Single-cell analysis can be used to estimate absolute differences in total mRNA content across cell types. (A) Schematic explanation of how plate composition and ERCC spike-ins are used to estimate absolute RNA levels. The plate organization for this study included cells from multiple sorting gates (HSPC, Prog, LT-HSCs) and each well contained ERCC spike-ins. The sequencing depth varies across lanes and cell types; therefore, ERCC spike-ins are used to normalize across cell types within a lane, in which the spike-in content becomes level within a lane but cell mRNA content may still vary. After this step, RNA content can be normalized across lanes. (B) Diffusion map of all cells was colored by RNA content. Estimates of total RNA content were calculated by summing the absolute normalized counts per cell. The scale ranges from blue to green to yellow to red with increasing RNA content. (C) Sum of normalized counts for E-SLAMs, LMPPs, GMPs, and MEPs colored by the scheme used in Figure 1A. Significance in differences in RNA content between cell types was calculated by using a 1-way analysis of variance test (**P < .001; ***P < .0001). (D) FSC-H for E-SLAMs, LMPPs, GMPs, and MEPs, colored by the scheme used in Figure 1A. FSC-H is used as an indicator of cell size. Significance in differences in FSC-H between cell types was calculated by using a 1-way analysis of variance test (**P < .001; ***P < .0001). (E) Most relevant significant terms from gene enrichment expression analysis on genes downregulated in absolute terms in E-only, GM-only, and E and GM trajectories. The numbers of genes showing downregulation along pseudotime in absolute terms is displayed in the Venn diagram. Terms with an adjusted P value <.05 (using Benjamini-Hochberg correction for multiple testing) were considered significant. The full tables of results can be found in the supplemental Data.