Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
Data files
Aug 07, 2022 version files 6.91 GB
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10XVisium.zip
4.99 GB
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IMC_1.zip
1.20 GB
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MALDI_1.zip
5.93 MB
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Methylation.zip
118.83 MB
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README.txt.rtf
13.84 KB
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scSlices_Environment.zip
544.50 MB
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scTumor_Tissue.zip
48.74 MB
Sep 04, 2022 version files 9.78 GB
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10XVisium_2.zip
7.87 GB
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IMC_1.zip
1.20 GB
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MALDI_1.zip
5.93 MB
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Methylation.zip
118.83 MB
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README2.txt.rtf
14.51 KB
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scSlices_Environment.zip
544.50 MB
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scTumor_Tissue.zip
48.74 MB
Jan 16, 2025 version files 7.65 GB
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10XVisium.zip
4.99 GB
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IMC_1.zip
1.20 GB
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MALDI_1.zip
747.28 MB
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Methylation.zip
118.83 MB
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README.md
9.49 KB
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scSlices_Environment.zip
544.50 MB
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scTumor_Tissue.zip
48.74 MB
Abstract
Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies (Couturier et al., 2020; Neftel et al., 2019; Richards et al., 2021). The source of the dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas in-depth by spatially resolved transcriptomics, metabolomics, and proteomics. By deciphering regionally shared transcriptional programs across patients, we infer that glioblastoma is organized by spatial segregation of lineage states and adapt to inflammatory and/or metabolic stimuli, reminiscent of the reactive transformation in mature astrocytes. Integration of metabolic imaging and imaging mass cytometry uncovered locoregional tumor-host interdependence, resulting in spatially exclusive adaptive transcriptional programs. Inferring copy-number alterations emphasizes a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. A model of glioblastoma stem cells implanted into human and rodent neocortical tissue mimicking various environments confirmed that transcriptional states originate from dynamic adaptation to various environments.
README: Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
Author Information
- Name: Dr. Vidhya M. Ravi\ Institution: University Clinic of Freiburg\ Email: vidhya.ravi@uniklinik-freiburg.de
- Name: PD. Dr. Dieter Henrik Heiland\ Institution: University Clinic of Freiburg\ Email: dieter.henrik.heiland@uniklinik-freiburg.de
Date of Data Collection
2020–2021
Geographic Location of Data Collection
Freiburg, Germany
Recommended Citation for This Dataset
Ravi, Vidhya; Will, Paulina; Kueckelhaus, Jan et al. (2022). Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma [Dataset]. Dryad. https://doi.org/10.5061/dryad.h70rxwdmj
1. Spatial Transcriptomics (10X Visium)
File/folder name: 10XVISIUM.zip
Inside 10XVISIUM.zip, each sample folder follows the convention:
#UKF241_C_ST
│
├─ outs/
│ ├─ aligned_fiducials.jpg
│ ├─ detected_tissue_image.jpg
│ ├─ scalefactors_json.json
│ ├─ tissue_hires_image.png
│ ├─ tissue_lowres_image.png
│ ├─ tissue_positions.csv
│ ├─ spatial_enrichment.csv
│ ├─ metrics_summary.csv
│ ├─ filtered_feature_bc_matrix/
│ ├─ filtered_feature_bc_matrix.h5
│ ├─ raw_feature_bc_matrix/
│ ├─ raw_feature_bc_matrix.h5
│ └─ molecule_info.h5
└─ H&E/
└─ [All raw H&E images in TIFF or PNG formats]
- Sample ID naming:
#UKF= University Clinic of Freiburg patient numberC= Cortex,T= tumor,TC= Tumor Core,TI= Tumor Infiltration,IDH= IDH tumor, etc.
- Spatial Transcriptomics Pipeline:\ Data generated with Space Ranger (10X Genomics).
- Software Requirements:
- R
- ImageJ or CellProfiler for image transformations
2. Imaging Mass Cytometry (IMC)
File/folder name: IMC_1.zip
Inside IMC_1.zip, you will find:
- A Raw folder containing .mcd files (e.g.,
GBM_#UKF_248_#UKF_259_#UKF_260.mcd), which include images for three tumor samples (#UKF248, #UKF259, #UKF260). README.txtdescribing the samples, antibody panels, and other parameters used in the IMC experiments.
