Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Non-invasive Transcriptomic Cell Profiling of the Human Endometrium with Generative Deep Learning

ABSTRACT: This study addresses the need for non-invasive methods to assess the human endometrium, which is critical for successful pregnancy and is implicated in unexplained infertility. The primary biological relevance lies in determining if extracellular vesicles in uterine fluid (UF-EVs) can serve as a proxy for the cellular state of the endometrial tissue. The intent is to validate a deep learning-based deconvolution approach that uses the transcriptomic profile of UF-EVs to accurately delineate the cellular composition of the endometrium throughout the menstrual cycle. Success in this area would enable detailed endometrial assessment without requiring invasive biopsies. The experimental workflow was designed to compare cellular profiles derived from UF-EVs against those from paired endometrial tissue. The primary dataset consisted of paired endometrial tissue and UF-EV transcriptomes collected from 19 fertile, healthy women across four menstrual cycle phases: proliferative (n=4), early-secretory (n=5), mid-secretory (n=5), and late-secretory (n=5). Two additional unpaired UF-EV samples were also included (phenotypes mapped in main_pheno.csv). For this cohort, small EVs were isolated using size-exclusion chromatography and RNA libraries were prepared with the TruSeq exome RNA library preparation kit. Raw sequencing data was processed with the nf-core/rnaseq pipeline (v3.14) and the RSEM outputs are provided. To map the spatial origin of EVs, the analysis also incorporated endometrial tissue Visium slides from two proliferative (n=2) and two early-secretory (n=2) phase samples (E-MTAB-9260). A deep learning deconvolution model, BulkTrajBlend, was trained on a human endometrial single-cell RNA-seq reference atlas. The trained model was then used to infer cell type proportions in the bulk transcriptomes and to generate pseudo-single-cell (pSC) data (weights in pseudosc_bio.pth and pseudosc_ev.pth), which was subsequently projected onto the spatial transcriptomic slides to visualize the potential tissue origins of the cells secreting EVs.

INSTRUMENT(S): HPC, QIAzol and miRNeasy micro kits; Bioanalyzer TapeStation 2100; Qubit microRNA assay, None, NextSeq 1000, NEXTflex small RNA library preparation kit v4; TruSeq exome RNA library preparation kit (Illumina); High Sensitivity DNA ScreenTape D1000

ORGANISM(S): Homo sapiens

SUBMITTER: Alvin Meltsov

PROVIDER: E-MTAB-15505 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Purpose: Extracellular vescicles (EVs) of uterine origin have recently been identified, but they are not comprised by current diagnostics on endometrial receptivity. In order to test the possibility to use EVs as biomarker reservoirs for non-invasive monitoring of the endometrial status, we defined two experimental designs: in step a) we aimed at assessing the concordance between the transcriptome of endometrial tissue and uterine fluid-derived EVs (UF-EVs); in step b) we aimed at unraveling the transcriptomic profile of UF-EVs released during the receptive phase and associated with successful implantation Methods: RNAseq analysis was performed on total RNA isolated from a) endometrial biopsies and UF-EVs concomitantly collected from regularly cycling women; b) UF-EVs collected in the pre-receptive (LH+2) versus the receptive (LH+7) phase of provenly fertile women and in the receptive phase (LH+7) of women undergoing assisted reproduction and transfer of an euploid blastocyst. In the latter group, the outcomes of the following embryo transfer attempt were recorded, in order to compare the transcriptomic profile of women with successful versus failed implantation. Differential Gene Expression (DGE) and Gene Set Enrichment (GSEA) analyses were performed. Results: In the first experimental design, a highly significant correlation was found between the transcriptional profiles of endometrial tissue biopsies and corresponding UF-EVs (r=0.71 p<0.001; ρ=0.65 p<0.001). In the second experimental design, using a large group of 73 samples, 16,777 genes were overall found to be expressed in UF-EVs. Of these, DGE analysis showed that 942 were up-regulated and 1,305 down-regulated during the receptive phase in women with proven fertility and 97 were up-regulated and 64 down-regulated during the receptive phase of women achieving successful implantation versus women failing to achieve pregnancy in the subsequent cycle. 41 genes had a statistically significant different expression in both comparisons. We additionally tested wether transcripts used by current endometrial receptivity tests based on endometrial biopsies (Endometrial Receptivity Assay, ERA test) could also be detected in UF-EVs: out of 238 ERA test gene data-set, GSEA highlighted 219 genes with similar transcriptional profile in UF-EVs. We then performed another comparison adding the list of genes consisting of the ‘metasignature’ transcriptome derived from the published meta-analysis of endometrial receptivity associated transcripts, comprising 57 genes. This last intersection identified 38 genes expressed in UF-EVs that i) show differential expression with the same transcriptional profile (same trends) in both EVs and ERA test assay; ii) belong to the leading edge of genes with the most significant enrichment score in our analysis; iii) are identified as receptivity-associated genes by the current meta-analysis on endometrial transcriptome. Conclusions: This is the first study showing the capacity of UF-EVs to reflect the endometrial tissue transcriptional status and to establish a signature of genes expressed in UF-EVs that might serve as putative biomarkers for non-invasive endometrial receptivity tests.

