Single cell RNA-seq and single molecule RNA-FISH approaches to study stochasticity of latent HIV-1 reactivation
Single cell RNA-seq and single molecule RNA-FISH approaches to study stochasticity of latent HIV-1 reactivation
批准号:
10082908
负责人:
GANJAM V KALPANA
金额:
$25.15万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-05-20 至 2022-04-30
关键词:
AntibodiesBiological AssayCD4 Positive T LymphocytesCell Culture TechniquesCellsComputational algorithmDataDevelopmentFishesGene Expression RegulationGenesGeneticGenetic HeterogeneityGenetic TranscriptionGoalsHIVHIV-1ImageImmunofluorescence ImmunologicIndividualInfectionLeadLengthMethodologyMethodsMicroscopyModelingMolecularNoisePathway interactionsPatientsProceduresProteinsProvirusesQuantitative Reverse Transcriptase PCRRNARNA ProbesResolutionRestSamplingScanningShockSiteSlideSourceSpeedStimulusT-LymphocyteTechniquesTechnologyTranscriptVariantViralViral GenomeViral ProteinsVirus Integrationbasecell typecombatdifferential expressionepigenetic regulationin vivoinnovationintegration siteknock-downmultiple omicsnew therapeutic targetnovelnovel markernovel therapeuticsreactivation from latencysingle cell analysissingle moleculesingle-cell RNA sequencingtranscriptometranscriptome sequencingviral RNA
中文摘要
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英文摘要
The persistent reservoirs are the ultimate hurdle to HIV-1 cure. ‘Shock and Kill’ strategy to eliminate HIV-
1 reservoirs requires reactivation of all latent proviruses with latency reversing agents (LRA), which is currently
not possible. Quantitation of patient derived latent reservoirs by QVOA (Quantitative Viral Outgrowth Assay)
indicated that only ~1/60th of full-length replication competent latent proviruses are activated by LRA. The reason
for this variability is not completely understood but has been attributed to differential epigenetic regulation of
provirus (due to different sites of proviral integration), differences in the viral genome (genetic variability),
differential expression of cellular factors or different transcriptional or post-transcriptional blocks in the patient-
derived latent cells. In the ExVivo and cell culture models of latency, where a single viral clone is used for infection
with no genetic heterogeneity, reactivation of only a small percentage of cells and different levels of expression
of viral RNA and proteins indicate cell-to-cell variation in reactivation. These studies also revealed that the
majority of latent cells are not activated by current approaches. Current strategies do not address cell-to-cell
variation in proviral reactivation and understanding this variability in vivo is essential to achieve either full
reactivation or full suppression of these reservoirs.
The goal of this application is to employ innovative single-cell and multi-omics platforms to investigate
mechanisms of HIV persistence at the single-cell level with greater precision and higher resolution than has been
achieved previously using traditional techniques. Stellaris-based quantitative single molecule RNA-FISH
(smRNA-FISH), that allows the greater resolution and precise quantitation of the number of RNA molecules
within single cells, have been used to study stochasticity in eukaryotic gene transcription. We have applied
smRNA-FISH combined with Immunofluorescence (IF) termed SMIRA (Single cell and single molecule IF and
RNA-FISH based Assay) to study HIV-1 reactivation in latency models and quantitated the single RNA molecules
using FISH-quant, a computational algorithm. In addition, we have combined these studies with a high speed
and high resolution scanning (HSHRS) microscopy to identify rare positive cell/s in a large pool of negative cells.
Using these methods, our goal is to quantitate the cell-to-cell variability in latent cells. Our hypothesis is that cell-
to-cell variation in HIV-1 reactivation is due to both ‘intrinsic’ and ‘extrinsic’ factors. By combining single cell and
single molecule approaches with single cell RNA-sequencing (scRNA-seq) methodologies we propose to identify
the determinants responsible for variation in the HIV-1 reactivation and extend these studies to identify these
markers in patient samples. In aim I, we will characterize and determine the genes and pathways involved
in the stochastic activation in HIV-1 latent cells using SMIRA, HSHRS and RNA-seq analysis. In aim II,
we will validate the genes and pathways identified to influence HIV-1 stochastic activation in models of
latency and in ART-suppressed patient samples. Our analyses will identify novel genes and pathways that
influence cell-to-cell variability in HIV-1 reactivation and ultimately will lead to the development of novel therapies
to combat HIV-1 latency.
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