CAPITAL: major advance in the analysis of single-cell RNA data

Researchers at Osaka University have developed a computational tool called CAPITAL that can perform precise comparative analysis of complex single-cell sequencing datasets

New developments in high-throughput biological studies mean that genes that are active in a single cell can now be determined. However, analyzing the resulting complex datasets can be challenging. Now a team from Osaka University has developed CAPITAL, a new computational tool for comparing complex data sets from single cells.

RNA sequencing provides information about the subset of the entire population of genes that are actively expressed or “turned on”. As technology has advanced, it has become possible to sequence the RNA population of a single cell. This can provide a lot of information about the specific changes in gene expression involved when a large population of mixed cells undergoes dynamic and transient processes, such as differentiation or cell death, because each individual cell can be specifically analyzed rather than all different cells. types being grouped.

CAPITAL is specifically designed to compare complex data sets from single cells undergoing transition processes. These analyzes are carried out by defining a “pseudo-temporal trajectory”, which places the cells along a hypothetical path reflecting their progress in the transition process. These trajectories are not always simple and linear; they can become very complex and branched. In the past, only linear trajectories could be aligned for comparison, but the team’s innovation means that complex branching trajectories can now be aligned and compared precisely and automatically.

After developing the algorithm used for CAPITAL, which implements a method known as tree alignment, they tested it on both synthetic datasets and authentic datasets from cells in bone marrow. The results demonstrated that CAPITAL is statistically more accurate and robust than previous computational algorithms, showing major advances over these methods.

Trajectory comparison is a powerful analysis that can, for example, identify the dynamics of gene expression between different species to provide insight into evolutionary processes. “We have shown in this study that CAPITAL can reveal the existence of different molecular patterns between humans and mice, even when the expression patterns are similar and appear to be conserved,” says lead author Reiichi Sugihara. “This will allow the identification of novel regulators that determine cell fate.” This technology is not limited to this type of data, as lead author Yuki Kato explains: “Our new computational tool can be applied to a wide range of high-throughput data sets, including pseudo data. -temporal, spatial and epigenetic.”

This powerful new technique will allow the global comparison of single-cell trajectories, which could lead to the identification of new disease-associated genes that could not be identified by previous comparative methods. Thus, CAPITAL represents a significant advance in the field of single-cell biology.

Fig. 1

Presentation of CAPITAL: an algorithm for comparing pseudo-temporal trajectories with branches

1 credit


Figure 2

CAPITAL is statistically better than data integration methods in conserving trajectories across 2,278 pairs of multi-branch synthetic datasets

Credit: 2022 Sugihara et al., Single-Cell Trajectory Tree Alignment with CAPITAL, Nature Communications


Figure 3

Dynamics of CSF1 gene expression along pseudo-time from hematopoietic stem cell to erythrocyte between human and mouse from bone marrow cell data

Credit: 2022 Sugihara et al., Single-Cell Trajectory Tree Alignment with CAPITAL, Nature Communications

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