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[NCP Pilot 3B] We needn't do MAP2 antibody staining of neurons because Cell Painting seems sufficient #8

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shntnu opened this issue Sep 29, 2020 · 36 comments
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[NCP Pilot 3B] We needn't do MAP2 antibody staining of neurons because Cell Painting seems sufficient #8

shntnu opened this issue Sep 29, 2020 · 36 comments
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@shntnu
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shntnu commented Sep 29, 2020 •
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Related pilot: #6

Goals

  1. How much does it augment the Cell Painting data? Specifically, do we need MAP2 antibody to distinguish branching? It is possible that the standard Cell Painting dyes capture this signal.
  2. How well we can detect differences between conditions with synaptic profiling?

^ We have very few conditions to come up with any strong conclusions, but it is a good start.

Experimental Design

Expected date for imaging: Done
Antibodies: MAP2, DAPI, [Synapsin1, Syngap1, Synaptophysin]
Cell Type: Day 28 Neurons
Plates: 384 well (Glia on all wells) plates in #2 without Glia were poor

Conclusion

We restricted our analysis to only inspecting whether branching was sufficiently captured using Cell Painting or not.
In #8 (comment), @mtegtmey concluded that indeed it is sufficient. We did not explore synaptic profiling further; will do so in the 22q data.
@mtegtmey said:

I think we won’t be able to capture the synapses at this magnification. Typically, we would want 40-60X. So I think we could close that step and we are in a strong position to move forward with using the standard CP stains for all of our cell types currently.
@shntnu
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shntnu commented Sep 29, 2020

We will meet with Beth's team to learn about current best practices for profiling these structures. @mtegtmey Could you add relevant notes here in preparation for our meeting?

@mtegtmey
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mtegtmey commented Oct 6, 2020

We have so far tested staining for 5 different markers as mentioned above: MAP2, DAPI, and one of three synaptic markers (Synapsin, Syngap1, Synaptophysin). Although in each case we were able to see synaptic staining, we need to next test for co-staining of pre- and post- synaptic markers. In addition, we need to use higher concentrations of each synaptic antibody. I've attached two representative images here.

ICC

Our primary aim will be to develop a synaptic assay which will enable us to identify structural changes in synapse count, size, shape, etc between 22q11 deletion carrier neurons and control neurons. Also to determine if it would be possible to multiplex/merge synaptic staining with cell profiling via Cell Painting

@mtegtmey
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@shntnu just following up from our discussion last week. Is there any particular place I should transfer the images from this experiments?

@shntnu
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shntnu commented Oct 14, 2020

@shntnu just following up from our discussion last week. Is there any particular place I should transfer the images from this experiments?

Please upload to /imaging/analysis/2019_05_28_Neuronal_Cell_Painting/NCP_PILOT_3/images/

@shntnu
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shntnu commented Oct 20, 2020

Please upload to /imaging/analysis/2019_05_28_Neuronal_Cell_Painting/NCP_PILOT_3/images/

@bethac07 it's here

@mtegtmey
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mtegtmey commented Oct 20, 2020 via email

@shntnu
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shntnu commented Oct 20, 2020

I’m getting an error saying the directory doesn’t exist. Where on the cluster is /imaging/analysis? This isn’t being transferred from microscope, I have the data in my directory here: /stanley/stemcell/Nehme_Lab/Matt_ICC_test/images

Can you try /imaging/dropbox/2019_05_28_Neuronal_Cell_Painting_NCP_PILOT_3_images? You'd need to create the folder 2019_05_28_Neuronal_Cell_Painting_NCP_PILOT_3_images

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bethac07 commented Oct 23, 2020 via email

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bethac07 commented Oct 23, 2020 via email

@mtegtmey
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mtegtmey commented Oct 23, 2020 via email •
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@mtegtmey
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mtegtmey commented Oct 23, 2020 via email

@bethac07
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What I've found so far is: the Assaylayout/Unnamed.xml (in 3 copies), the FFC_Profile Measurement 1.xml (in 2 copies), and, in the images subfolder, image files for an experiment that seems to be set as "Columns 9-16, Rows 1-16, 8 fields/well, 3 channels and 3 Z planes per field". All of those I've now uploaded to S3.

