The Advanta™ IO Gene Expression Assay is a 170-gene expression qPCR assay that enables profiling of tumor immunobiology and new biomarker identification.

Designed to meet the rigorous demands of human checkpoint research programs, the Advanta IO Gene Expression Assay includes 91 key markers of tumor immune response that were shown in a multicenter international clinical trial to inform tumor progression and checkpoint therapeutic response1,2. Working with researchers in the biopharmaceutical industry, we further expanded the panel to include 74 additional immuno-oncology markers.

The two-panel set includes genes for identification and functional analysis of immune and cancer cells, including markers found in defined T cell subsets, cytokines and chemokines and markers of immune regulation and immune cell fate. The Advanta IO Gene Expression Assay is a key component of a complete workflow from sample to data supporting your research in tumor immune response.

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Panel A

Panel B

Table 1. The Advanta IO Gene Expression Assay gene list by panel. Panel A includes 91 target genes and 5 housekeeping genes. Panel B includes 74 target genes and the same 5 housekeeping genes, along with 17 open assay inlets to facilitate the addition of custom content over time. Refer to Ordering information for the panel content of each component.

Complete workflow from sample to data analysis

Figure 1. Assay workflow. The gene expression assay workflow uses the Juno™ system for IFC preparation and the Biomark HD for qPCR thermal cycling and data acquisition. Generate 175–192 assay results per sample (up to 24 samples) in 4.5 hours with 1.3 hours of hands-on time using the GE 24.192 IFC. Alternatively, generate up to 96 assay results per sample (up to 96 samples) in 6.5 hours with 1.5 hours of hands-on time using the GE 96.96 IFC. After primary data analysis, results can be further analyzed with third-party software, such as GO Immuno-Oncology Workbench.

Figure 2. Manage sample throughput to meet your research needs with integrated fluidic circuits (IFCs). The Gene Expression 24.192 IFC (A) accommodates up to 24 samples per run. For laboratories with higher throughput needs, the Gene Expression 96.96 IFC (B) accommodates up to 96 samples per run. Respectively, the IFCs generate a total of 4,608 and 9,216 parallel reactions.

Table 3. Sample capacity per IFC.  The number of samples that can be processed in a single run depends on the IFC format and the number of replicates per sample. 

Ordering information

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For Research Use Only. Not for use in diagnostic procedures.

Improving the process

Download our product flyer to learn more about using Advanta FFPE RNA Extraction Kit to accelerate your investigative research.

The Advanta FFPE RNA Extraction Kit stands apart from other commercial kits by providing:

• A single-tube extraction purification process
• Enhanced safety with xylene-free paraffin removal
• More precious samples are qualified for downstream analysis
• Improved detection during gene expression analysis

Sirona Dx case study

Clinical research services provider Sirona Dx, Inc., based in Vancouver, Washington, performed gene expression analysis of FFPE samples extracted using the Advanta FFPE RNA Extraction Kit and a leading column-based commercial kit. After extraction, the group reverse-transcribed and preamplified the RNA and analyzed the cDNA on Biomark HD using the Advanta IO Gene Expression Assay panel containing 170 unique biomarkers for profiling tumor immunobiology and identification. Gene expression analysis data demonstrated more target gene detection in samples extracted using the Advanta FFPE RNA Extraction Kit. These samples typically showed lower cycle threshold values than those extracted using the alternative kit.

“Sirona Dx specializes in expression profiling of challenging FFPE samples and we welcomed the opportunity to partner with Fluidigm to develop an improved RNA extraction methodology,” said Sirona Dx CEO Nasry Yassa. “To date we have processed several hundred samples with the Advanta FFPE RNA Extraction Kit, and our pharma clients have been astonished by the quality of RNA extracted and delighted at our ability to interrogate more of their precious FFPE samples­.”

Contact us to learn more about the Advanta FFPE RNA Extraction Kit.

For Research Use Only. Not for use in diagnostic procedures.

Accurately tracking samples from acquisition to data analysis is important for any laboratory, especially in translational and clinical research programs. Implementation of a standardized genotyping workflow to confirm the identity and quality of each sample prior to analysis can help maximize confidence in study results.

