A Noninvasive Menstrual Blood-Based Diagnostic Platform for Endometriosis Using Digital Droplet Enzyme-Linked Immunosorbent Assay and Single-Cell RNA Sequencing

Han Wang, Zhouyi Gan, Zhending Gan, Yueyue Wang, Dingmeng Hu, Lexiang Zhang, Fangfu Ye, Ping Duan
Research (Washington, D.C.) · 2025 · vol. 8 , pp. 0652 · doi:10.34133/research.0652 · PMID:40171018 · W4408403298
article OA: gold CC0 ⤵ 1 in-corpus citation
📄 Open PDF View on OpenAlex View on PubMed View at publisher

Abstract

Endometriosis is marked by the ectopic growth, spread, and invasion of endometrial tissue beyond the uterus, resulting in recurrent bleeding, pain, reproductive challenges, and the formation of nodules or masses. Despite advancements in detection methods like ultrasound and laparoscopy, these techniques remain limited by low specificity and invasiveness, underscoring the need for a highly specific, noninvasive in vitro diagnostic method. This study investigates the potential of using menstrual blood as a noninvasive diagnostic sample for endometriosis by targeting genetic and inflammatory markers associated with endometriosis lesions. A novel digital droplet enzyme-linked immunosorbent assay (ddELISA) was developed, leveraging SiO 2 nanoparticles for the femtomolar-sensitive detection of inflammatory cytokines (OPN, IL-10, IL-6) in menstrual blood. Single-cell RNA sequencing revealed differentiation patterns across endometrial tissues and menstrual blood, affirming that menstrual blood replicates key inflammatory and immune properties of endometriosis. Furthermore, endometriosis menstrual blood endometrial cells derived from human menstrual blood displayed similar properties to endometrial stromal cells in endometriosis lesions, validating menstrual blood as a suitable in vitro diagnostic sample. In contrast to traditional ELISA, ddELISA supports multi-target detection with enhanced sensitivity and reduced processing time, allowing precise biomarker analysis from minimal sample volumes. Our ddELISA-based approach shows promise as a rapid, accessible, and accurate diagnostic tool for endometriosis, with potential for practical clinical application.

My notes (saved in your browser only)

Condition tags

endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (37)

Cited by (1)

Source provenance

europepmc
last seen: 2026-06-04T01:30:01.192114+00:00
openalex
last seen: 2026-06-04T00:00:01.174412+00:00
pmc
last seen: 2026-05-13T20:22:03.195721+00:00
pubmed
last seen: 2026-05-29T00:31:35.037505+00:00
License: CC0 · commercial use OK