The genes that are activated in a single cell may now be determined because of advances in high-throughput biological investigations. However, interpreting the resulting complex datasets might be difficult. CAPITAL, a novel computational tool for comparing big information from single cells, has now been developed by a team at Osaka University. Their findings were published in Nature Communications. RNA sequencing offers data on a subset of the whole population of genes that are actively expressed or turned on. With the advancement of technology, it is now feasible to sequence the RNA population of a single cell. Because each individual cell can be specifically analyzed rather than all the different cell types being pooled, this can provide a great deal of information on the specific changes in gene expression involved when a large population of mixed cells undergoes dynamic, transitional processes, such as differentiation or cell death.
CAPITAL was created primarily to compare complicated information from single cells moving through transitional stages. These investigations are carried out by creating a pseudotime trajectory, which sets the cells along a fictional path that represents their transitional process progress. These paths are not necessarily simple and linear; they can become highly convoluted and branched. Previously, only linear trajectories could be aligned for comparison, but the team's breakthrough means that complicated branching trajectories may now be aligned and compared correctly and automatically.
They tested the CAPITAL technique, which uses a method known as tree alignment, on both synthetic datasets and actual datasets derived from bone marrow cells after designing it. The results indicated that CAPITAL is statistically more accurate and resilient than earlier computational techniques, demonstrating significant gains over current methods.
A powerful approach is trajectory comparison, which may, for example, detect the gene expression patterns across various species to offer information on evolutionary processes.
According to the research's lead author, Reiichi Sugihara, "we demonstrated in this study that CAPITAL can reveal the existence of different molecular patterns between humans and mice even when the expression patterns appear to be conserved." This will facilitate the discovery of new regulators that control cell destiny.
According to senior author Yuki Kato, this technique isn't restricted to this sort of data. "Our novel computational tool can be applied to a wide range of high-throughput datasets, including pseudotemporal, spatial, and epigenetic data," he says.
This powerful new technology will allow for the worldwide comparison of single-cell trajectories, perhaps leading to the identification of novel disease-associated genes that were not previously recognized. As a result, CAPITAL marks a huge step forward in the realm of single-cell biology.
Journal Information: Reiichi Sugihara et al, Alignment of single-cell trajectory trees with CAPITAL, Nature Communications (2022). DOI: 10.1038/s41467-022-33681-3
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