Immuno-oncology
Autoimmune disorders
Vaccine and drug responses
Infectious disease
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
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).
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.
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