Proteomics Topics
As an IsoPlexis Certified Service Provider, FlowMetric offers comprehensive, multiplexed, functional proteomic and metabolomic analysis with the IsoLight™ Proteomics Hub from IsoPlexis. This system supports the discovery of biomarkers from the secretome and intracellular proteome, by describing each immune cell by its true functional biology.
Immuno-oncology
Autoimmune disorders
Vaccine and drug responses
Infectious disease
With more than 70 publications and 50 highly correlative clinical datasets, this innovative platform is providing unparalleled predictive data across diverse fields of clinical research and precision medicine.
At the heart of the IsoCode technology are the testing cassettes that mediate the capturing of tens of thousands of single cells into autonomous chambers, enabling the biological function of each of these cells to be measured in isolation. A series of optimized panels are available to resolve the cellular secretome, along with intracellular proteome and metabolome profiles in human, mouse and non-human primate model systems.
Isoplexis’ CodePlex solution provides a streamlined approach to multiplexed bulk cytokine analytics. With more than 30 parameters measured from one 11μL sample volume, panels are optimized for specific research applications to deliver unprecedented insights into functional drivers within biological systems. Sample types include:
Cerebrospinal Fluid
Serum
Plasma
Urine
Conditioned Cell Culture Media
Research Areas:
Human Adaptive Immune
Granzyme B, IFN-γ, MIP-1α, Perforin, TNF-α, TNF-β, GM-CSF, IL-2, IL-5, IL-7,IL-8, IL-9, IL-12, IL-15, IL-21, CCL 11, IP-10, MIP-1β, RANTES, IL-4, IL-10, IL-13, IL-22, TGFβ1, sCD137, sCD40L, IL-1β, IL-6, IL-17A, IL-17F, MCP-1, MCP-4
Non-Human Primate Adaptive Immune
TNF-α, MCP-1, IL-2, IL-4, MIP-1β, IL-6, IL-8, IL-1β, RANTES, IFN-g, IP-10, MIP-1α, MIF, GM-CSF
Mouse Adaptive Immune
Granzyme B, IFN-γ, MIP-1α, TNF-α, GM-CSF, IL-2, IL-5, IL-7, IL-12p70, IL-15, IL-21, sCD137, CCL11, CXCL1, CXCL13, IP-10, RANTES, Fas, IL-4, IL-10, IL-13, IL-27, TGFβ1, IL-6, IL-17A, MCP-1, IL-1β
Human Innate Immune
IFN-γ, MIP-1α, TNF-α, TNF-β, GM-CSF, IL-8, IL-9, IL-15, IL-18, TGF-α, IL-5, CCL11, IP-10, MIP-1β, RANTES, BCA-1, IL-10, IL-13, IL-22, sCD40L, IL-1β, IL-6, IL-12-p40, IL-12, IL-17A, IL-17F, MCP-1, MCP-4, MIF, EGF, PDGF-BB, VEGF
Human Inflammation
GM-CSF, IFN-γ, IL-2, IL-12, TNF-α, TNF-β, IL-4, IL-5, IL-7, IL-9, IL-13, CCL11, IL-8, IP-10, MCP-1, MCP-4, MIP-1α, MIP-1β, RANTES, IL-10, IL-15, IL-22, TGF-β1, IL-1β, IL-6, IL-17A, IL-17F, IL-21, Granzyme B, Perforin, sCD40L, sCD137
Research Areas:
Human Tumor Signaling
P-IkBA, Cleaved PARP, P-Stat5, Alpha Tubulin, P-p44-42 MAPK, P-Stat3, P-Rb, P-NF-kB p65, P-PRAS40, P-eIF4E, P-MEK1-2, P-S6 Ribosomal, P-p90RSK, P-Stat1, P-Met
Human Adaptive Immune (Coming Soon)
P-Akt, P-p53, P-PD1, P-LCK, P-CD3 zeta, P-Zap70, P-CCR7, P-CD28, P-41BB, P-MEK 1/2, P-P44/42 MAPK (ERK1/2), P-Jak1, P-Jak2, P-AMPK, P-PI3K, P-mTOR, P-P21, P-LAT, P-NF-kB p65, Alpha Tubulin
Research Areas:
Tumor Metabolome
P-PRAS40, P-IkBα, P-NF-kβ p65, P-Met, P-p44/42 MAPK, P-S6 Ribosomal, P-Rb, P-p90RSK, P-Stat3, P-MEK1/2, P-Stat1, P-Stat5, P-eIF4E, Cleaved PARP, Alpha Tubulin, Glucose-Biotin
Targets 15+ phosphoproteins and metabolites to evaluate independent trajectories that drive drug tolerance.
