Putative regulators for the continuum of erythroiddifferentiation revealed by single-cell transcriptomeof human BM and UCB cellsPeng 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 andEngineering Research Center for Molecular Diagnostics of Human Genetic Diseases, 510515 Guangzhou, China; cPrenatal Diagnostic Center, Institute ofBirth 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, LernerResearch 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 YorkBlood 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-topoietic stem cells (HSCs) involved in the generation of red cells iscritical for understanding dynamic developmental changes thataccompany human erythropoiesis. Using single-cell RNA sequenc-ing (scRNA-seq) of primary human terminal erythroid cells(CD34−CD235a+) isolated directly from adult bone marrow (BM)and umbilical cord blood (UCB), we documented the transcriptomeof terminally differentiated human erythroblasts at unprece-dented resolution. The insights enabled us to distinguish polychro-matic erythroblasts (PolyEs) at the early and late stages ofdevelopment as well as the different development stages of or-thochromatic erythroblasts (OrthoEs). We further identified a setof putative regulators of terminal erythroid differentiation andfunctionally validated three of the identified genes, AKAP8L, TER-F2IP, and RNF10, by monitoring cell differentiation and apoptosis.We documented that knockdown of AKAP8L suppressed the com-mitment of HSCs to erythroid lineage and cell proliferation anddelayed differentiation of colony-forming unit-erythroid (CFU-E)to the proerythroblast stage (ProE). In contrast, the knockdownof TERF2IP and RNF10 delayed differentiation of PolyE to OrthoEstage. Taken together, the convergence and divergence of thetranscriptional continuums at single-cell resolution underscorethe transcriptional regulatory networks that underlie human fetaland adult terminal erythroid differentiation.

scRNA-seq | terminal erythroid differentiation | cell clusters | regulator

Definitive human erythropoiesis, characterized by the move-ment of lineage-committed cells through progenitor, pre-

cursor, and mature RBC compartments, occurs in the fetal liverand in postnatal bone marrow. The human erythropoiesis pro-cess is divided into three distinct phases: early erythropoiesis,terminal erythroid differentiation, and reticulocyte maturation.The terminal erythroid differentiation phase is subdividedchronologically into four stages, proerythroblast (ProE), baso-philic erythroblast (BasoE), polychromatophilic erythroblast(PolyE), and orthochromatic erythroblast (OrthoE), based onthe morphological characteristics of the cells (1). The BasoE isfurther split into early and late stages based on cell surface ex-pression patterns of SLC4A1 (band 3) and ITGA4 (α4 integrin)(2). Differentiation of hematopoietic stem cells to red blood cellsis a continuous process with multiple distinct stages of devel-opment. During this process, cells express stage-specific genes,which are critical to regulate the complex process of stem cellcommitment to erythroid differentiation and subsequent termi-nal erythroid differentiation to generate enucleate red cells(3–5). During terminal erythroid differentiation, there is in-creasing expression of hemoglobin genes (HBA1/A2, HBB, and/

or HBG1/G2), and karyopyknosis and enucleation are importantbiological processes (3). However, significant gaps exist in de-tailed understanding of molecular mechanisms during this pro-cess. Thus, there is a critical need for detailed understanding ofthe dynamic changes in gene expression during erythropoiesis.Recently, bulk RNA sequencing studies and proteomic anal-

yses of human erythroblasts derived from in vitro cultures ofCD34+ cells revealed that the major dynamic changes in ex-pression pattern of genes during erythroid differentiation wereclustered into four major patterns: expression of the pattern 1 setof genes decreases during differentiation, the pattern 2 set ofgenes are highly expressed at the ProE stage and their expression

Significance

Using scRNA-seq, we identified a dynamic gene expressionprofile during terminal erythroid differentiation. Recent effortsrevealed the expression features at various developmentalstages during hematopoietic differentiation of human stemcells derived from human fetal cord blood and adult bonemarrow. However, the transcription dynamics for erythropoi-esis remain elusive. Here, we dissected the gene expressiondynamics from ProE to OrthoE by carrying out scRNA-seq oferythroblasts isolated from human cord blood and bone mar-row cells. We subdivided the human erythropoiesis PolyE andOrthoE into early-/late-PolyE and early-/late-OrthoE, re-spectively. We also predicted a list of regulators during termi-nal erythroid differentiation and tested in vitro differentiationexperiments. We provide a foundational human scRNA-seqdataset and candidate master regulators of erythropoiesis forfurther study.

Author contributions: P.H., X.P., and X.X. designed research; P.H. and H.Y. performedresearch; 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; andX.Z., Q.L., X.Q., and S.C. collected the UCB and BM samples.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

Published under the PNAS license.

Data deposition: The scRNA-seq raw datasets generated during this study are deposited inGene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150774). Code is available from Github (https://github.com/SMU-medicalgenetics/Single_cell).1P.H., Y.Z., and J.Z. contributed equally to this work.2To whom correspondence may be addressed. Email: panvictor@qq.com or xixm@smu.edu.cn.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1915085117/-/DCSupplemental.

First published May 26, 2020.