- Antibody panel (
Antibody_panel.csv):\ Contains columns such as Metal Tag, Full (Boolean), and elastic (Boolean) for controlling downstream analysis and training in ilastik. - Software Requirements:
- CellProfiler 3.1.8
- ilastik
- Anaconda for environment management
- R for downstream analysis
3. Spatial Metabolomics (MALDI)
File/folder name: MALDI_1.zip
Recent Update
We have updated the MALDI imaging data to include both .imzML and .ibd files for each sample. These files ensure full compatibility with various mass spectrometry software platforms.
Inside MALDI_1.zip, you will find:
- A raw folder containing the updated
.imzMLand.ibdfiles for each sample. -- XML-based imzML file contains metadata for the new MALDI dataset. It describes the spatial coordinates of each pixel in the image and the associated mass-to-charge (m/z) values. The ibd file is the companion binary data file that stores the actual intensity values for each m/z and pixel coordinate. Together, .imzML and .ibd allow you to fully reconstruct and analyze the new MALDI imaging dataset. README.txtdescribing the samples and any acquisition parameters.
- Data Import and Processing\
The raw files (
.imzML+.ibd) can be imported into R using thereadImzML()function from the Cardinal package. Cardinal provides a comprehensive set of tools for preprocessing, spatial segmentation, and classification of MSI (Mass Spectrometry Imaging) data. - File Format Description
.imzML: An XML-based format that describes the MSI experiment, including pixel coordinates, mass-to-charge (m/z) values, and more..ibd: Binary data containing the actual spectral intensities.
- Software Requirements:
- R
- Cardinal R package for mass spectrometry imaging data analysis
4. Methylation (Infinium Methylation Array)
File/folder name: Methylation.zip
Inside Methylation.zip, you will find:
- A raw folder containing .idat files (
*_Grn.idatand*_Red.idat) for each condition (hypoxia and normoxia). - A Meta.csv file describing the experimental conditions (e.g., sample IDs, condition types).
- Analysis:
- Methylation processing with the minfi R package.
- CNV analysis performed with the conumee package (functions:
cnv.fit()andcnv.bin()).
- Software Requirements:
- R (plus
minfiandconumeepackages)
- R (plus
5. Single Cell Sequencing
(a) Microenvironment Validation
File/folder name: scSlices_Environment.zip
We used primary tumor cells (#UKF233) injected into different microenvironments (e.g., 3-week-old rat, 2-year-old rat, human cortical slices) and performed 10X Chromium single-cell RNA sequencing after 7 days of co-culture.
- Folder Structure:
scSlices_Environment.zip │ ├─ #Rat_2Y/ │ └─ outs/ │ ├─ filtered_feature_bc_matrix/ │ ├─ filtered_feature_bc_matrix.h5 │ └─ ... ├─ #Rat_3W/ ├─ #Human_midAge/ └─ README.txt - Outs folder:
filtered_feature_bc_matrix/matrix.mtx.gzfeatures.tsv.gzbarcodes.tsv.gzfiltered_feature_bc_matrix.h5
- Software Requirements:
(b) Tumor Tissue Single Cell
File/folder name: scTumor_Tissue.zip
Directly processed patient tumor tissue (e.g., #UKF313_T_SCRNA), using 10X Chromium Single Cell technology.
- Folder Structure:
scTumor_Tissue.zip │ ├─ #UKF313_T_SCRNA/ │ └─ outs/ │ ├─ filtered_feature_bc_matrix/ │ ├─ filtered_feature_bc_matrix.h5 │ └─ ... └─ ... - Variable Descriptions (used in the manuscript):
nCount_RNA: Number of RNA molecules detected per cellnFeature_RNA: Number of genes detected per cellpercent.mt: Percentage of reads mapped to mitochondrial genesS.Score/G2M.Score: Cell cycle phase scoresPhase: Inferred cell cycle phaseseurat_clusters: Transcriptional clusters after fastMNN or Seurat-based clustering
6. Data Usage & Software Requirements Summary
- Spatial Transcriptomics:
- Requires Space Ranger (10X Genomics) outputs and R for downstream analysis.
- Image alignment: ImageJ or CellProfiler.
- Imaging Mass Cytometry (IMC):
.mcdraw data.- Requires CellProfiler, ilastik, and R.
- Spatial Metabolomics (MALDI):
- Updated
.imzML+.ibdraw data. - Requires Cardinal R package for analysis.
- Updated
- Methylation:
.idatfiles processed with minfi and CNV analysis using conumee in R.
- Single Cell Sequencing:
- 10X Genomics raw data (Cell Ranger outputs).
- Requires R and scRNA-seq analysis libraries (e.g., Seurat, Scanpy in Python if converted).
