# ELISA Qualitative

## Specs

| Label                     | Value                         |
| ------------------------- | ----------------------------- |
| **Version**               | 1.0.0 (updated on 2026-04-10) |
| **Developer**             | Labii Inc.                    |
| **Type**                  | Section                       |
| **Support Configuration** | Yes                           |

## Overview

[ELISA](https://www.thermofisher.com/us/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/overview-elisa.html) (enzyme-linked immunosorbent assay) is a plate-based assay technique used to detect and quantify substances such as proteins, antibodies, and hormones. A qualitative ELISA determines whether a target analyte is present or absent in a sample, rather than measuring its exact concentration. Labii's **ELISA Qualitative** widget consolidates plate layout design, raw data entry, control validation, and cutoff-based classification into a single ELN workflow. Users assign well roles (negative controls, positive controls, blanks, and samples), enter absorbance readings, and click **Analysis** to receive automated positive/negative results — all without leaving the notebook.

## Use Cases

* **Infectious Disease Screening** — Detect the presence of antibodies or antigens in patient samples to determine exposure or infection status
* **Vaccine Development** — Screen serum samples for seroconversion after immunization, returning a binary reactive/non-reactive result
* **Food Safety Testing** — Identify contamination by allergens, pathogens, or adulterants in food products using a pass/fail threshold
* **Quality Control** — Confirm the presence of a specific biomarker in a production batch, flagging non-conforming samples
* **Compliance Workflows** — Maintain audit-ready, traceable ELISA records within a 21 CFR Part 11–compliant ELN

## Interface

### Read-only View

The read-only view displays the completed qualitative analysis in the 96-well plate format. Each well shows its classification result — **Positive**, **Negative**, or control status — color-coded for rapid visual interpretation. The analysis summary, including mean negative control absorbance, mean positive control absorbance, and the calculated cutoff value, is shown alongside the plate map.

<figure><img src="https://3607108856-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LHVg57XIRcjV-Vbubtn%2Fuploads%2Fgit-blob-422b7b4bba248b5677ef8276b3e3db98116f7d05%2Fwidget-section-elisa-qualitative.webp?alt=media" alt="ELISA Qualitative read-only view showing the 96-well plate with positive/negative classification results"><figcaption><p>The read-only view displays positive/negative classification results for each well alongside the calculated cutoff value</p></figcaption></figure>

### Edit View

The edit view presents a 96-well plate interface with two input stages: **Layout** and **Data**. In the Layout stage, users define the plate map by assigning well roles (negative controls, positive controls, blanks, and sample identifiers). In the Data stage, users enter raw absorbance readings into the corresponding wells. Once both stages are complete, clicking the **Analysis** button triggers the automated classification pipeline.

## Configuration

Click the **Edit** icon in the widget header to open the configuration panel and adjust the following settings.

### Settings

* **Optical Density (OD)** — Wavelength used for absorbance measurement. Defaults to `450 nM`
* **NC Validation** — Validation rule applied to Negative Control wells to confirm the assay is performing correctly. If left blank, NC validation is skipped
* **PC Validation** — Validation rule applied to Positive Control wells to confirm the assay is performing correctly. If left blank, PC validation is skipped
* **Cutoff** — Formula used to calculate the classification threshold. Defaults to the mean NC absorbance

{% hint style="info" %}
For most standard qualitative ELISA protocols, a cutoff equal to the mean NC absorbance is sufficient. More stringent protocols may define the cutoff as NC mean + 2×SD or NC mean × a fixed multiplier.
{% endhint %}

## Additional Functions

### Prepare Layout

The widget supports 96-well plates. Define the plate map by assigning well roles.

