How Imaging Mass Cytometry transformed one lab’s discovery of the intrinsic behavior of a tumor

Breast cancer mortality is more than 40% higher for African American women in the US than Caucasian women. Yet, the cancer’s incidence across race is very similar.

Melissa Davis, PhD, is Scientific Director of the International Center for the Study of Breast Cancer Subtypes (ICSBCS) and Assistant Professor of Cell and Developmental Biology at Weill Cornell Medical College. She is working to uncover the various components that contribute to cancer mortality disparities between races.

While multiple factors play a role in cancer survival, tumor characteristics are believed to account for almost a quarter of racial differences. Early research on variations in tumor biology among breast cancer patients of different races labeled hormone receptor status as a differentiating factor. To take this further, Davis explains, “The triple-negative subtype in breast cancer lacks expression of hormone receptors that are the basis of common targeted therapies and has the highest rate in non-Hispanic black patients. It is important that we identify what molecular markers underly this subtype beyond hormone receptor status or genomic profiling, which can be impractical in a clinical setting.”

A look into African history explains much about the origins of the triple-negative subtype and what other related phenotypes might be present in women with this diagnosis. Davis focuses on the African diaspora, commonly used to describe the mass dispersion of African people during the transatlantic slave trade, recruiting patients for sample collection from Africa, the Caribbean islands and North America.

Africa has the lowest incidence of breast cancer in the world, yet the highest mortality rate due to the prevalence of triple-negative diagnoses. This component of African ancestry spreads to black Americans in the US and black South Africans, with similar trends in West Africa, all coinciding with the transatlantic slave trade across the Americas and the Caribbean.

“The triple-negative subtype in breast cancer lacks expression of hormone receptors that are the basis of common targeted therapies and has the highest rate in non-Hispanic black patients. It is important that we identify what molecular markers underly this subtype beyond hormone receptor status or genomic profiling, which can be impractical in a clinical setting.”

Exploring the DARC phenotype

Davis and her lab aim to find molecular markers and functional differences that might explain the frequency of the triple-negative subtype in these populations. They are currently focused on one particular allele, the Duffy-null allele of the Duffy antigen receptor for chemokines (DARC) gene, that arose in sub-Saharan Africa and plays a role in immune response regulation. It was fixed into this population because it confers resistance to malaria.

Its global distribution is vast, with over 70% of Africans carrying the allele in America alone, and tracks with movement along the transatlantic slave trade. Duffy-null carriers are also erythrocytic-null, expressing the gene only in endothelial cells. This is an important feature because it results in recruitment of immune cells into inflamed areas through the circulatory system. In a tumor, this would influence the microenvironment in an organ site, such as in breast cancer.

Recently, the Davis lab discovered that tumors expressing this gene also exhibit higher levels of inflammatory chemokines and infiltrating immune cells. Owing to this chemokine recruitment, a high level of DARC expression in a tumor is associated with significant survival benefits in all subtypes.

Imaging DARC with immunohistochemistry

In the effort to uncover how the DARC gene phenotype bestows a survival advantage, a look at gene signatures correlated with DARC status identified a gene subset that tended to be more highly expressed in DARC-positive tumors. Davis used these as a template to learn more at the single-cell level with traditional immunohistochemistry (IHC).

The goal was to determine whether these markers and the patterns of these markers correlate synchronously across the tumor space by compartmentalizing features and quantifying cell types. However, IHC quickly became its own challenge, with the inability to multiplex marker staining on single sections. The initial work was tedious but yielded results after months of simple cell identification. The team saw a higher rate of infiltrating CD3+ cells. Davis focused in on 20-plus markers, but with IHC, this would become exponentially more difficult. The solution was Imaging Mass Cytometry™ (IMC™).

“If we were going to describe and characterize a tumor phenotype based on heterogeneity, we needed a better platform. We turned to IMC with the Hyperion™ Imaging System at the Englander Institute for Precision Medicine to define spatial distinctions characterizing the DARC immune tumor type,” said Davis. “We were able to multiplex up to 30 markers as our target number of conjugated antibodies on a single slide and acquire this data in a way where we could quantify the markers and do computational analyses.”

Moving forward with IMC

A first step was careful consideration of the regions of interest (ROIs). With a need to compare populations, it was important to collaborate with a pathologist who could demarcate areas of interest to capture the stroma as well as the solid tumor space and to ensure that the captured area was comparable across different patient populations.