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decreases during differentiation, the pattern 3 set of genes areexpressed at low to midrange levels in ProE stage and increasetheir expression levels during later stages of differentiation, andthe pattern 4 set of genes exhibit an inverted V-shaped mirrorimage with a peak at the PolyE stage (4–7). In the present study,using single-cell RNA sequencing (scRNA-seq), we explored thetranscriptome of human erythroblasts derived from newbornumbilical cord blood and adult bone marrow. These studiesidentified subtypes of terminally differentiating erythroblasts andredefined their fate and differentiation processes, thereby doc-umenting the highly heterogenous composition of cell typesduring erythroblast differentiation, indicating cell-to-cell het-erogeneity (8–12). In addition, they provided transitional mo-lecular profiles in early progenitors during fetal hematopoiesis.Previous studies of human erythropoiesis relied almost ex-

clusively on the in vitro differentiation of hematopoietic stemand progenitor cells (HSPCs) from different sources, and onperforming bulk RNA-seq analysis on cells at different stages ofdifferentiation (13). Of note, molecular and cellular traits such ascellular morphology, gene expression profiles, and epigeneticsbetween primary erythroblasts in vivo and erythroblasts gener-ated in vitro have not been explored, as has been documented forother cell types (14). It is well known that there are two majordifferences between fetal and adult erythrocytes. First, the he-moglobin in fetal red blood cells is mainly HbF (about 98% oftotal hemoglobin), while, in adults, it is mainly HbA (about 95%of total hemoglobin). Second, the fetal umbilical cord blood (theperipheral blood of a fetus) contains a large number of nucleatedred blood cells, which are rare in adult peripheral blood (15, 16).Therefore, deciphering the differences in erythroblast differen-tiation trajectories between adult and fetal erythropoiesis shouldenable a better understanding of disordered erythropoiesis indistinct clinical conditions such as thalassemia and other bonemarrow failure syndromes with higher expression of HbF (4).In order to fill these gaps in our understanding of human

erythropoiesis, we performed scRNA-seq transcriptome analysisof terminal differentiated primary human erythroblasts isolateddirectly from umbilical cord blood (UCB) and bone marrow(BM) samples.

ResultsSingle-Cell Transcriptomes of Human Terminal Erythropoiesis. Toreliably construct an scRNA-seq library, we isolated primaryhuman terminally differentiated erythroblasts directly from adultBM and UCB samples using magnetic beads via CD34-negativeselection followed by CD235a (glycophorin A, GYPA) positiveselection (SI Appendix, Fig. S1 A and B). Single-cell sequencinglibraries were constructed based on the 10× Genomics Chro-mium protocols, and transcriptomic data were generated on theIllumina X 10 platform. Following rigorous quality control (QC),we obtained data on 8,668 cells from three BM samples and17,692 cells from three UCB samples. On average, we detected377 expressed genes and 5,870 mRNA molecules in each indi-vidual BM cell. In contrast, 602 expressed genes and 8,133mRNA molecules were noted in each UCB erythroblast. Weannotated each cell type and excluded the nonerythroid mono-cytes based on the criteria previously described (17) (SI Appen-dix, Fig. S2 A and B). We then performed imputation of thedropout data with the R package scImpute, followed by Pearsoncorrelation analysis. It appears that the expression pattern of thethree BM samples was highly consistent (Pearson coefficient R >0.97, P < 0.01) but substantially differed from UCB samples withhigh heterogeneity (Pearson coefficient R < 0.76, P < 0.01; SIAppendix, Fig. S3 A and B). Unsupervised clustering shows thathuman BM cells from the three samples are evenly distributed inmost clusters of cells except for one cluster unique to the BM1sample (SI Appendix, Fig. S3C). UCB1 cell clusters differ fromclusters of both UCB2 and UCB3 cell populations, which are

highly consistent, indicating the intersample heterogeneity fea-ture of human fetal erythropoiesis (SI Appendix, Fig. S3D). Wemerged the three BM-derived or three UBC-derived data setstogether prior to performing the bioinformatic analysis.

Heterogeneity and Clusters of Terminally Differentiated ErythroidCells in BM Samples. By applying the CellRanger package (18),we identified a set of 284 signature genes [between-clusters log2(fold change UMI counts), termed log2FC, >1.0; FDR < 0.05],enabling classification of 8,668 cells into 7 clusters, including acontinuous arc of 6 clusters (clusters 1–6) and one separatecluster (cluster 7; Fig. 1A and Dataset S1). Interestingly, cellsfrom cluster 7 were mostly from BM1 sample (SI Appendix, Fig.S3C). Based on the differentially expressed genes (DEGs) be-tween clusters and heat map analysis shown in Fig. 1B, clusters 3,4, and 5 were distinct from the others, while clusters 1, 2, and 6shared similar DEGs (Fig. 1B). Thus, clusters 1, 2, and 6 wererecognized as the same group of cells. To identify the order ofprogression of cell differentiation progress, we reconstructed celldifferentiation trajectory with Monocle (19), and the resultshowed that the sequential order of cell differentiation in clus-ters is as follows: 3 → 4 → 5 → (1, 2, 6) → 7 (Fig. 1C). In otherwords, cluster 3 belongs to a group of cells at the earliest dif-ferentiation stage, while cluster 7 belongs to cells at a very latestage of differentiation. Additionally, mRNA expression ofGYPA, the distinct marker of erythroblasts, is down-regulatedalong the cell’s maturation pathway (13). When the expressionlevel of GYPA mRNA among these clusters was compared,cluster 7 had the lowest value (Fig. 1D), implying that cluster 7most likely represents the very last stage of differentiation (verylate OrthoE or reticulocytes).Next, we identified cells at differentiation stages correspond-