{% stepper %}
{% step %}
Click **Edit Plate** to open the well-editor interface
{% endstep %}

{% step %}
Assign well roles using the following labels:

* **NC** — Mark a well as a Negative Control
* **PC** — Mark a well as a Positive Control
* **BLANK** — Mark a well as a blank for background subtraction
* Any other label is treated as a sample identifier; duplicate labels are treated as replicates
* Leave a well empty to exclude it from analysis
  {% endstep %}

{% step %}
Alternatively, paste layout values directly from Excel or Word, or drag and drop a tabular file to import the layout automatically
{% endstep %}
{% endstepper %}

### Prepare Data

{% stepper %}
{% step %}
Switch to the **Data** input mode within the widget
{% endstep %}

{% step %}
Copy absorbance readings from your plate reader output and paste them into the corresponding wells, or type values manually
{% endstep %}

{% step %}
Leave any well blank to exclude that data point from the analysis
{% endstep %}
{% endstepper %}

### Perform Analysis

Click the **Analysis** button to run the full automated pipeline. The analysis completes in seconds and executes the following six-step sequence:

1. **Generate mean NC absorbance** — The absorbance values of all NC wells are averaged to produce the mean NC value; standard deviation (SD) of NC is also calculated
2. **Generate mean PC absorbance** — The absorbance values of all PC wells are averaged to produce the mean PC value; standard deviation (SD) of PC is also calculated
3. **Validate Negative Controls** — If an NC Validation rule is configured, the widget checks whether the NC wells meet the acceptance criteria. If no rule is specified, this step is skipped
4. **Validate Positive Controls** — If a PC Validation rule is configured, the widget checks whether the PC wells meet the acceptance criteria. If no rule is specified, this step is skipped
5. **Calculate the cutoff value** — The cutoff is determined using the selected cutoff formula. By default, the cutoff equals the mean NC absorbance
6. **Classify samples** — Each sample's absorbance is compared against the cutoff. Samples with absorbance ≥ cutoff are classified as **Positive**; samples with absorbance < cutoff are classified as **Negative**. Results are displayed in the 96-well layout

### Result

Each well in the 96-well layout is color-coded to indicate its classification outcome:

* **Green** — The sample passed; absorbance is below the cutoff and the result is **Negative**
* **Red** — The sample failed; absorbance meets or exceeds the cutoff and the result is **Positive**

<figure><img src="https://3607108856-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LHVg57XIRcjV-Vbubtn%2Fuploads%2Fgit-blob-9d5b40817f830d12bf0d330b65b82b03069ed6c8%2Fwidget-section-elisa-qualitative-result.webp?alt=media" alt="Color-coded 96-well plate showing green wells for Negative results and red wells for Positive results"><figcaption><p>Wells are color-coded green (Negative / pass) or red (Positive / fail) based on comparison against the calculated cutoff value</p></figcaption></figure>

{% hint style="warning" %}
If NC or PC validation fails, review well assignments and raw data before proceeding. Classification results produced when controls fail may not be reliable.
{% endhint %}

## Best Practices

* **Run negative and positive controls on every plate** — Controls confirm that the assay is performing correctly and are required to generate a valid cutoff value
* **Run samples in replicate** — Test each sample in duplicate or triplicate to reduce the impact of pipetting variability; wells with the same label are treated as replicates
* **Define validation rules for controls** — Configure NC and PC Validation parameters to automatically flag plates where controls fall outside acceptance criteria, preventing unreliable results from being reported
* **Choose an appropriate cutoff formula** — The default NC mean cutoff works for many assays, but stricter formulas (e.g., NC mean + 2×SD) improve specificity in high-noise assay environments
* **Include blank wells** — Blanks enable background subtraction and help detect reagent or plate contamination

{% hint style="success" %}
For regulated environments (e.g., clinical diagnostics or GMP quality control), configure both NC Validation and PC Validation rules and document the cutoff formula in the widget settings to create a fully traceable, audit-ready record.
{% endhint %}

## Related Widgets

* [**ELISA Standard Curve**](https://docs.labii.com/widgets/section-widgets/biology/assay/elisa-standard-curve) — Companion widget for quantitative ELISA workflows. Use when you need to calculate sample concentrations from a standard curve rather than a binary classification
* [**Dose Response Curve**](https://docs.labii.com/widgets/section-widgets/biology/assay/dose-response-curve) — Performs dose-response analysis on 96-well plate data. Use when studying potency or inhibitory concentration (IC50) rather than qualitative presence/absence

## References

* [Overview of ELISA — ThermoFisher](https://www.thermofisher.com/us/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/overview-elisa.html)
* [Qualitative vs. Quantitative ELISA — Abcam](https://www.abcam.com/protocols/types-of-elisa)
* [21 CFR Part 11 — Electronic Records and Signatures](https://www.ecfr.gov/current/title-21/chapter-I/subchapter-A/part-11)


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