Evidence map›Paper›PMID 36307636›Full record

ArticleIn vitro cellular & developmental biology. Animal2022

Reprogramming cultured human fungiform (HBO) taste cells into neuron-like cells through in vitro induction.

Nadia Elkaddi, Bilal Malik, Andrew I Spielman, Mehmet Hakan Ozdener

Abstract read
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In one paragraph

Article in In vitro cellular & developmental biology. Animal, 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 86% 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

4 authors at 2 institutions in 1 country.

Nadia ElkaddiMonell Chemical Senses Center, 3500 Market Street, Philadelphia, PA, 19104, USA.
Bilal MalikMonell Chemical Senses Center, 3500 Market Street, Philadelphia, PA, 19104, USA.
Andrew I SpielmanNew York University College of Dentistry, New York, NY, 10010, USA.
Mehmet Hakan OzdenerMonell Chemical Senses Center, 3500 Market Street, Philadelphia, PA, 19104, USA. hozdener@monell.org.ORCID http://orcid.org/0000-0001-9259-4683
Monell Chemical Senses Center · USNew York University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human taste cells are a heterogeneous population of specialized epithelial cells that are constantly generated from progenitor taste cells. Type I and type III taste cells express some neural markers, and studies have reported that direct innervation by neurons is not required for taste cell development. To our knowledge, no previous study has demonstrated that taste cells can differentiate into neuron-like cells or any other non-taste cell type. Here, for the first time, we describe a simple in vitro method that uses a serum-free neural induction medium to differentiate cultured physiologically functional primary human taste (HBO) cells into neuron-like cells in 2-3 wk with high efficiency. We verified neural attributes of these HBO-derived neuron-like with immunocytochemistry, single-cell calcium imaging, and DiI staining and examined cell morphology using transmission electron microscopy. Induced neuron-like cells demonstrated neuron-specific proteins, dendritic and axonal morphology, and networking behaviors. This technique will open new avenues for translational medicine, autologous cell therapy, regenerative medicine, therapy for neurodegenerative disorders, and drug screening.

Indexed as

Taste BudsAnimalsCell DifferentiationCells, CulturedEpithelial CellsHumansNeuronsTasteDifferentiationNeuronal cellsReprogrammingTaste cells

Identifiers

PMID36307636
OpenAlexW4307472572

What OpenQuestion holds

Textmetadata
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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.