ing to ProE, BasoE, PolyE, and OrthoE by Seurat based on thepreviously reported expression of marker genes during humanterminal erythroid differentiation (13, 20). We noted that 67.1%of cells belong to OrthoE and 24.9% of cells belong to PolyE,while few ProE and BasoE cells were also recognized (Fig. 1E).This distribution of cells at different differentiation stages isconsistent with previously reported results (21) and consistentwith the human erythroid developmental program in bonemarrow. When gene expression patterns of cells in differentclusters were mapped to previously reported expression profilesof erythroblasts at different development stages (Fig. 1 A and E),both ProE and BasoE cells were part of cluster 3 rather thanseparating into two distinct clusters. This could be either due toinsufficient numbers of cells belonging to these two stages ana-lyzed and/or highly similar gene expression patterns at these twodevelopmental stages. Therefore, we chose not to focus on thesetwo early cell stages so as not to overinterpret the findings re-garding these specific development stages. The PolyE cellsgrouped into clusters 4 and 5 and part of cluster 3, and wedesignated these 3 clusters respectively as transit-PolyE (parts ofcluster 3), early-PolyE (cluster 4), and late-PolyE (cluster 5).Based on the fact that the gene expression pattern of transit-PolyE cells had some overlap with genes expressed in BasoEcells, we defined this particular stage of cells as a transitionalstage between BasoE and PolyE. OrthoE cells could also bedivided into two stages of development, early-OrthoE (clusters 1,2, 6) and late-OrthoE (cluster 7), respectively. We also notedthat the intranuclear MALAT1 gene was expressed at a lowerlevel in the late-OrthoE stage than in other stages (Fig. 1D).Therefore, we speculate that the late-OrthoE would be a tran-sition state with highly pyknotic nuclei and ready for enucleation.Taken together, these findings suggest that the conventionallydefined differentiation stages, PolyE and OrthoE, can be furthersubdivided along their maturation states based on gene expressionpatterns.

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We performed Gene Ontology (GO) analysis based on dif-ferentially expressed genes among the four groups (ProE/BasoE/transit-PolyE, early-/late-PolyE, early-OrthoE, and late-OrthoE)and identified associated enriched GO terms to gain insights intothe biological processes (Fig. 1 B and F and Dataset S2). A set of207 signature genes was identified in cluster 3 representing ProE,BasoE, and transit-PolyE cells, and the GO terms for this clusterwere significantly enriched (FDR < 0.01) for differentiallyexpressed genes related to ribosome biogenesis, protein target-ing, and RNA catabolic process. Early- and late-PolyE cellsshared similar differentially expressed genes, with only 13 dif-ferential expressed genes (|logFC| > 0.5, FDR < 0.01) betweenthem identified (Fig. 1B and Dataset S1), which were for celldivision, organelle fission, and cell cycle.The two most important biological processes in OrthoE stage

are karyopyknosis and enucleation, whose molecular mechanisms

remain to be fully defined. There is good evidence that Rho GTPaseand cytoskeleton contribute to these processes (22–24). For early-OrthoE stage, 30 differentially expressed genes were identified, in-cluding GTPase biological synthesis and activation regulators, es-pecially TMCC2, EIF5, and ARL4A (25–27). Moreover, XPO7 ishighly expressed at this stage, and is essential for regulating eryth-roblast maturation and karyopyknosis (28, 29). These findings con-firm that early-OrthoE stage is indeed the preparatory stage for cellenucleation. Interestingly, the antiviral gene IFIT1B is also highlyexpressed at this stage, implying a role in cellular immunity, whichhas previously been suggested as a potential function of erythroblasts(30). In addition, differentially expressed genes related to iron anderythrocyte homeostasis were also significantly enriched. At late-OrthoE stage, a transition period before generation of enucleatereticulocytes, differentially expressed genes related to oxygen trans-port and hemoglobin complex synthesis were significantly enriched.

Fig. 1. Single-cell transcriptomes of the terminal differentiation of human erythroid cells and their distinct biomarkers. (A) t-SNE visualization of adult BMerythroblasts (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 withMonocle. Each dot represents a single cell. Dots in colors indicate different cell clusters. Black arrow indicates cell differentiation trajectory. (D) t-SNE mapcolored 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 cellcycle are depicted in different colors, G0/G1 in orange, S in azure, and G2/M in green.

