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Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

ABSTRACT: The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations^1-4 as well as the makeup of the tumour microenvironment^5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables the de novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model of metastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.

SUBMITTER: Zhao T

PROVIDER: S-EPMC9301586 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

Zhao Tongtong T Chiang Zachary D ZD Morriss Julia W JW LaFave Lindsay M LM Murray Evan M EM Del Priore Isabella I Meli Kevin K Lareau Caleb A CA Nadaf Naeem M NM Li Jilong J Earl Andrew S AS Macosko Evan Z EZ Jacks Tyler T Buenrostro Jason D JD Chen Fei F

Nature 20211215 7891

The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations<sup>1-4</sup> as well as the makeup of the tumour microenvironment<sup>5,6</sup>. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatiall ...[more]

PMID: 34912115

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Project description:BackgroundNumerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation.MethodsTo address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively.ResultsA significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample.ConclusionsThis study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.

Dataset Information

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

Publications

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

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