AutoSpill: a method for calculating spillover coefficients in high-parameter flow cytometry

I am really thrilled to release AutoSpill onto BioRxiv. AutoSpill is a novel method for applying compensation to flow cytometry data, which reduces the error by ~100,000-fold. It is thanks to AutoSpill that we can push machines to their max colours, and actually get good quality 40+ parameter flow cytometry data. AutoSpill is a beautiful example of what maths can add to #immunology, led by the talented Dr Carlos Roca.

So how does AutoSpill work? If you just want to compensate your data, simply upload your single colour controls to https://autospill.vib.be and then copy the spillover matrix to your flow cytometry program of choice. Dr Carly Whyte made this easy two minute tutorial:

If you program your flow cytometry analysis in R, we have also released the AutoSpill full code, so you can add this to your bioinformatics pipeline.

Here are a few examples of the error reduction you can get with AutoSpill:

In high dimensional flow cytometry traditional compensation errors create artefacts. AutoSpill creates a perfect spillover matrix. What does a "perfect" spillover matrix mean? An error reduction of 100,000-fold on average, to the point where error is practically zero. (if you are using the script, you can reduce the error further - we stop the improvement at this point because it is functionally perfect). This means the large over/under-compensation effects can be completely removed from your data. If you want to run 28 colour flow cytometry on a 28 colour machine, you can spend hours-upon-hours compensating your data by hand, or 2 minutes with AutoSpill. AutoSpill is designed to run through the same operations that a skilled flow user does, just faster. But always remember - the compensation can still only be as good as the quality of the single colour controls!

How does AutoSpill work? It is a huge surprise to me, but with the enormous effort over decades to add extra lasers and new flurophores onto machines, the mathematics behind compensation hasn't been updated since 1993, where it was designed for 3 colour flow on computers with 100,000-fold less capacity. For decades we've been building more-and-more expensive machines, and haven't updated the basic maths that the machines run on!

Traditional compensation defines a positive and a negative population, finds the slope and uses that as the spillover matrix. It still works okay in most cases, it is just that the small errors start piling up when you are making 250+ calculations on a high parameter dataset.

AutoSpill is actually fairly simple at heart:

1. Draw an automatic live cell gate
2. Use linear regression to take into account every cell, not just the two data points of average positive and average negative
3. Calculate the error left, using the sum of errors in every compensation pair 
4. Use the residual error and return to step 2
5. Repeat the tweaking of the matrix until error is gone

The underlying mathematics is tougher though (never thought I'd write a paper  discussing "the linearity of the quantum mechanical nature of photons"!), because AutoSpill was built for actual flow cytometry users, not as a computational exercise. Carlos built the original pipeline, and then we extensively beta-tested it on 1000+ datasets over 20 months. Something as simple as a live gate becomes complex when you want it to robustly work on any dataset, cells or beads, collected on any machine. I'll spare you the details, but two stage tesselation and a 33% density estimation using a convex hull does the trick, successfully spotting the cells or beads even with heavy debris:

There are many advantages to using linear regression to calculate compensation. Why through out the data from 40,000 cells and instead turn it into two points, the way traditional compensation does? If you use linear regression you can use all of the data, which means AutoSpill works even if you have mostly negative or mostly positive data, just a shoulder of positive events or even a smear. So you can use the real antibodies on real cells to calculate your single colour compensations, rather than using beads or anti-CD4 in every channel.

As an added advantage to this approach, AutoSpill can remove most of the autofluroscence from your flow cytometry sample. For people working on cancer or myeloid cells this can be a complete game-changer. It turns out that while cells have different amounts of autofluorescence, the spectrum of that autofluirescence is fairly constant. You can collect empty data in the worst autofluorescent channel. The single in this channel can be used to calculate the autofluorescent spectrum, which can then be calculated on a per-cell basis and used to compensate it out of every other channel.

Here are two examples:

1. Back when I was a post-doc, there were many published reports of Foxp3 expression in macrophages, epithelium, cancer, etc. All autofluorescent artefacts that wasted years of research.  
AutoSpill removes this autofluorescence specifically from the macrophages:

2. Now we work on microglia, and it is still argued in the literature as to whether they express low levels of MHCII at homeostasis. Using AutoSpill to remove the autofluorescent signal it is quite simple - no they don't.

Of course, AutoSpill is a tool to get an optimal solution for good data. It can't turn bad data into good data. You should always work with your Core Facility staff to optimise the machines and run high quality single colour controls.

