Analyzing ELISA data with Copilot in Excel

Scope note: This walkthrough is for practice and research data only. Do not use it to produce reportable, clinical, or otherwise regulated assay results. Any thresholds shown here, including the %CV cutoff, are illustrative — always apply the acceptance limits in your own validated SOP and kit protocol, under qualified review.

A quick primer before the walkthrough:

In plain English: Use Microsoft 365 Copilot inside Excel to fit ELISA standard curves and calculate sample concentrations — without writing formulas from scratch.

Key terms:

• ELISA — a plate-based assay that measures how much of a specific protein is in your sample.
• 4-parameter fit (4PL) — a standard curve regression model commonly recommended in ELISA kit protocols.
• Copilot — Microsoft's AI assistant built into Excel/Word/Outlook.

What you'll need: Microsoft 365 with a Copilot license, and an ELISA plate export (CSV or XLSX). Note: Always check your organization's current agreement and tenant settings with the vendor regarding data processing, as vendor terms change over time.

Comfort level: Basic Excel (opening files, selecting cells). No macros or Python required.

The problem with pasting data into ChatGPT

You have a 96-well ELISA plate: standards in duplicate, samples in triplicate, and a background well. You want a 4-parameter logistic (4PL) fit, back-calculated sample concentrations with CVs, and a clear overview of which samples fall outside the standard curve calibration range.

Pasting raw plate layouts directly into web interfaces like ChatGPT can lead to formatting issues. You risk losing matrix layout formatting, row/column alignment, or the underlying formula chain needed to review calculations. Copilot in Excel operates directly within your workbook, generating native formulas that can be reviewed alongside your raw data. If your dataset contains proprietary or patient-related sample identifiers, ensure handling aligns with your organization's internal data governance and PHI policies.

What you will need

• Excel with a Microsoft 365 license that includes Copilot (check your organization's current enterprise agreement regarding vendor data terms).
• Standard curve concentrations specified by your assay kit protocol (typically 7–8 points plus blank).
• Raw optical density (OD) values with the plate layout preserved.
• A blank sheet named Analysis.

Step 1 — Structure the raw data

Put the plate in a rectangular block. Row 1 has plate column numbers (1–12), column A has plate row letters (A–H). Below the plate, add three named blocks:

• Layout — a matching 8×12 grid with sample IDs, standard concentrations, or "BLANK".
• Standards — two columns: concentration (pg/mL), and mean OD (leave OD blank; Copilot will fill it).
• Samples — one column of sample IDs, second column blank for back-calculated concentration.

Structure is critical when working with Copilot. It relies on your designated labels rather than guessing plate organization.

Step 2 — Ask Copilot to compute the standards

Open Copilot in the sheet and type:

For each concentration in the Standards table, look up the wells in Layout that match, average their OD values from the plate, and put the mean and %CV into columns C and D of Standards. Show the formula you used in cell F1.

Copilot will typically return an AVERAGEIFS or AVERAGE(FILTER(...)) formula. Manually verify at least one standard calculation against your laboratory SOP before applying formulas across the sheet.

Step 3 — Fit a 4PL curve

Excel does not have a native 4PL function, so you have two options depending on your assay requirements and laboratory SOPs.

Option A (recommended): use Copilot to generate a Solver setup.

Fit a 4-parameter logistic curve to the Standards table (concentration in col A, mean OD in col C). Put the parameters (Top, Bottom, LogEC50, Hillslope) in cells H2:H5. Add a "Predicted OD" column in Standards and set up Solver to minimize the sum of squared residuals between predicted and observed OD.

Copilot will lay out initial parameter estimates, the predicted-OD formula, and residual calculation steps, then describe how to configure Excel Solver (Data → Solver → Set objective, By changing H2:H5, Minimize, GRG Nonlinear). Run Solver once.

Option B: linearize with log/logit and use LINEST. Faster, but typically less accurate at curve extremes for non-linear responses. Evaluate whether linear approximations satisfy your assay's validated methodology before using this approach.

