Cutting Edge Session:

Wednesday, September, 28th, 2022: 03:30 pm

Chairs: Henrik Mei and Asylkhan Rakhymzhan

Cytometric technologies ranging from instruments and assays to data analysis are an important pillar of scientific advance in the study of life, with applications in basic and medical research, and diagnostics. The cutting-edge session aims to present the cytometry of tomorrow and features a variety of topics and speakers. This years` focus is the intersection of high-dimensional and functional single-cell cytometry. By capturing functional states of many cell types and differentiation states at a time, at single-cell levels and high throughput, these approaches promise to aid the understanding of human health and disease.

Emily Stephenson
Emily Stephenson


Haniffa Lab, Newcastle University and Visiting Scientist, Wellcome Sanger Institute

The COVID-19 pandemic has shown to be a complicated disease that continues to evolve as more variants of SARS-CoV-2 emerge. In the spring of 2020, very little was known about how the virus entered the body and how an individual’s immune system responded to the infection. The Human Cell Atlas (HCA) database enabled the identification of the specific cell types that were permissive to the virus and the particular entry receptors. We generated further HCA datasets using single cell RNA sequencing combined with surface proteome and T and B lymphocyte antigen receptor analyses from the blood of patients infected with COVID-19. This data revealed a coordinated immune response that contributed both to the resolution and the pathogenesis of the disease. We also generated reference maps that could be utilised in the generation of therapies and prophylaxes.
In 2022, the COVID-19 situation is different across the globe. With the rollout of many vaccination programmes and better therapies, the risk to the general population is much lower. However, for some patient cohorts, COVID-19 still poses a major threat. End stage renal failure has shown to have the highest mortality rate out of these groups. We have collected longitudinal data from ESRF patients with COVID-19 to study the reasons behind why the disease is worse for these individuals. The dataset has also given us the chance to study the kinetics behind the disease and the effect of treatments. 

Dr Emily Stephenson studied Biology at Newcastle University before beginning her career in a clinical laboratory carrying out genetic testing and providing a DNA sequencing service for researchers. Emily joined the lab of Professor Muzlifah Haniffa in a technical role to optimise and implement single cell genomics pipelines. Following many successful years in this position, Emily began a PhD in Immunogenomics. Emily’s research focused on mapping the immune system during development and disease using single cell technologies. Emily and her colleagues made several novel discoveries which were published in Nature, Science and Nature Medicine which contributed to her PhD thesis. Emily is now working as a Senior Research Associate in Prof Haniffa’s lab where she continues her research into immunology whilst training and supervising staff and students.

Rafael Jose Arguello
Rafael Jose Argüello

Metabolism in the single cell era: approaches to sharpen the cutting edge of the metabolism field

Metabolic regulation of immunity team
Centre d’Immunologie de Marseille-Luminy (CIML), Marseille, France.

Personalized medicine requires methods that capture biological markers that can predict cell function and response to treatment. There is compelling evidence that the response to treatments in the context of cancer and infection correlate with the metabolic state of cells. Indeed, the metabolic profile of immune cells, infected cells and cancer cell subsets is a universal hallmark of their functional state. Current methods to profile energy metabolism require large number of cells, cell culture media and are not adapted to analyze patient samples. We have recently developed SCENITH, a method to functionally profile energetic metabolism with single cell resolution by FACS. Here, we present a SCENITH based approach that allows to functionally determine the metabolic profile in micro-samples of whole blood (i.e. <500 ul) compatible with non-invasive blood extraction systems. Our approach is fixation and shipping compatible and compatible with epigenetic analysis. We present here a proof of concept using a very robust, 25 colors spectral flow cytometry panel that allows to determine the metabolic profile of all immune in the blood. This revolutionary approach has the potential to be used at home by end users to link their health status and response to treatment with their immune phenotype and functional immunometabolic profile. We envision that by using our SCENITH-based functional profiling as a personalised medicine approach. We predict that these high dimensional functional information of immune cells will contribute to predict disease development and response to treatment, one of the biggest challenges of modern medicine.