The Advanta™ Sample ID Genotyping Panel enables laboratories to accurately detect a unique genetic fingerprint from samples at each stage of the sample journey. Developed for use with the Biomark™ HD system and based on Fluidigm™ microfluidics, the panel contains 96 unique single-nucleotide polymorphism (SNP) variants, including 80 synonymous human variants located in exonic regions of the genome, 10 quality assessment variants, and 6 gender-confirming variants. New targets for specific sample populations can be easily added to customize the panel based on specific study goals.

Efficiently track sample identity

A. Sample journey

B. Advanta Sample ID Genotyping Panel workflow

By utilizing the high-performing Biomark HD system, which supports gene expression, genotyping, copy number variation and digital PCR analysis using Fluidigm microfluidic technology, the Advanta Sample ID Genotyping Panel workflow offers a number of additional benefits to sample identification, including decreased hands-on time and significant cost savings compared to traditional qPCR methods.

Advanta Sample ID Genotyping Panel SNPs  

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For Research Use Only. Not for use in diagnostic procedures.

Christophe Lancrin, PhD, leads a Research Group at the European Molecular Biology Laboratory (EMBL) in Rome, Italy. He recently spoke with Fluidigm about his 2018 eLife paper, “Single-cell transcriptomics reveals a new dynamical function of transcription factors during embryonic hematopoiesis,” describing how he used C1™ and Biomark™ HD to identify an intermediate cell population between endothelial and blood cells during endothelial to hematopoietic transition (EHT). In the publication, he also discusses using C1 for full-length mRNA sequencing to identify a new gene regulatory network involving seven key transcription factors.

His research group at EMBL explores cell fate decisions and how endothelial cells grow blood progenitors and stem cells during embryonic development. Understanding EHT depends on identifying essential genetic regulatory mechanisms involved in the biological process. For this study, he and first author Dr. Isabelle Bergiers, PhD, set out to reveal the gene regulatory network responsible for switching off the endothelial cell fate and switching on the blood program and generating blood stem cells.

Recognizing that ultrahigh-throughput 3ʹ end RNA sequencing may be insufficient for detecting low abundant transcripts such as those coding for transcription factors, Dr. Lancrin launched his study with a hypothesis: Using targeted gene expression as a starting point, genomicists can apply highly sensitive single-cell real-time PCR technology to gain valuable insights for investigations into health and disease research.

Starting with 95 target genes associated with hematopoietic, endothelial and vascular smooth muscle cells, the team isolated embryonic endothelial cells for targeted gene expression using the Biomark HD system and assessed the expression patterns by single-cell real-time PCR to understand the process of EHT. Dr. Lancrin’s group discovered a population with endothelial and hematopoietic characteristics co-expressing seven essential transcription factors at the single-cell level.

In the eLife paper, the authors make a strong case for hypothesis-driven low-throughput single-cell sequencing employing highly sensitive full-length transcriptome analysis with the C1 platform, noting that sequencing 192 single cells was enough to reveal a new gene regulatory network involving the Runx1, Gata2, Tal1, Fli1, Lyl1, Erg and Lmo2 transcription factors. This finding did not require ultrahigh-throughput RNA sequencing analysis.

“Single-cell analysis is powerful. When you work in bulk, you miss information. Working with single cells is definitely a big plus but you need to use the right technology for the right question.” —Christophe Lancrin, PhD, EMBL

A summary of the study 

The investigators produced data suggesting that even though the proto-oncogene-encoded protein Fli1 (transcription factor Friend leukemia integration 1) initially supports the endothelial cell fate, it acquires a prohematopoietic role when co-expressed with protein Runx1 (runt-related transcription factor 1).

Bergiers et al. concluded that “this work demonstrates the power of single-cell RNA sequencing for characterizing complex transcription factor dynamics.” The eLife paper notes that “although bulk transcriptomics can reveal crucial overall gene correlations between semi-stable cellular states, it cannot resolve subtler gene interactions occurring in complex transitional states. In addition, using a bulk approach makes it difficult to infer the direct consequences on the transcriptional landscape upon which these TFs are acting. These limitations can be overcome by the use of single-cell approaches.”

While stem cells are responsible for an organism’s development of specialized cell types, understanding how they form is necessary for assimilating cellular development and regenerative medicine applications. A vital function of many species, EHT is the embryonic process wherein vascular endothelial cells develop into blood stem cells.