If you like AutoSpill, and you come from a math, computer science or data science background, why not come and join the lab? We have a position open for a data science post-doc or senior scientist for another two weeks.

Training the PhD supervisors

I just completed another "training the PhD supervisors" course, in anticipation of my first Cambridge PhD students. I have a few thoughts on training supervisors, but first my credentials and context: 

1. Unlike most science professors, I took formal training in higher education, through a two year part-time Graduate Certificate program, and have published on PhD training.

2. 26 PhD students as supervisor (16) or co-supervisor (10). Of these, 18 graduations, 6 students still in progress and 2 drop-outs. Some easy experiences, where the students flew though. Some wonderful experiences, where I really got to help the student grow and flourish. Some steep learning curves, where the student and I took longer to get it together, but ultimately we both learned from the experience and the student suceeded. Some nightmares, that had me on the edge of quitting and occasionally still give me insomnia. I am a better supervisor today than I was 10 years ago, and hopefully I will be a better PhD supervisor in 10 years than I am today.

3. I see the PhD as a program where you create the environment that gives the student the opportunity to grow. This is difficult, since it involves understanding the student and pushing them just the right amount to stimulate them without intimidating them. The PhD for me is a highly versatile program, and I am happy for it to steer towards many different outcomes based on what the student is aiming for (academia, industry, etc).

So, my thoughts on training programs for PhD supervisors

First, they are necessary. The messages end up being fairly simple. Remember your PhD student is a person as well as a student. Learn that your student has different needs and expectations that you did as a PhD student. Learn to listen to their expectations, learn to be explicit in your expectations, be prepared to discuss and compromise. Document and revisit discussions. Learn the boundaries of reasonable expectations on both sides. Learn when to bring in extra help, learn where that help can come from. While these messages are simple, for many PhD supervisors it will be the first time they've explicitly heard them, and often new supervisors rely excessively on the lessons of their own n=1 PhD. 

This is the raison d'être of these training programs, and the central work is typically done well. There are several common failings, however:

1. Pedagogy has a teaching problem. Education is an advanced academic field, with a highly specialised language, just like other fields. Unfortunately, many education experts use this language when training PhD supervisors. It is a major turn-off, especially to STEM academics, where even common humanities terms can be opaque or even just mystifying. Most supervisors are going to get less than one undergrad credit worth of education training - the use of specialist language is unnecessary and a barrier to concept uptake. I fully acknowledge that STEM disciplines have the same language barrier. I hope that one day there is a concerted effort to bring knowledge from STEM into humanities - and at that point we will need to learn the language of humanities to effectively communicate. But during supervisor training the onus is clearly on the trainer to use discipline-neutral language.

2. Humanities and STEM are just too different. The PhD programs are so different, in style, outcome and supervision, that examples and advice end up being so generic it is of little value, or it jars completely with one of the fields. Just split up these training courses into humanities and STEM, replicate the common content and specialise the field-specific content. 

3. Supervisor training programs are too reactionary. A common mistake for new supervisors is to focus on correcting problems that they experienced during their own PhD. It can result in them being blindsided by different challenges. Ironically, the very classes that teach this are often guilty of the same problem. These courses are designed around the failings of current senior faculty. It is almost "what do we wish our senior lecturers had been taught 20 years ago?" in content and context. In STEM, the biggest failure in the senior supervisor population is the "sink or swim" mentality, which essentially assumes that any student who struggles is not cut out for a PhD (i.e., the failure is entirely in the student). This is demonstrably incorrect and propogates major problems of inequality. However, while this flaw is common in senior supervisors, it is becoming extremely rare in junior supervisors. When given problem examples, junior supervisors tend to first assume the failures are entirely in the supervisor. I have seen more issues arise from junior supervisors trying to be a friend to their students, or over-committing their time to a single student, then I have from junior supervisors neglecting their students. This is not to say that neglect is not a problem - it is, and needs to be addressed. However training courses for junior supervisors should better reflect the problems that are common in junior supervisors. 

