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Transient and stable vector transfection: Pitfalls, off-target effects, artifacts
Abstract
Transient and stable vector transfections have played important roles in illustrating the function of specific genes/proteins. The general assumption is that such a platform could effectively link a given gene/protein to gained phenotypes, revealing the mechanism of how a gene works. However, in reality, increased studies have surprisingly noticed some unexpected results. In this review, we demonstrate that an assumption that empty vector-transfected cells preserve the cytogenetic and phenotypic characteristics, and represent the adequate control in transfection experiments is not universally valid. A DNA vector, a transfection reagent, expression of an antibiotic resistance (trans)gene, expression of a reporter (trans)gene, and selection by acute/chronic antibiotic treatment may evoke cellular responses that affect the biochemical processes under investigation. We exemplify a number of studies, which reported obvious genomic, transcriptomic and phenotypic changes of tumor cells after transient/stable transfection of an empty vector. To further address the common mechanisms of these unexpected findings, we will apply the genome theory of somatic evolution to explain stress-mediated system dynamics and the limitations of predicting the system behavior solely based on targeted genes. We conceptualize that the diverse experimental manipulations (e.g., transgene overexpression, gene knock out/down, chemical treatments, acute changes in culture conditions, etc.) may act as a syste…
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“In the development of these lines, it is important to consider whether the proposed constructs and their locations in the genome would be likely to impact the intended functionality and utility of the cells (for a review, see Stepanenko and Heng, 2017), altering, for example, the constitutive expression of reprogramming factors in PSCs or silencing a reporter gene that is supposed to remain active”
From Guidance document on Good cell and tissue culture practice 2.0 (GCCP 2.0) (2021) · cites this paper as Stepanenko and Heng, 2017
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Metadata
- Authors
- Stepanenko, A. A., & Heng, H. H.
- Venue
- Mutation Research/Reviews in Mutation Research
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 773
- Pages
- 91-103
- ISSN
- 1383-5742
- Cited by
- 109
- References
- 148
- Language
- en
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.