Spatial single cell analysis of tumor microenvironment remodeling pattern in primary central nervous system lymphoma

Xia, Yuan; Sun, Tao; Li, Guosheng; Li, Mingying; Wang, Dongmei; Su, Xiuhua; Ye, Jingjing; Ji, Chunyan

doi:10.1038/s41375-023-01908-x

Cited by 8 publications

(4 citation statements)

References 53 publications

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“…Cold tumours had built up barriers of cells with tight intercellular junctions, and the FKBP5+ tumour subgroup was key in creating these barriers. CD19+ tumour cells were encapsulated inside these barriers, which explains why CAR T cells have difficulty penetrating solid tumour margins [88]. These results could help decide the proper course of therapy based on TME structure.…”

Section: Transcriptomicsmentioning

confidence: 96%

“…It can be utilized in tandem with scRNA-seq as well [87]. Xia et al performed spatial transcriptomics on hot and cold tumours and combined the data with 14 964 single-cell transcriptomes of primary central nervous system lymphoma (PCNSL) patients [88]. This integrative analysis annotated cancer cell subclones and immune cells and their spatial distribution to see which tumour cells reshape the TME and create a cold tumour.…”

Section: Transcriptomicsmentioning

confidence: 99%

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Applications of single‐cell omics for chimeric antigen receptor T cell therapy

Ghaffari,

Saleh,

Akbari

et al. 2023

Immunology

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Chimeric antigen receptor (CAR) T cell therapy is a promising cancer treatment modality. The breakthroughs in CAR T cell therapy were, in part, possible with the help of cell analysis methods, such as single‐cell analysis. Bulk analyses have provided invaluable information regarding the complex molecular dynamics of CAR T cells, but their results are an average of thousands of signals in CAR T or tumour cells. Since cancer is a heterogeneous disease where each minute detail of a subclone could change the outcome of the treatment, single‐cell analysis could prove to be a powerful instrument in deciphering the secrets of tumour microenvironment for cancer immunotherapy. With the recent studies in all aspects of adoptive cell therapy making use of single‐cell analysis, a comprehensive review of the recent preclinical and clinical findings in CAR T cell therapy was needed. Here, we categorized and summarized the key points of the studies in which single‐cell analysis provided insights into the genomics, epigenomics, transcriptomics and proteomics as well as their respective multi‐omics of CAR T cell therapy.

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Section: Transcriptomicsmentioning

confidence: 96%

Section: Transcriptomicsmentioning

confidence: 99%

Applications of single‐cell omics for chimeric antigen receptor T cell therapy

Ghaffari,

Saleh,

Akbari

et al. 2023

Immunology

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show abstract

“…Xia et al. ( 57 ) used Visium to analyze different categories of TME in PCNSL. They used pathological assessment to identify four PCNSL samples which resembled each of their four classifications; ‘hot’, ‘invasive margin excluded (IME)’, ‘invasive margin immunosuppressed (IMS)’ and ‘cold’.…”

Section: Spatial Transcriptomic Analysis In Lymphoma: Studies To Datementioning

confidence: 99%

Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics

Pickard,

Stephenson,

Mitchell

et al. 2023

Front. Oncol.

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Lymphomas are a heterogenous group of lymphoid neoplasms with a wide variety of clinical presentations. Response to treatment and prognosis differs both between and within lymphoma subtypes. Improved molecular and genetic profiling has increased our understanding of the factors which drive these clinical dynamics. Immune and non-immune cells within the lymphoma tumor microenvironment (TME) can both play a key role in antitumor immune responses and conversely also support lymphoma growth and survival. A deeper understanding of the lymphoma TME would identify key lymphoma and immune cell interactions which could be disrupted for therapeutic benefit. Single cell RNA sequencing studies have provided a more comprehensive description of the TME, however these studies are limited in that they lack spatial context. Spatial transcriptomics provides a comprehensive analysis of gene expression within tissue and is an attractive technique in lymphoma to both disentangle the complex interactions between lymphoma and TME cells and improve understanding of how lymphoma cells evade the host immune response. This article summarizes current spatial transcriptomic technologies and their use in lymphoma research to date. The resulting data has already enriched our knowledge of the mechanisms and clinical impact of an immunosuppressive TME in lymphoma and the accrual of further studies will provide a fundamental step in the march towards personalized medicine.

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“…In addition to examining the overall spatial landscape of tumors, identifying the spatiotemporal distribution of immune subsets is crucial to deconvolute the spatial niches within a tissue section ( 28 , 29 ). Functional and spatial characterization of cell types through an integrative approach can also give leads on potential intercellular signals in the TME ( 30 ). There are a number of studies on the TME that use a combination of spatial modelling approaches ( 31 , 32 ) to demonstrate how the malignant cells modulate the disease ( 33 , 34 ).…”

Section: Introductionmentioning

confidence: 99%

Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications

Kumar,

Pandurengan,

Parra

et al. 2023

Front. Immunol.

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Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient’s TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.

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Spatial single cell analysis of tumor microenvironment remodeling pattern in primary central nervous system lymphoma

Cited by 8 publications

References 53 publications

Applications of single‐cell omics for chimeric antigen receptor T cell therapy

Applications of single‐cell omics for chimeric antigen receptor T cell therapy

Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics

Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications

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