Translational single-cell research enabled by Singleron’s end-to-end research platform

Jonathan Scolnick

Keywords: single-cell RNA-seq, machine learning, translational predictions, targeted RNA panels, multiple myeloma, daratumumab, Singleron GEXSCOPE

TL;DR: Singleron will present its end-to-end single-cell platform and a multiple myeloma case showing how interpretable ML on single-cell data enables therapy-specific response predictions and compact gene signatures for clinical assay development.

Abstract:

We will present Singleron’s integrated single-cell platform and illustrate how these capabilities enable clinically relevant predictions.
Singleron’s end-to-end wet-lab workflow includes: optimized tissue dissociation; single cell isolation and RNA capture; modular targeted add-ons (TCR/BCR repertoire, custom panels including targeting non-polyA RNAs); and multiple data analysis tools. Together, these offerings provide reproducible, high-quality single-cell datasets suitable for translational research.
As an example use case, single-cell profiles from multiple myeloma were combined with interpretable machine learning to generate cell-level response scores to daratumumab-containing regimens. Patient-level aggregated single-cell predictions yielded accurate and therapy-specific predictions, and model interpretability highlighted a small gene set that can be transferred to simpler bulk gene expression analysis assays. This workflow demonstrates how Singleron’s wet-lab technologies and analytics can support the development of practical clinical prediction tools.

Submission Number: 3