Marina Suhorutshenko1,2, Viktorija Kukushkina3, Agne Velthut-Meikas1,4, Signe Altmäe1,5, Maire Peters1,2, Reedik Mägi3, Kaarel Krjutškov1,6, Mariann Koel1,7, Francisco M Codoñer8, Juan Fco Martinez-Blanch8, Felipe Vilella9, Carlos Simón9,10,11, Andres Salumets1,2,12,13, Triin Laisk1,2,3. 1. Competence Centre on Health Technologies, Tartu, Estonia. 2. Department of Obstetrics and Gynecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia. 3. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia. 4. Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia. 5. Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Granada, Granada, Spain. 6. Research Program of Molecular Neurology, Research Programs Unit, University of Helsinki, Helsinki, Finland. 7. Department of Cell Biology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia. 8. Lifesequencing SL, Valencia, Spain. 9. Igenomix Foundation/INCLIVA, Valencia, Spain. 10. Research Department, Igenomix SL, Valencia, Spain. 11. Department of Pediatrics, Obstetrics and Gynecology, Valencia University, Valencia, Spain. 12. Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. 13. Department of Biomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.
Abstract
STUDY QUESTION: Does cellular composition of the endometrial biopsy affect the gene expression profile of endometrial whole-tissue samples? SUMMARY ANSWER: The differences in epithelial and stromal cell proportions in endometrial biopsies modify the whole-tissue gene expression profiles and affect the results of differential expression analyses. WHAT IS ALREADY KNOWN: Each cell type has its unique gene expression profile. The proportions of epithelial and stromal cells vary in endometrial tissue during the menstrual cycle, along with individual and technical variation due to the method and tools used to obtain the tissue biopsy. STUDY DESIGN, SIZE, DURATION: Using cell-population specific transcriptome data and computational deconvolution approach, we estimated the epithelial and stromal cell proportions in whole-tissue biopsies taken during early secretory and mid-secretory phases. The estimated cellular proportions were used as covariates in whole-tissue differential gene expression analysis. Endometrial transcriptomes before and after deconvolution were compared and analysed in biological context. PARTICIPANTS/MATERIAL, SETTING, METHODS: Paired early- and mid-secretory endometrial biopsies were obtained from 35 healthy, regularly cycling, fertile volunteers, aged 23-36 years, and analysed by RNA sequencing. Differential gene expression analysis was performed using two approaches. In one of them, computational deconvolution was applied as an intermediate step to adjust for the proportions of epithelial and stromal cells in the endometrial biopsy. The results were then compared to conventional differential expression analysis. Ten paired endometrial samples were analysed with qPCR to validate the results. MAIN RESULTS AND THE ROLE OF CHANCE: The estimated average proportions of stromal and epithelial cells in early secretory phase were 65% and 35%, and during mid-secretory phase, 46% and 54%, respectively, correlating well with the results of histological evaluation (r = 0.88, P = 1.1 × 10-6). Endometrial tissue transcriptomic analysis showed that approximately 26% of transcripts (n = 946) differentially expressed in receptive endometrium in cell-type unadjusted analysis also remain differentially expressed after adjustment for biopsy cellular composition. However, the other 74% (n = 2645) become statistically non-significant after adjustment for biopsy cellular composition, underlining the impact of tissue heterogeneity on differential expression analysis. The results suggest new mechanisms involved in endometrial maturation, involving genes like LINC01320, SLC8A1 and GGTA1P, described for the first time in context of endometrial receptivity. LARGE-SCALE DATA: The RNA-seq data presented in this study is deposited in the Gene Expression Omnibus database with accession number GSE98386. LIMITATIONS REASONS FOR CAUTION: Only dominant endometrial cell types were considered in gene expression profile deconvolution; however, other less frequent endometrial cell types also contribute to the whole-tissue gene expression profile. WIDER IMPLICATIONS OF THE FINDINGS: The better understanding of molecular processes during transition from pre-receptive to receptive endometrium serves to improve the effectiveness and personalization of assisted reproduction protocols. Biopsy cellular composition should be taken into account in future endometrial 'omics' studies, where tissue heterogeneity could potentially influence the results. STUDY FUNDING/COMPETING INTEREST(S): This study was funded by: Estonian Ministry of Education and Research (grant IUT34-16); Enterprise Estonia (EU48695); the EU-FP7 Eurostars program (NOTED, EU41564); the EU-FP7 Marie Curie Industry-Academia Partnerships and Pathways (SARM, EU324509); Horizon 2020 innovation program (WIDENLIFE, EU692065); MSCA-RISE-2015 project MOMENDO (No 691058) and the Miguel Servet Program Type I of Instituto de Salud Carlos III (CP13/00038); Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and European Regional Development Fund (FEDER): grants RYC-2016-21199 and ENDORE SAF2017-87526. Authors confirm no competing interests.