IMC comparison of the tumor types showed that in the DARC-negative tumors, immune cell markers isolated to the stromal area with less infiltration into solid tumor space. Conversely, images of DARC-positive tumors showed more infiltration into the solid tumor space and increased association with endothelial cells and vascular structure. Differences in tumor architecture suggest that infiltration occurs because DARC-positive tumors are more vascularized. These findings blend nicely with prior bulk-tumor RNA-seq data indicating that the spatial distribution of immune cells is variable between tumors.

“These images provided a view of the distribution of markers and relationships of cell populations across DARC-positive and DARC-negative tumors. Quantifying behavior at a single-cell level with IMC offers more information about what is happening across the architecture and connects these findings to clinical implications in breast cancer,” clarifies Davis.

IMC made it easy to identify subpopulations of cells present only in a DARC-high vs. a DARC-low environment by combining all ROIs to view non-overlapping subsets. Davis found one subset of cells in DARC-positive tumors that co-expressed immune cell and mesenchymal markers, mapping these vimentin-positive immune cells as infiltrating into the solid tumor area and indicating a distinct DARC-regulated tumor microenvironment. Further study will include functional characterization of these subpopulations using models of DARC tumors in mice and ex vivo patient-derived organoids.

Initial clinical implications of these phenotypes for survival advantage are promising. Next steps in this project will be to correlate DARC phenotypes in tissues with clinical outcome and clinical annotation and to stratify populations by subtype.

Imaging Mass Cytometry empowered Davis not only to discover what cell populations are present in these tumors and where they are located but, simultaneously, also to detail the intricate behavior and interactions occurring among cells and how these actions affect clinical outcome in triple-negative breast cancer patients.

“Why is this important in the context of disparities? We know from research on human evolution that immune cells originating from people of African descent compared to European descent have completely different responses to the same bacterial pathogen. This indicates that evolutionary consequences or natural events, like the types of pathogens present in Europe or in Africa, have elicited unique immune responses,” Davis concludes.

“And we can apply this to other diseases, such as cancer, that are highly reliant on an immune response. Consider implications on therapeutics, and whether tumor cell subtypes could identify subpopulations in which immunotherapies are effective and predict treatment response based on these patterns.”

The Ihrie Lab at Vanderbilt develops new approach to associate cell type to overall survival

Sometimes the smallest things can help you see the bigger picture. That’s the idea behind one professor’s research into brain cancer. By revealing the abnormal cells that are most aggressive within brain tumors, we can find answers about which cell features may lead to which clinical outcomes.

Rebecca Ihrie, PhD, Associate Professor of Cell and Developmental Biology at Vanderbilt University, recently talked with us about glioblastoma, one of the most common and malignant forms of primary brain tumor.

Although glioblastoma has been a heavy focus of study for many years, prognosis has not improved much in recent decades. Current standard treatments center on the alkylating agent temozolomide in addition to radiotherapy. While use of this agent extends survival to some extent, overall prognosis and survival outcomes remain poor.

Mass cytometry is a primary research approach used in the Ihrie Lab, being particularly informative for glioblastoma investigation. “When we think about everything going on within a cell, DNA and RNA profiling, which have been widely used in brain tumors and many other tissues, are certainly very informative. But when we think about the functional repertoire of a cell, this information is incomplete,” explains Ihrie. “Mass cytometry allows us to complement what we have learned at the DNA and RNA level to better understand beyond copy number analysis, sequence analysis and transcript analysis how cells are dynamically responding to their neighbors and what their signaling capability at the protein level is.”

The complexity of glioblastoma is evident within tumors, which can contain multiple cancer lineage cells resembling different stages of neural stem cell differentiation; neurons, astrocytes, and glia (the resident cells of the brain); various immune cells; and endothelial cells. Three major subtypes of glioblastoma have been classified, distinguished primarily by alterations in growth factor receptor gene copy number as well as transcriptional signatures that resemble different progenitor cells within the brain.

Ihrie is interested in understanding these heterogeneous mixtures of cells and how they vary across patients. She discusses two recent questions her lab and collaborators are currently focused on: How can we identify highly malignant phenotypes across glioblastoma, finding the most aggressive cells that drive recurrence but that might be rare and variable across patients? And how can we do discovery work in smaller cohorts of patients? “We really wanted to leverage all of the information generated from approaches like mass cytometry, where we can obtain millions of cells and lots of data on specific parameters to figure out which of these parameters can help us find the cells that are driving the worst outcomes and then identify their targetable features,” Ihrie says.

“Mass cytometry allows us to complement what we have learned at the DNA and RNA level to better understand … how cells are dynamically responding to their neighbors and what their signaling capability at the protein level is,” says Ihrie.

