We focused on computational methods for
estimating single-cell aging status,
which could improve our understanding of
the heterogeneity of aging and replace
chronological age, which has a tendency
to introduce bias, in aging studies.
As the average human lifespan increases,
aging has become a global and urgent problem.
Older ages are associated with increased risks
of various disorders, calling for a better
understanding of the mechanisms of aging
and its relationship with diseases.
Using biological age rather than
chronological age is essential to
investigate the risk factors
that contribute to the aging process
and the diseases accompanying it.
Although various biological age estimators
have been proposed, few of them take into account
the differences among cells and
define the age of a single cell.
Single-cell sequencing techniques
have recently revolutionized biomedical research,
including the aging field.
Due to the lack of an accurate age model,
researchers have to use chronological age,
which introduces bias into aging research,
especially when the intervals between different groups
are short. Therefore, we developed a
single-cell aging level estimator, SCALE,
based on selected aging-related genes.
To allow us to develop our SCALE method
based on the expression levels of aging-related genes,
we curated aging genes from more than 10,000 related
scientific articles and 6 databases.
To facilitate access to these data by other researchers,
we created the web page to display these genes.
Aging is a highly complex process that is
difficult to study effectively by
traditional low-throughput methods,
so it is likely that several aging-related genes
have not been identified. Hence, we also identified
new aging genes by Elastic-net regression for each tissue.
By Using SCALE, we observed the cell-to-cell variations
of biological age,
possibly due to cell type characteristics
and the microenvironment.
With aging being increasingly recognized as
a global challenge to be solved,
our model could be used as a powerful tool
to study the aging process
at the single-cell level to
address this healthcare challenge.