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Record W2105446184 · doi:10.3791/4002

Detection of Viral RNA by Fluorescence <em>in situ</em> Hybridization (FISH)

2012· article· en· W2105446184 on OpenAlexafffund
Kishanda Vyboh, Lara Ajamian, Andrew J. Mouland

Bibliographic record

VenueJournal of Visualized Experiments · 2012
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsBiologyRNACell biologyCytoplasmGenomeViral replicationVirusGeneGene expressionVirologyGenetics

Abstract

fetched live from OpenAlex

Viruses that infect cells elicit specific changes to normal cell functions which serve to divert energy and resources for viral replication. Many aspects of host cell function are commandeered by viruses, usually by the expression of viral gene products that recruit host cell proteins and machineries. Moreover, viruses engineer specific membrane organelles or tag on to mobile vesicles and motor proteins to target regions of the cell (during de novo infection, viruses co-opt molecular motor proteins to target the nucleus; later, during virus assembly, they will hijack cellular machineries that will help in the assembly of viruses). Less is understood on how viruses, in particular those with RNA genomes, coordinate the intracellular trafficking of both protein and RNA components and how they achieve assembly of infectious particles at specific loci in the cell. The study of RNA localization began in earlier work. Developing lower eukaryotic embryos and neuronal cells provided important biological information, and also underscored the importance of RNA localization in the programming of gene expression cascades. The study in other organisms and cell systems has yielded similar important information. Viruses are obligate parasites and must utilise their host cells to replicate. Thus, it is critical to understand how RNA viruses direct their RNA genomes from the nucleus, through the nuclear pore, through the cytoplasm and on to one of its final destinations, into progeny virus particles. FISH serves as a useful tool to identify changes in steady-state localization of viral RNA. When combined with immunofluorescence (IF) analysis, FISH/IF co-analyses will provide information on the co-localization of proteins with the viral RNA. This analysis therefore provides a good starting point to test for RNA-protein interactions by other biochemical or biophysical tests, since co-localization by itself is not enough evidence to be certain of an interaction. In studying viral RNA localization using a method like this, abundant information has been gained on both viral and cellular RNA trafficking events. For instance, HIV-1 produces RNA in the nucleus of infected cells but the RNA is only translated in the cytoplasm. When one key viral protein is missing (Rev), FISH of the viral RNA has revealed that the block to viral replication is due to the retention of the HIV-1 genomic RNA in the nucleus. Here, we present the method for visual analysis of viral genomic RNA in situ. The method makes use of a labelled RNA probe. This probe is designed to be complementary to the viral genomic RNA. During the in vitro synthesis of the antisense RNA probe, the ribonucleotide that is modified with digoxigenin (DIG) is included in an in vitro transcription reaction. Once the probe has hybridized to the target mRNA in cells, subsequent antibody labelling steps (Figure 1) will reveal the localization of the mRNA as well as proteins of interest when performing FISH/IF.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.391
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2012
Admission routes2
Has abstractyes

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