Putative Regulators for the Continuum of Erythroid Differentiation Revealed by Single-Cell Transcriptome of Human BM and UCB Cells

Putative regulators for the continuum of erythroid differentiation revealed by single-cell transcriptome of human BM and UCB cells

Peng Huanga,b,c,d,1, Yongzhong Zhaoe,1, Jianmei Zhonga,b,d,1, Xinhua Zhangf, Qifa Liug, Xiaoxia Qiuc, Shaoke Chenc, Hongxia Yanh, Christopher Hillyerh, Narla Mohandash, Xinghua Pand,i,2, and Xiangmin Xua,b,d,2

aDepartment of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China; bGuangdong Technology and Engineering Research Center for Molecular Diagnostics of Human Genetic Diseases, 510515 Guangzhou, China; cPrenatal Diagnostic Center, Institute of Birth Defect Prevention and Control, Guangxi Zhuang Autonomous Region Women and Children Health Care Hospital, 530000 Nanning, China; dGuangdong Provincial Key Laboratory of Single Cell Technology and Application, 510515 Guangzhou, China; eDepartment of Cancer Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195; fDepartment of Hematology, 303rd Hospital of the People’s Liberation Army, 530021 Nanning, China; gDepartment of Hematology, Nanfang Hospital, Southern Medical University, 510515 Guangzhou, China; hRed Cell Physiology Laboratory, New York Blood Center, New York, NY 10065; and iDepartment of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China

Edited by Trudi Schüpbach, Princeton University, Princeton, NJ, and approved April 22, 2020 (received for review August 29, 2019) Fine-resolution differentiation trajectories of adult human hema- or HBG1/G2), and karyopyknosis and enucleation are important topoietic stem cells (HSCs) involved in the generation of red cells is biological processes (3). However, significant gaps exist in de- critical for understanding dynamic developmental changes that tailed understanding of molecular mechanisms during this pro- accompany human erythropoiesis. Using single-cell RNA sequenc- cess. Thus, there is a critical need for detailed understanding of ing (scRNA-seq) of primary human terminal erythroid cells the dynamic changes in gene expression during erythropoiesis. (CD34−CD235a+) isolated directly from adult bone marrow (BM) Recently, bulk RNA sequencing studies and proteomic anal- and umbilical cord blood (UCB), we documented the transcriptome yses of human erythroblasts derived from in vitro cultures of of terminally differentiated human erythroblasts at unprece- CD34+ cells revealed that the major dynamic changes in ex- dented resolution. The insights enabled us to distinguish polychro- pression pattern of genes during erythroid differentiation were matic erythroblasts (PolyEs) at the early and late stages of clustered into four major patterns: expression of the pattern 1 set development as well as the different development stages of or- of genes decreases during differentiation, the pattern 2 set of thochromatic erythroblasts (OrthoEs). We further identified a set genes are highly expressed at the ProE stage and their expression of putative regulators of terminal erythroid differentiation and functionally validated three of the identified genes, AKAP8L, TER- Significance F2IP, and RNF10, by monitoring cell differentiation and apoptosis. We documented that knockdown of AKAP8L suppressed the com- Using scRNA-seq, we identified a dynamic gene expression mitment of HSCs to erythroid lineage and cell proliferation and profile during terminal erythroid differentiation. Recent efforts delayed differentiation of colony-forming unit-erythroid (CFU-E) revealed the expression features at various developmental to the proerythroblast stage (ProE). In contrast, the knockdown stages during hematopoietic differentiation of human stem of TERF2IP and RNF10 delayed differentiation of PolyE to OrthoE cells derived from human fetal cord blood and adult bone stage. Taken together, the convergence and divergence of the marrow. However, the transcription dynamics for erythropoi- transcriptional continuums at single-cell resolution underscore esis remain elusive. Here, we dissected the gene expression the transcriptional regulatory networks that underlie human fetal dynamics from ProE to OrthoE by carrying out scRNA-seq of and adult terminal erythroid differentiation. erythroblasts isolated from human cord blood and bone marrow cells. We subdivided the human erythropoiesis PolyE and scRNA-seq | terminal erythroid differentiation | cell clusters | regulator OrthoE into early-/late-PolyE and early-/late-OrthoE, respectively. We also predicted a list of regulators during termi- efinitive human erythropoiesis, characterized by the move- nal erythroid differentiation and tested in vitro differentiation Dment of lineage-committed cells through progenitor, pre- experiments. We provide a foundational human scRNA-seq cursor, and mature RBC compartments, occurs in the fetal liver dataset and candidate master regulators of erythropoiesis for and in postnatal bone marrow. The human erythropoiesis pro- further study. cess is divided into three distinct phases: early erythropoiesis, terminal erythroid differentiation, and reticulocyte maturation. Author contributions: P.H., X.P., and X.X. designed research; P.H. and H.Y. performed The terminal erythroid differentiation phase is subdivided research; X.Z., Q.L., X.Q., and S.C. contributed new reagents/analytic tools; P.H., Y.Z., and J.Z. analyzed data; P.H., Y.Z., J.Z., C.H., N.M., X.P., and X.X. wrote the paper; and chronologically into four stages, proerythroblast (ProE), baso- X.Z., Q.L., X.Q., and S.C. collected the UCB and BM samples. philic erythroblast (BasoE), polychromatophilic erythroblast The authors declare no competing interest. (PolyE), and orthochromatic erythroblast (OrthoE), based on the morphological characteristics of the cells (1). The BasoE is This article is a PNAS Direct Submission. further split into early and late stages based on cell surface ex- Published under the PNAS license. pression patterns of SLC4A1 (band 3) and ITGA4 (α4 integrin) Data deposition: The scRNA-seq raw datasets generated during this study are deposited in Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? (2). Differentiation of hematopoietic stem cells to red blood cells acc=GSE150774). Code is available from Github (https://github.com/SMU-medicalgenetics/ is a continuous process with multiple distinct stages of devel- Single_cell). opment. During this process, cells express stage-specific genes, 1P.H., Y.Z., and J.Z. contributed equally to this work. which are critical to regulate the complex process of stem cell 2To whom correspondence may be addressed. Email: [email protected] or xixm@smu. commitment to erythroid differentiation and subsequent termi- edu.cn. nal erythroid differentiation to generate enucleate red cells This article contains supporting information online at https://www.pnas.org/lookup/suppl/ (3–5). During terminal erythroid differentiation, there is in- doi:10.1073/pnas.1915085117/-/DCSupplemental. creasing expression of hemoglobin genes (HBA1/A2, HBB, and/ First published May 26, 2020.

