Users can view general information of collected datasets during cell state transitions, e.g. origin cell type, terminal cell type, condition of induction in experiment, and data source. ⇓

In addition, users can click the links in the first column to perform analysis and visualization of the corresponding datasets. ⇓

Users can specify a transition between a pair of cell types to view the genes that play potential important roles, the trajectory of gene expression changes, and the patterns of gene expression during the transition.

Example:

Step 1: select Homo sapiens, Embryonic stem cell, and Cardiomyocyte as species, origin cell, and terminal cell, respectively. ⇓

Step 2: select dataset “GSE67152.D2.and.D3”. ⇓

Step 3: perform the transition trajectory function. ⇓

It appears that the 4th day (D4) is important for the state transition, since gene activities change drastically at that time. In the clustering diagram, the samples from adjacent time points tend to be in the same cluster, indicating the gene expression profiles are continuous during transition and the data is of good quality. ⇓

Step 4~6: perform the detailed incidents function. ⇓

The decreasing number of DEGs on D6 indicates that the transition processes may be completed on the 6th day. ⇓

The detailed incidents funcion reveals the genes that significantly change gene expression in each time point. The 2nd day that is an early stage in transition, in which the DEG detected are enriched with biological processes, such as cardiac cell fate specification, ventricular cardiac muscle tissue morphogenesis and heart development, ⇓

while the DEGs up-regulated on the 8th day are enriched in muscle contraction and ventricular cardiac muscle tissue morphogenesis, indicating the 8th day is the late stage of cardiac transition. ⇓

Step 7~8: perform expression pattern to obtain DEGs with different gene expression pattern including monotonically increasing, monotonically decreasing, and reaching a peak at different time points. ⇓

Step 9: perform functional analysis on these subsets of DEGs with specified patterns to reveal the functions of the DEGs with expression pattern of interest. ⇓

Step 10: convenient query gene expression for single interested gene in this dataset. ⇓

User can submit a gene list obtained from their own studies not only to predict the candidate transitions involving the queried genes, but also functions and gene expression profiles of the queried genes in the candidate datasets from previously studies.

Example:

Step 1~4: paste a list of genes and submit. ⇓

The list used in this example consists of 323 transiently either up- or down-regulated genes (clusters IV and VIII) during the reprogramming of mouse embryonic fibroblasts (MEFs) to induced pluripotent stem cells (iPSCs) identified by Polo et al (Polo, J., Anderssen, E., Walsh, R., Schwarz, B., Nefzger, C., Lim, S.M., Borkent, M., Apostolou, E., Alaei, S. and Cloutier, J. (2012) A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells. Cell, 151, 1617-1632). ⇓

CSTEA recovers the transition process from fibroblast to iPSCs and shows the relevant queried genes. ⇓

Step 5: perform functional enrichment analysis on overlapped genes to reveal the functions of these important genes. ⇓

The functional analysis reveals the candidate transition-associated genes annotated with transition-associated pathways and biological processes. ⇓

Step 6~7: view the datasets associated with the queried genes, and analyze the roles of these genes at different time points in specific dataset. ⇓

The result page will need 4~10 seconds to initiate, after which the transition trajectory and clustering of samples will be visualized based only on the genes overlapping with the signature defined in the specified dataset (GSE46532 in this case). However, the trajectory based on these overlapped genes may be much different from the one visualized based on all DEGs. limited by the few overlapped genes, the trajectory may not effectively represent the change of transcriptome during the transition. ⇓

The detailed incidents will also be performed on these overlapped genes to reveal the time points these queried genes differentially express, i.e. may regulate the transition, and to reveal the functions of the queried genes that differentially express at different time points, which benefits explaining the mechanism these queried genes take effect. ⇓

The expression patterns of these overlapped genes to reveal the time points these queried genes differentially express, i.e. may regulate the transition, and to reveal the functions of the queried genes that differentially express at different time points, which benefits explaining the mechanism these queried genes take effect. ⇓

There are 9 out of the 25 queried genes overlapping with the dataset-specific signature have the peak gene expression at the 26th day. ⇓

Among these 9 genes, Col2a1, Bcl11a and Insm are related to cell differentiation and organ development. These findings indicate that the 26th day may be the critical time point at which cells are converted to the desired pluripotent state. ⇓

Users can upload their custom time-series gene expression data to obtain the genes that play potential important roles, the trajectory of gene expression changes, and the patterns of gene expression during the transition represented by the user-uploaded data. In addition, user can compare the uploaded data with datasets from previously studies.

Step 1~4: The example .csv file test.csv is used for demonstration. Mus musculus and REFSEQ_MRNA are selected as the species and the gene accession, respectively. After the file is completed uploaded, click the “Analyse” button to perform visualization and analysis. ⇓

The result page need 5~10 seconds to be initiated.

Step 5: After the page is prepared, the visualization and analysis results are shown as in process query function (described in the tutorial for process query). ⇓

Step 6: Click “Signature Query” button to compare the uploaded data with datasets from previous studies, which queries the DEGs identified in the uploaded data against the signatures defined in CSTEA with signature query function. The result will be shown in the fashion of signature query function as described in the tutorial for signature query function. ⇓

After step 6, the result page shows the candidate transition processes with similar gene expression dynamic patterns in the user-uploaded data. The users can easily check what potential cell state transitions have been involved in their custom data, which may give useful clues for further exploration. ⇓

If a public dataset was chosen, the expression patterns of the common DEGs across different time points will be shown so that the users can check whether their custom data is similar to the public dataset, providing clues for further exploration. ⇓