Navigation
Public engagement

Virus Fighter

Build a virus or fight a pandemic!

Play online

Maya's Marvellous Medicine

Read online for free

Print your own copy

Battle Robots of the Blood

Read online for free

Print your own copy

Just for Kids! All about Coronavirus

Read online for free

Print your own copy

Archive
LabListon on Twitter
Sunday
Mar062022

Our paper discussed on RheumMadness podcast

The RheumMadness podcast scouted our paper on using machine learning and immunoprofiling to understand juvenile idiopathic arthritis. Their summary?

Future: Future implications for immunophenotyping machine learning include the diagnosis, treatment and prognosis of all rheumatologic conditions. With the increase in potential immunomodulating targeted therapies, along with the classification of disease based on those same immune targets, an exciting possibility of choosing precise individualized treatment plans for our patients exists.
In pediatric rheumatology, we are accustomed to using complicated clinical algorithms to properly diagnose and treat our patients. But is this really the most accurate system? Machine learning and immunophenotyping have the potential to turn the field inside out. 

Chances in the Tournament: As the only pediatric team in play, this team is the dark horse. However, the long-term clinical implications of this team are arguably more far-reaching than any other team in the Machine Region—and the entire tournament. Despite the small number of participants in this study, the exclusion of psoriatic and enthesitis-related JIA, and the lack of attention given to race, ethnicity and environmental factors that could potentially alter immune signatures, we still believe the strengths of this article make it a crucial one. We stand an excellent chance.

Immunophenotyping machine learning has implications for more than just anti-tumor necrosis factor response in RA, like our opponents would argue. Our study shows that its implications stretch far beyond one diagnosis or two therapy choices. In fact, pediatric rheumatologists have just begun to pave the way for better classifying patients in the adult world as well. Could eight subtypes of RA actually exist, and you just don’t know it yet? Immunophenotyping through machine learning could be the disrupter you’ve been waiting for. This study can go all the way.

 

Thursday
Feb032022

Manipulating brain Tregs to protect against neuropathology

From the GlobalImmuno Talks 20222:

Sunday
Jan232022

Maya's Marvellous Medicine

Tuesday
Dec212021

2021 Golden Pipette

The Golden Pipette has a long and illustrious record. Awarded at every lab retreat in recognition of a single very cool result, the Golden Pipette has been handed down through generations of talented scientists. This year the Golden Pipette was awarded to.... Ntombizodwa Makuyana, for her exciting new approach to creating an anti-inflammatory environment in the lung. Well done Tombi, for a stunning first year PhD result!

Saturday
Dec112021

The Seven Rules of Grant Writing

Wednesday
Dec082021

What is a t-SNE?

t-Distributed Stochastic Neighbor Embedding (t-SNE) is the most commonly used non-linear dimensionality reduction algorithm for single cell biology. In its common usage for visualising high-dimensionality single cell data, the algorithm starts with the single cells distributed at random points, along a Gaussian distribution, in transformed space. In an iterative process the cells move along a cost gradient, which provides a penalty for mismatch between the distances between two cells in the original high-dimensional space versus the representational low-dimensional space. In its common usage for visualising high-dimensionality single cell data, the cost gradient of t-SNE places greater weight on pairs of cells close to each other, with medium- and long-range pairs ignored. When sufficient iterations have occurred to reach stability, the outcome produces clusters of similar cells, based on the input data. Membership of a cluster indicates shared properties, however the non-linear nature of the penalty cost does not allow relationships to be inferred by the relative positioning of the clusters.

Running the same dataset through a t-SNE multiple times results in a visually distinct stable states, owing to the random placement of the input data at the first stage. The high cost of violation of local distances ensures that local clusters are maintained across runs, while the low cost of medium- and long-range pairs permits multiple stable states with rotational symmetry to develop. An under-appreciated aspect of the t-SNE algorithm is the early exaggeration of the penalty for violating local distances for the first 50 iterations. Visualising t-SNE runs at each iteration demonstrates that the early exaggeration phase involves a sharp contraction of all points, which then expand out into separate clusters when the exaggeration factor is removed. This early exaggeration is integral to the t-SNE calculation, as maintaining the high penalty throughout results in dense overlapping clusters, while maintaining the low penalty throughout permits splitting of clusters, as cells close together in high-dimensional space do not come in close enough proximity in representational space to drive clustering. It is important to allow both phases and sufficient iterations for the t-SNE to reach stability for consistency in results.

Iteration-by-iteration visualisation of the t-SNE for two unique seeds of the same dataset. Note the initial collapse of the sample into very small distances, and the rotational symmetry observed between the two runs as the samples slowly expand with extra iterations.

Iteration-by-iteration visualisation of the t-SNE for two unique seeds of the same dataset, with no initial exaggeration of the penalty. As the initial collapse of the sample is reduced, similar cells can avoid coming into close enough contact to drive cluster formation. As a result, biological clusters are split in the final representation and repeat runs vary greatly.

Friday
Dec032021

Responding to the COVID crisis

As well as exposing weaknesses in healthcare systems and supply chains, the coronavirus pandemic has underscored the importance of fundamental research and collective effort. During 2020, scientists rose to the challenge of developing new vaccines and effective treatments for Covid-19. Institute immunologists Dr Michelle Linterman and Professor Adrian Liston describe how their labs responded and the lessons we must learn.

 

In the early days of the coronavirus pandemic, as lockdowns loomed, workplaces closed and travel slowed to a trickle, Dr Michelle Linterman was certain of one thing – she wanted to make her group’s expertise available to the global vaccines effort.

