ORIGINAL RESEARCH article

Front. Cell Dev. Biol., 12 September 2022

Sec. Molecular and Cellular Reproduction

Volume 10 - 2022 | https://doi.org/10.3389/fcell.2022.918222

Characterization of multitype colonies originating from porcine blastocysts produced in vitro

  • 1. Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, South Korea

  • 2. Designed Animal and Transplantation Research Institute (DATRI), Institute of Green Bio Science and Technology, Seoul National University, Pyeongchang, South Korea

Article metrics

View details

2

Citations

2,4k

Views

1k

Downloads

Abstract

Many types of embryonic stem cells have been induced from pre-implantation blastocysts to study the specification of early lineages. Various cell lines have been established using chemicals, including excessive inhibitory molecules. Previous studies have also aimed to purify cell populations representing a single embryonic lineage from a protocol. In this study, we used a novel culture condition to induce cells from blastocyst seeding and analyzed their characteristics. Next, signaling inhibitors were introduced during the cell culture period. Furthermore, we investigated the cell types using RNA sequencing. Each type of cell population showed a distinct morphology and reactivity with alkaline phosphatase. Marker proteins enabled each cell type to be distinguished by immunocytochemistry, and genes such as Sox17, Gata4, Gata6, T, and Cdx2 showed applicability for the discrimination of cell types. Signaling inhibitors suppressed the production of some cell types, and gene expression and marker protein patterns were collapsed. RNA-sequencing suggested cell-type-specific marker genes and the correlation among samples. In conclusion, four types of cells could be induced from porcine embryos using a single protocol, and they could be isolated manually. Our data will help promote the study of lineage segregation based on embryonic cells.

Introduction

Every cell that comprises the body comes from a fertilized egg. From a few totipotent cells, embryonic lineages arise in a planned order. Following the compaction of embryonic cells, the outer cells form the trophectoderm (TE) and the inner cells form the inner call mass (ICM) (Yamanaka et al., 2006). Furthermore, cells in the ICM are segregated into epiblasts and primitive endoderm (PrE) (Hermitte and Chazaud, 2014). All of the cell populations of the fetus originate from these embryonic lineages (Rossant, 2007). To understand the early development of embryos, lineages in the early stages have been analyzed for various species (Teruel et al., 2000). However, some details remain unknown in mice, and even the principles of major specifications are unclear in model animals, including pigs.

To determine the mechanisms of lineage specification, many studies have tried to establish lineage-specific cell lines (Ralston and Rossant, 2005). Because embryonic events are rapid and the states of cells are constantly changing, maintenance of cellular conditions using chemicals is necessary (Teruel et al., 2000; Smith, 2001). Various embryonic stem cells representing specific lineages have been utilized to investigate the characteristics of each lineage.

In a previous study, we obtained an epiblast-like cell line from blastocyst seeding (Choi et al., 2019). Fetal bovine serum was excluded to reduce unknown factors, and many cytokines and chemicals were used to derive cells of the epiblast lineage (van der Valk et al., 2018). Inhibitors for the maintenance of stem cells were introduced to suppress unintended cell types. Many other studies have used various combinations of chemicals to lead cells on a path to a single lineage (Emre et al., 2007; Li and Ding, 2010). However, during the early stages of natural development, most signals originate from inside the embryo. Therefore, to follow the spontaneous development of early embryos, chemical barriers should be excluded. However, mimicry of the early differentiation of embryonic cell lineages outside of the reproductive tract still has challenges to overcome. The primary task is the isolation of cell populations representing embryonic lineages with the minimum use of inhibitors.

In this study, we minimized inhibitors to retain as many embryonic lineages as possible. Two inhibitors that are required to maintain pluripotent states in pigs, human leukemia inhibitory factor (hLIF) and basic fibroblast growth factor (bFGF), were used together under previously described culture conditions (Ezashi et al., 2012). We established four types of cells from in vitro fertilized blastocysts. To identify these cell types, first, the morphology of the cells was examined. Next, alkaline phosphatase staining and immunocytochemistry of marker proteins were conducted. Then, we isolated each cell type and used quantitative PCR to analyze the expression levels of marker genes. To identify detailed expression profiles, RNA sequencing (RNA-seq) was conducted with cell clumps. During the culture period, and to observe the response of cells, we also added additional signaling inhibitors (PDGFRA inhibitor, TGFβ inhibitor, and MEK inhibitor), which have the potential to control the pluripotency of pig embryos (Vrbsky et al., 2015; Oh et al., 2020). Finally, we isolated RNAs from each cell type and conducted RNA-seq. We selected upregulated genes from each cell type and showed the distribution of each type on a multidimensional scaling plot. Additionally, the results were compared with sequencing results from other studies.

Materials and methods

The care and experimental use of pigs were approved by the Institute of Laboratory Animal Resources, Seoul National University (SNU-140328-2). Unless otherwise stated, we obtained all chemicals from Sigma–Aldrich Corp. (St. Louis, MO, United States).

