Evidence map›Paper›PMID 37047770›Full record

ArticleInternational journal of molecular sciences2023

Protocol Optimization for Direct Reprogramming of Primary Human Fibroblast into Induced Striatal Neurons.

Nina Kraskovskaya, Anastasia Bolshakova, Mikhail Khotin, Ilya Bezprozvanny, Natalia Mikhailova

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

9 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Fundamental and Practical Perspectives in Regenerative Medicine.International journal of molecular sciences · 2024
    Article
  6. Article
  7. Article
  8. Direct Reprogramming of Somatic Skin Cells from a Patient with Huntington's Disease into Striatal Neurons to Create Models of Pathology.Doklady biological sciences : proceedings of the Academy of Sciences of the USSR, Biological sciences sections · 2024
    Article
  9. Novel advanced patient-derivedFrontiers in neurology
    Review
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

5 authors at 2 institutions in 2 countries.

Nina KraskovskayaCenter of Cellular Technologies, Institute of Cytology of the Russian Academy of Science, 194064 St. Petersburg, Russia.ORCID 0000-0002-1318-3868
Anastasia BolshakovaLaboratory of Molecular Neurodegeneration, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia.
Mikhail KhotinCenter of Cellular Technologies, Institute of Cytology of the Russian Academy of Science, 194064 St. Petersburg, Russia.ORCID 0000-0002-8293-6368
Ilya BezprozvannyLaboratory of Molecular Neurodegeneration, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia.ORCID 0000-0001-7006-6951
Natalia MikhailovaCenter of Cellular Technologies, Institute of Cytology of the Russian Academy of Science, 194064 St. Petersburg, Russia.ORCID 0000-0003-1650-9330
Peter the Great St. Petersburg Polytechnic University · RUInstitute of Cytology · RU

Funding

Ministry of Science and Higher Education of the Russian Federation 075-15-2021-1063Russian Science Foundation (RSF) grant 22-75-00106, 22-15-00049
6 · The paper itself

Abstract

The modeling of neuropathology on induced neurons obtained by cell reprogramming technologies can fill a gap between clinical trials and studies on model organisms for the development of treatment strategies for neurodegenerative diseases. Patient-specific models based on patients' cells play an important role in such studies. There are two ways to obtain induced neuronal cells. One is based on induced pluripotent stem cells. The other is based on direct reprogramming, which allows us to obtain mature neuronal cells from adult somatic cells, such as dermal fibroblasts. Moreover, the latter method makes it possible to better preserve the age-related aspects of neuropathology, which is valuable for diseases that occur with age. However, direct methods of reprogramming have a significant drawback associated with low cell viability during procedures. Furthermore, the number of reprogrammable neurons available for morphological and functional studies is limited by the initial number of somatic cells. In this article, we propose modifications of a previously developed direct reprogramming method, based on the combination of microRNA and transcription factors, which allowed us to obtain a population of functionally active induced striatal neurons (iSNs) with a high efficiency. We also overcame the problem of the presence of multinucleated neurons associated with the cellular division of starting fibroblasts. Synchronization cells in the G1 phase increased the homogeneity of the fibroblast population, increased the survival rate of induced neurons, and eliminated the presence of multinucleated cells at the end of the reprogramming procedure. We have demonstrated that iSNs are functionally active and able to form synaptic connections in co-cultures with mouse cortical neurons. The proposed modifications can also be used to obtain a population of other induced neuronal types, such as motor and dopaminergic ones, by selecting transcription factors that determine differentiation into a region-specific neuron.

Indexed as

Induced Pluripotent Stem CellsNeuronsAdultAnimalsCell DifferentiationCellular ReprogrammingFibroblastsHumansMiceTranscription FactorsTranscription Factorsagingcell synchronizationdendritic spinesdirect reprogramminglentivirusesmedium spiny neuronsmicroRNA

Identifiers

PMID37047770
PMCPMC10095147
OpenAlexW4362670962

What OpenQuestion holds

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LicenceCC BY
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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.