Rafael Argüello studied biological sciences, did his master in molecular biology and his PhD in Immunology at the University of Buenos Aires. After his postdoctoral stage in France and the USA in the labs of Philippe Pierre (CIML) and Max Krummel (UCSF), he obtained a tenured CNRS researcher position in France. He leads the “Metabolic regulation of immunity” team in the DeCiBEL lab, at the Centre d’Immunologie de Marseille Luminy (CIML). He has been studying regulation of protein synthesis and metabolism and has developed original methods to understand basic mechanisms of cell biology. Rafael patented and published SCENITH and envisions the use of SCENITH as a personalized medicine tool. For this, he created a website and an international network of collaborations ( Since 2019, he has been awarded grants including an Emergence grant, CoPoC, ECOS-Sud (2021-2023 France-Argentina) and the ¨ANR – young researcher award¨ of the French National Research Agency grant (2021-2025). He has recent publications in Cell, Cell Metabolism, Nature immunology, Nature Cancer and EMBO Journal. He is a Marylou Ingram scholar (2021-2024) of the emerging leadership program of the international society for the advancement of cytometry (ISAC) and in 2021 he obtained the “Diversity, Equality and Inclusion Paper of the Year award” from Society for Leukocyte Biology.

Marie Burns (Short Talk)

Deutsches Rheuma-Forschungszentrum Berlin, ein Leibniz-Institut, Immune Monitoring & Mass Cytometry, Berlin, Germany

Single cell phospho-signatures for precision medicine in SLE and other chronic inflammatory diseases
Chronic inflammatory diseases are complex and multifactorial, and often comprise clinically unapparent endotypes, differing in immunopathogenesis and pathology. Such heterogeneity is a major challenge for treatment-decision making and prognosis. We developed a novel pipeline for the analysis of the ex vivo phosphorylation states of nine important signal transduction proteins in patients´ deeply resolved blood leukocytes by highly multiplexed mass cytometry to capture cell type and -state-specific activation patterns in chronic inflammatory diseases.
Phosphorylation patterns differed most between haematopoietically and functionally distinct cell types. Also, profiles of naïve and memory B and CD4, and CD8 T cells differed by both lineage and differentiation state, showcasing highly polarized phospho-signatures across the immune system. The cellular fingerprint of 20 patients with active Systemic Lupus Erythematosus (SLE) was characterized by selective leukopenia affecting myeloid populations and B cells, an induction of plasmablasts and activated CD8 memory T cells, and a prominent and selective increase in ex vivo STAT phosphorylation. The latter was limited to specific cell subsets and highlighted an important role for monocytes, NK cells, PB/PC, as well as CD8 T cells in SLE.
Phospho-readouts improved the classification of healthy individuals vs. SLE, the representation of patient individuality and were associated with distinct immuno(patho)logical features of SLE. In line with the expectation that specific immune cell overactivity is associated with maintenance of disease, responsiveness to the JAK inhibitor Baricitinib was associated with high baseline levels of pSTAT3 in monocytes and T cells in distinguished one responder patient from three patients with poor to intermediate response.
Notably, cross-disease comparisons with patients suffering from rheumatoid arthritis and spondyloarthritis yielded disease-specific phosphorylation patterns when considering averages over patient groups, while single RA patients grouped with SLE patients in multidimensional clustering, identifying shared cross-disease patterns of immune activation, potentially requiring clinical consideration in individual patients.
By integrating ex vivo immune cell activation cues into cytometric immune profiling, functional phospho-mass cytometry provides a new platform to capture patient heterogeneity and disease endotypes, expected to contribute to precision medicine in chronic inflammation.

Daniel Kage (Short Talk)

Deutsches Rheuma-Forschungszentrum Berlin, ein Leibniz-Institut, Flow Cytometry Core Facility, Berlin, Germany

Cell sorting based on angle-resolved pulse shapes
Light scattering or imaging technologies can be used to characterize cells. However, in standard flow cytometers, information from scattered light is limited by the lack of angular resolution. Also, only integrated or peak intensities from the cell transit through the laser beam are provided. Although imaging flow cytometry techniques are advancing, they require high computational power and sorting is limited to certain simple metrics calculated from the images.
We recently developed a technique that enables the acquisition of full intensity pulse shapes during cell transit through the laser beam and provides angular resolution in the forward scatter direction. The technique was combined with clustering-based data analysis and has proven to be useful for labelfree cell cycle analysis. This method is now extended for cell sorting based on pulse shape features. First results were obtained with this custom-built setup in sorting different cell types only based on scattered light pulse shapes.