Evidence map›Paper›PMID 40749677›Full record

ArticleCurrent biology : CB2025

Live imaging endogenous transcription factor dynamics reveals mechanisms of epiblast and primitive endoderm fate segregation.

Rebecca P Kim-Yip, David Denberg, Denis F Faerberg, Hayden Nunley, Isabella Leite, Madeleine Chalifoux, Bradley Joyce, Jared Toettcher, Bin Gu, Eszter Posfai

Abstract read
In one paragraph

Article in Current biology : CB, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Rebecca P Kim-YipDepartment of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
David DenbergCenter for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA.
Denis F FaerbergLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
Hayden NunleyCenter for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA.
Isabella LeiteLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
Madeleine ChalifouxDepartment of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.
Bradley JoyceDepartment of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
Jared ToettcherDepartment of Molecular Biology, Princeton University, Princeton, NJ 08544, USA; Omenn-Darling Bioengineering Institute, Princeton University, Princeton, NJ 08544, USA.
Bin GuDepartment of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, East Lansing, MI 48824, USA; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
Eszter PosfaiDepartment of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. Electronic address: eposfai@princeton.edu.

Funding

Quantitavie and Computational Biology Graduate ProgramT32HG003284 · NHGRI · PRINCETON UNIVERSITY · PI Joshua Michael Akey, Stanislav Y. Shvartsman · 2004 to 2026
$9.9M
NRSA TrainingTL1TR003019 · NCATS · RUTGERS BIOMEDICAL/HEALTH SCIENCES-RBHS · PI SCOTTO, KATHLEEN W. · 2019 to 2023
$1.9M
Mechanisms of epiblast and primitive endoderm segregationR01HD110577 · NICHD · PRINCETON UNIVERSITY · PI Eszter Posfai · 2023 to 2026
$1.9M
NCATS NIH HHS TL1 TR003019NHGRI NIH HHS T32 HG003284NICHD NIH HHS R01 HD110577
6 · The paper itself

Abstract

The segregation of the epiblast (EPI) and primitive endoderm (PE) cell types in the preimplantation mouse embryo is not only a crucial decision that sets aside the precursors of the embryo proper from extraembryonic cells, respectively, but also has served as a central model to study a key concept in mammalian development: how much of developmental patterning is predetermined vs. stochastically emergent. Here, we address this question by quantitative live imaging of multiple endogenously tagged transcription factors key to this fate decision and trace their dynamics at a single-cell resolution through the formation of EPI and PE cell fates. Strikingly, we reveal an initial symmetry breaking event, the formation of a primary EPI cell lineage, and show that this is linked to the dynamics of the prior inner cell mass/trophectoderm fate decision through the expression of SOX2. This primary EPI lineage, through fibroblast growth factor (FGF) signaling, induces an increase in the transcription factor GATA6 in other inner cell mass cells, setting them on the course toward PE differentiation. Interestingly, this trajectory can switch during a defined developmental window, leading to the emergence of secondary EPI cells. Finally, we show that early expression levels of NANOG, which are seemingly stochastic, can bias whether a cell's trajectory switches to secondary EPI or continues as PE. Our data give unique insight into how fate patterning is initiated and propagated during unperturbed embryonic development through the interplay of lineage-history-biased and stochastic cell-intrinsic molecular features, unifying previous models of EPI/PE segregation.

Indexed as

EndodermGerm LayersTranscription FactorsAnimalsCell DifferentiationCell LineageFemaleGATA6 Transcription FactorGene Expression Regulation, DevelopmentalMiceSOXB1 Transcription FactorsGata6 protein, mouseGATA6 Transcription FactorSox2 protein, mouseSOXB1 Transcription FactorsTranscription FactorsblastocystepiblastGATA6ICMlive imagingmouseNANOGpreimplantation embryoprimitive endodermSOX2

Identifiers

PMID40749677
PMCPMC12406577

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.