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Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma [NextSeq]

ABSTRACT: Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma [NextSeq]

PROVIDER: PRJNA1193592 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR31591461.fastq.gz	Fastqsanger.gz
	SRR31591462.fastq.gz	Fastqsanger.gz
	SRR31591463.fastq.gz	Fastqsanger.gz
	SRR31591464.fastq.gz	Fastqsanger.gz
	SRR31591465.fastq.gz	Fastqsanger.gz

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Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma

Project description:Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma

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Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma [AVITI]

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Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma [AVITI]

Project description:Hepatocellular carcinoma (HCC) is one of the most lethal cancers for humans. HCC is highly heterogeneous. In this study, we performed ultra-depth (~1 million reads per spot) spatial sequencing on a case of HCC. Sixteen distinct spatial expression clusters were identified. Each of these clusters was spatially contiguous and had distinct gene expression patterns. In contrast, benign liver tissues showed minimal heterogeneity in terms of gene expression. Numerous immune cell-enriched spots were identified in both HCC and benign liver regions. Cells adjacent to these immune cell-enriched spots showed significant alterations in their gene expression patterns. Interestingly, the responses of HCC cells to the nearby immune cells were significantly more intense and broader, while the responses of benign liver cells to immune cells were somewhat narrow and muted, suggesting an innate difference in immune cell activities towards HCC cells in comparison with benign liver cells. When standard-depth sequencing was performed, significant numbers of genes and pathways that were associated with these changes disappeared. Qualitative differences in some pathways were also found. These results suggest that deep spatial sequencing may help to uncover previously unidentified mechanisms of liver cancer development.

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Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma [NextSeq]

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Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma

Project description:This SuperSeries is composed of the SubSeries listed below.

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Spatial transcriptomics revealing host-microbe interactions

Project description:We developed an analysis pipeline that can extract microbial sequences from Spatial Transcriptomic (ST) data and assign taxonomic labels, generating a spatial microbial abundance matrix in addition to the default host expression matrix, enabling simultaneous analysis of host expression and microbial distribution. We called the pipeline Spatial Meta-transcriptome (SMT) and applied it on both human and murine intestinal sections and validated the spatial microbial abundance information with alternative assays. Biological insights were gained from this novel data that that demonstrated host-microbe interaction at various spatial scales. Finally, we tested experimental modification that can increase microbial capture while preserving host spatial expression quality and, by use of positive controls, quantitatively demonstrated the capture efficiency and recall of our methods. This proof of concept work demonstrates the feasibility of Spatial Meta-transcriptomic analysis, and paves the way for further experimental optimization and application.

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Project description:The inter-patient variability of tumor proteomes has been investigated on a large scale but many tumors display also intra-tumoral heterogeneity (ITH) regarding morphological and genetic features. To what extent the local proteome of tumors intrinsically differs remains largely unknown. Here, we used hepatocellular carcinoma (HCC) as a model system, to quantify both inter- and intra-tumor heterogeneity across human patient specimens with spatial resolution. We first defined proteomic features that robustly distinguish neoplastic from the directly adjacent non-neoplastic tissue by integrating proteomic data from human patient samples and genetically defined mouse models with available gene expression data. We then demonstrated the existence of intra-tumoral variations in protein abundance that re-occur across different patient samples, and affect clinically relevant proteins, even in the absence of obvious morphological differences or genetic alterations. Our work demonstrates the suitability and the benefits of using mass spectrometry based proteomics to analyze diagnostic tumor specimens with spatial resolution

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