Question: About heterogeneous cell tissue bulk RNA-seq
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gravatar for tpx1234
9 weeks ago by
tpx12340
tpx12340 wrote:

Dear all, i have a question ,which have bother me for a long time, i have a group of bulk rna-seq data, which were from whole testis , contain control and treated samples, after i was running DEG pipeline , i have got many DEGs ,but when i use IHC to test some of those genes (cell type marker gene ), i found there were significant cell proportion change in different cell types between control and treatment groups ( testes are highly heterogeneous tissue). Are those DEGs represent the proportion change of those different cell types? or the different RNA expression level in specific cell type ? if those "DEGs" were not real DEGs between same cell types, do i have a way to correct the result ? i have exact cell counting of those marked cells, normalized to 1 mm2.

rna-seq • 153 views
ADD COMMENTlink modified 9 weeks ago by Kevin Blighe60k • written 9 weeks ago by tpx12340

i use IHC to test some of those genes (cell type marker gene ), i found there were significant cell proportion change in different cell types between control and treatment group

Can you elaborate a bit on this? I.e. how trust-worthy is that assessment?

Are those DEGs represent the proportion change of those different cell types?

Some of the genes may well show differential gene expression due to changes in the cell type population frequencies.

if those "DEGs" were not real DEGs between same cell types, do i have a way to correct the result ?

Only if you have the full range of population differences represented in both treatment and control conditions. If all of your control samples show the same frequencies and all of your treatment samples have a different cell type distribution then there's no way to correct for, i.e. the population frequencies would be confounded with the actual treatment of interest.

ADD REPLYlink written 9 weeks ago by Friederike5.7k

I used TissueFAXS Plus system to scan whole testis section , this technic can count all cell(by scanning cell nucleus which are hematoxylin positive) and specific marker antibody positive cells. I marked three major spermatogenic cell types, which were spermatogonia , spermatocyte and sperm, those cell marker i found were from two single-cell data set published in Development Cell and a paper from Cell Research. What really shock me was that in the treatment group, the absolute number of the three types of cells were decreased and the proportion changed significantly, compared to the control group. So I'm not sure these DEGs were reliable,and the biological meaning of it. For example, i found the expression levels of marker genes in spermatogonia cells were significantly different between the control and treatment groups. Is this due to a change in the proportion of different type of cells or the real spermatogonia transcriptome changes? or both? I have the proportion of major spermatogenic cell types and counting number of them, both in control and treatment group. I dont know if i can correct this mess.

ADD REPLYlink modified 9 weeks ago • written 9 weeks ago by tpx12340
1
gravatar for Kevin Blighe
9 weeks ago by
Kevin Blighe60k
Kevin Blighe60k wrote:

The DEGs, depending on how you conducted your analysis, are statistically significantly differentially expressed between control and treatment. Typically, a statistically significantly differentially expressed gene will, in addition to a low p-value, have an absolute log [base 2] fold-change that is > 2, but not always. An exception may occur in a dataset that has high variability or one that is heteroskedastic, like the one that you may have due to the natural variability in testes tissue.

If you would like further advice, please elaborate on the programs that you have used, and also the key lines from your code.

Kevin

ADD COMMENTlink written 9 weeks ago by Kevin Blighe60k

I used a software called kallisto , it generate a count and TPM matrix, then i used DESeq2 to find DEGs, as for the cutoff, I used relatively lax standards, p<0.05. The major problem i faced is the limitations of bulk sequencing in heterogeneous tissue. In this experimental design, i made a serious mistake, i should separate those different type of cells, and then sequencing.
Any way , thank you very much ,Kevin

ADD REPLYlink written 9 weeks ago by tpx12340
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