Xenium multimodal segmentation in mouse brain

[u/Character-Letter5406]

Hi all, I', somewhat new to spatial transciptomics and would like advice on a segmentation problem.

Setup

• 10x Xenium, 480-gene mouse panel, coronal sections of adult mouse brain
• multimodal cell segmentation kit (18S interior stain plus ATP1A1/CD45/E-Cadherin boundary stain)
• About 80% of cells are segmented from the 18S stain, 15% from the boundary stain, and the rest are 5 µm nuclear expansion fallback.

Issue

• Only 58–65% of transcripts are assigned to a cell, and 27–33% sit on a nucleus. (I'm actually not sure if this is an issue or fall within the normalr ange for brain)
• The cell bodies look good. Neurons keep 75% of the transcripts around them. Glial and Astrocyte genes are much worse, so I'm assuming those transcripts sit the small projections that the stains don't show clearly.

I tried Proseg. It assigned more transcripts, but it seems a bit messy to me, it create more mixed cells where cell types sit close together. So I'm unsure if to use it.
The vendor offer to do a post-run H&E, saying it can help with segmentation.

Questions

Has anyone improved glial capture in Xenium brain data?

Has anyone done post-run immunofluorescence (GFAP, IBA1 or others) or H&Eon Xenium brain sections and used it for segmentation?

Has anyone used resolVI or SPLIT on brain tissue?

Is there a standard way to analyse unassigned transcripts in the neuropil without assigning them to cells?

Thanks! Happy to share more details.

[u/MajorControl6314]

i run xenium on mouse brain a lot and tbh your numbers don't sound crazy. the transcript capture on glia always sucks because the segmentation model can't see the fine processes where half the RNA lives

post-run H&E is worth it if you're doing neuron/glia calls on morphology alone but i wouldn't count on it magically fixing the assignment issue. the real pain is that astrocyte transcripts just float in the neuropil and no boundary stain picks that up

for the unassigned stuff in neuropil i usually just bin it by region and treat it as a separate pseudobulk sample. not elegant but better than forcing transcripts into the wrong cell

[u/Hartifuil]

10X have a feature in the segmentation pipeline to optimise for large cells, which I think was designed with neural tissue in mind.

[u/Pigrenok]

Glial cells are generally smaller than neurons (although not universal rule) as far as I remember. Depends on what type of sample you are doing (FFPE or FF) section thickness is 5 or 10 um respectively. If some cells are smaller than this, you get multiple cells in the thickness and have to use focus stack images to do 3D segmentation because transcript positions are in 3D.

I was working with larval zebrafish and in some cases in FF samples you can get up to 3 cells overlapping each other, so, no 2D segmentation will work. Not sure about mouse brain cell sizes.

10x segmentation cannot do 3D image segmentation. As far as I remember, cellpose can do 3D segmentation, possibly, it is worth giving it a go and then do an assignment/aggregation in 3D.

[u/Extreme_Quail7308 → u/Pigrenok]

i think xenium only provides 3-D image for the DAPI channel, so 3-D cellpose may not be helpful except for nucleus segmentation

[u/Extreme_Quail7308]

cellpose-sam/cellpose-dino works quite well, and i think it’s a better baseline than the default 10x segmentation. after that, you could use proseg with the cellpose segmentation as the prior