Interactive book brings to life career pathways in science

A Fellow of St Catharine’s has co-authored a new interactive book that enables readers to trace the twists and turns of different career pathways in science. ‘The Professor, the Pipette & the Path Not Taken: Choose Your Science Career’  is a new spin on the branching narrative-style adventure genre written by Professor Adrian Liston (2023) and Professor James Dooley, with illustrations by Yulia Lapko. 

Intended for students at high schools or colleges making university choices and undergraduate science students starting on the path to a career in science, ‘The Professor, the Pipette & the Path Not Taken’ explores the setbacks, breakthroughs, friendships, ethical dilemmas, lucky breaks and spectacular mistakes that shape scientific lives. Readers can choose 277 different pathways through the book and arrive at one of ten different endings, all inspired by real careers in biomedical science today.

Professor Liston, who is Professor of Pathology at the University of Cambridge, explained:

“Science careers can lead to world-changing discoveries, but every scientist experiences bumps in the road before finding success. We want readers to experience the agency they have over their own lives, and by replaying their career multiple times learn that things don’t have to go perfectly from the start in order to have an impact in science. With each choice they make as they turn the pages, readers can discover the many different types of success that can emerge from starting a career in science, with dishonesty the only true barrier to progression.”

Professor Liston and Dr Dooley set up a research laboratory together in 2009. The Liston-Dooley lab is now based at Cambridge’s Department of Pathology and has grown to a team of 20 scientists. 

Professor Liston described the rationale behind the book, saying:

"Many students feel enormous pressure to get everything right from day one: the right exams, the right degree, the right internship, the perfect CV. But science does not really work like that. Careers are rarely linear and failure is not usually the end of the story. In our lab, we have trained more than 200 scientists, and we have seen people build brilliant careers through routes they never expected. We wanted this book to show students that there are many ways to build a life in science, and that the hard path can still lead somewhere meaningful. We thought a branching adventure book exploring these pathways would introduce young people to what it means to build a life in science." 

Professor Dooley added, “Those who know us well might spot the quirks of our own career choices among the scenarios that we’ve included. I was certainly one of those who took a more unconventional route into science, with career delays and detours that meant I had to take the hard path to success. We have also been inspired by some of the pathways taken by team members whom we’ve had the pleasure to teach, supervise or work with over the years. We hope the book is a fun way to show the next generation that science is not just about perfect choices, but about curiosity, resilience, and finding your own route forward.”

Beyond his research and teaching in Pathology at St Catharine’s, Professor Liston works extensively on communicating science to children, with the online game VirusFighter and the illustrated children’s books ‘All about Coronavirus’ (2020), ‘Battle Robots of the Blood’ (2020) and ‘Maya’s Marvellous Medicine’ (2021). He previously joined forces with Yulia Lapko on 'Becoming a Scientist: The Graphic Novel' (2024) which tells the story of the twelve scientists in his biomedical research laboratory to inspire readers between 12 and 18 years of age. Their latest collaboration is intended to be a companion project for the graphic novel. 

Yulia is Project & Communications Coordinator (Higher Education) for the Gatsby Plant Science Education Programme at the University of Cambridge’s Sainsbury Laboratory.

Read ‘The Professor, the Pipette & the Path Not Taken: Choose Your Science Career’. 

Congratulations Stevi Piliou!

Congratulations to Stevi Piliou for winning the 2026 Golden Pipette! Stevi won the Golden Pipette for her exceptional leadership in the lab over the past year, driving forward her project on multiple sclerosis therapeutics through both independent research and through leadership and mentoring of others. Well done Stevi!

Lab retreat 2026

Dr Arman Ghodsinia

Celebrating the successful PhD defence of Dr Arman Ghodsinia!

What is immunotherapy and how does it treat cancer and other conditions?

From infections and allergies to brain diseases and autoimmune disorders, a wave of trials offers hope

Ian Sample, the Guardian

Liston, the cofounder of Cambridge spin-out Aila Biotech, is developing a Treg therapy for multiple sclerosis, a disease caused by immune cells attacking the nervous system by mistake. The therapy aims to boost Tregs in the brain to call the attack off.

The potential for Tregs is vast. Therapies are in the pipeline for dementia and autoimmune diseases from type 1 diabetes and rheumatoid arthritis to lupus and chronic inflammation.... “Probably half of all deaths have a component that is immunological,” says Liston. “It is an underlying theme across ageing, autoimmune diseases, allergies, infectious diseases, inflammatory diseases like diabetes. But one of the great things about the immune system is that it is very easy to change. We can adapt it to our purposes.”

https://www.theguardian.com/science/2026/may/22/what-are-immunotherapies-and-how-do-they-treat-cancer-and-other-conditions

Failure in Science

Targeted gene delivery to the lung

A five minute primer on our new paper, out at Science Immunology!

Using local cytokine delivery to prevent lung pathology

We have an exciting new story out at Science Immunology! It uses AAV-mediated cytokine delivery to change the lung environment. We can boost lung Tregs or deliver anti-inflammatory cytokines, preventing fatal respiratory collapse.

This story started during the COVID pandemic. Our first Cambridge PhD students joined during lockdown, and a talented student, Ntombizodwa Makuyana (now Dr Ntombizodwa Gentry), wanted to work on a potential therapeutic.

James and I had only just moved to Cambridge, and we still had a team in Belgium. They were processing clinical samples from COVID patients, so we already had a good idea that the respiratory failure was driven by excessive inflammation.

At the time we had been working on a system to boost Tregs in the brain. Our AAV system was working great, so we thought "what if we tried to do the same thing in the lung?" It took quite some trial and error, but eventually we found that intranasal delivery of AAV6.2 with the CC10 promoter limited expression of our cargo limited to the lung.

