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

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

Immune response in respiratory cases focus of researchers in China

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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Advanta RNA-Seq workflow helps advance research

Scott Magness, PhD, is an Associate Professor and the Founder and Director of the Center for Gastrointestinal Biology and Disease Advanced Analytics Core at the University of North Carolina School of Medicine, and also serves as Founder and Chief Science Officer at Altis Biosystems. He found time to speak with us about his scientific interests and how the Fluidigm Advanta RNA-Seq NGS Library Prep Kit has helped support new and exciting research in his lab.

What is the primary focus of your research, and why did you go in that direction?

As an intestinal stem cell biologist, I am interested in understanding the genetic mechanisms that control stem cell maintenance and differentiation. Your entire gut lining is renewed every week in a stem cell-driven process. We really want to understand the basic biology of intestinal stem cells and learn more about the genes that control their behavior, ultimately leading us to develop translational approaches to stem cell-based therapies for human disease and injury of the intestine. To this end, we have a number of projects focused on elucidating genetic processes that underlie stemness and developing microscale cell culture models to establish a clearer understanding of stem cell-driven regeneration pathways in homeostasis and injury.

While organoids that grow in 3D hydrogels have revolutionized in vitro studies of the intestinal epithelium, there are limitations that prohibit physiological relevance and efficient analyses. To address this, we developed 2D epithelial monolayer systems that serve as ideal experimental models, are scalable and can be amenable to high-throughput analyses. We then created a microtech device to examine the intestinal epithelial response to oxygen deprivation or other environmental signals over time.

Can you give us an example of how the experiments you design in the lab translate to potential therapies for human disease?

Even though we have identified several pathways that regulate the maintenance and proper differentiation of stem cells, one thing we don’t fully understand is how environmental factors, disease states or injury events impact this process from start to finish. One way we are studying this is to examine how stem cells function during ischemia, which is loss of blood flow that can lead to reduced oxygen, or hypoxia, in cells and possibly result in cell death. Ischemia can occur due to stroke in the gut, blood flow interruption during surgery, twisting of the gut or necrotizing enterocolitis. If we can understand how and when stem cells react to ischemic events, we could potentially propose therapeutic interventions to restimulate the stem cells and repair the gut.

Using the gut-on-a-chip microtech device, we developed a hypoxia-on-a-chip environment allowing accurate oxygen level adjustment and detailed measurement of the magnitude of oxygen concentration in a single system at the same time. This approach enabled us to observe stem cell expression patterns with varying magnitude and duration of hypoxia.

Why did you pick the Advanta™ RNA-Seq NGS Library Prep Kit for your project?

After initial determination that we could elicit a response from stem cells to a hypoxic event, we wanted to analyze stem cell response at different time points and elucidate how different gene expression pathways reacted to allow the cells to survive in a hypoxic environment, or not. For us, the Advanta RNA-seq workflow was vital to our studies because we were challenged with only being able to retrieve a small number of cells from our model for subsequent analysis. The culture chamber in the gut-on-a-chip system is very small–the entire five-well platform is smaller than a microscope slide–and we only get about 50,000 cells that generate a confluent monolayer. This translates to small amounts of cellular material and small amounts of harvested total RNA to use in the library prep protocol. Conventional RNA-seq systems simply cannot achieve what we need on this system. Also, in order to perform all technical and biological replicates necessary for these experiments, conventional systems become very expensive. The Advanta system was a game changer, enabling us to use these microdevices and characterize as many transcriptional responses as we can while managing costs.

How did the Advanta RNA-Seq NGS Library Prep Kit help in your hypoxia-on-a-chip research?

The Advanta system generated excellent data quality from our samples with low cell numbers, providing a really nice overview of what was happening to these stem cells during a hypoxic event. For example, we obtained >30 million reads per sample, 98.5% aligned to the human genome reference and 80.2% aligned to the transcriptome reference. Principal component analyses further demonstrated that samples treated under similar conditions clustered together. This level of clean data helped us immensely in pulling out differential gene expression patterns that we could use to generate hypotheses for how the cells were responding to hypoxia over time.

In fact, we were able to determine the top 100 most differentially regulated genes at each measured time point for both normoxia and hypoxia and compare the data across the three time points. Duration of hypoxia on the stem cells induced stereotypical early and late gene expression patterns, where the number and type of unique and differentially regulated genes change over time to increase the cells’ chance of survival.

What’s next for you regarding this project?

With what we were able to accomplish in these experiments, we are excited to move forward with additional studies. One of the original reasons we did this work was to see how long stem cells can survive. Performing additional functional experiments to investigate our hypotheses for how certain pathways protect cells from hypoxia will determine whether we can ultimately lengthen stem cell life during hypoxic events.

Learn more about the Advanta RNA-Seq NGS Library Prep Kit

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Jan-Philipp Mallm, PhD, focuses on how to identify best treatment options for cancer patients

Jan-Philipp Mallm, PhD, works at the German Cancer Research Center (DKFZ), where he and colleagues are interested in how they can identify best treatment options for cancer patients by analyzing single-cell information. The center aims to generate a comprehensive understanding of cancer genome regulation, with the goal of encouraging novel approaches to personalized cancer treatment.

Single-cell sequencing techniques are at the heart of Mallm’s research as the head of DKFZ’s Open Lab for single-cell sequencing (scOpenLab), which provides single-cell sequencing technologies to DKFZ researchers.

