- Open Access
Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing
- Xiaoping Han†1, 2, 8,
- Haide Chen†1, 3, 5Email author,
- Daosheng Huang1, 8,
- Huidong Chen4, 6,
- Lijiang Fei1, 8,
- Chen Cheng7,
- He Huang2, 8,
- Guo-Cheng Yuan4Email author and
- Guoji Guo1, 2, 3, 8Email author
© The Author(s). 2018
Received: 29 November 2017
Accepted: 21 March 2018
Published: 5 April 2018
Human pluripotent stem cells (hPSCs) provide powerful models for studying cellular differentiations and unlimited sources of cells for regenerative medicine. However, a comprehensive single-cell level differentiation roadmap for hPSCs has not been achieved.
We use high throughput single-cell RNA-sequencing (scRNA-seq), based on optimized microfluidic circuits, to profile early differentiation lineages in the human embryoid body system. We present a cellular-state landscape for hPSC early differentiation that covers multiple cellular lineages, including neural, muscle, endothelial, stromal, liver, and epithelial cells. Through pseudotime analysis, we construct the developmental trajectories of these progenitor cells and reveal the gene expression dynamics in the process of cell differentiation. We further reprogram primed H9 cells into naïve-like H9 cells to study the cellular-state transition process. We find that genes related to hemogenic endothelium development are enriched in naïve-like H9. Functionally, naïve-like H9 show higher potency for differentiation into hematopoietic lineages than primed cells.
Our single-cell analysis reveals the cellular-state landscape of hPSC early differentiation, offering new insights that can be harnessed for optimization of differentiation protocols.
Thomson et al. derived human pluripotent stem cells (hPSCs) from human blastocysts for the first time in 1998 . hPSCs have the capacity of self-renewal and multilineage differentiation both in vitro and in vivo. These features of hPSCs have provided remarkable promise in developmental biology and regenerative medicine . hPSCs can be used to generate diverse cell-types from all three germ layers using different differentiation protocols [3–7]. However, most existing protocols suffer from low efficiency and functional deficiency.
hPSC differentiation is a complex process. Flow cytometry and immunostaining have been used to define cell types in hPSC differentiation cultures. However, these methods are limited by the number of fluorescent probes that can be used at the same time; the heterogeneity of the hPSC differentiation process cannot be fully resolved. Single-cell RNA-sequencing (scRNA-seq), first released in 2009 , has provided a promising alternative. During the past few years, the technology has been vastly improved by the development of numerous innovative approaches [24, 25], including C1 (SMARTer) , SMART-seq2 , CEL-seq , Drop-seq , InDrop , 10X Genomics , etc. To date, single-cell technology has been used to study cellular heterogeneity in a wide range of systems , including the hierarchy of tumor cells [32, 33], tissue and organs [34–37], developing embryos [38, 39] and in vitro differentiation systems [40–42].
We use the upgraded Fluidigm C1 system with optimized high-throughput integrated fluidics circuits (HT IFCs) to construct the early differentiation trajectories of various lineage-specific progenitors derived from hPSCs and to reveal the interaction between these precursor cells in EB differentiation system. We find key TFs and signaling pathways that direct the differentiation process. We show that liver may be involved in regulating the differentiation of other tissue cells through cell–cell interactions. We also use the C1 scRNA-seq platform to study the primed-to-naïve transition process and to reveal the differences in gene expression profiles between Primed and Naïve-like H9. Combined with the analysis of EB differentiation, genes related to hemogenic endothelium development and MAPK-ERK1/2 signaling pathway are enriched in Naïve-like H9 but not in Primed H9. Functionally, Naïve-like H9 show the differentiation bias to endothelial-hematopoietic lineages. Taken together, we construct a comprehensive single-cell level differentiation roadmap for hPSCs and offer new insights into early embryonic lineages that can guide the establishment and optimization of more sophisticated differentiation system.