12868–12876 | PNAS | June 9, 2020 | vol. 117 | no. 23 www.pnas.org/cgi/doi/10.1073/pnas.1915085117 Downloaded by guest on September 26, 2021 decreases during differentiation, the pattern 3 set of genes are highly consistent, indicating the intersample heterogeneity fea- expressed at low to midrange levels in ProE stage and increase ture of human fetal erythropoiesis (SI Appendix, Fig. S3D). We their expression levels during later stages of differentiation, and merged the three BM-derived or three UBC-derived data sets the pattern 4 set of genes exhibit an inverted V-shaped mirror together prior to performing the bioinformatic analysis. image with a peak at the PolyE stage (4–7). In the present study, using single-cell RNA sequencing (scRNA-seq), we explored the Heterogeneity and Clusters of Terminally Differentiated Erythroid transcriptome of human erythroblasts derived from newborn Cells in BM Samples. By applying the CellRanger package (18), umbilical cord blood and adult bone marrow. These studies we identified a set of 284 signature genes [between-clusters log2 identified subtypes of terminally differentiating erythroblasts and (fold change UMI counts), termed log2FC, >1.0; FDR < 0.05], redefined their fate and differentiation processes, thereby doc- enabling classification of 8,668 cells into 7 clusters, including a umenting the highly heterogenous composition of cell types continuous arc of 6 clusters (clusters 1–6) and one separate during erythroblast differentiation, indicating cell-to-cell het- cluster (cluster 7; Fig. 1A and Dataset S1). Interestingly, cells erogeneity (8–12). In addition, they provided transitional mo- from cluster 7 were mostly from BM1 sample (SI Appendix, Fig. lecular profiles in early progenitors during fetal hematopoiesis. S3C). Based on the differentially expressed genes (DEGs) be- Previous studies of human erythropoiesis relied almost ex- tween clusters and heat map analysis shown in Fig. 1B, clusters 3, clusively on the in vitro differentiation of hematopoietic stem 4, and 5 were distinct from the others, while clusters 1, 2, and 6 and progenitor cells (HSPCs) from different sources, and on shared similar DEGs (Fig. 1B). Thus, clusters 1, 2, and 6 were performing bulk RNA-seq analysis on cells at different stages of recognized as the same group of cells. To identify the order of differentiation (13). Of note, molecular and cellular traits such as progression of cell differentiation progress, we reconstructed cell cellular morphology, gene expression profiles, and epigenetics differentiation trajectory with Monocle (19), and the result between primary erythroblasts in vivo and erythroblasts gener- showed that the sequential order of cell differentiation in clus- ated in vitro have not been explored, as has been documented for ters is as follows: 3 → 4 → 5 → (1, 2, 6) → 7 (Fig. 1C). In other other cell types (14). It is well known that there are two major words, cluster 3 belongs to a group of cells at the earliest dif- differences between fetal and adult erythrocytes. First, the he- ferentiation stage, while cluster 7 belongs to cells at a very late moglobin in fetal red blood cells is mainly HbF (about 98% of stage of differentiation. Additionally, mRNA expression of total hemoglobin), while, in adults, it is mainly HbA (about 95% GYPA, the distinct marker of erythroblasts, is down-regulated of total hemoglobin). Second, the fetal umbilical cord blood (the along the cell’s maturation pathway (13). When the expression peripheral blood of a fetus) contains a large number of nucleated level of GYPA mRNA among these clusters was compared, red blood cells, which are rare in adult peripheral blood (15, 16). cluster 7 had the lowest value (Fig. 1D), implying that cluster 7 DEVELOPMENTAL BIOLOGY Therefore, deciphering the differences in erythroblast differen- most likely represents the very last stage of differentiation (very tiation trajectories between adult and fetal erythropoiesis should late OrthoE or reticulocytes). enable a better understanding of disordered erythropoiesis in Next, we identified cells at differentiation stages correspond- distinct clinical conditions such as thalassemia and other bone ing to ProE, BasoE, PolyE, and OrthoE by Seurat based on the marrow failure syndromes with higher expression of HbF (4). previously reported expression of marker genes during human In order to fill these gaps in our understanding of human terminal erythroid differentiation (13, 20). We noted that 67.1% erythropoiesis, we performed scRNA-seq transcriptome analysis of cells belong to OrthoE and 24.9% of cells belong to PolyE, of terminal differentiated primary human erythroblasts isolated while few ProE and BasoE cells were also recognized (Fig. 1E). directly from umbilical cord blood (UCB) and bone marrow This distribution of cells at different differentiation stages is (BM) samples. consistent with previously reported results (21) and consistent Results with the human erythroid developmental program in bone marrow. When gene expression patterns of cells in different Single-Cell Transcriptomes of Human Terminal Erythropoiesis. To clusters were mapped to previously reported expression profiles reliably construct an scRNA-seq library, we isolated primary A E human terminally differentiated erythroblasts directly from adult of erythroblasts at different development stages (Fig. 1 and ), BM and UCB samples using magnetic beads via CD34-negative both ProE and BasoE cells were part of cluster 3 rather than selection followed by CD235a (glycophorin A, GYPA) positive separating into two distinct clusters. This could be either due to selection (SI Appendix, Fig. S1 A and B). Single-cell sequencing insufficient numbers of cells belonging to these two stages ana- libraries were constructed based on the 10× Genomics Chro- lyzed and/or highly similar gene expression patterns at these two mium protocols, and transcriptomic data were generated on the developmental stages. Therefore, we chose not to focus on these Illumina X 10 platform. Following rigorous quality control (QC), two early cell stages so as not to overinterpret the findings re- we obtained data on 8,668 cells from three BM samples and garding these specific development stages. The PolyE cells 17,692 cells from three UCB samples. On average, we detected grouped into clusters 4 and 5 and part of cluster 3, and we 377 expressed genes and 5,870 mRNA molecules in each indi- designated these 3 clusters respectively as transit-PolyE (parts of vidual BM cell. In contrast, 602 expressed genes and 8,133 cluster 3), early-PolyE (cluster 4), and late-PolyE (cluster 5). mRNA molecules were noted in each UCB erythroblast. We Based on the fact that the gene expression pattern of transit- annotated each cell type and excluded the nonerythroid mono- PolyE cells had some overlap with genes expressed in BasoE cytes based on the criteria previously described (17) (SI Appen- cells, we defined this particular stage of cells as a transitional dix, Fig. S2 A and B). We then performed imputation of the stage between BasoE and PolyE. OrthoE cells could also be dropout data with the R package scImpute, followed by Pearson divided into two stages of development, early-OrthoE (clusters 1, correlation analysis. It appears that the expression pattern of the 2, 6) and late-OrthoE (cluster 7), respectively. We also noted three BM samples was highly consistent (Pearson coefficient R > that the intranuclear MALAT1 gene was expressed at a lower 0.97, P < 0.01) but substantially differed from UCB samples with level in the late-OrthoE stage than in other stages (Fig. 1D). high heterogeneity (Pearson coefficient R < 0.76, P < 0.01; SI Therefore, we speculate that the late-OrthoE would be a tran- Appendix, Fig. S3 A and B). Unsupervised clustering shows that sition state with highly pyknotic nuclei and ready for enucleation. human BM cells from the three samples are evenly distributed in Taken together, these findings suggest that the conventionally most clusters of cells except for one cluster unique to the BM1 defined differentiation stages, PolyE and OrthoE, can be further sample (SI Appendix, Fig. S3C). UCB1 cell clusters differ from subdivided along their maturation states based on gene expression clusters of both UCB2 and UCB3 cell populations, which are patterns.

