ELISA Standard Curve

Perform standard curve analysis to ELISA data

Overview

ELISA (enzyme-linked immunosorbent assay) is a plate-based assay technique designed for detecting and quantifying substances such as peptides, proteins, antibodies and hormones. In an ELISA, an antigen must be immobilized on a solid surface and then complexed with an antibody that is linked to an enzyme. Detection is accomplished by assessing the conjugated enzyme activity via incubation with a substrate to produce a measurable product. The most crucial element of the detection strategy is a highly specific antibody-antigen interaction.

After an ELISA has been run, the data must be analyzed. Currently, most of the scientists are using multiple tools and take several hours for such analysis. Specifically, Excel is used to prepare the data, Graphpad is used to fit the curve and do the prediction.

Labii's ELISA Data Analysis widget, for the first time, enable you to design ELISA layout, document ELISA results, analyze standard curve and predict the final concentration all in your ELN. No other 3rd party software is required, and you can do so in just a few clicks.

To use this widget is as simple as other widgets. Once the widget is added, you can follow the guideline to update the layout, and perform the analysis.

Step 1: Prepare layout

Currently, the widget only supports 96-wells plate. Please report back to us if another size plate is used in your experiments.

You can prepare the layout in 3 ways:

  1. Type in the layout into the layout wells

  2. Copy the layout from your excel/word and paste them into the layout wells

  3. Drag and drop and select a table format files you already have, the widget will import the data from your file.

  4. Click the edit icon to update the dilute factors, default to 1. Use this field to document how the samples been diluted.

When preparing the layout, here are a few rules to follow:

  1. Use the first cell as the concentration unit. For example ug/ml.

  2. Use CONC to mark a serious of standard concentration in either first column or first row.

  3. Use STD to mark a well as standard.

  4. Use BLANK to mark a well as blank.

  5. The same name is treated as duplicates.

  6. Leave a well blank to exclude the data point in the analysis.

Good practice:

  1. Run samples in replicate. To help evaluate the extent of error, each standard and sample should be tested in replicate (duplicate or triplicate, depending on the number of samples and room on the plate). Afterward, the average, standard deviation (SD), and coefficient of variation (CV) can be calculated to provide confidence in pipetting precision. As a best practice, the replicates should have a CV of less than 20%. If the CV is higher than 20%, consider the following (Reasons the CV may be high):

    1. Pipetting errors—Check pipetting technique. See the ELISA Troubleshooting Guide for proper pipetting techniques.

    2. Contamination of plates or reagents—Make sure plates and reagents are stored properly and don’t get cross-contaminated. Also, check reagents for expiration dates.

    3. Temperature across plate—Incubate plates in a stable environment to ensure even temperature.

    4. Evaporation—Use plate covers during all incubation steps.

  2. Run a standard curve on every plate. Every ELISA runs slightly differently depending on the operator, pipetting, incubations, and temperature. Taking these variables into account, it is a best practice to run a standard curve on each plate.

  3. Run a positive control sample. Running a control sample with a known concentration on each plate will indicate whether the ELISA was successfully executed. If the control sample represents the correct concentration, you can be confident in the results of the other unknown samples.

  4. Run blank samples. Blank samples are composed of the buffer or water with no protein sample included. These samples allow the subtraction of background absorbance from the rest of the data points to ensure the most accurate OD readings.

  5. Dilute samples so they fall within the linear range of the standard curve. To get the most accurate results, dilute the samples so they fall within the linear range of the standard curve. Values that fall toward the top or bottom of the curve tend to have a higher amount of error because of the assay’s limits. Many operators test samples at multiple dilutions to ensure that at least one of them falls within the linear range.

Step 2: Prepare data

The process to prepare the value is very similar to prepare the layout. Simply copy the absorbance data into the 96 wells and it is ready to go.

A few tips:

  • Leave a well blank to exclude the data point in the analysis.

Step 3: Perform the analysis

Once the data is ready, to perform the ELISA data analysis is as simple as a click. Click the Analysis button and the analysis will start right way. All results will be included in the analysis section once finished. The analysis only takes a few seconds.

Here are the details about the analysis:

1. Generate the average absorbance of blanks

Labii will use the layout you created at step 1 to find all blank wells. The average value will be calculated and displayed back to you.

2. Subtract background absorbance

The normalized value for all data points is calculated via subtracting the background absorbance (the average value of the blanks). If the blank samples are reading higher than usual, this may indicate that there was an error in the assay.

3. Generate the average absorbance of the standard

If multiple standards are used, the absorbance of the standard at the same concentration will be calculated. Labii also calculates the standard errors for the duplicates. This value will be used in plotting and fitting.

4. Fit the standard absorbance

On default, Labii uses linear regression to fit the standard absorbance with the standard concentrations. Labii also provide you the option to use other regression methods. You can click the edit icon to change the repression method:

  • linear - Fits the input data to a straight line with the equation y = mx + c.

  • exponential - Fits the input data to an exponential curve with the equation y = ae^bx.

  • logarithmic - Fits the input data to a logarithmic curve with the equation y = a + b ln x.

  • power - Fits the input data to a power law curve with the equation y = ax^b.

  • polynomial - Fits the input data to a polynomial curve with the equation anx^n ... + a1x + a0.

Once it is finished, the fit equation will be generated and displayed for references. The calculated standard point will be used for plotting.

5. Plot standard curve

A standard curve for the target protein by plotting the mean absorbance (y axis) against the protein concentration (x axis). A best fit curve through the points in the graph will also be added based on the calculated value from fitting.

6. Calculate SD, CV, and concentration

Concentration

The mean value of a sample/test/project will first generate. This mean value will then used in the fitting model to calculate the corresponding concentration. The final concentration will be multiple the dilution factor.

Standard Deviation (SD)

The standard deviation is a measure that is used to quantify the amount of variation or dispersion of a set of data values. The SD will be calculated for each sample.

Coefficient Variation (CV)

The coefficient variation (CV) is the ratio of the standard deviation σ to the mean µ:

Cv= σ / µ

This is expressed as a percentage of variance to the mean and indicates any inconsistencies and inaccuracies in the results. Larger variance indicates greater inconsistency and error. Labii calculates the CV values for each sample.

Once done, the sample, average value, SD, CV, and concentration will be displayed in a table. You can click the table header to sort the table.

Advanced

Some advanced parameters can be adjusted by clicking the "edit" icon.

  • Optical Density (OD) - You can change the wave length, default to 450nM

  • Dilute Factor - How many times do you dilute the sample. The widget will multiple the factor back to your final concentration.

  • Data Transform - How do you want to transform the data.

    • x

    • log(x)

    • 10^x

  • Fit Method - The regression method to use.

Summary

With Labii's ELISA Data Analysis widget, you can document and analyze the data in a few clicks, and the result is ready in a few seconds. Labii's ELISA Data Analysis widget is flexible and can meet all of your ELISA layout design. It also provides you multiple regression methods to meet your analysis for log files. Labii ELN & LIMS is the only ELN in the market that is able to provide this kind of applications. Please reach to us if you have any questions.