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Physiol. Genomics 29: 253-259, 2007. First published January 30, 2007; doi:10.1152/physiolgenomics.00067.2006 Free Article
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Received 18 April 2006; accepted in final form 23 January 2007.
Physiological Genomics 29:253-259 (2007)
1094-8341/07 $8.00 © 2007 American Physiological Society

MPSS profiling of embryonic gonad and primordial germ cells in chicken

Heebal Kim1,4, Tae Sub Park2, Woon Kyu Lee3, Sunjin Moon1, Jin Nam Kim2, Ji Hye Shin2, Jin Gyoung Jung1,2, Seon Duk Lee2, Sang Hyun Park1, Kyung Je Park1, Mi A Kim2, Sang Su Shin1, Tae Min Kim1, Jungrye Nam1, Yeonkyung Kang2, Jeong Mook Lim1 and Jae Yong Han1

1 Department of Agricultural Biotechnology, Seoul National University, Seoul
2 Department of Genomics, Avicore Biotechnology Institute, Gyeonggi-do
3 Department of Laboratory Animal, Medical Research Center, College of Medicine, Yonsei University
4 Graduate Program in Bioinformatics, Seoul National University, Seoul, Korea


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 GRANTS
 REFERENCES
 
The massively parallel signature sequencing (MPSS) provides a greater depth of coverage than expressed sequence tag scan or microarray and provides a comprehensive expression profile. We used the MPSS technology to uncover gene expression profiling in the early embryonic gonads and primordial germ cells (PGCs) in the chicken. Total numbers of sequenced signatures were 1,012,533 and 995,676 for the PGCs and gonad, respectively. Using a noise distribution model, we found that 1.67% of all signatures are expressed at a higher level in PCGs and 2.81% of all signatures are expressed at a higher level in the gonad. The MPSS data are presented via an interactive web interface available at http://snugenome.snu.ac.kr/MPSS. The MPSS data have been submitted to the Gene Expression Omnibus of the National Center for Biotechnology Information (accession number GSM137300 and GSM137301 for PGCs and gonad, respectively).

massively parallel signature sequencing


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 GRANTS
 REFERENCES
 
GERM CELLS ARE INTERESTING because their purpose is to convey the hereditary genetic information from one generation to the next. The germ cells develop from primordial germ cells (PGCs) in the early embryonic developmental stages, and become into functional gametes, sperm in male or oocyte in female, after sexual maturity. The PGCs have unique and different migration activities in birds and mammals. They temporally reside in the extraembryonic tissue and localize into embryonic gonads. In mammals, the PGCs are originated from the epiblast of the gastrulating embryo and move into embryonic gonads through hindgut by amoeboid movement (27). In birds, PGCs appear from the epiblast in the blastoderm at the first time, and translocate to the hypoblast of the area pellucida (12, 34). During the gastrulation, they temporarily circulate via the blood vascular system and finally migrate into the gonadal anlagen (27). Thus, avian PGCs can be collected from germinal crescent (14, 40) or blood vessel (25, 26) and embryonic gonads (7, 26).

On the other hand, the ultimate function of the gonads is to produce gametes and provide a niche for germ cells. Embryonic germ cell development is orchestrated by gene interactions between or within germ cells and various types of somatic cells. Patterns of gene expression in embryonic gonads are related to germ cell development and regulation. Gonadal development proceeds via the interaction between somatic mesodermal cells and colonizing germ cells. This development is coupled with sex differentiation. In the majority of vertebrates, sex is determined genetically, but sexual differentiation begins only during gonadal development. A critical gene for sex determination equivalent to SRY in mammals has not been identified in the chicken, and the primary sex-determining signal is unknown. Birds have ZW (female)/ZZ (male) sex chromosomes, which differ from the XX (female)/XY (male) system in mammals. Although recent studies have identified several genes essential for early gonadal development, the exact role of these genes remains to be elucidated (19, 33, 35). Morphologically, sex differentiation in the chicken begins on day 6 in the embryo, and the embryonic gonad and enclosed germ cells undergo important phases of gene expression.

The PGCs are an important cell type, in which either gene expression or suppression should be regulated temporally and spatially during embryonic developments. According to gene expression switching triggered by interactions with an environmental niche, PGCs could maintain their pluripotency or differentiate into germ cells. However, there are few reports of transcriptomic study in the germ cell and gonad in the chicken, especially in the early embryonic developmental stages due to technical difficulties for collecting early embryonic germ cells.