In vitro production of fertilized embryos

Ovaries of prepubertal gilts were obtained from a local slaughterhouse and transferred to the laboratory within warmed saline. Cumulus-oocyte complexes (COCs) were collected by aspirating 3- to 7-mm follicles from the prepubertal gilts with a 10-ml syringe and an 18-gauge needle. COCs with compact multiple layers of cumulus cells and fine cytoplasm were collected from aspirated porcine follicular fluid (pFF) and allowed to mature for 44 h at 39°C in tissue culture medium 199 (TCM 199) (Gibco, Grand Island, NY, United States) supplemented with 10% pFF, L-cysteine (0.1 mg/ml), sodium pyruvate (44 ng/ml), epidermal growth factor (10 ng/ml), insulin (1 mg/ml), and kanamycin (75 μg/ml). The COCs were matured for the first 22 h with 10 IU/ml gonadotropin hormones, pregnant mare serum gonadotropin (PMSG) (Lee Biosolutions, Maryland Heights, MO, United States), and human chorionic gonadotropin (hCG); the gonadotropins were excluded from the medium for the last 22 h. After maturation, cumulus cells were removed from the oocytes with hyaluronidase. Sperm cells were washed twice with Dulbecco’s phosphate buffered saline (DPBS) supplemented with 0.1% bovine serum albumin (BSA) at 1,400 rpm for 3 min. Washed sperm (4 × 104/ml in the final concentration) were co-incubated with the matured oocytes in 500 μl of modified tris-buffered medium (mTBM) for 4 h (Abeydeera and Day, 1997). mTBM consisted of 113.1 mM sodium chloride, 3 mM potassium chloride, 7.5 mM calcium chloride, 20 mM Trizma® base, 11 mM glucose, 5 mM pyruvate, 1 mM caffeine, and 0.8% BSA. Following this process, the eggs were incubated in 5% CO2 and 5% O2 at 39 °C in 20 μl of porcine zygote medium 3 (PZM3) (Yoshioka et al., 2002).

Culture of cells from blastocyst seeding (including AP staining)

Hatched blastocysts were attached to feeder cells (mitomycin C-treated mouse embryonic fibroblasts). In total, 12 to 24 blastocysts were used in each seeding experiment. The basal medium was DMEM/F-12 supplemented with MEM Non-Essential Amino Acids, Glutamax, 2-mercaptoethanol, antibiotic-antimycotic, and 15% KnockOut™ Serum Replacement (all from Gibco, NY, United States). Basic FGF and human LIF were added to the medium (10 ng/ml each). AG1296 (PDGF receptor inhibitor, 10 μM), SB431542 (TGFβ inhibitor, 4 μM), and PD0325901 (MEK inhibitor, 1 μM) were used for the inhibitor treatment experiment. Samples were cultured for 14 days after blastocyst seeding and stained with alkaline phosphatase (AP).

Immunocytochemistry of colonies

Cells were washed with DPBS supplemented and fixed with 4% paraformaldehyde (PFA) in DPBS at room temperature (RT) for 15 min. Fixed cells were permeabilized using 0.2% Tween-20 and 0.1% Triton X-100 in DPBS at RT for 15 min, followed by blocking with 10% donkey serum in DPBS at RT for 1 h. Samples were stained with anti-SOX2 (5 μg/ml) or anti-GATA6 (1 μg/ml) in DPBS containing 10% donkey serum at 4°C overnight. After washing three times in washing solution (DPBS with 0.2% Tween-20 for 10 min), cells were incubated with donkey anti-rabbit Alexa594 or donkey anti-goat Alexa488 (Invitrogen 1:5,000) in DPBS with 10% donkey serum at RT for 1 h and at 4 °C for 6 h. For double staining, samples were stained again with anti-SOX17 (1 μg/ml) or anti-NANOG (1 μg/ml). The procedures were the same as for the first staining. The antibodies used are listed in Table 1. A digital imaging system for microscopy (DS-L1, Nikon) was used to obtain fluorescence and bright-field images. We used ImageJ software to process the images.

TABLE 1

Primary antibodies
TargetHostCompanyCatalog number
SOX SOX172GoatR&D systemsAF1924
SOX2RabbitMilliporeAB5603
GATA6GoatR&D systemsAF1700
NANOGRabbitPeprotech500-P236
Secondary antibodies
Fluorescent dyeTarget/hostCompanyCatalog number
Alexa594Rabbit/donkeyInvitrogenA-21207
Alexa488Goat/donkeyInvitrogenA-11055

List of antibodies.

RNA extraction and quantitative PCR

Each cell population from the colonies was mechanically separated from the blastocyst seeding samples. RNA was extracted and cDNA was synthesized with a TaqManâ„¢ Gene Expression Cells-to-CTâ„¢ Kit (Invitrogen, MA, United States). Quantitative PCR was conducted using Power SYBRâ„¢ Green PCR Master Mix (Applied Biosystemsâ„¢, CA, United States). The levels of the transcripts were normalized to the expression level of the GAPDH gene. The list of primers used is described in Table 2.