When Dr. Lancrin and the researchers used Biomark with the classical endothelial genes and hematopoietic genes, they realized there were three different populations in the embryonic endothelium: endothelial, hematopoietic and one in between the two. Finding dual identity in both endothelial and hematopoietic genes, they identified a cell type in transition between the two stages. Looking closely, they discovered the seven transcription factors co-expressing both endothelial and hematopoietic genes were the only population expressing them all at single-cell resolution.

To clarify the findings, the team asked whether the genetic expression was a consequence or a cause of this dual identity. Using the embryonic stem cell (ESC) differentiation model into blood cells, they created an ESC line where all these factors could be over-expressed simultaneously at the single-cell level. Using this tool, they discovered that the co-expression of these transcription factors was in fact responsible for the dual endothelial and hematopoietic identity.

After proving their hypothesis, the EMBL scientists used C1 to identify genes linking to those transcription factors, to find certain targets for them. They discovered GPR-56 to be one of the target genes. There was an increase in the expression of this gene, which happens to be involved in hematopoiesis. The Lancrin group was surprised to learn that GPR-56 expression could not explain the dual endothelial and hematopoietic identity.

“Strikingly, we saw there were two transition factors linked to the same genes but in opposite relationships,” he said. “This suggested that some transcription factors were working against each other. We proved that this was not just correlation; it was actually the cause of the dual endothelial and hematopoietic identity.”

By modifying the relative quantity of these transcription factors, the Lancrin group eventually demonstrated that these factors were in a competition, which was responsible for the dual identity. “The competition is basically between two groups of transcription factors,” he said, “one supporting the blood cell fate and the other the endothelial identity.”

For this study, the investigators needed the C1 full-length mRNA sequencing technology because of its sensitivity. Dr. Lancrin explained, “We not only needed to detect transcription factors, we had to detect a range of gene expression levels. We required higher-quality sequencing data to perform these challenging analyses.”

Potential personalized medicine implications

The EMBL group realized that the Fli1 transcription factor supporting the endothelial cell fate essentially switches activity at some point during the EHT transition. Fli1 initially supports the endothelial cell fate but following expression of Runx1—master regulator of blood cell development—Fli1 starts to support the formation of blood cells. a football analogy, Dr. Lancrin likened it to two equal teams competing against each other. If suddenly a number of players switch sides, then one team must win, and it will be the team with more players. That study was the first to provide clear insights for him. “What we found could potentially help researchers to produce blood cells more efficiently, potentially by using small molecules affecting the interaction between Fli1 and Runx1.”

Dr. Lancrin plans to continue doing single-cell transcription analysis for more investigations, such as identifying what other aspects of transcription factor activity can be regulated. The eLife paper makes it clear that despite the small number of cells, his research was impactful because of how the scientists set up and modeled the study. “Single-cell analysis is powerful,” he said. “When you work in bulk, you miss information. Working with single cells is definitely a big plus but you need to use the right technology for the right question.”

Future applications

Generating embryonic stem cell-like (ESC-like) induced pluripotent stem cells (iPSCs) from fully-differentiated cell types such as skin fibroblasts was a breakthrough in regenerative medicine. Important work remains to be done to efficiently differentiate iPSC or ESC for specific blood cell progenitors like hematopoietic stem cells (HSCs), so Dr. Lancrin is focusing his research on revealing the mechanisms underlying HSC formation from endothelial cells. “Combining single-cell transcriptomics, computational biology, time-lapse microscopy and loss and gain of function experiments in vitro and in vivo, we plan to identify signaling pathways and transcriptional regulators involved in generating hematopoietic stem and progenitor cells during embryonic life,” he said. If all goes as planned, his research will lead to the development of new strategies to improve methods of blood cell generation from ESC or iPSC for regenerative medicine.

Dr. Lancrin believes his findings could potentially help researchers design more efficient approaches to generate blood cells from ESCs or iPSCs by influencing key transcription factor activity. Looking beyond hematopoiesis, investigators could apply the single-cell transcriptomics approaches from this study to gain insights about different cellular transitions such as the epithelial to mesenchymal transition occurring during development and in disease states such as cancer metastasis.

“Our computational approach for the study of transcription factor interaction will help to understand the formation of HSCs from endothelial cells,” he said.

Check out our new low-cost full-length mRNA sequencing application and other innovative single-cell protocols at C1 Script Hub™.  

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For Research Use Only. Not for use in diagnostic procedures.

