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

Nobel Prize for regulatory T cells

A small primer on the Nobel Prize awarded to Mary E. Brunkow, Fred Ramsdell and Shimon Sakaguchi today. This prize was for combining two separate fields of immunology research - genetic research on IPEX and immunology research of regulatory T cells (Tregs), with enormous impact on biology/medicine.

First, let's talk about IPEX. It is short for "Immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome", which is a bit of a mouth-full. Essentially, it is a severe autoimmune disease, impacting boys (inherited only from the mother), which is fatal in early childhood unless treated. By coincidence, there was a mouse strain with the same disease and inheritance pattern called “Scurfy”, allowing it to be studied in mice.

IPEX/Scurfy was rather mysterious, but because of the inheritance pattern it was quickly mapped to the X chromosome. Several teams of scientists worked on mapping this disorder down to the gene level, with Brunkow and Ramsdell leading the teams that identified FOXP3 as the causative gene in both humans and mice, with major papers in 2001.

Completely independent of this, we had the field of regulatory T cells. There were some misleading experiments on "suppressive T cells" early on, a field which rapidly built and then collapsed in the 80s. Few of those experiments had lasting impact in the field of immunology, but an exception were the papers of Nicole Le Douarin in 1987/1988. She grafted the wing buds of quail onto embryonic chickens, which developed into chickens with quail wings, which were then rapidly rejected by the immune system. The key finding, however, was that if the proto-thymus was also transplanted the chickens kept their wings long term. Here it was quite important that the chicken was used, as it has 10-16 anatomically-separated thymic lobes and you only need to transplant one to get transplant acceptance. This means that the chicken developed a form of tolerance mediated by T cells educated in the thymus but effective in the periphery.

It was a hard and unpopular field for decades, however, with the key pioneers being Fiona Powrie and Shimon Sakaguchi. They chased up independent sets of T cells with immunosuppressive properties, using different markers on what were ultimately the same cells – regulatory T cells, potent at shutting down immune responses in multiple different assays.

It wasn’t until 2003 that regulatory T cells gained wide uptake by the immunology community. This key breakthrough happened by the linking of FOXP3, the IPEX/Scurfy gene, and regulatory T cells. Three groups, lead by Sakaguchi, Ramsdell and Sasha Rudensky, all demonstrated that FOXP3 was acting as the master transcription factor that converted regular T cells into the immunosuppressive regulatory T cells. Suddenly everyone could study Tregs and manipulate their genetics, with tool after tool coming online (such as Foxp3GFP, Foxp3Cre, Foxp3DTR – Rudensky, Tim Spawasser and Jeff Bluestone, among others). It triggered an exponential increase in papers on regulatory T cells, linking them to disease after disease.

The impact has been enormous, with regulatory T cells going from being a niche frowned-upon subset of immunology, to underpinning our entire understanding of how the immune system works. This is obvious important for diseases where we want to shut down the immune system, such as autoimmunity, allergy, transplantation and inflammatory diseases. There anything to boost the number or function of regulatory T cells could be clinically beneficial, with the therapeutic interleukin 2 (IL2) being the prototype therapy and still in clinical use today. It was also a key discovery for contexts where we want to activate the immune system, in particular in cancers, which locally recruit regulatory T cells to protect themselves from immune clearance. Treatments such as anti-CTLA4 essentially allow inflammatory T cells to bypass suppression by regulatory T cells, and have transformed the oncology space. The pre-clinical pipeline is even richer, so we can expect many more regulatory T cell-based therapies to enter the market soon!

Huge congratulations not only to the team leaders who won this prize, but all the students, technicians and expert scientists who did the work that underpins this discovery. Their work, and the work of those following in their footsteps, is changing the future for patients!

Also see a few articles where I was quoted in the Guardian and Science.

Optimisation of blocking during flow cytometry

Another new flow cytometery guide drop from Oliver Burton, just published in Current Protocols.

This one is on optimising blocking while preserving signal, in particular how to overcome Fc interactions and dye-dye interactions, and preventing tandem break-down.

The details are in the protocol, with variants for intracellluar and cytokine staining, but generally-speaking normal mouse/rat serum, BioLegend Tandem stabilizer and Thermo/BD Brilliant Stain Buffer is an optimal combo. True-Stain and other additions aren't worth the extra $$$.

For Tandem Signal Enhancers, you don't really need them for mouse cells, and for human cells a cheaper alternative is simply to fix your cells and stain with tandems after fixation. Both eliminates non-specific tandem binding and also reduces tandem break-down. Since monocyte blocks reduce transcription factor detection, for some reason, leave them out if you are doing intracellular staining.

 

The Brilliant and Super Bright dyes really do need the Brilliant Stain buffers, but be aware that these buffers are mildly fluorescent, so leave them out if you don't need the dye, and titrate them down when you use them. 1/2 to 1/4 is normally good, and for many antibodies even lower is fine (and cheaper!).

For the tandem dyes, Tandem Stabilizer is good, but you can make it easier through panel design. Tandem breakdown is not purely chemical - it is higher on monocytes than lymphocytes, and is largely abolished in fixed cells. So move those tandem dyes to post-fix T cells if you can!

 

We've tried to cover all the main use cases, so take a look at Oliver's trouble-shooting guide to reduce off-target binding and preserve signal.

A near-universal ultra-cheap fix/perm protocol for flow cytometry

For our flow cytometry peeps, would you like to have a single fix/perm protocol that is optimised for everything? One that preserves fluorophores while allowing simultaneous TF and cytokine staining? How about a protocol that is 100-fold cheaper than your current one?

Over the last 8 years, Oliver Burton has tested >1000 different fix/perm combos, and here the final verdict is: "Burton's Best Buffer": 2% formalin, 0.05% Fairy dish soap, 0.5% Tween-20, 0.1% Triton X-100.

Yep, replace all of those expensive detergents with Fairy dishwashing liquid. It is as good as the BD Foxp3 fix/perm kit for transcription factors, as good as eBio perm for cytokines, preserves even weak endogenous GFP killed by most fix/perm combos, and preserves dye integrity too. Burton's Best Buffer is simply the best fix/perm protocol to use under any condition (except phospho-flow).

Plus it is dirt cheap - one bottle of Fairy (or Dreft, Dawn, Yes, JAR, or whatever they sell it as locally) will literally last your lab for decades.

Take a read of the protocol here.

ImmunoTea interview

I'm interviewed in the latest episode of ImmunoTea. Take a listen for all things Tregs and neuroimmunology!

Our lab in numbers

We've just had our 200th person join the lab! Welcome to Ida Jobe! I could write books about them as individuals (and have!), but here are our lab members in statistics:

• 38% domestic, 62% international from 54 countries
• 66% women, 33% men, 1% non-binary
• 78% from under-represented groups

Congratulations to Dr Katy Palios!

Congratulations to Dr Katy Palios for winning the 2025 Golden Pipette! Katy won the Golden Pipette for her exceptional scientific leadership and teamwork, bringing out the best in all those around her. Well done Dr Palios!

New lab photo