More speedy and higher-precision analysis for comprehensive gene expression data of a single cell!
A single cell analysis with next-generation sequencing makes it possible to evaluate the differentiation status of iPS cells more accurately. Furthermore, the bioinformatic analysis of a vast amount of gene expression data can reveal biomarkers for targeted/non-targeted cells and the best condition of differentiation induction. This enables to obtain target cells efficiently, we can accelerate your regenerative medicine research!
More accurate evaluation with a single-cell analysis
More speedy analysis by next-generation sequencer
Utilization of single-cell analysis
in the regenerative medicine area
Characteristic analysis in a single cellular level:
We detect the heterogeneity (sub-populations) within samples by using large-scale gene expression data from each cell, and estimate cell types in each subpopulations. This approach makes it possible to accurately evaluate whether differentiation to targeted cells has been achieved.
Moreover, by searching for biomarkers that can identify the targeted/non-targeted cell populations found, we support the efficient evaluation of cell quality.
Visualization and classification of the expression profile for a single cell
A 2-dimesional scatter diagram of expression profile for each cell. Cells with a similar expression profile are plotted near each other, and it lead to detection the subpopulations in samples visually.
Optimization of differentiation induction
the differentiation trajectory of cells and the time-series expression variation manner can be understood. By investigating the expression change of cells on pseudotime (meaning a pseudo-temporal axis) obtained from a single cell analysis. We use these data to identify the genes involved in cell lineage divergence and the transcription factors varying over time, and propose the best detailed conditions for differentiation to targeted cells.
Prediction of cell lineage
Virtual differentiation trajectory predicted by using gene expression data.
Cell lineage in samples can be inferred from the branched lineage.
Time-series expression change
The heat map expresses the gene expression change over time with colors.
Based on differences in time series, the genes involved in differentiation induction or divergence can be identified.
Example of utilization
in the regenerative medicine area
The results of a study using single-cell analysis were reported at general session (oral presentation) 38 in the 19th Congress of the Japanese Society for Regenerative Medicine.
“Profiling to improve the safety of human iPS cell-derived pancreatic islet-like cells for transplantation”
Kensuke Sakuma (T-CiRA Discovery, Takeda Pharmaceutical Company Limited) et al.
Coauthored by Dr. Asano, a researcher at Axcelead Drug Discovery Partners
Transplantation of pancreatic islet-like cells derived from human iPS cells is expected to be an alternative option for pancreatic transplantation facing a chronic donor shortage. In order to enhance the safety of cells for transplantation, the differentiation induction method has been modified for the creation of pancreatic islet-like cell populations from human iPS cells . As a result, the created pancreatic islet-like cell population achieved normalization of blood glucose levels in immunocompromised mice with diabetes mellitus 2-3 months after transplantation, with sustained benefit for over 6 months. This cell population also demonstrated physiological responses: the human C-peptide in blood rapidly increased with oral glucose loading after the blood glucose level normalized and, conversely, restored following transient decrease when insulin was administered to simulate a hypoglycemic attack.
Points of single cell analysis utilization!
In a histology analysis of grafts, a pancreatic islet-like cell structure with few notable untargeted cells was observed even 6 months after transplantation. The compositions of pancreatic islet-like cell populations were compared between those from pre- and post-modification of the differentiation induction method, resulting in the identification of subpopulations that appeared to be involved in untargeted-cell hyperplasia. In the cells created using the modified method, among pancreatic beta-cell fraction with insulin NKX6.1 expression, the proportion of cells expressing the maturation marker MAFA had increased. Therefore, it was suggested that the profile of cell populations suitable for clinical application, from the aspect of drug effect and safety, was obtained.
Choose Axcelead for evaluation systems
in regenerative medicine research!
Axcelead has experience and achievements in a variety of single cell analyses, using not only cultured cells but also xenograft models, clinical specimens, organoids, etc. We provide the results obtained from data analyses flexibly corresponding to the characteristics of samples and clients’ requests.
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|cell RNA-seq analysis||evaluation of gene expression changes over time|
|Metabolomics/ proteomics of cells and media||factors and prediction markers for cell differentiation|
|Low molecular weight compound screening||Phenotypic screening using iPS cells|
|Efficacy study/ engraftment evaluation study/ safety study using pathological model animal modelss||・Large animals ( pig、monkey）
・Small animals (mouse, rat)
|Other||Evaluation of Cell Delivery Device|
・ Please contact us for evaluation systems not shown in the above table.