A major advance in single-cell RNA data analysis – CAPITAL computational analysis software

New advances in high-throughput biological research mean that genes that are active in individual cells can now be identified
.
However, analyzing datasets with complex results can be challenging
.
Now, a team at Osaka University has developed a new computational tool CAPITAL for comparing complex datasets
from individual cells.

RNA sequencing provides information
about a subset of the entire gene population that is actively expressed or "turned on.
" As technology advances, it has become possible
to sequence RNA populations from individual cells.
When a large number of mixed cells undergo dynamic transition processes such as differentiation or cell death, this can provide a wealth of information about specific changes in gene expression, as each cell can be analyzed on a case-by-case basis, rather than clustering all the different types of cells together
.

CAPITAL is specifically designed to compare complex datasets from single cells undergoing a transition process
.
These analyses are performed by defining a "pseudo-temporal trajectory" that places the cell on a hypothetical path that reflects the cell's progression
during the transition.
These trajectories are not always direct 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 AN ALGORITHM FOR CAPITAL, WHICH IMPLEMENTS A METHOD CALLED TREE ALIGNMENT, THEY TESTED
IT ON SYNTHETIC DATASETS AND REAL DATASETS FROM BONE MARROW CELLS.
THE RESULTS SHOW THAT CAPITAL IS STATISTICALLY MORE ACCURATE AND ROBUST THAN PREVIOUSLY EXISTING COMPUTATIONAL ALGORITHMS, SHOWING MAJOR ADVANCES IN
THESE METHODS.

Trajectory comparison is a powerful analytical method, for example, that can identify gene expression dynamics between different species, informing
the evolutionary process.
Lead author Reiichi Sugihara said: "We show in this study that CAPITAL can reveal different molecular patterns between humans and mice, even if expression patterns
are similar and appear to be conserved.
This will allow the identification of new regulators that determine cell fate
.
The technique is not limited to this type of data, as senior author Yuki Kato explains: "Our novel computational tools can be applied to a wide range of high-throughput datasets, including pseudotemporal, spatial, and epigenetic data
.
"

This powerful new technique will allow for global comparisons of single-cell trajectories, which could lead to the identification of new disease-related genes
that earlier comparison methods could not identify.
As such, CAPITAL represents a major advance in
the field of single-cell biology.

Alignment of single-cell trajectory trees with CAPITAL