Single-cell RNA sequencing reveals evolution of immune landscape during glioblastoma progression

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Abstract

Glioblastoma (GBM) is an incurable primary malignant brain cancer hallmarked with a substantial protumorigenic immune component. Knowledge of the GBM immune microenvironment during tumor evolution and standard of care treatments is limited. Using single-cell transcriptomics and flow cytometry, we unveiled large-scale comprehensive longitudinal changes in immune cell composition throughout tumor progression in an epidermal growth factor receptor-driven genetic mouse GBM model. We identified subsets of proinflammatory microglia in developing GBMs and anti-inflammatory macrophages and protumorigenic myeloid-derived suppressors cells in end-stage tumors, an evolution that parallels breakdown of the blood–brain barrier and extensive growth of epidermal growth factor receptor ⁺ GBM cells. A similar relationship was found between microglia and macrophages in patient biopsies of low-grade glioma and GBM. Temozolomide decreased the accumulation of myeloid-derived suppressor cells, whereas concomitant temozolomide irradiation increased intratumoral GranzymeB ⁺ CD8 ⁺T cells but also increased CD4 ⁺ regulatory T cells. These results provide a comprehensive and unbiased immune cellular landscape and its evolutionary changes during GBM progression.

Abstract

Single-cell RNAseq during initiation and progression of mouse glioblastoma reveals a dynamic immune microenvironment transitioning from pro-inflammatory microglia in early tumors towards an infiltrating macrophage and suppressor cell-centric immune landscape in late-stage tumors.

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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Is Open Access

limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Author and article information

Contributors

Vassiliki A. Boussiotis:

ORCID: http://orcid.org/0000-0002-3963-1518

vboussio@bidmc.harvard.edu

Al Charest:

ORCID: http://orcid.org/0000-0002-1548-6950

acharest@bidmc.harvard.edu

Journal

Journal ID (nlm-ta): Nat Immunol

Journal ID (iso-abbrev): Nat Immunol

Title: Nature Immunology

Publisher: Nature Publishing Group US (New York )

ISSN (Print): 1529-2908

ISSN (Electronic): 1529-2916

Publication date (Electronic): 27 May 2022

Publication date PMC-release: 27 May 2022

Publication date (Print): 2022

Volume: 23

Issue: 6

Pages: 971-984

Affiliations

[1 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Department of Medicine, , Beth Israel Deaconess Medical Center, Harvard Medical School, ; Boston, MA USA

[2 ]GRID grid.67033.31, ISNI 0000 0000 8934 4045, Sackler School of Graduate Studies, , Tufts University School of Medicine, ; Boston, MA USA

[3 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Department of Neurosurgery, , Massachusetts General Hospital, Harvard Medical School, ; Boston, MA USA

[4 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Department of Neurology, , Beth Israel Deaconess Medical Center, Harvard Medical School, ; Boston, MA USA

[5 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Department of Pathology, , Beth Israel Deaconess Medical Center, Harvard Medical School, ; Boston, MA USA

[6 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Cancer Research Institute, , Beth Israel Deaconess Medical Center, ; Boston, MA USA

Author information

Vaughn A. Rogers http://orcid.org/0000-0002-6423-5197

Vassiliki A. Boussiotis http://orcid.org/0000-0002-3963-1518

Al Charest http://orcid.org/0000-0002-1548-6950

Article

Publisher ID: 1215

DOI: 10.1038/s41590-022-01215-0

PMC ID: 9174057

PubMed ID: 35624211

SO-VID: a2d0adbb-c5d6-4932-b54a-35af1b4230d9

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 21 May 2021

Date accepted : 18 April 2022

Funding

Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);

Award ID: R01 CA229784

Award Recipient : Al Charest

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ScienceOpen disciplines: Immunology

Keywords: tumour immunology,cancer immunotherapy

Data availability:

ScienceOpen disciplines: Immunology

Keywords: tumour immunology, cancer immunotherapy

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Single-cell RNA sequencing reveals evolution of immune landscape during glioblastoma progression

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Most cited references 80

STAR: ultrafast universal RNA-seq aligner.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

limma powers differential expression analyses for RNA-sequencing and microarray studies

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