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In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG)pathway enrichment analysis with expression genes in each clusterrevealed that the autophagy pathway was enriched at OrthoE stage,which indicated that cell autophagy contributes to the enucleationand maturation processes of erythroblasts.Based on expression of genes involved in regulating the cell

cycle using Seurat, we noted that, among the 8,668 BM eryth-roblasts analyzed, 1,103 cells were in S phase, 1,712 cells in G2/Mphase, and 5,853 cells in G0/G1 phase. Further analysis showedthat ProE/BasoE cells were mostly at S phase, early-/late-PolyEcells were mostly at G2/M phase, and early-/late-OrthoE cellswere mostly at G0/G1 phase (Fig. 1G). Due to the condensednucleus of early-/late-OrthoE cells, we speculate that these cellswere at phase G0 rather than at G1 phase.Furthermore, we noted a decrease in numbers of genes

expressed as erythroblast differentiation progressed, which isconsistent with the decrease in number of proteins detected (7),while, at the same time, there is a marked increase in expressionof genes encoding hemoglobin, especially HBA1/HBA2, HBB,and/or HBG1/HBG2 (SI Appendix, Fig. S4A). In addition tohemoglobin genes, we also found a few other highly expressedgenes, including previously reported AHSP (α hemoglobin sta-bilizing protein) (6) and a set of genes (SI Appendix, Table S1)that are related to regulation of hemoglobin synthesis, structuralremodeling of erythroblasts, and karyopyknosis. While we notedthe expression of TPT1, EEF2, and TFRC genes, it contrasts withprevious reports of their high level of expression in cultured cells(6). They were expressed at low levels in our set of single-cellsequencing data with primary unmanipulated erythroblasts.

Heterogeneity and Clusters of Terminal Erythroid Cells in UCBSamples. With the same stratification strategy as detailed ear-lier, seven clusters of cells and their stages of differentiation andcell cycle phases were also identified in UCB erythroblasts (SIAppendix, Fig. S5 A, B, and D and Dataset S3). When comparedto BM erythroblasts, the most significant difference noted be-tween them is the differentiation stage of the cells. Most of theerythroblasts in UCB samples were OrthoE (87%), while theproportion of OrthoE in BM samples was 67%. This noted dif-ference in developmental stage was validated by flow cytometryanalysis (SI Appendix, Fig. S5C). Another difference noted was inthe expression levels of γ- and β-hemoglobin genes. While bothHBB and HBG1/G2 were highly expressed in UCB erythroblasts,only HBB was expressed in BM erythroblasts (SI Appendix, Fig.S4B). Interestingly, the scRNA-seq analysis for the three UCBsamples revealed a complicated heterogeneity in the OrthoEstages, which are mostly in the G0/G1 stage, with a small portionin the S phases, of the cell cycle (SI Appendix, Fig. S5D).

Gene Expression Dynamics during Human Terminal ErythroidDifferentiation. We sought to profile gene expression dynamicsat single-cell transcriptome resolution across different stages ofhuman terminal erythroid differentiation with Monocle. We as-sembled genes into three subsets according to their expressiontrend along the differentiation trajectory (Fig. 2A). Although thenumber of expressed genes identified at single-cell transcriptomeresolution was not as high as previously reported from se-quencing of large numbers of cells, the noted expression trendsbased on scRNA-seq data in large part matched data from bulksequencing (6). Panel one encompasses a set of 1,145 geneswhose expression is high at very early stages of erythroiddifferentiation but are rapidly down-regulated as erythroid dif-ferentiation proceeds. This set included genes encoding tran-scription factors KLF1 and BCL11A, which are critical at earlyphases of erythroid differentiation. A second panel encompassesa set of 647 genes whose expression begins at early stages ofdifferentiation and is sustained as the differentiation proceeds,followed by down-regulation at very late stages. The third panel

is a set of 674 genes that are expressed at low levels at earlystages of terminal erythroid differentiation phase and whoseexpression progressively increases at late stages of differentia-tion. Interestingly, we noted different patterns of gene expres-sion in the third panel: a subset of genes that are specificallyenriched at PolyE stage, a second subset highly expressed atearly-OrthoE stage, and a third subset marking the late-OrthoEstage.To validate the noted differential gene expression patterns

from our scRNA-seq analysis, we performed in vitro culturing ofadult BM CD34+ cells to generate erythroblasts at various stagesof differentiation and monitored expression patterns of TER-F2IP, SOX6, IFIT1B, CFL1, and ARL4A genes that encompassthe three noted patterns of expression (Fig. 2A) using quantita-tive PCR (qPCR) (31). Morphological examination followingWright’s–Giemsa staining showed that ProE was the dominantpopulation on day 7, BasoE was dominant on day 9 and day 11,PolyE was dominant on day 13 and day 15, and OrthoE wasdominant on day 17 (Fig. 2B). Indeed, qPCR showed us that theexpression of CFL1 (gene belonging to Fig. 2A, panel 1) washighly expressed at day 7 but rapidly decreased as differentiationproceeded. SOX6 (gene belonging to Fig. 2A, panel 2) increasedits expression from day 7 to day 13 and its expression decreasedon day 17. Regarding IFIT1B, TERF2IP, and ARL4A (genesbelonging to Fig. 2A, panel 3), their expression began at day 7and exhibited high levels of expression at very late stages oferythroid differentiation (Fig. 2C). Furthermore, the well-knownerythroid regulators SNCA and FOXO3 were up-regulated, whileGATA1, BCL11A, and KLF1 were down-regulated during ter-minal erythroid differentiation (SI Appendix, Fig. S6A). In-triguingly, we observed the decreasing expression of NFE2L1,NDEL1, EPB41, USO1, MARK2, LGALS9, NEK1, and AXIN1(SI Appendix, Fig. S6B); however, a recent study using culturedcells from CD34+ cells showed increased expression up toOrthoE stage (32). Thus, it appears that the gene expressionprofiles of primary isolated cells may differ from the in vitrodifferentiation experiments.