A subset of genes that are expressed in a given cell or tissue type is defined as a transcriptome conveying the identity of each expressed gene and its level of expression for a defined population of cells (38). Sequence-based transcript-documenting technologies such as expressed sequence tags (ESTs) (1) and serial analysis of gene expression (37) can determine the gene expression patterns in a cell population or a specific tissue type. Recently, to uncover the gene expression pattern and identify novel transcripts, we have collected a large amount of ESTs in embryonic gonads (30) and PGCs (13), and constructed a chicken germ cell EST database (17). The EST sequencing technology for gene expression profiling works well if there are enough sequences because, for example, the physiological activity and cell differentiation of a mammalian cell are controlled by 10,000 or more protein-coding genes associated with about 300,000–500,000 mRNA transcripts (2, 20). Thus, the EST sequencing is not a cost-effective way to analyze a precise gene expression profiling. A more cost-effective sequence-based transcriptome analysis can be achieved by a recent alternative technology, massively parallel signature sequencing (MPSS) (5). The MPSS technology has been applied successfully to gene expression profiling in Arabidopsis (22) and human (8, 15, 16). The MPSS provides a greater depth of coverage than EST scan or microarray and provides a comprehensive expression profile (4) and allows the measurement of expression levels ranging between >105 copies per cell and <2. Thus, individual genes can show very high degrees of tissue specificity, and be classified accordingly (15). Here, we used the alternative technology to uncover gene expression profiling in a greater depth in the early embryonic gonads and PGCs in the chicken.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 GRANTS
 REFERENCES
 
All procedures for animal management, reproduction, and surgery were performed in accordance with the standard protocols of the Division of Animal Genetic Engineering, Seoul National University. The Institutional Review Board of Seoul National University approved the research proposal and the relevant experimental procedures in January 2003.

Retrieval of chicken gonad and PGCs.
Experimental animals provided for this experiment were maintained at the University Animal Farm, Seoul National University, and all experimental procedures were performed at the affiliated laboratories of the university. Gonadal cells were retrieved from the gonads of 6.5-day-old (stage 29) White Leghorn embryos by our standard procedure (28). Embryos were freed from the yolk by rinsing with calcium- and magnesium-free PBS, and the gonads were retrieved by dissection of embryo abdomen with sharp tweezers under a stereomicroscope. Embryonic gonads were collected from a total of 1,947 embryos in eight separated experimental batches by 10 highly skilled persons. Gonadal tissues were dissociated by gentle pipetting in 0.05% (vol/vol) trypsin solution supplemented with 0.53 mM EDTA. After being centrifuged at 200 g for 5 min, total gonadal cells were loaded into a magnetic-activated cell sorter (MACS, Miltenyi Biotech), and the separated PGCs were immediately stored in liquid nitrogen (–190°C) until being processed further. The numbers of PGCs in cell population before and after loading were counted.

MACS treatment for chicken PGCs and counting PGC number.
Chicken gonadal cells were incubated with PGC-specific primary antibody, anti-stage-specific embryo antigen (anti-SSEA)-1 antibody for chicken PGCs (mouse IgM isotype), for 20 min at the room temperature of 20–25°C. Anti-SSEA-1 antibody developed by Solter and Knowles (31) was obtained from the Developmental Studies Hybridoma Bank developed under the auspices of the National Institute of Child Health and Human Development and maintained by the University of Iowa, Department of Biological Science. After being washed with 1 ml of buffer (PBS supplemented with 0.5% BSA and 2 mM EDTA), the supernatant was completely removed. The pellet was mixed with 100 µl of buffer containing 20 µl of rat anti-mouse IgM microbeads for 15 min at 4°C. Treated cells were carefully washed by the addition of 500 µl of buffer and subsequently loaded with MACS (18). For counting cell numbers, chicken PGCs before or after MACS treatment were fixed with 1% (vol/vol) glutaraldehyde for 5 min and rinsed with 1x PBS twice. The anti-SSEA-1 ascites fluid diluted 1:1,000 in PBS was added, and subsequent steps were carried out using DAKO universal LSAB kit, Peroxidase (DAKO), according to the manufacturer's instruction.

