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Scientists establish techniques for inferring single-cell trajectories


On July 31, Hu Zheng’s research group from the Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, and Zhou Da’s research group from the School of Mathematical Sciences of Xiamen University, published a report entitled PhyloVelo enhances transcriptomic velocity field mapping using monotonically The research paper expressed genes, which proposes a novel algorithmic framework for single-cell differentiation trajectory inference, is named PhyloVelo. This method combines single-cell transcriptome data and lineage tracing data to identify genes with monotonically increasing or decreasing expression with cell division, that is, monotonically expressed genes, and uses evolutionary methods to estimate the RNA transcription change rate of monotonic genes to construct single-cell transcriptome velocity fields to achieve high-precision inference of cell differentiation trajectories.

Cell differentiation and fate determination are one of the mysteries of life and the core problems in the field of life sciences, revealing its laws and mechanisms is of great significance for exploring life phenomena and promoting the development of medicine. However, it is not easy to accurately track the dynamic differentiation trajectory of cells, especially in the process of cell fate transition under disturbances such as diseases, which are relatively random and unpredictable. Currently, single-cell transcriptome sequencing (scRNA-seq) is a powerful technique to study cell differentiation, and the dynamic process of cell differentiation can be inferred by analyzing the transcriptome state of individual cells. In particular, RNA velocity models developed based on the shear kinetics of messenger RNA (mRNA) can predict the state of single-cell transcriptomes in “past” or “future” time, which is a classical algorithm for single-cell trajectory inference. However, due to the high complexity of single-cell sequencing technology and mRNA transcription/shear dynamics, RNA velocity models often have the problem of wrong inference or unrobustness in practical applications. 

In this study, we propose to use the branch length information of single-cell phylogenetic trees instead of physical time, combined with single-cell transcriptome data, to explore the change of gene expression with branch length (i.e., time), especially to identify genes with monotonically increasing or decreasing expression with cell division (i.e., monotonically expressed genes), and their RNA change rate can be quantitatively estimated using molecular evolution models, so as to construct a single-cell transcriptome velocity field and achieve high-precision inference of cell differentiation trajectories (Figure 1).Figure 1. Schematic diagram of the PhyloVelo algorithm framework 

The PhyloVelo algorithm is mainly divided into two parts. The first step is the identification of monotonically expressed genes (MEGs), using single-cell transcriptome data and cell phylogeny information, through the diffusion process model with drift and the zero-expansion negative binomial distribution (ZINB) to estimate the expression level of each gene at various time points on the phylogenetic tree; Furthermore, using the correlation between the expression level estimate and the corresponding phylogenetic tree time, those genes that increase or decrease linearly monotonically with lineage time, that is, monotonically expressed genes MEGs (Figure 1). The second step is the estimation of the rate of change in gene expression, assuming that the rate of change of the expression level of each MEG over time is constant, that is, the drift coefficient in the diffusion equation is constant; Through the maximum likelihood estimation, the drift coefficient of each MEG can be obtained, so as to obtain the vector of monotonic gene expression change rate per cell; Mapping this vector to a dimensionality-reducing space (tSNE, UMAP, etc.) to reconstruct the RNA velocity field allows inference of the transcriptome state of each cell at one unit time (e.g., cell division or mutation) in the past, i.e., counter-time reconstruction of cell differentiation trajectories (Figure 1).    

In this study, the accuracy and robustness of the PhyloVelo algorithm are verified on a variety of simulation data and real data. PhyloVelo is capable of inferring linear, bifurcated, and convergence complex differentiation structures in simulation data with high accuracy, which are highly consistent with the true differentiation trajectory (Figure 2). In addition, PhyloVelo also demonstrated superior RNA velocity performance in early embryonic development of mice, accurately identifying blood/endothelial progenitor cells in the red blood cell family as the earliest cell type, and strongly correlated with cell proliferation capacity (Figure 3).Figure 2. PhyloVelo accurately infers cell differentiation trajectories in simulated data

Figure 3. PhyloVelo reconstructs the developmental cell differentiation trajectories of the mouse blood system

In addition to mouse embryonic development, PhyloVelo accurately resolves complex differentiation trajectories in other biological processes such as tumor evolution and immune cell dynamic development in mice and humans, and quantifies state transition probabilities between cell types. For example, in a lung cancer model, PhyloVelo revealed the reverse differentiation trajectories of cancer cells. In CD8+ T cells after anti-PD-1 treatment, PhyloVelo found that the source of activated CD8+ T cells changed significantly before and after immunotherapy, indicating a high degree of fate plasticity of T cells. 

In conclusion, PhyloVelo is a new method for reconstructing cell fate transitions using single-cell lineage and transcriptome data with high accuracy and robustness. PhyloVelo can overcome the limitations of traditional RNA velocity methods and can uncover “clock genes” hidden in transcriptome data, providing clues to the molecular mechanisms underlying cell differentiation. PhyloVelo provides a powerful tool for studying biological development and disease progression, as well as new perspectives for future single-cell lineage and transcriptome data analysis. For ease of use, the research team published manuals and specific application examples online (see https://phylovelo.readthedocs.io/en/latest/).

Figure 4. Screenshot of the PhyloVelo user manual website

The research work has been supported by the National Key R&D Program, the National Natural Science Foundation of China, the Guangdong Outstanding Youth Fund, the Basic Research Fund of Central Universities, the China Postdoctoral Fund and the Shenzhen Innovation Institute of Synthetic Biology. The study was jointly completed by Shenzhen Advanced Institute and Xiamen University. (Source: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences)

Related paper information:https://doi.org/10.1038/s41587-023-01887-5

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