GBM360

GBM360 software

GBM360 is a software that harnesses the power of machine learning to investigate the cellular heterogeneity and spatial architecture of glioblastoma (GBM). <br> The software takes H&E-stained histology image as input and predicts the distribution of transcriptional subtype and aggressiveness of GBM cells.

A fully functional software is available at: https://gbm360.stanford.edu.

<img src="pictures/demo.png" width = "70%" height = "70%"> <br/>

System requirements

The software is written with Streamlit (V 1.12). Software dependencies can be found in requirements.txt

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Reference / Citation

Zheng, Y., Carrillo-Perez, F., Pizurica, M. et al. Spatial cellular architecture predicts prognosis in glioblastoma. Nat Commun 14, 4122 (2023). https://doi.org/10.1038/s41467-023-39933-0

Installation

This repository contains the source code of GBM360 for demonstration purpose only.

1. Clone this Git repository: <br> git clone https://github.com/gevaertlab/GBM360.git to your local file system.

2. Create a new conda environment: <br> conda create --name GBM360 python=3.9 and activate: conda activate GBM360

3. Install the required packages: <br> pip install -r requirements.txt

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Instructions for use

1. Visit https://gbm360.stanford.edu in a web browser.
2. Click the Run tab located at the top of the page.
3. To start the analysis, user can either upload a new histology image or simply click Use an example slide. <br> Note:
   • We currently support images saved in tif, tiff or svs format. <br>
   • Ideally, the image should be scanned at 20X magnification with a pixel resolution of 0.5um / pixel.

<img src="pictures/screenshot_file_upload.png" width = "70%" height = "70%">

A thumbnail of the image will display when the upload is complete

<img src="pictures/screenshot_thumbnail.png" width = "70%" height = "70%">

4. Select the mode for running the job. <br> Note:

   • The default mode is set to the Test mode, which will only predicts a limited portion of the image (1,000 patches). This is meant to speed up the process by generating a quick preview of the results.
   • To predict the entire image, please switch to Complete mode.
   • We are currently working on obtaining GPU support for this software, which will significantly accelerate its performance.

<img src="pictures/screenshot_mode.png" width = "70%" height = "70%"> <br/>

5. Click the Get cell type visualization button to predict the spatial distribution of transcriptional subtype for tumor cells.

<img src="pictures/screenshot_option.png" width = "70%" height = "70%">

The image will be colored by the predicted transcriptional subtype:

<img src="pictures/screenshot_cell_type_vis.png" width = "70%" height = "70%"> <br/>

6. Based on the spatial subtype prediction, the software will automatically make several statistical analysis to quantify subtype compositions and spatial cellular organization:

   (1) Subtype fraction

   <img src="pictures/screenshot_cell_fraction.png" width = "70%" height = "70%"> <br/>

   (2) Subtype interaction

   <img src="pictures/screenshot_interaction.png" width = "70%" height = "70%"> <br/>

   (3) Clustering coefficient

   <img src="pictures/screenshot_cc.png" width = "70%" height = "70%">

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6. Finally, click the Get prognosis visualization button to predict the aggressive score of the cells.

Blue indicates low aggressiveness, while Red indicates high aggressiveness

<img src="pictures/screenshot_agg.png" width = "70%" height = "70%">

Preprocessing codes

Data from 10X Genomics were first converted into Seurat or AnnData object using the Seurat or Scanpy package.

1. Quality control and data integration were performed using the Seurat package: quality_control.R.
2. Run inferCNV.py to infer copy number variation using transcriptomics profiles.
3. Run tumor_frac.py to infer tumor cell fraction for each spot based on the CNV profiles.