Project description:Endometriosis (EMs) is a common infertility-related disease in women of reproductive age. Impaired endometrial decidualization is one of the most important factors contributing to the embryo implantation failure EMs patients. However, the exact mechanism remains unclear. Previous studies have shown collagen I deposition in the eutopic endometrium of EMs patients, which may lead to impaired endometrial decidualization. The level of collagen I in eutopic endometrium of EMs was analyzed. We performed a proteomic analysis of ectopic endometrial stromal cell-derived extracellular vesicles (EMs-EVs) and extracellular vesicles derived from endometrial stromal cells in the endometrium of control patients (CTL-EVs). An endometrial transcriptional profiles of EMs patients and normal controls in the mid-secretory phase of menstrual cycle was compared. Endometrial stromal cells (ESCs) were stimulated with chloroquine or rapamycin to evaluate the association between autophagy and collagen I. The expression of PKM2 in EMs-EVs and serum extracellular vesicles from EMs patients were examined by western blotting. PKM2 was overexpressed or knockdown in ESCs to investigate the level of autophagy and collagen I. ESCs were treated with ectopic ESC-derived extracellular vesicles with highly expressed PKM2 protein, and the potential molecular mechanisms were further confirmed through western blotting and immunohistochemical analysis. We found that endometrial collagen I expression during the mid-secretory phase was increased in the EMs group. We demonstrated that autophagy is defective in eutopic endometrial stromal cells (ESCs) of EMs patients. In ESCs, pharmacological inhibition of autophagy by chloroquine (CQ) promoted collagen I deposition. Ectopic endometrial stromal cell-derived extracellular vesicles (EMs-EVs) inhibited autophagy of ESCs and promoted collagen I deposition in vivo and in vitro. Mechanistically, EMs-EVs encapsulating PKM2 impair eutopic endometrial autophagy via Akt/mTOR signaling pathway. Together, we demonstrated that EMs-EVs encapsulating PKM2 impaired endometrial autophagy inducing collagen I deposition in EMs, which provided a potential target for therapeutic implications.

Project description:Extracellular vesicles (EVs) exert important functions in the extracellular space and in cell-to-cell communication. Ultracentrifugation, the most frequently used EV isolation technique, requires specialized equipment and provides relatively low purity and yield. A more recently applied and easily accessible EV isolation technique is size exclusion chromatography (SEC). Here, we hypothesized that EVs isolated from dilute cell culture media by concentration on a 10 kDa filter followed by SEC are suitable for proteomic and functional studies. We also hypothesized that the protein-rich SEC fractions are suitable controls to assess biological effects of the non-EV associated secretome. Cell-depleted medium from BEAS-2B bronchial epithelial cells (60 ml) was 0.22 µm filtered, concentrated to 0.5 ml by 10 kDa UF and run over a sepharose CL-4B column. Fractions were assessed for protein and EV content. EV-rich and protein-rich fractions were concentrated by 10 kDa UF. EV concentrates were characterized by tuneable resistive pulse sensing, cryo transmission electron microscopy and nanoLC-MS/MS. Finally, we assessed the effect of EVs or free secreted protein from unexposed cells or cells exposed to 1% (v/v) cigarette smoke extract (CSE) on IL-8 release by naïve BEAS-2B cells or on adhesion of THP-1 monocytes to endothelial cells. UF concentrated the EVs with approximately 100% recovery. Most of EVs eluted in SEC fractions 6-11, while protein became detectable at fraction 12. The final EV recovery was 40 ± 4%, as compared to 27 ± 8% by ultracentrifugation. Mass Spectrometry of the EV fractions identified 388 different proteins, including various EV markers. Only the protein, but not the EV fractions from media of CSE-exposed cells induced IL-8 release by naïve bronchial epithelial cells, whereas only EVs, but not the protein fractions induced THP-1 adhesion to endothelial cells. Thus, UF followed by SEC provides EV isolates with sufficient yield and purity for proteomic studies and allows directly comparing the functional effects of EVs and the non-EV associated secretome.

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Non-invasive Transcriptomic Cell Profiling of the Human Endometrium with Generative Deep Learning

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