Regulators of Human Terminal Erythroid Differentiation.We appliedCellRouter analysis to reconstruct single cell trajectory, in whicha k-nearest neighbor (kNN) cellular network can be built viadimensionality reduction (Fig. 1A). The key algorithm of Cell-Router leverages a scoring scheme, termed gene regulatorynetworks score (GRN), to identify transcriptional regulators bytheir state of activation of predicted target genes (33). By cal-culating GRNs (from ProE to late-OrthoE), we could documenta continuous regulatory network during terminal erythroid dif-ferentiation, including positive regulators (plus score of GRNand increased levels of target gene expression) and negativeregulators (minus score of GRN and decreased expression oftarget genes; Fig. 3). Based on GRNs, we identified a set of top-ranked genes that encompass the well-known regulators oferythropoiesis, NFE2, HMGB2, YBX1, SOX6, FOXO3, andSNCA (34–39), and other candidate regulatory genes for whichlittle information is currently available regarding their roles interminal erythroid differentiation (SI Appendix, Table S2). Wewould like to note that GATA1 and KLF1 are in the list of ourpredictive regulators, but with a lower predictive power, likelydue to their decreased mRNA expression during later stages oferythroid differentiation.During terminal erythroid differentiation, we noted that the

GRN scores for SNCA, FOXO3, NFE2, NFIX, RNF10, TER-F2IP, and AKAP8L substantially increased, while the GRNscores of SOX6 and YBX3 continuously decreased. It is likelythat the genes with increasing GRN scores during terminal ery-throid differentiation are much more relevant to regulatingerythroid differentiation than the genes with decreasing GRNscores. Furthermore, we noted that different positive regulators

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increased their expression level at different stages of terminalerythroid differentiation, which implies that they would functionat specific stages during differentiation. To validate this hy-pothesis, we selected TERF2IP, AKAP8L, and RNF10 to carryout knockdown experiments using CD34+ cells, since theirfunction during erythroid differentiation has not been pre-viously characterized (40). These regulators were chosen on thebases of their high GRN scores and their up-regulated ex-pression patterns at different stages of differentiation (SI Ap-pendix, Fig. S6C).To explore the roles of AKAP8L, TERF2IP, and RNF10 in

regulating erythroid differentiation at different developmentalstages, we used the shRNA-mediated knockdown approach andmonitored in vitro cell differentiation by flow cytometry. Theknockdown efficiency was quantitated through all stages of dif-ferentiation. On day 7 of culture, the knockdown efficiencies forAKAP8L-shRNA, TERF2IP-shRNA, and RNF10-shRNA were58%, 79%, and 72%, respectively (Fig. 4A and SI Appendix, Fig.S7). Erythropoiesis can be functionally divided into two stages:early-stage erythropoiesis, which encompasses transition from

HSC to CFU-E, and terminal erythroid differentiation of ProEto OrthoE. We compared the BFU-E and CFU-E populationsbetween control and knockdown groups based on the surfaceexpression levels of CD34 and CD36 using the previously de-scribed flow cytometry-based strategy (41). On day 7, the BFU-Epopulation in a control group was 19%, while, in the AKAP8L-shRNA group, it was significantly higher at 37% (P < 0.01). Incontrast, the CFU-E population was higher at 46% in a controlgroup than in AKAP8L-shRNA at 13% (P < 0.01). Thus,AKAP8L knockdown resulted in delayed maturation of erythroidprogenitors. No such effect was seen in either BFU-E or CFU-Epopulation following knockdown of TERF2IP and RNF10. BFU-E population was 27% and 28% (vs. 19% in the control group),and CFU-E cells were 40% and 41% (vs. 46% in the controlgroup) in TERF2IP-shRNA and RNF10-shRNA cells, re-spectively (Fig. 4B). These findings imply that knockdown ofAKAP8L delayed the commitment of HSC to erythroid lineage,while knockdown of TERF2IP and RNF10 did not affectthis process.