Huang et al. PNAS | June 9, 2020 | vol. 117 | no. 23 | 12869 Downloaded by guest on September 26, 2021 Fig. 1. Single-cell transcriptomes of the terminal differentiation of human erythroid cells and their distinct biomarkers. (A) t-SNE visualization of adult BM erythroblasts (n = 8,668 cells) by CellRanger in distinct clusters, with cell numbers of 1,792, 1,753, 1,226, 894, 695, 391, and 1,917 in clusters 1–7, respectively.

(B) The heat map illustrates discriminant gene sets for each cluster with cutoff threshold of |log2FC| > 1.0. (C) Cell differentiation trajectory reconstructed with Monocle. Each dot represents a single cell. Dots in colors indicate different cell clusters. Black arrow indicates cell differentiation trajectory.(D) t-SNE map colored by expression levels of GYPA (Left) and MALAT1 (Right). (E) t-SNE shows the distribution of erythroblast differentiation stages. ProE, BasoE, PolyE, and OthoE are shown in distinct colors. (F) Enriched GO terms and P values of the four stages of cells. (G) Cell cycling phases identified by Seurat. Phases of cell cycle are depicted in different colors, G0/G1 in orange, S in azure, and G2/M in green.

We performed Gene Ontology (GO) analysis based on dif- remain to be fully defined. There is good evidence that Rho GTPase ferentially expressed genes among the four groups (ProE/BasoE/ and cytoskeleton contribute to these processes (22–24). For early- transit-PolyE, early-/late-PolyE, early-OrthoE, and late-OrthoE) OrthoE stage, 30 differentially expressed genes were identified, in- and identified associated enriched GO terms to gain insights into cluding GTPase biological synthesis and activation regulators, es- the biological processes (Fig. 1 B and F and Dataset S2). A set of pecially TMCC2, EIF5,andARL4A (25–27). Moreover, XPO7 is 207 signature genes was identified in cluster 3 representing ProE, highly expressed at this stage, and is essential for regulating eryth- BasoE, and transit-PolyE cells, and the GO terms for this cluster roblast maturation and karyopyknosis (28, 29). These findings con- were significantly enriched (FDR < 0.01) for differentially firm that early-OrthoE stage is indeed the preparatory stage for cell expressed genes related to ribosome biogenesis, protein target- enucleation. Interestingly, the antiviral gene IFIT1B is also highly ing, and RNA catabolic process. Early- and late-PolyE cells expressed at this stage, implying a role in cellular immunity, which shared similar differentially expressed genes, with only 13 dif- has previously been suggested as a potential function of erythroblasts ferential expressed genes (|logFC| > 0.5, FDR < 0.01) between (30). In addition, differentially expressed genes related to iron and them identified (Fig. 1B and Dataset S1), which were for cell erythrocyte homeostasis were also significantly enriched. At late- division, organelle fission, and cell cycle. OrthoE stage, a transition period before generation of enucleate The two most important biological processes in OrthoE stage reticulocytes, differentially expressed genes related to oxygen trans- are karyopyknosis and enucleation, whose molecular mechanisms port and hemoglobin complex synthesis were significantly enriched.