TABLE 2

Cell lineageGeneForward sequenceReverse sequenceAnnealing temperature (°C)Product size (base pairs)
ReferenceGAPDHTGCTCCTCCCCGTTCGACATGCGGCCAAATCCGTTC60100
EpiblastOCT4ACTT​GGA​GAG​CCC​TGG​TTT​TAC​TGCC​AGG​TCC​GAG​GAT​CAA​C68159
SOX2CGGCGGTGGCAACTCTACTCG​GGA​CCA​CAC​CAT​GAA​AG64100
NANOGCAT​CTG​CTG​AGA​CCC​TCG​ACGGG​TCT​GCG​AGA​ACA​CAG​TT60195
HNF4AGCT​TCT​TTC​GGA​GGA​GTG​TGTTG​ACC​TGC​GAG​TGC​TGA​T60183
KLF4GGA​CCA​CCT​TGC​CTT​ACA​CACTT​TCC​AGC​TGG​GTT​CCT​CC60146
MYCGAA​AAA​GAC​GTG​CTG​CGG​AACCA​GCC​AAG​GTT​GTG​AGG​TT60253
PrEPDGFRAGGT​CAC​CTG​TGC​CGT​CTT​TATTT​GAT​GGA​CGG​GAC​CTT​GG60115
PDGFAGCT​GTG​GAT​ACC​TCG​CCA​ATCTT​CTC​TTC​CTC​CGA​ACG​GG60132
SOX17GCAAGATGCTGGGCAAGTTTG​TAG​TTG​GGG​TGG​TCC​TG60112
GATA4GACCACCACCACCACGCTAAT​CCC​CTC​TTT​CCG​CAT​T60121
GATA6CGG​CCT​CTA​CAG​CAA​GAT​GAAGT​TGG​CAC​AGG​ACA​ATC​CA6098
TECDX2CAGCGGCGGAACCTGTGACT​CGG​TAT​TTG​TCT​TTC​GTC​CTG6392
DAN2TGG​GAG​TGA​GGC​CCT​AAT​GAGGA​CTA​CTT​AGG​TCG​GGA​GGT60111
GATA3GCGGGCTCTACCACAAAACGT​TGG​CAT​TTC​TTC​TCC​A60141
XENSALL4CAG​GAG​TAC​CAG​AGC​CGA​AGACC​TCG​GGA​GAC​TTG​GAC​TT60107
SNAI1TTT​TCA​GCA​GCC​CTA​TGA​CCCCA​GGA​GAG​AGT​CCC​AGA​TG60107
SPARCGGA​CCA​TCA​GTC​CTC​TGG​AAAGT​TCT​GCG​TCT​CCC​AAA​GA60111
MesodermTGGG​CAA​GGG​ATG​GGA​ATA​AGGACC​GCT​GAG​GAT​GGA​CAA​AG60112
GSCGAA​GCC​CTG​GAG​AAC​CTC​TTGCT​TTC​GAC​GAC​GTC​TTG​TT60200
GATA5GAAACCCGAGCCCAGCCGGA​GTG​AAG​AGG​CAG​CGA​G60172
MIXL1AGA​TGT​GAA​CTG​CCT​GCC​CATT​CTG​GTG​TGT​GTC​TCC​CTG60232
Germ cellIFITM3TTC​GTG​GCT​TTC​GCC​TAC​TCCCA​GTG​GTG​CAA​ACG​ATG​AT60161
DDX4GAA​CCC​AGT​TGG​GGC​ATT​CATTT​GAT​GGC​ATT​CCT​GGG​CA64211
PRDM1GTT​CAG​GCA​GAG​GCA​TCC​TTGAG​TGT​GCT​GGG​TTC​ACG​TA60272
PTENCCA​GTC​AGA​GGC​GCT​ATG​TGTGG​CAG​ACC​ACA​AAC​TGA​GG64151

List of oligonucleotides for quantitative PCR.

RNA sequencing

RNA was purified from each type of cell (Clear-S™, Invirustech, Korea). Libraries were prepared using the SMART-Seq® v4 Ultra® Low Input RNA Kit for Sequencing (Takara Bio, CA, United States). An Illumina Novaseq 6000 (CA, United States) was used to produce read counts of samples. For the comparative study, previous reports were used (Table 3). A list of the tools used for the analysis is given in Table 4.