I don't see anywhere in that folder the Index.idx.xml file (Usually in the 'Images' subdirectory); as far as I can tell that's all that missing, IF those are the correct columns/rows/fields/channels/planes you expect.

If it's easy for you to dig up/find that file, that would be helpful for me (it will save ~30-60 minutes total), if not, I can proceed without it if a) you can confirm to me the rest of the files are as you expect and b) you can let me know what's in each channel.

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mtegtmey commented Oct 23, 2020 via email

@bethac07
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Are the rows/columns what you expect? Which rows/columns have each of the 3 synaptic markers, in case they're different enough that they need different pipelines?

If it takes you <~30 minutes to get the file, and you don't mind, that would be great, otherwise we'll proceed on Monday without.

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mtegtmey commented Oct 23, 2020 via email

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mtegtmey commented Oct 25, 2020 via email

@bethac07
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Thanks for sending the file Matt, that was super helpful!

So @pearlryder and I started on this today, but we had two major questions we think should be answered before we should go forward (and a couple of minor questions/comments while we're here).

Major questions-

  1. It seems like, similar to last time, the plating is a mixture of glia (MAP2 negative) and neurons (MAP2 positive), right? And the neurons themselves are in multiple focal planes? Should we filter down to only strongly MAP2 positive nuclei being going ahead and doing tracing, etc? Here's a snapshot of a region that makes us ask that- to me, there are two strongly positive neuronal cell bodies (blue at bottom left and teal at top right), one thing that I THINK is a neuron but just very out of focus (yellow-green bottom right), and two things that I think are glial nuclei (orange and light green in the top left) that, for at least one of them, is then picking up some processes that actually don't belong to it, but to some other neuron somewhere else. So, should we only try to measure the ones with high soma MAP2? It's less apples-to-apples than the CellPainting experiment, where of course we couldn't do that, but it might be more accurate (see also major question 2), especially in crowded images.

image

  1. Should there be an intensity cutoff point of MAP2 foci for processes before we trace them? Here's a good example region- there are some dim processes that are barely visible in the top region (normalized intensity), but are definitely there and real looking in the bottom region (intensity slightly blown out)- possibly they're in a different focal plane. Should we try to grab each and every one? Just the brightest ones?

Minor questions/comments

  1. It does not look like we measured cell branching in our last cell painting experiment, assuming you would like us to/that it would be helpful to rerun with that measurement added?

  2. What if anything should we do with synapsin- just measures its levels and "clumpiness" inside the cell bodies as defined by MAP2, or try to actually identify it as objects? My vote is for the former but if we think there's usefulness in doing the latter it's not tricky to add. We'd definitely want to know which wells were which synapse marker if we're going to do the latter.

  3. Is it correct to say the cells were plated in increasing density left-to-right on the plate? Anything else we should know about compound treatment, etc?

I feel like I had one other question but can't come up with it now, will add it later if/when it resurfaces ;)

@mtegtmey
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mtegtmey commented Oct 27, 2020 •
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@bethac07 @pearlryder thanks for working on this! I'll try answering the questions below, but please let me know if you need more detail.

big questions:

(1) So, should we only try to measure the ones with high soma MAP2?

I think this will be a best approach. This way we can be 'sure' we are capturing the neurons and avoiding the weird glia below. If referring to these images, i would use the cells which are blue and teal.

(2) Should there be an intensity cutoff point of MAP2 foci for processes before we trace them?

Ideally we would want to trace any processes we can confidently identify their 'source' soma. Although they may dive down to another focal plane, we would want to try capturing them. If we have, say, low intensity processes that we can't link to a soma with high-confidence, I would not include them in tracing. If it's possible to segment them in such a way!

baby questions:

(3) It does not look like we measured cell branching in our last cell painting experiment, assuming you would like us to/that it would be helpful to rerun with that measurement added?

Yes, this is a feature we would like to capture! By branching, you mean the projections/processes? One of the key things we would like to find out is whether doing this will the CP stains is sufficient to capture the branching or if we will need to use more conventional stains like MAP2 used here.

(4) What if anything should we do with synapsin- just measures its levels and "clumpiness" inside the cell bodies as defined by MAP2, or try to actually identify it as objects? My vote is for the former but if we think there's usefulness in doing the latter it's not tricky to add. We'd definitely want to know which wells were which synapse marker if we're going to do the latter.

