Review for "Thanatotranscriptome: genes actively expressed after organismal death"

Completed on 25 Jun 2016 by Peter Ellis . Sourced from http://biorxiv.org/content/early/2016/06/11/058305.

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"We see this pattern in many of the transcriptional profiles. This is difficult to explain by the 'stable gene transcript' idea."

This is incorrect - it is trivial to explain almost any pattern in terms of differential cell death and differential transcript stability. To illustrate this, consider the following toy model system:

Cell composition
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In the model, the tissue you are analysing has two cell types in equal proportions - muscle cells and fibroblasts. We will assume that muscle cells have a high requirement for oxygen and will die within minutes of organismal death. Fibroblasts are more resilient and will live for a week. Transcription continues until the cell dies, after which point the mRNA begins to decay.

Gene expression per cell
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In the model, we assume that each cell type expresses just three genes, at equal absolute levels.
1) An unstable housekeeping gene (U in both cell types) where the transcript takes 1 day to decay.
2) A stable housekeeping gene (S in both cell types) where the transcript takes two days to decay.
3) A highly stable tissue specific gene (M for muscle and F for fibroblasts) where the transcript takes 3 days to decay.

What happens in the experiment?
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***TIME ZERO.
Both cell types are alive at the instant of organismal death.
- Muscle cells contain 1 unit of M, 1 unit of U and 1 unit of S
- Fibroblasts contain 1 unit of F, 1 unit of U and 1 unit of S

You measure:
M = 16.7% of the total sample
F = 16.7% of the total sample
U = 33% of the total sample
S = 33% of the total sample

The muscle cells now die, and the transcripts within those cells start to decay.

***DAY 1
Transcripts in dead muscle cells are decaying with rates as specified above.
Fibroblasts are still alive
- Dead muscle cells contain 2/3 unit of M, 0 units of U and 1/2 units of S
- Fibroblasts contain 1 unit of F, 1 unit of U and 1 unit of S

You measure:
M = 16.3% of the total sample = DOWNREGULATED FROM D0
F = 24.2% of the total sample = UPREGULATED FROM D0
U = 24.2% of the total sample = DOWNREGULATED FROM D0
S = 36.3% of the total sample = UPREGULATED FROM D0

***DAY 2
Transcripts in dead muscle cells are decaying with rates as specified above.
Fibroblasts are still alive
- Dead muscle cells contain 1/3 unit of M, 0 units of U and 0 units of S
- Fibroblasts contain 1 unit of F, 1 unit of U and 1 unit of S

You measure:
M = 10% of the total sample = DOWNREGULATED FROM D1
F = 30% of the total sample = UPREGULATED FROM D1
U = 30% of the total sample = UPREGULATED FROM D1
S = 30% of the total sample = DOWNREGULATED FROM D1

***DAYS 3 THROUGH 7
RNA in Dead muscle cells has completely vanished
Fibroblasts are still alive
- Dead muscle cells contain nothing
- Fibroblasts contain 1 unit of F, 1 unit of U and 1 unit of S

You measure:
M = 0% of the total sample = DOWNRGULATED FROM D2, ABSENT HEREAFTER
F = 33% of the total sample = UPREGULATED FROM D2, THEN PLATEAU
U = 33% of the total sample = UPREGULATED FROM D2, THEN PLATEAU
S = 33% of the total sample = UPREGULATED FROM D2, THEN PLATEAU

On day 7 the fibroblasts die

***DAY 8
All cells now dead, fibroblast mRNAs now decaying
- Fibroblasts contain 2/3 unit of F, 0 units of U and 1/2 unit of S

You measure:
U = 0% of the total sample = DOWNREGULATED FROM D7, ABSENT HEREAFTER
F = 57% of the total sample = UPREGULATED FROM D7
S = 44% of the total sample = UPREGULATED FROM D7

***DAY 9
All cells now dead, fibroblast mRNAs almost completely decayed
- Fibroblasts contain 1/3 unit of F, 0 units of U and 0 units of S

You measure:
F = 100% of the total sample = UPREGULATED FROM D8
S = 0% of the total sample = DOWNREGULATED FROM D8

So, what are the final profiles?
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* U shows an initial dip, then a slow rise, then a plateau, then a sudden fall to zero.
* S fluctuates, showing an initial rise, then a dip, then another rise, then finally disappearing.
* M shows a slow fall which accelerates down to zero
* F increases in a jerky non-linear fashion to very high levels, just before it vanishes.

And note - this model exhibits all that complexity using just four genes in two cell types. I have done a lot of expression profiling on mixed populations (my area is testis development), and I assure you these results are virtually uninterpretable without a good fundamental understanding of the detailed cellular composition of your sample at each stage.

"Reply: We are confused by the comment about how we report RNA concentrations. "

Your table S2 purports to show a consistent increase in the amount of mRNA in the liver from time zero through to 48 hours. In my experience the precipitation step in your RNA extraction is not quantitative in the first place. Even if the extraction itself is quantitatively accurate, it may be simply that you processed a larger chunk of liver for the 48 day samples than for the time zero samples.

