Glioblastoma stem cells show transcriptionally correlated spatial organization

TL;DR

Glioblastoma stem cells form spatial patterns in culture that correlate with their gene activity, which can be predicted using computer vision.

Contribution

A novel use of interpretable computer vision to link GSC spatial organization with transcriptional state.

Findings

GSCs form multicellular patterns that correlate with their transcriptional state.

Image features predict transcriptional state across varying cell confluency levels.

This approach can identify how GSC biology changes in response to perturbations via imaging.

Abstract

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.

Computer vision coupled with gene expression shed light on the collective organizational patterning of glioma stem cells, which correlates with glioma stem cell phenotypes

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