Quantification noise in single cell experiments

Abstract

In quantitative single-cell studies, the critical part is the low amount of nucleic acids present and the resulting experimental variations. In addition biological data obtained from heterogeneous tissue are not reflecting the expression behaviour of every single-cell. These variations can be derived from natural biological variance or can be introduced externally. Both have negative effects on the quantification result. The aim of this study is to make quantitative single-cell studies more transparent and reliable in order to fulfil the MIQE guidelines at the single-cell level. The technical variability introduced by RT, pre-amplification, evaporation, biological material and qPCR itself was evaluated by using RNA or DNA standards. Secondly, the biological expression variances of GAPDH, TNFα, IL-1β, TLR4 were measured by mRNA profiling experiment in single lymphocytes. The used quantification setup was sensitive enough to detect single standard copies and transcripts out of one solitary cell. Most variability was introduced by RT, followed by evaporation, and pre-amplification. The qPCR analysis and the biological matrix introduced only minor variability. Both conducted studies impressively demonstrate the heterogeneity of expression patterns in individual cells and showed clearly today's limitation in quantitative single-cell expression analysis.

Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles

“…Maximum-likelihood inference reconstructs the single-cell expression distribution without the need to measure single cells. Ignoring the technical challenges of global single-cell methods (17,20,21,27), it should also be theoretically possible to recreate the complete expression distribution by measuring many individual cells. However, it was not clear whether single-cell profiling would be as effective as stochastic profiling when reconstructing from a limited number of 1-or 10-cell samples.…”

“…At the transcript level, global methods have been developed to profile single cells by oligonucleotide microarrays (14,15) or RNA sequencing (16)(17)(18)(19). However, generally such approaches overlook the considerable technical variation in RNA extraction (20) and reverse transcription (21) when applied to the limited starting material of single cells. Single-cell profiles also retain the biological noisiness (22) associated with each cell's isolation and handling.…”

Isolation of intact astrocytes from the optic nerve head of adult mice

“…However, it should be noted that the variability of qPCR experiments in single cells is considerable and cannot completely be eliminated by optimizing the reaction conditions for each step (Reiter et al, 2011). Rather, the variability reflects the “stochastic” nature of gene expression over time and the heterogeneity even amongst neighboring cells (Cai et al, 2006; Junker and van Oudenaarden, 2014).…”

Single‐cell technologies to study the immune system

“…These computational methods become crucial for data interpretation because this new technology generates an incredible amount of data, which require faster and more standardized computational methods. The data are also ‘corrupted’ by numerous confounding factors and biases that need to be corrected for, using automated methods 16, 17, 18, 19, 20…”

“…Alternatively, the count of the mRNA molecules per cell, in which each molecule is individually labelled with random DNA sequences (Unique Molecular Identifiers21). According to the sample preparation method, different computational approaches can be used to calculate gene expression level 16, 19, 22…”