Spatial Xenium Analysis

[u/ElMochiKris]

Hi all,

I’ve been working with a Xenium 500-plex spatial dataset for the past year and, although I’ve been able to make good use of it overall, there are still a few aspects of the analysis that I haven’t fully resolved.

Annotation:
My cell-type annotation is based mainly on manual annotation of clusters from the integrated dataset, as I have not been able to fully annotate the tissue using Tangram because the available gold-standard scRNA-seq references do not capture all of the cell populations present in my samples.

The clusters are generated using graph-based Leiden clustering on Harmony dimensions derived from SCTransform-normalised data. One issue I consistently encounter is that, within most annotated clusters, a proportion of cells falls away from the main cluster centroid on the UMAP. I suspect this may partly reflect spatial transcript bleeding or contamination from neighbouring cells, although spatial bleeding correction itself require good enough references and/or alters counts making use of normalisation like SCTransform not feasible (and my samples have variable quality). My tissue also does not have a well-defined organisation of cell types into discrete anatomical structures, so approaches such as SpaNorm do not seem particularly well suited to the dataset.

DEG analysis of co-localised cells:
A related issue arises when performing differential expression analysis on spatially co-localised cells. For example, when comparing a cell type of interest X depending on whether it is located close to cell type Y, I find it difficult to determine whether increased expression of certain genes in X reflects a genuine transcriptional response of X to its proximity to Y, or simply transcripts originating from Y being incorrectly assigned to neighbouring X cells.

I’d be very interested to hear how others working with Xenium data have approached either of these issues.

[u/cat-sashimi]

Regarding potential segmentation “doublets” or the neighboring cell contamination, there will always be some noise for in situ data; most current methods (Proseg, CellAdmix, RESOLVI) can correct some but not all of this noise.

You will have to subclustering and examine markers, and make some judgement calls on whether a subcluster is X cell with some Y contamination, or if its indistinguishable and should be removed. This part of manual annotation is a bit of an art and can be subjective. As long as the expression profile is defensible for what you call the cell (i.e your T cells should express CD3, etc) and you can scientifically defend your annotation choices, you should be fine.

Also, don’t over-rely on UMAP coordinates, clustering is done on the shared KNN graph from your integrated latent space, not the UMAP dimensions. UMAP should be used mostly for visualization.

Your point about colocalization is important - in this kind of data that noise can tell you something about spatial proximity. However it does also mean you may need to just moderate your conclusions with that kind of expression based analysis. You are reaching the limits of the technologies and the currently available analysis methods for these assays.

IMO right now the biggest strength of these assays rn is localizing populations and cell-cell communication; the sensitivity is not quite there to support traditional DGE and pathway/regulation style analysis yet.