In the quest to establish processes that decipher tumor heterogeneity using single-cell genomics, transcriptomics and epigenomics approaches, Mallm was driven to use the Fluidigm C1™ platform and solid-state microfluidic devices. “The C1 system allows us to explore single cells in depth, where we could not before, in a straightforward and easy-to-set-up, all-inclusive instrument,” Mallm explains. While several systems provide good overviews of a given sample, the DKFZ community is looking deeper in an effort to understand regulation and mechanisms in cell processes. The C1 platform provides this enhanced level of detail and the flexibility to optimize custom assays to obtain the highest sensitivity possible.

Applying the C1 platform to scATAC-seq and multi-omic cell characterization

Mallm is interested in correlating open chromatin regions, particularly for promoters, through single-cell analysis. Currently, he studies different types of leukemia to investigate possible common origins in the epigenome. Comparing samples could provide clues to how cancer types relate to one another and whether they contain variable cell populations. The lab also monitors reactions in embryonic stem cells when triggered by external stimuli. This study explores the types of mechanisms that are present in each cell, any differences from cell to cell, changes in gene activation and how this is all regulated with the stem cell state.

The ability to analyze single cells and specific cell populations sheds new light into processes within each cell and in cell-to-cell interactions. To accurately calculate correlations between chromatin regions in these types of samples, a high number of insertion points is necessary to infer which promoter is open and relevant for transcription regulation of a given gene. Mallm uses single-cell ATAC-seq prepared on C1 to ensure sensitive and accurate results—not easily accomplished with single-cell analysis on other systems.

Initially, the scOpenLab tested ATAC-seq on the C1 platform with a lower throughput integrated fluidic circuit (IFC), the C1 Single-Cell Open App™ IFC. With increasing demand and optimal results, the lab moved to the high-throughput (HT) IFC, the C1 Single-Cell mRNA Seq HT, which enabled significantly increased output and better comparisons between samples. “With the HT IFC, we can compare tumor malignancies, treated-to-untreated or malignant-to-non-malignant, on the same IFC. This helps to reduce bias from IFC to IFC and allows for direct comparison of two populations,” says Mallm.

For a multi-omics approach, C1 offers the potential to analyze proteins, RNA, the epigenome and the metabolome all on one platform instead of having separate platforms for each analysis, increasing reproducibility of experiments. The closed IFC system provides a large advantage over standard 96-well or 384-well plates, in which there is expected carryover, sample interaction and/or contamination. Furthermore, each well can be inspected to determine whether it contains a single cell or a doublet. Even an empty well offers additional data about experimental robustness as a built-in control.

With scATAC-seq, Mallm emphasizes the need for many insertion sites, which not every system can provide. With the growing interest in regulation and correlation within and between cells, the higher-level sensitivity of C1 is imperative. The DKFZ also uses C1 because the system can use frozen material, allowing for reuse of tumor samples that have already been analyzed and for deeper analysis of tumor heterogeneity and epigenetic differences from tumor to tumor or within the same tumor.

Deploying highly controlled perturbations, the team discovered that calcium responses are cell type-specific and change dynamically as stem cells differentiate to diverse types of neurons. Additionally, Mayer and her colleagues observed cellular diversity not captured by single-cell mRNA sequencing alone. For example, they found a specific serotonin receptor that selectively activates radial glia cells (a type of stem cell) in the developing human brain, but not the mouse brain. Further, inhibiting this receptor in the human brain disrupts the radial glial scaffold, a mechanism for new neurons to travel to their final destination. In the study, they also show neurotransmitter signaling during neurogenesis and highlight potential mechanisms in the evolution of physiological signaling.

When used in this way, the C1 system supports the characterization of cell populations and allows identification of mechanisms, where other instruments focus purely on cell type identification and signatures. “Many different approaches can identify cell types. But if you want to understand how the cell type is different epigenetically and driven by epigenetics, this is something only C1 can tell you and is something that is more and more of interest among the cancer research community,” asserts Mallm.

Polaris is the solution to enable researchers to understand the functional heterogeneity of cell populations characterized by single-cell mRNA sequencing.

Can current research shape future endeavors?

“Taking this to the next level, using the C1 platform could give us the opportunity to gather information from RNA, protein and chromatin in just one go from the same cell. Sensitivity becomes even more crucial to obtain an individual readout for each cell in a combined protocol. The C1 system and its microfluidics would help to achieve this goal.”

“This approach is highly versatile and could be used for pretty much any organ system in any biological question in mammalian cells. I think that a great next application would be to use a different intracellular indicator, for example, for pH levels,” she said. “I think it's one of these instruments that's very nice to have downstream of assessing single-cell transcriptomics studies—especially when you have a good hypothesis—that allows you to explore functional signaling pathways at the single-cell level.”

To generate a comprehensive overview that includes genotypic influence on the transcriptome, DNA must also come into play. Single-cell DNA analysis could assist in identifying driving mutations in a population and further exploration of a mutation in the same patient over time. This type of analysis could serve to determine whether treatment drives accumulation of mutations or if a mutation originally present in the starting population expands. The ability to detect this expansion before initiating treatment could help predict whether the treatment will be tolerated and ultimately successful.

With commitment among cancer researchers to guide development of individual treatments based on a better understanding of tumor evolution and cell behavior, Mallm would like to see single-cell sequencing taken to the clinic and to the patient. The community is motivated to find ways to improve treatment and prevention, and the use of single-cell analysis could drive this bench-to-bedside translation.