ATAC-seq of Low-Input and Cryopreserved Primordial Germ Cells Reveals Functional Enhancers
Bibliographic record
Abstract
Dynamic changes in chromatin accessibility at cis-regulatory elements underlie cell fate transitions during development. Primordial germ cells (PGCs) represent a rare population whose chromatin dynamics remain poorly understood compared to known epigenetic landscapes. Here, we utilized the chicken PGC model to bridge this gap, leveraging its capacity for in vitro expansion and in vivo colonization. We adapted the ATAC-seq workflow to obtain reproducible accessible chromatin region (ACR) profiles from as few as 200 cells, even after cryopreservation. Integrative analysis identified over 10,000 PGC-specific ACRs, many absent from somatic tissues, and revealed inherent Tn5 transposase sequence biases in the chicken genome. To validate these ACRs, we established an in vitro PGC differentiation system and utilized in vivo embryonic transplantation. Reporter assays confirmed enhancer activities in cultured PGCs, while transcriptome integration associated these ACRs with genes expressed at embryonic day 2.5. In vivo transplantation demonstrated that these enhancers exhibited early stage-specific activity, becoming silenced upon gonadal settlement. Our results provide a practical strategy for identifying functional regulatory elements from minimal starting material, facilitating the study of chromatin dynamics in rare cell populations.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".