After eight batches of cell preparation, total cell numbers of PGC-enriched fraction and gonadal stromal cells were 5.26 x 106 and 1.76 x 108, respectively. These cell populations were further used for total RNA isolation and MPSS analysis.

Generation of MPSS datasets.
The generation of MPSS datasets in the gonad and PGC samples was performed by Takara Biotechnology (Shiga, Japan). Total RNA was isolated from 11 samples by TRIzol Reagent (Invitrogen) and checked with an Agilent 2100 Bioanalyzer (Agilent Technologies). After a quality check, seven samples of PGCs and four samples of gonad were mixed, respectively. The mixed RNAs were treated with RNase-free DNase I and checked by the Agilent 2100 Bioanalyzer. The RNAs were processed according to the MPSS protocol as outlined in the references (5, 6, 29). In brief, each total RNA was reverse transcribed, and the cDNA was digested with DpnII. The MmeI site-containing adapter was ligated, and the 3'-most DpnII fragment (signature) was obtained and cloned into a Tag vector. The resulting libraries were amplified and loaded onto microbeads. More than 1.0 million microbeads were loaded into each flow cell, and the signature sequences were determined by a series of enzymatic reactions as outlined in the references (5, 6, 21, 29). Signatures representing transcripts were generated with 17-base sequence signature. The abundance for each signature was converted to transcripts per million (tpm). These MPSS data have been submitted to the GEO database under accession no. GSM137300,1.

Signature annotation and classification.
To generate complete, annotated Gallus signature database, we extracted all the possible signatures from the Gallus genome sequence (UCSC galGal2) and the Gallus UniGene sequences. Each virtual signature was ranked on the basis of on its position and orientation in the original sequence. The annotation for that sequence was then assigned to the signature, and the resulting signature database was used to annotate the data from the experiments. Criteria were set to classify signatures: 1) the position of the signatures relative to polyadenylation signals and poly-A tails and 2) the orientation of the signatures relative to the 5'- to 3'-direction of the source mRNA. Each virtual signature was ranked, as outlined in the Supporting Table S1 (the online version of this article contains supplemental material).

Statistical analysis of differentially expressed signatures.
We classified signatures into two cases: 1) a nonzero measurement that has nonzero tpm measurements for both tissues and 2) a one-zero measurement that has a zero-tpm measurement for gonad or a nonzero measurement for PGC. Then, we obtained the tpm value from {theta}i {equiv} log10 [(vi/Ns) x 106], where the vi and the Ns are the bead counts for the given signature i and the total number of sequenced beads in each sample. For case 1, we evaluated significance of the difference between the expression value {theta}pgc and {theta}gonad for each signature by a statistical model. This model provides a noise distribution for each measurement as a function of the observed tpm that based on other replicate measurements of other MPSS datasets (32). For case 2, we calculated the P value as the area of the significance region from the probability distribution of the {theta}i.

GO annotation and significance test of GO terms.
To classify and compare the differentially expressed signatures (DESs) between the two samples with Gene Ontology (GO) terms, we used The Institute for Genomic Research (TIGR) Gallus gallus Gene Index (GgGI, release 10.0). A sequence similarity comparison between the tentative consensus sequences of the GgGI and our DES in the two samples was conducted using the stand-alone BLASTN program of the National Center for Biotechnology Information (version 2.2.10), with perfect identity as the cutoff value. The GO annotation was performed by extracting information with the term already annotated in the GgGI. Pearson's {chi}2 test was used to test the significance of which GO terms were enriched in one sample of DES, but relatively depleted in the other. As described previously (42), a particular GO term can be viewed as a function that maps gene G in go (G) = 0 or 1, according to the corresponding GO term. The null hypothesis of no association between gene lists and a particular GO term is translated into equal distribution of binary random variables. A Bonferroni correction (3) was applied to correct the multiple test problems. The significance tests were performed from the 2nd level of GO terms to leaf terms. We define the levels of GO terms on the basis of hierarchical list view. The first level includes molecular function (MF), biological process (BP), and cellular component (CC) terms. We used the 0.05 significance level to reject the null hypothesis. We identified only the significant leaf nodes with the following algorithm; Finding Significant Leaf Nodes (FSLN) is

FSLN (G, v):

Perform the "visit" action for node v;
For each child w of v do
If the child w has significant p-value then remove the v node;
Recursively traverse the subgraph rooted at w by calling FSLN(G, w);
In brief, the algorithm looks up all child nodes of a node, and if any of its child nodes shows significant P value it is excluded from the significant GO terms.


