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Proximity Network Assay

Proximity Network Assay (PNA) is the name of the second generation of spatial network technology assays pioneered by Pixelgen Technologies. PNA creates a spatial network across a cell surface with the use of barcoded antibodies, and a linkage oligo. The linkage oligo has the ability to record which two antibodies were adjacent to each other, thus creating a spatial network of neighborhoods of individual proteins.

PNA is a method to assess the spatial distribution and abundance of proteins on the surface of single cells. The method is optimized for the study of samples of 1000 cells with a multiplexed panel of 155 proteins, and up to 8 samples in parallel.

PNA technology overview

Technology Overview​

PNA creates an interconnected spatial network of detected proteins across the surface of individual cells. The assay starts with fixating cells to immobilize the surface proteome, after which a panel of barcoded antibodies are bound to each cell. A Rolling Circle amplification product (RCP) is then generated from each barcoded antibody, which enables hybridization of linkage oligos that connect proximate RCPs on each cell.

PNA RCA

Each RCP contains multiple copies of a unique molecular identifier (UMI) that is specific for the barcoded antibody molecule. UMI sequences from two UMIs from proximate antibodies are incorporated onto an hybridized linkage oligo via a gapfill ligation reaction, thus incorporating the information of which antibodies were adjacent into the linkage oligo itself. PNA linker

Upon sequencing of the generated molecules, this information is recorded. From the set of sequenced DNA linker molecules, each represents a link between two neighboring proteins. An interconnected spatial network is generated from each assayed cell, unlocking the potential to perform spatial statistics of proteins and make 3D visualizations of each of the 1000 single cells.

From Cells to Graphs​

After sequencing, the resulting FASTQ files will contain a set of reads that describe the protein-protein spatial adjacencies that were generated from the PNA assay. However, in order to unlock the full potential of the data, these reads must be processed and compiled into the spatial protein networks that comprise the individual cells we started with. Fortunately, Pixelgen Technologies offers free, open-source software for you to process and analyze PNA data to do exactly that.

From cell to graph

Pixelator is the main tool for processing PNA raw reads into structured data outputs, ready for analysis. Pixelator also exists as a Nextflow pipeline as part of the nf-core community, providing an efficient and reproducible way to run the workflow. In brief, processing of PNA sequencing data in Pixelator is divided into the following steps:

  1. Amplicon: Filtering of sequencing reads by quality metrics and by expected sequence motifs.
  2. Demux: Decoding marker barcode sequences to proteins using a panel file.
  3. Collapse: Deduplication and error correction of sequenced DNA molecules.
  4. Graph: Construction of a full spatial graph and identification of cells.
  5. Analysis: Calculation of spatial proximity scores and other metrics.
  6. Layout: Computation of x, y, z node coordinates for 3D visualization of each cell graph

The pipeline outputs a QC report that can help you understand the quality of your sample and data, and a PXL file that contains all data points to describe your assayed cells, including both abundance and spatial metrics, as well as the interconnected spatial networks the comprise each cell and 3D layouts that can be used to visualize them. Find out more how to harness this data in our tutorials.