(a) Boxplot of transcript count per cell distribution for LSK cells, LMPPs and CLPs. The sample sizes are 370, 748, and 494 cells for LSK cells, LMPPs, and CLPs, sorted from two independent experiments with n = 2 mice. (b, c) Saturation analysis. (b) The number of UMIs detected in cells of the LMPP sample is shown as a function of the fraction of reads used for analysis. (c) The number of genes detected in cells of the LMPP sample is shown as a function of the fraction of reads used for analysis. (d) To benchmark the quality of our dataset, we compared Cd34+ LMPPs to mouse (and human) Cd34+ (CD34+) datasets generated with different sequencing technologies1–3, including Smart-seq24, the commercial 10x GemCode technology and MARS-seq5. Boxplot of UMI count distribution in Cd34+ (or CD34+) cells from different mouse (or human) hematopoietic progenitor datasets. Herman: Cd34+ LMPPs from this study (725 cells from n = 2 mice). Paul2: Cd34+ cells from the common myeloid progenitor gate sequenced with MARS-seq (2,370 cells from n = 4 mice). Zheng (PB)3: Cd34+ (mRNA-)positive cells from the CD34+ surface protein–positive peripheral blood population generated with 10x GemCode technology (3,213 cells from n=1 donor). Zheng (BM)3: Cd34+ cells from post-transplantation bone-marrow of an acute myeloid leukemia patient (AML027) generated with 10x GemCode technology (290 cells from n = 1 donor). Selection was always based on Cd34 (CD34) mRNA levels. (e) Boxplot of the number of detected genes per cell for the same samples as in (d) and a high-sensitivity dataset sequenced with a non-UMI based full-length coverage technology. Velten1: CD34+ human bone marrow cells from individual 1 sequenced with Smart-seq2 (1,035 cells from n = 1 donor). In (a-e), bold line indicates the median and box limits represent interquartile range. The whiskers extend to the most extreme data point, within 1.5 times the interquartile range from the box. Outliers are indicated. (f-j) Shown are t-SNE maps highlighting fluorescence intensity measured by index-sorting to enable simultaneous quantification of the transcriptome and cell surface marker expression for (f) Kit, (g) Sca-1, (h) Flt3, (i) Il7r, and (j) Ly6d. In (f-j) 1,949 cells from n=4 animals are shown. (k) Barplot of Spearman’s correlation coefficient between surface protein expression quantified by fluorescence intensity and mRNA expression measured by single-cell RNA-seq for the same cell. The correlation coefficient was calculated for 1,949 cells from three independent experiments with n = 4 mice.
1. Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).
2. Paul, F. et al. Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors. Cell 163, 1663–1677 (2015).
3. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
4. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–8 (2013).
5. Jaitin, D. A. et al. Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types. Science 343, 776–779 (2014).