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Record W7018305136

Deciphering synaptic receptor distributions, clustering and stoichiometry using spatial intensity distribution analysis (SpIDA)

2011· dissertation· en· W7018305136 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2011
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsReceptorCytoplasmCompartmentalization (fire protection)Cluster analysisFluorescenceG protein-coupled receptorHistogramCell membraneDistribution (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Measuring protein interactions in subcellular compartments is key to understanding cell signalling mechanisms, but quantitative analysis of these interactions in situ has remained a major challenge. This thesis presents a novel analysis technique, spatial intensity distribution analysis (SpIDA), which may be applied to images obtained using fluorescence microscopy. SpIDA measures fluorescent particle densities and oligomerization states within individual images. The method is based on fitting intensity histograms from single images with super-Poissonian distributions to obtain density maps of fluorescent molecules and their quantal brightness. Since distributions are acquired spatially rather than temporally, this analysis may be applied to both live and chemically fixed cells and tissue. The technique does not rely on spatial correlations, freeing it from biases due to subcellular compartmentalization and heterogeneity within tissue samples. First, we validated the analysis technique evaluating its limits and demonstrating how it can be used to obtain useful information from complex biological samples. Analysis of simulations and heterodimeric GABAB receptors in spinal cord samples shows that the approach yields accurate estimates over a broad range of densities. SpIDA is applicable to sampling within subcell areas and reveals the presence of monomers and multimers with single dye labeling. We show that the substance P receptor (NK-1r) almost exclusively forms homodimers on the membrane and is primarily monomeric in the cytoplasm of dorsal horn neurons. Triggering receptor internalization caused a measurable decrease in homodimer density on the membrane surface. Finally, using GFP-tagged receptor subunits, we show that SpIDA can resolve dynamic changes in receptor oligomerization in live cells and is applicable to detection of high order oligomerization states. We then compared SpIDA results with those obtained from fluorescence lifetime imaging, and used it to extract information on receptor tyrosine kinase (RTK) dimerization at the cell membrane in response to GPCR activation. We show that RTK dimerization can be used as an index of activation or transactivation and then characterize the level of transactivation of many RTK-GPCR pairs, with cell cultures and primary neuron cultures with endogenous levels of RTKs and GPCRs. Dose-response curves were obtained from which pharmalogical parameters can be compared for each GPCR studied. Our data demonstrates that by allowing for time and space quantification of heterogenous oligomeric states, SpIDA enables systematic quantitative mechanistic studies not only of RTK transactivation at the cell membrane, but also of other cell signaling processes involving changes in protein oligomerization, trafficking and activity in different subcellular localizations. Finally, we studied the changes in number of synaptic sites in the neurons of the dorsal horn of the spinal cord of rats after a peripheral nerve injury (PNI), which consists of our model for chronic pain. We show that, after the PNI, there is a general decrease in synaptic sites together with a scaling or increasing of some of the GABAA receptor subunits. This scaling of the GABAA receptors at the postsynaptic sites was replicated by incubating the histological sections in a brain derivative nerve factor. Furthermore, we use SpIDA to obtain stoichiometry information for the GABAA receptor subunits directly at the postsynaptic sites. In short, we observe a switch from receptors containing two alpha1 to receptors containing two alpha2 and alpha3. This general change in subunits will have a direct effect on the cell as it will have different effects on the cell membrane conductance in response to GABA. As demonstrated, the advantages and greater versatility of SpIDA over current techniques opens the door to a new level of quantification for studies of protein interactions in native tissue using standard fluorescence microscopy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2011
Admission routes1
Has abstractyes

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