4. Training programs are less valuable because they are siloed. This training is focused on the well-being of the student, and is essentially dedicated entirely to situations where the student has a problem that can be fixed by behaviour-change in the supervisor. We know, however, that junior faculty are under enormous stress, rife with anxiety. One of the biggest sources of stress can be the very rare cases of problem students. This situation, of a problem that requires behaviour-change in the student, is almost entirely neglected in supervisor training. We are trying to fix one side of the equation in this training, and the other side is often entirely neglected or dealt with in a generic "stress resilience" training course (which also assumes the flaw is in the faculty not being able to deal with the stress). What we need is integrated training. Pitch us the same problem scenario twice, but with different missing context. Walk through the problem scenario with missing context A, where you need to change. Walk through the problem scenario with missing context B, where the student needs to change. Discuss how to identify developing problems, how to reflect on whether you are dealing with a context A or context B issue, and what practical steps to take in each context. I really dislike the problem scenarios where we are expected to take a one paragraph description at face value - real lab problems are never that simple, and always involve looking at a problem from multiple perspectives. Real solutions always involve trade-offs. Let's not pretend to junior supervisors that they will be in a situation where they can just invest limitless time - there needs to be hard barriers to stop work-life imbalance on their side. Let's also not pretend that a supervisor-student relationship exists in isolation - it has impacts on the entire lab, and trade-offs are always required. Perhaps this comes from a STEM vs humanities divide, but I see the concept of the team/lab almost entirely neglected in problem scenarios and trouble-shooting.

Finally, a little self-reflection. I would give this particular training course a 9/10 - probably the best I've been through. And yet 90% of what I wrote is a criticism. Occupational hazard? I think in STEM we move very quickly on from the success to trying to fix the failures. I know that when I run evaluations I need to force myself to stop, and say "well done on X, Y and Z. These are important. Congratulations. Now let's talk about A, B and C, which need some improvement...... Again, well done on X, Y and Z."

Position open for a data scientist

We have an exciting opportunity for a post-doc or staff scientist to join the Liston lab at The Babraham Institute. The role will be responsible for leading the development of data analysis methods and bioinformatics pipelines for immunology projects. This is a perfect position for a computationally-orientated scientist who wishes to model real biological data and is willing to learn immunology.

Applicants will have a PhD in mathematics, computer science or bioinformatics. For those with additional postdoctoral experience, a more senior position with added responsibilities is available. Applicants from diverse academic backgrounds are encouraged to apply, immunology experience is not essential. Experience at being embedded in a wet-lab environment, prior work on flow cytometry or single-cell RNA-seq data, or experience in machine learning will all be considered strong assets.

This job is ideal for a strong mathematical candidate who wants to apply their knowledge to biological problems. The successful candidate will work as part of a wet/dry mixed lab, develop novel tools, analyse data, create mathematical models and aid in the design of experiments to test those models. The job has potential career development into a long-term staff scientist, or would suit candidates looking to develop skills for an independent position.

The Liston lab is a fun, international and multi-disciplinary environment, which welcomes diversity and supports the career growth of lab members. 

Learning all about the immune system

Happy readers of "Battle Robots of the Blood"!

Coronavirus is infectious before illness

Coronavirus science simplified: number 6. This article in Nature Medicine looked at the amount of virus present in patients before and after they got symptoms. The data is clear: you can spread COVID19 before you actually get sick, so wear a mask! Read the original paper, or see the illustrated abstract by Tenmai.

What we are doing during the COVID-19 pandemic

This is a strange time for any workplace. People suddenly working from home, large changes in job duties, some people left without much to do while others are expected to manage whole new realms of bureaucracy while also continuing their full-time job. For us, as an immunology lab, this pandemic has an added dimension of peculiarity: our work is directly relevant to the ongoing situation.

Looking back on how we dealt with the outbreak, we were ahead of the curve. We put in place strict social distancing and work-from-home measures well before our institutes / government did (and, I would argue as an immunologist, our lab rules were more science-based than those later imposed on us). We also started a public education program on COVID-19, with an interactive Virus Outbreak simulator, an illustrated series translating scientific  articles into lay language and even released a kid's book explaining Coronavirus (with special thanks to lab members Dr Teresa Prezzemolo, Julika Neumann and Dr Mathijs Willemsen for translating this into different languages).

We also had lab members head back to the clinic to help with the capacity issues created by COVID-19. Dr Frederik Staels and Dr Erika Van Nieuwenhove suspended research to increase their clinical duties, and Dr Stephanie Humblet-Baron and Dr Mathijs Willemsen were on-call in case the system was overwhelmed.

Silke Janssen, processing patient blood

Our lab never completely shut-down though - we had important work that needed to be done. I'd like to call out Dr Susan Schlenner, Dr James Dooley and Dr Lubna Kouser who led the unglamorous but key administration on securing the safety of team members who had to be in the lab. Our Leuven lab was central to the processing of clinical COVID-19 samples. We usually think of clinical trials being run by MDs, but the work does not end after the blood is collected. I really want to call out the key contributions of Silke Janssens and Dr Teresa Prezzemolo. Without them, coming in all day, every day to process blood samples, clinical research of COVID-19 would have been crippled.

