Background: The commercially available 10x Genomics protocol to generate droplet-based single-cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as three silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also robustness of a dozen methods. Results: We found that some methods, including Seurat and Cell Ranger, outperform other methods, although performance seems to be dependent on the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this, we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.

Background: The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods. Results: We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.

High-grade gliomas (HGGs) are aggressive primary brain cancers with poor response to standard regimens, driven by immense heterogeneity. In isocitrate dehydrogenase (IDH) wild-type HGG (glioblastoma, GBM), increased intratumoral heterogeneity is associated with more aggressive disease. Spatial technologies can dissect complex heterogeneity within the tumor ecosystem by preserving cellular organization in situ. We employed GeoMx digital spatial profiling, CosMx spatial molecular imaging, Xenium in situ mapping and Visium spatial gene expression in experimental and validation patient cohorts to interrogate the transcriptional landscape in HGG. Here, we construct a high-resolution molecular map of heterogeneity in GBM and IDH-mutant patient samples to investigate the cellular communities that compose HGG. We uncovered striking diversity in the tumor landscape and degree of spatial heterogeneity within the cellular composition of the tumors. The immune distribution was diverse between samples, however, consistently correlated spatially with distinct tumor cell phenotypes, validated across tumor cohorts. Reconstructing the tumor architecture revealed two distinct niches, one composed of tumor cells that most closely resemble normal glial cells, associated with microglia, and the other niche populated by monocytes and mesenchymal tumor cells. This primary study reveals high levels of intratumoral heterogeneity in HGGs, associated with a diverse immune landscape within spatially localized regions.

In the past decade, the identification of gene co-expression has become a routine part of the analysis of high-dimensional microarray data. Gene co-expression, which is mostly detected via the Pearson correlation coefficient, has played an important role in the discovery of molecular pathways and networks. Unfortunately, the presence of systematic noise in high-dimensional microarray datasets corrupts estimates of gene co-expression. Removing systematic noise from microarray data is therefore crucial. Many cleaning approaches for microarray data exist, however these methods are aimed towards improving differential expression analysis and their performances have been primarily tested for this application. To our knowledge, the performances of these approaches have never been systematically compared in the context of gene co-expression estimation. Using simulations we demonstrate that standard cleaning procedures, such as background correction and quantile normalization, fail to adequately remove systematic noise that affects gene co-expression and at times further degrade true gene co-expression. Instead we show that a global version of removal of unwanted variation (RUV), a data-driven approach, removes systematic noise but also allows the estimation of the true underlying gene-gene correlations. We compare the performance of all noise removal methods when applied to five large published datasets on gene expression in the human brain. RUV retrieves the highest gene co-expression values for sets of genes known to interact, but also provides the greatest consistency across all five datasets. We apply the method to prioritize epileptic encephalopathy candidate genes. Our work raises serious concerns about the quality of many published gene co-expression analyses. RUV provides an efficient and flexible way to remove systematic noise from high-dimensional microarray datasets when the objective is gene co-expression analysis. The RUV method as applicable in the context of gene-gene correlation estimation is available as a BioconductoR-package: RUVcorr.

The glioblastoma (GBM) microenvironment is enriched in immunosuppressive factors that potently interfere with the function of cytotoxic T lymphocytes. Cancer cells can directly affect the immune system, but the mechanisms driving these interactions are not completely clear. Here, we demonstrate that the polyamine metabolite spermidine (SPD) was elevated in the GBM tumor microenvironment. Exogenous administration of SPD drove tumor aggressiveness in an immune-dependent manner in preclinical mouse models via reduction of CD8 ⁺ T cell frequency and reduced cytotoxic function. Knockdown of ornithine decarboxylase, the rate-limiting enzyme in SPD synthesis, did not affect cancer cell growth in vitro but did result in extended survival. Furthermore, patients with GBM with a more favorable outcome had a significant reduction in SPD compared with patients with a poor prognosis. Our results demonstrate that SPD functions as a cancer cell–derived metabolite that drives tumor progression by reducing CD8 ⁺ T cell numbers and function.

Targeted epigenome editing tools allow precise manipulation and investigation of genome modifications, however they often display high context dependency and variable efficacy between target genes and cell types. While systems that simultaneously recruit multiple distinct ‘effector’ chromatin regulators can improve efficacy, they generally lack control over effector composition and spatial organisation. To overcome this we have created a modular combinatorial epigenome editing platform, called SSSavi. This system is an interchangeable and reconfigurable docking platform fused to dCas9 that enables simultaneous recruitment of up to four different effectors, allowing precise control of effector composition and spatial ordering. We demonstrate the activity and specificity of the SSSavi system and, by testing it against existing multi-effector targeting systems, demonstrate its comparable efficacy. Furthermore, we demonstrate the importance of the spatial ordering of the recruited effectors for effective transcriptional regulation. Together, the SSSavi system enables exploration of combinatorial effector co-recruitment to enhance manipulation of chromatin contexts previously resistant to targeted editing.