The system works great! AAV6.2.CC10 driving IL2 production gives an expansion of lung Tregs without impacting other sites, even the draining LN of the lung. And it is not just IL2 - in the same way we can drive IL10 or IL1RA in the lung without altering systemic levels. Pick your own cargo!

We never ended up testing it in SARS-CoV2 infection - by that point the vaccine had come along. But COVID isn't the only important lung infection. You might not even have heard of one of the most deadly: Influenza-Associated Pulmonary Aspergillosis (IAPA).

Aspergillus is a fungus common in decaying soil, that can infect the lung. For healthy individuals it is rarely a problem, and is easily cleared. But Aspergillus is a hidden killer. The Joost Wauters, Greetje Vande Velde and Stephanie Humblet-Baron teams teams had found that coinfection of influenza and aspergillus is extremely deadly, with an ICU mortality rate of >50%, and lethal coinfection in mice.

So James, Oliver and Milla headed over to Belgium and teamed up with Laura Seldeslachts and Lauren Michiels to test our AAV-cytokine delivery approach in coinfected mice. Success! In every measure we tested, our treatment reduced severity from fatal respiratory failure down close to a regular flu infection.

The best part is, because we only altered lung immunology, the anti-viral responses from the LN were intact, so we could reduce lung inflammation without giving the infection a free-pass. This is why tissue immunology has such potential - only hit the site needed!

Thanks to the ERC, Wellcome Trust and FWO for funding. 

Read the full story here

AI solves enigmatic immune disease?

Unfortunately, in my write up of our new paper, I committed the cardinal sin of science communication:

I forgot to say we used machine learning!

Clearly I should have led with "AI solves enigmatic immune disease!"

This paper is actually a pretty good example of what AI actually does for understanding immune disorders. Maybe it gives an incremental advance over modern statistical methods. Random Forests (the best performing AI approach) identified the same immunological signatures as multivariable logistic regression (a conventional statistical analysis), so we didn't learn any new biology from AI.

It did improve diagnostic AUC by ~2.6% though. That's not nothing - incremental improvements are at the heart of clinical advances. I'll take a ~2.6% improvement where ever I can get it.

But will even that ~2.6% increase in diagnostic discrimination be reproducible and useful? Here I am not so sure. The main immunological signatures look very robust, but the "AI boost" doesn't come from identifying additional signature factors, it comes from interaction between these signals, and that interaction is the part that is most prone to over-fitting. On a repeat study, the overall diagnostic capacity is likely to drop (regression to the mean), and I suspect that the "AI boost" of ~2.6% may drop to 0% or even be negative. If we increase the complexity of the AI model, we do find that it performs worse than the classical statistical model, which points in that direction.

I also doubt that any clinical diagnostic test would use the "AI boost", even if it was reproducible and robust. Actual clinical diagnostics are usually highly simplified, focused on fewer parameters than the research-grade complexity we used here. So if this advance gets into routine clinical diagnostics (which I hope!), I suspect it would focus on just the very strongest individual immune signals, which were identified by both AI and classical statistics.

So overall, AI was a marginally-useful tool in this study, which helped at the edges. But it wasn't transformative. That isn't just a feature of our study - and it isn't simply a matter of better machine-learning algorithms being built. It is a fundamental limitation of AI - medical data is rarely of the right data structure to give the types of advantages you see in other areas (like weather prediction or molecular structure prediction). AI is another useful tool, and every new tool helps. But it will hardly "transform medicine", and only replaces experiments in niche use cases.

Let's use AI where it helps, without contributing to the excessive hype.

AI solves enigmatic immune disease?

Unfortunately, in my write up of our new paper, I committed the cardinal sin of science communication:

I forgot to say we used machine learning!

Clearly I should have led with "AI solves enigmatic immune disease!"

This paper is actually a pretty good example of what AI actually does for understanding immune disorders. Maybe it gives an incremental advance over modern statistical methods. Random Forests (the best performing AI approach) identified the same immunological signatures as multivariable logistic regression (a conventional statistical analysis), so we didn't learn any new biology from AI.

It did improve diagnostic AUC by ~2.6% though. That's not nothing - incremental improvements are at the heart of clinical advances. I'll take a ~2.6% improvement where ever I can get it.

But will even that ~2.6% increase in diagnostic discrimination be reproducible and useful? Here I am not so sure. The main immunological signatures look very robust, but the "AI boost" doesn't come from identifying additional signature factors, it comes from interaction between these signals, and that interaction is the part that is most prone to over-fitting. On a repeat study, the overall diagnostic capacity is likely to drop (regression to the mean), and I suspect that the "AI boost" of ~2.6% may drop to 0% or even be negative. If we increase the complexity of the AI model, we do find that it performs worse than the classical statistical model, which points in that direction.

I also doubt that any clinical diagnostic test would use the "AI boost", even if it was reproducible and robust. Actual clinical diagnostics are usually highly simplified, focused on fewer parameters than the research-grade complexity we used here. So if this advance gets into routine clinical diagnostics (which I hope!), I suspect it would focus on just the very strongest individual immune signals, which were identified by both AI and classical statistics.

So overall, AI was a marginally-useful tool in this study, which helped at the edges. But it wasn't transformative. That isn't just a feature of our study - and it isn't simply a matter of better machine-learning algorithms being built. It is a fundamental limitation of AI - medical data is rarely of the right data structure to give the types of advantages you see in other areas (like weather prediction or molecular structure prediction). AI is another useful tool, and every new tool helps. But it will hardly "transform medicine", and only replaces experiments in niche use cases.

Let's use AI where it helps, without contributing to the excessive hype.