Biology subjects

Taylor, S. E. B.

Publications and source records attributed to Taylor, S. E. B..

3 recordsLinked to original sources

bioRxiv2025

Biomarker Quantification in Breast Cancer using Xenium In Situ

Advances in spatial transcriptomics enable high-throughput quantitation of both established and novel biomarkers at single cell resolution, offering the potential to transform diagnostics. Using Xenium in situ technology in FFPE human breast samples, we address two challenges in the cancer field: 1. achieving reliable normalization of gene expression across heterogeneous sample populations; 2. identifying biomarkers that predict invasion or metastasis. We describe a scalable approach to identify low-variation housekeeping (HK) genes within any given sample set, then use those HK genes for cross- and intra-sample normalization of biomarkers. Analyzing 12 FFPE human breast samples-primarily ductal carcinoma in situ (DCIS)-with a custom 280-gene panel, we identified four HK genes (EEF1G, EEF2, MALAT1, and RPLP0) that exhibited minimal variability in tumor cells, four tumor cell biomarkers (LDHA, SDC1, PIGR, SFRP1) that increased or decreased with tumor grade, and one tumor-associated myoepithelial biomarker (LAMC2). Normalizing biomarkers to the four HK genes preserved the dynamic range of expression necessary for distinguishing tumor grades, outperforming HKs from legacy RT-PCR diagnostic panels. Lastly, we employed a cell-agnostic approach in the tumor periphery to quantify MMP11, a biomarker correlated with proliferative and potentially pre-invasive ducts. Our results establish a single cell normalization method for spatial in situ transcriptomics and reveal and quantitate biomarkers relevant to DCIS risk and progression.

cancer biology↗

bioRxiv2024

Characterization of immune cell populations in the tumor microenvironment of colorectal cancer using high definition spatial profiling

Colorectal cancer (CRC) is the second-deadliest cancer in the world, yet a deeper understanding of spatial patterns of gene expression in the tumor microenvironment (TME) remains elusive. Here, we introduce the Visium HD platform (10x Genomics) and use it to investigate human CRC and normal adjacent mucosal tissues from formalin fixed paraffin embedded (FFPE) samples. The first assay available on Visium HD is a probe-based spatial transcriptomics workflow that was developed to enable whole transcriptome single cell scale analysis. We demonstrate highly refined unsupervised spatial clustering in Visium HD data that aligns with the hallmarks of colon tissue morphology and is notably improved over earlier Visium assays. Using serial sections from the same FFPE blocks we generate a single cell atlas of our samples, then we integrate the data to comprehensively characterize the immune cell types present in the TME, specifically at the tumor periphery. We observed enrichment of two pro-tumor macrophage subpopulations with differential gene expression profiles that were localized within distinct tumor regions. Further characterization of the T cells present in one of the samples revealed a clonal expansion that we were able to localize in the tissue using in situ gene expression analysis. In situ analysis also allowed us to perform in-depth characterization of the microenvironment of the clonally expanded T cell population and we identified a third macrophage subpopulation with gene expression profiles consistent with an anti-tumor response. Our study provides a comprehensive map of the cellular composition of the CRC TME and identifies phenotypically and spatially distinct immune cell populations within it. We show that the single cell-scale resolution afforded by Visium HD and the whole transcriptome nature of the assay allows investigations into cellular function and interaction at the tumor periphery in FFPE tissues, which has not been previously possible.

cancer biology↗

bioRxiv2022

Comparing 10x Genomics single-cell 3' and 5' assay in short-and long-read sequencing

Barcoding strategies are fundamental to droplet-based single-cell sequencing, and understanding the biases and caveats between approaches is essential. Here, we comprehensively evaluated both short and long reads of the cDNA obtained through the two marketed approaches from 10x Genomics, the "3 assay" and the "5 assay", which attach barcodes at different ends of the mRNA molecule. Although the barcode detection, cell-type identification, and gene expression profile are similar in both assays, the 5 assay captured more exonic molecules and fewer intronic molecules compared to the 3 assay. We found that 13.7% of genes sequenced have longer average read lengths and are more complete (spanning both polyA-site and TSS) in the long reads from the 5 assay compared to the 3 assay. These genes are characterized by long average transcript length, high intron number, and low expression overall. Despite these differences, cell-type-specific isoform profiles observed from the two assays remain highly correlated. This study provides a benchmark for choosing the single-cell assay for the intended research question, and insights regarding platform-specific biases to be mindful of when analyzing data, particularly across samples and technologies.

bioinformatics↗