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
GRANTS
 REFERENCES
 
MPSS signatures matched to the chicken genomic sequence.
The expressed MPSS signatures were compared with the chicken genomic sequences to assign the expression signatures to specific genes and genomic positions. As shown in Table 1, from the filtered total signatures with significantly expressed signatures (>3 tpm) (23), a total of 20.4% signatures were matched with unique location in the genome, 7.4% signatures were matched with duplicated locations, and 7.8% signatures were unmatched. The unmatched signatures may have resulted from sequencing errors, unidentified spliced 3'-end and the physical map of chicken covering ~91% of the chicken genome that might bring transcripts found in nonsequenced regions (39). The MPSS was performed on the whole transcripts isolated from the two sample libraries, which were PGC and embryonic gonad samples.


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  		Table 1. Libraries and signature summary statistics

 
We followed the filtering procedure as described elsewhere (23, 24). Almost one-third of the filtered signatures in each library were found in the range of 4–100 tpm. Less than 1% of signatures were highly expressed at levels >1,000 tpm. About 60% of signatures were observed in an unreliable level of expression at 1, 2, or 3 tpm. Besides, totals of 15.4 and 11.0% signatures were unique for the PGC and gonad (Table 2), respectively. From these unique signatures, ~40% of signatures were seen in cDNAs of unknown strand in both PGC and gonad. These transcripts are expected to be novel transcripts and ncRNAs. Signatures with multiple genome hits were identified: 35 and 34% in the PGC and gonad, respectively; 9 and 18% signatures were produced by sense-strand expression, and the remaining fraction (5%) was associated with cis-antisense transcripts based on the annotated genes.


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  		Table 2. Unique genomic signature and MPSS data from two libraries

 
Alternative polyadenylated transcripts.
We estimated the number of alternative transcripts directly from the MPSS data and signature classification (Table 3). Signatures in classes 1, 2, 3, 4, and 5 matching an annotated gene that have a unique match in the genome were summed for each library. In the PGC with >3 tmp, the sum of these signatures amounts to 3,354. Meanwhile, the total number of annotated genes that are identified by the signatures is 2,310. The difference between these two values is 1,044 (31.1%), which is the number of all types of transcriptional variants for PGC. In the same way, the difference is 824 in the gonad, in which 29.4% of the total transcripts are alternative splicing isoforms. We also calculated the number of genes with alternative splicing variants, i.e., the number of distinct gene that have more than two signatures matched. Only 614 (26.6%) and 469 (23.7%) of expressed genes in the PGC and gonad with >3 tpm have alternatively spliced transcripts. When one considers all tpms, 1,367 (43.9%) of expressed genes in the two libraries produce alternative splicing transcripts (Table 4).


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  		Table 3. Genes and alternative transcripts detected by MPSS signatures for PGC and gonad

 

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  		Table 4. Genes and alternative transcripts detected by MPSS signatures

 
Pattern of transcription compared between PGC and gonad libraries.
The genes having the most abundant signatures are listed in Table 5. The most abundant transcripts in the libraries consisted of 5/4 (PGC/gonad) mitochondrial genes and 3/4 ribosomal protein genes. It is notable that ß-actin, one of the actin isoforms associated with cell motility, is more than twofold abundant in the gonad than in PGC. There is a ubiquitously expressed chaperone, heat shock protein (HSP) 90, in the top-10 list of PGC, but not in the gonad. Then we compared PGCs and gonads to identify signatures that have tissue specificity. The tissue-specific genes were defined with 100-fold higher in one library than the other. We show that the PGC library had about fourfold more tissue-specific genes than the gonad library (Table 6). While the abundance varies in a range of 4–165 tpm, the abundance in another library was 0–1 tpm. The constantly expressed genes are determined by calculating the overlapping proportion of signatures in between PGC and gonad libraries. The criterion was that sharing signatures showed a difference of 0.5–2.0 between the libraries; 1,702 signatures were expressed at relatively constant levels in both libraries. Most of these signatures were produced by genes with high variation between 4 and 13,030 tpm.


