Tajvar, P. | bioRxiv Science

bioRxiv2025

Single-Cell Protein Interactomes by the Proximity Network Assay

Cellular functions depend on dynamic interactions of proteins and their spatial organisation. While transcriptomic and proteomic methods of molecular parts-lists have enabled single-cell profiling based on abundance, scalable technologies allowing high-resolution measurements of protein organization and interactions at scale are lacking. Here we present the Proximity Network Assay (PNA), a DNA-based method for constructing three-dimensional nanoscale maps of 155 plasma membrane proteins in single cells without the use of optics. PNA employs barcoded antibodies and in situ rolling circle amplification to generate >40,000 spatial nodes per cell, which are linked through proximity-dependent gap-fill ligation and decoded by DNA sequencing forming single cell Proximity Networks. PNA captures abundance, self-clustering, and [~]12,000 pairwise colocalization relationships per single-cell, validating established protein interactions. This new modality provides a framework to uncover novel spatial biomarkers, reveal functional mechanisms, and advance translational studies in immunology, oncology, and cell therapy.

bioengineering↗

bioRxiv2024

Cluster-free annotation of single cells using Earth mover's distance-based classification

Grouping individual cells in clusters and annotating these based on feature expression is a common procedure in single-cell analysis pipelines. Multiple methods have been reported for single-cell mRNA sequencing and cytometry datasets where the vast majority rely on sequential 2-step procedures involving I) cell clustering based on notions of similarity and II) cluster annotation via manual or semi-automated methods. However, as arbitrary borders are drawn between more or less similar groups of cells, one cannot guarantee that all cells within a cluster are of the same type. Further, dimensionality reduction has been shown to cause considerable distortion in high-dimensional datasets and is prone to variable annotations of the same cell when relative changes occur in data composition. Another limitation of existing methods is that simultaneous analyses of large sets of cells are computationally expensive and difficult to scale for growing datasets or metanalyses across multiple datasets. Here we present an alternative method based on calculation of Earth Movers Distance and a Bayesian classifier coupled to Random Forest, which annotates one cell at a time removing the need for prior clustering and resulting in improved accuracy, better scaling with increasing cell numbers and less computational resources needed.

bioinformatics↗