# Molecular Imaging and Diagnosis of Endometriosis using Mass Spectrometry Technologies

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2022 · $184,635

## Abstract

ABSTRACT: Endometriosis is a chronic pathologic condition that affects approximately 176 million worldwide,
and can cause severe abdominal pain and subfertility. Despite its prevalence, there is currently no widely
accepted theory of pathogenesis for endometriosis. Further, there are currently no clinical biomarkers for non-
invasive diagnosis as most patients are only diagnosed at the time of exploratory laparoscopy. Thus, application
of new molecular technologies to advance the understanding, diagnosis, and intraoperative detection of
endometriosis is critically needed to improve patient care and management. Ambient mass spectrometry (MS)
techniques offer the chemical specificity and sensitivity to perform rapid and in-depth molecular analysis of
unprocessed tissue samples. We propose to conduct a rigorous study applying MS techniques in conjunction
with statistical methods to identify, characterize, and validate metabolic and protein markers of endometrial and
endometriosis tissues correlated to patient symptoms and disease presentation. Further, we propose to evaluate
the performance of ambient MS techniques as tools for rapid diagnosis of endometrial tissues, and in vivo
intraoperative disease detection. Combining our team’s expertise in MS, statistics, gynecological surgery,
pathology and endometriosis, we are unique poised to successfully carry out the following proposed aims:
Aim 1. Characterize metabolic and proteomic markers of endometriosis tissues from endometriosis
patients using MS imaging. Characterization of molecular markers that are specific to endometriosis lesions
within tissue microenvironment can lead to new understanding of the biological mechanisms governing
endometriosis development and pinpoint potential targets for treatment. Using a large cohort of tissues
prospectively collected from surgeries, we will use MS imaging and statistical analysis to identify molecular
signatures of endometriosis tissues and uncover significant molecular alterations correlated to patient
phenotypes and disease presentation within controlled patient subgroups.
Aim 2. Define diagnostic markers of endometriosis using endometrial tissues from endometriosis and
unaffected patients. New methods for accurate and rapid diagnosis endometriosis are critically needed to
advance patient care. We propose to use MS imaging and statistical analyses to identify diagnostic markers of
endometriosis using endometrial tissue samples obtained from eutopic endometrium of endometriosis patients,
and endometrium tissues from unaffected patients undergoing other benign gynecological procedures, with the
goal of identifying molecular markers that could use for diagnosis of endometriosis from tissue biopsies.
Aim 3. Test the MasSpec Pen as an intraoperative tool for in vivo endometriosis detection. Complete
excision of endometriosis lesions and preservation of healthy adjacent tissues is of utmost importance in the
surgical treatment of patients. We propose to conduc...

## Key facts

- **NIH application ID:** 10406313
- **Project number:** 5R01HD101560-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Livia Schiavinato Eberlin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $184,635
- **Award type:** 5
- **Project period:** 2021-09-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10406313

## Citation

> US National Institutes of Health, RePORTER application 10406313, Molecular Imaging and Diagnosis of Endometriosis using Mass Spectrometry Technologies (5R01HD101560-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10406313. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*