TABLE 3

Access number of GEOName of sampleRunDescriptionSample name in this study
GSE189477GSM5702418Not published (private until publication)Epiblast-like cellsType A (A)
GSM5702419Not published (private until publication)Epiblast-like cellsType A (A1)
GSM5702420Not published (private until publication)Epiblast-like cellsType A (A2)
GSM5702421Not published (private until publication)Primitive endoderm-like cellsType B (B)
GSM5702422Not published (private until publication)Primitive endoderm-like cellsType B (B1)
GSM5702423Not published (private until publication)Primitive endoderm-like cellsType B (B2)
GSM5702424Not published (private until publication)Trophectoderm-like cellsType C (C)
GSM5702425Not published (private until publication)Trophectoderm-like cellsType C (C1)
GSM5702426Not published (private until publication)Trophectoderm-like cellsType C (C2)
GSM5702427Not published (private until publication)Mesoderm-like cellsType D (D)
GSM5702428Not published (private until publication)Mesoderm-like cellsType D (D1)
GSM5702429Not published (private until publication)Mesoderm-like cellsType D (D2)
GSE120031GSM3391893SRR7851658Pig fetal fibroblastsPEF
GSM3391894SRR7851659Pig fetal fibroblastsPEF
GSM3391895SRR7851660Pig fetal fibroblastsPEF
GSM3391902SRR7851667Pig ESCsIVF-ES
GSM3391903SRR7851668Pig ESCsIVF-ES
GSM3391904SRR7851669Pig ESCsIVF-ES
GSE112380GSM3069020SRR6904203Pig embryo: MorulaMorula
GSM3069022SRR6904205Pig embryo: MorulaMorula
GSM3069025SRR6904208Pig embryo: MorulaMorula
GSM3069058SRR6904241Pig embryo: EB ICMEB_ICM
GSM3069061SRR6904244Pig embryo: EB ICMEB_ICM
GSM3069065SRR6904248Pig embryo: EB ICMEB_ICM
GSM3069081SRR6904264Pig embryo: EB TEEB_TE
GSM3069092SRR6904275Pig embryo: EB TEEB_TE
GSM3069094SRR6904277Pig embryo: EB TEEB_TE
GSM3069096SRR6904279Pig embryo: LB EPILB_Epi
GSM3069113SRR6904296Pig embryo: LB EPILB_Epi
GSM3069118SRR6904301Pig embryo: LB EPILB_Epi
GSM3069131SRR6904314Pig embryo: HYPOLB_PrE
GSM3069134SRR6904317Pig embryo: HYPOLB_PrE
GSM3069142SRR6904325Pig embryo: HYPOLB_PrE
GSE66507GSM1624222SRR1825955Human embryo: TETE
GSM1624225SRR1825958Human embryo: TETE
GSM1624228SRR1825961Human embryo: EPIEpiblast
GSM1624229SRR1825962Human embryo: TETE
GSM1624232SRR1825965Human embryo: PEPrE
GSM1868810SRR2240580Human embryo: PEPrE
GSM1868810SRR2240581Human embryo: PEPrE
GSM1868823SRR2240644Human embryo: EPIEpiblast
GSM1868823SRR2240645Human embryo: EPIEpiblast

List of RNA-seq data.

TABLE 4

Tools_1Name of toolVersionParameterReference
Preprocessing
 Low-quality reads and adapter sequences were filtered according to the following parameterscutadapt2.8quality-cutoff (20) and minimum-length (50)Martin, (2011)
Alignment
 Filtered reads were mapped to the reference genome related to the species. The parameters have been set based on ENCODE standard options.STAR2.7.1aoutFilterType (BySJout), outFilterMultimapNmax (20), alignSJoverhangMin (8), alignSJDBoverhangMin (1), outFilterMismatchNmax (999), outFilterMismatchNoverLmax (0.04), alignIntronMin (20), alignIntronMax (1000000), and alignMatesGapMax (1000000)Dobin et al. (2013)
Abundance estimation
 The expression levels of genes and transcripts were calculated using the read mapping information obtained from the aligner in the following mannerRSEM1.3.1—Li and Dewey, (2011)
featureCounts2.0.0—Liao et al. (2014)
HTSeq-count0.11.2minaqual (0) and mode (intersection-nonempty)Anders et al. (2015)
Cufflinks2.2.1multiread-correct and frag-bias-correctTrapnell et al. (2010)
Tools_2Name of the toolVersionRef
 Differentially expressed gene (DEG) analysis
  Using genes and transcript expression information, genes were predicted with statistically significant differences in expression between samples or groupsTCC1.26.0Sun et al. (2013)
edgeR3.28.1Robinson et al. (2010)
DESeq1.38.0Anders and Huber, (2010)
DESeq21.26.0Love et al. (2014)
EnhancedVolcano-Kevin et al. (2018)
 Functional study
  The biological function of DEGs is being investigated to identify biological clues for research using differences in the expression levelsgoseq1.38.0Young et al. (2010)
GOplot1.0.2Walter et al. (2015)
 Data quality control (QC)
  Data QC was performed to verify sequencing and alignment data, etc.FastQC0.11.9Andrews, (2010)
PCAtools—Kevin Blighe, (2019)
Gviz1.30.0Hahne and Ivanek, (2016)

Tools and parameters.

Statistical analysis

Statistical analysis of the data was performed using GraphPad Prism Software (version 5.01; San Diego, CA, United States). Significant differences among experimental groups were determined by one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test, while unpaired t-tests were used for the binomial data. A p-value < 0.05 was considered significant. Data are presented as the mean± the standard error.

