Flow Cytometry Methods and Reagents

Flow Cytometry Reagents

• Conjugated Antibodies:

  • Antibodies specific to a particular marker (cell surface, activation, or cytokine).

  • Conjugated with a fluorophore for detection in flow cytometry.

• Maltamers (Tetramers, Pentamers, Dextromers):

  • Peptide-HLA complexes to detect peptide-specific T cells.

  • Tetramers are more common due to fewer non-specific binding issues compared to dextromers.

  • Consist of peptide HLA complex with a fluorescently labelled backbone.

  • Used to identify specific T cells (CD8 or CD4) based on HLA class I or class II.

• Cell Viability Dyes:

  • Bind to proteins in dying cells, especially free amines.

  • Help differentiate live from dead cells.

• Proliferation Dyes (e.g., Cell Trace Violet):

  • Track cell proliferation by measuring the dilution of the dye with each cell division.

  • Cells are stained with a maximum amount of dye which splits in half when the cell divides.

  • Loss of intensity dye measures proliferation.

  • Can track up to 9 divisions using Cell Trace Violet because the halving intensity is easy to see on a log scale.

Maltamer Details

• Types: Tetramers (4), Pentamers (5), Dextromers (10) - referring to the number of molecules.

• Tetramer Structure: 4 peptide HLA complexes conjugated to a backbone with a fluorophore.

• Dextromers: Higher propensity for non-specific binding due to many interactions; rarely used.

Cell Phenotype Markers

• Cell Type Identification:

  • Examples: CD3, CD4, CD8.

• Cell Status:

  • Naive vs. Memory cells.

  • Markers: CD27, CD45RA, CCR7, CD95.

• Activation State:

  • PD1 (over-activated/exhausted cells).

  • CD107A (degranulation - release of perforin and granzymes).

  • CD69 (activated cells).

• Each antibody should have a different fluorophore to distinguish markers at a single-cell level.

Cell Function Markers

• Functional markers are things like interferon gamma and TNF alpha.

• CD4 and CD8 T cells: Functional markers (Interferon gamma, TNF alpha).

• Transcription factors: e.g. FOP3.

• Internal functional markers: Granzyme B expression, TNF alpha, interferon-gamma production.

• Functional markers are often tracked intracellularly due to their secretion.

• Combining cell function markers with phenotypic markers provides comprehensive information.

Cell Viability Markers Significance

• Dead cells are sticky and non-specifically bind antibodies.

• Essential to exclude dead cells from analysis to ensure accurate results.

Proliferation Dyes (Cell Trace Violet)

• Cells stained with dye, which is halved with each division.

• Measure loss of dye intensity to track proliferation.

• Can distinguish up to 9 cell divisions due to the log scale separation.

• Applications:

  • Assessing activation effectiveness.

  • Comparing activation methods.

  • Measuring division time following stimulation.

Experimental Design Considerations (Phenotype)

• Cell Sample (PBMCs, T cell line, etc.) + Epitope-specific cells

• Add Tetramer (streptavidin backbone + fluorophore + HLA complexes with peptide).

• Wash off excess tetramer.

• Add Surface Markers (CD3, CD4, CD8, CD27, CD45RA, CCR7, CD95).

• Add Cell Viability Dye.

Data Interpretation (Phenotype)

• Identify tetramer-specific cells.

• Analyze surface marker expression to determine cell phenotype (naive, memory).

• Exclude dead cells from analysis.

• Determine proportion of tetramer-specific cells.

• Correlate tetramer positivity with naive or memory markers.

Experimental Design Considerations (Function)

• Cell Population (PBMCs, T cell lines).

• Add Tetramer (for epitope-specific cells).

• Add Peptide of Interest (to activate cells).

• Add Proliferation Dye.

• Add Viability Dye.

• Add Surface Markers (cell type, phenotype).

• Add Cytokines and Degranulation Markers.

Data Interpretation (Function)

• Exclude dead cells.

• Assess cytokine production (activation).

• Measure proliferation rate.

• Correlate function with cell phenotype.

Data Analysis: Gating Strategies

• Size and Complexity (Forward and Side Scatter):

  • First step in gating.

  • Red blood cells, lymphocytes, and granulocytes can be separated this way.

  • Lymphocytes are usually the cells of interest when looking at T cells.

• Fluorescence Analysis:

  • Cells are stained with different fluorophores.

  • Examples:

    • Double Negative (no yellow or blue antibody).

    • Blue Positive (blue antibody).

    • Yellow Positive (yellow antibody).

    • Double Positive (yellow and blue antibodies).

  • Each dot on a FACS plot represents a single cell.

Gating Strategy Steps

• Forward and Side Scatter (select cells of interest).

• Singlets (exclude doublets or clumps of cells).

  • Cells stuck together may fluoresce with all different combinations of antibodies, so we can't trust them.

  • Area vs Height: cells that are stuck together won't change height, but size will, so we get them out that way.

• Color-based gating (e.g., select blue population).

• Iterate to refine population.

Real-Life Gating Example

• Select lymphocytes.

• Select single cells.

• Gate on CD3 and CD8 (double negative, single positives, double positives).

• Remember that unused cells are still there, we are just not looking at them right now.

• The order of the gates can matter, but doesn't matter. Take the regular M&Ms first and get your singlets; then you can look at the different parameters in any different order.

Data Representation Methods

• Histograms (1 Parameter):

  • Single sample with one homogeneous population and one peak.

  • Cells with 4 fluorescent tags vs no fluorescent tags exhibit two distinct peaks.

• Overlays (Multiple Samples):

  • Compare different samples.

  • Time courses, different cell populations, different donors, different reagent concentrations.

  • Can directly overlay or use offset histograms.

  • Different samples and how things have changed with regards to the different samples using this sort of offset histogram.

• 2-Parameter FACS Plots:

  • Double negative, single positive, double positive cells.

  • Can also do things like overlay the samples, similar to the histograms, where in orange, we've got one sample, and in purple, we've got another sample, and you can see how the populations sit with respect to each other in the different types of samples.

• 3-Parameter FACS Plots:

  • More complex analysis.

  • These typically aren't seen in literature publications, but they are doable.

  • Can also group cells based on 20 different parameters.