🔬 The Four Types of Assays and How They Work

In drug discovery and diagnostics, an assay is any laboratory test that measures a biological or chemical activity. Assays answer questions — does a compound bind this protein? Does a cell die after treatment? Is a specific nucleic acid present in a sample?

Quick overview: Most assays used in pharma and diagnostics fall into four broad categories — biochemical, cell-based, immunoassays, and molecular. Each has distinct strengths, limitations, and typical applications.

Table of contents

• Biochemical assays (enzyme activity, binding)
• Cell-based assays (phenotypic, viability)
• Immunoassays (ELISA, lateral flow)
• Molecular assays (PCR, sequencing)
• How to pick the right assay
• Short glossary & further reading

1. Biochemical assays — the simplest direct readouts 🧪

Biochemical assays measure the function or interaction of isolated biomolecules (proteins, enzymes, receptors) outside of cells. They are often the first-line assays in target-based drug discovery because they are fast, scalable, and relatively easy to interpret.

Common formats

• Enzyme activity assays — measure substrate conversion (e.g., colorimetric, fluorometric, luminescent readouts).
• Binding assays — measure direct interaction (e.g., fluorescence polarization, surface plasmon resonance).
• Reporter fragment complementation — split enzymes or reporters that reconstitute activity on binding.

Why use biochemical assays?

• High throughput: amenable to 384/1536-well screening.
• Direct mechanistic insight — you can measure enzyme kinetics (Km, Vmax) and inhibitor types (competitive, noncompetitive).
• Lower biological complexity: fewer variables than cell assays.

Limitations

They lack cellular context — compounds active in a purified-enzyme assay may fail in cells due to permeability, metabolism, or off-target effects.

2. Cell-based assays — biology in context 🧬

Cell-based assays measure cellular responses: viability, proliferation, signaling, morphology, reporter expression, or more complex phenotypes (e.g., migration, differentiation). They add physiological context and are crucial for understanding whether hits from biochemical screens work in a living system.

Types of cell-based assays

• Viability / cytotoxicity — MTT, resazurin, ATP-based luminescence assays.
• Reporter assays — luciferase or fluorescent reporters driven by pathways (e.g., NF-κB, Wnt).
• High-content imaging (HCI) — automated microscopy to extract multi-parameter phenotypes.
• Phenotypic screens — observe complex biological outcomes without a single molecular target.

Pros

• Biologically relevant readouts — includes uptake, metabolism, and cellular machinery.
• Enables toxicity and off-target profiling earlier.
• High-content methods can uncover unexpected mechanisms.

Cons

More variability (cell line differences, passage number), often lower throughput and higher cost than biochemical assays. Assay development can be longer (optimizing transfection, expression, readout windows).

3. Immunoassays — selective detection using antibodies 🧫

Immunoassays exploit the specificity of antibodies to detect proteins, peptides, or other antigens. Common formats include ELISA, lateral flow assays (LFAs), and multiplexed bead-based assays (e.g., Luminex).

Popular formats

• ELISA (Enzyme-Linked Immunosorbent Assay) — plate-based sandwich or competitive formats that use enzyme-linked secondary antibodies for readout.
• Lateral flow — rapid point-of-care tests (pregnancy tests, many rapid antigen tests).
• Multiplex bead assays — measure multiple analytes in a single sample using bead barcodes.

Strengths

• High specificity when validated antibody pairs are used.
• Sensitive and quantitative (depending on format and reagents).
• Well-suited to clinical and diagnostic workflows.

Weaknesses

Dependent on antibody quality — cross-reactivity or poor affinity can cause false positives/negatives. Matrix effects (serum, plasma) often require blocking and careful controls.

When to choose an immunoassay

Use immunoassays when the target is a protein and high specificity in complex samples (blood, CSF, cell lysate) is required. For detailed ELISA development tips, see our internal post: ELISA Assay Development.

4. Molecular assays — detect nucleic acids and genetic info 🧬🔎

Molecular assays detect DNA, RNA, or other nucleic acid signatures. PCR and sequencing are the most familiar molecular tools, and they power diagnostics, pathogen detection, gene expression analysis, and genotyping.

Common molecular techniques

• qPCR / RT-qPCR — quantifies DNA or RNA with high sensitivity.
• Digital PCR — absolute quantification via partitioning (droplet dPCR).
• Next-Generation Sequencing (NGS) — broad profiling of sequences, mutation detection, transcriptomics.
• Isothermal amplification — LAMP, RPA for rapid diagnostics without thermal cyclers.

Pros

• Extraordinary sensitivity (can detect single copies with dPCR).
• Specificity via primers/probes targeting unique sequences.
• Powerful for pathogen ID, gene expression, and variant detection.

Cons

Prone to contamination if workflows are not physically separated. Interpretation of sequencing data requires bioinformatics. Sample prep and extraction quality heavily influence results.

How to pick the right assay for your project ✅

1. Define your question: Mechanism (does it bind?), function (does it change activity?), biological outcome (does it improve cell survival?).
2. Consider throughput: For large compound libraries, start with biochemical or simple cell-based assays; for focused validation use higher-content cell assays or immuno/molecular methods.
3. Sample matrix: Is your analyte in buffer, cell lysate, serum, or tissue? Immunoassays and some molecular assays handle complex matrices better when optimized.
4. Sensitivity & specificity: Molecular assays excel at sensitivity; immunoassays can be highly specific if antibodies are well-validated.
5. Resources & timelines: Sequencing and high-content imaging require more time and analysis than a plate reader-based biochemical assay.

Practical tips for assay development and validation 🛠️

• Start with controls: positive, negative, and no-template/no-cell controls are essential.
• Assay window and Z'-factor: quantify dynamic range and robustness (Z' > 0.5 is a healthy assay for HTS).
• Reagent QC: batch-to-batch variation in antibodies, enzymes, or plates can ruin reproducibility — validate each lot.
• Matrix effects: spike-and-recovery and dilution linearity tests expose interference from serum or lysate.
• Documentation: record SOPs, acceptance criteria, and sample handling to ensure transferability and regulatory readiness.

Glossary & further reading

Assay: a test to measure the presence, amount, or activity of a substance.
Z'-factor: statistical measure of assay quality that compares positive and negative controls.
Matrix effect: interference from sample components (lipids, salts) causing signal suppression or enhancement.

Want a deeper overview of assay strategy? Read our comprehensive pillar post: Assay Development: From Concept to Validation. For hands-on cell assay guidance see: Cell-based Assay Development and for plate-based immuno methods: ELISA Assay Development.