From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing

Deeper sequencing for scRNA-seq will do little to improve quantification as the number of individual mRNA molecules in a cell is small (100-300,000 transcripts) and only a fraction are successfully reverse-transcribed to cDNA.

“Deeper sequencing for scRNA-seq will do little to improve quantification as the number of individual mRNA molecules in a cell is small (in the order of 100–300,000 transcripts) and only a fraction of them are successfully reverse-transcribed to cDNA [8, 176]”

Some facets of cellular identity are transient in time and space, and temporal processes may progress along trajectories, oscillate, or be influenced by spatial position.

“First, in contexts such as development and physiology, some facets of cellular identity are transient in time and space. Temporal processes may, for example, progress along one or more trajectories (e.g., differentiation); oscillate continuously between cellular states (e.g., the cell cycle98,99 or circadian rhythm100); or be influenced by the physical position and neighborhood of the cell71,93.”

Canonical structure is likely in most normal tissues (e.g., brain structures) and physiological processes (e.g., embryogenesis).

“While this is a likely situation in most normal tissues (e.g., brain structures71) and physiological processes (e.g., embryogenesis93)”

Seurat and a method by Achim et al. combine limited spatial cues for landmark genes with scRNA-seq from dissociated tissue to infer the spatial location of dissociated cells.

“This has inspired the development of two methods, Seurat (A.R. and colleagues)93 and a method by Achim et al.71 that combine limited spatial cues for landmark genes with single-cell RNA-seq of cells from the same type of dissociated tissue to infer the spatial location of the dissociated cells through the patterns reflected by the landmark genes.”

Even without new experimental methods, computational techniques can help relate distinct data types across single cells, as was effectively shown for relating spatially resolved in situ hybridization data to scRNA-seq to derive a spatial mapping of cells to positions.

“Even without new experimental methods, computational techniques can help relate distinct data types across single cells, as was effectively shown for relating spatially resolved in situ hybridization data to scRNA-seq to derive a spatial mapping of cells to positions71,93.”

Protein abundance is often challenging to estimate via scRNA-seq data due to post-transcriptional regulation and relatively low RNA capture rates.

“Protein abundance is often challenging to estimate via scRNA-seq data due to post-transcriptional regulation and relatively low RNA capture rates10,11.”

The greater variability within VTA microglia is commonly seen in RNAseq of small cell numbers and does not reflect differences in sample quality.

“this phenomenon is commonly observed in RNAseq analysis of small numbers of cells (Marinov et al., 2014) and does not reflect differences in sample quality”

Gene expression variation may cause enough transcriptome diversity in scRNA-seq that distinct populations of cells blend together and become indistinguishable.

“Of course, gene expression can vary widely across phenotypically similar cells (Kaufmann and van Oudenaarden, 2007; Marinov et al., 2014), which may cause enough transcriptome diversity in scRNA-seq that distinct populations of cells blend together and become indistinguishable from each other.”

Gene expression can vary widely across phenotypically similar cells.

“Of course, gene expression can vary widely across phenotypically similar cells (Kaufmann and van Oudenaarden, 2007; Marinov et al., 2014), which may cause enough transcriptome diversity in scRNA-seq that distinct populations of cells blend together and become indistinguishable from each other.”

Variability in gene counts between phenotypically identical single cells is not exclusively due to drop-out events, but also due to differential sampling of the transcriptome.

“Moreover, as has been previously shown (45), our results indicate that variability in gene counts between phenotypically identical single cells is not exclusively due to drop-out events, but also due to differential sampling of the transcriptome (fig. S4).”