Single-Cell Data

Guides, FAQs, and protocols to help you get the most out of your system

Frequently Asked Questions

Protein arrays have been known to be unreliable in regards to being quantitative. How is your technology able to deliver quantitative data?

Our IsoCode technology is very different from common spotted protein microarrays. During our chip manufacturing process, we are using precision microfluidics to flow defined amounts of capture antibodies in precise patterns onto our detection array yielding high-density antibody barcodes.

How much single cell data is typically generated per chip?

A typical run yields data from roughly 1200-1500 single cells across a panel of 25-35 analytes, for a total of roughly 40,000 data points.

The number of single cell data obtained for one of my chips is low. Can I still use the data?

We have empirically determined that data obtained from as little as 200 single cells is generally sufficient for functional profiling. However, we recommend at least 500 single cells per chip in order to capture the full heterogeneity of single-cell responses.

What is the variability of the single-cell data?

Overall, we have about a 20-30% inter-assay variability, which positions our assay between the gold standards Flow-ICS and ELISPOT in terms of variability.

Do you have data on technical reproducibility?

Yes, we have performed extensive testing to determine technical reproducibility.

The secretion profile of my sample looks different than the expression profile I am accustomed to seeing using flow cytometry or RNAseq.

The IsoCode Chip has published a series of comparator assay metrics to both Fluorospot and flow cytometry Intracellular Cytokine Staining. At the same time, Flow cytometry and single-cell RNAseq assays have their merits and we view them as complentary tools to our technology. Single cell secretion profiles are expected to be different from single cell RNAseq assays. Our functional profiling assays quantify the secretion capacity of single cells whereas cytokine profiling by flow cytometry typically relies on artificially disrupting protein transport within the cell. Many publications exists demonstrating poor correlation between single cell RNAseq and actual protein expression. Furthermore, our technology was able to link secretion profiles to patient outcome with statistical significance in several retrospecitive studies, whereas flow cytometry and other bulk assays were not able to. Please see our publications for more information.

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