12870 | www.pnas.org/cgi/doi/10.1073/pnas.1915085117 Huang et al. Downloaded by guest on September 26, 2021 In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) is a set of 674 genes that are expressed at low levels at early pathway enrichment analysis with expression genes in each cluster stages of terminal erythroid differentiation phase and whose revealed that the autophagy pathway was enriched at OrthoE stage, expression progressively increases at late stages of differentia- which indicated that cell autophagy contributes to the enucleation tion. Interestingly, we noted different patterns of gene expres- and maturation processes of erythroblasts. sion in the third panel: a subset of genes that are specifically Based on expression of genes involved in regulating the cell enriched at PolyE stage, a second subset highly expressed at cycle using Seurat, we noted that, among the 8,668 BM eryth- early-OrthoE stage, and a third subset marking the late-OrthoE roblasts analyzed, 1,103 cells were in S phase, 1,712 cells in G2/M stage. phase, and 5,853 cells in G0/G1 phase. Further analysis showed To validate the noted differential gene expression patterns that ProE/BasoE cells were mostly at S phase, early-/late-PolyE from our scRNA-seq analysis, we performed in vitro culturing of cells were mostly at G2/M phase, and early-/late-OrthoE cells adult BM CD34+ cells to generate erythroblasts at various stages were mostly at G0/G1 phase (Fig. 1G). Due to the condensed of differentiation and monitored expression patterns of TER- nucleus of early-/late-OrthoE cells, we speculate that these cells F2IP, SOX6, IFIT1B, CFL1, and ARL4A genes that encompass were at phase G0 rather than at G1 phase. the three noted patterns of expression (Fig. 2A) using quantita- Furthermore, we noted a decrease in numbers of genes tive PCR (qPCR) (31). Morphological examination following expressed as erythroblast differentiation progressed, which is Wright’s–Giemsa staining showed that ProE was the dominant consistent with the decrease in number of proteins detected (7), population on day 7, BasoE was dominant on day 9 and day 11, while, at the same time, there is a marked increase in expression PolyE was dominant on day 13 and day 15, and OrthoE was of genes encoding hemoglobin, especially HBA1/HBA2, HBB, dominant on day 17 (Fig. 2B). Indeed, qPCR showed us that the and/or HBG1/HBG2 (SI Appendix, Fig. S4A). In addition to expression of CFL1 (gene belonging to Fig. 2A, panel 1) was hemoglobin genes, we also found a few other highly expressed highly expressed at day 7 but rapidly decreased as differentiation genes, including previously reported AHSP (α hemoglobin sta- proceeded. SOX6 (gene belonging to Fig. 2A, panel 2) increased bilizing protein) (6) and a set of genes (SI Appendix, Table S1) its expression from day 7 to day 13 and its expression decreased that are related to regulation of hemoglobin synthesis, structural on day 17. Regarding IFIT1B, TERF2IP, and ARL4A (genes remodeling of erythroblasts, and karyopyknosis. While we noted belonging to Fig. 2A, panel 3), their expression began at day 7 the expression of TPT1, EEF2, and TFRC genes, it contrasts with and exhibited high levels of expression at very late stages of previous reports of their high level of expression in cultured cells erythroid differentiation (Fig. 2C). Furthermore, the well-known (6). They were expressed at low levels in our set of single-cell erythroid regulators SNCA and FOXO3 were up-regulated, while sequencing data with primary unmanipulated erythroblasts. GATA1, BCL11A, and KLF1 were down-regulated during ter- DEVELOPMENTAL BIOLOGY minal erythroid differentiation (SI Appendix, Fig. S6A). In- Heterogeneity and Clusters of Terminal Erythroid Cells in UCB triguingly, we observed the decreasing expression of NFE2L1, Samples. With the same stratification strategy as detailed ear- NDEL1, EPB41, USO1, MARK2, LGALS9, NEK1, and AXIN1 lier, seven clusters of cells and their stages of differentiation and (SI Appendix, Fig. S6B); however, a recent study using cultured cell cycle phases were also identified in UCB erythroblasts (SI cells from CD34+ cells showed increased expression up to Appendix, Fig. S5 A, B, and D and Dataset S3). When compared OrthoE stage (32). Thus, it appears that the gene expression to BM erythroblasts, the most significant difference noted be- profiles of primary isolated cells may differ from the in vitro tween them is the differentiation stage of the cells. Most of the differentiation experiments. erythroblasts in UCB samples were OrthoE (87%), while the proportion of OrthoE in BM samples was 67%. This noted dif- Regulators of Human Terminal Erythroid Differentiation. We applied ference in developmental stage was validated by flow cytometry CellRouter analysis to reconstruct single cell trajectory, in which analysis (SI Appendix, Fig. S5C). Another difference noted was in a k-nearest neighbor (kNN) cellular network can be built via the expression levels of γ- and β-hemoglobin genes. While both dimensionality reduction (Fig. 1A). The key algorithm of Cell- HBB and HBG1/G2 were highly expressed in UCB erythroblasts, Router leverages a scoring scheme, termed gene regulatory only HBB was expressed in BM erythroblasts (SI Appendix, Fig. networks score (GRN), to identify transcriptional regulators by S4B). Interestingly, the scRNA-seq analysis for the three UCB their state of activation of predicted target genes (33). By cal- samples revealed a complicated heterogeneity in the OrthoE culating GRNs (from ProE to late-OrthoE), we could document stages, which are mostly in the G0/G1 stage, with a small portion a continuous regulatory network during terminal erythroid dif- in the S phases, of the cell cycle (SI Appendix, Fig. S5D). ferentiation, including positive regulators (plus score of GRN and increased levels of target gene expression) and negative Gene Expression Dynamics during Human Terminal Erythroid regulators (minus score of GRN and decreased expression of Differentiation. We sought to profile gene expression dynamics target genes; Fig. 3). Based on GRNs, we identified a set of top- at single-cell transcriptome resolution across different stages of ranked genes that encompass the well-known regulators of human terminal erythroid differentiation with Monocle. We as- erythropoiesis, NFE2, HMGB2, YBX1, SOX6, FOXO3, and sembled genes into three subsets according to their expression SNCA (34–39), and other candidate regulatory genes for which trend along the differentiation trajectory (Fig. 2A). Although the little information is currently available regarding their roles in number of expressed genes identified at single-cell transcriptome terminal erythroid differentiation (SI Appendix, Table S2). We resolution was not as high as previously reported from se- would like to note that GATA1 and KLF1 are in the list of our quencing of large numbers of cells, the noted expression trends predictive regulators, but with a lower predictive power, likely based on scRNA-seq data in large part matched data from bulk due to their decreased mRNA expression during later stages of sequencing (6). Panel one encompasses a set of 1,145 genes erythroid differentiation. whose expression is high at very early stages of erythroid During terminal erythroid differentiation, we noted that the differentiation but are rapidly down-regulated as erythroid dif- GRN scores for SNCA, FOXO3, NFE2, NFIX, RNF10, TER- ferentiation proceeds. This set included genes encoding tran- F2IP, and AKAP8L substantially increased, while the GRN scription factors KLF1 and BCL11A, which are critical at early scores of SOX6 and YBX3 continuously decreased. It is likely phases of erythroid differentiation. A second panel encompasses that the genes with increasing GRN scores during terminal ery- a set of 647 genes whose expression begins at early stages of throid differentiation are much more relevant to regulating differentiation and is sustained as the differentiation proceeds, erythroid differentiation than the genes with decreasing GRN followed by down-regulation at very late stages. The third panel scores. Furthermore, we noted that different positive regulators