Fig. 2. Single-cell gene expression dynamics in human terminal erythroid differentiation. (A) Patterns of gene transcriptional trends in human BM cellsduring terminal erythroid differentiation analyzed with Monocle. The graphs on the right side indicate the trends of gene expression. (B) Morphology oferythroid 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, andPolyE cells are marked with red, blue, and black, respectively (objective lens, 100×). (C) Predicted master gene regulator tested by qRT-PCR. Cells wereharvested on days 7, 9, 11, 13, 15, and 17 from in vitro differentiated CD34+ cells. Gene expression level is illustrated with ΔCt value.

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Next, we examined the effects of AKAP8L, TERF2IP, andRNF10 knockdown on terminal erythroid differentiation. It hasbeen documented that the transition of CFU-E to ProE ischaracterized by the expression of GYPA (CD235a) (2). Flowcytometry showed that, on day 7 of culture, only 22% ofAKAP8L-knockdown cells were CD235a-positive, whereas 43%of control group cells were CD235a-positive, demonstrating thatthe knockdown of AKAP8L delayed the differentiation of CFU-E to ProE. No such delay was noted following the knockdown ofeither RNF10 or TERF2IP (Fig. 4C). Furthermore, monitoringof surface expression levels of α4 integrin and band 3 to assessterminal erythroid differentiation showed that AKAP8L knock-down delayed the progression of erythroid differentiation at day13, TERF2IP knockdown on day 15, and RNF10 knockdownon day 17 (Fig. 4D). Staining with Wright’s–Giemsa of cells fromcultures at different time points showed that BasoE was thedominant population on day 11, early-PolyE on day 13, late-PolyE on day 15, and OrthoE on day 17 (Fig. 2B). Thus,knockdown of AKAP8L suppressed cell proliferation and delayeddifferentiation of CFU-E to terminal erythroblast stages, whileknockdown of TERF2IP delayed differentiation of early-PolyE tolate-PolyE and knockdown RNF10 delayed differentiation of late-PolyE to early-OrthoE.We also examined the effects of AKAP8L, TERF2IP, and

RNF10 knockdown on cell growth. The growth curves showedonly small differences in cell numbers between control andknockdown groups until day 7. However, cell numbers for AKAP8L

knockdown showed a decrease starting on day 9 that persisted untilthe end of the culture period. We also noticed a slight decrease incell numbers in TERF2IP and RNF10 knockdown groups on day 9,followed by a burst of proliferation on day 11 (Fig. 4E). The de-creased final output of erythroid cells at the end of culture is mostlikely due to apoptosis.

DiscussionTerminal erythroid differentiation phase is critical for the gen-eration and final stages of maturation of red blood cells. Duringthis phase, erythroblasts are classified as ProE, early-BasoE, late-BasoE, PolyE, and OrthoE based on morphology and cell sur-face expression of membrane proteins. To explore the proteomiccomposition and biological functions of cells at these distinctstages, large numbers of purified cells are required. Whileexisting strategies can be used for isolating large number of cellsproduced in vitro, to date, it has not been possible to isolatesufficient numbers of primary erythroblasts at distinct de-velopmental stages from human bone marrow. scRNA-seq is apowerful and robust tool to capture transcriptome-wide insightsfrom samples of mixed cell populations of cells with finite cellnumbers. In this study, we profiled and analyzed the regulatorylandscape via scRNA-seq of primary unmanipulated human ter-minally differentiated erythroid cells (CD34−CD235a+) isolateddirectly from healthy adult BM and neonate UCB samples. Bysingle cell sequencing, we obtained a fine-resolution phasing of hu-man terminal erythroid differentiation and a set of gene signatures

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.

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related to the development and maturation of erythroid cells. Thefindings that only a small number of ProE and BasoE cells are foundin BM samples is possibly attributed to their relative low abundancecompared to later stages of differentiation, since every 1 ProE cellwill generate 16–32 OrthoE cells in bone marrow as a result of 4–5mitosis. Hence, our results primarily identified the heterogeneity inthe more abundant PolyE and OrthoE stages of erythroid differ-entiation. In fetal UCB, not surprisingly, we found that cells that arereleased into circulation under stress erythropoiesis are mostly late-OrthoE. It has been previously reported that, in addition to fetalUCB, late-OrthoE cells are also found in circulation of thalassemiapatients (16).A number of important regulatory genes have been previously

identified by bulk RNA-seq in the early stages of erythropoiesiswith cultured cells (13); a few of them are well recognized, e.g.,KLF1, GATA1, NFE2, BCL11A, LRF, TET2/3, and MYB(42–44). In order to understand the nature of erythropoiesisin vivo, in the present study, we used unmanipulated primaryerythroid cells derived from healthy adult BM. In addition to thewell-known erythropoiesis regulators GATA1, KLF1, and MYB,we identified a set of regulatory genes, particularly AKAP8L,

