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. 2022 Mar 28;2(3):100188.
doi: 10.1016/j.crmeth.2022.100188. Epub 2022 Mar 21.

Integrating transcription-factor abundance with chromatin accessibility in human erythroid lineage commitment

Reema Baskar  1   2   3 , Amy F Chen  4   3 , Patricia Favaro  1 , Warren Reynolds  1 , Fabian Mueller  4 , Luciene Borges  1 , Sizun Jiang  1 , Hyun Shin Park  5 , Eric T Kool  5   6 , William J Greenleaf  4   7   8 , Sean C Bendall  1   9
Affiliations

Integrating transcription-factor abundance with chromatin accessibility in human erythroid lineage commitment

Reema Baskar et al. Cell Rep Methods. .

Abstract

Master transcription factors (TFs) directly regulate present and future cell states by binding DNA regulatory elements and driving gene-expression programs. Their abundance influences epigenetic priming to different cell fates at the chromatin level, especially in the context of differentiation. In order to link TF protein abundance to changes in TF motif accessibility and open chromatin, we developed InTAC-seq, a method for simultaneous quantification of genome-wide chromatin accessibility and intracellular protein abundance in fixed cells. Our method produces high-quality data and is a cost-effective alternative to single-cell techniques. We showcase our method by purifying bone marrow (BM) progenitor cells based on GATA-1 protein levels and establish high GATA-1-expressing BM cells as both epigenetically and functionally similar to erythroid-committed progenitors.

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Conflict of interest statement