Huang et al. PNAS | June 9, 2020 | vol. 117 | no. 23 | 12871 Downloaded by guest on September 26, 2021 Fig. 2. Single-cell gene expression dynamics in human terminal erythroid differentiation. (A) Patterns of gene transcriptional trends in human BM cells during terminal erythroid differentiation analyzed with Monocle. The graphs on the right side indicate the trends of gene expression. (B) Morphology of erythroid cells on days 7, 9, 11, 13, 15, and 17 of in vitro differentiated human CD34+ cells. Cells were stained with Wright’s–Giemsa Stain. ProE, BasoE, and PolyE cells are marked with red, blue, and black, respectively (objective lens, 100×). (C) Predicted master gene regulator tested by qRT-PCR. Cells were harvested on days 7, 9, 11, 13, 15, and 17 from in vitro differentiated CD34+ cells. Gene expression level is illustrated with ΔCt value.

increased their expression level at different stages of terminal HSC to CFU-E, and terminal erythroid differentiation of ProE erythroid differentiation, which implies that they would function to OrthoE. We compared the BFU-E and CFU-E populations at specific stages during differentiation. To validate this hy- between control and knockdown groups based on the surface pothesis, we selected TERF2IP, AKAP8L, and RNF10 to carry expression levels of CD34 and CD36 using the previously de- + out knockdown experiments using CD34 cells, since their scribed flow cytometry-based strategy (41). On day 7, the BFU-E function during erythroid differentiation has not been pre- population in a control group was 19%, while, in the AKAP8L- viously characterized (40). These regulators were chosen on the shRNA group, it was significantly higher at 37% (P < 0.01). In bases of their high GRN scores and their up-regulated ex- SI Ap- contrast, the CFU-E population was higher at 46% in a control pression patterns at different stages of differentiation ( group than in AKAP8L-shRNA at 13% (P < 0.01). Thus, pendix,Fig.S6C). AKAP8L knockdown resulted in delayed maturation of erythroid To explore the roles of AKAP8L, TERF2IP, and RNF10 in progenitors. No such effect was seen in either BFU-E or CFU-E regulating erythroid differentiation at different developmental TERF2IP RNF10 stages, we used the shRNA-mediated knockdown approach and population following knockdown of and . BFU- monitored in vitro cell differentiation by flow cytometry. The E population was 27% and 28% (vs. 19% in the control group), knockdown efficiency was quantitated through all stages of dif- and CFU-E cells were 40% and 41% (vs. 46% in the control ferentiation. On day 7 of culture, the knockdown efficiencies for group) in TERF2IP-shRNA and RNF10-shRNA cells, re- AKAP8L-shRNA, TERF2IP-shRNA, and RNF10-shRNA were spectively (Fig. 4B). These findings imply that knockdown of 58%, 79%, and 72%, respectively (Fig. 4A and SI Appendix, Fig. AKAP8L delayed the commitment of HSC to erythroid lineage, S7). Erythropoiesis can be functionally divided into two stages: while knockdown of TERF2IP and RNF10 did not affect early-stage erythropoiesis, which encompasses transition from this process.

