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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Resolving Single-Cell Gene Expression by Pseudotemporal Integration of Transcriptomic and Proteomic Datasets.

Craig P Barry¹, Gert H Talbo², Aiden Beauglehole¹

¹Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, Australia.

Molecular & Cellular Proteomics : MCP

|November 29, 2025

Summary

This summary is machine-generated.

This study integrates single-cell RNA sequencing and proteomics to reveal dynamic transcription-translation responses to hypoxia. Pseudotemporal ordering uncovers immediate transcriptomic and delayed proteomic expression patterns.

Keywords:

hypoxia multiomics integration scRNA-Seq single-cell proteomics

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Area of Science:

Molecular Biology
Systems Biology
Cellular Dynamics

Background:

Single-cell omics technologies like single-cell RNA sequencing (scRNA-Seq) and single-cell proteomics provide deep insights into cellular heterogeneity.
Integrating diverse single-cell data types, such as transcriptomics and proteomics, is crucial for understanding complex cellular processes but remains a significant challenge.
Understanding transcription-translation dynamics is key to deciphering cellular responses to various stimuli.

Purpose of the Study:

To develop and apply a novel method for integrating single-cell RNA sequencing and single-cell proteomics data.
To analyze transcription-translation expression dynamics during cellular responses to hypoxic stress.
To construct comprehensive transcription-translation profiles from longitudinal single-cell samples.

Main Methods:

Longitudinal single-cell samples were collected following induced hypoxia.
Pseudotemporal cell ordering was employed to align and integrate single-cell RNA sequencing and single-cell proteomics data.
Key markers were utilized to construct pseudotemporal trajectories for each data type.

Main Results:

The integrated analysis revealed distinct temporal patterns in transcriptional and translational responses to hypoxia.
An immediate transcriptomic response was observed, followed by a delayed proteomic expression.
The study successfully generated a unified profile of single-cell mRNA and protein expression under hypoxic conditions.

Conclusions:

Pseudotemporal integration of single-cell transcriptomic and proteomic data offers a powerful framework for studying transcription-translation dynamics.
This approach elucidates distinct regulatory mechanisms and cellular phenotypes under stress, such as hypoxic stress.
The findings provide a foundation for future investigations into complex gene and protein expression regulation at the single-cell level.