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When I am reading papers that compares bulk RNA sequencing and single-cell RNA sequencing, we often see papers describe bulk RNA seq measures the average cell expression.

For example, in this paper Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

bulk RNA-seq mainly reflects the averaged gene expression across thousands of cells

And this paper A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications

the averaging that occurs in pooling large numbers of cells does not allow detailed assessment of the fundamental biological unit—the cell—or the individual nuclei that package the genome

And this paper Differential expression analyses for single-cell RNA-Seq: old questions on new data

Standard RNA-Seq experiments need millions of cells for sequencing [2,3], and therefore can only get averaged measurements of gene expressions of the cells sequenced.

My question is, when we perform the bulk RNA seq and calculates parameters like TPM, we don't actually divide the total number of mRNA transcripts with the number of cells (I think sometimes we don't even have an accurate number on the number of cells that we used in bulk RNA seq), we simply normalise it with the transcript length and count.

Therefore, if we DID NOT divide the number of transcripts with the total cell number, how are we measuring the average of expression, but not the total expression?

Or in other words, what is the "average expression" referring to? Average to what?

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What these statements refer to is that bulk RNA-seq will overlook the different transcriptional cell types, hence the value of single cell RNA-seq in unraveling heterogeneous cell populations/tissues.

I think "total" is also avoided because total RNA has another meaning related to capturing all the RNA transcripts in a cell (e.g. including non polyA transcripts), which is still not the case for most RNA-seq experiments.

Also note that RNA-seq experiments generally (without spikins) are only relative and not giving absolute quantifications of the amount of RNA in a cell or groups of cells.

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  • $\begingroup$ Thank you for your answer! I understand what you said, but I'm asking about total expression, not total RNA. You said "bulk RNA-seq will overlook the different transcriptional cell types", yes I agree, but then it also means that it is interpreting the total transcriptional actives within the mixture of cells. I'm still not sure why the word "average" is used in these publications. Or in other words, what is the "average expression" referring to? Average to what? $\endgroup$
    – benson23
    Feb 26, 2022 at 4:19
  • $\begingroup$ @benson23 As I said, these numbers are generally relative and not absolute so these statements are not implying an actual mathematical calculation of mean expression (or indeed summed total expression). 'Average' also means 'typical' and that is the essence here, but this is semantics $\endgroup$
    – Chris_Rands
    Feb 26, 2022 at 8:51
  • $\begingroup$ Thank you for explaining again! I agree that these numbers would be relative, however, I do not quite agree with you that the use of the word "averaged" is semantic. Based on the first paper I quoted, it said "averaged gene expression across thousands of cells", in the second paper "the averaging that occurs in pooling large numbers of cells" and in the third paper "only get averaged measurements of gene expressions of the cells sequenced", which to me, certainly implies some mathematical operations... $\endgroup$
    – benson23
    Feb 26, 2022 at 9:18
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    $\begingroup$ I believe using the average is fair for this purpose. The moment you break thousands or millions of cells you are already averaging relative mRNA content; you are making a soup of it. Keep in mind that when you sequence RNA (RNA-seq), you are not sequencing every single molecule in the sample. In other words, you do not know how many molecules of each mRNA you have. $\endgroup$
    – Supertech
    Mar 1, 2022 at 0:56
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    $\begingroup$ What you are doing is comparing relative level of certain mRNAs between two samples in a giving sequencing depth. It's like comparing salinity iin two different seas: Dead Sea has salinity of 337 g/kg and Black Sea has salinity of 20 g/kg. You can consider the salinity a single mRNA. You do not need to know the exact size of Dead Sea to say that salt concentration of Dead Sea is ~16-fold higher than Black Sea. $\endgroup$
    – Supertech
    Mar 1, 2022 at 0:56

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