Stratifying patients with RAPID

Risk Assessment Population IDentification (RAPID), an unsupervised and automated machine learning algorithm, identifies phenotypically distinct cell populations and determines whether these populations stratify patient survival. The algorithm couples the per-cell data obtained using mass cytometry to continuous features like progression-free survival, measured in days or weeks, and overall survival, which in glioblastoma has a median of about 15 months.

Initially, the team analyzed over 2 million glioblastoma cells from 28 patients by mass cytometry, using a panel of 40 different parameters to find immune, endothelial and other infiltrating cells within brain tumors.

The team assigned all cells to clusters based on phenotype and determined which clusters were meaningfully associated with a better or worse outcome. RAPID analysis identified both glioblastoma-negative prognostic (GNP) cells, whose abundance is predictive of especially bad outcome, and glioblastoma-positive prognostic (GPP) cells, whose abundance indicates a long overall survival. She notes that since these survival differences had not been discovered through other data methods at the genomic DNA or RNA level, “seeing something that stratified survival was very exciting, because it gives us an opportunity to better understand the biology of these tumors and think about how to target them.”

The in-depth data gleaned from their mass cytometry experiments revealed distinct and mixed phenotypes with opposing expression patterns that further distinguished the two prognostic groups. “Access to the high-dimensional data from CyTOF® was fantastic for us to be able to do additional ongoing discovery work in these tumors and to understand these phenotypes,” adds Ihrie. “But we want to be able to translate this to approaches that neuropathologists typically use.”

Translating CyTOF findings to neuropathology

Ihrie wondered if brain tumor location could affect cancer cells and immune cells within the tumor. The goal was to apply the phenotypes discovered from suspension mass cytometry and pinpoint their location within individual tumors or across tumor classifications.

Based on MRI images, the team asked whether glioblastoma tumors contacting specific structures within the brain have better or worse outcomes. At initial diagnosis, about 50% of patients had a tumor that contacts the lateral ventricles and the stem cell niche that resides there. These patients had poorer overall survival. A smaller subset had a tumor that contacts the other major stem cell niche of the brain, the dentate gyrus—but surprisingly, contact with this niche did not correlate with survival differences.

Overlaying their originally determined cell phenotypes with tumor location classification, they found a cluster of cells that is enriched in noncontacting tumors with protein expression levels very closely resembling the glioma-positive prognostic phenotype.

Other known predictors of outcome, such as the size of the tumor, the ability of the surgeon to resect it and bulk DNA and transcriptomic profiles, did not explain the effects of ventricular contact and raised novel questions about the biology behind these differences in outcome. Moving forward, Ihrie is interested in coupling MRI images, a routine visualization for glioblastoma patients, to this type of information to help decide on treatment options.

Connecting the dots on the influence of tumor location on potential tumor prognosis

One challenge with traditional imaging approaches is the inability to view and analyze all parameters simultaneously. “Wouldn't it be nice if we could measure everything together? That's something we are working on now, together with the Bursky Center for Human Immunology & Immunotherapy at Washington University in St. Louis using Imaging Mass Cytometry™,” Ihrie says. “We are eager to take all the parameters we used on dissociated tumors in suspension and apply them across the tissue to get more information, including spatial data for better classification of patients.”

Ihrie next used Imaging Mass Cytometry to identify and associate cell-to-cell variation and distribution within different tumors to phenotypes. The team developed and validated a brain-centered imaging panel that included lineage-specific transcription factors, phosphoproteins and other cancer-centric markers that overlap with the panel used for earlier RAPID studies.

In glioblastoma samples, images revealed a nonrandom distribution of signaling, with separate clusters of cells signaling together. Further study could isolate these signaling groups to decipher their role in the tumor and whether distance from the stem cell niche might come into play.

Particularly in glioblastoma, where transcriptomic and other genomic analyses have not readily allowed stratification of patients into different groups based on outcome, mass cytometry measurements at the protein level that include phosphorylation events have helped classify prognostic subgroups. The custom workflow, RAPID, is now published, and it generates simple stratification assays that use high-dimensional data to compare findings in fixed tissue and suspension.

“Ultimately, we are very excited to find that stem cell niche contact changes the cancer cell and immune cell features and is linked to some of the differences in prognosis that we found before. And importantly, these phenotypes are not uniformly distributed through the tissue. The ability to go back and forth between what we can discover in suspension and in fixed tissue has been an incredibly useful tool for us,” concludes Ihrie.

