Sandwich ELISA with diluted serum samples
Estimate cytokine concentrations in duplicate serum samples.
8 standards · 10 samples · duplicates
Paste standard and sample ODs to fit the curve and get sample concentrations.
| Name | Mean OD | OD %CV | Interpolated (pg/mL) | Dilution | Concentration (pg/mL) | Flag |
|---|---|---|---|---|---|---|
| S1 | 0.192 | 2.3 | — | 2 | < 31.3 pg/mL (below range) | Below standard range |
| S2 | 0.353 | 5.2 | 23.4 | 2 | 46.8 | |
| S3 | 0.538 | 1.4 | 39.0 | 2 | 78.0 | |
| S4 | 0.924 | 24.6 | 80.2 | 2 | 160.4 | CV > 20% |
| S5 | 1.210 | 3.8 | 124.8 | 2 | 249.6 | |
| S6 | 1.473 | 6.1 | 187.1 | 2 | 374.2 | |
| S7 | 1.821 | 2.9 | 348.6 | 2 | 697.3 | |
| S8 | 2.035 | 4.4 | 595.7 | 2 | 1191.4 | |
| S9 | 2.131 | 1.8 | 840.8 | 2 | 1681.6 | |
| S10 | 2.194 | 7.2 | — | 2 | > 2000.0 pg/mL (above range) | Above standard range |
| Nominal (pg/mL) | Mean OD | OD %CV | Back-calculated (pg/mL) | Recovery % |
|---|---|---|---|---|
| 0.0 | 0.060 | 2.9 | 0.02 | — |
| 15.6 | 0.253 | 6.2 | 15.63 | 100.0 |
| 31.3 | 0.448 | 4.1 | 31.22 | 99.9 |
| 62.5 | 0.776 | 1.7 | 62.54 | 100.1 |
| 125.0 | 1.211 | 5.3 | 124.91 | 99.9 |
| 250.0 | 1.648 | 2.2 | 250.41 | 100.2 |
| 500.0 | 1.973 | 3.4 | 498.42 | 99.7 |
| 1000.0 | 2.170 | 1.3 | 1002.70 | 100.3 |
| Parameter | Estimate | SE |
|---|---|---|
| Equation | y = d + (a − d) / (1 + (x/c)ᵇ) | |
| a | −8.998×10⁻⁵ | 0.02038 |
| b | 1.149 | 0.04883 |
| c | 125.1 | 5.756 |
| d | 2.303 | 0.04314 |
| R² | 0.9987 | — |
| Sy.x | 0.03095 | — |
| n | 16 | — |
| df | 12 | — |
Explore common assays and see how the model and results change.
Estimate cytokine concentrations in duplicate serum samples.
8 standards · 10 samples · duplicates
Fit a real DNase assay and compare measurements from a second run.
Source: R datasets (DNase; Davidian & Giltinan 1995)
Analyze a descending response curve with diluted samples.
6 standards + zero · 8 samples · duplicates
Fit a quadratic curve to BSA standards and diluted lysates.
Source: Pierce BCA kit; example ODs
Use a linear fit for microassay protein measurements.
Source: Bio-Rad; example ODs
Analyze a complete plate after wavelength correction.
96 wells · 7 standards + zero · 40 samples
Fit an ELISA standard curve, check replicate precision, and export dilution-corrected concentrations.
Paste concentrations and replicate absorbances or enter a plate grid.
Add sample ODs and a dilution factor for each sample.
Select the model and weighting recommended for your assay.
Review the QC flags, then download the graph and results.
Use the assay instructions and the shape of your standards to select a fit.
A 4PL standard curve describes a symmetric sigmoidal response. A 5PL curve adds an asymmetry parameter g. Findlay & Dillard (2007)
x: concentration; y: response; a/d: plateaus; b: slope; c: midpoint.
x: concentration; y: response; a/d: plateaus; b: slope; c: location; g: asymmetry.
y: measured response; x: concentration; a, b, c, d and g: fitted parameters (g = 1 for 4PL).
A linear calibration curve is useful over a limited response range; a quadratic fit can capture curvature. Check for a turning point before interpreting inverse values.
BCA and Bradford assays can use protein standards to estimate concentrations. Select the fit and range recommended by the kit.
Review recovery, replicate CV and the standard range before interpreting sample concentrations.
| QC check | What to review |
|---|---|
| Recovery | Compare back-calculated and nominal concentrations. |
| Replicate CV | Inspect large variability between replicate wells. |
| Standard range | Retest samples outside the calibration range when appropriate. |
Average replicate ODs, subtract the zero standard if required, fit the standards, and interpolate the sample OD. Multiply the interpolated concentration by the sample dilution factor.
Follow the kit instructions, which often recommend 4PL. A 5PL curve can describe asymmetry; a linear fit is generally suitable only over a limited range.
Multiply the interpolated concentration by the dilution factor. The dilution factor is the final volume divided by the original sample volume.
Subtract the mean zero-standard OD when the assay instructions call for it. A constant shift does not change the inverse concentrations of an unconstrained 4PL or linear fit, but it affects a log–log fit.
Retest high samples at a suitable dilution and report low samples according to the assay instructions. This calculator does not calculate a limit of detection.
A CV of 20% or less is a common criterion, although individual assays may set a tighter limit. CV is the standard deviation divided by the mean, multiplied by 100.
No. Also inspect standard recovery, replicate precision and residual patterns across the concentration range.
Excel can fit a 4PL model with Solver. Fits in GraphPad Prism should agree when the equation, data and weighting match.
Weighting can help when response variability changes across the range. Use the weighting specified by the assay validation; the default here is unweighted.
Turn your absorbance readings into a standard curve and sample concentrations.
Calculate your ELISA results