New insights into a mysterious inflammatory disease

We have a new story out at Nature Communications!

This time our lab tackled the enigmatic "autoinflammation of unknown origin". These #autoinflammatory patients don't fit the criteria of classical syndromes. It wasn't even clear they were a single group, to be honest

We worked with the amazing Dr Carine Wouters and clinicians across Europe to collect samples for a systems immunology analysis. Critically, we we able to access samples at the point of diagnosis, many still untreated, so we could see the primary immunological effects. A decade (yes! planning started in 2016) of sample collection and flow cytometry data generation followed, led by the KU Leuven team under the leadership of Prof Stephanie Humblet-Baron. Our own Dr Rafael Veiga led the data analysis.

Fast-forward through the slow science and the final outcome is that immune status distinguishes patients compared to healthy controls (AUC 0.83), with CD38+ T cells elevated and memory B cells way down. This shared phenotype suggests autoinflammation of unknown is a distinct condition, not a diverse set of patients let down by the diagnostic process. That by itself was a significant clinical observation!

But the patients already knew they weren't healthy. The clinical challenge is to distinguish them from other autoinflammatory conditions. Fortunately, we ran in parallel samples from confounding conditions with similar demographics, and the patients still had a distinct immune profile! It was striking, however, that the signature immunological changes were shared with patients with Still's Disease. We could still distinguish the conditions (AUC 0.79), but changes such as CD38 and BAFF moved in parallel between the conditions.

We wrote the paper up, submitted and waited for the reviewer comments. Quite constructive and improved the clinical aspects of our paper. The hardest ask was for serum proteomics on all samples - fortunately Dominique De Seny's team at Universite de Liege had being doing just this! Sometimes luck is on your side!

Completely independent immune phenotype platform, and the same message - autoinflammation of unknown origin patients clustered together, and shared many signature changes with Still's disease patients

Could autoinflammation of unknown origin and Still's disease share an immunological basis? Could the treatments used in Still's disease work in the other patients? Right now, we don't know, but perhaps we are finally on the right path to finding out!

Many thanks to the research teams at Cambridge, Leuven and Liege, the clinical team, and most of all the patients who were at the heart of the study! May your contribution help find new treatments!

Read the full paper here.

Tissue Tregs in Annual Reviews of Immunology

Our latest review is out, a comprehensive synthesis of tissue Tregs. It has been a decade since Annual Reviews of Immunology last reviewed tissue Tregs, and there have been enormous advances and conceptual leaps forward in the field.

Tissue Tregs have now been found in essentially all tissues, and have broadly conserved properties of enhancing repair and rejuvenation as well as controlling local inflammation. While the impact on tissues differ, molecular mediators are largely shared across tissues. The molecular cues that induce the tissue Treg phenotype are only partially understood, but key external signals from the tissue environment seem to be important in upregulating a core transcription factor set, which remodels the epigenetic and transcriptional landscape.

The cellular kinetics are not fully understood, however the majority of evidence using parabiosis, TCR retrogenics, cell transfers and fate-mappers suggest that the majority of tissue Tregs are pan-tissue, multi-tissue or tissue-cycling in their behaviour during homeostasis.

We also cover the increasingly promising attempts to exploit the properties of tissue Tregs in the clinic, and outline the key open questions for the field. 

Read the full article here.

Wetenschapper in wording

Spreek je Nederlands? Wetenschapper in wording is nu ook in het Nederlands verkrijgbaar! Dankjewel Liesbeth Aerts en Annelies Van Dyck.

Self-Doubt: An Anthology of Experiences in the Biomedical Sciences

Introducing our new book, "Self-Doubt: An Anthology of Experiences in the Biomedical Sciences". 

Have you ever had a crisis of self-doubt? A feeling that you are out of your depth and are not cut out for a career in science? I have. At the time I thought it was just me.

After decades of mentoring PhD students and postdocs, I now believe self-doubt is near-ubiquitous, an occupational hazard in science. The hardest part of dealing with your self-doubt believe you are alone in your thoughts. So my lab members and alumni have shared their own stories of career self-doubt.

If you know anyone in science who is doubting their path, please share this book with them (Amazon, Great British Bookshop) so that they know they are not alone

Lung Tregs at the Midwinter Conference

I am very fortunate to be at the Harald von Boehmer Midwinter Conference, courtesy of amazing conference organisers Ludger Klein and Lisa von Boehmer. Highly recommended as one of the best immunology conferences around - I've really been inspired by the great talks in every session. For those of you who would like a taster of the conference, here is my presentation - all unpublished work, covering FlowCode analysis of tissue Tregs, resolving spectral flow cytometry limitations through AutoSpectral, and using a novel AAV-based system of cytokine delivery to the lung to treat Influenza-Associated Pulmonary Aspergillosis. A sneak peak at the work currently going on in the lab! 

Where are they now?