Whether researchers are following the immune response to treatments for cancer, infection or autoimmune disease, or searching for biomarkers of disease onset and progression, they need a comprehensive, reliable method to monitor changes in immune cell populations.

Mass cytometry has proven to be a uniquely empowering platform for immune profiling and biomarker discovery. Now, Fluidigm makes this powerful technology more accessible than ever with the Maxpar^® Human Immune Monitoring Panel Kit designed for use on Helios™, a CyTOF^® system.

The Maxpar Human Immune Monitoring Panel Kit

This 29-marker panel kit maximizes the information obtained from every drop of precious sample, enabling identification of over 25 key immune cell populations, all in a single tube (Figure 1).

Figure 1. Cell subsets that can be identified with the single-tube Maxpar Human Immune Monitoring Panel Kit.

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Panel Kit Contents and Workflow

Whether you are new to mass cytometry or are a seasoned pro, the Maxpar Human Immune Monitoring Panel Kit provides the most convenient and efficient approach to identifying all major immune subsets in peripheral blood mononuclear cells (PBMC).

This kit contains all reagents needed to prepare PBMC samples for mass cytometric analysis on Helios, plus:

• 29 individually packaged, pre-titrated antibodies ready to use straight from the kit to prepare the antibody cocktail
• A detailed, kit-specific protocol for sample preparation, instrument setup and sample collection on Helios systems
• A Helios acquisition template pre-populated with the appropriate parameter names and metals required

Table 1. The 29 anti-human antibodies included in the Maxpar Human Immune Monitoring Panel Kit. The eight open channels are 145Nd, 150Nd, 156Gd, 159Tb, 161Dy, 170Er, 172Yb, 209Bi.

Figure 2. Workflow for the Maxpar Human Immune Monitoring Panel Kit. No panel design or antibody titrations need to be performed. Stain PBMC samples according to the custom protocol provided, collect data on a Helios or CyTOF 2-to-Helios instrument using the customized kit acquisition template, and analyze samples.

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Data analysis options are flexible

FCS 3.0 data files are generated when acquiring samples with the Maxpar Human Immune Monitoring Panel Kit on Helios and can be analyzed by any capable third-party software tool. Manual gating can be performed with third-party software tools such as Cytobank.

Automated analysis software such as GemStone™ can also be used, providing the following features:

• Automated gating of viable single cells
• 2D dot plot and cell stage progression profiles
• High-dimensional Cen-se’™ map of identified populations
• Sample report for each population, with cell number, percent of total cells and percent of parent population

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Complete the form below to receive more information on the new Maxpar Human Immune Monitoring Panel Kit.

For Research Use Only. Not for use in diagnostic procedures.

Embarking on the launch of a landmark technology designed to reveal unprecedented biological insights and improve the future of health care, we at Fluidigm are pleased to collaborate with scientists at the birthplace of Imaging Mass Cytometry™, the University of Zurich (UZH). Computational biologist Denis Schapiro of the Bodenmiller Lab talked with us about developing the data analysis software distributed with the Hyperion™ Imaging System. Here he shares what he’s learned in recent years as a pioneer in the emerging field of Imaging Mass Cytometry, and how the technology stands to impact life sciences by integrating cellular biology with molecular and spatial analysis.

Schapiro joined the Bodenmiller Lab to build a computational pipeline for Imaging Mass Cytometry, which was first featured in a published paper in the March 2014 issue of Nature Methods. He became the main developer of the histoCAT™ (histology topography cytometry analysis toolbox) software for analyzing highly multiplexed tissue data such as that produced by the Hyperion™ Imaging System, which relies on IMC™. The software has emerged as a key component of the technology.

In his August 2017 Nature Methods paper on histoCAT design and application, Schapiro and co-first author Hartland Jackson demonstrated how they analyzed 49 breast cancer samples and identified cellular social networks associated with tumor grading. Researchers see different topographic structures in Grade 1 patients versus Grade 3. By quantifying patterns of interaction in the context of clinical outcomes, histoCAT could improve precision medicine applications across experimental cohorts for a range of diseases in the future. Doing so requires looking at both the composition of cell types and their spatial interactions.