STUDY QUESTION: Does cellular composition of the endometrial biopsy affect the gene expression profile of endometrial whole-tissue samples? SUMMARY ANSWER: The differences in epithelial and stromal cell proportions in endometrial biopsies modify the whole-tissue gene expression profiles and affect the results of differential expression analyses. WHAT IS ALREADY KNOWN: Each cell type has its unique gene expression profile. The proportions of epithelial and stromal cells vary in endometrial tissue during the menstrual cycle, along with individual and technical variation due to the method and tools used to obtain the tissue biopsy. STUDY DESIGN, SIZE, DURATION: Using cell-population specific transcriptome data and computational deconvolution approach, we estimated the epithelial and stromal cell proportions in whole-tissue biopsies taken during early secretory and mid-secretory phases. The estimated cellular proportions were used as covariates in whole-tissue differential gene expression analysis. Endometrial transcriptomes before and after deconvolution were compared and analysed in biological context. PARTICIPANTS/MATERIAL, SETTING, METHODS: Paired early- and mid-secretory endometrial biopsies were obtained from 35 healthy, regularly cycling, fertile volunteers, aged 23-36 years, and analysed by RNA sequencing. Differential gene expression analysis was performed using two approaches. In one of them, computational deconvolution was applied as an intermediate step to adjust for the proportions of epithelial and stromal cells in the endometrial biopsy. The results were then compared to conventional differential expression analysis. Ten paired endometrial samples were analysed with qPCR to validate the results. MAIN RESULTS AND THE ROLE OF CHANCE: The estimated average proportions of stromal and epithelial cells in early secretory phase were 65% and 35%, and during mid-secretory phase, 46% and 54%, respectively, correlating well with the results of histological evaluation (r = 0.88, P = 1.1 × 10-6). Endometrial tissue transcriptomic analysis showed that approximately 26% of transcripts (n = 946) differentially expressed in receptive endometrium in cell-type unadjusted analysis also remain differentially expressed after adjustment for biopsy cellular composition. However, the other 74% (n = 2645) become statistically non-significant after adjustment for biopsy cellular composition, underlining the impact of tissue heterogeneity on differential expression analysis. The results suggest new mechanisms involved in endometrial maturation, involving genes like LINC01320, SLC8A1 and GGTA1P, described for the first time in context of endometrial receptivity. LARGE-SCALE DATA: The RNA-seq data presented in this study is deposited in the Gene Expression Omnibus database with accession number GSE98386. LIMITATIONS REASONS FOR CAUTION: Only dominant endometrial cell types were considered in gene expression profile deconvolution; however, other less frequent endometrial cell types also contribute to the whole-tissue gene expression profile. WIDER IMPLICATIONS OF THE FINDINGS: The better understanding of molecular processes during transition from pre-receptive to receptive endometrium serves to improve the effectiveness and personalization of assisted reproduction protocols. Biopsy cellular composition should be taken into account in future endometrial 'omics' studies, where tissue heterogeneity could potentially influence the results. STUDY FUNDING/COMPETING INTEREST(S): This study was funded by: Estonian Ministry of Education and Research (grant IUT34-16); Enterprise Estonia (EU48695); the EU-FP7 Eurostars program (NOTED, EU41564); the EU-FP7 Marie Curie Industry-Academia Partnerships and Pathways (SARM, EU324509); Horizon 2020 innovation program (WIDENLIFE, EU692065); MSCA-RISE-2015 project MOMENDO (No 691058) and the Miguel Servet Program Type I of Instituto de Salud Carlos III (CP13/00038); Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and European Regional Development Fund (FEDER): grants RYC-2016-21199 and ENDORE SAF2017-87526. Authors confirm no competing interests.