I was reminiscing about the amazing people who have passed through our lab over the years. We have a constant stream of new team members and people finishing up. So what happens after you leave an academic lab? Here are our outcomes:

12 technicians:

• 40% moved to higher education (MPhil/PhD)
• 60% moved to another academic laboratory job 

30 Masters students:

• 43% started a PhD
• 7% started another higher education degree
• 23% technician positions in academia
• 20% moved to biotech/pharma
• 7% moved to agtech

22 PhD students:

• 30% became post-docs
• 8% went to other positions in academia
• 40% to biotech/pharma
• 13% to clinical posts
• 4% to law

27 post-docs:

• 27% to tenure-track / tenured positions
• 27% to another post-doc position
• 42% to biotech/pharma

All of them now successful!

Out of Caution or Protest, Foreign Scholars Skip U.S. Conferences

As President Donald Trump deploys National Guard troops to American cities and ICE arrests pick up, some international scholars are opting out of U.S. travel

By Emma Whitford

...

Adrian Liston, a professor of pathology at the University of Cambridge, lived in Seattle while he completed his postdoc and has typically traveled to the U.S. for conferences two or three times a year. But now, he has opted out of all U.S. travel.

“That was a decision made after Trump won re-election. I had already been booked in on a couple of conferences, and the following week, I decided that I wasn’t going to be going to America under Trump,” Liston said. “So I canceled the conferences that I’d already been invited to and I’ve said no to any travel to America for professional or personal work since.”

He’s not concerned about his own safety in the States; like Murakami Wood, his boycott is a protest rooted in ethics.

“I don’t agree with the politicization of scientific funding, the destruction of data collection and openness, the violation of grant agreements, the dismantling of public health that’s been happening with Trump, and I feel like traveling to America at this point is a partial endorsement of what’s going on,” Liston said.

Read the full article at Inside Higher Ed

Burton's Best Buffer featured in the Cambridge Independent

Novel analytical pipeline reduces spectral flow cytometry errors up to 9000-fold

Pre-print alert! And this one really is a must read for anyone that does spectral flow cytometry. It is a complete, fully-automated spectral unmixing pipeline that reduces error up to 9000-fold, created by our cytometry guru Oliver Burton, of Colibri Cytometry fame.

We've all seen the problems - spreading, skewing, autofluorescence intrusion. Unmixing errors are so ubiquitous in high parameter panels they are often thought of as unavoidable, intrinsic to the way the hardware works. Surprisingly, they are largely artefacts of the unmixing software being used.

The problem is that spectral unmixing is complex. The basis is a linear regression of positive versus negative signals, a highly error-prone process. This issue is largely solved by the use of robust linear regression with iterative rounds of improvement (which we pioneered with AutoSpill). However there are three additional problems, which become bigger the more fluorophores are used:

1)This unmixing solution still requires ideal positive-negative matching to find the right linear regression. This isn’t trivial, as the cells positive for one marker might have completely different autofluoroscence profiles to the cells positive for another marker. Using the same negative population gives you spillover calculation errors.

2) Cells have variation in background fluorescence. An unmixing matrix that doesn't account for autofluorescence will force all signal into one of the flurophore channels, giving misassigned signal. Past approaches only use a single autofuorescence index, which means heterogenous mixtures have cells with misassigned signal.

3) Fluorophores actually stuck on cells have variation in emissions, and using only a single profile will lead to misassigned signal on some cells.

Some of these problems can be tackled (partially) by a highly skilled flow cytometrist, willing to spend days on each unmixing matrix, manually selecting populations for positive and negative cells and running multiple sets of calculations depending on which markers they want to assess. AutoSpectral does it all in a completely automated pipeline, using a robust statistical model that is highly reproducible and visibly reduces the error.

For positive-negative calculations, intrusive events are purged and scatter-matching is used to identify the suitable negative population for each positive population. We then use robust linear regression with iterative improvement to find the ideal unmixing matrix.

We can also deal with heterogeneity in the cells by identifying all autofluorescence patterns in the unstained sample, then applying each pattern to each individual cell in the real sample. We select the autofluorescence index that leaves the least residual, subtract that signal and unmix the rest.

The same is true for fluorophore variation - we can test the different fits on a per cell basis, and use the fit that leaves the least residual. It means more signal is attributed to the correct fluorophore.

 

The cumulative effect of these improvements is enormous. For tough samples, like lung, incorrectly assigned signals are reduced by up to 9000-fold, and a 10- to 3000-fold improvement is common. We demonstrate the improvement in synthetic experiments with known ground truth, and multiple real-world complex panels, where we can use known biology to see the improvements. For example, look at this experiment, where the wildtype has no GFP signal and the GFP transgenic should have GFP in CD4 and CD8 T cells. Since this sample is from the lung, the autofluorescence of macrophages gives a huge GFP signal in the wildtype mouse, which completely confounds the genuine GFP signal in T cells. Switching over to AutoSpectral, and exactly the same samples, with exactly the same cells, behave just as you would expect, little signal in the wildtype and a CD4+ and CD4- population in the GFP transgenic.

The whole pipeline is available right now on GitHub. Don't be intimidated by R, it includes comprehensive notes on every step from installation to utilisation, and only takes a couple of minutes to run per experiment. Hopefully soon (like with AutoSpill) it becomes standard on commercial platforms too.

Full article is available here.

Interview with iiSIAR podcast