{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["Alvin Meltsov"],"organism":["Homo sapiens"],"software":["R","Illumina BCL convert; nf-core/rnaseq; Trim Galore!; STAR; RSEM; QualiMap; featureCounts"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-15505"],"description":["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."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Sample Collection - The detailed sample collection criteria are described in Apostolov et al., 2025. According to the provided summary, paired transcriptomic samples of endometrial tissue and uterine fluid extracellular vesicles (UF-EVs) were obtained from 19 fertile, healthy women. These samples were collected across four specific phases of the menstrual cycle: proliferative (n=4), early-secretory (n=5), mid-secretory (n=5), and late-secretory (n=5). Additionally, two unpaired UF-EV samples were collected from two healthy donors, one in the mid-secretory and one in the late-secretory phase.","Nucleic Acid Extraction - The nucleic acid library construction protocol began with RNA extraction using QIAzol and miRNeasy micro kits, a process which consecutively separated the large and small RNA fractions. Quality and quantity of the extracted RNA were then assessed; a Bioanalyzer TapeStation 2100 was used for general quality checks, while the small RNA fraction was specifically quantified using a Qubit microRNA assay.","Library Construction - For library construction, different kits were used depending on the target. Small RNA libraries were generated from 20 ng of starting material using a NEXTflex small RNA library preparation kit v4. In contrast, mRNA libraries were constructed using the TruSeq exome RNA library preparation kit (Illumina). This method enriches for coding regions via sequence-specific probes and does not depend on a poly(A) tail. The starting amount for uterine fluid extracellular vesicle (UF-EV) samples was 100 ng of RNA, or the maximum available quantity. Once the libraries were complete, they were pooled and evaluated with a High Sensitivity DNA ScreenTape D1000.","Sequencing - Finally, a 1 nM concentration of the library pool was sequenced on the NextSeq 1000 platform using a single-end 80 bp read setting."],"figure_sub":["Organization","MINSEQE Score","Assays and Data","Processed Data","MAGE-TAB Files"],"data_protocol":["Sequence Alignment - Demultiplexing was done with Illumina BCL convert v3.10.12 and preprocessed with the nf-core/rnaseq pipeline (v3.14). The reads were trimmed with Trim Galore! (v0.6.10) to remove adapter sequences and low-quality bases. Reads were aligned to the GRCh37.p13 reference genome, selected for its compatibility with previously published datasets, using the STAR aligner (v2.7.10a). Gene expression levels were quantified with the RSEM algorithm (v1.3.3). Genomic feature quantification was done with QualiMap and featureCounts (v2.0.3)","Data Transformation - Raw reads were normalised separately with Transcripts Per Million (TPM) normalisation method"],"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["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"],"study_type":["RNA-seq of coding RNA"],"species":["Homo sapiens"],"pubmed_authors":["Alvin Meltsov"],"additional_accession":[]},"is_claimable":false,"name":"Non-invasive Transcriptomic Cell Profiling of the Human Endometrium with Generative Deep Learning","description":"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.","dates":{"release":"2025-11-01T00:00:00Z","modification":"2025-11-01T02:01:43.927Z","creation":"2025-08-14T11:07:34.878Z"},"accession":"E-MTAB-15505","cross_references":{"ENA":["ERP178777"],"EFO":["EFO_0002944","EFO_0004170","EFO_0004917","EFO_0005518","EFO_0003816","EFO_0003738","EFO_0004184"]}}