RNF10, and TERF2IP, that could potentially play a key role inregulating terminal differentiation of primary human erythroidcells, especially in regulating erythroblast karyopyknosis andenucleation. These candidate regulators could be prioritized infuture studies to develop a comprehensive understanding ofkaryorrhexis and also the underlying mechanism of diversity incell stage differences in various different types of anemia, in-cluding congenital dyserythropoietic anemias.Recent studies have documented the dynamics of erythroid

gene expression patterns by bulk RNA-seq of human erythro-blasts derived from culturing of CD34+ cells from adult and cordblood (13). While our findings are in large part consistent withthese reports, we also found that the profiles of erythroid cellsisolated directly from BM (in vivo) exhibit some differences. Forexample, in contrast to the reported increased expression ofmaster regulators GATA1 and KLF1 during differentiation ofcultured CD34+ cells into OrthoE cells and decreasing expres-sion of hemoglobin switching genes SOX6 and BCL11A at PolyEstage (45), the expression patterns of these genes, GATA1,KLF1, SOX6, and BCL11A, were different in primary unculturedcells. According to our data, GATA1, SOX6, and KLF1 were

Fig. 4. Effects of AKAP8L, TERF2IP, and RNF10 knockdown on human erythroid differentiation. (A) Expression levels of AKAP8L, TERF2IP, and RNF10 ex-amined 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 progenitorpopulations, 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 dataare 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 inpercentage (mean ± SEM, n = 3; *P < 0.01). (D) Terminal erythroid differentiation was examined on indicated days by flow cytometric analysis based on theexpression of band 3 and α4 integrin. Representative plots of α4 integrin versus band 3 of CD235a+ cells are shown, and the erythroblasts are separated intoseven 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 normalcontrol CD34+ cells and cells following knockdown of AKAP8L, TERF2IP, and RNF10, respectively (mean ± SEM, n = 3; *P < 0.01, differential significance whencompared to normal control groups).

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down-regulated all the way through the terminal stage, whileBCL11A is continuously expressed at low levels. The noted dif-ferences might be likely due to the differences in gene expressionpatterns of in vivo- and in vitro-derived erythroblasts. Un-derstanding how cultured erythroblasts differ from those isolatedin vivo is important. Future studies should critically address thisissue to obtain comprehensive understanding of this difference inhuman erythropoiesis.

MethodsInformed Consent and Sample Collection. This study was approved by themedical ethics committee of the 303rd Hospital of the People’s LiberationArmy. All donors provided informed consent and voluntarily donated thesamples for our study. A 10-mL BM sample was collected from six younghealthy male donors, and 20-mL UCB samples were collected from sixhealthy neonate umbilical cords. Samples were collected into 50-mL sterilecentrifuge tubes containing 30 IU/mL sodium heparin and stored on ice priorto analysis.

Isolation and Enrichment of Terminal Differentiated Erythroid Cells. Thirtymilliliters of MACS labeling buffer [phosphate-buffered saline (PBS), 0.5%(wt/vol) BSA, and 2 mM disodium EDTA] was added to each sample andmixed gently. Monocytes were isolated from BM/UCB samples by usingFicoll–Paque density gradient centrifugation as previously described (46–48).In brief, 5 mL Ficoll was overlaid carefully with 10 mL diluted BM/UCBsamples in a 15-mL sterile centrifuge tube and centrifuged at 300 × g for35 min at room temperature. The middle interphase with mononuclear cellswas aspirated with a Pasteur pipette into a 15-mL sterile centrifuge tube.The isolated cells were washed with 5 mL of MACS labeling buffer, and thecell pellet was collected. MACS (Miltenyi Biotec) immune cell separationprocess was used to enrich for CD34+ and CD34−/CD235a+ cells from isolatedmononuclear cells according to previously published protocols (49, 50).

Single-Cell Capturing and cDNA Preparation. After the MACS cell isolationprocedure, cells were suspended in 0.4% BSA–PBS at a concentration of0.5∼1 × 106 cells per milliliter, and cell viability was monitored by a CountessII Automated Cell Counter (LIFE Invitrogen). Viability of cells was >70%, and8,000 cells per sample were added to each channel with the objective ofobtaining at least 5,000 cells for analysis. The cells were then partitionedinto Gel Beads in Emulsion by running the Chromium Controller System (10×Genomics) with Chromium Single Cell 3′ Reagent Kit (10× Genomics; v2Chemistry). Full-length cDNA(GEM-RT) was generated using the followingincubation protocol: −53 °C for 45 min, 85 °C for 5 min, 4 °C hold. cDNA wasamplified with the following incubation conditions: 12 cycles of amplifica-tion at 98 °C for 3 min, 98 °C for 15 s, 67 °C for 20 s, 72 °C for 1 min. Theamplified product was subjected to Post Amplification QC and Quantifica-tion by running a 2100 Bioanalyzer system (model G2939B; Agilent) (51–53).

Sequencing Library Construction and Sequencing. Library construction wasperformed using Chromium Single Cell 3′ Reagent Kit (v2) according to themanufacturer’s instructions. Ligated P5 primer, P7 primer, sample index, andread 2 primer were added, and cDNA and Illumina bridge amplification PCRwas performed. In brief, fragmentation, end-repair, and A-tailing wereperformed with the following incubation protocol: precool block at 4 °Chold, fragmentation at 32 °C for 5 min, end-repair and A-tailing at 65 °C for30 min. Following cleanup of the sample, 50 μL Adaptor Ligation Mix wasadded and incubated at 20 °C for 15 min. Sample index PCR was performedwith Amplification Master Mix and SI-PCR Primer added to each sample andsubjected to the following protocol: 98 °C for 45 s, 98 °C for 20 s, 54 °C for 30s, 72 °C for 20 s, repeated 12 cycles, 72 °C for 1 min, and 4 °C hold. Sampleswere processed for quantification following protocol. Libraries were se-quenced on an Illumina HiSeq X Ten.

