Additional file 1 of Identifying quantitatively differential chromosomal compartmentalization changes and their biological significance from Hi-C data using DARIC

Additional file 1: Figure 1. Introduction of the DARIC framework. A Scatter plot showing the high correlation between PIS and PC1 values from the H1ESC Hi-C data. B MA plot showing the systematic differences between H1ESC and K562 cells. Each dot represents a 50kb bin. The Red dashed line represents the fitted line from the M and A values. C MA plot after normalization showing the elimination of the systematic differences between the two cell types. D-E The emission matrix (D) and state coverage matrix (E) for the 5-state HMM model. F Confusion matrix showing the overlap between the states of 5-state model and those of the 4-state model. Numbers represent 50kb bins. Figure 2. Functional association between gene regulation and differential compartments revealed by DARIC. A-B Heatmap showing the enrichment of cell type-specific genes (A) and superenhancers. (B) in the four states identified by DARIC. Values show the log2(observed/expected) enrichment. C Bar plots showing the expression of SOX2 and MYB genes in H1ESC and K562 cells. Figure 3. Comparison between DARIC and existing methods. A Venn diagram presenting the overlap between the ‘Strong-’ state revealed by DARIC and the ‘AB’ state in conventional analyses. The numbers in the plot represent the numbers of 50kb bins. B Violin plot showing the PIS differences for the three types of domains defined in (A). C-D Violin plots showing the comparisons of Lamina1-DamID signal changes (C), and gene expression fold changes (D) in the three types of domains defined in (A). E Venn diagram showing the overlap of genomic bins identified with decreased PIS/PC1 values in K562 by DARIC and dcHiC. Numbers of 50kb bins were shown in the diagram. F Enrichment of H1ESC-specific genes for the three types of genomic regions defined in (E). G An exemplary region showing DARIC and dcHiC output with decreased PIS in K562 cells. H-K Performance comparison between DARIC and HOMER using H1ESC versus K562 as an example. (H) Venn diagram showing the overlap of genomic bins identified with increased PIS values in K562 by DARIC and HOMER. Numbers of 50kb bins were shown in the diagram. (I) Enrichment of K562-specific genes for the three types of genomic regions defined in (H). (J) Venn diagram showing the overlap of genomic bins identified with decreased PIS values in K562 by DARIC and HOMER. Numbers of 50kb bins were shown in the diagram. (K) Enrichment of H1ESC-specific genes for the three types of genomic regions defined in (J). Figure 4. DARIC is robust to technical variations in Hi-C data, such as choices of restriction enzymes and sequencing depth. A Snapshot of chromosome 6 showing the comparison in scaling differences in PIS from three different restriction enzymes before and after the normalization step performed by DARIC. B Snapshot of chromosome 6 showing the high similarity of PIS from Hi-C data at different sequencing depths. Figure 5. Applying DARIC to delineating compartment changes during cardiomyocyte differentiation. A Emission matrix resulting from the HMM model trained in the cardiomyocyte system. B Cardiomyocyte-specific genes associated with significant PIS increases during the differentiation tend to be involved in longer loops than those without PIS increases. C GO enrichment analysis for two sets of cardiomyocyte-specific genes classified by whether associated with significant PIS changes. Figure 6. Applying DARIC to a compendium of Hi-C datasets across many cell types. A Distribution of TSA-seq signals in the five variability states in the three cell lines. B Distribution of DamID signals in K562 cells. C Stacked bar plots showing the composition percentages of the five sub-compartments in the five variability states. D PIS variability comparison for the five sub-compartments

A Cell-Permeant Nanobody-Based Degrader That Induces Fetal Hemoglobin

Proximity-based strategies to degrade proteins have enormous therapeutic potential in medicine, but the technologies are limited to proteins for which small molecule ligands exist. The identification of such ligands for therapeutically relevant but “undruggable” proteins remains challenging. Herein, we employed yeast surface display of synthetic nanobodies to identify a protein ligand selective for BCL11A, a critical repressor of fetal globin gene transcription. Fusion of the nanobody to a cell-permeant miniature protein and an E3 adaptor creates a degrader that depletes cellular BCL11A in differentiated primary erythroid precursor cells, thereby inducing the expression of fetal hemoglobin, a modifier of clinical severity of sickle cell disease and β-thalassemia. Our strategy provides a means of fetal hemoglobin induction through reversible, temporal modulation of BCL11A. Additionally, it establishes a new paradigm for the targeted degradation of previously intractable proteins

A Cell-Permeant Nanobody-Based Degrader That Induces Fetal Hemoglobin

Proximity-based strategies to degrade proteins have enormous therapeutic potential in medicine, but the technologies are limited to proteins for which small molecule ligands exist. The identification of such ligands for therapeutically relevant but “undruggable” proteins remains challenging. Herein, we employed yeast surface display of synthetic nanobodies to identify a protein ligand selective for BCL11A, a critical repressor of fetal globin gene transcription. Fusion of the nanobody to a cell-permeant miniature protein and an E3 adaptor creates a degrader that depletes cellular BCL11A in differentiated primary erythroid precursor cells, thereby inducing the expression of fetal hemoglobin, a modifier of clinical severity of sickle cell disease and β-thalassemia. Our strategy provides a means of fetal hemoglobin induction through reversible, temporal modulation of BCL11A. Additionally, it establishes a new paradigm for the targeted degradation of previously intractable proteins