scRNA-seq analysis of hPSCs and EBs
In order to systematically map hPSCs early differentiation pathways, Naïve-like H9, Primed H9, and EBs were prepared as single-cell samples for sequencing using Fluidigm C1 system with HT IFCs (Fig. 1a). This system can be used to analyze up to 800 cells at a time and detect an average of 5000 genes per cell. A major advantage of this technology is the balance of throughput and resolution. After sequencing and data processing, we got high-quality transcriptomic data from 4822 single cells, including 2636 EB samples (683 day 4 EBs and 1953 day 8 EBs), 1491 Naïve-like H9 samples, and 695 Primed H9 samples. The scRNA-seq data had high read depth, which can map to 5000 genes for most of the single-cell samples (Fig. 1b and Additional file 1: Figure S1a); and Naïve-like H9 datasets show weak batch effect of Fluidigm C1 system (Additional file 1: Figure S1b). The random differentiation of EBs causes the batch effect (Additional file 1: Figure S1c). We used Seurat to perform principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analysis . Seurat divided our samples into four main clusters, including two EB clusters (EB-ectodetm and EB-mesendoderm), one Primed H9 cluster, and one Naïve-like H9 cluster (Fig. 1c). hPSCs (i.e. Naïve-like and Primed H9) have relative homogeneity. EB cells show significant heterogeneity, which indicated well spontaneous differentiation of hPSCs and provided a variety of samples for Monocle pseudotime analysis . To reveal the gene expression dynamics and key regulators of hPSC early differentiation, we used Seurat and Monocle to analyze these data.
Mapping cellular landscape for early embryonic lineages
Cluster muscle cell consists of four sub-clusters, including muscle-LDHA-3, muscle-FBN2–4, muscle-CRYAB-13, and muscle-GABRP-12. These sub-clusters have specific gene expression pattern and gene expression distribution (Fig. 3c and d and Additional file 1: Figure S3e–h). Muscle-LDHA-3 is enriched for LDHA, POSTN, and IGF2 ; muscle-FBN2–4 is enriched for FBN2, NCAM1, and SERPINE2 ; muscle-CRYAB-13 is enriched for CRYAB, COL1A1, and LGALS1 [67, 68]; muscle-GABRP-12 is enriched for GABRP, CXCL12, and TRIML2 . Combined with the differentiation trajectory, we think these muscle sub-clusters were divided for both different cell types and different differentiation stages (Additional file 1: Figure S4c). Skeletal muscle cell differentiation related genes are enriched in muscle-FBN2–4 and muscle-CRYAB-13; angiogenesis related genes are enriched in muscle-GABRP-12; glycolytic process and insulin receptor signaling pathway related genes are enriched in muscle-LDHA-3 (Additional file 1: Figure S4b). The sub-clusters analysis indicated the diversity of differentiation direction in neural and muscle cell clusters.
Construction of hPSC early differentiation trajectory
The differentiation trend of EBs is similar to the development in vivo, because 3D EBs have complex cell adhesions and paracrine signaling system, which can establish various interactions among different cell types . Based on the expression of ligands or complementary receptors on every cell, we calculated the number of interactions among different cell types and showed potential cell-cell interactions in a network (Additional file 1: Figure S6) . These ligand-receptor pairings suggest extensive crosstalk among six types of progenitor cells (Fig. 4c). The one-to-many and many-to-one relationships exist between receptors and ligands. For example, liver cell receptor CLEC2B can bind ligand CLEC3A from muscle cells, stromal cells, endothelial cells, and neural cells; liver ligand SHBG can bind to receptor CLDN4 on all types of cells, which indicate the important roles of liver cells in the differentiation of other cell types. These ligand-receptor pairings may reveal the cell–cell interactions during the development in vivo.
Construction of naïve hPSC reset trajectory
We performed pseudotime analysis to study cell state transition process (from day 0 to day 20) using scRNA-seq data (Fig. 6b). Day 10 RSeT samples are at the intermediate state followed Primed H9. Day 20 RSeT samples are divided into two branches (cell fate 1 and cell fate 2). The RSeT culture process causes the heterogeneity of the Naïve-like H9. The expression pattern shows that only cell fate 2 branch directs to the naïve state with high expression of pluripotent TFs (e.g. POU5F1, NANOG, and PRDM14) (Fig. 6c and d). Cell fate 1 branch directs to a differentiation state with gradual downregulation of pluripotent TFs (e.g. POU5F1 and NANOG) and upregulation of lineage specifier genes (e.g. HAND1, SNAI2, and PAX6). Though these lineage specifier genes are upregulated at the middle stage in both branches, they are downregulated at the terminal stage of cell fate 2. The differential expression dynamics of lineage specifier genes may help us to understand the reset process.