7. Contact and Support
For any questions about file formats, software usage, or analysis pipelines, please contact:
- Dr. Vidhya M. Ravi (vidhya.ravi@uniklinik-freiburg.de)
- PD. Dr. Dieter Henrik Heiland (dieter.henrik.heiland@uniklinik-freiburg.de)
8. License and Restrictions
This dataset is published under a CC0 license waiver. However, we ask that you please cite the recommended citation above when using this dataset. Consult your institution’s guidelines for any restrictions regarding patient data usage. The data provided has been de-identified where applicable.
Change log
Version 1.0 (2022-09-04)
-Initial release of the dataset to Dryad.
-Provided four .zip archives: IMC_1.zip, MALDI_1.zip, Methylation.zip, and scSlices_Environment.zip.
-Included a main README.txt file with instructions on how to load each data type.
Version 1.1 (2025-01-16)
-Added the missing .ibd file and combined the newly provided .imzML and .ibd files into MALDI1.zip for easier handling.
-Revised the MALDI section of the README to describe the newly included .imzML and .ibd files.
-Corrected minor typos and clarified software requirements
End of README
Thank you for your interest in our dataset. We hope this information facilitates straightforward data access and analysis.
Methods
The dataset was collected using:
1) 10X Visium spatila gene expression kit: And all the instructions for Tissue Optimization and Library preparation were followed according to manufacturer’s protocol. Data were analyzed and quality controlled by the cell ranger pipeline provided by 10X. For further analysis we developed a framework for spatial data analysis. The cell ranger output can be imported into SPATA by either a direct import function (SPATA:: initiateSpataObject_10X) or manually imported using count matrix and barcode-coordinate matrix as well the H&E staining.
2) MALDI-FTICR-MSI: Tissue preparation steps for MALDI imaging mass spectrometry (MALDI-MSI) analysis was performed as previously described (Aichler et al., 2017; Sun et al., 2018). We imported the files into R using the readImzML function from the cardinal package(Bemis et al., 2015). We reshaped the pixel data matrix into an intensity matrix and a matrix of coordinates for each tumor separately. We filtered the m/z matrix to annotated peaks (METASPACE database) using the match.closest function from the MALDIquant package resulting in a metabolic intensity matrix (Gibb and Strimmer, 2012). The intensity matrix and the corresponding spatial coordinates were imported into a SPATA object for further spatial data analysis using the SPATA::initiateSpataObject_MALDI.
3) Imaging mass cytometry: A 39-marker IMC panel was designed including structural and tumor markers as well as markers to assess several innate and adaptive immune cells . Metal-labeled antibodies were either obtained pre-conjugated (Fluidigm) or labeled in-house by conjugating purified antibodies to lanthanide metals using the Maxpar X8 antibody labelling kit (Fluidigm) according to the manufacturer’s instructions. Two to three 1000 μm² images per patient were acquired using a Hyperion Imaging System (Fluidigm). Briefly, tuning of the instrument was performed according to the manufacturer‘s instructions. Tissue sections were laser ablated spot-by-spot at 200 Hz resulting in a pixel size/resolution of 1 μm². Preprocessing of the raw data was conducted using the CyTOF software v7.0 (Fluidigm) and image acquisition control was performed using MCD Viewer v1.0.560.6 (Fluidigm).
4) Single cell RNA-sequencing: Single cell RNA-sequencing was performed according to the Chromium Next GEM Single Cell 3´v3.1 protocol (10x Genomics), based on a droplet scRNA-sequencing approach. In brief, collected cells were added to a prepared master mix containing reagents for a reverse transcription reaction and loaded onto separate lanes of a Chromium Next GEM Chip G. After running the chip on a Chromium Controller, generated GEMs were transferred to a tube strip. Following reverse transcription, GEMs were broken, and cDNA was purified from leftover reagents. Amplified cDNA was fragmented and size-selected using SPRIselect reagent (Beckman Coulter, B23318). i7 indexes as well as P5 and P7 Illumina primers were added to the libraries. The average length of final libraries was quantified using a Fragment Analyzer (HS NGS Fragment kit, Agilent, DNF-474) and the concentration of libraries was determined using a Qubit 1X dsDNA HS kit (Thermo Fisher, Q33231). Final libraries were diluted to 4nM, pooled and denatured before sequencing on an Illumina NextSeq 550 Sequencing System (Illumina, San Diego, CA, USA) using NextSeq 500/550 High Output kit v2.5 (Illumina, 20024906) with 28 cycles for read 1, 8 cycles for i7 index and 56 cycles for read 2.