Highly Multiplexed Deep Cell Phenotyping with ChipCytometry™

Immune cell phenotyping is an important capability for immunology and immuno-oncology research, clinical trials, and therapeutic monitoring. More specifically, immune phenotyping applications include:

  • Understanding immune responses to pathogens (viruses, bacteria, parasites, etc.)
  • Diagnosing diseases and determining disease severity, particularly for autoimmune disorders and certain types of cancer
  • Immunotherapy planning and treatment monitoring
  • Determining the impacts of new medications on specific immune populations
  • Monitoring transplant patients for immune responses to the transplanted tissue
  • Personalized medicine

Technologies for deep immune cell phenotyping

Cell phenotyping classifies cells based on the proteins present on the cell surface. Immune cell phenotyping includes the identification and quantification of lymphocytes (T cells, B cells, and natural killer cells), monocytes (dendritic cells and macrophages), and several subtypes within those categories. The surface markers for cell identification are tagged with fluorescently labeled antibodies and detected using either flow cytometry or immunofluorescence microscopy.

<strong>Basic immune cell phenotyping.</strong> Immune cells are classified and quantified from biomarker expression patterns shown in the table.
Basic immune cell phenotyping. Immune cells are classified and quantified from biomarker expression patterns shown in the table.

ChipCytometry provides several key benefits compared to flow cytometry and other methods for analyzing cell suspensions:

  • Streamlined assay validation—using cycles of up to 5 antibodies at a time, the ability to re-use the same fluorophores, and the availability of a collection of pre-validated multiplexed assays significantly shortens the validation time compared to validating individual, unique fluorophores for flow cytometry.
  • Expand experiment later—because the ChipCytometry workflow is non-destructive, samples fixed in ChipCytometry microfluidic chips can be stored for future re-interrogation if detection of additional biomarkers are needed after the initial study is completed.
  • More data from less material—a ChipCytometry microfluidic chip only requires about 2 mL of blood (250,000 cells), which is less than half the volume required for most flow cytometry assays. This advantage makes ChipCytometry a preferred choice for small samples, such as infant blood, or samples that need to be split into multiple assays for analysis.

How high is “highly multiplexed”?

There is not a standard definition for “highly multiplexed.” At Canopy Biosciences, we are still looking for the ceiling for how high we can go with our CellScape cyclic multiplex immunofluorescence imaging platform. As the workflow is non-destructive, the maximum multiplex is theoretically only limited by the availability of specific and fluorescently labeled antibodies to the desired targets.

To demonstrate this idea, ChipCytometry was used to detect, quantify, and analyze a large number of clinically relevant biomarkers from a PBMC sample. This highly multiplexed dataset enabled deep immune profiling and cell phenotyping, including analysis of B cell subtypes, T cell subtypes, monocytes, dendritic cells, apoptosis indicators, and activation markers, all from a single sample. After cyclic labeling, imaging, and photo-inactivation, 74 biologically relevant biomarkers were detected from the same sample. Using the highly multiplexed dataset, immune cells were then deeply phenotyped based on biomarker expression profiles.

The use of such a highly multiplexed assay enabled far more detailed analyses of immune cell subtypes and profound phenotyping than the standard/basic immune cell classification illustrated in the first graph above. Expression of exhaustion indicators, immune checkpoint receptors like CD274/CD279 (PD-L1/PD1), and transcription factors were analyzed in the T and B cell populations. Monocytes and dendritic cells were also subtyped based on immune activation expression patterns.

<strong>Example images of individual cell types.</strong> Immune cells are identified from image overlays of 74-plex PBMC cell phenotyping analysis, with select biomarkers shown as indicated in legend.
Example images of individual cell types. Immune cells are identified from image overlays of 74-plex PBMC cell phenotyping analysis, with select biomarkers shown as indicated in legend.
<strong>Deep phenotyping of T and B cells.</strong> Use of a highly multiplexed assay detecting 74 biomarkers enables deep phenotyping of T and B cell subtypes, including expression analysis of exhaustion markers, immune checkpoint receptors, and transcription factors.
Deep phenotyping of T and B cells. Use of a highly multiplexed assay detecting 74 biomarkers enables deep phenotyping of T and B cell subtypes, including expression analysis of exhaustion markers, immune checkpoint receptors, and transcription factors.
<strong>Deep phenotypic analysis of monocytes and dendritic cells.</strong> With the 74-plex assay, subtyping of other immune cell types can also be completed by measuring additional biomarkers, including activation indicators and interleukins.
Deep phenotypic analysis of monocytes and dendritic cells. With the 74-plex assay, subtyping of other immune cell types can also be completed by measuring additional biomarkers, including activation indicators and interleukins.

Deep immune profiling data analysis

Perhaps the most significant hurdle in cell phenotyping is the analysis of large datasets. Compiling the image-based readouts from many biomarkers requires massive computing power and specific software algorithms. Fortunately, the cloud computing BIOS platform of Enable Medicine provides the capability to access cell segmentation, gating, and unsupervised clustering algorithms, leveraging the power of AI to generate meaningful results from highly multiplexed datasets.

Summary