12872 | www.pnas.org/cgi/doi/10.1073/pnas.1915085117 Huang et al. Downloaded by guest on September 26, 2021 DEVELOPMENTAL BIOLOGY

Fig. 3. Predicted regulators involved in human terminal erythroid differentiation. (A) Kinetic profile of each regulator along the differentiation trajectory (from ProE to OrthoE). Each row represents a gene expression variation during time trajectory of erythroid differentiation. Genes are listed on the left side. Color indicates expression value, which is scaled from 0 (blue) to 1 (red). Black arrow on the bottom indicates cell differentiation trajectory from left to right. (B) Predicted candidate regulators in reprogramming terminal erythroid differentiation based on gene regulatory networks (GRNs) score. Positive genes (GRN > 0, bars in red) or negative genes (GRN < 0, bar in blue) indicate two types of transcriptional regulators in BM erythroblasts.

Next, we examined the effects of AKAP8L, TERF2IP, and knockdown showed a decrease starting on day 9 that persisted until RNF10 knockdown on terminal erythroid differentiation. It has the end of the culture period. We also noticed a slight decrease in been documented that the transition of CFU-E to ProE is cell numbers in TERF2IP and RNF10 knockdown groups on day 9, characterized by the expression of GYPA (CD235a) (2). Flow followed by a burst of proliferation on day 11 (Fig. 4E). The de- cytometry showed that, on day 7 of culture, only 22% of creased final output of erythroid cells at the end of culture is most AKAP8L-knockdown cells were CD235a-positive, whereas 43% likely due to apoptosis. of control group cells were CD235a-positive, demonstrating that the knockdown of AKAP8L delayed the differentiation of CFU- Discussion E to ProE. No such delay was noted following the knockdown of Terminal erythroid differentiation phase is critical for the gen- either RNF10 or TERF2IP (Fig. 4C). Furthermore, monitoring eration and final stages of maturation of red blood cells. During of surface expression levels of α4 integrin and band 3 to assess this phase, erythroblasts are classified as ProE, early-BasoE, late- terminal erythroid differentiation showed that AKAP8L knock- BasoE, PolyE, and OrthoE based on morphology and cell sur- down delayed the progression of erythroid differentiation at day face expression of membrane proteins. To explore the proteomic 13, TERF2IP knockdown on day 15, and RNF10 knockdown composition and biological functions of cells at these distinct on day 17 (Fig. 4D). Staining with Wright’s–Giemsa of cells from stages, large numbers of purified cells are required. While cultures at different time points showed that BasoE was the existing strategies can be used for isolating large number of cells dominant population on day 11, early-PolyE on day 13, late- produced in vitro, to date, it has not been possible to isolate PolyE on day 15, and OrthoE on day 17 (Fig. 2B). Thus, sufficient numbers of primary erythroblasts at distinct de- knockdown of AKAP8L suppressed cell proliferation and delayed velopmental stages from human bone marrow. scRNA-seq is a differentiation of CFU-E to terminal erythroblast stages, while powerful and robust tool to capture transcriptome-wide insights knockdown of TERF2IP delayed differentiation of early-PolyE to from samples of mixed cell populations of cells with finite cell late-PolyE and knockdown RNF10 delayed differentiation of late- numbers. In this study, we profiled and analyzed the regulatory PolyE to early-OrthoE. landscape via scRNA-seq of primary unmanipulated human ter- − We also examined the effects of AKAP8L, TERF2IP, and minally differentiated erythroid cells (CD34 CD235a+) isolated RNF10 knockdown on cell growth. The growth curves showed directly from healthy adult BM and neonate UCB samples. By only small differences in cell numbers between control and single cell sequencing, we obtained a fine-resolution phasing of hu- knockdown groups until day 7. However, cell numbers for AKAP8L man terminal erythroid differentiation and a set of gene signatures

Huang et al. PNAS | June 9, 2020 | vol. 117 | no. 23 | 12873 Downloaded by guest on September 26, 2021 Fig. 4. Effects of AKAP8L, TERF2IP, and RNF10 knockdown on human erythroid differentiation. (A) Expression levels of AKAP8L, TERF2IP, and RNF10 examined by qRT-PCR on day 7 of culture with GADPH as internal control. Error bars indicate SEM (n = 3). (B) Flow cytometric analysis of erythroid progenitor populations, including BFU-E (CD34+CD36−) and CFU-E (CD34−CD36+) cells. Cells cultured for 7 d were stained with antibody against CD34 and CD36. The data are shown as mean ± SEM of three independent biological replicates (*P < 0.01). (C) The expression of CD235a on day 7 of culture of CD235a+ cells in percentage (mean ± SEM, n = 3; *P < 0.01). (D) Terminal erythroid differentiation was examined on indicated days by flow cytometric analysis based on the expression of band 3 and α4 integrin. Representative plots of α4 integrin versus band 3 of CD235a+ cells are shown, and the erythroblasts are separated into seven populations: ProE (I), early-BasoE (II), late-BasoE (III), early-PolyE (IV), late-PolyE (V), early-OrthoE (VI), and late-OrthoE (VII). (E) Growth curves of normal control CD34+ cells and cells following knockdown of AKAP8L, TERF2IP, and RNF10, respectively (mean ± SEM, n = 3; *P < 0.01, differential significance when compared to normal control groups).