Results

Morphology and protein localization in each type of cell

The morphological features of each cell population are shown in Figure 1A. Types A, B, and C cultured as single layers, but type D formed multiple layers. Lipid droplets (LDs) were determined by microscopy with normal light, following a previous study (Niu et al., 2015). Cell types A and C contained LDs, while B and D did not show LDs. The density order of the cells was type D, B, A, and C. In the AP treatment experiment, type A cells were stained, but cell types B and C were not AP-positive. Within type D, the central part of the colony (high cellular density) showed an AP-positive signal (Figure 1B). Because the type D cells grew in multiple layers, the AP staining always showed the same pattern as shown in Figure 1B. SOX17 protein was detected only in the nuclei of type A cells. SOX2 was localized in the nuclei of type A, B, and D cells, but this protein was localized in the cytoplasm of type C cells (Figure 1C; Supplementary Figure S1). Similar to SOX17, GATA6 was detected in the nuclei of type A cells. Type B cells were negative for GATA6. Cell types C and D showed a positive signal for GATA6, but it was not negative in type B. NANOG was observed in the nuclei of type A and B cells, while it was observed in the cytoplasm of type C cells (Figure 1D). All of the results from this section are summarized in Figure 3A, and representative images are shown in Figure 3B.

FIGURE 1

Relative expression level of lineage-specific marker genes in each type of cells

Relative expression levels of marker genes were normalized with porcine embryonic fibroblasts (PEFs) as a control (Figure 2). Among the epiblast markers, Oct4a and Hnf4a showed high levels of expression in cell types A and B. The expression of Sox2 was restricted in type D cells. Nanog expression in type C cells showed significantly lower expression than that in PEFs. The expression levels of Klf4 and Myc were lower in the four types of cells than in somatic cells. PDGFRA expression was high in type A and B cells, but PDGFα expression was lower in the four cell types than in PEFs. Other PrE-specific genes, Sox17, Gata4, and Gata6, were highly expressed in cell types A and B. Among TE-specific markers, Cdx2 showed specific expression in type C cells. Dab2 expression in the four cell types was lower than that in PEFs, while Gata3 expression was only high in type B cells. Among XEN cell markers, Snail and Sparc expression in the four types of cells was lower than that in PEFs. Sall4 was highly expressed in cell types A and B. Mesoderm marker T showed exclusive expression in type D cells. Type D cells also showed the highest expression of Gata5. Additionally, the expression of Gata5 in type C cells was lower than that in type D cells but higher than that in cell types A and B. Gsc expression was high in cell types A and B, and the expression of Mixl1 was significantly high in cell types A, B, and D. In the case of germ cell markers, the expression levels of Prdm1 were significantly high in cell types A and B. However, Ddx4 expression was only higher in cell type D than in the other cell types. Moreover, Pten expression in the four cell types was lower than that in PEFs. All of the results from this section are summarized in Figure 3A.

FIGURE 2

FIGURE 3

Treatment of colonies with signaling inhibitors

The inhibitors we used were powerful, therefore some cell types were not detected after the culture period (Figure 4A). The four types were observed following TGFβ inhibitor treatment. However, AG1296 suppressed the development of cell type D, and only type C cells survived MEK inhibitor treatment. We selected five marker genes (Sox17, Gata4, Gata6, T, and Cdx2) from the above experiment (Figure 4B). In type A cells, PrE markers were greatly reduced by a TGFβ inhibitor, and these markers were decreased in type B cells by AG1296. The TE marker Cdx2 in type C cells was downregulated by all three inhibitors; in particular, the MEK inhibitor strongly suppressed Cdx2. The mesoderm marker T was repressed in type D cells by a MEK inhibitor. Next, the cells were immunostained with the antibodies listed in Figure 5. No difference was detected in the pattern of SOX2 following inhibitor treatment, but the SOX17 signal was not observed under all conditions. In the case of GATA6 and NANOG, AG1296 and MEK inhibitors did not affect the pattern of protein expression. Under TGFβ inhibitor treatment, the nucleus-specific positive signals of GATA6 and NANOG in type A cells disappeared, and NANOG was translocated to the cytoplasm in type B cells. Additionally, type C cells were not positive for either protein.

FIGURE 4

FIGURE 5

RNA sequencing of the four cell types

A summary of the RNA sequencing is given in Supplementary Table S1. Raw data were aligned with reference genome information of pigs (Supplementary Table S2). Mapped reads were categorized by the value of reads per kilobase per million (RPKM) (Supplementary Table S3). We found up- or down-regulated genes in each cell type (Table 5). Only triplicates were used for each type, so no significant differences were detected in the lineage marker results. However, to show any tendencies of the marker genes, relative RPKMs were visualized (Figure 6A). Of the epiblast markers, Oct4, Nanog, and Hnf4a showed peaks in cell type A. All the PrE markers had the highest value in cell type A. The TE markers Cdx2, Dab2, and Gata3 showed peaks in cell type C. Correlations among the 12 samples were analyzed by multidimensional scaling (Figure 6B). Samples were gathered according to their types. This result means that each type showed a distinct pattern of RNA expression. We also compared 12 samples with RNA sequencing data of previous studies (Figure 6C). In the comparison with porcine embryonic stem cells and somatic cells, the 12 samples showed a closer correlation with embryonic stem cells than somatic cells. Next, the 12 samples were compared with single-cell RNA-seq samples of porcine and human embryos (Figures 6D,E, respectively). In this analysis, no correlation was found between our samples and the single-cell RNA-seq data.

