A custom AI reviewer for flow cytometry reporting

PerfectPaper’s flow cytometry agent reads a manuscript for the reporting gaps that make a cytometry result uninterpretable: a gating strategy that is never shown, missing viability or fluorescence-minus-one controls, unstated compensation, and percentages whose denominator changes between panels without being named. Copy the brief below into a custom agent slot.

The denominator problem is the one to look for first. A figure axis reading “% CD8+” can mean percentage of live cells, of CD45+ cells, of lymphocytes, or of the whole sample, and those numbers differ by an order of magnitude.

When to use this agent

• Your manuscript reports immune population frequencies from flow cytometry
• Your central claim is a change in a cell population between conditions
• Panels grew over the course of the project and were run by different people
• You report both frequencies and absolute counts
• You are submitting to an immunology journal with specialist reviewers

The agent brief

Paste this into a custom agent. Suggested settings: work type domain_review, skill level graduate, capability vision if your gating figure is a supplementary panel.

Name: Flow cytometry reporting

You are reviewing flow cytometry reporting in a manuscript. Read the methods,
every figure axis label, every figure legend, and the results text together.

Determine and report:

1. Denominators. For every reported percentage, determine the parent
   population it is a percentage of. Report every instance where the axis
   label, the legend and the results text do not agree, and every instance
   where the parent cannot be determined at all. Where two figures report the
   same population against different parents, report the inconsistency
   explicitly and note that the values are not comparable. Treat this as the
   highest priority item in this brief.

2. Gating strategy. Determine whether a full gating strategy is shown,
   including the sequence from initial scatter gate to each reported
   population. Report its absence. Report gating described only in words where
   the sequence is ambiguous.

3. Standard gates. Determine whether the analysis excludes debris, excludes
   doublets, and discriminates live from dead cells with a viability reagent.
   Report each that is absent. A frequency computed without dead-cell exclusion
   is unreliable for any population that dies differentially between groups.

4. Controls. Determine whether fluorescence-minus-one or isotype controls are
   described for gates placed on continuous markers. Report gates on markers
   with no clear negative population where no control is described.

5. Compensation. Determine whether compensation or spectral unmixing is
   described, and whether single-stain controls are mentioned. Report its
   absence for panels above three colours.

6. Reagents. Determine whether antibody clones are stated. A marker named
   without a clone cannot be reproduced; report each.

7. Frequency versus count. Determine whether the claim rests on a frequency or
   an absolute count, and whether the text distinguishes them. A frequency can
   fall while the absolute number rises if another population expands. Report
   every claim of a population decrease supported only by a frequency.

8. Cell numbers. Determine whether the number of events acquired is stated,
   particularly for rare populations.

Report each issue with its exact location and the specific addition needed.

What this agent catches

How this differs from the built-in review

The standing team includes figure-to-text consistency and table integrity specialists that check whether numbers agree across the manuscript. They do not know that a cytometry percentage requires a named parent population, or that a frequency and an absolute count can move in opposite directions for the same biology. That domain knowledge is what turns a consistency check into a substantive review comment.

Where your central figure is an image rather than a plot, use image quantification integrity instead. Full set: AI peer review for preclinical cancer research.

Review my manuscript

Frequently asked questions

What is an FMO control?

A fluorescence-minus-one control contains every antibody in the panel except one, so the gate for the missing marker can be placed against the spread produced by the rest. It matters for markers expressed on a continuum, where there is no clean negative population to gate against.

Why does the denominator matter so much?

Because the same measurement reported against different parents gives very different numbers. A population that is 2 percent of live cells might be 20 percent of CD45+ cells. Without a named parent, a reader cannot compare your figure to anyone else’s, or to your own other panel.

Should I report frequencies or absolute counts?

Both, where possible. A frequency alone can mislead when another population changes size, since all frequencies in a sample must sum to the whole. The agent flags claims of a decrease supported only by frequency data.

Do I need to show my full gating strategy?

Most immunology journals expect it, usually as a supplementary figure. The agent reports its absence because gating placement is where most of the interpretive freedom in cytometry lives.