related to the development and maturation of erythroid cells. The RNF10, and TERF2IP, that could potentially play a key role in findings that only a small number of ProE and BasoE cells are found regulating terminal differentiation of primary human erythroid in BM samples is possibly attributed to their relative low abundance cells, especially in regulating erythroblast karyopyknosis and compared to later stages of differentiation, since every 1 ProE cell enucleation. These candidate regulators could be prioritized in will generate 16–32 OrthoE cells in bone marrow as a result of 4–5 future studies to develop a comprehensive understanding of mitosis. Hence, our results primarily identified the heterogeneity in karyorrhexis and also the underlying mechanism of diversity in the more abundant PolyE and OrthoE stages of erythroid differ- cell stage differences in various different types of anemia, in- entiation. In fetal UCB, not surprisingly, we found that cells that are cluding congenital dyserythropoietic anemias. released into circulation under stress erythropoiesis are mostly late- Recent studies have documented the dynamics of erythroid OrthoE. It has been previously reported that, in addition to fetal gene expression patterns by bulk RNA-seq of human erythro- UCB, late-OrthoE cells are also found in circulation of thalassemia blasts derived from culturing of CD34+ cells from adult and cord patients (16). blood (13). While our findings are in large part consistent with A number of important regulatory genes have been previously these reports, we also found that the profiles of erythroid cells identified by bulk RNA-seq in the early stages of erythropoiesis isolated directly from BM (in vivo) exhibit some differences. For with cultured cells (13); a few of them are well recognized, e.g., example, in contrast to the reported increased expression of KLF1, GATA1, NFE2, BCL11A, LRF, TET2/3, and MYB master regulators GATA1 and KLF1 during differentiation of (42–44). In order to understand the nature of erythropoiesis cultured CD34+ cells into OrthoE cells and decreasing expres- in vivo, in the present study, we used unmanipulated primary sion of hemoglobin switching genes SOX6 and BCL11A at PolyE erythroid cells derived from healthy adult BM. In addition to the stage (45), the expression patterns of these genes, GATA1, well-known erythropoiesis regulators GATA1, KLF1, and MYB, KLF1, SOX6, and BCL11A, were different in primary uncultured we identified a set of regulatory genes, particularly AKAP8L, cells. According to our data, GATA1, SOX6, and KLF1 were