Mass cytometry assists research on role of immune system in disorders: A COVID-19 update

Lori Turner, PhD, a post-doc in the NIHR Cambridge BRC Cell Phenotyping Hub and the Department of Psychiatry at the University of Cambridge, had been using the Maxpar® Direct™ Immune Profiling Assay™ in a potentially groundbreaking search for treatments for depression that act through the immune system.

But with the onset of the COVID-19 pandemic, the multi-site Antidepressant Trial With P2X7 Antagonist JNJ-54175446 (ATP), the first clinical study by the Wellcome Trust Consortium, came to a halt in March 2020. While the team waited for the ATP trial to restart, its members decided to repurpose their entire workflow to do a COVID-19 study. “We had everything sitting there ready to go. We thought, this is a perfect opportunity to immunophenotype COVID-19,” says Turner.

The team got back to work using that same workflow staining, fixing and freezing COVID-19 samples and then thawing them later for analysis. The ability to work with frozen samples was a defining factor of the new study, since more samples were coming in than could be analyzed fresh. The team also purified white blood cells and performed immunophenotyping on them.

The study gave Turner a unique opportunity to compare flow versus mass cytometry data for quite a large number of patients, since their new analysis used both. She found that across the various types of immune cells—naive B cells, memory B cells, gamma delta T cells, MAIT cell, and CD4 naive central memory cells, to name a few—there was “a remarkable correlation” in the data. “I was really impressed to see that,” says Turner.

Moving forward, Turner is considering using the workflow in other studies as well, especially as things have changed with the pandemic. “We are not recalling patients into the hospital, and so we’re planning lots of multi-site studies in the future,” she notes. By validating the workflow for general use, it will become very easy for the lab to adopt quickly. “You add the whole blood to the [Maxpar Direct Assay] tube, stain it for 30 minutes, add a stabilizer for 10 minutes, and then stick it at –80 °C. Or stick it on dry ice and ship it to a central site where samples can be analyzed.” With several trials using the workflow in planning stages, Turner thinks it’s going to make a lot of their work much simpler going forward.

The Influence of Inflammation on Mental Health

How a new clinical trial using CyTOF® could change treatment options for depression

As an immunologist with a background in host-pathogen interactions, Turner explores immune system function and dysregulation in various psychiatric disorders.

With depression . . . we see a spectrum of symptoms. We believe that different symptoms correlate with numbers of certain immune cells and changes occurring in these cells.”

Before ATP was put on hold, Turner was coordinating the biomarker studies in the clinical trial as part of the Wellcome Trust Consortium for the Neuroimmunology of Mood Disorders and Alzheimer’s Disease (NIMA). Rather than target the nervous system, as do many current medicines for depression and neurodegeneration, the NIMA consortium focused on the immune system. Several studies have already linked the immune system to psychiatric and neurodegenerative disorders, revealing increased levels of inflammation in patients with depression^1,2 and Alzheimer’s disease³. By bringing together academia and pharmaceutical companies, NIMA investigates whether targeting the immune system could become an effective alternative treatment.

Immune cell biomarkers in depression

Certain immune cell subsets are increased in depression while others are decreased, and these changes relate to the level of severity of specific symptoms. Turner focuses on whether new treatments could influence these changes and subsequently affect symptoms. “With depression, some people feel anxiety or fatigue for example, and some people don’t—we see a spectrum of symptoms. We believe that different symptoms correlate with numbers of certain immune cells and changes occurring in these cells. This is what we’ve set out to identify,” explains Turner.

In the Phase 2 clinical trial, the research team intends to test an anti-inflammatory that crosses the blood-brain barrier to block activity of the P2X7 receptor, present on various immune cells and linked to stress-related depression. The group will perform immune profiling by mass cytometry to monitor the immune system before, during and after treatment. With any observed response, further study could identify immune biomarkers related to that response. Patients will also complete additional blood tests, questionnaires and magnetic resonance imaging brain scans throughout the trial to more comprehensively understand anti-inflammatory effects on the immune system and the brain.

Neuro-immunophenotyping with CyTOF

Turner came into mass cytometry through her work with the flow cytometry core facility at Cambridge. While her early research used flow cytometry, her more recent work at the Cambridge Biomedical Research Centre (BRC), part of the National Institute for Health Research (NIHR), requires larger panels that can only be analyzed by mass cytometry. In the first phases of the study, Turner directed preclinical work to assess depressed participants and healthy controls by mass cytometry, using the Fluidigm customizable human immune monitoring kit. Initial data focused on the P2X7 receptor and potential associated biomarkers.

