We’re back to more scandals in senescence research. The problem is not just limited to the dozens of studies using the wrong antibody, but to the subsequent use of AI that incorporates these wrong results to make false conclusions and point in wrong directions for future research. AI is the great multiplier of errors which we have no way of catching ahead of time while the errors become deeply imbedded and woven into the science of the whole field, fatally polluting everything it comes into contact with. It seems to me critical to validate whatever it is that AI synthesizes, but that’s extremely time and labor intensive, so I personally use AI very sparingly and only under strictly defined parameters. There are settings where AI is enormously useful (like image based diagnostics), but great care is needed. I never ask AI to summarize papers for me, or reach conclusions about complex processes. I’m always amazed at how trustingly people ask AI various medical questions where the chances of error strike me as exceptionally high. Obviously, the ease of simply being able to ask a question and have it answered by AI is super convenient, but that convenience comes with a price.
For the fans of senolytics these new revelations cannot be comforting news, but we can be quite certain that this is not the last of the scandals. Of course greater rigor in validating reagents being ordered by scientists in any medical setting should be a priority, but that’s time consuming and expensive, so here we are.
Sleuth identifies dozens of studies that used the wrong antibody
The case is the latest example of a scientific workhorse being misused in experiments.
https://www.nature.com/articles/d41586-026-02352-4
“In the latest case, David says that researchers seem to have chosen the wrong antibodies — instead of using ones that target mammalian proteins that are thought to reveal information about ageing, their papers listed antibodies that bind to bacterial proteins. This highlights a larger problem in biology, in which reagents in experiments are used poorly or not adequately characterized, says Aled Edwards, a biochemist at the Structural Genome Consortium in Toronto, Canada. In an ideal world, he says, researchers would validate the antibodies, to check they work as planned, before starting an experiment. But this can be time-consuming and expensive, he adds.
He thinks the issue will get worse as more researchers use artificial-intelligence models to review the literature and make predictions about which proteins could be good targets for disease treatments. If the wrong antibodies are reported in papers, it could throw such predictions off, he adds.”
“Cell-ageing studies
Researchers studying ageing use an antibody to identify when cells stop dividing but remain alive — a state called senescence. Scientists want to understand this process because non-dividing cells accumulate in tissues, causing inflammation and secreting proteins that can harm nearby cells.
A hallmark of senescent cells is the activity of an enzyme called β-galactosidase. By using a mammalian antibody that binds to β-galactosidase, scientists can detect the protein using imaging techniques such as immunostaining or western blotting and, in doing so, theoretically identify senescence. (There is some dispute about whether this technique can accurately flag cells in senescence, but some researchers still use it.)
However, David says that he has identified at least 54 papers in which the authors state they used an antibody that targets β-galactosidase from Escherichia coli bacteria. Using this antibody to try to identify β-galactosidase expression in mammalian cells is not going to work, says David. “This is a big blunder,” he writes on the blog.”