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8 critical tips to consider when developing and optimising an immunoassay or ELISA.

Updated: Apr 30

Optimising an ELISA method involves several steps to enhance the accuracy, sensitivity, specificity, and overall performance of the assay. At Fleet Bioprocessing, we have the expertise in immunoassay development to support your specific project, but here are some points to consider.


  1. Antibody and antigen selection: Your assay will only ever be as good as the reagents used. Whilst for a novel target the choice of antibody may be limited, where a range of antibodies are available, or if raising new antibodies, it’s important to take the time to make sure the selection is right for the assay purpose. Choose antibodies that specifically bind the target analyte with high affinity and minimal cross-reactivity. Validate the antibody’s specificity and selectivity with rigorous testing and characterisation.

  2. Optimise antibody/antigen concentrations: Determine the optimal concentrations of the target antigen and antibodies to achieve the desired sensitivity and signal-to-noise ratio. Perform titration experiments to identify the optimal working concentrations for each component and to identify the antibody concentration that provides maximum signal with minimal non-specific binding.

  3. Optimise incubation conditions: Evaluate the effects of incubation time, temperature, and buffer composition on the assay performance. Assess the effects of pH, ionic strength, and buffer components on non-specific binding. Conduct time course experiments to determine the optimal duration of incubation for maximum signal generation while minimising non-specific binding. Increasing the incubation time may enhance specific binding, but it could also increase non-specific binding, so finding the optimal balance is crucial.

  4. Blocking and washing steps: Optimise the blocking agent and washing buffer to reduce non-specific binding and background noise. Evaluate different blocking agents and washing protocols (e.g., number of washes, duration, and buffer composition) to achieve optimal assay sensitivity and specificity. Common blocking agents include bovine serum albumin (BSA), non-fat milk, casein, or specialized blocking buffers. Determine the appropriate number of washes, duration of each wash, and buffer composition (e.g., PBS, TBS) based on the specific assay requirements. Gentle but thorough washing is essential to remove non-specifically bound substances without disrupting the specific antibody-antigen interactions. If non-specific binding is caused by interfering substances in the sample, consider pre-treatment methods such as dilution, filtration, or depletion of the interfering substances., or preferably investigate inclusion of appropriate blockers in buffer formulations.

  5. Signal detection and amplification: Consider different detection methods, such as colorimetric, chemiluminescent, or fluorescent detection, depending on the assay requirements. Assess the need for signal amplification techniques, such as enzyme-based amplification systems, to enhance the sensitivity of the assay. If the antibody is being directly labelled for use in the assay, ensure that the conjugation methods used are robust, repeatable, and scalable.

  6. Validation and standardisation: Validate the optimised method using well-characterised reference materials or clinical samples to assess accuracy, precision, linearity, and robustness. Recognised study designs to evaluate the various aspects of assay performance are available (eg. CLSI) but the design of any performance verification programme should always take into account the function of the assay, with additional studies potentially being required to validate specific aspects. Standardise the assay protocol to ensure consistent and reproducible results across different operators, instruments, and laboratories.

  7. Data analysis and interpretation: Implement appropriate data analysis algorithms and statistical methods to accurately quantify and interpret the assay results. Consider curve fits and weighting for accurate interpolation of results. Establish appropriate cut-off values and reference ranges based on control samples or established clinical standards.

  8. Quality control and troubleshooting: Implement quality control measures, including the use of positive and negative controls, to monitor assay performance and detect any deviations or issues. Negative controls should include samples or wells with no target analyte to verify the absence of non-specific binding. Positive controls should contain known concentrations of the target analyte to validate the assay's sensitivity and specificity. It is also important to include controls at any critical levels, such as clinical decision points, cut-off, etc. Routinely monitoring controls in your assay allows you to assess the day-to-day performance and identify runs generating acceptable data. It also aids in identifying issues early so that troubleshooting can take place and any problems are quickly resolved.

It's important to note that the optimisation process may vary depending on the specific immunoassay method, target analyte, and intended application. Therefore, it is recommended to consult relevant scientific literature, assay-specific guidelines, and collaborate with experienced researchers or assay developers to ensure the best optimization strategy for your specific immunoassay.


Fleet Bioprocessing are well-known worldwide for our expertise in Immunoassay and ELISA development. We would be happy to talk to you about your assay needs and help you avoid some of the pitfalls that can arise.

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