TABLE 5

SampleUpregulated genesDownregulated genes
Type AKRT8, APOE, RBP4, TF, FETUB, GPC3, CLDN6, P3H1, MDH1, APOA1, GSN, ISYNA1, CKB
Type BCXCL14, COX3, S100A6, CRABP1, APOA1, ENSSSCG00000032599, ENSSSCG00000034846, ENSSSCG00000041596, ENSSSCG00000041875
Type CLGMN, S100A6, TACSTD2, ANXA2, B2M, CLDN4, CST6, PTGS2, MEST, ENSSSCG00000017061, GRN, COX1, CD9, MT1A, ENSSSCG00000024911, PLBD1, CSTB, TIMP3, PLET1, ENSSSCG00000035724, ENSSSCG00000037567, KRT7, HSPB1, CTSD, ENSSSCG00000048235TMSB10, MDK, PRDX2, ENSSSCG00000014540, UBB, ENO1, H3-3A, ENSSSCG00000032003, IGFBP2
Type DCKB, CRABP1, HMGN2, S100A11, TMSB10, H2AFZ, ENSSSCG00000009327, ENSSSCG00000012119, MDK, PRDX2, ENSSSCG00000017202, UBB, COX2, ATP8, COX3, ENO1, H3-3A, STMN1, ENSSSCG00000032599, ENSSSCG00000034846, PTMA, ENSSSCG00000039506, ID3KRT8, KRT18

List of type-specific up- or down-regulated genes from the four cell types.

FIGURE 6

Discussion

Unlike previous studies, we tried to produce multiple types of embryonic cells with minimum signaling chemicals. We used both hLIF and bFGF together with the recipe described in our previous report (Choi et al., 2019). We induced four types of cell populations using a single protocol, and their characteristics were analyzed.

From the patterns of marker proteins and profiles of gene expression, we concluded that these four cell types could represent four embryonic lineages of early blastocysts (type A, PrE; type B, epiblast; type C, TE; type D, mesoderm). First, we could easily classify the types based on their morphology. Well-known protein markers of pluripotent cells, SOX2, GATA6, NANOG, and SOX17, showed their availability as standards to distinguish the four types. Additionally, among candidate genes, some genes demonstrated that their expression levels could be used as type-specific markers of gene expression. The nuclei of type A cells were highly positive for the PrE-specific markers SOX17 and GATA6. Also, similar to PrE tissue in embryos, type A cells only grew in a single layer. Type B cells shared many marker patterns with type A cells, including NANOG protein and mRNA expression. One of the major differences between cell types A and B was morphology. Type B cells could grow in multiple layers with cuboidal shapes. In the early stages of embryonic development, only epiblast cells can have many layers between two monolayer cell types, TE and PrE. Additionally, early epiblast cells have an apolar cell shape similar to that of the type B cells (Sheng, 2015). SOX2 is known as a marker of pluripotent cells such as ICM cells and epiblasts (Avilion et al., 2003). However, SOX2 has also been detected in the cytosol of TE and cancer cells (Keramari et al., 2010; Artus et al., 2011). Our results corresponded with those reports. Type C cells showed similar characteristic to TE cells. This type only expanded in the monolayer, and when the density of cells became too high, it detached from the feeder cells to form a bubble-like structure. The strong mesoderm marker T was only expressed in type D cells among the four cell types, and these cells were AP-positive (Herrmann et al., 1990). Therefore, type D cells were assumed to be a mesoderm lineage.

AG1296, an inhibitor of the PDGF receptor, repressed the induction of type D cells during the culture period. Type A cells maintained GATA6-positivity but lost SOX17 intensity. TGFβ inhibitors and MEK inhibitors are well-known cytokines that suppress the epiblast lineage in embryos and embryonic stem cells (Vrbsky et al., 2015). The four cell types we cultured can be produced with TGFβ inhibitor. However, type A cells lost the expression of both SOX17 and GATA6. In our results, the MEK inhibitor was a much stronger regulator than TGFβ in porcine embryos. Only type C-like cells could be obtained, while the expression level of Cdx2, a TE marker, was significantly reduced. Therefore, these cells could be suggested as type C, whereas it is hard to conclude them as type C cells. Treatment of AG1296 also resulted in changes in marker gene expression.

Based on the RNA-seq results, comparitive analyses were conducted. Up- or down-regulated genes were selected for each type of cell. Epiblast and TE markers showed peaks in epiblast-like type A and TE-like type D cella, respectively. Together with the qPCR and ICC data, type A could be inferred to be an epiblast lineage. Additionally, by the same token, type D cells might represent the TE lineage. However, the RPKM of PrE markers was highest for type A cells. In the case of PrE, the RNA-seq data were not consistent with the data above. The correlation of samples was examined, and a two-dimensional plot was produced. The same types of samples were clustered on the plot, but one of the type B samples was located near the type A samples. Mesoderm-like type D samples localized closer to type B than type A samples. During embryonic development, mesoderm cells originate from epiblasts, and PrE cells promote the induction of mesoderm formation. The developmental signal from PrE seemed to make mesoderm cells similar to PrE. The locations of TE-like type C samples were separate from the other samples. In the metadata analysis, we found that our samples were more like embryonic stem cells than somatic cells. However, because of the differences of data sources, single-cell RNA-seq data could not be used to characterize the four cell types in our study.

