- 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.
- Single-cell RNA-sequencing
- Primed human pluripotent stem cell
- Embryoid body
- Naïve human pluripotent stem cell
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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The authors declare that they have no competing interests.
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- Thomson JA, Itskovitz-Eldor J, Shapiro SS, Waknitz MA, Swiergiel JJ, Marshall VS, et al. Embryonic stem cell lines derived from human blastocysts. Science. 1998;282:1145–7.View ArticlePubMedGoogle Scholar
- Tabar V, Studer L. Pluripotent stem cells in regenerative medicine: challenges and recent progress. Nat Rev Genet. 2014;15:82–92. https://doi.org/10.1038/nrg3563 View ArticlePubMedPubMed CentralGoogle Scholar
- Vodyanik MA, Bork JA, Thomson JA, Slukvin II. Human embryonic stem cell-derived CD34+ cells: efficient production in the coculture with OP9 stromal cells and analysis of lymphohematopoietic potential. Blood. 2005;105:617–26. https://doi.org/10.1182/blood-2004-04-1649 View ArticlePubMedGoogle Scholar
- Doulatov S, Vo LT, Chou SS, Kim PG, Arora N, Li H, et al. Induction of multipotential hematopoietic progenitors from human pluripotent stem cells via respecification of lineage-restricted precursors. Cell Stem Cell. 2013;13:459–470. https://doi.org/10.1016/j.stem.2013.09.002.
- Kroon E, Martinson LA, Kadoya K, Bang AG, Kelly OG, Eliazer S, et al. Pancreatic endoderm derived from human embryonic stem cells generates glucose-responsive insulin-secreting cells in vivo. Nat Biotechnol. 2008;26:443–452. https://doi.org/10.1038/nbt1393.
- Chambers SM, Fasano CA, Papapetrou EP, Tomishima M, Sadelain M, Studer L. Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Nat Biotechnol. 2009;27:275–80. https://doi.org/10.1038/nbt.1529 View ArticlePubMedPubMed CentralGoogle Scholar
- Kriks S, Shim JW, Piao J, Ganat YM, Wakeman DR, Xie Z, et al. Dopamine neurons derived from human ES cells efficiently engraft in animal models of Parkinson’s disease. Nature. 2011;480:547–551. https://doi.org/10.1038/nature10648.
- Nichols J, Smith A. Pluripotency in the embryo and in culture. Cold Spring Harb Perspect Biol. 2012;4:a008128. https://doi.org/10.1101/cshperspect.a008128 View ArticlePubMedPubMed CentralGoogle Scholar
- Nichols J, Smith A. Naive and primed pluripotent states. Cell Stem Cell. 2009;4:487–92. https://doi.org/10.1016/j.stem.2009.05.015 View ArticlePubMedGoogle Scholar
- Gafni O, Weinberger L, Mansour AA, Manor YS, Chomsky E, Ben-Yosef D, et al. Derivation of novel human ground state naive pluripotent stem cells. Nature. 2013;504:282–286. https://doi.org/10.1038/nature12745.
- Theunissen TW, Friedli M, He Y, Planet E, O’Neil RC, Markoulaki S, et al. Molecular Criteria for Defining the Naive Human Pluripotent State. Cell Stem Cell. 2016;19:502–515. https://doi.org/10.1016/j.stem.2016.06.011.
- Theunissen TW, Powell BE, Wang H, Mitalipova M, Faddah DA, Reddy J, et al. Systematic identification of culture conditions for induction and maintenance of naive human pluripotency. Cell Stem Cell. 2014;15:471–487. https://doi.org/10.1016/j.stem.2014.07.002.
- Ware CB, Nelson AM, Mecham B, Hesson J, Zhou W, Jonlin EC, et al. Derivation of naive human embryonic stem cells. Proc Natl Acad Sci U S A. 2014;111:4484–4489. https://doi.org/10.1073/pnas.1319738111.
