Understanding the immunophenotype of Covid-19 patients could play a key role in anticipating the course of the disease in a patient, thereby assisting healthcare professionals in making critical personalised treatment decisions.
In collaboration with Guy’s and St Thomas’ NHS Foundation Trust and the Francis Crick Institute in London, and the European Bioinformatics Institute (EMBL-EBI) in Cambridge, UK, King’s College London (KCL) launched the Covid-IP (Covid–ImmunoPhenotype) project in March 2020, to better understand the immunophenotype of patients infected with SARS-CoV-2 (the coronavirus responsible for Covid-19).
Immunophenotypes vary greatly across different individuals, giving strong clues as to what mechanisms the human immune system must employ to provide protection from Covid-19, and indicating ways in which it can go wrong, worsening rather than improving the patient’s condition.
The Covid-IP project performed immunophenotyping on blood samples from >120 Covid-19 patients, consisting of eight complementary flow cytometry panels per patient to capture the major populations of immune cells and rare populations, as well as activation markers. This generated thousands of FCS files, requiring significant manpower to organise the analysis, which is manual. Individual scientists had to create each gate and process each file individually. In a study of this scale and urgency, this created a dual challenge of manpower and the possibility of variability in the data due to the manual gating.
Dr Adam Laing, Senior Post-Doctoral Research Assistant, Hayday Group at King’s College London, described the scale of the problem: “The challenges of the project were substantial. We typically run projects of similar complexity and scale over a timeframe of 3-5 years. Due to the urgency of the unfolding situation, this project was completed in 114 days. We had 26 scientists working on the analysis and experiments and while every attempt was made to standardise the gating approach, inter-operator variability was one of the confounding sources of variation that was, in this instance, inescapable.”
As a direct comparison to the manual gating that was carried out on this project, automated pipelines were implemented for flow cytometry analysis using the CytoML Suite in the Aigenpulse Platform. It automated all steps from data import, QC, gating, statistical analysis and visualisation. This enabled researchers to apply guided algorithms to mimic human gating strategies to the entire dataset without manual intervention.
Compared with the KCL manual pipeline, automated processing provided a strong correlation (Pearson’s R = 0.93) (Figure 1a) and reduced variation for each gating step (Figure 1b). The fast processing time reduced the full time equivalent (FTE) from >10 over eight weeks using manual gating, to 1.5 over two weeks using automated gating.
Figure 1. A) Direct comparison of population sizes from 210 samples gated using manual or automated strategies. Colours represent gated populations. Covariance was measured with Pearson’s correlation coefficient, R = 0.93, p-value = 2.2-16. B) Coefficient of variation for gated populations (normalised to live cells), comparing manual and automated gating strategies.
Dr Laing concluded: “The results from the CytoML automated gating technique are really promising. As research into Covid-19 continues, it would be incredibly valuable if we could alleviate a lot of the analysis work from researchers to enable them to focus on the underlying biology of the virus and the immune response of patients. To be able to achieve that, we need automated, high throughput computational approaches, such as this platform.”
CytoML removes the barrier to data processing, automating the mundane, routine tasks of gating and allowing scientists to quickly process data and focus their time on data exploration and assessments and testing their hypotheses. It allows the sharing of gated cytometry data between researchers working across different platforms, making it an invaluable tool for validating and verifying the reproducibility of analyses.
Automated flow cytometry data processing platforms, such as CytoML, not only take the labour out of routine analysis and facilitate traceability and consistency, but enable the reuse of processed cytometry data, integrating population counts identified by manual gating to increase the value of the data and enable cross-project analysis. This is vital for projects such as Covid-IP, where numerous laboratories from different institutions rely on sharing data in real-time and obtaining insights that could aid the diagnosis and treatment of the virus. More information about the project is available in this recently published Nature article.
To find out more about CytoML and our collaboration with the Covid-IP project, download our new whitepaper.