

We know that manuscripts evolve over many months or even years and that pulling together data from different experiments all supporting a key conclusion into one multi-panel figure can be a challenge. Not only have editors had to do this ourselves when we were working at the bench, but in our current role, we see a lot of figures!

Although we heard about some common figure manipulations from my colleague Debbie Sweet earlier this week, I thought I'd share, based on this experience, a few pointers to help avoid common mistakes editors see people make that all too often can lead to problems after publication, including challenges to the integrity of the work in online forums, publications of Errata, investigations into the original data, and sometimes—if the mistakes are sufficiently pervasive—even retraction.

1. Placeholder panels

One of the most common reasons an Erratum needs to be published for a paper is that an image was duplicated or an incorrect a blot or graph was included in the figure.

This often occurs because the authors want to "hold a place" in a figure for forthcoming data and then forget to substitute the place-holding data with the actual relevant data. Here's our recommendation: if advance figure preparation helps you organize and prioritize your remaining experiments, save panel slots with a blank square filled only with text describing what you are hoping to show there. That would not easily be missed in the final proofing of a paper before submission.

2. Loading controls

More questions get raised about duplication or misuse of loading controls in figures with western blot or RT-PCR data than almost any other issue. Just to make sure we are all on the same page, the point of a loading control is to be able to see how consistently samples were loaded onto and run/transferred through the same gel. It is against this control that changes in levels of the band of interest are calibrated, so it's important that the loading control be run through the gel from the same loading well as the experimental sample you want to probe.

That is to say, running two separate gels won't yield a good loading control even if using the same pool/lysate of nucleic acids/proteins and the same pipetman setting for loading. Therefore, it is rarely appropriate for the same loading control bands to appear in more than one figure panel, and, in an exceptional case, a clear explanation of why it is warranted should be included in the figure legends.

3. Splicing of gels and blots

It's OK to remove irrelevant or blank lanes from a gel in order to present your data in a streamlined way to readers and reviewers, but when you do it, you need to mark it clearly so that there is obvious transparency about how the figure was prepared.

You can indicate where an irrelevant lane was spliced out with a white space or a thin black line and state how you have altered the original scan in the figure legend.

In a different but related scenario, on occasion, it may be necessary to run one experiment on two different gels if all the samples you would like to analyze don't fit on one gel. In this case, a common sample should be loaded on both gels, and the fact that the experiment was performed across two gels must be explained in the legend and transparent in the figure panel—the two gels should not be spliced together to look like one.

4. Beautifying data

Most people know that it's definitely not OK to alter data in a figure to give a different impression than the originally collected data, and it's rare that we come across instances of this type of manipulation. However, we do see cases in which areas of the figure that don't include the actual data have been "doctored" to make them look nicer.

For example, a smudge on a blot or some background in a micrograph may get covered up in the figure. Or the brightness/contrast may be adjusted to render a fainter background band invisible. Even if the appearance of the key data haven't changed, this is nevertheless still inappropriate figure preparation.

Reviewers and readers need to know what you saw in the lab and how you interpreted it, and they need to have confidence that you are not making selective alterations to what they can and cannot see. Everyone that has done experiments knows that not every data point looks pitch perfect. Leave the warts, they're part of the experiment.