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Jean Fan, PhD
Associate Professor, Department of Biomedical Engineering, Center for Computational Biology, Johns Hopkins University

Comparative Spatial Transcriptomics Analysis

Advances in high-throughput spatial transcriptomics (ST) technologies enable high-throughput molecular profiling of cells while maintaining their spatial organization within tissues. Such high-throughput ST data demand new statistical approaches and computational analysis methods to identify genes that spatially change in their expression patterns between conditions, such as in diseased versus healthy tissues. In this talk, I will provide an overview of the latest ST computational analysis methods developed by my lab. In particular, to facilitate spatial molecular comparisons across structurally matched tissue sections from replicates, case-control settings, and within and across technologies, we previously develop STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. Likewise, to enhance the scalability of ST data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. More recently, we developed STcompare to integrate STalign and SEraster into a statistical framework for comparative analysis of ST data by testing for differences in spatial correlation and spatial fold-change across structurally matched locations while robustly controls for false positives even in the presence of spatial autocorrelation common in ST data. Alternatively, to facilitate spatial molecular comparisons across structurally unmatched tissues, we previously developed CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, to quantify cell-type spatial relationships across multiple length scales. We have applied CRAWDAD to compare cell-type spatial organizations across samples as well as across functional tissue units within samples. Overall, we anticipate that such computational methods for analyzing ST data will contribute to important biological insights regarding spatial molecular changes across comparative axes of interest.

Caleb Hallinan
PhD Student, Johns Hopkins University

Primer: Understanding Spatial Transcriptomics and Evaluating Probe Accuracy in 10x Genomics Xenium Technology

Primer Abstract:
Spatial transcriptomics promises to reveal where genes are expressed within intact tissue, transforming how we study development, disease, and tumor biology. But how accurate are these spatial gene expression maps? Focusing on the widely used 10x Genomics Xenium technology, we uncovered evidence that off‑target probe binding can distort gene-specific expression patterns and alter biological conclusions. By comparing Xenium breast cancer data (an imaging-based technology) with matched Visium CytAssist (a sequencing-based technology) and single‑cell RNA‑seq data from the same breast tumor, we identified cases where spatial gene expression reflects contributions from unintended off-target transcripts rather than the intended target gene alone. In this talk, I will introduce the core ideas of spatial transcriptomics and highlight a subtle but consequential technical artifact that can affect imaging‑based technologies.

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