DOI: 10.5061/dryad.wpzgmsbv6
title: Data from: Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma

description: Diffuse invasion of glioblastoma cells through normal brain tissue is a
 key contributor to tumor aggressiveness, resistance to conventional
 therapies, and dismal prognosis in patients. A deeper understanding of how
 components of the tumor microenvironment (TME) contribute to overall tumor
 organization and to programs of invasion may reveal opportunities for
 improved therapeutic strategies. Towards this goal, we applied a novel
 computational workflow to a spatiotemporally profiled GBM xenograft
 cohort, leveraging the ability to distinguish human tumor from mouse TME
 to overcome previous limitations in analysis of diffuse invasion. Our
 analytic approach, based on unsupervised deconvolution, performs
 reference-free discovery of cell types and cell activities within the
 complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor
 cell programs set within the spatiotemporal context of 90 mouse brain and
 TME cell types, cell activities, and anatomic structures. Distinct tumor
 programs related to invasion were aligned with routes of perivascular,
 white matter, and parenchymal invasion. Furthermore, sub-modules of genes
 serving as program hubs were highly prognostic in GBM patients. The
 compendium of programs presented here provides a basis for rational
 targeting of tumor and/or TME components. We anticipate that our approach
 will facilitate an ecosystem-level understanding of immediate and
 long-term consequences of such perturbations, including identification of
 compensatory programs that will inform improved combinatorial therapies.
description: The dataset includes 10x Genomics Visium spatial transcriptomic
 profiles of 23 GBM xenograft samples established from six brain tumor
 initiating cell lines (BTIC) derived from four patients (BT143, BT238,
 BT134 and BT161). BTIC lines from two patients (BT143 and BT238) were
 established from samples obtained from the tumor core (x), the
 contrast-enhancing highly vascularized tumor margin (y), and the highly
 diffuse leading edge of the tumor (z) through pre-operative MRI-guided
 resections. Upon implantation, early, mid, and late timepoints of growth
 are sampled based on known time to endpoint. Biological replicates of
 these mouse brain sections are obtained 30-40um apart. Raw spatial
 transcriptomic data were processed using the SpaceRanger software (v1.3.1)
 to generate FASTQ files. Sequences were aligned to the hybrid genome
 reference sequence GRCh38-mm10-2020-A and barcode/UMI counting was
 performed using Space Ranger pipeline’s default settings and all samples
 were aggregated using the SpaceRanger Aggr pipeline. The aggregated gene
 expression data was factorized into gene expression programs using the
 Consensus Non-negative Matrix Factorization algorithm to generate two
 matrices – one specifying the contribution of each spot to the identified
 programs (usage matrix) and the other specifying contribution of each gene
 to the programs (gene-score matrix). The factorization was performed
 separately for human (Tumor) and mouse (Brain & TME) across all
 spots. Rank 15 and Rank 90 were chosen for the human and mouse data,
 respectively. The usage matrix from each factorization result was
 normalized such that the usage values per spot sum to 1. Next to quantify
 the signal of programs relative to the total signal from the two genomes,
 we further scaled the cNMF-usage matrices whereby human program usages per
 spot were multiplied by genome admixture ratios and mouse program usages
 per cell by 1-admixture. The admix-scaled usage matrices were used for
 further downstream analyses. 
description: # Spatiotemporal modeling reveals high-resolution invasion states in
 glioblastoma
 [https://doi.org/10.5061/dryad.wpzgmsbv6](https://doi.org/10.5061/dryad.wpzgmsbv6) The dataset includes 10x Genomics Visium spatial transcriptomic profiles of 23 GBM xenograft samples established from six brain tumor initiating cell lines (BTIC) derived from four patients (BT143, BT238, BT134 and BT161). BTIC lines from two patients (BT143 and BT238) were established from samples obtained from the tumor core (x), the contrast-enhancing highly vascularized tumor margin (y), and the highly diffuse leading edge of the tumor (z) through pre-operative MRI-guided resections. Upon implantation, early, mid, and late timepoints of growth are sampled based on known time to endpoint. Biological replicates of these mouse brain sections are obtained 30-40um apart. Raw spatial transcriptomic data were processed using the SpaceRanger software (v1.3.1) to generate FASTQ files. Sequences were aligned to the hybrid genome reference sequence GRCh38-mm10-2020-A and barcode/UMI counting was performed using Space Ranger pipelines default settings and all samples were aggregated using the SpaceRanger Aggr pipeline. The aggregated gene expression data was factorized into gene expression programs using the Consensus Non-negative Matrix Factorization algorithm to generate two matrices one specifying the contribution of each spot to the identified programs (usage matrix) and the other specifying contribution of each gene to the programs (gene-score matrix). The factorization was performed separately for human (Tumor) and mouse (Brain & TME) across all spots. Rank 15 and Rank 90 were chosen for the human and mouse data respectively. The