MYT1-RNF41 exploration for MorphMap paper (ORF)

[afermg]

From: Anne Carpenter anne@broadinstitute.org
Date: Fri, Dec 1, 2023 at 2:44 PM

Hi Ardigen/Ksilink,
Using this attached plot from Ardigen of the top 25 most anti-correlated pairs, I saw MYT1-RNF41.

So, I found someone who studies MYT1 who is willing to collaborate. We told her that MYT1 has the 'opposite' profile to RNF41 and she looked in some unpublished data of hers and she sees that MYT1 (a repressor) binds to the promoter of RNF41! Neat! (please keep that confidential)

She may design further followups to add to MorphMap. I'm not sure it's necessary but it may help to have more info about MYT1:

How feasible is it to generate a list of top-10 genes that correlate/anti-correlate to MYT1 (with the correlation values so we see the strength)? This was from the CRISPR data, but it looks like we tested the gene in ORFs too so we could create both sets of lists.

We'd also like to tell her what is the morphology diff for MYT1 vs neg controls, but I think we will start by pulling up a few images on our side, because that is a hard Q to answer via informatics (we wrote a blog post of steps but it's fairly unsatisfying/time-consuming).

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From: Anne Carpenter anne@broadinstitute.org
Date: Tue, Dec 5, 2023 at 11:52 AM

I'm adding Alán on our team who has been working on this connection specifically. He pulled images for us but we didn't see anything dramatic by eye (there is a LOT of variation from one replicate to the next, which makes this harder and is discouraging but I guess it explains batch effects we see in the data!)

Alán found the attached relationships at the replicate level in CRISPR data (Harmony-corrected). We expected a negative correlation between the two genes, and it looks like we see that for 2 replicates of RNF41 but not all of them. That is rather odd, as is the fact that a single replicate of the MYT1s is anti-correlated with ALL of the RNF41.

I'm guessing this pair of genes was in the top-25 of Ardigen's "negative correlation" list because after averaging (median, I think) it was still a strong relationship, but still it's a little surprising.

Ardigen, can you confirm you see a similar thing with replicates? I believe you are using the exact same Harmony-corrected profiles.

I guess this evidence is strong enough to continue working on this pair even though a few replicates were not consistent... Ksilink have you seen anything odd going on between replicates in the data in general?

I imagine this could either be:

1. bad batch effects that weren't corrected enough.
2. batch effects that were corrected too much, in a way that introduced errors.
3. some replicates (maybe some whole batches) just didn't CRISPR-knock-down effectively for technical reasons.

I welcome any thoughts anyone has on the matter! I will proceed to meet with the scientist in ~24 hours by zoom to see if she has some experiments to run, or if we can include the unpublished data she has already.

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Edit by Niranj: Code for generating the following list: https://github.com/jump-cellpainting/morphmap/blob/c3393f985cb0a2c1a906ca8438f105eb785ce4de/12.explore-correlations-anticorrelations/4.explore-myt1.ipynb

From: Niranj Chandrasekaran csriniva@broadinstitute.org
Date: Wed, Dec 6, 2023 at 6:45 PM

I created the list of top correlated and anti correlated genes in the ORF dataset. RNF41 appears at the top. I have not checked whether the results look different at the replicate level or not.

Top correlated

Metadata_broad_sample_1	Metadata_broad_sample_2	Cosine_similarity	Gene_1	Gene_2
ccsbBroad304_10986	ccsbBroad304_04392	0.65285605	MYT1	LZTS2
ccsbBroad304_10986	ccsbBroad304_01581	0.62539047	MYT1	SOX15
ccsbBroad304_10986	ccsbBroad304_10195	0.59890395	MYT1	LGALS9C
ccsbBroad304_10986	ccsbBroad304_09189	0.57654506	MYT1	LZTS2
ccsbBroad304_10986	ccsbBroad304_04393	0.55127573	MYT1	LCOR
ccsbBroad304_10986	ccsbBroad304_11168	0.53719974	MYT1	TACC1
ccsbBroad304_10986	ccsbBroad304_13816	0.5334892	MYT1	ASCL2
ccsbBroad304_10986	ccsbBroad304_15450	0.529622	MYT1	HNRNPC
ccsbBroad304_10986	ccsbBroad304_01085	0.529373	MYT1	NEUROD1
ccsbBroad304_10986	ccsbBroad304_04658	0.52150875	MYT1	ERMAP

Top anti-correlated

Metadata_broad_sample_1	Metadata_broad_sample_2	Cosine_similarity	Gene_1	Gene_2
ccsbBroad304_10986	ccsbBroad304_02346	-0.6578171	MYT1	RNF41
ccsbBroad304_10986	ccsbBroad304_11513	-0.5148919	MYT1	DDX17
ccsbBroad304_10986	ccsbBroad304_00946	-0.512445	MYT1	LMNA
ccsbBroad304_10986	ccsbBroad304_10479	-0.4869117	MYT1	FAM81A
ccsbBroad304_10986	ccsbBroad304_04413	-0.4827406	MYT1	TRIM55
ccsbBroad304_10986	ccsbBroad304_04404	-0.460314	MYT1	TUBB6
ccsbBroad304_10986	ccsbBroad304_08471	-0.4582101	MYT1	AGGF1
ccsbBroad304_10986	ccsbBroad304_04931	-0.4466559	MYT1	FOXR2
ccsbBroad304_10986	ccsbBroad304_04798	-0.4455448	MYT1	KRT40
ccsbBroad304_10986	ccsbBroad304_16059	-0.444838	MYT1	CCDC74A