- Yang Y, Liu B, Xu J, Wang J, Wu J, Shi C, et al. Derivation of pluripotent stem cells with in vivo embryonic and extraembryonic potency. Cell. 2017;169:243–257.e225. https://doi.org/10.1016/j.cell.2017.02.005.
- Zimmerlin L, Park TS, Huo JS, Verma K, Pather SR, Talbot CC Jr, et al. Tankyrase inhibition promotes a stable human naive pluripotent state with improved functionality. Development. 2016;143:4368–4380. https://doi.org/10.1242/dev.138982.
- Liu X, Nefzger CM, Rossello FJ, Chen J, Knaupp AS, Firas J, et al. Comprehensive characterization of distinct states of human naive pluripotency generated by reprogramming. Nat Methods. 2017;14(11):1055–1106. https://doi.org/10.1038/nmeth.4436.
- Itskovitz-Eldor J, Schuldiner M, Karsenti D, Eden A, Yanuka O, Amit M, et al. Differentiation of human embryonic stem cells into embryoid bodies compromising the three embryonic germ layers. Mol Med. 2000;6:88–95.Google Scholar
- Boxman J, Sagy N, Achanta S, Vadigepalli R, Nachman I. Integrated live imaging and molecular profiling of embryoid bodies reveals a synchronized progression of early differentiation. Sci Rep. 2016;6:31623. https://doi.org/10.1038/srep31623 View ArticlePubMedPubMed CentralGoogle Scholar
- Magli A, Schnettler E, Swanson SA, Borges L, Hoffman K, Stewart R, et al. Pax3 and Tbx5 specify whether PDGFRalpha+ cells assume skeletal or cardiac muscle fate in differentiating embryonic stem cells. Stem Cells. 2014;32:2072–2083. https://doi.org/10.1002/stem.1713.
- Fehling HJ, Lacaud G, Kubo A, Kennedy M, Robertson S, Keller G, et al. Tracking mesoderm induction and its specification to the hemangioblast during embryonic stem cell differentiation. Development. 2003;130:4217–27.View ArticlePubMedGoogle Scholar
- Ogawa S, Tagawa Y, Kamiyoshi A, Suzuki A, Nakayama J, Hashikura Y, et al. Crucial roles of mesodermal cell lineages in a murine embryonic stem cell-derived in vitro liver organogenesis system. Stem Cells. 2005;23:903–13. https://doi.org/10.1634/stemcells.2004-0295 View ArticlePubMedGoogle Scholar
- Lee MS, Jun DH, Hwang CI, Park SS, Kang JJ, Park HS, et al. Selection of neural differentiation-specific genes by comparing profiles of random differentiation. Stem Cells. 2006;24:1946–1955. https://doi.org/10.1634/stemcells.2005-0325.
- Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6:377–382. https://doi.org/10.1038/nmeth.1315.
- Haque A, Engel J, Teichmann SA, Lonnberg T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 2017;9:75. https://doi.org/10.1186/s13073-017-0467-4 View ArticlePubMedPubMed CentralGoogle Scholar
- Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. The technology and biology of single-cell RNA sequencing. Mol Cell. 2015;58:610–20. https://doi.org/10.1016/j.molcel.2015.04.005 View ArticlePubMedGoogle Scholar
- Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014;32:1053–1058. https://doi.org/10.1038/nbt.2967.
- Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096–8. https://doi.org/10.1038/nmeth.2639 View ArticlePubMedGoogle Scholar
- Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2:666–73. https://doi.org/10.1016/j.celrep.2012.08.003 View ArticlePubMedGoogle Scholar
- Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–1214. https://doi.org/10.1016/j.cell.2015.05.002.
- Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161:1187–1201. https://doi.org/10.1016/j.cell.2015.04.044.
- Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049. https://doi.org/10.1038/ncomms14049.
- Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–782. https://doi.org/10.1038/nbt.2282.
- Chung W, Eum HH, Lee HO, Lee KM, Lee HB, Kim KT, et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat Commun. 2017;8:15081. https://doi.org/10.1038/ncomms15081.
- Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol. 2018;18:35–45. https://doi.org/10.1038/nri.2017.76 View ArticlePubMedGoogle Scholar
- Bjorklund AK, Forkel M, Picelli S, Konya V, Theorell J, Friberg D, et al. The heterogeneity of human CD127(+) innate lymphoid cells revealed by single-cell RNA sequencing. Nat Immunol. 2016;17:451–460. https://doi.org/10.1038/ni.3368.
- Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature. 2014;509:371–375. https://doi.org/10.1038/nature13173.
- Gaublomme JT, Yosef N, Lee Y, Gertner RS, Yang LV, Wu C, et al. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell. 2015;163:1400–1412. https://doi.org/10.1016/j.cell.2015.11.009.
- Petropoulos S, Edsgard D, Reinius B, Deng Q, Panula SP, Codeluppi S, et al. Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell. 2016;165:1012–1026. https://doi.org/10.1016/j.cell.2016.03.023.
- Blakeley P, Fogarty NM, del Valle I, Wamaitha SE, Hu TX, Elder K, et al. Defining the three cell lineages of the human blastocyst by single-cell RNA-seq. Development. 2015;142:3151–3165. https://doi.org/10.1242/dev.123547.
- Chu LF, Leng N, Zhang J, Hou Z, Mamott D, Vereide DT, et al. Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol. 2016;17:173. https://doi.org/10.1186/s13059-016-1033-x.
- Semrau S, Goldmann JE, Soumillon M, Mikkelsen TS, Jaenisch R, van Oudenaarden A. Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat Commun. 2017;8:1096. https://doi.org/10.1038/s41467-017-01076-4 View ArticlePubMedPubMed CentralGoogle Scholar
- Han X, Yu H, Huang D, Xu Y, Saadatpour A, Li X, et al. A molecular roadmap for induced multi-lineage trans-differentiation of fibroblasts by chemical combinations. Cell Res. 2017;27:842. https://doi.org/10.1038/cr.2017.77.
- Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33:495–502. https://doi.org/10.1038/nbt.3192 View ArticlePubMedPubMed CentralGoogle Scholar
- Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–386. https://doi.org/10.1038/nbt.2859.
- Boutilier JK, Taylor RL, Ram R, McNamara E, Nguyen Q, Goullee H, et al. Variable cardiac alpha-actin (Actc1) expression in early adult skeletal muscle correlates with promoter methylation. Biochim Biophys Acta. 2017;1860:1025–1036. https://doi.org/10.1016/j.bbagrm.2017.08.004.
- Despars G, Periasamy P, Tan J, Abbey J, O’Neill TJ, O’Neill HC. Gene signature of stromal cells which support dendritic cell development. Stem Cells Dev. 2008;17:917–27. https://doi.org/10.1089/scd.2007.0170 View ArticlePubMedGoogle Scholar
- Kilari S, Remadevi I, Zhao B, Pan J, Miao R, Ramchandran R, et al. Endothelial cell-specific chemotaxis receptor (ECSCR) enhances vascular endothelial growth factor (VEGF) receptor-2/kinase insert domain receptor (KDR) activation and promotes proteolysis of internalized KDR. J Biol Chem. 2013;288:10265–10274. https://doi.org/10.1074/jbc.M112.413542.
- Kimura C, Takeda N, Suzuki M, Oshimura M, Aizawa S, Matsuo I. Cis-acting elements conserved between mouse and pufferfish Otx2 genes govern the expression in mesencephalic neural crest cells. Development. 1997;124:3929–41.PubMedGoogle Scholar
- Silos-Santiago I, Yeh HJ, Gurrieri MA, Guillerman RP, Li YS, Wolf J, et al. Localization of pleiotrophin and its mRNA in subpopulations of neurons and their corresponding axonal tracts suggests important roles in neural-glial interactions during development and in maturity. J Neurobiol. 1996;31:283–296.Google Scholar
- Qu Y, Huang Y, Feng J, Alvarez-Bolado G, Grove EA, Yang Y, et al. Genetic evidence that Celsr3 and Celsr2, together with Fzd3, regulate forebrain wiring in a Vangl-independent manner. Proc Natl Acad Sci U S A. 2014;111:E2996–E3004. https://doi.org/10.1073/pnas.1402105111.
