Advanced Flow Cytometry and Immunophenotyping

Lesson 1 • Instrument Quality Control Basics

Introduces daily QC using calibration beads and standardized protocols. Ensures students can verify instrument performance before acquiring experimental samples.

Lesson 2 • Fluidics and Cell Interrogation

Explains hydrodynamic focusing and sample flow rates that position cells in the laser path. Connects fluidic control to data quality and event rate consistency.

Lesson 3 • Laser Sources and Optical Configurations

Surveys common laser wavelengths and bandpass filter arrangements used in modern cytometers. Links optical layout choices to fluorochrome compatibility and sensitivity.

Lesson 4 • Principles of Light Scatter and Fluorescence

Covers forward scatter, side scatter, and fluorescence emission as diagnostic signals. Establishes the physical basis for all subsequent gating and panel design decisions.

Lesson 1 • Fluorochrome Spectral Properties

Examines excitation peaks, emission spectra, and brightness rankings of common fluorochromes. Provides the spectral knowledge needed to build non-conflicting panels.

Lesson 2 • Spectral Overlap and Compensation Theory

Explains why fluorochrome emission bleeds into adjacent detectors and how compensation corrects it. Builds the conceptual framework before students apply compensation matrices.

Lesson 3 • Antibody Conjugation and Validation

Covers conjugation chemistry, fluorochrome-to-protein ratios, and lot-to-lot validation. Connects reagent quality to reproducible staining intensity.

Lesson 4 • Viability Dyes and Live-Dead Discrimination

Introduces amine-reactive and DNA-intercalating viability dyes and their spectral placement. Ensures dead-cell exclusion is integrated into every panel from the start.

Lesson 1 • Intracellular and Intranuclear Staining

Teaches fixation and permeabilization chemistries for cytokines, transcription factors, and nuclear antigens. Addresses compatibility with surface staining and viability dyes.

Lesson 2 • Blood and Tissue Sample Processing

Covers whole-blood lysis, density-gradient separation, and tissue dissociation methods. Connects processing choice to downstream cell viability and antigen preservation.

Lesson 3 • Fixation and Biosafety Considerations

Reviews fixative options for inactivating infectious agents and their effects on fluorochrome stability. Aligns sample handling with biosafety-level requirements.

Lesson 4 • Controls and Experimental Design

Defines FMO, isotype, and biological controls and their roles in gating accuracy. Students design a complete control set for a multicolor experiment.

Lesson 1 • Threshold and Trigger Settings

Explains trigger channel selection, threshold levels, and their effect on debris exclusion. Students set thresholds that capture target populations without excess noise events.

Lesson 2 • Acquisition Templates and Protocols

Covers building reusable acquisition templates with predefined gates and statistics. Standardizes data collection across operators and experimental runs.

Lesson 3 • Compensation Matrix Setup

Guides students through acquiring single-color controls and calculating a compensation matrix. Reinforces the theory from Chapter 2 with hands-on instrument application.

Lesson 4 • Voltage Optimization Strategies

Teaches peak-2 and reference bead methods for setting PMT voltages consistently. Links voltage choices to dynamic range and sensitivity for each detector.

Lesson 1 • Population Frequency and Absolute Counts

Covers percent-of-parent, percent-of-total, and absolute count calculations using counting beads. Ensures students report data in clinically and experimentally meaningful units.

Lesson 2 • FMO-Based Gate Placement

Demonstrates using fluorescence-minus-one controls to set biologically justified gate boundaries. Reduces operator bias in low-frequency population identification.

Lesson 3 • Hierarchical Gating Fundamentals

Establishes sequential gating logic from scatter to lineage to functional markers. Provides a reproducible framework that minimizes subjective gate placement.

Lesson 4 • Software Platforms for Analysis

Surveys major analysis software environments and their gating, statistics, and export capabilities. Students apply at least one platform to a complete dataset.

Lesson 1 • T Cell Subset Identification

Defines canonical markers for naive, effector, memory, and regulatory T cell subsets. Students gate CD4 and CD8 lineages with activation and exhaustion markers.

Lesson 2 • NK Cell and Innate Lymphoid Cells

Identifies NK cell subsets by CD56 and CD16 expression and ILC1, ILC2, ILC3 by lineage-negative gating. Addresses the challenge of rare ILC enumeration.

Lesson 3 • Monocyte and Dendritic Cell Subsets

Distinguishes classical, intermediate, and non-classical monocytes and myeloid vs. plasmacytoid DCs. Students apply HLA-DR, CD14, and CD16 gating hierarchies.

Lesson 4 • Granulocyte and Mast Cell Phenotyping

Covers neutrophil, eosinophil, basophil, and mast cell identification in blood and tissue. Addresses scatter-based and marker-based strategies for granulocyte gating.

Lesson 1 • Cytotoxicity and Degranulation Assays

Introduces CD107a degranulation, target-cell killing, and LAMP-1 surface mobilization assays. Students measure NK and CTL cytotoxic activity in co-culture systems.

Lesson 2 • Intracellular Cytokine Staining

Teaches stimulation, Brefeldin A treatment, and ICS protocols for detecting cytokine-producing cells. Students quantify antigen-specific T cell responses at the single-cell level.

Lesson 3 • Phosphoflow and Signaling Assays

Covers methanol fixation, phospho-specific antibodies, and barcoding for signaling pathway analysis. Students map kinase activation states across multiple stimulation conditions.

Lesson 4 • Proliferation Assays

Covers CFSE, CellTrace Violet, and BrdU-based proliferation tracking methods. Connects dye dilution kinetics to division index and precursor frequency calculations.

Lesson 1 • Reporting and Reproducible Workflows

Covers scripted R and Python pipelines, version control, and community data-sharing standards. Students produce a fully documented, reproducible analysis workflow.

Lesson 2 • Differential Abundance Analysis

Teaches statistical frameworks for comparing cluster frequencies across experimental groups. Students identify significantly altered populations while controlling for multiple comparisons.

Lesson 3 • Principles of High-Dimensional Data

Explains the curse of dimensionality and why traditional bivariate gating fails at 20-plus parameters. Motivates the need for computational approaches introduced in subsequent sections.

Lesson 4 • Automated Clustering Algorithms

Introduces FlowSOM, Phenograph, and Leiden clustering for unsupervised population discovery. Students evaluate cluster stability and biological relevance against manual gates.

Lesson 5 • Dimensionality Reduction Methods

Covers PCA, tSNE, and UMAP algorithms, their parameters, and appropriate use cases. Students run each method on a real dataset and compare resulting visualizations.