Advances and applications in single-cell and spatial genomics

This review, led by a team of prominent researchers from Zhejiang University (Guoji Guo, Hongshan Guo, Yijun Ruan, Yongcheng Wang), Peking University (Zemin Zhang, Dong Xing), and BGI Research (Xun Xu), highlights the transformative capabilities of single-cell and spatial genomics. These revolutionary technologies go beyond traditional genomic methods by enabling detailed analysis of individual cells and their location within tissues. This advancement paves the way for the creation of comprehensive cell atlases, which provide critical insights for understanding disease mechanisms and developing innovative therapies.

The section on ‘Single-Cell Sequencing Technologies’ traces the evolution of these technologies, starting from initial RNA sequencing and progressing towards comprehensive genomic, epigenomic, and proteomic sequencing (see Fig.1). It highlights a significant shift from single-omics to multi-omics approaches, and from focusing on whole cells to investigating subcellular compartments. Crucially, this review also underscores the dramatic increase in sequencing capacity that has been achieved.

The section on ‘Single-Cell Multi-omics Technologies’ delves into the development of these sophisticated techniques. Building upon single-omics methods, multi-omics approaches incorporate advanced techniques like microfluidics, pipetting, and flow cytometry for cell separation and complex labeling strategies, like split-pool barcoding. Central to multi-omics is the principle of separating different omics data while ensuring data integrity and efficient analysis.

This review then dives into the history of spatial transcriptomics and multi-omics, detailing the remarkable advancements over the past 25 years. Four major technologies are described, highlighting the historical applications in fields like developmental biology, neuroscience, pathology, and plant sciences.

Turning to computational aspects, this review summarizes strategies for analyzing single-cell genomic data, including deep learning-based cell atlas data modeling. It also addresses the computational challenges presented by cross-platform, cross-species big data analysis.

This review then underscores the history of cross-species, cross-tissue cellular atlas research, using examples of mouse and human atlases, since the launch of the Human Cell Atlas (HCA) in 2016.

The section on ‘Single-cell Insights for Translational Medicine’ turns to the clinical applications of single-cell genomics, especially in cancer treatment, treatment of non-cancer diseases, and drug target discovery and precision medicine (see Fig.2). It emphasizes the importance of single-cell insights in translating research findings into clinical treatments.

The section on ‘Perspectives, Challenges, and Opportunities of Single-Cell Genomics’ examines the future of single-cell and spatial genomics, emphasizing the transformative potential of these technologies through improved throughput, sensitivity, and modalities (see Fig.3). The review outlines the promise of AI-driven models for accelerating disease target discovery and drug development. It concludes with a vision of a new biomedical era, fueled by fundamental research translated to clinical applications to combat diseases and safeguard human health.

 

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Advances and applications in single-cell and spatial genomics

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