“Since standardization is extremely important for a multi-site clinical trial, we needed a pre-validated, as-is kit that included the markers we were focused on and that could be used across multiple sites …”

For the clinical trial, the team moved to the Maxpar Direct Immune Profiling Assay. “Since standardization is extremely important for a multi-site clinical trial, we needed a pre-validated, as-is kit that included the markers we were focused on and that could be used across multiple sites since the trial will be carried out across five centers in the UK,” says Turner. “The idea of being able to add a whole blood sample directly to a pre-prepared assay tube, reducing site-to-site variation, was a huge factor for us. And the inclusion of Maxpar Pathsetter™ software allows us to quickly generate a report that goes into our online database, which is then shared with the clinical team for complete quality control.”

As the trial moves forward, the group is optimistic about the opportunity to identify related biomarkers that indicate response to the treatment. Good results could then initiate an expanded trial with more and different types of participants. Turner notes that the Maxpar Direct Immune Profiling Assay and Maxpar Pathsetter software can evolve with the study, allowing addition of new markers to investigate other aspects of immune cell subsets. For a list of this and other clinical trials employing mass cytometry, go to clinicaltrials.gov.

Learn more about the award-winning Maxpar Direct Immune Profiling Assay and Maxpar Pathsetter software.
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The critical role CyTOF plays in helping Nadia Roan understand viral persistence and clearance mechanisms

How a dual-mode technology enables better insight

Xianting Ding, PhD, is a professor in the School of Biomedical Engineering and deputy director of the Institute for Personalized Medicine at Shanghai Jiao Tong University. The Ding lab focuses on two main objectives: detecting disease-correlated biomarkers to enable early diagnoses and manipulating these targets to develop effective and personalized combinatorial drug therapies. “We believe that the exploration of immunity, metabolism and cell activity in clinical specimens is important for optimizing treatment response and improving patients’ survival time,” he says.

Biological systems are made up of complex layers of molecules interacting to generate a healthy homeostasis with the ability to respond to infiltrating disease. A dysfunctional system can cause aberrant expression and other signaling errors. Ding points out that research on SARS-CoV-2, for example, shows evidence that immune system abnormalities are highly correlated with disease progression. Likewise, he and his team aim to correlate abnormal immune profiles with clinical phenomics of other diseases by applying high-dimensional single-cell analysis.

"Compared to other high-multiplex technologies, IMC excels at providing both spatial information and high-multiplexity."

Given that available samples, typically blood or tissue, tend to be small or acquired sparingly, Ding must apply various techniques to comprehensively gather enough data to formulate solutions. The group uses a combination of CyTOF® in mass cytometry and Imaging Mass Cytometry™ (IMC™) and fluorescence-activated cell sorting (FACS), Orbitrap™ for mass spectrometry and liquid chromatography-mass spectrometry (LC-MS) to study the different aspects of a disease.

The lab recently published work on colon cancer, where IMC allowed them to map more complex characteristics and related immune cell networks. These types of studies require labeling more markers than allowed by conventional techniques. With IMC, the team can increase its marker count to meet this demand using as many as 37 markers in one panel.

“IMC helps us understand the cell population distribution in a diseased area in contrast to a non-diseased area,” he explains. “Compared to other high-multiplex technologies, IMC excels at providing both spatial information and high multiplexity. It allows us to see spatial context more intuitively and enables accurate detection, treatment and prognosis of the disease by analyzing the immune system in the peripheral blood.”

Taking advantage of dual mode

One successful approach in generating an in-depth view of tissue architecture and spatial distribution of cells in addition to an overall immune cell profile is the dual-mode capability of the Hyperion™ Imaging System, which enables analysis of both suspension and tissue. Ding discussed his use of mass cytometry and IMC for an in-depth characterization of the immune cell subsets and protein profiles involved in signaling pathways in the peripheral blood and skin of patients with psoriasis (PS).

The group analyzed peripheral blood mononuclear cell (PBMC) samples isolated from four patients who were newly diagnosed with graft-versus-host disease (GVHD) and four samples from healthy controls. Based on a combination of surface markers, they used CyTOF to simultaneously analyze T cell and B cell subsets using mass cytometry. Results showed that the frequencies of total T and B cells were statistically significantly decreased in GVHD samples, while frequencies of specific CD8+ T effector memory cells were increased.

Following CyTOF mass cytometry analysis, two samples of PS lesions were obtained and stained for 31 immune markers for Imaging Mass Cytometry. In the IMC images, the team observed colocalization of immune cell subsets, validating their expected cell distribution and indicating the stability and reliability of the technique. Using dual-mode capability on the Hyperion Imaging System, the team identified 15 major immune cell populations in T cell lineages and characterized various other T cell populations simultaneously.