In conclusion, we have proposed a novel method for the induction of PrE, epiblasts, TE, and mesoderm-like cells from blastocysts using a single method. The limitation of suppressor molecules led to the survival of embryonic lineages during attachment culture. With additional inhibitors, some cell types were not observed, and the obtained cell types had transitions in marker proteins and mRNAs. Each cell type showed a unique response to signaling inhibitors. In our RNA-seq results, we found cell type-specific genes. Additionally, the similarity of the cell types was examined. As a further study, we are preparing type-specific culture conditions to maintain the characteristics of each cell type. Functional experiments such as differentiation of each cell type will be possible due to the extension of the culture period. This study will broaden the understanding of lineage specification in early embryos. In particular, we might develop a method to isolate cell populations in their natural state.

Statements

Data availability statement

The datasets presented in this study can be found in online repositories. The name of the repository and accession number can be found below: NCBI Gene Expression Omnibus; accession number GSE189477.

Ethics statement

The animal study was reviewed and approved by the Institute of Laboratory Animal Resources, Seoul National University.

Author contributions

J-NO and C-KL conceptualized the study. J-NO conducted the overall experiments and data curation. JJ contributed to feeder cell preparation and sampling for RNA purification. ML, GC, D-KL, K-HC, S-HK, and JJ participated in writing, review, and editing. D-KL, K-HC, and C-KL supervised the research. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the BK21 Four program, the Korea Evaluation Institute of Industrial Technology (KEIT) through the Alchemist project funded by the Ministry of Trade, Industry and Energy (MOTIE; 20012411), and the National Research Foundation of Korea (NRF) grant funded by the government of Republic of Korea (NRF-2021R1A2C4001837).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2022.918222/full#supplementary-material

References

  • 1

    AbeydeeraL. R.DayB. N. (1997). In vitro penetration of pig oocytes in a modified Tris-buffered medium: Effect of BSA, caffeine and calcium. Theriogenology48, 537–544.

  • 2

    AndersS.HuberW. (2010). Differential expression analysis for sequence count data. Genome Biol.11, R106. 10.1186/gb-2010-11-10-r106

  • 3

    AndersS.PylP. T.HuberW. (2015). HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics31, 166–169. 10.1093/bioinformatics/btu638

  • 4

    AndrewsS. (2010). FastQC: A quality control tool for high throughput sequence data.

  • 5

    ArtusJ.PiliszekA.HadjantonakisA. K. (2011). The primitive endoderm lineage of the mouse blastocyst: Sequential transcription factor activation and regulation of differentiation by Sox17. Dev. Biol.350, 393–404. 10.1016/j.ydbio.2010.12.007

  • 6

    AvilionA. A.NicolisS. K.PevnyL. H.PerezL.VivianN.Lovell-BadgeR. (2003). Multipotent cell lineages in early mouse development depend on SOX2 function. Genes Dev.17, 126–140. 10.1101/gad.224503

  • 7

    ChoiK. H.LeeD. K.KimS. W.WooS. H.KimD. Y.LeeC. K. (2019). Chemically defined media can maintain pig pluripotency network in vitro. Stem Cell Rep.13, 221–234. 10.1016/j.stemcr.2019.05.028

  • 8

    DobinA.DavisC. A.SchlesingerF.DrenkowJ.ZaleskiC.JhaS.et al (2013). Star: Ultrafast universal RNA-seq aligner. Bioinformatics29, 15–21. 10.1093/bioinformatics/bts635

  • 9

    EmreN.ColemanR.DingS. (2007). A chemical approach to stem cell biology. Curr. Opin. Chem. Biol.11, 252–258. 10.1016/j.cbpa.2007.04.024

  • 10

    EzashiT.TeluguB. P.RobertsR. M. (2012). Induced pluripotent stem cells from pigs and other ungulate species: An alternative to embryonic stem cells?Reprod. Domest. Anim.47 (4), 92–97. 10.1111/j.1439-0531.2012.02061.x

  • 11

    HahneF.IvanekR. (2016). Visualizing genomic data using gviz and bioconductor. Methods Mol. Biol.1418, 335–351. 10.1007/978-1-4939-3578-9_16

  • 12

    HermitteS.ChazaudC. (2014). Primitive endoderm differentiation: from specification to epithelium formation. Philos. Trans. R. Soc. Lond. B Biol. Sci.369, 20130537. 10.1098/rstb.2013.0537

  • 13

    HerrmannB. G.LabeitS.PoustkaA.KingT. R.LehrachH. (1990). Cloning of the T gene required in mesoderm formation in the mouse. Nature343, 617–622. 10.1038/343617a0

  • 14

    KeramariM.RazaviJ.IngmanK. A.PatschC.EdenhoferF.WardC. M.et al (2010). Sox2 is essential for formation of trophectoderm in the preimplantation embryo. PLoS One5, e13952. 10.1371/journal.pone.0013952

  • 15

    KevinB.RanaS.MylesL. (2018). EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling.

  • 16

    Kevin BligheA. L. (2019). PCAtools: Everything principal components analysis.