Step 4 — Back-calculate samples

Add a column to Samples:

For each sample ID in Samples, average the triplicate OD values from the plate (using Layout to locate the wells) and back-calculate concentration by inverting the 4PL with parameters in H2:H5. Also compute %CV across the triplicate wells.

The inverted 4PL formula is:

conc = EXP( LogEC50 + (1/Hillslope) * LN( (Top - Bottom)/(OD - Bottom) - 1 ) )  // LogEC50 is the natural log of EC50, matching LN()

Ask Copilot to place that formula in the Samples table. Verify the sign of the Hillslope term, as slope direction depends on whether OD increases or decreases with concentration in your specific assay type (e.g., sandwich vs. competitive ELISA).

Step 5 — Flag out-of-range samples

Add a "Status" column to Samples. If the sample mean OD falls outside the dynamic range of the standard curve (e.g., above the highest standard OD or below the lowest standard OD), mark it "OUT OF RANGE". If %CV exceeds our target threshold (e.g., > 20%), mark it "REPEAT". Otherwise mark it "OK".

Copilot generates structured nested logical statements cleanly. Always adapt the criteria and resulting actions (such as dilution, re-assay, or concentration) to match your assay's validated protocol, kit instructions, and laboratory SOPs.

Step 6 — Sanity checks before trusting the calculations

Note: The following quality control checks are illustrative examples. All acceptance criteria and data management actions must be governed by your laboratory's validated assay protocol and standard operating procedures (SOPs).

• Goodness of fit: Validated assay protocols typically set defined acceptance thresholds for curve fit (e.g., target R² thresholds such as ≥ 0.99 or specific residual limits). If fit criteria are not met, consult your SOP for curve refitting procedures. Selective removal or "dropping" of standard points must strictly follow your lab's formal, validated data-exclusion protocol.
• Background OD: Verify that blank well OD values comply with baseline limits specified in your assay kit insert (e.g., within expected standard deviation ranges relative to lower standards).
• Asymptote boundaries: Ensure back-calculated sample values do not sit inside or beyond the theoretical upper or lower asymptote parameters (Top/Bottom) defined by your curve fit model.
• Controls and matrix effects: Check that vehicle or matrix controls yield expected baseline values per your experimental design. Unexpected signals may indicate matrix interference requiring protocol adjustments.

What Copilot cannot do

• Judge overall assay validity. Final evaluation of assay execution remains the responsibility of the researcher and must align with institutional quality standards.
• Select sample re-testing protocols. Copilot can flag values outside the standard range, but appropriate dilution factors or handling steps must be determined by qualified staff based on validated SOPs.
• Correct for spatial plate artifacts. Edge effects or systematic thermal gradients require specialized spatial normalization methods outside Copilot's default formula generation.

The workbook you are left with

At the end of this workflow, you have an Excel workbook containing raw plate data, layout mapping, standard curve parameters, and back-calculated sample results with visible, auditable formulas. This transparent structure supports peer review and aligns with routine data review processes under your laboratory's established procedures.

If something goes wrong

• The curve fit looks off. Check that standards are sorted, blanks subtracted, and that you asked for the fit your assay actually uses (4PL for most ELISAs, not a straight line).
• Copilot cannot see the data. Your range must be a proper Excel Table and the file must be saved to OneDrive or SharePoint.
• Concentrations come back negative or wildly high. Values like these often fall outside the range of the standard curve. Flag them for review and consider whether a diluted re-run is appropriate, following your assay's validated SOP for out-of-range results.
• Replicates disagree. Looking at the CV per sample is a reasonable first check. A high CV can point to pipetting, plate handling, reagent, or sample-related causes as well as analysis issues — investigate according to your lab's troubleshooting procedure rather than assuming one cause.
• The numbers do not match your plate reader software. Recomputing one sample by hand can help you locate where the two calculations diverge. Validated instrument software and your quality procedures remain the authority for reportable results; raise persistent discrepancies through your normal deviation process.