The scRNA-seq platform we used is Fluidigm C1 system with the HT IFCs . The old version of IFCs can only capture 96 cells at most [40, 92], so cell sorting or other enrichment strategies are usually performed before scRNA-seq for the recovery of rare cell types. However, HT IFCs can efficiently analyze thousands of single cells without prior enrichment from heterogeneous systems such as EB differentiation (Fig. 2a).
In contrast to monolayer differentiation, EB differentiation system provides 3D structure to establish complex cell adhesions and paracrine signaling, which promote the differentiation and morphogenesis similar to the native tissue development . The interactions between different cell types are important for the development and differentiation. Liver is the essential site for the early hematopoietic development in the embryo stage . Cardiomyocytes and endothelial cell were reported important for the differentiation of liver in EB differentiation . We used the random EB differentiation system to generate various tissue cells of three germ lineages for our hPSC early differentiation trajectory construction (Fig. 9). We found complex interactions among different cell types (Fig. 4c and Additional file 1: Figure S6). Interestingly, liver cells build specific interactions with other cell types using specific ligands and receptors in EBs. The functions of these interactions should be verified in further studies. It may indicate the important role of liver cells in the differentiation of other cell types.
We used the scRNA-seq to study the reset trajectory of Naïve-like H9. In the tree-like trajectory, we found two branches, one directs to success and the other directs to failure (Fig. 6b). After comparing gene expression dynamics, we revealed the cell state transition process, from Primed to Naïve-like H9. We found that some lineage specifier genes (PAX6, HAND1, et al.) are upregulated at the middle stage (Fig. 6d). In the success branch, those lineage specifier genes are downregulated before the terminal stage. However, in the failure branch, the upregulation is persistent, which lead to differentiation but not the naïve state. The balance of lineage specifier genes can keep the pluripotency of stem cell . We therefore suspected that in the cell state transition process Primed H9 is reprogramed to a pluripotent intermediate state with the balance of lineage specifiers (Fig. 9). When this balance is broken, the intermediate state cells lose their pluripotency and differentiate. Understanding the mechanisms that control the balances of these lineage specifier genes may help us to regulate the pluripotency of hPSCs and optimize differentiation protocols.
Differentiation bias of different hPSCs might be harnessed for better lineage differentiation protocols. Here, we found better potency of hematopoietic differentiation in Naïve-like H9. MAPK-ERK1/2 related genes are highly expressed in Naïve-like H9 but not in Primed H9 (Fig. 7f and Fig. 8e and Additional file 1: Figure S9c). We therefore suspect that MAPK-ERK1/2 contributes to the hematopoietic differentiation bias of Naïve-like H9 (Fig. 9). Though LIF is the key cytokine to keep the “naïve” state of hPSCs , it can also activate the MAPK-ERK1/2 signaling pathway , which is involved in the differentiation of hPSCs towards endothelial lineages (Additional file 1: Figure S7), and hematopoietic development . The commercial naïve medium RSeT contain the MAPK-ERK1/2 inhibitor (such as PD0325901 ). The inhibitor may lead to perturbation of MAPK-ERK1/2 pathway. When the inhibitor is removed and differentiation media are added, hemogenic fate is enhanced for Naïve-like hPSC culture.
In this study, we used scRNA-seq to map the early differentiation of hPSCs. We identified various lineage-specific progenitor cells and constructed the differentiation trajectories by pseudotime analysis. The gene expression dynamics offer new insights into molecular pathways of early embryonic lineages that can be harnessed for optimization of differentiation protocols.