Based on the behavior of the immune cell subsets as seen by CyTOF, Ding was able to hypothesize how cell migration affects the progression of the disease. For example, a reduction of lymphocytes in peripheral blood suggests that they may migrate to the lesion area. That hypothesis can be verified with IMC data. Ding adds that the use of single-cell mass cytometry allows systemic-level characterization of lymphocyte subpopulations and dysregulated signaling pathways in the blood, allowing him to identify abnormalities of different immune cell subsets.

The use of both suspension and imaging modes on the Hyperion Imaging System has the potential to benefit many research areas, including oncology, immunology, immunophenotyping and more. Mass cytometry data can be used to predict the occurrence and progression of diseases, and IMC data represents the mechanism in lesions or other diseased tissue. Further study using these technologies has prompted Ding to develop novel probes for IMC to support successful imaging acquisition and offer new opportunities for diagnosing malignancies in the future.

Mass Cytometry Key in Coronavirus Science Publication

Impaired interferon response found in severe cases

IMC a key to early data on SARS-CoV-2

Immune response in respiratory cases focus of researchers in China

University of Virginia researcher focuses on role of immune cell subsets in chronic inflammatory diseases

Junior Investigator Hema Kothari, PhD, is one of many researchers who have been able to pivot their research to study mechanisms that influence the risk of severe COVID-19 progression. Dr. Kothari is a Research Assistant Professor in Dr. Coleen McNamara’s lab at the University of Virginia (UVA) in Charlottesville and focuses on understanding the role of immune cell subsets in cardiovascular disease and other chronic inflammatory diseases.

In an effort to develop approaches that better enable personalized treatments for immunomodulatory and anti-inflammatory therapies, the lab created a mass cytometry-based pipeline for in-depth analysis of human peripheral blood mononuclear cells (PBMC) to identify specific immune phenotypes that associate with disease severity and response to therapy. Work is ongoing using these customized panels and study populations including subjects with rheumatoid arthritis, coronary artery disease and other inflammatory illnesses.

Recently, IL-1β, a proinflammatory cytokine investigated by Kothari, has emerged as a key mediator of the cytokine storm linked to high morbidity and mortality from COVID-19. In addition, blocking the IL-1 receptor with anakinra has entered clinical trials in COVID-19 patients.

Upon the realization that her current study could easily translate to and possibly accelerate COVID-19 research, Kothari refocused her efforts on the specific immune cell subsets targeted by IL-1β and IL-1β-induced signaling pathways in humans.

Cardiovascular problems are a known issue with cytokine storm onset in COVID-19 patients and correlate with disease severity and mortality. Applying her knowledge from cardiovascular research to SARS-CoV-2 activated inflammation, Kothari has initiated a new study that is funded by the UVA Manning Fund for COVID-19 Research. The study’s goals are to phenotype PBMC samples collected from COVID-19 patients at UVA by mass cytometry to identify circulating immune cell subtypes that are most responsive to IL-1β stimulation and immune phenotypes that correlate with disease severity, and to develop a customized diagnostic biomarker assay for early identification of those at risk of a cytokine storm for improved patient outcomes.

With high interindividual heterogeneity in response, when some patients get better and some progress to critical stages of the disease, the ability to look at broad intracellular signaling supports a greater understanding of what is occurring upstream of cytokine production. This will allow earlier identification and possible prediction of which patients are at a higher risk of cytokine storm and which are most likely to benefit from anakinra therapy.

Uniquely CyTOF

Kothari works with CyTOF® technology for the majority of her projects because it offers comprehensive immune cell analysis, the ability to more deeply profile immune cell subtypes and the flexibility to adjust and expand panels easily. With flow cytometry or other conventional cell analysis methods, it can be challenging to expand a panel to more than 20 markers. That ceiling tends to be just enough to identify different immune cell types in the PBMC sample but does not allow the opportunity to ask more in-depth questions about what these cells are doing.

“CyTOF is really helpful because you can identify all cell subsets as well as the specific proteins that you are interested in while avoiding challenges with fluorophore spectrum overlap,” explains Kothari. “For example, we were able to use a 37-marker panel in the COVID-19 study. I am interested in looking for cell surface receptors such as IL-1 and IL-6 receptors, intracellular signaling proteins activated downstream of IL-1β and IL-6, intracellular cytokines and other markers across different immune cell types. The power of CyTOF is that you can have a lot of different markers in a panel and analyze them all at once.”

