Life science
Medical & healthcare

Software development in single-cell RNA
data analysis considering pseudotime
Department of RNA Biology and Neuroscience, Graduate School of Medicine
Associate Professor Yuki Kato
https://researchmap.jp/read0207242?lang=en

Single-cell RNA sequencing data provide information on the RNA
population of just a single cell from a large population of mixedcells, which enable us to perform cell trajectory analysis for clarifying cellular transitional processes. Early methods for comparing
cell trajectories, however, require selection of a pair of linear paths
in branching trajectories using prior knowledge. Our team has developed CAPITAL, whose innovation means that complex branching trajectories can now be accurately and automatically aligned
and compared using a method knows as tree alignment.

A population of cells taken from a tissue at a certain time can beconsidered as a snapshot that contains cells ranging from undifferentiated cells to matured cells. These heterogeneous cells with
their expression profiles are grouped and placed in a time series,
resulting in a cell trajectory along a hypothetical path that reflects
their progress through the transitional process. This approach hasa major advantage of short time and low cost, which can “see thewood for the trees.” If two cell trajectories from different experiments are aligned, potential regulatory genes varying across theconditions can be identified.
Here our team has developed CAPITAL (comparative analysisof pseudotime trajectory inference with tree alignment), where twocell trajectory trees that consist of cell types are aligned, and thencell-cell alignment for each pair of linear paths is performed. Afterdeveloping the algorithm, we tested it on synthetic datasets withmultiple branches. The results demonstrated that CAPITAL is statistically more accurate and robust than the data integration methods
that existed previously, showing major advances over these methods. We next tested CAPITAL on authentic datasets from humanand mouse bone marrow cells, indicating that CAPITAL can reveal
the existence of different molecular patterns across different species even when the expression patterns are similar and appear to
be conserved.

!"#$%&'(".*)/(' ,'--"#$%&&'('#)%*)'$+,'--)*"+,-&'("!"#$%&'(&%%)*+,+!#$%&'$()*+%,#$*(-&.#+*/&-"-./0&11"2# ,0&#3

Fig. 1: Overview of cell trajectory analysis using single-cell data<'--9*)* :<-/1)'(9*)* ;!"#$%&"'(")%*+,-./-,,"0$1$)-1)

2,6)1-&"1&--"$,%+*3-*12-,,",%*-$&"$,%+*3-*1

9*)* :

9*)* ;
21'/$3)%4'
4-*-"-5#&-))%'*"1&-*0

Fig. 2: Overview of our algorithm for aligning branching cell trajectories

P a t e n t
Sugihara, Reiichi; Kato, Yuki et al. Alignment of single-cell trajectory trees with CAPITAL. Nature Communications. 2022, 13, 5972.
doi: 10.1038/s41467-022-33681-3
URL https://www.med.osaka-u.ac.jp/pub/rna/ykato/en/research.html
K e y w o r d single cell, gene expression, trajectory inference, alignment
Tr e a t i s e

8'#' !

21'/$3)%4'4&%% ,0+5&(,206▍ Significance of the research and Future perspective
CAPITAL can compare complex datasets from single cells undergoing transitional processes. For one promising example, consider a population of blood cells, which are known to differentiatewith multiple branches. We can first obtain a single-cell dataset ofa diseased patient and that of a healthy control, and then run CAPITAL to identify novel disease-associated genes. We aim at characterizing disease states using multi-omics data, including transcriptome, epigenome, and proteome. Our powerful new technique is
not limited to just the type of transcriptome data but applicable to awide range of those high-throughput datasets. Thus, CAPITAL represents a significant advance in the field of single-cell biology.

567('11%3#▍

0%11/'

Background & Results

567('11%3# 3& ='#' !

Life science

Abstract