W155 - Integrating Spatial Transcriptomics and Single-Cell RNA Sequencing to Spatially Characterize Human Lymphatic Tissue

Advances in single cell analysis technologies have revolutionized efforts to understand cell state and heterogeneity in conditions of health and disease. However, tissue dissociation procedures do not always encompass the full cellular constituents within a tissue, particularly for many stromal subsets. Moreover, cell:cell interactions and localization within functional units are often lost upon tissue digestion. Emerging spatial transcriptomic (ST) technologies address this limitation by assessing gene expression across a tissue section while preserving positional information that is otherwise lost with single cell RNA-seq approaches. Despite further advancements, ST approaches currently do not offer the degree of throughput and resolution that is achieved with scRNA-seq, thushighlighting the need to integrate single-cell and spatial data.Here, we present spatial transcriptomics data (Visium, 10x Genomics) generated from fresh frozen, OCT-embeddedthymus, spleen, and pancreatic lymph node obtained fromcontrol human organ donors (N=2, age=1.5, 32 y.o.).Furthermore, we integrated these datasets with reference gene expression signatures derived from scRNAseq datasets to deconvolute the relative cell type abundances across the tissue section. We then employ the TRUST4 algorithm to extract candidate TCR and BCR reads from Visium data in order to reconstruct the immune receptor repertoire of ab and gd T cells as well as B cells to assess their clonal diversity and relative positions in situabgd. Together, this multi-modal analysis is expected to enable detailed studies capturing the cellular heterogeneity, spatial organization, and clonal diversity withincomplex tissues during health and disease.