Single-cell Quantitation of mRNA and Surface Protein Expression in Simian Immunodeficiency Virus-infected CD4+ T Cells Isolated from Rhesus macaques
September 25th, 2018
10.1K views
Described is a methodology to quantitate the expression of 96 genes and 18 surface proteins by single cells ex vivo, allowing for the identification of differentially expressed genes and proteins in virus-infected cells relative to uninfected cells. We apply the approach to study SIV-infected CD4+ T cells isolated from rhesus macaques.
Overview
This article describes a methodology to quantitate the expression of 96 genes and 18 surface proteins by single cells ex vivo. This approach allows for the identification of differentially expressed genes and proteins in virus-infected cells relative to uninfected cells, specifically applied to SIV-infected CD4 + T cells isolated from rhesus macaques.
Key Study Components
Area of Science
- Neuroscience
- Immunology
- Host-pathogen interactions
Background
- Understanding the unique characteristics of productively infected cells.
- Identifying host cofactors that facilitate viral infections.
- Developing targeted therapeutic interventions.
- Providing quantitative measures for protein and mRNA within single cells.
Purpose of Study
- To quantify gene and protein expression in single cells.
- To compare SIV-infected and uninfected CD4 + T cells.
- To enhance understanding of host-pathogen interactions.
Methods Used
- Cell sorting procedure demonstrated by a senior technician.
- Preparation of assay mix with 96 gene expression assays.
- Pipetting of assay into a 96 well PCR plate.
- Use of commercially available reagents for the assay.
Main Results
- Identification of differentially expressed genes and proteins.
- Quantitative data on protein and mRNA levels in single cells.
- Insights into the characteristics of SIV-infected cells.
Conclusions
- The methodology provides a robust framework for studying viral infections.
- It facilitates the exploration of therapeutic targets in infected cells.
- Future applications may extend to other viral pathogens.
Frequently Asked Questions
What is the main advantage of this technique?
The technique provides quantitative measures for both protein and mRNA within single cells.
Who demonstrates the cell-sorting procedure?
Matthew Creegan, a senior technician from the flow cytometry core in Doctor Michael Eller's laboratory.
What is the purpose of the assay mix?
The assay mix quantifies the expression of multiple genes in single cells.
How are the assays prepared?
Assays are combined into an RNase, DNase free tube and diluted appropriately.
What type of cells are studied in this research?
SIV-infected CD4 + T cells isolated from rhesus macaques.
What can this method help researchers understand?
It helps answer key questions about host-pathogen interactions and therapeutic targeting.
Executive Industry Relevance
Single-cell proteo-transcriptional profiling enables biopharma R&D to resolve heterogeneous infected cell populations and identify therapeutic targets with mechanistic precision. By linking viral gene expression to host protein and transcript levels at single-cell resolution, the method supports target validation and de-risks early discovery decisions. This approach enhances predictive confidence in host-pathogen interaction studies and informs portfolio prioritization for antiviral interventions.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by linking viral gene expression states to host cofactor profiles in infected cells.
- Operational Value: Provides quantitative protein and mRNA readouts that clarify biological mechanisms underlying productive versus non-productive infection.
- Strategic Value: Supports predictive confidence in target selection by revealing differentially expressed host factors associated with viral life cycle stages.
Screening & Assay Development
- Scientific Value: Generates standardized, multiplexed quantitative data from sorted single cells, enabling reproducible assay development for host-pathogen studies.
- Operational Value: Integrates FACS isolation with high-throughput qPCR to create a scalable workflow for linked protein-transcriptional profiling.
- Strategic Value: Facilitates screening readiness by producing multidimensional single-cell datasets that inform compound effect on infected cell phenotypes.
Translational & Preclinical Research
- Scientific Value: Reveals in vivo post-transcriptional gene regulation and viral RNA expression heterogeneity, supporting biomarker discovery in relevant disease models.
- Operational Value: Enables direct comparison of infected and uninfected cells from the same specimen to assess host response modulation.
- Strategic Value: Informs risk-adjusted advancement decisions by characterizing host-pathogen interactions at single-cell resolution in preclinical systems.
Pipeline & Workflow Integration
The method bridges discovery biology and preclinical validation by enabling mechanistic de-risking of antiviral targets through quantitative single-cell analysis of infected cell states.
