Abstract P5-02-01: A FACS-free purification method to study estrogen signaling, organoid formation, and metabolic reprogramming in mammary epithelial cells
Bibliographic record
Abstract
Abstract Mammary epithelial cells (MECs) are known to have their metabolism reprogrammed following pregnancy to allow the increased energy requirements for lactation. The estrogen receptor α (ERα) is mainly associated with the regulation of biological pathways linked to mammary gland development but its influence on MECs metabolism is still unknown. Our hypothesis is that ERα reprograms cell metabolism in normal MECs, a phenomenon that would be reprogrammed during breast carcinogenesis. Few in vitro models are used to study MECs, and most of them do not express ERα. Primary MECs can be used to overcome this issue, but methods to purify these cells generally require flow cytometry and fluorescence-activated cell sorting (FACS), which require specialized instruments and expertise. Herein, we present in detail a FACS-free protocol for purification and primary culture of mouse MECs to study ERα metabolic functions using mass spectrometry (MS). Purified MECs from nulliparous mice remain differentiated for up to six days with >85% luminal epithelial cells in two-dimensional culture. When seeded in Matrigel, they form organoids that recapitulate the mammary gland morphology in vivo by developing lumens, contractile cells, and lobular structures. MECs express a functional ERα signaling pathway in both two- and three-dimensional cell culture, as shown at the mRNA and protein levels and by the phenotypic characterization. Extracellular metabolic flux analysis showed that estrogens induce a metabolic switch favouring aerobic glycolysis over mitochondrial respiration in MECs grown in two-dimensions, a phenomenon known as the Warburg effect. We also performed (MS)-based metabolomics in organoids. Estrogens altered the levels of metabolites from various pathways, including aerobic glycolysis, citric acid cycle, urea cycle, and amino acid metabolism, demonstrating that ERα reprograms cell metabolism in mammary organoids. To further understand this reprogramming, stable isotope tracer analysis in primary culture organoids are currently performed. In addition, pregnancy and breast-feeding are known to be protective against breast carcinogenesis. Consequently, we also performed MEC purification and organoid culture using mammary glands from multiparous mice. Intriguingly, organoid phenotypic characterization indicated a difference in organoid structures between MECs from nulliparous and multiparous mice. Furthermore, we observe significant differences in estrogenic response between both conditions, suggesting that pregnancy and/or lactation promotes the establishment of specific epigenetic marks that are preserved even ex vivo. Chromatin immunoprecipitation of specific histone marks and MS-based metabolic studies are ongoing to better understand the different responses of mammary organoids to estrogens between nulliparous and multiparous mice. Overall, we have optimized mouse MEC isolation and purification for two- and three-dimensional cultures and for MS-based metabolomics. We demonstrated that these organoids retain a functional ERα pathway over time and that ERα significantly reprograms multiple metabolic pathways. This model represents a valuable tool to study how estrogens modulate mammary gland biology, and particularly how these hormones reprogram metabolism during lactation and breast carcinogenesis. Citation Format: Aurélie Lacouture, Cynthia Jobin, Alisson Clemenceau, Cindy Weidmann, Line Berthiaume, Dominic Bastien, Isabelle Laverdière, Martin Pelletier, Caroline Diorio, Francine Durocher, Étienne Audet-Walsh. A FACS-free purification method to study estrogen signaling, organoid formation, and metabolic reprogramming in mammary epithelial cells [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-02-01.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".