- Cyr AR, Kulak MV, Park JM, Bogachek MV, Spanheimer PM, Woodfield GW, et al. TFAP2C governs the luminal epithelial phenotype in mammary development and carcinogenesis. Oncogene. 2015;34:436–444. https://doi.org/10.1038/onc.2013.569.
- Mirkovitch J, Darnell JE Jr. Rapid in vivo footprinting technique identifies proteins bound to the TTR gene in the mouse liver. Genes Dev. 1991;5:83–93.View ArticlePubMedGoogle Scholar
- Lee CS, Friedman JR, Fulmer JT, Kaestner KH. The initiation of liver development is dependent on Foxa transcription factors. Nature. 2005;435:944–7. https://doi.org/10.1038/nature03649 View ArticlePubMedGoogle Scholar
- Fish RJ, Vorjohann S, Bena F, Fort A, Neerman-Arbez M. Developmental expression and organisation of fibrinogen genes in the zebrafish. Thromb Haemost. 2012;107:158–66. https://doi.org/10.1160/TH11-04-0221 View ArticlePubMedGoogle Scholar
- Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. https://doi.org/10.1038/nprot.2008.211 View ArticlePubMedGoogle Scholar
- Vitureira N, McNagny K, Soriano E, Burgaya F. Pattern of expression of the podocalyxin gene in the mouse brain during development. Gene Expr Patterns. 2005;5:349–54. https://doi.org/10.1016/j.modgep.2004.10.002 View ArticlePubMedGoogle Scholar
- Islam SM, Shinmyo Y, Okafuji T, Su Y, Naser IB, Ahmed G, et al. Draxin, a repulsive guidance protein for spinal cord and forebrain commissures. Science. 2009;323:388–393. https://doi.org/10.1126/science.1165187.
- Shinmyo Y, Asrafuzzaman Riyadh M, Ahmed G, Bin Naser I, Hossain M, Takebayashi H, et al. Draxin from neocortical neurons controls the guidance of thalamocortical projections into the neocortex. Nat Commun. 2015;6:10232. https://doi.org/10.1038/ncomms10232.
- Cushion TD, Paciorkowski AR, Pilz DT, Mullins JG, Seltzer LE, Marion RW, et al. De novo mutations in the beta-tubulin gene TUBB2A cause simplified gyral patterning and infantile-onset epilepsy. Am J Hum Genet. 2014;94:634–641. https://doi.org/10.1016/j.ajhg.2014.03.009.
- Li P, Sun X, Ma Z, Liu Y, Jin Y, Ge R, et al. Transcriptional reactivation of OTX2, RX1 and SIX3 during reprogramming contributes to the generation of RPE cells from human iPSCs. Int J Biol Sci. 2016;12:505–517. https://doi.org/10.7150/ijbs.14212.
- Murisier F, Guichard S, Beermann F. Distinct distal regulatory elements control tyrosinase expression in melanocytes and the retinal pigment epithelium. Dev Biol. 2007;303:838–47. https://doi.org/10.1016/j.ydbio.2006.11.038 View ArticlePubMedGoogle Scholar
- Abe M, Ruest LB, Clouthier DE. Fate of cranial neural crest cells during craniofacial development in endothelin-A receptor-deficient mice. Int J Dev Biol. 2007;51:97–105. https://doi.org/10.1387/ijdb.062237ma View ArticlePubMedPubMed CentralGoogle Scholar
- Prasad MK, Reed X, Gorkin DU, Cronin JC, McAdow AR, Chain K, et al. SOX10 directly modulates ERBB3 transcription via an intronic neural crest enhancer. BMC Dev Biol. 2011;11:40. https://doi.org/10.1186/1471-213X-11-40.
