Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Felipe Segato Dezem, Wani Arjumand, Hannah DuBose, Natalia Silva Morosini, Jasmine Plummer
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引用次数: 0

Abstract

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

空间分辨单细胞图像学:方法、挑战和未来展望》。
将全局组学数据叠加到空间生物维度上是一项前景广阔的技术,可提供对正常和疾病状态下组织样本分子微环境组织的相互作用组和细胞异质性的高分辨率洞察。空间全息技术可分为三种主要模式:(a) 基于新一代测序的检测,(b) 基于成像的空间分辨转录组学 RNA 方法,包括原位杂交/原位测序,以及 (c) 基于成像的蛋白质组学。这些方法可在细胞水平评估转录本和蛋白质,产生大量计算难度高的数据集。由于缺乏标准化的计算管道来分析和整合这些非统一结构的数据,因此有必要应用人工智能和机器学习策略来最好地可视化和转化其复杂性。在这篇综述中,我们总结了目前可用的技术和计算策略,强调了它们的优势和局限性,并讨论了它们在科学领域的未来前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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