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The commercially available PhenoCycler Solution by Akoya Biosciences relies on Co-Detection by Indexing (CODEX), a technique enables highly multiplexed single-cell quantification of membrane protein expression in fresh-frozed or formalin fixed paraffin embedded (FFPE) tissues. In CODEX, antigens are stained simultaneously with unique oligonucleotide-conjugated antibodies. Antibody binding events are visualized iteratively via successive rounds of fluorescent microscopy. At each imaging round, cells are exposed to a mix of two non-fluorescent index nucleotides and two fluorescent labeling nucleotides. The DNA barcodes are designed such that only two antibodies can be labeled in a single imaging cycle, and only if the indexing nucleotide has been incorporated. Notably, CODEX completes a 30-antibody visualization in approximately 3.5 hours and is adaptable to most standard three-color fluorescence microscope platforms.

Pros and cons


  • Commercial kit
  • Single cell resolution
  • Formalin fixed, paraffin embedded (FFPE) tissue compatible


  • Requires panels of proteins usually with a few dozens of antibodies, but this is standard for highly multiplexed immunofluorescence. Akoya sells curated panels.

Getting Started

Several CODEX datasets were generated by the HuBMAP Consortium and are available for download from their data portal. Raw and processed data are typically avaiable for several fields of view and can be readily combined into a single SpatialFeatureExperiment(SFE) object. A tutorial for processing the output of various spatial transcriptomics technologies into a SFE object for use with Voyager is available in the vignette linked below.

Vignette Colab Notebook Description
Create a SFE object Colab Notebook Download data, create SFE object

Analysis Workflows

The vignettes below demonstrate workflows that can be implemented with Voyager using data generated with CODEX technology.

Vignette Colab Notebook Description
CODEX colon analysis Colab Notebook Perform basic QC, data normalization, dimension reduction, spatial autocorrelation metrics, local spatial statistics, non-spatial clustering, differential expression