RESUMO
Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of cluster-free detection of gene expression, offering more profound insights into the spatial architecture of transcriptomes. The algorithm is embedded in SPATA2, an open-source framework written in R, which provides a comprehensive set of tools for investigating gene expression within tissue.
Assuntos
Algoritmos , Perfilação da Expressão Gênica , Transcriptoma , Animais , Camundongos , Perfilação da Expressão Gênica/métodos , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patologia , Neoplasias Encefálicas/metabolismo , Humanos , Análise de Célula Única/métodosRESUMO
Spatial resolution of the T cell repertoire is essential for deciphering cancer-associated immune dysfunction. Current spatially resolved transcriptomic technologies are unable to directly annotate T cell receptors (TCR). We present spatially resolved T cell receptor sequencing (SPTCR-seq), which integrates optimized target enrichment and long-read sequencing for highly sensitive TCR sequencing. The SPTCR computational pipeline achieves yield and coverage per TCR comparable to alternative single-cell TCR technologies. Our comparison of PCR-based and SPTCR-seq methods underscores SPTCR-seq's superior ability to reconstruct the entire TCR architecture, including V, D, J regions and the complementarity-determining region 3 (CDR3). Employing SPTCR-seq, we assess local T cell diversity and clonal expansion across spatially discrete niches. Exploration of the reciprocal interaction of the tumor microenvironmental and T cells discloses the critical involvement of NK and B cells in T cell exhaustion. Integrating spatially resolved omics and TCR sequencing provides as a robust tool for exploring T cell dysfunction in cancers and beyond.
Assuntos
Receptores de Antígenos de Linfócitos T , Linfócitos T , Receptores de Antígenos de Linfócitos T/genética , Regiões Determinantes de Complementaridade/genética , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Perfilação da Expressão Gênica , Receptores de Antígenos de Linfócitos T alfa-beta/genéticaRESUMO
BACKGROUND: In glioblastoma (GBM), the effects of altered glycocalyx are largely unexplored. The terminal moiety of cell coating glycans, sialic acid, is of paramount importance for cell-cell contacts. However, sialic acid turnover in gliomas and its impact on tumor networks remain unknown. METHODS: We streamlined an experimental setup using organotypic human brain slice cultures as a framework for exploring brain glycobiology, including metabolic labeling of sialic acid moieties and quantification of glycocalyx changes. By live, 2-photon and high-resolution microscopy we have examined morphological and functional effects of altered sialic acid metabolism in GBM. By calcium imaging we investigated the effects of the altered glycocalyx on a functional level of GBM networks. RESULTS: The visualization and quantitative analysis of newly synthesized sialic acids revealed a high rate of de novo sialylation in GBM cells. Sialyltrasferases and sialidases were highly expressed in GBM, indicating that significant turnover of sialic acids is involved in GBM pathology. Inhibition of either sialic acid biosynthesis or desialylation affected the pattern of tumor growth and lead to the alterations in the connectivity of glioblastoma cells network. CONCLUSIONS: Our results indicate that sialic acid is essential for the establishment of GBM tumor and its cellular network. They highlight the importance of sialic acid for glioblastoma pathology and suggest that dynamics of sialylation have the potential to be targeted therapeutically.
Assuntos
Neoplasias Encefálicas , Glioblastoma , Humanos , Glioblastoma/patologia , Ácido N-Acetilneuramínico/metabolismo , Ácidos Siálicos/metabolismo , Transdução de Sinais , Linhagem Celular TumoralRESUMO
Immunotherapy has revolutionized cancer treatment but has yet to be translated into brain tumors. Studies in other solid tumors suggest a central role of B-cell immunity in driving immune-checkpoint-blockade efficacy. Using single-cell and single-nuclei transcriptomics of human glioblastoma and melanoma brain metastasis, we found that tumor-associated B-cells have high expression of checkpoint molecules, known to block B-cell-receptor downstream effector function such as plasmablast differentiation and antigen-presentation. We also identified TGFß-1/TGFß receptor-2 interaction as a crucial modulator of B-cell suppression. Treatment of glioblastoma patients with pembrolizumab induced expression of B-cell checkpoint molecules and TGFß-receptor-2. Abrogation of TGFß using different conditional knockouts expanded germinal-center-like intratumoral B-cells, enhancing immune-checkpoint-blockade efficacy. Finally, blocking αVß8 integrin (which controls the release of active TGFß) and PD-1 significantly increased B-cell-dependent animal survival and immunological memory. Our study highlights the importance of intratumoral B-cell immunity and a remodeled approach to boost the effects of immunotherapy against brain tumors.
RESUMO
Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies. The source of dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas 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 adapts 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.