Cell culture and differentiation
H9 and H1 human ES cells were maintained in mTeSR™1 media (STEMCELL Technologies) on tissue culture plates coated with Matrigel (BD Bioscience) routinely . H9 and H1 were reset into a naïve-like state by RSeT™ media (STEMCELL Technologies) following the instruction [81, 96]. We generated EBs by clone suspension. EBs were differentiated in DMEM/F12 (GIBCO) supplemented with 20% FBS (GIBCO), 50 U/mL penicillin/streptomycin (GIBCO), 2 mM L-Glutamine (GIBCO), 1 × non-essential amino acids, and 100 μM ß-mercaptoethanol (Sigma). In brief, H9 was digested using 0.5 mg/mL Dispase (Invitrogen) for 30 min. Then cell clumps suspended in differentiation media were seeded into an Ultra-Low attachment 6 well plate (Corning). After four days or eight days of culture, EBs were harvested and digested into single-cell suspension in 3 × 105 cells/mL using TrypLE (GIBCO). We did not use cell sorting or other enrichment strategies before single-cell capture. The hematopoietic differentiation of hPSCs was performed using STEMdiff Hematopoietic Kit (STEMCELL Technologies) following the instructions. At day 12, cells were analyzed with flow cytometry. CD34+ cells were enriched with EasySep™ CD34 positive selection kit (StemCell Technologies) for CFU assays.
Colony-forming unit (CFU) assays
CFU assays were performed with MethoCult™ H4034 Optimum methylcellulose-based media (StemCell Technologies) following manufacturer’s instructions. In brief, 3 mL MethoCult™ media with 1 × 104/mL CD34+ cells and penicillin-streptomycin were added into each 35 mm low adherent plastic dish. Colonies were counted and identified after 10–14 days of incubation.
Flow cytometry analysis of cell phenotype
Cells suspended in 100 μL of PBS were incubated with antibodies at 4 °C for 30 min. The samples were measured on BD Fortessa and analyzed by FlowJo software (Tree Star). Antibodies used in our study were listed: anti-Human CD34 (BioLegend, Pacific Blue, clone 581), anti-human CD34 (BioLegend, PE, clone 581), anti-human CD201 (BioLegend, APC, clone RCR-401), anti-Human CD43 (BioLegend, APC, clone 10G7), anti-Human CD45 (BioLegend, FITC, clone HI30), anti-Human CD90 (BD Pharmingen, APC, clone 5E10), and CD24 (BioLegend, PE, clone ML5).
Immunofluorescence staining and confocal image analysis
Cells were seeded into glass-bottom culture dishes (NEST, 35/15 mm) coated with Matrigel. Cultured cells were fixed in 4% paraformaldehyde at room temperature for 30 min. Then permeabilized treatment was performed at room temperature for 30 min with PBS + 0.2% TritonX-100. Cells were blocked with PBS + 1% BSA + 4% FBS + 0.4% TritonX-100 at room temperature for 1 h. Then cells were incubated with primary antibodies, diluted in PBS + 0.2% BSA + 0.1% TritonX-100, at 4 °C overnight. Cells were incubated with AlexaFluor secondary antibodies (Invitrogen) for 1 h at room temperature. Then cells were incubated with DAPI for 5 min at room temperature. After the second round of fixation, cells were ready for imaging. Olympus IX81-FV1000 was used to collect immunofluorescence images and FV10-ASW 2.1 Viewer was used to process images. The primary antibodies used in our study were listed in Additional file 2: Table S1.
Western blot analysis
Whole-cell protein were isolated from Primed H9 and Naïve-like H9. Protein samples were incubated with the following primary antibodies in 5%BSA: anti-ERK (Servicebio, Wuhan, China, GB13003–1), anti-JNK (Epitomics, 3496-s), anti-P38 (ABCAM, ab31828), and anti-β-actin (Servicebio, Wuhan, China, GB13001–1). Secondary antibodies were HRP-linked goat anti-mouse, goat anti-rabbit (Servicebio, Wuhan, China, GB23303). Blots were developed using ECL (Servicebio, Wuhan, China, G2014). The primary antibodies used in our study were listed in Additional file 2: Table S1.