For now, we want to just quantify total intensity for synaptic markers - I know we need to redo the experiment with higher concentrations of the ABs and at higher resolutions to really investigate the synapses. Our goal here would be to say that the number of synapses or intensity of the synapses (by stain intensity) per neuron varies by condition or well-to-well. I'm not particular concerned with the differences between the specific stains themselves because more optimization is needed! But am happy to provide a which-is-which map if needed :)

(5) Is it correct to say the cells were plated in increasing density left-to-right on the plate? Anything else we should know about compound treatment, etc?

Yes! They're in a sort of strange quadrant pattern. It was the best i could do with human hands towards randomization the plate. No compound treatments for this plate! Though every two rows will have a different genetic condition, visual map here:

Screen Shot 2020-10-27 at 12 35 25 PM

@bethac07
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3A (with added neuron branching measurements) is rerunning now, alongside an initial run of 3B, they should be ready for you in the morning @shntnu .

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shntnu commented Nov 15, 2020 •
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Checking platemaps

MAtt_ICC_test.txt

image

image

@mtegtmey
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@shntnu good here!

@shntnu
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shntnu commented Nov 15, 2020

Great! I'll proceed with this.

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shntnu commented Nov 18, 2020

@mtegtmey Were there 128 wells imaged in this plate? i.e. row 01-16 and columns 09-16?

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shntnu commented Nov 18, 2020 •
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A high-level summary of the dataset is here
https://github.com/broadinstitute/neuronal-cell-painting/blob/54ec670f185179e38010cacb2941afd1c76ff5f7/1.run-workflows/knit_notebooks/2.inspect-profiles-pilot3b.md
The first thing that popped up was the cell counts are much lower. I forgot if this expected @mtegtmey

image

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mtegtmey commented Nov 18, 2020 via email •
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@gwaybio gwaybio mentioned this issue Mar 1, 2021
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shntnu commented Jun 17, 2021

@ruifanp

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shntnu commented Jun 18, 2021 •
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Goal 1: Do we need MAP2 antibody to distinguish branching? It is possible that the standard Cell Painting dyes capture this signal.

Here are the branching features measured

  • NumberTrunks: The number of trunks. Trunks are branchpoints that lie within the seed objects
  • NumberNonTrunkBranches: The number of non-trunk branches. Branches are the branchpoints that lie outside the seed objects.
  • NumberBranchEnds: The number of branch end-points, i.e, termini.
  • TotalObjectSkeletonLength: The length of all skeleton segments per object.

Documentation on the measurement module from here:

Documentation

MeasureObjectSkeleton
MeasureObjectSkeleton measures information for any branching structures, such as neurons, root or branch systems, vasculature, or any skeletonized system that originates from a single point (such as neurites branching from a single nucleus/soma).

This module measures the number of trunks and branches for each branching system in an image. The module takes a skeletonized image of the object plus previously identified seed objects (for instance, each neuron’s soma) and finds the number of axon or dendrite trunks that emerge from the soma and the number of branches along the axons and dendrites. Note that the seed objects must be both smaller than the skeleton, and touching the skeleton, in order to be counted.

The typical approach for this module is the following:

  • Identify a seed object. This object is typically a nucleus, identified with a module such as IdentifyPrimaryObjects.
  • Identify a larger object that touches or encloses this seed object. For example, the neuron cell can be grown outwards from the initial seed nuclei using IdentifySecondaryObjects.
  • Use the Morph module to skeletonize the secondary objects.
  • Finally, the primary objects and the skeleton objects are used as inputs to MeasureObjectSkeleton.

The module determines distances from the seed objects along the axons and dendrites and assigns branchpoints based on distance to the closest seed object when two seed objects appear to be attached to the same dendrite or axon.

The module records vertices which include trunks, branchpoints, and endpoints.