You *have* to give your figures as amount of RNA per mg of tissue input, rather than per ul of lysate. Even then, it is very likely that as interstitial tissue fluids drain / pool / evaporate following death, the same weight of input tissue will contain more actual cells.

"Reply: We do not see this as a confounding factor. It occurs."

OK, so let's say you have a liver biopsy at time zero which is full of blood. Very high levels of globin mRNA from all the red blood cells, plus immunoglobulin genes from the B and T cells, etc. Hepatocyte mRNA is only a fraction of the total.

Now, take a sample a couple of hours later after much of the blood has drained out. Your globin mRNAs and immune-genes go down, because you no longer have as many red or white blood cells in the sample. All the hepatocyte genes go up, because there is now a higher proportion of these cells in the sample.

HOW IS THIS NOT A CONFOUNDING FACTOR?

You will measure this as potentially quite dramatic changes in gene abundance, which you are in turn interpreting as transcriptional regulation. All it is really telling you is that (1) Mammals have blood, and (2) gravity exists.

You absolutely have to know what you are putting into your experiment, and what your RNA sample was actually made from!

To sum up: there are two principles that should be applied to the interpretation of ALL transcriptomic studies looking at whole tissue extracts.

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1) You are profiling a mixed collection of multiple cell types. If the cellular composition of the tissue changes between the samples, then this will cause changes in transcript abundance that have nothing to do with genuine transcriptional regulation.

Therefore, if the cellular composition of the tissue of interest changes between your samples, it is VERY HARD to distinguish between genuine transcriptional regulation, and uninteresting passive consequences of the change in cell numbers. To have even a chance of doing so so, you have to either
(a) quantitate the cellular makeup of each sample and adjust for this, or
(b) isolate specific cell types - e.g. by flow sorting - and profile them individually

(a) would be easiest to do histologically, but in some cases you can recover elements of this data from the final RNA profile. For example, if you know that genes A & B are only expressed in one cell type, while genes D and E are only expressed in a different cell type, then you can use these genes as "tracers" to measure the abundance of each cell type. Conversely changes in the expression level of A relative to B, or C relative to D represent genuine transcriptional changes within each cell type.

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2) Almost all RNA quantitation methods measure the level of mRNA in the cytoplasm. This reflects the balance between historic transcription levels and the rate of mRNA degradation, and is poorly correlated with active transcription. Demonstrating de novo transcription is also a VERY HARD problem.

The gold standard method is RNA FISH, which directly detects the nascent transcripts within the nucleus on a per-cell basis. This technique however is very taxing, hard to compare between studies, and not amenable to global transcription profiling.

A possible approach would be to label newly-synthesised mRNA - e.g. by incorporation of ethynyluridine (EU) - and then purify the newly-synthesised mRNA for expression profiling. There is a commercial kit available for this, but I don't know if it has been applied to whole tissue samples or just to cell lines.
https://www.thermofisher.com/o...

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" ...specific gene transcripts cannot be stable at one time, unstable the next time, and then stable again [...] One can only conclude that the mRNA is being synthesized."

As covered above - the multiple levels of confounding factors in this analysis can easily mimic this pattern, or any other pattern you choose to name. At the risk of repeating myself:

1) Highly stable transcripts will show an artifactual peak after the cell dies, because the unstable transcripts vanish and only the stable transcripts remain.

2) If there is more than one cell type present, and these die at different times, then you will get more than one artifactual peak in the profile for the stable transcripts.

In the spirit of usefulness, here are some references that may be helpful.

http://www.ncbi.nlm.nih.gov/pu...
http://www.omicsonline.org/ope...
These two demonstrate that individual cells can survive for a very long time after the organism dies. There are many more such papers, going back to the 70s and earlier.

http://www.ncbi.nlm.nih.gov/pu...
http://www.ncbi.nlm.nih.gov/pm...
These two look at differential survival of various cell types - one study focusing on blood, and another on the inner ear. The latter study showed that stem cells survive better than terminally differentiated cells. This is biologically unsurprising, as stem cells in an adult organism are in general maintained in quiescent "tick-over" mode. This is a possible explanation for the observed upregulation of Hox genes and other early embryonic genes: if the stem cells are the last ones to die, then the global transcriptome will become more "stem-like".

http://www.ncbi.nlm.nih.gov/pu...
This one looked at the rate of mRNA degradation in two specific cell types (chondrocytes from arthritis sufferers versus control), and showed that different transcripts degrade at different rates, and this varies between cell types.

http://www.sciencedirect.com/s...
This one looks at mRNA decay rates in mouse brain and liver after post mortem (you might want to compare their liver data to yours if the time points match). Importantly, they found that none of the upregulated genes could be validated by quantitative PCR, while the downregulated genes did validate.