Biology subjects

Ohn, J.

Publications and source records attributed to Ohn, J..

3 recordsLinked to original sources

bioRxiv2024

Spatial transcriptomics unveils landscape of resistance to concurrent chemo-radiotherapy in hypopharyngeal squamous cell carcinoma: the role of SPP1+ macrophages

Hypopharyngeal squamous cell carcinoma (SCC) is a highly aggressive cancer with a poor prognosis, particularly in advanced stages where concurrent chemoradiotherapy (CCRT) is used for treatment. However, resistance to CCRT poses a significant challenge, often leading to treatment failure and disease progression. This study explores the tumor microenvironment (TME) of hypopharyngeal SCC to understand the molecular mechanisms underlying CCRT resistance. Using spatial transcriptomics (ST), we analyzed tissue samples from patients with locally advanced hypopharyngeal SCC, distinguishing between those who were CCRT-resistant and those who were CCRT-naive. The analysis revealed six distinct cellular clusters within the TME, including a prominent epithelio-immune cellular area in CCRT-resistant tissues. SPP1 was identified as a key gene with significantly higher expression in CCRT-resistant samples, specifically within macrophages. Further investigation showed that SPP1+ macrophages interacted with malignant epithelial cells through SPP1-CD44 and SPP1-ITGB1 ligand-receptor pairs. These interactions were primarily localized in the peri-tumoral and intra-tumoral regions, highlighting their potential role in driving CCRT resistance. Our findings suggest that SPP1+ macrophages contribute to the resistant phenotype in hypopharyngeal SCC by modulating the TME and interacting with cancer cells. Understanding these interactions offers valuable insights into the mechanisms of CCRT resistance and may inform the development of targeted therapies to improve patient outcomes.

cancer biology↗

bioRxiv2023

SpatialSPM: Statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets

Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insights into their biological functions. Even though ST dataset provides information on the gene expression and its location for each sample, it is challenging to compare spatial gene expression patterns across tissue samples with different shapes and coordinates. Here, we propose a method that reconstructs ST data into multi-dimensional image matrices to ensure comparability across different samples through spatial registration process. We demonstrated the applicability of this method by using two mouse brain ST datasets to investigate and directly compare gene expression in a specific anatomical region of interest, pixel by pixel, across various biological statuses. It can produce statistical parametric maps to find specific regions with differentially expressed genes across tissue samples. Our approach provides an efficient way to analyze ST datasets and may offer detailed insights into various biological conditions.

bioinformatics↗

bioRxiv2023

GeneDART: Extending gene coverage in image-based spatial transcriptomics by deep learning-based domain adaptation with barcode-based RNA-sequencing data

Spatial transcriptomics (ST) technologies provide comprehensive biological insights regarding cell-cell interactions and peri-cellular microenvironments. ST technologies are divided into two categories: imaging-based (I-B) and barcode-based (B-B). I-B ST technologies provide high resolution and sensitivity but have limited gene coverage. B-B ST technologies can analyze the whole transcriptome but have lower spatial resolution. To address these limitations, we propose a deep learning-based model that integrates I-B and B-B ST technologies to increase gene coverage while preserving high resolution. A model, trained by a neural network with an adversarial loss based on I-B and B-B datasets from human breast cancer tissue, was able to extend gene coverage to whole transcripts-level and accurately predict gene expression patterns in the I-B dataset with a high resolution. This novel methodology, named GeneDART, could enable researchers to utilize B-B and I-B ST datasets in a complementary way.

bioinformatics↗