  • 17

    LiB.DeweyC. N. (2011). Rsem: Accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinforma.12, 323. 10.1186/1471-2105-12-323

  • 18

    LiW.DingS. (2010). Small molecules that modulate embryonic stem cell fate and somatic cell reprogramming. Trends Pharmacol. Sci.31, 36–45. 10.1016/j.tips.2009.10.002

  • 19

    LiaoY.SmythG. K.ShiW. (2014). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics30, 923–930. 10.1093/bioinformatics/btt656

  • 20

    LoveM. I.HuberW.AndersS. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.15, 550. 10.1186/s13059-014-0550-8

  • 21

    MartinM. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J.17, 10. 10.14806/ej.17.1.200

  • 22

    NiuY.WangC.XiongQ.YangX.ChiD.LiP.et al (2015). Distribution and content of lipid droplets and mitochondria in pig parthenogenetically activated embryos after delipation. Theriogenology83, 131–138. 10.1016/j.theriogenology.2014.09.002

  • 23

    OhJ. N.LeeM.ChoeG. C.LeeD. K.ChoiK. H.KimS. H.et al (2020). Identification of the lineage markers and inhibition of DAB2 in in vitro fertilized porcine embryos. Int. J. Mol. Sci.21, E7275. 10.3390/ijms21197275

  • 24

    RalstonA.RossantJ. (2005). Genetic regulation of stem cell origins in the mouse embryo. Clin. Genet.68, 106–112. 10.1111/j.1399-0004.2005.00478.x

  • 25

    RobinsonM. D.MccarthyD. J.SmythG. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics26, 139–140. 10.1093/bioinformatics/btp616

  • 26

    RossantJ. (2007). Stem cells and lineage development in the mammalian blastocyst. Reprod. Fertil. Dev.19, 111–118. 10.1071/rd06125

  • 27

    ShengG. (2015). Epiblast morphogenesis before gastrulation. Dev. Biol.401, 17–24. 10.1016/j.ydbio.2014.10.003

  • 28

    SmithA. G. (2001). Embryo-derived stem cells: Of mice and men. Annu. Rev. Cell Dev. Biol.17, 435–462. 10.1146/annurev.cellbio.17.1.435

  • 29

    SunJ.NishiyamaT.ShimizuK.KadotaK. (2013). Tcc: an R package for comparing tag count data with robust normalization strategies. BMC Bioinforma.14, 219. 10.1186/1471-2105-14-219

  • 30

    TeruelM.SmithR.CatalanoR. (2000). Growth factors and embryo development. Biocell.24, 107–122.

  • 31

    TrapnellC.WilliamsB. A.PerteaG.MortazaviA.KwanG.Van BarenM. J.et al (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol.28, 511–515. 10.1038/nbt.1621

  • 32

    van der ValkJ.BiebackK.ButaC.CochraneB.DirksW. G.FuJ.et al (2018). Fetal bovine serum (FBS): Past - present - future. ALTEX35, 99–118. 10.14573/altex.1705101

  • 33

    VrbskyJ.TerehT.KyrylenkoS.DvorakP.KrejciL. (2015). MEK and TGF-beta inhibition promotes reprogramming without the use of transcription factor. PLoS One10, e0127739. 10.1371/journal.pone.0127739

  • 34

    WalterW.Sanchez-CaboF.RicoteM. (2015). GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics31, 2912–2914. 10.1093/bioinformatics/btv300

  • 35

    YamanakaY.RalstonA.StephensonR. O.RossantJ. (2006). Cell and molecular regulation of the mouse blastocyst. Dev. Dyn.235, 2301–2314. 10.1002/dvdy.20844

  • 36

    YoshiokaK.SuzukiC.TanakaA.AnasI. M.IwamuraS. (2002). Birth of piglets derived from porcine zygotes cultured in a chemically defined medium. Biol. Reprod.66, 112–119. 10.1095/biolreprod66.1.112

  • 37

    YoungM. D.WakefieldM. J.SmythG. K.OshlackA. (2010). Gene ontology analysis for RNA-seq: Accounting for selection bias. Genome Biol.11, R14. 10.1186/gb-2010-11-2-r14

Summary

Keywords

embryo, lineage, segregation, RNA-seq, blastocyst attachment

Citation

Oh J-N, Jeong J, Lee M, Choe GC, Lee D-K, Choi K-H, Kim S-H and Lee C-K (2022) Characterization of multitype colonies originating from porcine blastocysts produced in vitro. Front. Cell Dev. Biol. 10:918222. doi: 10.3389/fcell.2022.918222

Received

12 April 2022

Accepted

01 August 2022

Published

12 September 2022

Volume

10 - 2022

Edited by

Shahryar Kavoussi, Austin Fertility & Reproductive Medicine/Westlake IVF, United States

Reviewed by

In-Hyun Park, Yale University, United States

Sercin Karahuseyinoglu, Koç University, Turkey

Updates

Copyright

*Correspondence: Chang-Kyu Lee,

This article was submitted to Molecular and Cellular Reproduction, a section of the journal Frontiers in Cell and Developmental Biology

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Outline

Figures

Cite article

Copy to clipboard


Export citation file


Share article

Article metrics