Evidence map›Paper›PMID 37396969›Full record

ArticleTrends in developmental biology2022

Simplifying cell fate map by determining lineage history of core pathway activation during fate specification.

Zhen Huang

Open access · greenAbstract read
In one paragraph

Article in Trends in developmental biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 85% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

1 author at 1 institution in 1 country.

Zhen HuangDepartments of Neuroscience and Neurology, University of Wisconsin-Madison, Madison, WI 53705 USA.
University of Wisconsin–Madison · US

Funding

Mechanisms regulating vessel development in the embryonic and postnatal brainR01NS076729 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI HUANG, ZHEN · 2012 to 2016
$1.6M
NINDS NIH HHS R01 NS076729
6 · The paper itself

Abstract

A fundamental question in developmental biology is how a single genome gives rise to the diversity of cell fates. In essence, each cell fate in the human body is a unique but stable output state of the genome, maintained by positive and negative feedbacks from both inside and outside the cell (a stable cell state). Traditionally, defining a cell fate means identifying a unique combination of transcriptional factors expressed by the specific cell type. The hundreds of transcriptional factors in the genome, however, have complicated the task of simplifying cell fate representation and obtaining insights into its regulation. Moreover, results from this approach provides only a mostly static picture, with each cell fate/state disconnected from one another. An alternative approach instead defines cell fates by determining their relationship to each other, through identifying the signaling pathways that control each step of their lineage transition from a common progenitor during development. Decades of studies have shown only a handful of signaling pathways are sufficient to specify all cell fates in the body, simplifying the execution of such a strategy. In this review, I will argue this alternative approach is not only feasible but also has the potential of simplifying the cell fate landscape as well as facilitating the engineering of different cell fates for regenerative medicine.

Indexed as

Cell fatecell fate transitioncell statelineage historypathway activity recorderpathway activity reportersignaling pathway

Identifiers

PMID37396969
PMCPMC10312135
OpenAlexW4382919475

What OpenQuestion holds

Textmetadata
LicenceTDM
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.