Golden Pipette won by Dr Wenson Karunakaran

Congratulations to Dr Wenson Karunakaran! 

It was tough competition for the sixth Golden Pipette at the Cambridge-Leuven joint lab retreat. The final prize had to go to Dr Karunakaran for his work on brain CD4 T cells.

Many neuroscientists assume there are no CD4 T cells inside the healthy brain, but there are in fact around 5000 per gram of brain tissue. How do we know? Wenson imaged and counted them, one by one. 

That is what it takes to win the Golden Pipette.

Immune profiling ‘will be a revolution in medicine’

A revolution in medicine is coming.

It could aid the diagnosis of diseases, guide the way patients are treated and inform the discovery of new therapies.

Immune profiling seeks to explain how our body’s own defences are affected by and are responding to disease.

At the Babraham Institute, Professor Adrian Liston is working on the translation of this technique from the laboratory to the clinic.

“The immune profile is much more powerful than genomic data, but it’s much easier to get genomic data,” he tells the Cambridge Independent. “You can take blood, send it overnight and get it sequenced off-site. We are not at that stage with immune system data.

“But the more we know about different diseases, the more we realise there are inflammatory, or immune-mediated, components.

“It can be a revolution in medicine. Once the infrastructure is set up and hospitals are doing the analysis routinely, we will see an explosion in utility. Right now, it’s a research tool only.”

Read the full article at the Cambridge Independent

Dokter Algoritme

Algoritmen kunnen inzichten bereiken waar een mens moeilijk toe komt. Computeralgoritmen kunnen almaar beter moeilijke diagnosen stellen, soms zelfs beter dan artsen. Immunologe Erika Van Nieuwenhove van de Leuvense tak aan het Vlaams Instituut voor Biotechnologie (VIB) en haar collega’s melden in Annals of the Rheumatic Diseases dat ze een zelflerend algoritme hebben ontwikkeld dat met bijna 90 procent zekerheid artritis bij kinderen kan vaststellen, louter op basis van een bloedtest.

Het gaat om de vaakst voorkomende vorm van reuma bij kinderen, maar omdat de ernst en de evolutie van de symptomen sterk kunnen variëren, is een diagnose stellen niet altijd gemakkelijk. Het algoritme evalueert alleen de samenstelling van het immuunsysteem van de patiënten. Het zal nuttig zijn om te bepalen welke behandeling aangewezen is.

Knack - 24 Apr. 2019 - Page 86

Is the Scandinavian model the solution for STEM parents?

The Scandinavian model for parental leave is often touted as the world’s best. 12 months parental leave, split between both parents. This is a great model for a lot of careers, but is it actually a part of the solution to the issues that women face in the STEM fields?  In some cases, perhaps, but as a blanket solution I find it lacking:

1)     First, it must be stated that extended parental leave is only part of the Scandinavian model. When it was first legislated, the leave was typically taken almost exclusively by the mother, with the father just taking the mandated “Daddy’s two weeks”. Essentially, it reinforced the traditional model that put parenting on to the woman, often truncating women’s careers. This has changed substantially over the years, but those changes are due to the evolution of Scandinavian culture and the increasing normality of equality in the Scandinavian countries. Implementing just extended parental leave will not recreate the full advances seen in women’s careers across Scandinavia in the past decade

2)     Extended parental leave is ideal for workplaces where workers can be readily replaced during this period. Large employers are capable of hiring extra staff which can shift between different positions, and employees that only need days or weeks of training to get up to speed are easier to replace. Academic science generally fulfils neither of these categories. First, while universities are large, labs are essentially independent small businesses. Few if any labs are large enough to have standing rotating staff that substitute in for parental leave. Second, for scientific staff, their skills require months or years of training. As an employer, I generally write off the first 3 months of a post-doc’s time as just getting up to scratch of new techniques and the project. For a Masters student starting a PhD, often the entire first year is spent mastering the field without actual productive experiments being performed. That level of expertise is just not readily replaceable, which means the science suffers. This will then leave a negative mark on the applicants' CV beyond the one-year gap.

