High-Dimensional Multi-Omic Mapping of Post-Mortem Human Brain Using Iterative Indirect Immunofluorescence Imaging on Xenium-Processed Tissues

Abstract

Spatial transcriptomics approaches provide crucial insights into gene expression distribution within intact tissue architecture, but they encounter limitations in detecting morphologically complex cell types, assessing their spatial associations with pathology, and accurately annotating cell types using RNA data alone. Therefore, we developed a robust post-processing workflow integrating Xenium spatial technology with iterative indirect immunofluorescence imaging (4i) on formalin-fixed paraffin-embedded (FFPE) human brain tissue. The post-Xenium 4i protocol presented here allows multi-omic tissue mapping, enabling deeper investigation of pathological microenvironments defined by the spatial distribution of neuropathological hallmarks and enrichment of specific cell populations. We applied this workflow on calcarine cortex tissue sections where cerebral amyloid angiopathy (CAA) burden is present, in addition to amyloid plaques and tau pathology, generating a 15-plex image that captures the complexity of the pathological microenvironment. MOTIVATION The expansion of spatial transcriptomics (ST) technologies has enhanced our ability to dissect the complexity and diversity of the molecular organization of the human brain. Among these techniques, the Xenium platform enables high-plex, in situ gene expression profiling at subcellular resolution while preserving tissue architecture, thereby offering powerful insights into tissue organization. However, transcriptomic data can be greatly enriched by the inclusion of morphological, anatomical, pathological, and cellular markers at the protein level. Here, we report a post-spatial profiling workflow that combines the Xenium platform with iterative indirect immunofluorescence imaging (4i) on formalin-fixed paraffin-embedded (FFPE) human brain tissue to capture key pathological features for Alzheimer’s disease (AD) and facilitate cell segmentation. This approach enables cost-effective and flexible multiplexed, multi-omic mapping of the same tissue section by registering Xenium transcript data with subsequent 4i immunostaining, which maximizes the RNA quality. Incorporating immunofluorescence (IF)-based cell-type markers alongside RNA profiles enables cell types to be annotated using robust protein markers rather than relying solely on transcriptomic information, thus reducing an important source of noise in downstream analyses. Immunofluorescence staining of FFPE tissue following Xenium processing also preserves cellular morphology, allowing the morphological complexity of distinct cell types to be captured and quantitated, especially for glial cells with highly ramified processes. To demonstrate the practical application of this workflow to AD studies, we developed a proof-of-concept deep learning framework to classify nuclei into specific major cell types of the brain using the IF data. Thus, we present an integrated experimental and analytic workflow optimized for the multi-omic characterization of human brain tissue in an AD context; these pipelines provide spatially resolved insights into pathological microenvironments found in the older human brain and can readily be adjusted to detect other proteins of interest or other pathological features. Altogether, this approach provides a comprehensive view of spatial architecture within pathologically affected regions and enables accurate transcript-independent cell-type annotation. Highlights Integrate Xenium and 4i to build a multi-omic map on the same FFPE brain tissue Improvement of cell-type annotation Identify pathologic features and cross-register them into the transcriptomic and proteomic spatial matrix