Single-Cell RNA Sequencing Analysis. After sequencing, UMI counts wereobtained for gene expression via gene-barcode matrix with Cell Ranger, a setof analysis pipelines that process Chromium single-cell RNA-seq output. Inorder to access unbiased gene expression in which transcriptome data are

unaffected by excess zero or near-zero counts called dropout events, weapplied the R software package scImpute described by Li and Li (54) (https://github.com/Vivianstats/scImpute) to recover gene expression data on whichdownstream analyses can be performed. We assumed that there were fivesubpopulations according to prior knowledge and set k = 5 to impute. Afterimputation, assignment of cell-cycle stage was performed using the cyclonefunction in the scanner package. We kept all genes expressed (defined bynonzero counts) in ≥10 cells (∼0.2% of the data). For cells, at least 200 genesexpressed are required, indicating a line and intact cell. We also limited thatpercentage of mitochondria genes expressed to be lower than 0.05 and li-brary size of cells within 2 SDs around mean value. Global-scaling normali-zation method “LogNormalize” was performed on the filtered data at thenext step. In the analysis of gene dynamics, normalized expression cells datawere inputted into CellRouter to construct complex single-cell trajectoriesand identify regulators and genes participating in erythroid terminal dif-ferentiation (5). Code is available from Github (https://github.com/SMU-medicalgenetics/Single_cell).

In Vitro Erythroid Differentiation. CD34+ stem/progenitor cells (AllCells) wereobtained from three mobilized BM and three neonate UCB samples fromindependent donors. Cells were expanded at 37 °C and 5% CO2 in StemSpanSFEM (StemCell Technologies) supplemented with 10% FBS, the humanrecombinant cytokine SCF 50 ng/mL, and 10 ng/mL IL-3 (PeproTech). Fol-lowing 4 d of expansion, referred herein to as day 0 of differentiation, cellswere transferred to an erythroid differentiation medium consisting of SFEMmedium with 10% FBS and 3 U/mL EPO. Cells were harvested every 48 h (4).To conduct knockdown experiments, cultured CD34+ cells were transducedseparately with three different shRNA-expressing lentiviruses on day 4, andthe transduced cells were selected with puromycin. The shRNA sequencesused for constructing knockdown lentivirus are listed in SI Appendix,Table S3.

Quantitative RT-PCR. Total RNA was extracted from CD235a+ cells enrichedfrom BMs and UCB samples using traditional phenol-chloroform protocol.cDNA (20 μL per sample) was synthesized from 200 ng of RNA using Pri-meScript RT Master Mix (Takara) and diluted to 60 μL before performingqPCR. RT-qPCR was performed on Applied Biosystems 7300/7500 Real-TimePCR Systems using SYBR Green qPCR Master Mix (Takara). Each gene was runin triplicate and normalized to the housekeeping gene GAPDH. Amplifica-tion cycle was as follows: 95 °C for 30 s, 95 °C for 3 s, and 57 °C for 30 s for 40cycles. Primers for RT-qPCR are listed in SI Appendix, Table S4.

Flow Cytometry Analysis. Isolated CD235a+ cells and cells from CD34+ culturesat various time points were washed three times with PBS, resuspended in25 μL PBS, and blocked with 2.5 μL human Fc blocking buffer. For flowcytometry analysis of BFU-E and CFU-E populations, cells were stained withanti-human antibody CD36-APC (Miltenyi, 130–100-307), CD34-FITC (Milte-nyi, 130–098-142), CD123-AF488 (BioLegend, 306023), and CD235a-PE (Mil-tenyi, 130–100-269). To assess erythroid terminal differentiation, cells werestained with anti-human CD235a-FITC (Miltenyi, 130–100-266), CD233-PE(Miltenyi, 130–105-728), and CD49d-APC (130-099-226). Prior to subjectingcells to analysis by BD FACS-Melody cytometer, 5 μL of 7-AAD was added toselect for live cells (2). For histologic staining, cells were spun onto glassslides at 400 × g for 3 min using Centrifuge Automatic Preparation R(Bio-Rad). Cells were stained with Wright’s–Giemsa Stain (Baso). Imageswere acquired on an Olympus DP22 system.

Data Availability. The scRNA-seq raw datasets generated during this studyare deposited in Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150774).

ACKNOWLEDGMENTS. The authors thank Professor Xiuli An (School of LifeScience, Zhengzhou University) for helping experiment design. This researchwas supported by grants from National Key R&D Program of China(2018YFA0507800; 2018YFA0507803), National Natural Science Foundationof China (31871265; 81770173), National Institutes of Health (NIH DK32094),and Guangdong Natural Science Foundation (2018B030308004).

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