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

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

PROVIDER: PRJNA1193594 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR31588971.fastq.gz	Fastqsanger.gz
	SRR31588972.fastq.gz	Fastqsanger.gz
	SRR31588973.fastq.gz	Fastqsanger.gz
	SRR31588974.fastq.gz	Fastqsanger.gz
	SRR31588975.fastq.gz	Fastqsanger.gz

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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

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

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Utility analyses of AVITI sequencing platforms

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Project description:Next-Generation Sequencing (NGS) catalyzed breakthroughs across various scientific domains. Illumina's sequencing by synthesis method has long been essential for NGS but emerging technologies like Element Biosciences' sequencing by avidity (AVITI) represent a novel approach. It has been reported that AVITI offers improved signal-to-noise ratios and cost reductions. However, the method relies on rolling circle amplification which can be impacted by polymer size, raising questions about its efficacy sequencing small RNAs (sRNAs) libraries including microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and others that are crucial regulators of gene expression and involved in various biological processes. In addition, capturing capped small RNAs (csRNA-seq) has emerged as a powerful method to map active or “nascent” RNA polymerase II transcription initiation in tissues and clinical samples. Finally, we present an accelerated and optimized protocol for small fragment sequencing with AVITI that aligns seamlessly with available sRNA library kits to meet this need.

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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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