Used in combination with functional proteomic assessment, this enables researchers to better develop combination therapies to combat drug-resistance.
Easy Analysis if limited sample volumes. Only 11μL needed.
Each chip can run up to 8 Individual samples, or run In duplicate, triplicate for robust analysis.
Human Adaptive Immune
GM-CSF, Granzyme B, IFN-γ, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-13, IL-15, IL-17A, IP-10, MCP-1, MIP-1α, MIP-1β, Perforin, sCD137, TNF-α, TNF-β
Mouse Adaptive Immune
GM-CSF, IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12, IL-17A, IP-10, KC, MCP-1, MIP-1α, RANTES, TNF-α
Non-human Primate Adaptive Immune
GM-CSF, IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IP-10, MCP-1, MIP-1α, MIP-1β, RANTES, TNF-α
Human Innate Immune
EGF, GM-CSF, Granzyme B, IFN-γ, IL-1β, IL-4, IL-6, IL-7, IL-8, IL-10, IL-15, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-BB, sCD137, TNF-α, VEGF
Human Cytokine Storm Panel
GM-CSF, IFN-γ, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-13, IL-17A, IP-10, MCP-1, MIP-1α, MIP-1β, Perforin, TNF-α
Human Stem Cell Signaling
IL-17A, MIP-1α, IL-6, IL-4, MIP-1β, IL-8, IFN-γ, GM-CSF, IL-10, TNF-α, MCP-1, IL-2, IL-15, RANTES, IL-1α, IL-1β, CXCL5
Human Cancer Signaling
EGF, IFN-γ, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-13, MCP-1, MIF, PDGF-BB, RANTES, TNF-α
Mouse Cancer Signaling - Coming Soon
EGF, IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-10, IL-13, MCP-1, MIF, PDGF-BB, RANTES, TNF-α, VEGF
Mouse Inflammation- Coming Soon
IFN-γ, TNF-α, MIP-1α, IL-2, IL-5, IL-10, IL-13, IL-4, IL-6, IL-1β, IL-17A, IL-12, MCP-1, IP-10, KC, GM-CSF
Mouse Stem Cell Signaling - Coming Soon
GM-CSF, IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-15, IL-17A, MCP-1, MIP-1α, MIP-1β, RANTES, TNF-α
Mouse Innate Immune- Coming Soon
IFN-γ, TNF-α, MIP-1α, IL-15, GM-CSF, IL-5, IL-10, IL-13, IL-6, IL-17A, MCP-1, IP-10, MIP-1β, EGF, PDGF-BB, MIF
Research Areas:
Tumor Metabolome
P-PRAS40, P-IkBα, P-NF-kβ p65, P-Met, P-p44/42 MAPK, P-S6 Ribosomal, P-Rb, P-p90RSK, P-Stat3, P-MEK1/2, P-Stat1, P-Stat5, P-eIF4E, Cleaved PARP, Alpha Tubulin, Glucose-Biotin
Targets 15+ phosphoproteins and metabolites to evaluate independent trajectories that drive drug tolerance.
Used in combination with functional proteomic assessment, this enables researchers to better develop combination therapies to combat drug-resistance.
Research Areas:
Human Tumor Signaling - (Coming Soon - Ask About Early Access)
P-PRAS40, P-IkBα, P-NF-kβ p65, P-Met, P-p44/42 MAPK, P-S6 Ribosomal, P-Rb, P-p90RSK, P-STAT3, P-MEK1/2, P-Stat1, P-Stat5, P-eIF4E, Cleaved PARP, Alpha Tubulin
Human Adaptive Immune Panel - (Coming Soon - Ask About Early Access)
P-Akt, P-p53, P-PD1, P-LCK, P-CD3 zeta, P-Zap70, P-CCR7, P-CD28, P-41BB, P-MEK 1/2, P-P44/42 MAPK (ERK1/2), P-Jak1, P-Jak2, P-AMPK, P-PI3K, P-mTOR, P-P21, P-LAT, P-NF-kB p65, Alpha Tubulin
The system provides an automatic Quality Control ‘IsoQ-Score’ which grades the overall run based on several performance parameters. Signal background noise levels are determined per analyte on each chip by processing the
image data generated on the IsoLight.
The software analyzes the fluorescence intensity in ~1000 empty “zero-cell” microchambers on the same chip. A cutoff equal to the average background intensity plus three times the standard deviation is used to determine which readouts are true cytokine secretions and which ones are not.
As with flow cytometry, the IsoLight system generates very large, multiparameter datasets. The IsoSpeak software provides a comprehensive suite of tools for the downstream analysis of single-cell and bulk sample data obtained through the IsoLight platform. It is fully integrated with the IsoLight, allowing you to plan your project end-to-end, and combine multiple runs into a single analysis. For example, analysis can be readily performed across clinical trial cohorts or time-courses.