Dr Teresa Prezzemolo in the L2 labOur team, lead by Dr Stephanie Humblet-Baron, also analysed the samples prepared. We performed an ultra-high parameter analysis (far beyond state-of-the-art hospital diagnostics) of the T cell phenotype of COVID-19 patients: months of work from Dr Teresa Prezzemolo, Silke Janssens, Julika Neumann and Dr Mathijs Willemsen. Data analysis by Julika Neumann, Dr Carlos Roca, Dr Oliver Burton and Dr Stephanie Humblet-Baron identified a novel link between IL-10-producing Tregs and COVID-19 severity. We are now following this up to see if the link is useful as a biomarker or even is mechanistic in disease program. We have made our data an open resource (link), allowing other groups around the work to analyse our work. We are continuing to follow these patients and will soon have more and more information about why some patients remain completely healthy and others develop severe, even fatal, disease.

Dr Dooley and Dr Kouser (pre-COVID-19)We are not just clinical immunologists - we are also basic research immunologists. Mysterious virus triggering immune-mediated destruction of the tissue? We can deal with that. The whole lab contributed to the design of a new potential therapeutic, but I would especially like to call out the contributions of Dr James Dooley, Dr Oliver Burton, Dr Lubna Kouser and Fran Naranjo. Manufacturing is now complete and we are moving to pre-clinical testing. Hopefully we have a vaccine for SARS2 before our treatment is complete, but it is designed to deal with an unknown SARS3 equally well.

Suffice it to say, we have been as busy as we've ever been, and we will likely remain just as busy well after COVID-19 stops making the headlines. Which brings me to my final plea. Don't forget about scientific research. Unsung heroes during the pandemic, our staff are putting in an enormous effort. And yet we face an incredibly uncertain funding situation. Universities and research institutes have taken an enormous financial blow with this pandemic, and unless governments step in with a large financial rescue package, those scientific research staff who got us through the pandemic are going to be laid off in huge numbers. Even if you don't care about the moral imperative of looking after the people who stepped up when we needed them, there will be a SARS3 or novel flu pandemic in the future. We need to secure the research infrastructure to combat them right now. Science is not a factory that can be switched on and off at will - we need to maintain research excellence, scientific equipment and most of all key staff contracts over the long-term.

Laboratory Code of Conduct

Code of conduct

As a member of the Liston Lab, I understand

• There is no use of mice except under the mouse user principles
• There is no use of human samples or data without signing the patient data agreement
• Racism, sexism and homophobia are not acceptable
• Scientific fraud or plagerism are not acceptable

The laboratory is a shared facility with shared space and shared responsibility. As such

• I have read and agree to follow the laboratory protocols
• I will perform my lab duties as described in the “lab protocols” well and with regularity
• When I go on holidays I will prearrange for my lab duties to be fulfilled by someone else
• If I know in advance that performing my duties on schedule is not possible, I will consult with the lab manager on a case-by-case basis

Research laboratories can be a place of great stress, making inter-personal relationships intense. When dealing with other members of the laboratory:

• I will try to treat every person with professional respect
• I will try to be considerate of other’s feelings
• I will try to be respectful of other’s working conditions (eg, noise, tidiness)
• I understand that I work in a multicultural workplace, and that different people can have different cultural assumptions, reactions and methods of response than I do myself
  • I will try to assume the best rather than the worst from others’ actions
  • I will try to see the situation from the perspective of the other in a dispute
• I understand that tensions will rise and that we will not always be perfect
  • When I err, I will apologise
  • When I am apologised to, I will try to forgive
  • I will try not to hold a grudge
• I understand that public displays of affection or anger are not appropriate in the laboratory
• When disputes arise, I understand there is a responsibility to keep the impact on others minimal, as well as a responsibility to resolve the problem
• I understand that this code of conduct is an ambition for myself to achieve, rather than a standard against which I should judge others

Giving a virtual guest lecture

Cartoon courtesy of Simon Gumble, GSK

Welcome to a PhD

This recent twitter thread from a first-gen graduate asking about a PhD got me thinking. As a first-gen graduate myself, what advice would I give to someone starting a PhD? The below is tailored towards a biomed PhD in the UK/EU/Australian systems, but some points are more generalisable:

First, getting into a PhD program is tough. You've made it, congratulations! By definition you have the intellectual ability to finish. Never doubt that. That said, you will doubt it. Especially at the 3-6 month period and at ~2 years in. That is normal. Even though the people around you look confident, they all went through a similar period. 

