Biology subjects

Ayyadhury, S.

Publications and source records attributed to Ayyadhury, S..

4 recordsLinked to original sources

bioRxiv2026

Spatially resolved gene expression and tumour-microenvironment interactions across malignant transformation in IDH-mutant glioma

Isocitrate dehydrogenase (IDH)-mutant gliomas represent most lower-grade diffuse gliomas in young adults. Although IDH-mutant gliomas initially behave indolently relative to IDH-wildtype counterparts, they undergo inevitable malignant transformation. The mechanisms driving this progression remain poorly defined. We analyzed a unique longitudinal cohort of two patients with IDH-mutant diffuse gliomas, each with matched WHO grade 2, 3, and 4 tumours. Spatial transcriptomics (NanoString GeoMx and 10X Visium), combined with differential expression, spatially variable gene analysis, and gene ontology enrichment, were used to define transcriptional programs underlying progression. Unexpectedly, across both patients, higher-grade tumours demonstrated reduced proliferation at the infiltrative margin as measured by Ki67. Spatial transcriptomic analysis identified an enrichment of invasion-associated genes associated with increasing tumour grade. In an independent bulk RNA-seq cohort, expression of this spatially derived program did not outperform grade-only PFS or OS analysis. These observations underscore the need to develop new metrics that account for spatial evolution that occurs across progression, but escapes detection in bulk analyses and provide the rationale for prioritizing migration-, cytoskeletal- and stress-associated programs for further investigation.

cancer biology↗

bioRxiv2024

Glioblastoma stem cells show transcriptionally correlated spatial organization

Glioblastoma (GBM) is an aggressive brain cancer with a poor survival rate. Despite hundreds of clinical trials, there is no effective targeted therapy. Glioblastoma stem cells (GSCs) are an important GBM model system. In culture, these cells form spatial structures that share morphological aspects with their source tumors. We collected 17,000 phase contrast images of 15 patient-derived GSC lines growing to confluence. We find that GSCs grow in characteristic multicellular patterns depending on their transcriptional state. Interpretable computer vision algorithms identified specific image features that predict transcriptional state across multiple cell confluency levels. This relationship will be useful in developing GSC screens where image features can be used to identify how GSC biology changes in response to perturbations simply by imaging cultured cells on plates.

cancer biology↗

bioRxiv2021

A comparison of data integration methods for single-cell RNA sequencing of cancer samples

Tumours are routinely profiled with single-cell RNA sequencing (scRNA-seq) to characterize their diverse cellular ecosystems of malignant, immune, and stromal cell types. When combining data from multiple samples or studies, batch-specific technical variation can confound biological signals. However, scRNA-seq batch integration methods are often not designed for, or benchmarked, on datasets containing cancer cells. Here, we compare 5 data integration tools applied to 171,206 cells from 5 tumour scRNA-seq datasets. Based on our results, STACAS and fastMNN are the most suitable methods for integrating tumour datasets, demonstrating robust batch effect correction while preserving relevant biological variability in the malignant compartment. This comparison provides a framework for evaluating how well single-cell integration methods correct for technical variability while preserving biological heterogeneity of malignant and non-malignant cell populations.

bioinformatics↗

bioRxiv2018

Single-cell RNA-seq reveals that glioblastoma recapitulates normal brain development

SummaryCancer stem cells are critical for cancer initiation, development, and resistance to treatments. Our understanding of these processes, and how they relate to glioblastoma heterogeneity, is limited. To overcome these limitations, we performed single-cell RNA-sequencing on 38 296 glioblastoma cells and 22 637 normal human fetal brain cells. Using an unbiased approach, we mapped the lineage hierarchy of the developing human brain and compared the transcriptome of each cancer cell to this roadmap. We discovered a conserved neural trilineage cancer hierarchy with glial progenitor-like cells at the apex. We also found that this progenitor population contains the majority of cancers cycling cells and is the origin of heterogeneity. Finally, we show that this hierarchal map can be used to identify therapeutic targets specific to progenitor cancer stem cells. Our analyses show that normal brain development reconciles glioblastoma development, unravels the origin of glioblastoma heterogeneity, and helps to identify cancer stem cell-specific targets.

cancer biology↗