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

Graduation week for Dr Dashwood, Dr Gentry and Dr Ali!

Huge congratulations to Dr Amy Dashwood, Dr Ntombizodwa Gentry and Dr Magda Ali! All three graduating this week with their PhDs from University of Cambridge! Our first Cambridge PhD students, who I find out from reading the acknowledgements were known as "Adrian's Angels" or "The Three Musketeers". Fantastic scientists all, I'm really proud to have been part of their career journey. I look forward to following their successes into the future, already started with a postdoc at the University of Manchester, a postdoc at the MRC Laboratory of Molecular Biology (LMB), and a commercialisation position at Cambridge Enterprise. Well done!

Understanding tissue migration

We have an exciting new bioRxiv story that just went live! This one takes a computational immunology approach to understanding tissue-resident lymphocytes. The story highlights the extra value mathematical modelling can bring to biology.

It starts with a large multi-tissue multi-timepoint parabiosis experiment we ran to understand tissue Tregs. Václav Gergelits, lead author on the study, saw greater potential in this dataset to understand the kinetics of lymphocyte migration broadly.

We extracted turnover data for CD4, CD8, Treg, B cells and NK cells from 17 tissue sources, and generated a sophisticated model of migration, activation and death for each lineage and tissue. The Markov chain modelling found high-confidence solutions that matched the empirical data beautifully.

This tells us a probabilistic model and three distinct states (resting/activated/resident) are sufficient to recapitulate the complex migratory and tissue-residency behaviour of these cells. The cell states change probabilities, but the behaviour is still *probabilistic*.

This means lymphocytes do not have a residency "clock". We can measure the average dwell times for resident cells, but if this average residency time is 3 weeks, it does not mean cells have a 3-week timer. It means the cells have a daily probability of leaving that gives a 3 week average. The dice roll comes up earlier for some cells than for others, within those cells being intrinsically different. Like radioactive decay of atoms, it is just probability - there is nothing intrinsically different about the uranium atoms that decay after a week vs those that decay after a million years, they just had different rolls of the dice.

This approach can explain much of the variation in cell fate without needing to invoke cellular heterogeneity! Two identical cells can have highly divergent outcomes simply because of probability, without different underlying biology. In fact, we can create thousands of identical "digital cells", model them with these simple rules, and we get the empirically-observed range of dwell-times. There is no need to invoke TCR clonality or the like - it is simply an emergent property of cells with probabilistic kinetics!

A great example of applied mathematics informing biology!

Take a read of the pre-print here.

Understanding vaccination in transplant patients

We have a new systems vaccinology paper out at npj Vaccines!

The study tackles the problematic question of why transplant patients responded so poorly to the COVID vaccine. While most people had great antibodies from a single dose, only half of transplant patients have responded even after three!

We took blood from 20 healthy, 31 lung transplant and 59 kidney transplant patients prior to vaccination, and profiled 444 immunological parameters, to get a comprehensive systems immunology profile. We then followed who did and didn't respond to the vaccine, to find the immunological associates.

First up, there are clinical effects: Vaccine response was especially poor soon after transplantation, and in patients on immunosuppressive cocktails, especially those including MMF. Even taking this into account, there were immunological drivers associated with poor response.

As you might predict, the patients that responded best were those with an immune profile that had returned closer to normal post-transplantation. In fact, you could predict vaccine response with 93% accuracy just based on 10 immune parameters.

 

Oddly though, some patients were able to hobble together a poor but detectable response after two shots. These patients didn't have a more normal immune profile, and had quite unusual relationships between immune populations, suggesting that they had put together a poor-but-functional "kludge".

This study was a joint initiative from our lab, Arnaud Marchant's lab at at ULB and Stephanie Humblet-Baron's lab at KU Leuven. 

Huge thanks to all team members, especially Nicolas Gemander, Julika Neumann, Rafael Veiga and Isabelle Etienne for their leadership roles.

Biggest thanks of all to the patients who volunteered for the study!

Read the full paper here

Lab punting