3)     Scientific funding and scientific projects rarely have the flexibility to make this work. Consider a PI who hires a post-doc to work on a 3 year project. One year into the project, the post-doc goes on maternity leave for a year. The PI cannot put the project on hold for a year – since the funding clock is still running. Instead they need to transfer the project to a new person. Is it fair or reasonable for that project to be transferred again when the parental leave post-doc comes back? Potentially, but it is something that needs to be solved on a case-by-case basis. Even if the funding could be put on hold for parental leave (as in some fellowships), scientific careers are built on advancing science. If the work is scooped in the meantime, original work becomes confirmatory work – which would be a negative for the lab, the PI and the post-doc.

Consider two hypothetical cases, and whether extended parental leave helps or hinders a woman’s career in science.

Scenario 1. A young female PI goes on extended parental leave. What happens to her lab? You can’t shut the lab down for a year. PIs have responsibilities to their students and post-docs, they have responsibilities to their grant funders. So either those students get shuffled to another PI, or they have to work independently (and sub-optimally), or the PI on leave actually spends a chunk of unrecognised time managing the lab remotely. After the return, authorship on papers can often become murky and grants have been spent inefficiently.

Scenario 2. A young post-doc employed on a grant goes on extended parental leave. The PI hires a replacement post-doc to continue the project (the grant and science must go on). A year later the post-doc returns and (best case scenario) the PI manages to find enough funding to keep both staff on for a year to finish up. The paper may end up with a joint-first authorship, or maybe the new post-doc was able to push things fast enough that the original post-doc becomes second author. Afterall, the year back after parental leave is hardly your most productive. There is no easy fix – the PI needs to consider the contribution of both staff members in making a decision.

In short, I think that the Scandinavian model is excellent, and an incredible advance for some careers. However the particular aspect of extended parental leave is not suitable for all people (not everyone wants it), and it can have a negative effect on STEM careers. I would suggest a more flexible approach to STEM researchers who have children. This approach would allow researchers to make the choice to take extended leave, or make the choice to stay active in their field:

• Extended parental leave should be an option available to all
• Implement broad structural changes that promote equality in STEM, most importantly hiring women at senior levels and normalising a healthy life balance
• Infant daycare and before/after-hours childcare should be cheap and readily available for parents who chose that pathway.
• A “parental sabbatical” should be available to PIs who have children, where the PI becomes excused from all teaching and committee duties for a year, but is still able to work on research
• Grants should be automatically extended by a year if the PI has a child or if the staff paid on those grants go on leave
• Ethics protocols, biosafety protocols, etc should be automatically extended by a year if the PI has a child or the staff working under those protocols go on leave
• Review board decisions should be delayed by two years if a PI has a child
• Please comment if you have additional suggestions

If you are interested in how my family handled having a child and a career in STEM, you can read an interview I had with eLife on being a scientist parent. 

Using machine learning to diagnose disease

Profiling the immune system in paediatric arthritis patients offers hope for improved diagnosis and treatment

A team of scientists from VIB and KU Leuven has developed a machine learning algorithm that identifies children with juvenile arthritis with almost 90% accuracy from a simple blood test. The new findings, published this week in Annals of the Rheumatic Diseases, pave the way for the use of machine learning to improve diagnosis and to predict which juvenile arthritis patients may respond best to different treatment options. The work was led by Professor Adrian Liston, a group leader at the Babraham Institute in Cambridge, UK and at VIB and KU Leuven in Leuven, Belgium.

Juvenile idiopathic arthritis is the most common rheumatic disease in children, but it presents in many different severities and forms. This diversity makes clinical assessment and patient classification difficult.

A team of researchers at Belgian research organisations VIB, KU Leuven and UZ Leuven undertook a detailed biological characterisation of the immune system of hundreds of children with and without juvenile arthritis to help the diagnosis or treatment decisions for this disease.

“Essentially, we took blood samples from more than 100 children, two thirds of whom had childhood arthritis,” explains Erika Van Nieuwenhove (VIB-KU Leuven), and first author of the study. “We analysed their immune system at a greater level of detail than was ever done before for this disease, and simply using this data we then used machine learning to see if we could tell which children had arthritis.”

The results were quite remarkable: the algorithm was about 90% accurate at identifying the children with the disease. “Using only information on the immune system, and no clinical data at all, we could design a machine learning algorithm that was about 90% accurate at spotting which kids had arthritis,” says Professor Adrian Liston (Babraham Institute, Cambridge, UK and VIB-KU Leuven). “This result is a proof-of-principle demonstration that immune phenotyping combined with machine learning holds huge potential to diagnose disease. Similar approaches could be applied to improve patient selection for treatments and clinical trials.”