The ongoing analysis in here

  • We consider both datasets
    • NCP-Pilot-3A has 4 compounds + Untreated x 2 conditions x 4 seeding densities, assayed using Cell Painting.
    • NCP-Pilot-3B has only Untreated x the same 2 conditions x the same 4 seeding densities, assaying using DAPI, MAP2, and Synapsin1 (or related)
  • We inspect only the four branching measurements. These have the patternNuclei_ObjectSkeleton_[measurement]_CellBodiesPlusNeuritesSkel. If I understand correctly, the nucleus (Nuclei) is the seed object, and the cell body (CellBodiesPlusNeuritesSkel) is the enclosing object.
  • The Cell Painting dataset (3A) have branching measurements with names containing _mito_skel; we ignored these

Here are plots across the NCP-Pilot-3A and NCP-Pilot-3B combined, for untreated wells only. Note that these are per well means i.e. each data point is the mean of that readout for a well. Red is from Cell Painting images (Untreated_CP) and blue is from MAP2 staining (Untreated_MAP2). The NumCells readout is actually Nuclei_Number_Object_Number, which is a reasonable proxy broadinstitute/cell-health#105 (comment). The actual cell count of the well is approximately 2 x number of sites / well x Nuclei_Number_Object_Number = 2 x 9 x Nuclei_Number_Object_Number= ~18 x Nuclei_Number_Object_Number

image

The most striking difference is that there are far fewer cells per image in the MAP2 images (assuming the Nuclei_Number_Object_Number proxy for cell count is not totally off here; I can check that), also seen here:

image

Additional plots

unnamed-chunk-15-1

unnamed-chunk-16-1

@shntnu
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shntnu commented Jun 18, 2021

@mtegtmey #8 (comment) is ready for you to stare at. I'll ping Pearl on this thread next week for inputs on how to interpret the measurements.

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shntnu commented Jun 21, 2021 •
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@pearlryder This might be distant memory because I wasn't able to get to it until now (!). Let me know if you're able to address these questions or if you need a recap on what's going on here

w.r.t. #8 (comment):

Note: There's a pretty large disparity in the number of cells per image. I am flagging this so that @mtegtmey is aware because the feature comparisons can be affected by this.
Update: It's possible the difference is because the Cell Painting readouts (Pilot 3A) include glial cells but the MAP2 (Pilot 3B) does not; @bethac07 's notes here might help verify this #6 (comment).

  1. Can you help us translate the four branching measurements in the context of this experiment? Here is my interpretation

    • NumberTrunks: The number of trunks. Trunks are branchpoints that lie within the seed objects --> This is the number of trunks, literally, so the number of dendrites

    • NumberNonTrunkBranches: The number of non-trunk branches. Branches are the branchpoints that lie outside the seed objects. --> This is the number of branches across all the dendrites

    • NumberBranchEnds: The number of branch end-points, i.e, termini. --> this seems self-explanatory and it makes sense that it is correlated with NumberNonTrunkBranches (below) and that it is always lower than NumberNonTrunkBranches.

    • TotalObjectSkeletonLength: The length of all skeleton segments per object. --> this seems self-explanatory as well

  2. Do you know why it is that we see higher values for NumberTrunks in Cell Painting vs MAP2 but fewer for NumberNonTrunkBranches. I guess this is a direct consequence of having many more cells per site?

image

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shntnu commented Jun 23, 2021 •
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PS We now have a lookup table that provides URLs to the images, in case we want to inspect them.

It is also rendered in this notebook and copied below (Only untreated rows)