 

Among those working on a vaccine against SARS-CoV-2 (the coronavirus that causes Covid-19) was Dr Teresa Lambe at the Jenner Institute in Oxford. “I already knew Tess, so once it became clear they had a vaccine candidate, my first instinct was to ask her what we could do to help,” Linterman recalls.

As an immunologist, Linterman’s work focuses on how the immune system responds to vaccines. In particular, she wants to understand why older people respond less well to vaccines, something she studies using human vaccination studies and in aged mice. “I thought the most useful thing was for us to offer something that nobody else could contribute quickly – and that was our ability to use aged mice as a pre-clinical test of how this vaccine is likely to work in an ageing immune system,” she says.

 

Quote

When Lambe said yes, Linterman set up trials to compare immunological responses to the Oxford/AstraZeneca vaccine in young and aged mice, and discovered that although aged mice responded more poorly than young mice to a single dose, after two doses of the vaccine, the immune responses were very good in both groups.

 

The study helped both institutes. For the Jenner, it showed two doses of the vaccine would give good protection against infection in all adults. For Babraham, it provided new insights into vaccine responses at a cellular and molecular level, expanded research into new vaccine platforms and led to new collaborations. Most importantly, it illustrated the value of publicly-funded research.

 

“Because we’re funded by the BBSRC – in other words the tax payer – it was incredibly important to use our knowledge and expertise to contribute to vaccine development in the midst of the pandemic,” she says.

 

Fellow immunologist Professor Adrian Liston also stepped up to the mark, using his research to help clinicians make the best treatment choices for Covid-19 patients and his communication skills to provide accurate information to journalists and the public.

 

“We need to develop good systems for treating emerging viruses before we know much about them, which is something my lab is working on,” explains Liston. “We are coming up with treatments that are vaccine agnostic, treatments that will work for most viruses with the potential to become pandemic, regardless of the actual virus.”

 

Liston’s group is also interested in systems immunology – exploring what makes people’s immune systems so different from each other.

 

Quote

 This variation has been graphically illustrated during the pandemic, some people experiencing mild symptoms while others died. “Diversity is intrinsically important to the immune system. It’s the most genetically-diverse system in the human body, and there are other factors at play, such as age, gender and weight,” he explains.

 

Being so close to events has taught Liston and Linterman many lessons – lessons, they say, that are vital for political leaders to learn. First, zoonoses (diseases spread between animals and humans) with pandemic potential are far from rare events. “They occur every couple of years,” says Liston. “We’ve had coronavirus outbreaks before, like SARS and MERS; they happen like clockwork. In the previous outbreaks we had better luck and better preparation. These are things we must prepare for.”

 

Secondly, we must guard against complacency. “If we pat each other on the back for a job well done, and then slash science budgets, the next outbreak will be as bad as this one,” he warns. “We must fund surveillance as well as immunology and virology research, because if you scale down this science it takes a decade or more to rebuild that intellectual capital.” This preparation extends to supporting fundamental research in a broad range of areas. “We need to fund fundamental research because you’re never sure which bit of it will save you in the future,” says Linterman.


 Third, a global approach to research, and funding to support this, is essential, because scientific discoveries are not bounded by borders, adds Linterman: “One of the reasons the Oxford vaccine was developed so fast was because of years of work on Ebola and MERS using the same adenoviral vaccine vector.”

 

As vaccines are rolled out, and countries emerge from lockdown, we might usefully reflect on what we would have done without a vaccine. It’s a scenario that frightens Linterman. “There wasn’t another exit strategy,” she says. “The vaccines are great, far better than we expected. But there are pathogens that we don’t have good vaccines for. For me, that’s the scary thing. We’re lucky the vaccines are so effective – but that doesn’t mean the same will be true for the next pandemic.”

 

This feature was written by Becky Allen for the Annual Research Report 2019-2020.

Thursday
Dec022021

Wednesday
Dec012021

Public Engagement award for the VirusFighter team

Congratulations to the VirusFighter team for winning the Babraham Institute Public Engagement Award! VirusFighter is the reincarnation of VirusBreak. Over the last year I've worked with the PhD students in our lab, Amy Dashwood, Ntombizodwa Makuyana and Magda Ali, together with lab alumni David Posner, to create missions for VirusFighter - allowing the player to be Prime Minister of the UK during different virus outbreaks. GameDoctor created the interface, with liason via the PE team here at the Babraham Institute.

Congrats to Amy, Tombi, Magda and David - a huge contribution to scientific communication, and all during the first year of their PhDs!

 

Monday
Nov292021

VirusFighter

I'm very excited to announce the release of our new game, VirusFigher!

VirusFighter simulates the outbreak of viruses in the UK. Try the free play mode, and learn how small tweaks to viral lethality, virulence or incubation time change the dynamics of an outbreak. Test whether quarantine, social distancing or vaccination is the best approach for different viruses, and track the lives lost, the burden to the NHS and the economic cost.  

Or perhaps try mission mode: how would you do as Prime Minister during an outbreak of flu or COVID? Think you could do better? Listen to the advice of an immunologist at each critical decision point, and see how many lives your decisions cost. It is not always easy - see what the economist says and keep an eye on the economic cost too! When you've got that down pat, give a shot at something more exotic - an Ebola outbreak, or the "Big One" that gives virologists nightmares - Ebola gone airborne. Don't get too cocky with your good results managing COVID though, different outbreaks are best managed with different strategies. 

VirusFighter is a neat tool for understanding how viruses spread and can be contained, for adults and children alike. Thanks to Simon Andrews for generating the original VirusBreak engine, GameDoctor for the new game engine behind VirusFighter, the Babraham PE team, and the science team: Amy Dashwood, Ntombizodwa Makuyana, Magda Ali and David Posner.