Evidence map›Paper›PMID 33083240›Full record

ArticleMethodsX2020

Reproducible differentiation and characterization of neurons from mouse embryonic stem cells.

Sonal Saxena, Sumana Choudhury, K Naga Mohan

Abstract read
In one paragraph

Article in MethodsX, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

3 authors.

Sonal SaxenaDepartment of Biological Sciences, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Jawahar Nagar, Hyderabad 500 078, India.
Sumana ChoudhuryDepartment of Biological Sciences, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Jawahar Nagar, Hyderabad 500 078, India.
K Naga MohanDepartment of Biological Sciences, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Jawahar Nagar, Hyderabad 500 078, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Investigation on the effects of disease-associated mutations on neurodevelopment is an essential approach to understand the molecular basis of neurological disorders and can be achieved by generating suitable animal models. However, some of the mutations preclude development of animal models, leaving cell-based models as the only options. Mouse embryonic stem cells (mESCs) are attractive because of the well-established technologies for introducing disease-associated mutations and the feasibility of investigating the abnormalities during different stages of neurogenesis. Importantly, such transgenic mESCs enable large-scale screening and identification of the most promising small molecules and/or drug candidates before undertaking expensive animal studies. Although neuronal differentiation from mESCs is one of the earliest methods to be developed, we observed that the published as well as publicly available methods did not yield neurons consistently. Here, we describe a 16-day differentiation protocol that consistently induced differentiation of mESCs into neurons. This step-wise protocol enables monitoring of the neuronal differentiation process at different stages as well as characterization using the markers for immature and mature neurons by using immunocytochemistry and quantitative real-time PCRs.•Development of a method for differentiating mouse ES cells into neurons.•Differentiating the mouse ES cells into embryoid bodies prior to induction of neuronal differentiation results in better neuron formation.

Indexed as

Cell-based modelsMouse embryonic stem cellsNeurological disordersNeuronal differentiation

Identifiers

PMID33083240
PMCPMC7551330

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Registered trials

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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.