DECLARATION OF INTERESTS The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
InTAC-seq data from fixed cells is of comparable quality to ATAC-seq data from live cells and allows interrogation of chromatin accessibility associated with TF protein abundance (A) Overview of InTAC-seq experimental protocol. (B) Genome coverage of ATAC-seq data generated from live cells, fixed cells using InTAC-seq, or fixed cells using piATAC at the EBF1 locus in GM12878 cells. (C) Normalized Tn5 insertion profiles centered at transcription start sites (TSSs) for the indicated ATAC-seq libraries. (D) Scatterplot of estimated library size versus normalized TSS insertion score across all replicates of compared protocols. (E) Scatterplot of reads in consensus peaks averaged across replicates between InTAC-seq and live ATAC samples, with calculated Spearman correlation coefficient as shown. (F) FACS plot of forward scatter (linear scale) versus GATA-1 protein abundance (log10 scale) and the gating strategy to isolate the highest and lowest 15% of GATA-1-expressing K562 cells. (G) MA plot of log2 fold change in accessibility between GATA-1-high and GATA-1-low K562 populations versus log2 mean number of reads at all consensus peaks. Peaks with significant changes in accessibility are highlighted in red or blue. (H) Most significantly enriched TF motifs in differentially accessible peaks in GATA-1-high cells calculated using Fisher’s test. (I) Average accessibility of GATA-1 motif sites across all consensus ATAC-seq peaks binned by GATA-1 motif score. Accessibility is defined here as the area under the curve of a plot of bias-corrected, normalized Tn5 insertions centered at GATA-1 motif sites (as in Figure S1G), integrated from −50 to +50 bp and excluding the TF footprint from −10 to +10 bp. (J) Difference in GATA-1 motif accessibility between GATA-1 high and GATA-1 low samples normalized to the accessibility in the GATA-1 low population for each motif score bin.
Figure 2
Figure 2
GATA-1-high BM progenitors show strong priming for erythroid lineage (A) BM aspirate is ficolled and enriched for CD34+ cells before gating for CD34+/CD38+ cells and selecting GATA-1-high population (top ∼8%) and remaining GATA-1-expressing cells (denoted as GATA-1 mid/low) (bottom ∼87%). (B) InTAC-seq genome coverage plots at GATA1 and SPI1 loci for GATA-1-high and -mid/low BM progenitors. (C) Heatmap of chromVAR deviation scores across GATA-1 high and mid/low BM progenitors for top 50 most variable motifs. (D) MA plot of log2 fold change in accessibility between GATA-1 high and mid/low BM progenitors versus log2 mean number of reads in consensus peaks. Peaks with significant changes in accessibility are highlighted in red or blue. (E) Most significantly enriched TF motifs in differentially accessible peaks between GATA-1-high and -mid/low BM progenitors calculated using Fisher’s test. (F) (Top) Average accessibility of GATA-1 motif sites across all consensus ATAC-seq peaks binned by GATA-1 motif score. Accessibility is defined here as the area under the curve of a plot of bias-corrected, normalized Tn5 insertions centered at GATA-1 motif sites, integrated from −50 to +50 bp and excluding the TF footprint from −10 to +10 bp. (Bottom) Difference in GATA-1 motif accessibility between GATA-1 high and mid/low samples normalized to the accessibility in the GATA-1 mid/low population for each motif score bin. (G) UMAP of previously published and annotated BM scATAC dataset with Seurat clusters manually annotated as key BM populations. (H) Normalized GATA1 gene expression across BM progenitors in UMAP space (expression derived from scRNA-seq data integrated with scATAC-seq data). (I) Bulk BM progenitor InTAC-seq data simulated as scATAC counts and projected onto scATAC UMAP space.
Figure 3
Figure 3
High-dimensional, single-cell proteomic analysis of BM progenitors identifies TF and surface-marker trends in erythroid commitment (A) BM aspirate is ficolled and enriched for CD34+ cells before staining with antibodies to surface marker panel and key TF to capture single-cell protein abundance using mass cytometry. High-dimensional data with over 1 million cells were used to delineate heterogeneity in BM progenitors and find surface-marker surrogates for GATA-1 TF abundance for further functional validation. (B) Force-directed layout (ForceAtlas2) of density downsampled (to 250,000 cells) CD45+-gated BM progenitors colored by manually gated populations. (C) Normalized marker expression of key surface and TF proteins (in orange) across force-directed layout. Orange arrow denotes GATA-1-high region in single-cell map. (D) Leiden clusters of BM progenitors (resolution = 1) visualized on force-directed layout. (E) Barplot of frequency of manually gated BM progenitor populations across 11 Leiden clusters. (F) Normalized diffusion pseudotime calculation visualized on force-directed layout with trajectory from HSCs to erythroid-primed progenitors across Leiden clusters 3, 1, and 11 (in red). (G) Row-normalized heatmap of median marker abundance at 100 bins across diffusion pseudotime-aligned trajectory. Orange font and arrows indicate TF protein trends, and bold font with black arrows indicate key surface marker trends in trajectory. (H) Column-normalized heatmap of mutual information scores calculated on cells in Leiden clusters 2, 7, and 8 and normalized across key TF and surface-marker pairs.
Figure 4
Figure 4
Surface-marker-defined BM population surrogate for high GATA-1 protein abundance clonally enriches for erythroid lineage (A) Spearman correlation plot of surface markers and GATA-1 abundance in BM progenitors as measured by mass cytometry. (B) Top 8% of GATA-1-expressing cells in CD34+/CD38+ BM progenitors gated as GATA-1-high BM cells, and remaining GATA-1-mid/low cells used for subsequent analysis. (C) Boxplots of normalized surface-marker abundance of GATA-1-high BM progenitors (top ∼8% of expression) and GATA-1-mid/low BM progenitors (bottom ∼87% of expression) from mass cytometry. (D) Violin plots of GATA-1 protein abundance in manually gated target populations (as defined by CD71+, CD84+, CD33–), CD123– MEP populations, and in other BM progenitor populations. (E) Boxplot of clonal differentiation frequency of target population and CD123- MEP population to different lineages/population types across 4 biological replicates. (p values calculated using Student's t test)
Figure 5
Figure 5
High GATA-1 protein abundance delineates epigenetic program for erythroid commitment in RBC developmental trajectory (A) BM aspirate is ficolled and enriched for CD34+ cells before gating for CD123– MEP population (CD34+/CD38+/CD10–/CD45RA–/CD123–) and selecting high- (25%–40%) and mid- (∼lower 30%) GATA-1-expressing cells within each compartment. (B) GATA-1-high cells from CD34+/CD38+ and CD123– MEP compartments InTAC-seq data were simulated as scATAC counts and projected on scATAC UMAP space. (C) Putative erythropoiesis trajectory constructed from HSCs to late erythoid populations and overlaid on scATAC UMAP. (D) Heatmap of top variable TFs by ChromVAR deviation scores across constructed erythroid trajectory with the projected position of GATA-1-high InTAC-seq samples indicated in red as the point of GATA-1-high overlap, in blue as the before point, and in green as the after point. Top: line plot of InTAC-seq-denoted GATA-1-high-simulated scATAC cells as binned across pseudotime. (E) Top 20 genes significantly enriched (of fold change 2 and above) in integrated scRNA-seq data between the 3 bins, before, at, and after GATA-1-high overlap points in trajectory. (F) Summary schematic of continuous differentiation to erythrocytes in BM with downregulation of lymphoid/myeloid TF activity and gene expression programs and upregulation of erythroid TF activity and gene expression programs. High GATA-1 protein abundance overlaps epigenetic program shift to erythroid lineage commitment in human BM.
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