Various analytics are available to assess the polyfunctional profiles of each sample within an experiment and characterize single-cell level differences across samples.
Two of the most unique and powerful biomarkers/ endpoints are the Single-Cell Polyfunctionality, and the Polyfunctional Strength Index.
Polyfunctionality refers to the number of cytokines (2 or more) secreted by a cell. The highly polyfunctional cells represent the functionally super-active cells that, for example, uniquely correlate with anti-tumor activity or therapeutic resistance.
The Polyfunctional Strength Index merges all single-cell, multiparameter secretions into a single readout, by combining the polyfunctionality of the cells within a sample, with the signal intensity from each single-cell across all of the cytokines analyzed. The PSI can also be displayed in a color-coded form to show the contribution from different categories of cytokines (effector, inflammatory, chemo-attractive, regulatory, and stimulatory).
Polyfunctionality of CD4 T-cells and CD8 T-cells following stimulation with CD3/CD28 (left) and PMA/Ionomycin (right). Polyfunctionality describes cells that secrete two or more cytokines. Highly polyfunctional cells secreting 5 or more cytokines are represented here in deep orange- these super functioning cells have been associated with clinical end points. This represents a completely new way to examine the function of immune cells.
The profile of the cytokines contributing to the Polyfunctionality, can be visualized through the Polyfunctional Strength Index. The PSI profiles of CD4 T-cells and CD8 T-cell following CD3/CD28 both show largest contributions from Effector cytokines, however whereas the CD4 T-cells reveal inflammatory and regulatory cytokine profiles, CD8 T-cell PSI is driven by stimulatory and chemo-attractive cytokines. PMA/Ionomycin stimulation primarily drives effector functions in T-cells, we can also reveal chemo-attractive, stimulatory and regulatory cytokine profiles in both CD4 and CD8 populations. The Polyfunctional Strength Index takes the analysis to a completely different level by combining the Polyfunctionality of the cells, with the intensity of the cytokine production. Furthermore, the functional drivers can be deciphered from the types of cytokines secreted.
Heatmaps provide an easy means of visualizing individual cytokine contribution to polyfunctionality and identify key functional drivers.
t-SNE is a non-linear dimensionality reduction algorithm that is used to explore high-dimensional data sets by mapping data to two or more dimensions for observation. t-SNE algorithms utilize probability distributions with a random walk on neighborhood graphs to find structure within data sets, through representing similar data points in close-proximity, while maintaining both local and global structure of the data in an unbiased manner. For high complexity biomarker analysis, this process generates plots that differentiates data points (single cells) based on their greatest differences. The closer two data points are represented on the t-SNE, the more similar they are, the farther apart they are, the more diverse their biomarker profiles.
Polyfunctional Activation Topology (PAT) PCA shows the functional and polyfunctional groups of the selected samples. In this case we are looking at both CD4 and CD8 from the responding and non-responding cohorts.
Each circle corresponds to a single functional group, with the label next to it showing which analytes are in the group. The color-coded dots within each circle represent the frequency of single cells that secreted this group in each sample. The overall color-profile of each group represents a combination of the cytokine class color that is secreted within the group with highest frequency.
The axes correspond to the principal components of the data, i.e., these variables account for as much of the variability in the data as possible. These variables are linear combinations of the specified analytes; the analytes most strongly present in each component are listed at the ends of the axes. If the value of a principal component is high for a particular group, it is more likely to contain those analytes.
PCA functions by considering the fluorescence values of each single cell signature, and there is no presumption on how the data will partition; PCA simply determine similarities and differences between samples. In the case of PCA, unsupervised dimensional reduction enables characteristics to be compared between samples, with minimum loss of information. These tools are therefore best implemented to identify trends and build models of predictive biomarker profiling and cellular responses within pre-clinical studies.
Dominant subgroups will emerge in PAT PCA graphs, representing significant multi-functional subsets driving the overall response. Samples with higher response dominate the graph. Utilize Cytokine Secretion Frequency and Polyfunctional Contribution graphs to verify the largest components of those subgroups and provide insights into the subgroups of polyfunctional cells you are looking to identify and interrogate.
As a Certified Isoplexis Service Provider, FlowMetric is proud to offer the complete suite of IsoCode and CodePlex analysis for comprehensive functional proteome, secretome and metabolome profiling. By coupling this capability with FlowMetric’s cell sorting services, we are providing unparalleled omics profiling of specific cell populations, including rare cell types such as TILs and Tregs.
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