12874 | www.pnas.org/cgi/doi/10.1073/pnas.1915085117 Huang et al. Downloaded by guest on September 26, 2021 down-regulated all the way through the terminal stage, while unaffected by excess zero or near-zero counts called dropout events, we BCL11A is continuously expressed at low levels. The noted dif- applied the R software package scImpute described by Li and Li (54) (https:// ferences might be likely due to the differences in gene expression github.com/Vivianstats/scImpute) to recover gene expression data on which patterns of in vivo- and in vitro-derived erythroblasts. Un- downstream analyses can be performed. We assumed that there were five = derstanding how cultured erythroblasts differ from those isolated subpopulations according to prior knowledge and set k 5 to impute. After imputation, assignment of cell-cycle stage was performed using the cyclone in vivo is important. Future studies should critically address this function in the scanner package. We kept all genes expressed (defined by issue to obtain comprehensive understanding of this difference in nonzero counts) in ≥10 cells (∼0.2% of the data). For cells, at least 200 genes human erythropoiesis. expressed are required, indicating a line and intact cell. We also limited that percentage of mitochondria genes expressed to be lower than 0.05 and li- Methods brary size of cells within 2 SDs around mean value. Global-scaling normali- Informed Consent and Sample Collection. This study was approved by the zation method “LogNormalize” was performed on the filtered data at the medical ethics committee of the 303rd Hospital of the People’s Liberation next step. In the analysis of gene dynamics, normalized expression cells data Army. All donors provided informed consent and voluntarily donated the were inputted into CellRouter to construct complex single-cell trajectories samples for our study. A 10-mL BM sample was collected from six young and identify regulators and genes participating in erythroid terminal dif- healthy male donors, and 20-mL UCB samples were collected from six ferentiation (5). Code is available from Github (https://github.com/SMU- healthy neonate umbilical cords. Samples were collected into 50-mL sterile medicalgenetics/Single_cell). centrifuge tubes containing 30 IU/mL sodium heparin and stored on ice prior to analysis. In Vitro Erythroid Differentiation. CD34+ stem/progenitor cells (AllCells) were obtained from three mobilized BM and three neonate UCB samples from Isolation and Enrichment of Terminal Differentiated Erythroid Cells. Thirty independent donors. Cells were expanded at 37 °C and 5% CO2 in StemSpan milliliters of MACS labeling buffer [phosphate-buffered saline (PBS), 0.5% SFEM (StemCell Technologies) supplemented with 10% FBS, the human (wt/vol) BSA, and 2 mM disodium EDTA] was added to each sample and recombinant cytokine SCF 50 ng/mL, and 10 ng/mL IL-3 (PeproTech). Fol- mixed gently. Monocytes were isolated from BM/UCB samples by using lowing 4 d of expansion, referred herein to as day 0 of differentiation, cells Ficoll–Paque density gradient centrifugation as previously described (46–48). were transferred to an erythroid differentiation medium consisting of SFEM In brief, 5 mL Ficoll was overlaid carefully with 10 mL diluted BM/UCB medium with 10% FBS and 3 U/mL EPO. Cells were harvested every 48 h (4). samples in a 15-mL sterile centrifuge tube and centrifuged at 300 × g for To conduct knockdown experiments, cultured CD34+ cells were transduced 35 min at room temperature. The middle interphase with mononuclear cells separately with three different shRNA-expressing lentiviruses on day 4, and was aspirated with a Pasteur pipette into a 15-mL sterile centrifuge tube. the transduced cells were selected with puromycin. The shRNA sequences The isolated cells were washed with 5 mL of MACS labeling buffer, and the cell pellet was collected. MACS (Miltenyi Biotec) immune cell separation used for constructing knockdown lentivirus are listed in SI Appendix, − process was used to enrich for CD34+ and CD34 /CD235a+ cells from isolated Table S3. DEVELOPMENTAL BIOLOGY mononuclear cells according to previously published protocols (49, 50). Quantitative RT-PCR. Total RNA was extracted from CD235a+ cells enriched Single-Cell Capturing and cDNA Preparation. After the MACS cell isolation from BMs and UCB samples using traditional phenol-chloroform protocol. μ procedure, cells were suspended in 0.4% BSA–PBS at a concentration of cDNA (20 L per sample) was synthesized from 200 ng of RNA using Pri- μ 0.5∼1 × 106 cells per milliliter, and cell viability was monitored by a Countess meScript RT Master Mix (Takara) and diluted to 60 L before performing II Automated Cell Counter (LIFE Invitrogen). Viability of cells was >70%, and qPCR. RT-qPCR was performed on Applied Biosystems 7300/7500 Real-Time 8,000 cells per sample were added to each channel with the objective of PCR Systems using SYBR Green qPCR Master Mix (Takara). Each gene was run obtaining at least 5,000 cells for analysis. The cells were then partitioned in triplicate and normalized to the housekeeping gene GAPDH. Amplifica- into Gel Beads in Emulsion by running the Chromium Controller System (10× tion cycle was as follows: 95 °C for 30 s, 95 °C for 3 s, and 57 °C for 30 s for 40 Genomics) with Chromium Single Cell 3′ Reagent Kit (10× Genomics; v2 cycles. Primers for RT-qPCR are listed in SI Appendix, Table S4. Chemistry). Full-length cDNA(GEM-RT) was generated using the following incubation protocol: −53 °C for 45 min, 85 °C for 5 min, 4 °C hold. cDNA was Flow Cytometry Analysis. Isolated CD235a+ cells and cells from CD34+ cultures amplified with the following incubation conditions: 12 cycles of amplifica- at various time points were washed three times with PBS, resuspended in tion at 98 °C for 3 min, 98 °C for 15 s, 67 °C for 20 s, 72 °C for 1 min. The 25 μL PBS, and blocked with 2.5 μL human Fc blocking buffer. For flow amplified product was subjected to Post Amplification QC and Quantifica- cytometry analysis of BFU-E and CFU-E populations, cells were stained with tion by running a 2100 Bioanalyzer system (model G2939B; Agilent) (51–53). anti-human antibody CD36-APC (Miltenyi, 130–100-307), CD34-FITC (Milte- nyi, 130–098-142), CD123-AF488 (BioLegend, 306023), and CD235a-PE (Mil- Sequencing Library Construction and Sequencing. Library construction was tenyi, 130–100-269). To assess erythroid terminal differentiation, cells were performed using Chromium Single Cell 3′ Reagent Kit (v2) according to the stained with anti-human CD235a-FITC (Miltenyi, 130–100-266), CD233-PE manufacturer’s instructions. Ligated P5 primer, P7 primer, sample index, and (Miltenyi, 130–105-728), and CD49d-APC (130-099-226). Prior to subjecting read 2 primer were added, and cDNA and Illumina bridge amplification PCR cells to analysis by BD FACS-Melody cytometer, 5 μL of 7-AAD was added to was performed. In brief, fragmentation, end-repair, and A-tailing were select for live cells (2). For histologic staining, cells were spun onto glass performed with the following incubation protocol: precool block at 4 °C slides at 400 × g for 3 min using Centrifuge Automatic Preparation R hold, fragmentation at 32 °C for 5 min, end-repair and A-tailing at 65 °C for (Bio-Rad). Cells were stained with Wright’s–Giemsa Stain (Baso). Images μ 30 min. Following cleanup of the sample, 50 L Adaptor Ligation Mix was were acquired on an Olympus DP22 system. added and incubated at 20 °C for 15 min. Sample index PCR was performed with Amplification Master Mix and SI-PCR Primer added to each sample and Data Availability. The scRNA-seq raw datasets generated during this study subjected to the following protocol: 98 °C for 45 s, 98 °C for 20 s, 54 °C for 30 are deposited in Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/ s, 72 °C for 20 s, repeated 12 cycles, 72 °C for 1 min, and 4 °C hold. Samples geo/query/acc.cgi?acc=GSE150774). were processed for quantification following protocol. Libraries were se- quenced on an Illumina HiSeq X Ten. ACKNOWLEDGMENTS. The authors thank Professor Xiuli An (School of Life Science, Zhengzhou University) for helping experiment design. This research Single-Cell RNA Sequencing Analysis. After sequencing, UMI counts were was supported by grants from National Key R&D Program of China obtained for gene expression via gene-barcode matrix with Cell Ranger, a set (2018YFA0507800; 2018YFA0507803), National Natural Science Foundation of analysis pipelines that process Chromium single-cell RNA-seq output. In of China (31871265; 81770173), National Institutes of Health (NIH DK32094), order to access unbiased gene expression in which transcriptome data are and Guangdong Natural Science Foundation (2018B030308004).

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