expressing Cas13d and gRNA. ( B and C ) Quantification showed decreased levels of poly-GP ( B ) or poly-GA ( C ) in C9-500 BAC mice but not in the WT mice, when the mice were treated with AAV9 expressing Cas13d-S24 or Cas13d-S30, compared with those treated with control AAV9 expressing the non-targeting Cas13d-NT30. The number of dots in each group indicates the number of mice in the corresponding group. Data are presented as means ± SD and were analyzed with unpaired 1-tailed Student’s t test. * P < 0.05, **** P < 0.0001.

    Article Snippet: Cas13d coding sequence was amplified from a plasmid (Addgene, 109049) using a forward primer introducing an NdeI site (TACCACATATGATCGAAAAAAAAAAGTCCTTCGCCAA) and a reverse primer introducing a BamHI site (TTGCAGGATCCTTAGGAATTGCCGGACACCTTCTTTTTCTC).

    Techniques: Expressing, Control