- Discovery Biology: Supports hypothesis testing and pathway clarification by correlating viral gene expression with host surface protein and transcript levels in sorted cell subsets.
- Screening: Delivers assay-ready, reproducible quantitative outputs from multiplexed protein and mRNA measurement in single cells.
- Analytics: Provides normalized gene expression and protein quantification data that enable statistical comparison of infected versus uninfected conditions.
- Translational Research: Connects early discovery to preclinical continuity by identifying differentially expressed host genes and proteins in SIV-infected cells ex vivo.
- Enterprise Reuse: Establishes a reusable platform for linked proteo-transcriptional profiling applicable to any cell population defined by surface markers, host genes, or pathogen expression.
Operational & Enterprise Impact
- Scientific Value: Reduces mechanistic ambiguity in host-pathogen interactions by providing quantitative, multimodal single-cell resolution of infected cell states.
- Operational Value: Ensures standardization and reproducibility through indexed flow cytometry coupled with controlled qPCR pre-amplification and detection.
- Strategic Value: Improves go/no-go decisions by enabling early identification of host cofactors and viral reservoirs with therapeutic relevance.
- Portfolio Impact: Supports risk-adjusted prioritization of antiviral targets based on validated mechanistic insights from primary infected cell analysis.
Implementation Considerations
- Requires expertise in flow cytometry, single-cell sorting, and multiplexed qPCR assay design and execution.
- Dependent on access to fluorescence-activated cell sorters, microfluidic qPCR platforms, and RNase-free molecular biology workspaces.
- Necessitates cross-team standardization between immunology, virology, and genomics for consistent sample preparation and data integration.
- Involves adaptation considerations when applying the method to different tissue sources or cell types beyond CD4+ T cells.
- Practical limitations include assay multiplexing capacity constrained by microfluidic chip design and the need for immediate cell lysis post-sort to preserve RNA integrity.
Biopharma FAQs
Why does quantitating multiple viral RNA species matter for target validation?
Quantitating multiple viral RNA species enables classification of infected cells into distinct stages of the viral life cycle, such as productive versus non-productive infection. This stratification supports target validation by linking specific viral gene expression profiles to host cofactor states. It provides a mechanistic basis for prioritizing therapeutic interventions aimed at defined infection stages.
How does isolating live cells before lysis support discovery pipeline objectives?
Isolating viable cells via FACS prior to lysis preserves RNA and protein integrity for accurate multimodal measurement. This step ensures that surface protein markers and intracellular transcripts are linked to the same single cell, enabling correlated analysis. It supports discovery pipeline objectives by generating high-fidelity, matched proteo-transcriptional datasets from sorted subsets.
What do quantitative dependent variable measurements enable in host-pathogen studies?
Quantitative measurements of host genes, viral transcripts, and surface proteins enable statistical comparison between infected and uninfected cells from the same specimen. These dependent variables allow researchers to identify differentially expressed molecules associated with infection status. The resulting datasets support mechanistic de-risking by revealing host pathways modulated by viral infection.
Why do replication requirements matter for cross-functional collaboration in this method?
Replication requirements ensure that linked protein and transcriptional data are reproducible across sorted wells and independent experiments. Consistent well-position indexing between FACS and qPCR platforms enables reliable data merging for downstream analysis. This reproducibility supports cross-functional collaboration by providing standardized, auditable single-cell datasets for virology, immunology, and drug discovery teams.
What statistical analysis capabilities are required before implementing this method in drug discovery?
Implementation requires statistical software capable of normalizing qPCR data, mapping expression by sample and well position, and performing group-based comparisons such as fit Y by X analysis. The method depends on tools that can merge FACS and qPCR data by plate and well to generate combined protein-transcriptional profiles. These capabilities are essential for extracting mechanistic insights from heterogeneous infected cell populations.
Chapters in this video
0:04
Title
0:46
Gene Expression Assay Preparation and Surface Staining of Viable Cells
3:18
Prepare Cell Collection Plates, Perform FACS Sort, and Generate cDNA
5:17
Multiplex qPCR on the Biomark Platform
11:26
Results: Single-cell Quantitation of mRNA and Surface Protein Expression
12:56
Conclusion
