DOI: 10.5061/dryad.h70rxwdmj
title: Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma

description: 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.
description: The dataset was collected using: <br><br>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.<br><br>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.<br><br>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).<br><br>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.
description: # 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](mailto:vidhya.ravi@uniklinik-freiburg.de) * **Name**: PD. Dr. Dieter Henrik Heiland\ **Institution**: University Clinic of Freiburg\ **Email**: [dieter.henrik.heiland@uniklinik-freiburg.de](mailto: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](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 number * `C` = Cortex, `T` = tumor, `TC` = Tumor Core, `TI` = Tumor Infiltration, `IDH` = IDH tumor, etc. * **Spatial Transcriptomics Pipeline**:\ Data generated with [Space Ranger (10X Genomics)](https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/output/overview). * **Software Requirements**: * [R](https://www.r-project.org/) * [ImageJ or CellProfiler](https://imagej.net/software/fiji/) for image transformations --- ## 2. Imaging Mass Cytometry (IMC) **File/folder name**: `IMC_1.zip` Inside `IMC_1.zip`, you will find: 1. 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). 2. `README.txt` describing 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](https://cellprofiler.org/) * [ilastik](https://www.ilastik.org/) * [Anaconda](https://www.anaconda.com/) for environment management * [R](https://www.r-project.org/) 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: 1. A **raw** folder containing the updated **`.imzML`** and **`.ibd`** files 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. 2. `README.txt` describing the samples and any acquisition parameters. * **Data Import and Processing**\ The raw files (`.imzML` + `.ibd`) can be imported into [R](https://www.r-project.org/) using the **`readImzML()`** 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](https://www.r-project.org/) * [Cardinal R package](http://cardinal.stanford.edu/) for mass spectrometry imaging data analysis --- ## 4. Methylation (Infinium Methylation Array) **File/folder name**: `Methylation.zip` Inside `Methylation.zip`, you will find: 1. A **raw** folder containing **.idat** files (`*_Grn.idat` and `*_Red.idat`) for each condition (hypoxia and normoxia). 2. A **Meta.csv** file describing the experimental conditions (e.g., sample IDs, condition types). * **Analysis**: 1. **Methylation processing** with the **minfi** R package. 2. **CNV analysis** performed with the **conumee** package (functions: `cnv.fit()` and `cnv.bin()`). * **Software Requirements**: * [R](https://www.r-project.org/) (plus `minfi` and `conumee` packages) --- ## 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.gz` * `features.tsv.gz` * `barcodes.tsv.gz` * `filtered_feature_bc_matrix.h5` * **Software Requirements**: * [R](https://www.r-project.org/) (plus [Seurat](https://satijalab.org/seurat/) or comparable single-cell analysis packages) ### (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 cell * `nFeature_RNA`: Number of genes detected per cell * `percent.mt`: Percentage of reads mapped to mitochondrial genes * `S.Score` / `G2M.Score`: Cell cycle phase scores * `Phase`: Inferred cell cycle phase * `seurat_clusters`: Transcriptional clusters after fastMNN or Seurat-based clustering --- ## 6. Data Usage & Software Requirements Summary 1. **Spatial Transcriptomics**: * Requires [Space Ranger (10X Genomics)](https://www.10xgenomics.com/) outputs and [R](https://www.r-project.org/) for downstream analysis. * Image alignment: [ImageJ](https://imagej.net/software/fiji/) or [CellProfiler](https://cellprofiler.org/). 2. **Imaging Mass Cytometry (IMC)**: * `.mcd` raw data. * Requires **CellProfiler**, **ilastik**, and **R**. 3. **Spatial Metabolomics (MALDI)**: * **Updated** `.imzML` + `.ibd` raw data. * Requires **Cardinal** R package for analysis. 4. **Methylation**: * `.idat` files processed with **minfi** and CNV analysis using **conumee** in R. 5. **Single Cell Sequencing**: * 10X Genomics raw data (Cell Ranger outputs). * Requires [R](https://www.r-project.org/) 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](mailto:vidhya.ravi@uniklinik-freiburg.de)) * **PD. Dr. Dieter Henrik Heiland** ([dieter.henrik.heiland@uniklinik-freiburg.de](mailto: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 Version 1.2 (2025-03-06) -Added the missing 10XVisium dataset. --- **End of README** Thank you for your interest in our dataset. We hope this information facilitates straightforward data access and analysis.