空间转录组的解读与探究:数据分析方法学研究现状与展望

Decoding spatial transcriptomics:Current trends and future prospects in data analysis methodologies

作为近年来快速发展的新兴技术,空间转录组学已经极大地改变了生物和医学多个领域的研究范式.这一技术保留了复杂组织中细胞的空间定位信息,并且以多细胞团、单细胞或亚细胞分辨率进行转录组分析.细胞空间定位信息与其分子特征谱的耦合产生了新型的多模态高通量数据资源,这对高效的数据挖掘分析方法的开发提出了新的挑战.由于生理组织样本的复杂性与技术的局限性,空间转录组数据具有高度的非理想性.其数据结构复杂、信噪比低、稀疏性强且覆盖度不均一,给数据的深度分析和生物学信息的解析带来了一系列挑战,目前许多困难尚未解决.此外,多个技术路线的空间转录组学正在快速迭代发展,需要现有数据分析理论的发展和创新技术工具的开发.本文概述了当前空间转录组数据的常用解析方法,针对不同角度与层面的信息挖掘需求,讨论了现有方法的设计思路与潜在的局限性,并对未来方法学设计的方向和策略进行了展望,以期激发对新的数据分析理论、模型和算法的讨论和开发.

In recent years,spatial transcriptomics has emerged as a rapidly advancing technology,fundamentally transforming research paradigms across various fields of biology and medicine.This innovative technology retains the spatial localization information of cells within complex tissues and performs transcriptome analysis at multicellular,single-cell,or subcellular resolution.By coupling spatial localization information with molecular profiles,spatial transcriptomics generates new types of multimodal high-throughput data.These data sets serve as insightful resources for exploring molecular and cellular mechanisms within physiological contexts.For instance,spatial transcriptomics can elucidate the intricate architecture of the tumor microenvironment,revealing how cancer cells interact with surrounding stromal and immune cells.Similarly,it can map the spatial organization of neural circuits in the brain,offering deeper insights into neural development,function,and disease.This capacity to link cellular function to precise tissue architecture marks a significant advancement over traditional transcriptomics,which often lacks spatial context.Despite its transformative potential,spatial transcriptomic data exhibit several highly non-ideal characteristics,including low signal-to-noise ratio,high sparsity,and uneven coverages.These inherent challenges pose significant obstacles to indepth data analysis and information mining.The low signal-to-noise ratio often results from technical limitations in capturing and amplifying the RNA transcripts,leading to significant background noise that can obscure true biological signals.High sparsity is a consequence of the limited sensitivity of current technologies,where many genes may not be detected in all cells,resulting in numerous zero counts in the data matrix.Uneven coverage refers to the inconsistent detection of transcripts across different regions of the tissue,which can bias the analysis and interpretation of spatial patterns.These challenges necessitate the development of sophisticated computational methods to preprocess and analyze the data effectively.For instance,denoising algorithms are essential to enhance the signal quality,enabling more accurate downstream analyses.Imputation techniques aim to address the issue of sparsity by predicting missing values,thereby providing a more complete picture of the transcriptomic landscape.Furthermore,normalization strategies are required to correct for uneven coverage,ensuring that comparisons across different regions and samples are valid.Many of these challenges remain unresolved,complicating efforts to fully leverage the technology’s capabilities.Furthermore,the rapidly evolving nature of spatial transcriptomics necessitates continuous adaptation of existing methodologies and the development of more innovative analytical tools to keep pace with technological advancements.As new platforms and techniques are introduced,there is a constant need for updating analytical pipelines and validation frameworks to ensure robust and reproducible results.The integration of spatial transcriptomics with other omics data,such as genomics,proteomics,and metabolomics,also requires the development of novel multi-omics analysis methods that can handle the increased complexity and data dimensionality.