Furthermore, CyTOF allows more flexibility in experimental approach as the metal-tagged antibodies used are compatible with harsh cell fixation and permeabilization treatments. The compatibility of CyTOF with methanol treatment is key for performing analyses such as phosphoflow in the high-parameter space, mentions Kothari. Many fluorophores used in high-parameter fluorescence cytometry can be destroyed when cells are treated with methanol.

Further exploration takes many directions

With their CyTOF based approach already in place, the team can easily look for IL-1β-mediated signaling pathways across different immune cell subtypes and identify immune cell targets for IL-1β in humans, for more general cardiovascular implications as well as for COVID-19 specifically. One of the key findings uniquely enabled by CyTOF and advanced computational analysis was the discovery that CCR6-positive T cells are a major target of IL-1β in humans. Next steps could include looking for downstream effects of this interaction and whether it plays a role in IL-1β-mediated inflammatory mechanisms in cardiovascular diseases or in other inflammatory diseases. Kothari would also like to take these findings back into murine systems and investigate the impact of CCR6-positive T cells on atherosclerosis.

Kothari is able to expand her investigations in many directions using the lab’s current approach involving custom panel development and CyTOF technology to identify mechanisms, predict disease outcomes and characterize therapeutic response.

One therapy does not fit all. With this in mind, Kothari ultimately would like to apply her findings to developing precision and personalized approaches for the treatment of cardiovascular disease.

Learn more about Fluidigm’s response to the COVID-19 pandemic.

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Coupling immunology and microfluidics: How one lab explores the immune system one T cell at a time

There is an art to moving from complex scientific concept to simple question. How can we connect a single molecule to a disease state? How can our understanding of a disorder help us discover effective immunosuppressive drugs? What can we model in the laboratory that might impact the efficacy of a new vaccine? Dr. Luc Teyton, MD, PhD, has routinely pursued answers to scientific questions like these throughout his career as a clinical immunologist-turned-professor who now challenges his students to do the same.

Putting these basic questions into practice, researchers in the Teyton lab at the renowned Scripps Research institute in La Jolla, California, primarily focus on autoimmunity by investigating the association between human leukocyte antigen (HLA) molecules and various immune conditions using genomics, transcriptomics and proteomics. By approaching research one simple question at a time, Teyton is able to look broadly at a scientific hypothesis instead of limiting himself to a particular technique or discipline. “Rather, we collaborate with specialists in various fields to perform whatever is needed to give us different parts of an answer," he says.

Teyton’s pioneering approach to taking on any technique that makes a project successful has led the lab to adopt an array of methods from biochemistry, structural biology and biophysics to genetics and cell biology. However, a fundamental challenge in the study of the immune system involves isolating and analyzing small numbers of cells. As Teyton explains, “Sampling humans dictates the approaches you can use. Human cells are accessible from only three origins: saliva, blood and biopsies, which provide a very limited number of immune cells.” Teyton and his team have spent years developing enrichment tools and techniques that would allow them to isolate the needle in the haystack, such as the major histocompatibility complex (MHC) tetramer that enables labeling and sorting of single CD4 T cells based on their antigen recognition. “That being said, we usually end up with ten to a hundred cells of interest from each sample, leaving us no choice but to have recourse to single-cell approaches.”

The microfluidics entourage

Traditional manual analysis of single T cells to determine sequence and structure equated to a daunting amount of work and dismal efficiency. It took one post-doctoral fellow five years to sequence 250 cells from diabetic mice—work that now represents two experiments and two weeks of work, with efficiency of cell recovery reaching 60–70%, thanks to microfluidics.

Fluidigm microfluidics technologies have transformed the time and workload of these single-cell experiments with automation and conservation of sample and reagents. Single-cell isolation and sample preparation upstream of next-generation sequencing and real-time PCR analysis can be done using the C1™ system, and bulk cell and tissue analysis is easily completed on the Biomark™ HD system. Researchers rely heavily on Biomark to quantitatively examine series of 96 genes of interest in single cells. They can also comprehensively sequence T cell receptor chains from the same cell to analyze gene expression patterns after preparation on C1. “We have been impressed by the quality of the data generated by C1 with a depth of interrogation that was on average beyond 10,000 genes per cell,” adds Teyton.

Teyton describes as an example how microfluidics has allowed his lab to perform an in-depth analysis of the early anti-insulin response in the nonobese diabetic (NOD) mouse, a model of type 1 diabetes (T1D). Now the group can work on translating these observations to humans, particularly at-risk populations like first-degree relatives of a newly diagnosed T1D patient. Teyton hopes that his lab’s data on activation of an anti-insulin T cell response will explain the progression of autoimmunity in real time and offer an improved monitoring tool to replace tools that are unreliable and poorly predictive. Upon further study, the same approach could potentially be used to evaluate the efficacy of immune intervention during the pre-clinical phase of disease.