“Now it’s possible to identify different cell types—potentially even all cell types—in a tissue and their exact location. You can then go beyond exploring which cell types are present in these tissues to ask questions about their signaling and functional states, and how the cells communicate.”
—Denis Schapiro, Bodenmiller Lab, University of Zurich

histoCAT workflow

Essentially, histoCAT combines imaging data with cytometry. The imaging community is trained to segment, annotate and analyze imaging data. It characterizes millions of cells using spatial features, but because of limited multiplexing capabilities has been unable to identity the cell lines or types in the tissue. In contrast, cytometry researchers have mastered single-cell analysis using scatter plots, tSNE (t-distributed stochastic neighbor embedding) visualizations and associated gating tools to investigate highly multiplexed CyTOF^® datasets and identify cell types or phenotypes. But they have lacked spatial information.

Imaging Mass Cytometry multiplexing enables identification of many cell types in a tissue. “We know where the tumor cells and immune cells are located, and we know what cellular state they represent,” Schapiro said. “histoCAT unlocks this information so we can interactively select cells and ask questions about whether certain cell types and locations correlate with specific clinical information or outcomes. This could lead to the discovery of a new class of biomarkers: topographic biomarkers.”

“We designed histoCAT as a unique tool for combining cellular phenotypes with spatial information,” Schapiro said. “You can identify individual cells of interest on a scatter plot and visualize them directly in their spatial context. Alternatively, it’s possible to select cells or structures in their spatial context and visualize those in scatter plots and other single-cell visualizations. We call this interactive workflow between cytometry and image analysis the round-trip.

“Additionally, you can capture the tissue architecture quantitatively through permutation testing to see how the structure differs from a random cell type distribution,” he said, offering examples of using histoCAT in the context of immune infiltration and stroma-to-tumor interactions.

There are considerations, Schapiro observed: Strong and abundant membrane markers are key to successful segmentation, and thus to data analysis and interpretation. Schapiro and co-author Peter Schüffler have shown that Imaging Mass Cytometry enables improved segmentation by providing multiple membrane-associated markers through multiplexing (Schüffler, Schapiro et al., Cytometry Part A, 2015).

He offered this advice to new users: “Imaging Mass Cytometry is the right tool if you have a heterogeneous system with a lot of hidden spatial information. With histoCAT and other open source tools, you can do preliminary analysis leading to exciting results. With the right question, you can then follow up with more advanced tools and computational collaborations.”

histoCAT applications

Schapiro expects histoCAT to benefit scientists exploring a range of health and disease applications including histology, digital pathology and translational research. The capability of IMC to measure abundant information on a single cell with spatial resolution makes it possible to identify patterns in immuno-oncology and diseases on a single rather than consecutive sections. “The amazing part of developing histoCAT is that people can use it to do things I haven’t even thought of yet.”

He described how IMC and histoCAT support his own research: “What I value most about the Hyperion Imaging System technology is getting multiplexed spatial measurements. Now it’s possible to identify different cell types—potentially even all cell types—in a tissue, and their exact location. You can then go beyond exploring which cell types are present in these tissues to ask questions about their signaling and functional states and how these cells communicate.”

He plans to extend the methods he helped develop for dozens of samples to analyze hundreds or even thousands of samples for even deeper clinical research insights. He’s enthusiastic about optimizing the data analysis pipeline to answer questions such as how to analyze data efficiently with thousands or even 100,000 samples, and how to learn from this data. How do we normalize and segment the data and correct for errors? “I’m excited to focus on this cutting-edge technology,” he said. “We need to step-by-step continue to improve it, and then work with the broader community to make it even better.”

Developing histoCAT represents a significant step in advancing the potential of mass cytometry to deeply interrogate tissue samples. “That is the dream: histoCAT combines the immune system knowledge of the cytometry community with the pattern knowledge of pathologists and histologists, all in one software,” Schapiro said. “This is growing into a great community with so many new ideas. There will be lots of possibilities for collaborations.”

Supported by the Swiss National Science Foundation and a UZH BioEntrepreneur-Fellowship, Schapiro continues to work on multiplexed imaging data analysis as an Independent fellow affiliated with Harvard Medical School and the Broad Institute at MIT and Harvard. “The next step is to combine single-cell multiplexed imaging with single-cell transcriptomics.”

For news and updates and to learn more about how the Hyperion Imaging System can advance your research and reveal insights about cancer and other areas of health and disease research, send us your contact information below.

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For Research Use Only. Not for use in diagnostic procedures.