- Simoes-Costa M, Bronner ME. Establishing neural crest identity: a gene regulatory recipe. Development. 2015;142:242–57. https://doi.org/10.1242/dev.105445 View ArticlePubMedPubMed CentralGoogle Scholar
- Alzhanov DT, McInerney SF, Rotwein P. Long range interactions regulate Igf2 gene transcription during skeletal muscle differentiation. J Biol Chem. 2010;285:38969–77. https://doi.org/10.1074/jbc.M110.160986 View ArticlePubMedPubMed CentralGoogle Scholar
- Chern SR, Li SH, Lu CH, Chen EI. Spatiotemporal expression of the serine protease inhibitor, SERPINE2, in the mouse placenta and uterus during the estrous cycle, pregnancy, and lactation. Reprod Biol Endocrinol. 2010;8:127. https://doi.org/10.1186/1477-7827-8-127 View ArticlePubMedPubMed CentralGoogle Scholar
- Chan J, O’Donoghue K, Gavina M, Torrente Y, Kennea N, Mehmet H, et al. Galectin-1 induces skeletal muscle differentiation in human fetal mesenchymal stem cells and increases muscle regeneration. Stem Cells. 2006;24:1879–1891. https://doi.org/10.1634/stemcells.2005-0564.
- Wojtowicz I, Jablonska J, Zmojdzian M, Taghli-Lamallem O, Renaud Y, Junion G, et al. Drosophila small heat shock protein CryAB ensures structural integrity of developing muscles, and proper muscle and heart performance. Development. 2015;142:994–1005. https://doi.org/10.1242/dev.115352.
- Subramanian P, Karshovska E, Reinhard P, Megens RT, Zhou Z, Akhtar S, et a. Lysophosphatidic acid receptors LPA1 and LPA3 promote CXCL12-mediated smooth muscle progenitor cell recruitment in neointima formation. Circ Res. 2010;107:96–105. https://doi.org/10.1161/CIRCRESAHA.109.212647.
- Murry CE, Keller G. Differentiation of embryonic stem cells to clinically relevant populations: lessons from embryonic development. Cell. 2008;132:661–80. https://doi.org/10.1016/j.cell.2008.02.008 View ArticlePubMedGoogle Scholar
- Camp JG, Sekine K, Gerber T, Loeffler-Wirth H, Binder H, Gac M, et al. Multilineage communication regulates human liver bud development from pluripotency. Nature. 2017;546:533–538. https://doi.org/10.1038/nature22796.
- Weinberger L, Ayyash M, Novershtern N, Hanna JH. Dynamic stem cell states: naive to primed pluripotency in rodents and humans. Nat Rev Mol Cell Biol. 2016;17:155–69. https://doi.org/10.1038/nrm.2015.28 View ArticlePubMedGoogle Scholar
- Zambidis ET, Peault B, Park TS, Bunz F, Civin CI. Hematopoietic differentiation of human embryonic stem cells progresses through sequential hematoendothelial, primitive, and definitive stages resembling human yolk sac development. Blood. 2005;106:860–70. https://doi.org/10.1182/blood-2004-11-4522 View ArticlePubMedPubMed CentralGoogle Scholar
- Sun S, Zhang W, Chen X, Peng Y, Chen Q. A complex insertion/deletion polymorphism in the compositionally biased region of the ZFHX3 gene in patients with coronary heart disease in a Chinese population. Int J Clin Exp Med. 2015;8:7890–7.PubMedPubMed CentralGoogle Scholar
- Nakamura H, Edward DP, Sugar J, Yue BY. Expression of Sp1 and KLF6 in the developing human cornea. Mol Vis. 2007;13:1451–7.PubMedGoogle Scholar
- Teo AK, Arnold SJ, Trotter MW, Brown S, Ang LT, Chng Z, et al. Pluripotency factors regulate definitive endoderm specification through eomesodermin. Genes Dev. 2011;25:238–250. https://doi.org/10.1101/gad.607311.