The researchers are hopeful about the impact of this research in improving patient outcomes. “The tool needs further validation but otherwise there are no scientific barriers to this approach being quickly translated to the clinic,” comments Professor Carine Wouters (UZ Leuven), who was the clinical lead for this study. “Down the line, we could use this kind of detailed classification information—and machine learning analysis—to identify which patients will respond best to specific treatment options.”

Interview with "The Optimist" magazine

Read the article at The Optimist

Thinking back to your PhD, how would you describe this experience.

I quite enjoyed my PhD. The key success in a PhD is to find a match between supervisor and student. I only spoke to my supervisor every 3-4 months, and it was always about concepts and strategy rather than trouble-shooting. For me, I loved the independence that this gave me, and the amazing post-docs in the lab gave me more than enough technical advice. However, some of the other students around me did not like this approach to mentoring, and would have preferred weekly meetings going into the detail of their experiments. This was pure luck on my behalf - I could easily have ended up in a lab that I found stifling, because I didn't ask the right questions going in. This independence let me mould my PhD to my strengths. I learned just a few basic techniques and then applied them to novel questions. It was an approach that let me generate data and papers quickly, and led to an "easy PhD". 

To ask the famous question, is there anything you would like to change retrospectively regarding your PhD?

The flip side of having an "easy PhD" is that I never really had to leave my comfort zone. Since I didn't spend months (or years) painfully learning and optimising new techniques, I never became as technically skilled as the other PhD students around me. Science is so fast moving, that the best strategy is to learn how to learn, which you only get the hard way. Instead, I had my couple of techniques and I had learned how to plan experiments and writing papers. This made my post-doc really difficult - I didn't have the versatility or skill of other post-docs around me, who were picking up and using the latest techniques with trained ease, earned by blood, sweat and tears earlier on. Now as a PI, this deficit is not so important, since my job is all planning and writing, but even now I regret never learning to become a great experimentalist.   

 Which advice or tips would you give us PhDs on our way?

1. Analyse experiments as you go. I started the habit very early on of always analysing experiments once I finished them. By this I mean a full analysis, including a publication level graph, a figure legend and a few lines of text describing the result. It takes a little time, but it means you get real-time feedback on the quality of your experiments - give you have all the right controls, were the numbers high enough to make conclusions, are my conclusions solid enough to plan the next stage, etc. It also made writing papers and a thesis very simple - I just cut and paste my analysed data in, and I was half-way there. Since editing is much less intimidating than writing, I never developed that writing paralysis that some students get.

2. Don't stress about careers! The infamous "bottleneck" in the academic career is mostly illusional. In Flanders, perhaps 15-20% of PhD students go on to a long-term academic career, but even in countries with lower rates (2-5% would be normal) this this not due to a bottleneck. A PhD in biomedical science is one of the most desirable training programs possible for a modern career. The vast majority of people who leave academia are not pushed out; instead, biomed PhDs are leaving academic because of pull-factors - they find highly desirable jobs that they believe they will enjoy more. When I look around at the PhDs that I trained with or that I have trained, I can honestly say that not one has had a career failure. Yes, very few are now research professors, but that is because almost all of them found something else they liked more. Doctor, CEO, start-up company, scientific writer, senior public servant - all great jobs. Very, very few of the 100+ academic careers that I have followed have ended with someone getting pushed out of academia (i.e., timing out of the post-doc fellowship system), and those that did landed on their feet and found a great career that they now say is better suited to them. So.... don't stress about your future career. Concentrate on doing well in your PhD, and start planning your career a year in advance of any decision, but don't make yourself unhappy about uncertainty in which successful career path you will end up taking.

EMBO Young Investigator meeting

Great meeting with great people

Punting on the Cam

Visiting the original lab books of Rosalind Franklin

Front cover of Blood

Our study on myeloproliferative disorder is on the front cover of Blood

Farewell to John Barber

Sad to see John Barber leaving us to go back to his medical degree. He has spent the last year uncovering a novel genetic cause of neutropenia - details to follow soon!

Best of luck John, you'll be missed!