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pmid:35092277

EndoTime: non-categorical timing estimates for luteal endometrium.

Julia Lipecki · Andrew E Mitchell · Joanne Muter · Emma S Lucas · Komal Makwana · Katherine Fishwick · Joshua Odendaal · Amelia Hawkes · Pavle Vrljicak · Jan J Brosens · Sascha Ott

Human reproduction (Oxford, England) · 2022 doi:10.1093/humrep/deac006

Paper

complete

Experiment

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Data

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partial

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title
EndoTime: non-categorical timing estimates for luteal endometrium.
doi
10.1093/humrep/deac006
pmid
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Julia Lipecki
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Andrew E Mitchell
[2]
Joanne Muter
[3]
Emma S Lucas
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Komal Makwana
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Katherine Fishwick
[6]
Joshua Odendaal
[7]
Amelia Hawkes
[8]
Pavle Vrljicak
[9]
Jan J Brosens
[10]
Sascha Ott
journal
Human reproduction (Oxford, England)
year
2022
abstract
STUDY QUESTION: Can the accuracy of timing of luteal phase endometrial biopsies based on urinary ovulation testing be improved by measuring the expression of a small number of genes and a continuous, non-categorical modelling approach? SUMMARY ANSWER: Measuring the expression levels of six genes (IL2RB, IGFBP1, CXCL14, DPP4, GPX3 and SLC15A2) is sufficient to obtain substantially more accurate timing estimates and to assess the reliability of timing estimates for each sample. WHAT IS KNOWN ALREADY: Commercially available endometrial timing approaches based on gene expression require large gene sets and use a categorical approach that classifies samples as pre-receptive, receptive or post-receptive. STUDY DESIGN, SIZE, DURATION: Gene expression was measured by RTq-PCR in different sample sets, comprising a total of 664 endometrial biopsies obtained 4-12 days after a self-reported positive home ovulation test. A further 36 endometrial samples were profiled by RTq-PCR as well as RNA-sequencing. PARTICIPANTS/MATERIALS, SETTING, METHODS: A computational procedure, named 'EndoTime', was established that models the temporal profile of each gene and estimates the timing of each sample. Iterating these steps, temporal profiles are gradually refined as sample timings are being updated, and confidence in timing estimates is increased. After convergence, the method reports updated timing estimates for each sample while preserving the overall distribution of time points. MAIN RESULTS AND THE ROLE OF CHANCE: The Wilcoxon rank-sum test was used to confirm that ordering samples by EndoTime estimates yields sharper temporal expression profiles for held-out genes (not used when determining sample timings) than ordering the same expression values by patient-reported times (GPX3: P < 0.005; CXCL14: P < 2.7e-6; DPP4: P < 3.7e-13). Pearson correlation between EndoTime estimates for the same sample set but based on RTq-PCR or RNA-sequencing data showed a high degree of congruency between the two (P = 8.6e-10, R2 = 0.687). Estimated timings did not differ significantly between control subjects and patients with recurrent pregnancy loss or recurrent implantation failure (P > 0.05). LARGE SCALE DATA: The RTq-PCR data files are available via the GitHub repository for the EndoTime software at https://github.com/AE-Mitchell/EndoTime, as is the code used for pre-processing of RTq-PCR data. The RNA-sequencing data are available on GEO (accession GSE180485). LIMITATIONS, REASONS FOR CAUTION: Timing estimates are informed by glandular gene expression and will only represent the temporal state of other endometrial cell types if in synchrony with the epithelium. Methods that estimate the day of ovulation are still required as these data are essential inputs in our method. Our approach, in its current iteration, performs batch correction such that larger sample batches impart greater accuracy to timing estimations. In theory, our method requires endometrial samples obtained at different days in the luteal phase. In practice, however, this is not a concern as timings based on urinary ovulation testing are associated with a sufficient level of noise to ensure that a variety of time points will be sampled. WIDER IMPLICATIONS OF THE FINDINGS: Our method is the first to assay the temporal state of luteal-phase endometrial samples on a continuous domain. It is freely available with fully shared data and open-source software. EndoTime enables accurate temporal profiling of any gene in luteal endometrial samples for a wide range of research applications and, potentially, clinical use. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by a Wellcome Trust Investigator Award (Grant/Award Number: 212233/Z/18/Z) and the Tommy's National Miscarriage Research Centre. None of the authors have any competing interests. J.L. was funded by the Biotechnology and Biological Sciences Research Council (UK) through the Midlands Integrative Biology Training Partnership (MIBTP, BB/M01116X/1).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35092277", "kind": "science-record", "revision": 5, "content_hash": "sha256:b56484576ccce3c51df7c1e19a72c7b90fca69d5bf4844e1a9c58b96537b8665", "created_at": "2026-11-01T17:36:49Z", "updated_at": "2026-11-01T22:05:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "EndoTime: non-categorical timing estimates for luteal endometrium.", "doi": "10.1093/humrep/deac006", "pmid": "35092277", "authors": ["Julia Lipecki", "Andrew E Mitchell", "Joanne Muter", "Emma S Lucas", "Komal Makwana", "Katherine Fishwick", "Joshua Odendaal", "Amelia Hawkes", "Pavle Vrljicak", "Jan J Brosens", "Sascha Ott"], "journal": "Human reproduction (Oxford, England)", "year": 2022, "abstract": "STUDY QUESTION: Can the accuracy of timing of luteal phase endometrial biopsies based on urinary ovulation testing be improved by measuring the expression of a small number of genes and a continuous, non-categorical modelling approach?\nSUMMARY ANSWER: Measuring the expression levels of six genes (IL2RB, IGFBP1, CXCL14, DPP4, GPX3 and SLC15A2) is sufficient to obtain substantially more accurate timing estimates and to assess the reliability of timing estimates for each sample.\nWHAT IS KNOWN ALREADY: Commercially available endometrial timing approaches based on gene expression require large gene sets and use a categorical approach that classifies samples as pre-receptive, receptive or post-receptive.