- Bastide P, Darido C, Pannequin J, Kist R, Robine S, Marty-Double C, et al. Sox9 regulates cell proliferation and is required for Paneth cell differentiation in the intestinal epithelium. J Cell Biol. 2007;178:635–648. https://doi.org/10.1083/jcb.200704152.
- Mansouri A, Pla P, Larue L, Gruss P. Pax3 acts cell autonomously in the neural tube and somites by controlling cell surface properties. Development. 2001;128:1995–2005.PubMedGoogle Scholar
- Warrier S, Van der Jeught M, Duggal G, Tilleman L, Sutherland E, Taelman J, et al. Direct comparison of distinct naive pluripotent states in human embryonic stem cells. Nat Commun. 2017;8:15055. https://doi.org/10.1038/ncomms15055.
- Wang J, Singh M, Sun C, Besser D, Prigione A, Ivics Z, et al. Isolation and cultivation of naive-like human pluripotent stem cells based on HERVH expression. Nat Protoc. 2016;11:327–346. https://doi.org/10.1038/nprot.2016.016.
- Collier AJ, Panula SP, Schell JP, Chovanec P, Plaza Reyes A, Petropoulos S, et al. Comprehensive cell surface protein profiling identifies specific markers of human naive and primed pluripotent states. Cell Stem Cell. 2017;20(6):874-890.e7. https://doi.org/10.1016/j.stem.2017.02.014.
- Buhusi M, Demyanenko GP, Jannie KM, Dalal J, Darnell EP, Weiner JA, et al. ALCAM regulates mediolateral retinotopic mapping in the superior colliculus. J Neurosci. 2009;29:15630–41. https://doi.org/10.1523/JNEUROSCI.2215-09.2009 View ArticlePubMedGoogle Scholar
- Nakaya N, Sultana A, Lee HS, Tomarev SI. Olfactomedin 1 interacts with the Nogo A receptor complex to regulate axon growth. J Biol Chem. 2012;287:37171–84. https://doi.org/10.1074/jbc.M112.389916 View ArticlePubMedPubMed CentralGoogle Scholar
- Gregianin E, Pallafacchina G, Zanin S, Crippa V, Rusmini P, Poletti A, et al. Loss-of-function mutations in the SIGMAR1 gene cause distal hereditary motor neuropathy by impairing ER-mitochondria tethering and Ca2+ signalling. Hum Mol Genet. 2016;25:3741–3753. https://doi.org/10.1093/hmg/ddw220.
- Ditadi A, Sturgeon CM, Keller G. A view of human haematopoietic development from the Petri dish. Nat Rev Mol Cell Biol. 2017;18:56–67. https://doi.org/10.1038/nrm.2016.127 View ArticlePubMedGoogle Scholar
- Zovein AC, Hofmann JJ, Lynch M, French WJ, Turlo KA, Yang Y, et al. Fate tracing reveals the endothelial origin of hematopoietic stem cells. Cell Stem Cell. 2008;3:625–636. https://doi.org/10.1016/j.stem.2008.09.018.
- Geest CR, Coffer PJ. MAPK signaling pathways in the regulation of hematopoiesis. J Leukoc Biol. 2009;86:237–50. https://doi.org/10.1189/jlb.0209097 View ArticlePubMedGoogle Scholar
- Fiori JL, Billings PC, de la Pena LS, Kaplan FS, Shore EM. Dysregulation of the BMP-p38 MAPK signaling pathway in cells from patients with fibrodysplasia ossificans progressiva (FOP). J Bone Miner Res. 2006;21:902–9. https://doi.org/10.1359/jbmr.060215 View ArticlePubMedGoogle Scholar
- Lee SY, Yoon J, Lee MH, Jung SK, Kim DJ, Bode AM, et al. The role of heterodimeric AP-1 protein comprised of JunD and c-Fos proteins in hematopoiesis. J Biol Chem. 2012;287:31342–31348. https://doi.org/10.1074/jbc.M112.387266.
