Modeling and interpretation of single-cell proteogenomic data

TL;DR

This paper discusses the development of methods for analyzing and interpreting high-throughput single-cell proteogenomic data, emphasizing measurement reliability and modeling approaches to understand cellular molecular mechanisms.

Contribution

It introduces measurement modes and reliability estimation techniques for single-cell proteogenomics, along with models for biological interpretation of multi-modal data.

Findings

Different measurement modes support single-cell proteogenomic analysis.

Approaches for estimating measurement reliability are discussed.

Models for interpreting proteogenomic differences are proposed.

Abstract

Biological functions stem from coordinated interactions among proteins, nucleic acids and small molecules. Mass spectrometry technologies for reliable, high throughput single-cell proteomics will add a new modality to genomics and enable data-driven modeling of the molecular mechanisms coordinating proteins and nucleic acids at single-cell resolution. This promising potential requires estimating the reliability of measurements and computational analysis so that models can distinguish biological regulation from technical artifacts. We highlight different measurement modes that can support single-cell proteogenomic analysis and how to estimate their reliability. We then discuss approaches for developing both abstract and mechanistic models that aim to biologically interpret the measured differences across modalities, including specific applications to directed stem cell differentiation and to inferring protein interactions in cancer cells from the buffing of DNA copy-number variations. Single-cell proteogenomic data will support mechanistic models of direct molecular interactions that will provide generalizable and predictive representations of biological systems.