Reverse transcription (RT) and qPCR analysis
Total RNA prepared with EasyPure RNA Kit (Transgen) was reverse transcribed into complementary DNA (cDNA) by TransScript All-in-One First-Strand cDNA Synthesis SuperMix for qPCR kit (Transgen). The diluted cDNA was used as temples in qPCR (ChamQ SYBR qPCR Master Mix-Q311 (Vazyme)). The qPCR platform we used was LightCycler 480 (Roche) and data were analyzed by the ∆∆Ct method. The primers used in our study were listed in Additional file 3: Table S2, including the reference gene (ACTB).
Single-cell capture and scRNA-seq library preparation
We used Fluidigm C1 system and C1 high-throughput integrated fluidics circuits (HT IFCs) to perform the single-cells capture and library construction as instruction described. A total of 4000–8000 cells were loaded onto a medium-sized (10–17 μm) HT IFCs. The efficiency of capture was measured under the microscope. The capture sites without cell or with more than one cell were marked and excluded from further analysis. C1 system captured and converted all polyadenylated messenger RNA (mRNA) into cDNA with the cell-specific barcodes. After reverse transcription and preamplification, cDNA was prepared as samples for next-generation sequencing using library tagmentation and 3’end enrichment. Samples harvested from HT IFCs were used to create libraries for Illumina sequencing with Illumina Nextera XT DNA Library kit.
Bulk RNA-seq library construction
We used mRNA Capture Beads (VAHTS mRNA-seq v2 Library Prep Kit for Illumina, Vazyme) to extract mRNA from total RNA. PrimeScript™ Double Strand cDNA Synthesis Kit (TaKaRa) was used to synthesize double-stranded cDNA from purified polyadenylated mRNA templates. We used TruePrep DNA Library Prep Kit V2 for Illumina (TaKaRa) to prepare cDNA libraries for Illumina sequencing.
Sequencing data analysis
The sequenced reads were mapped against the reference GRCh38 using STAR v2.5.2a . scRNA-seq expression data, quantified by counts via featureCounts v1.5.1 , were analyzed with Seurat v2.0.1 (PCA, Cluster, t-SNE and cluster) . In brief, the Seurat object was generated from digital gene expression matrices. The parameter of “Filtercells” is nGene (2000 to 8800) and transcripts (-Inf to 6e + 05). In the standard pre-processing workflow of Seurat, we selected 8706 variable genes for following PCA. Then we performed cell cluster and t-SNE. Fifteen principal components were used in cell cluster with the resolution parameter set at 1.5. Marker genes of each cell cluster were outputted for GO and KEGG analysis, which were used to define the cell types. Cell clusters were annotated with the information of cell types and germ layers. Digital gene expression matrices with annotations from Seurat were analyzed by Monocle v2.3.6 (pseudotime analysis) . TFs from AnimalTFDB  and surface genes  were used to filter the gene lists. The cell–cell interactions were constructed by igraph v1.12 as previously reported . The count of cell–cell interactions was based on the ligands-receptors pairings . We used DAVID  to perform GO and KEGG analysis. GO terms were visualized by REVIGO  and Cytoscape . Bulk RNA-seq data, quantified by FPKM via RSEM v0.4.6 , were analyzed with DEseq2 v1.14.1 .
We thank Junfeng Ji at Zhejiang University School of Medicine for his assistance in this study. We thank Mengmeng Jiang for critical reading of this manuscript. We thank Huiyu Sun and Yang Xu for their assistance in RNA-seq data analysis. We thank G-BIO, Annoroad, VeritasGenetics, and Novogene for deep sequencing experiments, LongGene for supplying Gradient Thermal Cycler and Vazyme for supplying customized enzymes for the study.
This work was supported by grants from National Natural Science Foundation of China (31722027, 81770188, and 31701290), Fundamental Research Funds for the Central Universities (2016XZZX002–04), Zhejiang Provincial Natural Science Foundation of China (R17H080001), National Key Program on Stem Cell and Translational Research (2017YFA0103401) and 973 Program (2015CB964900).
Availability of data and materials
The RNA-seq data used in our study have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO accession number GSE107552 .
XH and HC designed and conducted experiments, including EB and hematopoietic differentiation. HH provided the guide to hematopoietic differentiation. HC, DH, HC, LF, and CC analyzed data and performed statistical analysis. GG and GY designed and supervised the study and wrote the manuscript. All authors read and approved the final manuscript.
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