Facilitated by SciLifeLab’s National Bioinformatics platform (NBIS), Single Cell Proteomics (now part of Affinity Proteomics), National Genomics infrastructure (NGI), SciLifeLab researchers from Uppsala University and Stockholm University, have describe a new approach that can accurately predict cellular states by simultaneous measurements of mRNA expression levels and protein abundance in single cells.
Continuous advances in single cell analysis have made it possible to investigate biological systems at a much smaller scale and increased resolution than ever before. The majority of these advances focus on mRNA which could pose a problem since transcriptomes might not reflect the actual state of the cell due to due the stochastic gene expression and transcriptional bursts.
Proteins are more stable than mRNA and typically exist in larger and more stable quantities within the cell. For this reason, the researchers behind the new study argue that “combined mRNA and protein single cell measurement approaches are necessary to better understand cellular states and to decipher regulatory circuits and pathways”.
During recent years, a number of approaches that focus on measuring both mRNA and protein levels at single cell resolutions have emerged. Many of them are limited to surface proteins or require invasive cell fixation.
In the study, published in Nature Communications Biology, the researchers describe a new powerful approach, called Single-Cell Protein and RNA Co-profiling (SPARC). SPARC combines single-cell RNA-sequencing with proximity extension essays to simultaneously measure global mRNA and 89 intracellular proteins in individual cells.
The researchers used the approach to investigate if the mRNA levels are predictive of the levels of the corresponding protein in cells at steady-state or undergoing a state-transition in human embryonic stem cells (hESCs).
The results showed that mRNA expression failed to accurately reflect protein abundance at the time of measurement, that protein levels of transcription factors better predict their downstream effects than do their corresponding transcripts, and that protein expression variation is overall lower than mRNA variation.
SPARC presents a state-of-the-art co-profiling method that overcomes current limitations in throughput and protein localization, including removing the need for cell fixation.