Teiko.bio

The Method

We read millions of cells using mass cytometry

Mass Cytometry is a mass spectrometry method used to determine specific properties of cells.

It is an advanced cytometry method that builds upon the established science of flow cytometry while eliminating many of the common challenges associated with the technique.

Armed with in-depth immune information, researchers can make informed decisions around patient response and drive new pathway discoveries.

High-resolution analysis of specific immune markers can provide insight into patient response

High-resolution analysis of specific immune markers can provide insight into patient response

Mass vs. Flow Cytometry

Mass Cytometry is a mass spectrometry method used to determine specific properties of cells.

It is an advanced cytometry method that builds upon the established science of flow cytometry while eliminating many of the common challenges associated with the technique.

Armed with in-depth immune information, researchers can make informed decisions around patient response and drive new pathway discoveries.

Flow Cytometry

Flow Cytometry

  • 10-15 markers per assay
  • Significant signal overlap due to fluorescence
  • Extensive time to optimization
  • Multiple sample consumption for result
MASS Cytometry

MASS Cytometry

  • Up to 45 unique markers per assay
  • High-resolution, minimal signature overlap
  • Reduced time to optimization
  • Single sample consumption for result
Expert Panel Design

Expert Panel Design

The TokuProfile Intro Panel was constructed based on extensive prior knowledge, input from our collaborative network, and experience over the course of dozens of unique mass cytometry projects. The panel includes:

  • Markers to identify every major immune cell population and subsets.
  • Expression of key co-stimulatory and co-inhibitory molecules that regulate cell function
  • Markers of cell proliferation and markers of recent activation.
  • Ten free channels for customization

Turning Data into Insights

Single cells in the data are classified based on their protein expression profiles.

Abundances of different immune cell subsets are quantified.

Activation states of each immune cell subset are quantified.

A regularized regression model is trained on these ~500 features to build a classifier of response.