usage matrix from each factorization result was normalized such that the usage values per spot sum to 1. Next, to quantify the signal of programs relative to the total signal from the two genomes, we further scaled the cNMF-usage matrices whereby human program usages per spot were multiplied by genome admixture ratios and mouse program usages per cell by 1-admixture. The admix-scaled usage matrices were used for further downstream analyses. ## Description of the data and file structure **1. GBM_Xenograft_Visium.zip** This file includes SpaceRanger outputs from the 23 samples. These files are required as input for further processing and visualization with a package such as Seurat. * For each sample, the following files/folders are included:\ \**(a)**filtered_feature_bc_matrix.h5 - Feature barcode matrix in h5 format.\ \**(b)**filtered_feature_bc_matrix folder - This folder includes gzipped TSV files of a list of spatial barcodes (barcodes.tsv.gz), a list of gene IDs (features.tsv.gz) and a matrix of UMI counts of features per barcode (matrix.mtx.gz).\ \**(c)**spatial folder - This folder contains quality control images of aligned fiducials of the tissue, detected tissue and spots, high- and low-resolution images of the tissue. Scale factors in json format and a csv file spatial barcodes and the coordinates specifying the spots are also included. * Further, all samples were aggregated using SpaceRanger Aggr pipeline.\ \**(d)**aggr\_ filtered_feature_bc_matrix.h5 - Feature barcode matrix of the aggregated data in h5 format. **2. cNMF_outputs.zip** This file includes cNMF outputs of human and mouse gene expression data factorizations. **(a)** Human.usages.k_15.consensus_normalized_scaledwithadmix.txt - Human programs usage matrix normalized and scaled with admix.\ **(b)** Mouse.usages.k_90.consensus_normalized_scaledwithadmix.txt - Mouse programs usage matrix normalized and scaled with admix.\ **(c)** Human.gene_spectra_score.k_15.txt - Human programs gene-score matrix.\ **(d)** Mouse.gene_spectra_score.k_90.txt - Mouse programs gene-score matrix. \**3.****Spot_metadata.csv**- Spot-level metadata including annotation of samples, genome admixture, density groups, invasion routes, clones, and module scores of GBM cell-states used in core analyses in this project. Following are the descriptions of column headers:\ A) Barcode Barcode of the Visium spot.\ B) Sample Sample ID of the spot.\ C) Patient Patient ID of cell lines from which the xenograft samples were established (BT143, BT134, BT161, BT134 or BT238) \ D) Line Region of tumor sampled by pre-operative MRI-guided resections (x - the tumor core, y - the contrast-enhancing highly vascularized tumor margin, z - the highly diffuse leading edge of the tumor or m unlabelled) \ E) Mouse Mouse number \ F) Day Time point of tumor growth in days\ G) Site Hemisphere of the sampled mouse brain section (i injection side or c contralateral side) \ H) Replicate Distance between sequential mouse brain sections sampled as replicates in micrometers.\ I) Type Sample type (ctrl control)\ J) Time_point Time point of tumor growth classified as early, mid or late. \ K) Hs_admix Human:Mouse genome admixture ratio calculated as the ratio of UMI counts from human genes relative to the total UMI count per spot.\ L) Density_group Categorization of spots based on tumor density i.e., genome admixture as high (admixture 80-100%; D4), moderate (50-80%; D3), low (20-50%; D2), and very low (5-20%; D1). Tumor-free mouse brain was comprised of spots with admixture &lt;5% (D0). \ M) D1_annot Invasion routes / programs enriched among D1 spots. Invasion routes include Whitematter, Parenchyma and Perivasculature (m30, m52, m59 and m86). \ N) InferCNV_clone Tumor clones associated with spots, inferred from the R package inferCNV. \ O) Neftel_NPC_module_score Module score for neural progenitor-like (NPC) cell-state gene signature (Neftel, C. et al. 2019), calculated using the *AddModuleScore()* function in the R package Seurat. Scores were first calculated for NPC1 and NPC2 states and then averaged to obtain the NPC module score. \ P) Neftel_OPC_module_score - Module score for oligodendrocyte-like (OPC) cell-state gene signature (Neftel, C. et al. 2019), calculated using the *AddModuleScore()* function in the R package Seurat.\ Q) Neftel_AC_module_score - Module score for astrocyte-like (AC) cell-state gene signature (Neftel, C. et al. 2019), calculated using the *AddModuleScore()* function in the R package Seurat. \ R) Neftel_MES_module_score - Module score for neural progenitor-like (MES) cell-state gene signature (Neftel, C. et al. 2019), calculated using the *AddModuleScore()* function in the R package Seurat. Scores were first calculated for MES1 and MES2 states and then averaged to obtain the MES module score. \**3.****Spatial_Program_Usage_Plots.zip** This folder includes spatial plots of program usage derived from the cNMF analyses. \ *(a)* spatial_plots_human_programs_rank15.pdf - spatial usage of 15 human tumor programs across 23 samples in the cohort.\ *(b)* spatial_plots_mouse_programs_rank90.pdf - spatial usage of 90 mouse tumor programs across 23 samples in the cohort. Missing data code: n/a ## **Code/Software** Code used to perform analyses presented in this manuscript are available on Github at [https://github.com/MorrissyLab/GBM_Xenograft_SpatialTranscriptomics](https://github.com/MorrissyLab/GBM_Xenograft_SpatialTranscriptomics).