- Perry SS, Zhao Y, Nie L, Cochrane SW, Huang Z, Sun XH. Id1, but not Id3, directs long-term repopulating hematopoietic stem-cell maintenance. Blood. 2007;110:2351–60. https://doi.org/10.1182/blood-2007-01-069914 View ArticlePubMedPubMed CentralGoogle Scholar
- Begley CG, Green AR. The SCL gene: from case report to critical hematopoietic regulator. Blood. 1999;93:2760–70.PubMedGoogle Scholar
- Guo G, Pinello L, Han X, Lai S, Shen L, Lin TW, et al. Serum-based culture conditions provoke gene expression variability in mouse embryonic stem cells as revealed by single-cell analysis. Cell Rep. 2016;14:956–965. https://doi.org/10.1016/j.celrep.2015.12.089.
- Shu J, Wu C, Wu Y, Li Z, Shao S, Zhao W, et al. Induction of pluripotency in mouse somatic cells with lineage specifiers. Cell. 2013;153:963–975. https://doi.org/10.1016/j.cell.2013.05.001.
- Niwa H, Ogawa K, Shimosato D, Adachi K. A parallel circuit of LIF signalling pathways maintains pluripotency of mouse ES cells. Nature. 2009;460:118–22. https://doi.org/10.1038/nature08113 View ArticlePubMedGoogle Scholar
- Ludwig T, A Thomson J. Defined, feeder-independent medium for human embryonic stem cell culture. Curr Protoc Stem Cell Biol. 2007;Chapter 1:Unit 1C 2. https://doi.org/10.1002/9780470151808.sc01c02s2.
- Vallot C, Patrat C, Collier AJ, Huret C, Casanova M, Liyakat Ali TM, et al. XACT noncoding RNA competes with XIST in the control of X chromosome activity during human early development. Cell Stem Cell. 2017;20:102–111. https://doi.org/10.1016/j.stem.2016.10.014.
- Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. https://doi.org/10.1093/bioinformatics/bts635.
- Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30. https://doi.org/10.1093/bioinformatics/btt656 View ArticlePubMedGoogle Scholar
- Zhang HM, Liu T, Liu CJ, Song S, Zhang X, Liu W, et al. AnimalTFDB 2.0: a resource for expression, prediction and functional study of animal transcription factors. Nucleic Acids Res. 2015;43:D76–D81. https://doi.org/10.1093/nar/gku887.
- da Cunha JP, Galante PA, de Souza JE, de Souza RF, Carvalho PM, Ohara DT, et al. Bioinformatics construction of the human cell surfaceome. Proc Natl Acad Sci U S A. 2009;106:16752–16757. https://doi.org/10.1073/pnas.0907939106.
- Ramilowski JA, Goldberg T, Harshbarger J, Kloppmann E, Lizio M, Satagopam VP, et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun. 2015;6:7866. https://doi.org/10.1038/ncomms8866.
- Supek F, Bosnjak M, Skunca N, Smuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011;6:e21800. https://doi.org/10.1371/journal.pone.0021800 View ArticlePubMedPubMed CentralGoogle Scholar
- Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol. 2011;696:291–303. https://doi.org/10.1007/978-1-60761-987-1_18 View ArticlePubMedGoogle Scholar
- Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323. https://doi.org/10.1186/1471-2105-12-323 View ArticlePubMedPubMed CentralGoogle Scholar
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. https://doi.org/10.1186/s13059-014-0550-8 View ArticlePubMedPubMed CentralGoogle Scholar
- Han X, Chen H, Huang D. Mapping human pluripotent stem cell differentiation pathways via high throughput single-cell RNA-sequencing. Gene Expression Omnibus. 2018, GSE107552. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE107552. Accessed 6 Mar 2018.