Single-Cell Proteomics Reveal that Quantitative Changes in Co-expressed Lineage-Specific Transcription Factors Determine Cell Fate

Abstract

Hematopoiesis provides an accessible system for studying the principles underlying cell-fate decisions in stem cells. Proposed models of hematopoiesis suggest that quantitative changes in lineage-specific transcription factors (LS-TFs) underlie cell-fate decisions. However, evidence for such models is lacking as TF levels are typically measured via RNA expression rather than by analyzing temporal changes in protein abundance. Here, we used single-cell mass cytometry and absolute quantification by mass spectrometry to capture the temporal dynamics of TF protein expression in individual cells during human erythropoiesis. We found that LS-TFs from alternate lineages are co-expressed, as proteins, in individual early progenitor cells and quantitative changes of LS-TFs occur gradually rather than abruptly to direct cell-fate decisions. Importantly, upregulation of a megakaryocytic TF in early progenitors is sufficient to deviate cells from an erythroid to a megakaryocyte trajectory, showing that quantitative changes in protein abundance of LS-TFs in progenitors can determine alternate cell fates.

Keywords: CyTOF; FLI1; KLF1; cell fate; erythropoiesis; hematopoiesis; mass cytometry; proteomics; single cell; transcription.

Figure 1.. Time Course Analysis of Human Erythropoiesis by Mass Cytometry

(A) Schematic of sample collection, temporal barcoding, and mass cytometry analyses. CD34⁺ HSPCs were isolated from cord blood and differentiated ex vivo along the erythroid lineage. Cells stained with May-Grünwald-Giemsa are shown (magnification 40×). (B) Cell subset identification by unsupervised clustering with the PhenoGraph algorithm. Each dot represents a cell (48,076 cells total). t-stochastic neighbor embedding (t-SNE) plot of colored PhenoGraph clusters is shown. (C) Temporal deconvolution of PhenoGraph clusters. (D) Reconstitution of the human erythroid trajectory based on temporal appearance of PhenoGraph clusters (Figure S1B). MPP → CMP → E-MEP → CFU-e1 → CFU-e2 → ProEB → Baso_EB → Poly_EB → Ortho_EB. Non-erythroid clusters are shown in gray. (E) Expression level plots highlighting the hematopoietic early marker CD34 and the erythroid marker GPA. (F) Single-cell population trends over time visualized by the algorithm SPRING applied to mass cytometry data. Each dot represents a cell (20,314 cells total) and is color coded to indicate the day of identification. See also Figure S1 and Tables S1 and S2.

Figure 2.. Co-expression of Lineage-Specific Transcription Factors in Individual Bipotential Progenitors

(A) Biaxial dot plots of mass cytometry data showing co-expression of KLF1 (erythroid) and FLI1 (megakaryocytic) proteins in E-MEPs. (B) 2D scatterplots showing gradual changes in KLF1 and FLI1 protein levels in individual cells across time and populations. (C) Ridge plots showing relative expression of KLF1 and FLI1 proteins in temporally ordered cell populations along the erythroid trajectory. (B) and (C) use PhenoGraph cell populations. (D) Biaxial dot plots of mass cytometry data showing co-expression of GATA1 and PU.1 proteins in CMPs. See also Figure S2.

Figure 3.. KLF1 and FLI1 Protein Levels Change Gradually during Erythropoiesis

(A) Absolute quantification of KLF1 and FLI1 proteins across time by SID-SRM mass spectrometry. (B and C) Representative results showing quantification of the indicated KLF1 (B) and FLI1 (C) peptides. The main picture shows co-eluting peptides—the spiked-in “heavy” standard peptides in blue and the endogenous “light” peptides in red. Inset picture shows specific transitions measured for each light peptide. These transitions are summed to produce the red peak in the main graph.

Figure 4.. Overexpression of FLI1 in Bipotential Progenitors Is Sufficient to Deviate the Erythroid Trajectory toward a Megakaryocytic Fate

(A) Schematic of sample collection, temporal barcoding, and mass cytometry analyses upon expression of a FLAG-tagged FLI1 protein in early progenitors. (B) Single-cell population trends over time visualized by SPRING applied to CyTOF data. Each dot represents a cell (9,000 cells total), and its color indicates the measured day. (C) Graph indicating the percentage of FLAG-negative and FLAG-positive cells that follows a megakaryocytic path (defined as CD41+) over time. (D) SPRING plot from (B) colored for FLAG expression (yellow for positive; black for negative). (E) SPRING plots from (B) colored for the indicated markers. Coloring scale from black (no expression) to green (high expression) is shown. (F) Cytobank histogram overlays of CyTOF data showing temporal variations in the relative level of each indicated marker in cells expressing FLAG-FLI1 (back histogram) versus FLAG-FLI1-negative cells (front histogram). Peaks are shaded using a color scale based on the raw values of medians for each x axis channel. See also Figures S3 and S4 and Table S3.