Computational Molecular Biology 2025, Vol.15, No.2, 91-101 http://bioscipublisher.com/index.php/cmb 99 simultaneously at the single-cell level. Currently, although the Multiome kit provided by 10x Genomics can measure two types of data of the same cell, its cost is several times that of the single-mode, and the data quality is also slightly compromised. In the future, more efficient simultaneous sequencing platforms will be needed, which can even measure DNA methylation, protein expression, etc. at the same time, to achieve true panoramic single-cell analysis. With the advancement of technology, the types of omics we can measure are constantly increasing. Besides transcription and accessibility, there are also DNA methylation, histone modification, proteins, metabolites, and so on. How to integrate such multi-level information is a huge challenge in the future. Most of the existing analytical methods deal with bimodal, and the complexity increases exponentially if there are more. Moreover, the time scales of signals in different modes vary. For instance, transcription can be at the minute level, while methylation changes may be at the hour or day level. How to consider time lag in analysis? In addition, the quality of multimodal data is uneven. Some omics techniques are very mature (RNA), while others are very new and noisy (proteomics such as CITE-seq). Simply piecing them together may be dominated by noise. It is necessary to develop a new computing framework, perhaps introducing physical models or graph models to fit the relationships at each layer. Another frontier is the spatiotemporal dimension. Cell fate determination often occurs in specific tissue structures and at specific developmental times. The classic method of single-cell sequencing disintegrates tissues and loses spatial information. Sequencing also damages cells and makes it impossible to track time. Although spatial transcriptomics and lineage tracing and other methods have emerged, integrating these with conventional single-cell data remains a new challenge. For instance, how can spatial position constraints be introduced into cell clustering or trajectory inference? How to correlate the lineage tree with the transcriptional trajectory? Currently, attempts have been made to integrate spatial and single-cell data through algorithms such as SpaceFlow, but they are still in the initial stage. In the future, it is hoped to establish a 4D (spatial 3D+ temporal) cell fate model, which requires a dual approach of experiments and computations: experimentally, information is obtained through time point sampling, lineage tracing, and in vivo imaging; computationally, dynamic network modeling, spatio-temporal point process analysis, etc. are developed for integration. If successful, we will be able to "see" how cells in a living embryo determine their fate and when and where they send out what signals-this will be a dream come true for developmental biology. As sequencing costs further decrease and operations become simplified, single-cell sequencing will become a standard feature in many biomedical studies. It might be as widespread as PCR is now. At that time, a vast amount of data will emerge, and the community needs to establish a unified data storage and analysis platform to achieve data sharing and reuse. Standardized analysis procedures and quality control will also be established to make the results from different laboratories comparable. Research on single-cell fate determination will be deeply integrated with fields such as physics, engineering, and clinical practice. Physically, analogical phase transition theory and stochastic process theory can help understand the randomness and certainty of cell state transitions. In engineering, microfluidics, high-dimensional data visualization, and AI algorithms will continuously inject new vitality. Clinically, the accumulation of a large number of single-cell data from patient samples will give rise to a new concept of "single-cell pathology", and doctors may be able to diagnose and classify diseases based on single-cell maps. For example, distinguishing different tumor subtypes and determining treatment plans, etc. Single-cell technology may be applied in sensitive fields such as reproduction and development, for instance, in the study of the fate of cells before human embryo implantation, which involves ethics. Society should establish norms for such research. At the same time, when technology is applied to human enhancement (such as optimizing stem cells for anti-aging), the impact also needs to be evaluated. Acknowledgments We would like to thank Mr. Jiao continuous support throughout the development of this study.
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