Lookup table
Metadata_Batch Metadata_plating_density Metadata_line_condition Metadata_compound_ID URL_Outline Metadata_Plate Metadata_Well Metadata_Row Metadata_FieldID Metadata_line_ID Metadata_line_source
NCP_PILOT_3 5000 control NA Outline BR_NCP_PILOT_3 F07 6 3 NA NA
NCP_PILOT_3 5000 control NA Outline BR_NCP_PILOT_3 F07 6 5 NA NA
NCP_PILOT_3 7500 control NA Outline BR_NCP_PILOT_3 F24 6 3 NA NA
NCP_PILOT_3 7500 control NA Outline BR_NCP_PILOT_3 F24 6 5 NA NA
NCP_PILOT_3 5000 deletion NA Outline BR_NCP_PILOT_3 H13 8 3 NA NA
NCP_PILOT_3 5000 deletion NA Outline BR_NCP_PILOT_3 H13 8 5 NA NA
NCP_PILOT_3 7500 deletion NA Outline BR_NCP_PILOT_3 H22 8 3 NA NA
NCP_PILOT_3 7500 deletion NA Outline BR_NCP_PILOT_3 H22 8 5 NA NA
NCP_PILOT_3 2500 control NA Outline BR_NCP_PILOT_3 I17 9 3 NA NA
NCP_PILOT_3 2500 control NA Outline BR_NCP_PILOT_3 I17 9 5 NA NA
NCP_PILOT_3 10000 deletion NA Outline BR_NCP_PILOT_3 K20 11 3 NA NA
NCP_PILOT_3 10000 deletion NA Outline BR_NCP_PILOT_3 K20 11 5 NA NA
NCP_PILOT_3 10000 control NA Outline BR_NCP_PILOT_3 M06 13 3 NA NA
NCP_PILOT_3 10000 control NA Outline BR_NCP_PILOT_3 M06 13 5 NA NA
NCP_PILOT_3 2500 deletion NA Outline BR_NCP_PILOT_3 O09 15 3 NA NA
NCP_PILOT_3 2500 deletion NA Outline BR_NCP_PILOT_3 O09 15 5 NA NA
NCP_PILOT_3B 10000 deletion NA Outline MAtt_ICC_test C16 3 3 NA NA
NCP_PILOT_3B 10000 deletion NA Outline MAtt_ICC_test C16 3 5 NA NA
NCP_PILOT_3B 7500 control NA Outline MAtt_ICC_test F12 6 3 NA NA
NCP_PILOT_3B 7500 control NA Outline MAtt_ICC_test F12 6 5 NA NA
NCP_PILOT_3B 2500 control NA Outline MAtt_ICC_test M13 13 3 NA NA
NCP_PILOT_3B 2500 control NA Outline MAtt_ICC_test M13 13 5 NA NA
NCP_PILOT_3B 7500 deletion NA Outline MAtt_ICC_test P12 16 3 NA NA
NCP_PILOT_3B 7500 deletion NA Outline MAtt_ICC_test P12 16 5 NA NA

@pearlryder
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Hi @shntnu ,

Here are a few answers to your questions:

  1. Can you help us translate the four branching measurements in the context of this experiment? Here is my interpretation

    • NumberTrunks: The number of trunks. Trunks are branchpoints that lie within the seed objects --> This is the number of trunks, literally, so the number of dendrites
    • NumberNonTrunkBranches: The number of non-trunk branches. Branches are the branchpoints that lie outside the seed objects. --> This is the number of branches across all the dendrites
    • NumberBranchEnds: The number of branch end-points, i.e, termini. --> this seems self-explanatory and it makes sense that it is correlated with NumberNonTrunkBranches (below) and that it is always lower than NumberNonTrunkBranches.
    • TotalObjectSkeletonLength: The length of all skeleton segments per object. --> this seems self-explanatory as well

Yes, your descriptions are accurate. Here are a couple of screenshots that might help for folks who prefer visual representations. This image shows a CellBodiesPlusNeuritesSkel image from the NCP_PILOT_3A experiment after it has been measured. Trunks are colored red (originating at the nucleus, which is shaded gray), branchpoints are green, and branch ends are blue. For context, the corresponding cell (in yellow) is show in the image below.

Small point - my understanding is that any projection that originates from the nucleus could be either a dendrite or an axon (not exclusively dendrites), with most neurons having 1 axon and multiple dendrites -- although @mtegtmey will be the expert on that aspect!

Screen Shot 2021-06-23 at 10 06 21 AM

Screen Shot 2021-06-23 at 10 06 08 AM

  1. Do you know why it is that we see higher values for NumberTrunks in Cell Painting vs MAP2 but fewer for NumberNonTrunkBranches. I guess this is a direct consequence of having many more cells per site?

image

My suspicion is that the higher value for NumberTrunks in the Cell Painting vs MAP2 data is driven by differences in the morphology of the cells (which may relate to the density aka cells per site). I built plate montages to help get an overview of the images per well. These stitched images have one Overlay image per site (#5) per each well (the PILOT_3A images were very large, so I split the montages into 8 separate files; BR_NCP_PILOT_3_1.tif contains rows A & B, BR_NCP_PILOT_3_2.tif contains rows C & D, etc.).