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  		Table 5. Top-10 most abundant signatures in PGCs and gonad tissues

 

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  		Table 6. Tissue-specific or constantly expressed genes

 
DESs and GO annotation.
The total signatures were divided into two cases, in which case 1 is nonzero measurement that has nonzero tpm measurements for both tissues and case 2 is one-zero measurement that has a zero-tpm measurement for either gonad or PGC. There were 26,599 for case 1 and 115,423 for case 2. We identified 2,377 and 3,991 signatures that are highly expressed in the PGCs and gonad samples, respectively, at a significance level of 5% (Table 7). Figure 1, A and B, shows the 2nd-level GO term annotation of the upregulated signatures in gonad and PGCs. However, the 2nd-level terms are rather general. Thus, we conducted a significance test of GO terms from the 2nd level to the leaf nodes and identified significant leaf GO terms. We used Pearson's {chi}2 test to evaluate the significance of GO terms that were enriched in one sample of DES but relatively depleted in the other. To classify and compare the DESs between the two samples using GO terms, sequence similarity comparisons were performed between TIGR GgGI and the DES. As shown in Table 8, of the 2,377 upregulated signatures in the PGC sample, there were 7, 10, and 2 significant leaf nodes identified from the MF, BP, and CC GO terms, respectively. On the other hand, of the 3,991 upregulated signatures in the gonad sample, there were 3, 10, and 4 significant leaf nodes identified from the MB, BP, and CC of GO terms, respectively. These are not mutually exclusive terms. We identified the significant leaf nodes of the two samples using Pearson's {chi}2 test (Table 8). A Bonferroni correction (3) was applied to correct the multiple test problems. Descriptions of the gene in the significant leaf nodes are shown in Supporting Table S2. It was interesting that HSPs were enriched GO terms in the PGC samples.


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  		Table 7. Number of upregulating signatures for PGCs and gonad tissue

 

Figure 1
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  		Fig. 1. Gene Ontology annotation of the differentially upregulated signatures in the gonad (A) and in the primordial germ cells (PGCs, B). A1/B1, A2/B2, and A3/B3 indicate molecular function, biological process, and cellular component, respectively.

 

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  		Table 8. List of the Gene Ontology significant leaf terms enriched in PGC or gonad sample among differentially expressed signatures

 
HSPs are group of proteins whose expression level is dramatically increased in response to the various environmental conditions as well as heat exposure from bacteria to human. HSP expression is reciprocally regulated in the various tissues and also during embryo development, especially in the germ-line (11, 36). Therefore, the higher level of HSP expression in PGCs, in this study, suggested that HSP functions as a signal transducer or a developmental regulator, not in response to biological stress. Importins, nuclear importer of proteins and microtubule organizer, are critically involved in oogenesis and spermatogenesis in Drosophila (9, 10). In this study, importin {alpha}-4 subunit was expressed highly in PGC population, which might reflect the possibility that the differentiation of PGCs is actively initiated at this stage followed by induction of germ cell-related transcript expression. Centromere protein ZW10 is required for accurate chromosome segregation during mitosis, as well as meiosis, in Drosophila (41). Although the biological processes of ZW10 are not clearly identified in the early embryonic germ cell development, it is suggested that ZW10 is involved in accurate chromosome segregation during germ cell proliferation in the early embryonic stages. However, its functionality and role(s) during the embryo development remain to be further investigated.

Database construction.
We implemented the data in mySQL, and the web interface and visualization were performed using PHP scripting in combination with the mySQL database. The MPSS data are presented via an interactive web interface available at http://snugenome.snu.ac.kr/MPSS, including simple query, query by chromosome position, bulk query, query by sequence, search by library and signature abundance range, search by class, and search by tissue specificity. The design of the web interface and tools of the database were modified from the Arabidopsis MPSS database (22). The overview of the data processing pipeline is presented in Supporting Fig. S1.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 GRANTS
REFERENCES
 
This work was supported by a grant from BioGreen 21 Program (20050301034487), Rural Development Administration, Republic of Korea; and by the Brain Korea 21 Project of the Ministry of Education.


   FOOTNOTES
 
Address for reprint requests and other correspondence: J. Y. Han, Dept. of Food and Animal Biotechnology, Seoul National Univ. (SNU), Seoul, 151-742, Korea (e-mail: jaehan{at}snu.ac.kr).

Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 GRANTS
 REFERENCES
 

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