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.

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.