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Transcriptome profiling of sheep granulosa cells and oocytes during early follicular development obtained by laser capture microdissection.

ABSTRACT: BACKGROUND: Successful achievement of early folliculogenesis is crucial for female reproductive function. The process is finely regulated by cell-cell interactions and by the coordinated expression of genes in both the oocyte and in granulosa cells. Despite many studies, little is known about the cell-specific gene expression driving early folliculogenesis. The very small size of these follicles and the mixture of types of follicles within the developing ovary make the experimental study of isolated follicular components very difficult.The recently developed laser capture microdissection (LCM) technique coupled with microarray experiments is a promising way to address the molecular profile of pure cell populations. However, one main challenge was to preserve the RNA quality during the isolation of single cells or groups of cells and also to obtain sufficient amounts of RNA.Using a new LCM method, we describe here the separate expression profiles of oocytes and follicular cells during the first stages of sheep folliculogenesis. RESULTS: We developed a new tissue fixation protocol ensuring efficient single cell capture and RNA integrity during the microdissection procedure. Enrichment in specific cell types was controlled by qRT-PCR analysis of known genes: six oocyte-specific genes (SOHLH2, MAEL, MATER, VASA, GDF9, BMP15) and three granulosa cell-specific genes (KL, GATA4, AMH).A global gene expression profile for each follicular compartment during early developmental stages was identified here for the first time, using a bovine Affymetrix chip. Most notably, the granulosa cell dataset is unique to date. The comparison of oocyte vs. follicular cell transcriptomes revealed 1050 transcripts specific to the granulosa cell and 759 specific to the oocyte.Functional analyses allowed the characterization of the three main cellular events involved in early folliculogenesis and confirmed the relevance and potential of LCM-derived RNA. CONCLUSIONS: The ovary is a complex mixture of different cell types. Distinct cell populations need therefore to be analyzed for a better understanding of their potential interactions. LCM and microarray analysis allowed us to identify novel gene expression patterns in follicular cells at different stages and in oocyte populations.

SUBMITTER: Bonnet A

PROVIDER: S-EPMC3166951 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Transcriptome profiling of sheep granulosa cells and oocytes during early follicular development obtained by laser capture microdissection.

Bonnet Agnes A Bevilacqua Claudia C Benne Francis F Bodin Loys L Cotinot Corinne C Liaubet Laurence L Sancristobal Magali M Sarry Julien J Terenina Elena E Martin Patrice P Tosser-Klopp Gwenola G Mandon-Pepin Beatrice B

BMC genomics 20110818

<h4>Background</h4>Successful achievement of early folliculogenesis is crucial for female reproductive function. The process is finely regulated by cell-cell interactions and by the coordinated expression of genes in both the oocyte and in granulosa cells. Despite many studies, little is known about the cell-specific gene expression driving early folliculogenesis. The very small size of these follicles and the mixture of types of follicles within the developing ovary make the experimental study ...[more]

PMID: 21851638

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Project description:The etiology of Type 1 Diabetes (T1D) remains elusive. Enzymatically isolated and cultured (EIC) islets cannot fully reflect the natural protein composition and disease process of in vivo islets, because of the stress from isolation procedures. In order to study islet protein composition in conditions close to the natural environment, we performed proteomic analysis of EIC islets, and laser capture microdissected (LCM) human islets and acinar tissue from fresh-frozen pancreas sections of three cadaveric donors. 1104 and 706 proteins were identified from 6 islets equivalents (IEQ) of LCM islets and acinar tissue, respectively. The proteomic profiles of LCM islets were reproducible within and among cadaveric donors. The endocrine hormones were only detected in LCM islets, whereas catalytic enzymes were significantly enriched in acinar tissue. Furthermore, high overlap (984 proteins) and similar function distribution were found between LCM and EIC islets proteomes, except that EIC islets had more acinar contaminants and stress-related signal transducer activity proteins. The comparison among LCM islets, LCM acinar tissue and EIC islets proteomes indicates that LCM combined with proteomic methods enables accurate and unbiased profiling of islet proteome from frozen pancreata. This paves the way for proteomic studies on human islets during the progression of T1D.The etiological agent triggering autoimmunity against beta cells in Type 1 diabetes (T1D) remains obscure. The in vitro models available (enzymatically isolated and cultured islets, EIC islets) do not accurately reflect what happens in vivo due to lack of the natural environment where islets exist and the preparation-induced changes in cell physiology. The importance of this study is that we investigated the feasibility of laser capture microdissection (LCM) for the isolation of intact islets from frozen cadaveric pancreatic tissue sections. We compared the protein profile of LCM islets (9 replicates from 3 cadaveric donors) with that of both LCM acinar tissues (6 replicates from the same 3 cadaveric donor as LCM islets) and EIC islets (at least 4 replicates for each sample with the same islets equivalents) by using proteomics techniques with advanced instrumentation, nanoLC-Q Exactive HF Orbitrap mass spectrometry (nano LC-MS/MS). The results demonstrate that the LCM method is reliable in isolating islets with an intact environment. LCM-based islet proteomics is a feasible approach to obtain good proteome coverage for assessing the pathology of T1D using cadaveric pancreatic samples, even from very small sample amounts. Future applications of this LCM-based proteomic method may help us understand the pathogenesis of T1D and identify potential biomarkers for T1D diagnosis at an early stage.

Dataset Information

Transcriptome profiling of sheep granulosa cells and oocytes during early follicular development obtained by laser capture microdissection.

Publications

Transcriptome profiling of sheep granulosa cells and oocytes during early follicular development obtained by laser capture microdissection.

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