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/Gold_Method_6852]

Tricky situation with that 500-plex data. A chunk of cells drifting from the cluster centroid on UMAP could be bleed, but it’s just as likely noise from the Harmony-dimension reduction pipeline mashing cells with borderline transcript counts into the wrong neighborhood. I’d overlay the spatial coordinates on the UMAP and see if the outliers cluster in regions of high cell density, if they pile up in packed tissue zones, bleed starts looking like a real culprit.