Here's a screenshot of images from the PILOT_3A plate:
Screen Shot 2021-06-23 at 12 46 34 PM

And for comparison, a screenshot from the PILOT_3B plate:
Screen Shot 2021-06-23 at 12 46 13 PM

Looking at these montages, it seems to me like the PILOT_3B images have fewer nuclei per image and less density of projections. As a result, we would expect lower values for NumberTrunks compared to the PILOT_3A images, so the numbers make sense in that regard. With higher nuclear density, any given projection is also more likely to overlap with a nucleus and be consider a Trunk rather than a NonTrunkBranch, so that may also account for why you get more Trunks and fewer NonTrunkBranches in the NCP_PILOT_3A data.

The full plate montages are available at s3://imaging-platform/projects/2019_05_28_Neuronal_Cell_Painting/workspace/stitched_images/ if that's helpful.

Please let me know if I can provide any further clarity! Tagging @bethac07 here in case she has any additional insights that I missed.

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shntnu commented Jun 23, 2021

Thanks for clarifying, @pearlryder !

Looking at these montages, it seems to me like the PILOT_3B images have fewer nuclei per image and less density of projections. As a result, we would expect lower values for NumberTrunks compared to the PILOT_3A images, so the numbers make sense in that regard.

All the reported measurements in those plots, including NumberTrunks, are per-well means i.e. the average value of that readout per cell for that well (and not the sum of the readout across all cells for that well).

Does your conclusion change based on this?

It's difficult to figure out from the images if indeed there are indeed more trunks per cell in PILOT_3A vs PILOT_3B but maybe you are saying that is the case (i.e. that's what you mean by "less density of projections")?

Ah, maybe this explanation might be it:

With higher nuclear density, any given projection is also more likely to overlap with a nucleus and be consider a Trunk rather than a NonTrunkBranch, so that may also account for why you get more Trunks and fewer NonTrunkBranches in the NCP_PILOT_3A data.

Ok, will tag @mtegtmey to peek into all this now and weigh in.

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@pearlryder @shntnu sorry just catching up.

"With higher nuclear density, any given projection is also more likely to overlap with a nucleus and be consider a Trunk rather than a NonTrunkBranch, so that may also account for why you get more Trunks and fewer NonTrunkBranches in the NCP_PILOT_3A data."

This makes sense to me. Ralda and I discussed this data today and it seems to me that the standard CP stains are sufficient for all of the metrics. Though there is a less of a range in the MAP2 conditions, so it could be better to use MAP2 as a validation for the metrics from the CP downstream if this were to be a point of revision.

It also seems, excitingly, that we have some clear differences across these metrics between cases and controls, which is something we would expect to see.

In terms of moving ahead, I'll run the standard CP stains on the neurons from the cohort!

@shntnu shntnu changed the title [NCP Pilot 3B] How do we profile synapses? [NCP Pilot 3B] Is antibody staining of neurons significantly more informative than Cell Painting for profiling? Jun 24, 2021
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shntnu commented Jun 24, 2021 •
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Thanks for reviewing and making a call on this @mtegtmey.

I've updated #8 (comment) with some notes on our conclusions here.

What is your conclusion about staining for synapses? We haven't explored that here.

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mtegtmey commented Jun 24, 2021 via email

@shntnu shntnu changed the title [NCP Pilot 3B] Is antibody staining of neurons significantly more informative than Cell Painting for profiling? [NCP Pilot 3B] We needn't to do MAP2 antibody staining of neurons because Cell Painting seems sufficient Jun 24, 2021
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shntnu commented Jun 24, 2021

We're all set then!

Thanks again @pearlryder and @bethac07 for getting all this done!

@shntnu shntnu closed this as completed Jun 24, 2021
@shntnu shntnu mentioned this issue Jun 24, 2021
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@shntnu shntnu changed the title [NCP Pilot 3B] We needn't to do MAP2 antibody staining of neurons because Cell Painting seems sufficient [NCP Pilot 3B] We needn't do MAP2 antibody staining of neurons because Cell Painting seems sufficient Aug 19, 2021
@shntnu shntnu mentioned this issue Oct 7, 2021
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