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From: Anne Carpenter anne@broadinstitute.org
Date: Thu, Dec 7, 2023 at 10:04 AM

Oh, awesome! I didn't even dare hope they were both existing in the ORF dataset.

So now these two genes are anti-correlated in both CRISPR and ORF data; that is a nice story (and makes me much less worried about the inconsistent replicates in CRISPR).

FYI, I've started to write up this section of the paper in the MorphMap draft at the end of the results here. I talked to the researcher (Sui Wang) and she says she can have a lab member make constructs to do a reporter assay to add to this section in addition to the promoter-binding unpublished results I mentioned before.

GIven how clean a story this is, I think for the paper we ought to make a full list of gene pairs that have this behavior: anti-correlating in both CRISPR and ORF datasets. This is the strongest evidence for them regulating each other and would be a nice finding (with one such pair, MYT1+RNF41, confirmed).

[afermg]

Providing the email log that led to this collaboration (skipping my email to her):
Sui’s email to Anne:

We mainly study the function of Myt1 in neurons (previously in pancreas) in mice in vivo. As far as I know, the connection between Myt1 and RNF41/Insyn1 is novel.

I have just checked our Myt1 Cut&RUN data (unpublished data) using mouse retinas. Myt1 binds to the promoter of rnf41, indicating a direct regulation, but doesn’t bind to the Insyn1 gene. Myt1 mainly functions as a transcriptional repressor.
..
My email to Sui after our chat Dec 6:

Please do share our results & plan with your lab member who might like to work on this project. I am happy that our data uncovered something interesting and that it can benefit you also.

The plan is that you will send us:
1- Cut & run track data for the particular gene (not the full dataset, because you will be publishing that later; if our reviewers complain about this, hopefully it will be middle of 2024 and perhaps you will be getting ready to preprint that data by then anyway)
2- Reporter results from 293 cells (you hope they behave well so that we don’t need to do the experiment in U2OS where we originally found the gene-gene relationship).
3- Author information for the researchers who should be added to the paper for sharing the cut & run data and the reporter data.

We will continue to work on:

• figuring out why the replicates are inconsistent.
• writing up the rest of the results/paper.
• figuring out what is the morphology change that makes the genes look the ‘opposite’ of each other. Looking by eye, we didn’t see a dramatic change but this is usually the case. We will share info as we uncover it.

Our 1st big paper (nickname “MorphMap") will be written in this doc here (https://docs.google.com/document/d/160QYCeJXMJMPgvYcirkkKaY99r6dZq6FMqYGE61CfFc/edit#) so you can see where your results will go (towards the end, as demonstration that findings from our data are useful!) If your results are ready by end of January it would be ideal to put in this 1st paper. If we are ready to submit but the reporter results aren’t, we can decide whether to wait for them or put the whole story in our 2nd big paper about this dataset.
..
Her reply:
Nice to talk with you😊

I will send you the figure ASAP.

Your plan sounds great to me.
..
My response
Update! Attached are the genes that most correlate / anticorrelate with MYT1 overexpression (the results we talked about so far are CRISPR knockout), in terms of morphology.

You can see RNF41 is the top anti-correlating gene here, too, so that is very nice confirmation!

The rest of the list may interest you in general, as these would be likely potential important genes in these pathways. We already have enough for a nice story for MYT1-RNF41 but let me know if you see anything else interesting in here for future work and feel free to follow it up.

[niranjchandrasekaran]

@zahrahanifehlou shared the following

I have created a list of genes that are top correlated and anti-correlated with MYT1 and RFN41 in CRISPR.
Please find the list in the attached file.

gene_corr_list.xlsx

to which I replied

Thank you for sharing the list. I took a look at it and found that RNF41 was not in MYT1's list of anti correlated genes. Could it be because this list was generated using all genes and not just replicable genes? I ask because for the ORF dataset, I only kept the replicable genes for generating the list (this was my code) and for the correlation heatmaps.

Anne, since only replicable genes are in the heatmap, it perhaps further alleviates our concern about the replicates not looking alike in the CRISPR data (assuming the heatmap was generated using the same code as that of the ORF data).

Zahra replied

In the CSV file that was sent, I didn't consider replicability when searching for correlated and anticorrelated profiles.
If we consider replicability with "q_value==True", MYT1 and RNF41 are not replicable.
And if we consider "p_value==True", only MYT1 is replicable.
In the "MYT1.nlargest.csv" and "MYT1.nsmallest.csv" files, you can find crispr profiles that are correlated/anticorrelated (with MYT1) and replicable ( "p_value==True").

