OR01-06 Single Nucleus Multi-omics Analysis Identifies Cellular Trajectories And Dynamic Changes In Gene Expression And Chromatin Accessibility In The Pituitary During The Mouse Estrous Cycle
Bibliographic record
Abstract
Abstract Disclosure: F.M. Ruf-Zamojski: None. W. Cheng: None. Z. Zhang: None. M. Zamojski: None. G.R. Smith: None. X. Chen: None. N. Mendelev: None. G. Strupinsky: None. C.A. Alonso: None. L. Ongaro Gambino: None. X. Zhou: None. E. Brule: None. M.S. Amper: None. P. Hanna: None. V.D. Nair: None. C.L. Andoniadou: None. J.L. Turgeon: None. O. Troyanskaya: None. E. Zaslavsky: None. D.J. Bernard: None. S.C. Sealfon: None. Single cell multi-omics datasets provide an unparalleled power to resolve gene regulatory circuits underlying cellular function in complex tissues such as the pituitary gland [1, 2]. To better understand cellular plasticity and dynamics in the mouse pituitary during the estrous cycle in vivo, we performed same-cell single nucleus (sn) multi-omics for gene expression and chromatin accessibility on individual pituitaries collected from mice at 9am on each day of the cycle, as well as at 6pm and 11pm on proestrus and at 2am on estrus to capture surge events. Cycle stage was determined by vaginal cytology and post-mortem measurement of serum LH and FSH levels. In total, 102,069 cells passed rigorous quality control (QC, [2]), with over 5,000 cells analyzed per sample, ∼2,000 genes/cell, ∼15,000 ATAC median high-quality fragments, and Transcription Start Site (TSS) enrichment scores above 9 for all samples. We identified 13 well-separated clusters in the snRNAseq and 10 in the snATACseq datasets representing the pituitary cell types that were followed over time. We integrated the gene expression and chromatin accessibility datasets and analyzed changes in cell type proportions, gene expression, and chromatin accessibility through time. We detected major differential gene expression changes in the gonadotropes and lactotropes, which we further investigated using pseudotime trajectory analyses. Several upstream Fshb loci showed dynamic changes during the estrous cycle. Additionally, we uncovered regulatory components of major pituitary genes over time using linkage data analysis.To our knowledge, this is the first study to present detailed and comprehensive data on gene expression and chromatin structure changes at sn resolution in all pituitary cell types during a dynamic physiological process. Thus, it provides critical new resources to the field of endocrinology. References:[1] Nat Comm, 2020, 12(2677), PMID:33976139.[2] Cell Reports, 2022, 38(10): 110467, PMID:35263594. Presentation: Thursday, June 15, 2023
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".