Second, it is your PhD, but the lab's project. You should aim to become the intellectual leader of the project after around a year, but always lead with humility. Others around you will always know more than you on specific techniques or domain knowledge. Being the leader doesn't mean be the boss. It means being the person who makes sure that things are on track, who takes responsibility for keeping up with the literature and following up with people who are part of the team.

Ask for advice, and listen to that advice. Take particular note when it comes from experience. Don't be that student who ignores technicians. When a tech is telling you something, listen. If your supervisor tells you something, listen. Feel free to disagree, but first listen. If someone suggests a protocol for an experiment, do not go back to them for help until you have actually followed their protocol word for word. Don't change protocols that work until you've got a lot more experience. Include every control that is suggested, even the ones you don't think are necessary.

Being a PI is a tough job, very time-demanding. So use their time wisely. Prep before a meeting, take notes during, follow-up. If you can answer a question via a quick google search or conversation with another lab member, do that instead of knocking on their door. A PI can be a valuable asset to you if you use their time wisely. If you start wasting their time they will schedule you out,

The lab environment can be a pressure-cooker of stress. Experiments don't work, trouble-shooting is horrible, publication can be nightmarish. At its best, the shared adversity will create unbreakable bonds between lab members. To make this happen, be considerate, be kind, forgive. Be the team member who helps out. Smile when someone frowns - they may have just had the most horrid day. Soon enough you have a day where you snap or frown - treat them the way you would like to be treated on your worst. Especially keep in mind that science is highly international and multi-cultural, and people may not mean things the way you perceive them (and vice versa). 

At the start, get into the lab and learn how it works. Where the tip-boxes go, who refills them and makes up new solutions, how plastics get ordered. Ask the lab manager or senior tech what you can do to help out. There are no magic fairies - every task is done by the team. If you leave the centrifuge messy, use the last reagent without ordering more, you will annoy people. If you clog up their personal pipettes and don't tell anyone, you will really annoy people. Be a good lab citizen.

The first six months is basically you learning how to be in the lab, reading the basic literature and just learning how to do the techniques. You won't actually make any advances - this is all on-the-job training.

Don't hide mistakes. You are going to make mistakes. You are going to make mistakes that will cost your monthly rent's worth in grant money. I remember the horror of breaking a haemocytometer during my first week in the lab. $500 at a time where that was an unbelievable amount of money to me. You make make mistakes that cost your annual salary's worth. Own them. Admit to them. Don't make them again. Never blame others for your mistake. Someone breaking the centrifuge is bad, but if I know they will never do it again I move on. If they blamed someone else for leaving it unbalanced (while they didn't check) then I worry that they haven't taken responsibility and are more likely to make the same expensive mistake again.

Six months in, and you are ready to go solo. Things that worked when you were shown how stop working. You will feel like a failure. It is tough, you will doubt yourself. You will look at senior students and think you will never be that good. You will, it just takes time. Here first-gen graduates have an advantage. They don't expect things to come easy to them, so they grit your teeth, try again, fail again and try again. A lot of success in science comes from having the personality to be able to deal with failure, over and over again. 

It starts working, you get results! Now you need to switch techniques, and you go through the same process. Much of the next year is this in repeat. You are now a real scientist, but you won't feel like you have made any actual progress on your PhD. Your lab mates try to pick you up, but you doubt. Again, this is normal. These are the "PhD blues". You might think about other careers, do a few training courses, lose motivation to go to work. This period can drag on, but once you get back into the lab and push, things will crack.

You are now the senior PhD student that juniors look at. They see you as calm and capable. You have become a data machine. In about six months you pump out 90% of the data of your PhD. At the same time, you probably see yourself as a bit of a fraud: you know you can handle the day-to-day of the lab, but you doubt you can handle the intellectual side still.  Your supervisor now becomes your key asset, probably for the first time, as you start to write up. 

Don't spend much time on your first draft, it will be rubbish anyway. Everyone's is. Just write it up and get feedback. Just like any technique, writing is a skill that you will learn, you just have to be willing to give it a shot, get feedback, and try again. This means when you your draft back full of revisions, don't just accept the changes. Try to understand them. What change did you PI make, and could you incorporate that strategy yourself next time? In particular, read papers while writing. Compare your paper to published papers, sentence by sentence to see if your work looks like the real thing. What do figure legends have in your field? Does your draft figure legend have all of these attributes? Remember you are the paper lead, but it is a consensus document. Be generous on coauthorship, and remember who helped you out early on. It means a lot to people to be acknowledged, and it doesn't hurt you to have extra authors on your paper.

