Sponsored content brought to you by
Perkin Elmer logo


Michelle Fraser, PhD
Michelle Fraser, PhD

Single-cell transcriptional profiling is poised to revolutionize our understanding of many biological processes. Obtaining high-quality, intact RNA is the first critical step of single-cell profiling. Several factors affect mRNA quality, including mRNA degradation, inter-site and inter-study variability, and loss of diversity.

Prevent mRNA Degradation

Recent studies have estimated the average half-life of mRNA is in the range of 2 to 4.8 minutes.1,2 This is significantly shorter than the 10-hour half-life predicted in earlier studies.3 To get an accurate representation of a transcriptional profile, it is critical to quickly stabilize the samples to be analyzed to prevent mRNA degradation and preserve the original transcriptome.

Reduce Variability

Inter-site and inter-study variability both can result in batch effects, variations in data that are caused by processing samples at different times to each other—not by biological differences in the primary samples. This is a significant factor contributing to the lack of data reproducibility.4 While multiple bioinformatic batch correction methods exist,5 the ideal way to counter them is to eliminate as many potential sources of technical variability as possible. Preserving the integrity of a sample during storage and processing samples taken from multiple sites and time points in a single batch is one way to eliminate variability.

Increase Diversity

There is a rise in the investigation of fragile cells, such as neutrophils, in our attempts to understand inflammation, autoimmune diseases, and prognosis of outcomes from cancer treatments.6  Not all single-cell sample processes are gentle enough to protect the integrity of fragile cells. The consequence is some cell types can be lost or underrepresented compared to the original sample.

There are currently 20 FDA-registered clinical trials specifically investigating the transcriptome of single cells.7 As technologies advance, so will our understanding of health and disease.


Learn more



  1. Baudrimont A, Voegeli S, Viloria E C, Stritt F, Lenon M, Wada T, Jaquet V, Becskei A. Multiplexed gene control reveals rapid mRNA turnover. Sci. Adv. 2017; 3(7).
  2. Chan L Y, Mugler C F, Heinrich S, Vallotton P, Weis K. Non-invasive measurement of mRNA decay reveals translation initiation as the major determinant of mRNA stability. ELife 2018; 7.
  3. Yang E, van Nimwegen E, Zavolan M, Rajewsky N, Schroeder M, Magnasco M, Darnell J E, Jr. Decay rates of human mRNAs: correlation with functional characteristics and sequence attributes. Gen. Res. 2003; 13(8): 1863–1872.
  4. Parker HS, Leek JT. The practical effect of batch on genomic prediction. Stat. Appl. Genet. Mol. Biol. 2012; 11(3): Article 10.
  5. Ma S, Sung J, Magis A T, Wang Y, Geman D, Price N D. Measuring the Effect of Inter-Study Variability on Estimating Prediction Error. PLoS ONE 2014; 9(10): e110840.
  6. Garratt W. Current understanding of the neutrophil transcriptome in health and disease. Cells 2021, 10(9): 2406:
  7. NIH. ClinicalTrials.gov. (October 13, 2021).


Michelle Fraser, PhD, is the General Manager of Next-Generation Sequencing at PerkinElmer. Previously she held the positions of Managing Director at PerkinElmer Health Sciences (Australia), CEO and Managing Director of RHS, (which was acquired by PerkinElmer in June 2018), CEO of Viswa Biotechnology, CEO of Benephex Biotechnologies, and Manager-Business Development at Bio Innovation. Dr. Fraser received a doctorate and graduate degrees from the University of Adelaide.


Previous articleSingle-Cell Functional Proteomics
Next articleBuilding a CAR-T Toolbox for More Comprehensive Assessment of Cell Therapies