\nSTUDY DESIGN, SIZE, DURATION: Gene expression was measured by RTq-PCR in different sample sets, comprising a total of 664 endometrial biopsies obtained 4-12 days after a self-reported positive home ovulation test. A further 36 endometrial samples were profiled by RTq-PCR as well as RNA-sequencing.\nPARTICIPANTS/MATERIALS, SETTING, METHODS: A computational procedure, named 'EndoTime', was established that models the temporal profile of each gene and estimates the timing of each sample. Iterating these steps, temporal profiles are gradually refined as sample timings are being updated, and confidence in timing estimates is increased. After convergence, the method reports updated timing estimates for each sample while preserving the overall distribution of time points.\nMAIN RESULTS AND THE ROLE OF CHANCE: The Wilcoxon rank-sum test was used to confirm that ordering samples by EndoTime estimates yields sharper temporal expression profiles for held-out genes (not used when determining sample timings) than ordering the same expression values by patient-reported times (GPX3: P < 0.005; CXCL14: P < 2.7e-6; DPP4: P < 3.7e-13). Pearson correlation between EndoTime estimates for the same sample set but based on RTq-PCR or RNA-sequencing data showed a high degree of congruency between the two (P = 8.6e-10, R2 = 0.687). Estimated timings did not differ significantly between control subjects and patients with recurrent pregnancy loss or recurrent implantation failure (P > 0.05).\nLARGE SCALE DATA: The RTq-PCR data files are available via the GitHub repository for the EndoTime software at https://github.com/AE-Mitchell/EndoTime, as is the code used for pre-processing of RTq-PCR data. The RNA-sequencing data are available on GEO (accession GSE180485).\nLIMITATIONS, REASONS FOR CAUTION: Timing estimates are informed by glandular gene expression and will only represent the temporal state of other endometrial cell types if in synchrony with the epithelium. Methods that estimate the day of ovulation are still required as these data are essential inputs in our method. Our approach, in its current iteration, performs batch correction such that larger sample batches impart greater accuracy to timing estimations. In theory, our method requires endometrial samples obtained at different days in the luteal phase. In practice, however, this is not a concern as timings based on urinary ovulation testing are associated with a sufficient level of noise to ensure that a variety of time points will be sampled.\nWIDER IMPLICATIONS OF THE FINDINGS: Our method is the first to assay the temporal state of luteal-phase endometrial samples on a continuous domain. It is freely available with fully shared data and open-source software. EndoTime enables accurate temporal profiling of any gene in luteal endometrial samples for a wide range of research applications and, potentially, clinical use.\nSTUDY FUNDING/COMPETING INTEREST(S): This study was supported by a Wellcome Trust Investigator Award (Grant/Award Number: 212233/Z/18/Z) and the Tommy's National Miscarriage Research Centre. None of the authors have any competing interests. J.L. was funded by the Biotechnology and Biological Sciences Research Council (UK) through the Midlands Integrative Biology Training Partnership (MIBTP, BB/M01116X/1).", "claims": [{"id": "claim-method", "statement": "The Wilcoxon rank-sum test was used to confirm that ordering samples by EndoTime estimates yields sharper temporal expression profiles for held-out genes (not used when determining sample timings) than ordering the same expression values by patient-reported times (GPX3: P < 0.005; CXCL14: P < 2.7e−6; DPP4: P < 3.7e−13).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35092277", "locator": "sections[0] \"Abstract\" paragraph 6", "quote": "The Wilcoxon rank-sum test was used to confirm that ordering samples by EndoTime estimates yields sharper temporal expression profiles for held-out genes (not used when determining sample timings) than ordering the same expression values by patient-reported times (GPX3: P < 0.005; CXCL14: P < 2.7e−6; DPP4: P < 3.7e−13)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA-seq data are available in the Gene Expression Omnibus under accession GSE180485. The RTq-PCR data are available in the GitHub repository for the EndoTime software at https://www.github.com/AE-Mitchell/EndoTime.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35092277", "locator": "sections[21] \"Data availability\" paragraph 2", "quote": "The RNA-seq data are available in the Gene Expression Omnibus under accession GSE180485. The RTq-PCR data are available in the GitHub repository for the EndoTime software at https://www.github.com/AE-Mitchell/EndoTime."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Measuring the expression levels of six genes (IL2RB, IGFBP1, CXCL14, DPP4, GPX3 and SLC15A2) is sufficient to obtain substantially more accurate timing estimates and to assess the reliability of timing estimates for each sample.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35092277", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Measuring the expression levels of six genes (IL2RB, IGFBP1, CXCL14, DPP4, GPX3 and SLC15A2) is sufficient to obtain substantially more accurate timing estimates and to assess the reliability of timing estimates for each sample."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Measuring the expression levels of six genes (IL2RB, IGFBP1, CXCL14, DPP4, GPX3 and SLC15A2) is sufficient to obtain substantially more accurate timing estimates and to assess the reliability of timing estimates for each sample.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35092277"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE180485"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE180485"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35092277", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/humrep/deac006", "sha256": "sha256:63a811306e03dba7ab3cf56ddda4575179e6d873d102ebcbe28a3a0ab8140160", "bytes": 50451, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/humrep/deac006", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8971653", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE180485", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180485", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE180485", "version": null, "note": "The RNA-seq data are available in the Gene Expression Omnibus under accession GSE180485. 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