Additionally, I computed similarity across all replicates (without median aggregation), and the results are available in "final.csv".

Also on this plot you can see similarity between MYT1 and RNF41 in different run(replicate):

MYT1.xlsx

[niranjchandrasekaran]

Hi @zahrahanifehlou, thanks for sharing these. Your heatmap replicates the heatmap that @afermg had previously shared, which is good. But I had a question about your following statement

If we consider replicability with "q_value==True", MYT1 and RNF41 are not replicable.
And if we consider "p_value==True", only MYT1 is replicable.

I assumed that when Tomasz generated the top n pairs heatmap in the first comment in this issue, that was after filtering out non-replicable genes. Is that not true? I guess this is also a question for @tjetkaARD.

[AnneCarpenter]

Here is the "track file" from our collaborator Dr Sui Wang showing Myt1 binding to RNF41's promoter!

[AnneCarpenter]

STATUS: this project is awaiting reporter assay results from the Wang lab, although what we have already is a nice result on its own

Here is my summary from the manuscript main text (moving everything here now to track better)

Myt1 has the opposite impact as RNF41 (& to a lesser extent, INSYN1) in CRISPR data
Emailed suiwang@stanford.edu https://mail.google.com/mail/u/0/#sent/QgrcJHsHnNjJtWszNdwGctjDfRPknnWjVHl
Because of their R01 grant that is current, on Myt1
IN PROGRESS - we met Dec 6!
The plan is that Sui will send us:
DONE - 1- Cut & run track data (not the full dataset, because you will be publishing that later; if our reviewers complain about this, hopefully it will be middle of 2024 and perhaps you will be getting ready to preprint that data by then anyway)
2- Reporter results from 293 cells (we hope they behave well so that we don’t need to do the experiment in U2OS where we originally found the relationship).
3- Author information for the researchers who should be added to the paper for sharing the cut & run data.

We will continue to work on:

• figuring out why the replicates are inconsistent.
• writing up the rest of the results/paper.
• figuring out what is the morphology change that makes the genes look the ‘opposite’ of each other. Looking by eye, we didn’t see a dramatic change but this is usually the case.

[AnneCarpenter]

Should doublecheck the story with the info provided here now #8

[tjetkaARD]

As mentioned here: #8
assignment of significant anti-correlation between MYT1 and RNF41 in CRISPR datasets was a mistaken on my side originiating from ORF data.

Nonetheless, there are two other evidence to fill in this story at least from molecular perspective:

1. There are only two studies with raw data for gene perturbations of MYT1: https://doi.org/10.1002/jcb.26636 and https://doi.org/10.1038/s41388-020-1268-6

• In the first one, we have expression induction of MYT1 (analougs to ORFs?), and the corresponding data shows significant over-expression of RNF41 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103790)
• In the second one, we have knock-down of MYT1 (by shRNA), and the corresponding data shows non-significant over-expression of RNF41 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128054)

2. The correlation of CRISPR effects as measured by pooled CRISPR and viability screens shows negative (-15%) anti-correlation between effects of RNF41 and MYT1 (in cancer cell lines, DepMap data). They are both within top 150 anti-correlated genes for each other.

3.There is almost no correlation within co-expression datasets between both of them.

[AnneCarpenter]

Just clarifying here: if I understand, these two genes DO anti-correlate in JUMP ORF and DO NOT correlate in JUMP CRISPR, do I have it correct?

And are we sure that both genes "have a phenotype" (ie are above our threshold for being distinct from negative control)?

[tjetkaARD]

JUMP-ORF: anti-correlation; coefficient: -0.65 (cosine similarity)

• both have the phenotype

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JUMP-CRISPR: insignificant result; coefficient: 0.10 (cosine similarity)

• RNF41 does not have the phenotype

[AnneCarpenter]

Thank you! It seems the remaining tasks here are:

• Figuring out what is the morphology change that makes the genes look the ‘opposite’ of each other. Looking by eye, we didn’t see a dramatic change but this is usually the case. We should discuss how we'd like to attack this, because it will be a recurring task for several vignettes. Decision: let's just look at things like feature names and feature categories (skipping generative AI for now, paper is quite full/nice already and it's not yet been proven such methods are actually useful for decision making even though they are pretty and interesting) per our blog post https://carpenter-singh-lab.broadinstitute.org/blog/help-interpreting-image-based-profiles. Niranj will assign to someone in the lab. New pycytominer function to easily get feature groups: Parse CellProfiler features cytomining/pycytominer#298
• Reporter results from suiwang@stanford.edu - I will ping her to see if the experiment is on track
• Making final figures of everything described above @niranjchandrasekaran
• confirm that our concern about 'replicates not reproducing' is in the CRISPR data? If so that isn't relevant to this vignette anyway so we can ignore it. Tomasz says this concern was in the CRISPR data where profiles for that gene were low/noisy anyway so this does NOT indicate a problem w the data overall.