Integration of microfluidics across hypotheses

In addition to C1 and Biomark HD, Teyton’s lab recently acquired the Juno™ system, which can process and prepare all Fluidigm microfluidic devices. The lab will incorporate Juno into a project that extensively uses C1 for scRNA-seq, where they will examine immune cell populations in colonic biopsies of patients with inflammatory bowel diseases.

Teyton would like to translate some of his approaches to the clinic for use in diagnosis and therapeutic monitoring. “To be successful, we have to validate the genomics data obtained using the Fluidigm suite of instruments by proteomics data to address protein expression for each cell we analyze,” he explains. “Ultimately, our success will depend on the efficiency of our collaboration and the integration of data from each platform. I believe that single-cell analysis has a huge potential in the clinic, but I am also aware that this potential is matched by challenges of a similar magnitude.”

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Machine learning and infectious disease: how we can better understand vaccine efficacy at the patient level

In one of the 20 most-read papers of 2019, Stanford University and University of Oxford researchers developed an automated machine learning system, Sequential Iterative Modeling “OverNight” (SIMON), that compares high-dimensional datasets from heterogeneous clinical studies focusing on vaccine response (Figure 1). The aim of the project is to build more accurate predictive models and accelerate analysis and discovery. “SIMON is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets,” says Adriana Tomic, PhD, lead author on the paper.


Figure 1. Graphical illustration of SIMON.

Blending machine learning with systems immunology

Taking a systems immunology approach to evaluate changes in immune cells and molecules during an infection or treatment can accelerate the understanding of an effective immune response. Integration of machine learning algorithms into systems immunology can further facilitate the discovery of unseen patterns in relevant immune cell subsets and genes. However, current approaches are challenged by heterogeneous or incomplete datasets. To overcome these obstacles and tailor machine learning to adapt to the variability of multiple clinical studies, Tomic and team developed SIMON open source software for the application of machine learning to high-dimensional clinical datasets. Put more simply, SIMON can predict if an individual will respond appropriately to a vaccine based on a set of immune system parameters.

SIMON was developed to improve computational analysis of influenza vaccination responses in humans, a task that is typically unreliable due to smaller sample sizes and single-target studies. Instead, SIMON analyzes multiple parameters from numerous individuals across several studies to accurately capture biological variability and to increase statistical significance. The inclusion of machine learning helps minimize sample loss and identify algorithms that fit any given data distribution, maximizing predictive accuracy and other performance measurements.

Initially, SIMON analyzed data from the Stanford Human Immune Monitoring Center (HIMC) in a novel project, FluPRINT. Five separate clinical studies of seasonal influenza vaccination were included, with various platforms and expanding parameters, including single-cell analysis at the gene and protein levels using mass cytometry to capture both immune system and individual variation. “By using SIMON, we identified subsets of immune cells not previously described to provide protection against the virus. These results are important for the development of the next generation of vaccines and have the capacity to fundamentally change vaccinology by application of machine learning to speed up discovery,” explains Tomic.

The data generated from this study was collected in an open-access FluPRINT database, meant to enable large-scale studies exploring the cellular and molecular basis of successful antibody responses to influenza vaccines. The resource can be used to uncover new markers and mechanisms that are important for influenza vaccine immunogenicity.

The move to any infectious disease

The SIMON systems immunology approach used in the FluPRINT project to predict flu vaccine response could also become crucial for understanding other infectious diseases, such as SARS-CoV-2 (Figure 2), and developing a vaccine to stop the COVID-19 pandemic. “Generating a similar dataset from ongoing clinical trials on SARS-CoV-2 will be critical for understanding this virus and generating an efficient vaccine,” says Tomic.


Figure 2. Coronavirus morphology.

Evaluating interindividual variation in response to an infection or a vaccine aligns with clinical presentation of COVID-19, where such a wide and unpredictable range of immune responses is observed from patient to patient. SIMON could assist in determining which patients might progress to critical stages of the disease based on their immune profiles and which would respond to certain treatments. To this end, Tomic is part of a University of Oxford team that has now identified a SARS-CoV-2 vaccine candidate and is working toward the first clinical testing phase.

The FluPRINT project is described in detail at fluprint.com and SIMON is available for download at genular.org.

Read the official release about the new vaccine candidate against COVID-19: ovg.ox.ac.uk/news/covid-19-vaccine-development

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