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Velthut-Meikas, A.

Publications and source records attributed to Velthut-Meikas, A..

2 recordsLinked to original sources

Endometrial receptivity revisited: endometrial transcriptome adjusted for tissue cellular heterogeneity

STUDY QUESTIONDoes cellular composition of the endometrial biopsy affect the gene expression profile of endometrial whole-tissue samples?\n\nSUMMARY ANSWERThe differences in epithelial and stromal cell proportions in endome-trial biopsies modify whole-tissue gene expression profiles, and also affect the results of differential expression analysis.\n\nWHAT IS ALREADY KNOWNEach 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 way and tools used to obtain the tissue biopsy.\n\nSTUDY DESIGN, SIZE, DURATIONUsing 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.\n\nPARTICIPANTS/MATERIAL, SETTING, METHODSPaired early- and mid-secretory endometrial biopsies were obtained from thirty-five healthy, regularly cycling, fertile volunteers, aged 23 to 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 epithelial and stromal cells proportions in endometrial biopsy. The results were then compared to conventional differential expression analysis.\n\nMAIN RESULTS AND THE ROLE OF CHANCEThe estimated average proportions of stromal and epithelial cells in early secretory phase were 65% and 35%, and during mid-secre-tory phase 46% and 54%, respectively, that correlated well with the results of histological evaluation (r=0.88, p=1.1x10-6). 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=2,645) 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.\n\nLIMITATIONS, REASONS FOR CAUTIONOnly 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.\n\nWIDER IMPLICATIONS OF THE FINDINGSThe 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.\n\nTRIAL REGISTRATION NON/A

genomics

TAC-seq: targeted DNA and RNA sequencing for precise biomarker molecule counting

Targeted next-generation sequencing based biomarker detection methods have become essential for biomedical diagnostics. In addition to their sensitivity and high-throughput capacity, absolute molecule counting based on unique molecular identifier (UMI) has high potential to increase biomarker detection accuracy even further through the reduction of systematic technical biases. Here, we present TAC-seq, a simple and cost-effective targeted allele counting by sequencing method that uses UMIs to estimate the original molecule counts of different biomarker types like mRNAs, microRNAs and cell-free DNA. We applied TAC-seq in three different applications and compared the results with standard sequencing technologies. RNA samples extracted from human endometrial biopsies were analyzed using previously described 57 mRNA-based receptivity biomarkers and 49 selected microRNAs at different expression levels. Cell-free DNA aneuploidy testing was based on cell line (47,XX,+21) genomic DNA. TAC-seq mRNA biomarker profiling showed identical clustering results to full transcriptome RNA sequencing, and microRNA detection demonstrated significant reduction in amplification bias, allowing to determine minor expression changes between different samples that remained undetermined by standard sequencing. The mimicking experiment for cell-free DNA fetal aneuploidy analysis showed that TAC-seq can be applied to count highly fragmented DNA, detecting significant (p=4.8x10-11) excess of molecules in case of trisomy 21. Based on three proof-of-principle applications we show that TAC-seq is a highly accurate and universal method for targeted nucleic acid biomarker profiling.

genomics