Publication. Ah, this is a horrible ordeal. You will get rejected multiple times, it will feel rubbish. Flip that paper to the next journal and don't take it personally. Don't dwell too much on the comments, the next reviewers may be completely different. If your paper is given a "major revision" - congratulations! That is actually really good news. Now you need to do all the experiments that the reviewers asked that are at all possible. Yes, you could argue the point, but save this for the cases where the experiment is impossible. It is much better to deal with a major revision than to start fresh with a new journal. At the end, you may feel more exhaustion and relief that the paper is off your plate, rather than actual satisfaction. This is (unfortunatey) normal. So make sure you celebrate every intermediate stage (submission, going out to review, major revisions coming back, etc). 

Remember you don't actually need to publish to graduate (in the UK or Australia, in much of the EU you do, but there is a journal home for every paper). You just need to produce a body of work suitable for publication. Like your paper, just push out the first thesis draft quick and dirty. It is a formality, nothing more. Your contribution is in your papers, while your thesis will be read by the jury only, and then gather dust on the shelves somewhere. 

Congratulations! You have a PhD. The highest degree possible. You are now an expert in your chosen field (although we all have more to learn!). You have many, many good career options available to you. A PhD in biomedical sciences is a gateway to so many interesting careers. Go down a pathway that looks interesting to you, and if it doesn't work out, pick a new path. The world is your oyster!

Researchers identify new genetic cause of severe immune disorder

Severe congenital neutropenia leaves young patients to contract infection after infection, leading to life-threatening situations. A team of Leuven scientists has identified a novel genetic mutation, pointing to a new causative mechanism for this severe immune disorder.

The story starts with patient Jane Doe, now 19 years old, but diagnosed with severe congenital neutropenia when she was just 2 years old. By that time, she had already suffered an ear abscess, recurring ear infections, bronchitis, sinusitis, tonsillitis and several gum infections.

After yet another infection, this time of her intestine, a detailed investigation revealed a striking shortage of neutrophils, white blood cells that are recruited as first-responders to the site of injury or infection within our body. Having an abnormally low concentration of neutrophils in the blood is referred to as neutropenia. When it is severe and present from birth (congenital), that is where the diagnosis of severe congenital neutropenia comes in.

“Severe congenital neutropenia is very scary, because these kids develop serious infections that can be lethal for infants,” explains Erika Van Nieuwenhove. “As if that’s not enough, they are also at increased risk for other conditions such as leukemia.”

Van Nieuwenhove is both an MD and PhD, who combines clinical work in the university hospital with Carine Wouters, with research at VIB and KU Leuven under the guidance of Adrian Liston and Stephanie Humblet-Baron.

Together with John Barber and several other colleagues, she set out to understand why Jane Doe developed SCN in the first place. Van Nieuwenhove: “For up to 50% of severe congenital neutropenia patients, we have no clue what causes the disease. It was the same for our patient, whose parents are both healthy.”

A new mutation in a familiar gene

After Jane Doe tested negative for mutations in all the genes with known ties to neutropenia, the researchers performed whole exome sequencing, probing every gene in the DNA, to trace back the genetic defect underlying the disorder.

“We identified a new mutation in a gene called SEC61A1, which encodes one of three subunits of the Sec61 complex. This molecular complex plays a crucial role in both protein transport and in maintaining the calcium balance of the cell,” explains Humblet-Baron. “Our experiments revealed that the genetic defect led to both a lower expression and a reduced efficacy of the SEC61A1 protein, and that these quantitative and qualitative defects in turn disturb neutrophil differentiation and maturation.”

Interestingly, SEC61A1 has recently been picked up in other studies that were not focused on neutropenia. Different mutations in the same gene were reported in two families with a rare kidney disease and in two additional families with an antibody deficiency.

“The fact that there are different mutations in the same gene indicates there may be overlapping mechanisms among the different disorders. With the low number of currently known patients, it is still too early to predict which mutations can lead to which symptoms,” explains Liston.

“What’s clear from our findings is that SEC61A1 mutations can also cause severe congenital neutropenia. Considering this gene’s link with other disorders, the clinical implications of our work reach far beyond the patient with whom it all started here in Leuven.”

—

Read the original paper: Defective SEC61α1 underlies a novel cause of autosomal dominant severe congenital neutropenia. Van Nieuwenhove et al. JACI 2020