With the advance in single-cell (sc) sequencing techniques, it's not only RNAs that can be sequenced to measure gene expressions in individual cells, but also other elements, and even from the same cell. For example, ATAC-seq, a genome-wide assay to investigate the chromatin states in the genome, is now able to be performed at the cell level. Compared to the bulk ATAC-seq, single-cell ATAC-seq is capable of differentiating cell types or stages. Single-cell TCR-seq, another advancing single-cell level sequencing technique, characterizes T-cell receptor (TCR) repertoires that can be served as immune response markers. By integrating these different types of single-cell omics data, we can reveal the in-depth biological mechanisms. For example, with the availability of sc-TCR and sc-ATAC sequencing data, we can uncover the epigenetic signatures of T cell clones.
The goals of the related research include in-depth biological mechanism study, drug discovery, and individualized medicine, with the aid of single-cell sequencing data integrative analysis. To perform an in-depth biological mechanism study, the single-cell sequencing data provides cell-level information hence it should enhance the biological mechanism discovered by bulk sequencing data analysis. For example, integrating RNA-seq and ATAC-seq demonstrates a general view of the gene regulatory network (GRN). While with cell-level data, we can construct clone-level, cell-type-specific, or cell-stage specific GRN and consequently determine the key regulators to drive the clones, cell types, or cell stages. This also leads to more efficient drug discovery since we have more specific regulators or genes to target. Chimeric antigen receptor (CAR) T-cell therapy is a good example that we can enhance individualized medicine with single-cell sequencing data. With CAR-T therapy, T cells are extracted from blood and engineered in the lab so that they can better target the tumor cells. However, different T cell clones may fight against the tumor cells with different efficacy. Sc-TCR and sc-RNA sequencing data analysis can help to improve the efficiency of the therapy by identifying T-cell clones that show a better response to tumor cells individually.
We are looking for manuscripts on the following areas:
1. New single-cell sequencing techniques that help to enhance biological mechanism study, drug discovery, and individualized medicine.
2. New computational pipelines or tools to perform the integrative analysis with single-cell multiomics data. They could be new tools that are built from the ground up or pipelines that integrate other tools but provide novel angles to interpret the results.
3. New statistical, machine learning, or deep learning models to perform the integrative analysis with single-cell multiomics data.
4. Reviews about the advanced single-cell sequencing techniques.
5. Reviews of the pipelines and tools for integrative analysis with single-cell multiomics data
With the advance in single-cell (sc) sequencing techniques, it's not only RNAs that can be sequenced to measure gene expressions in individual cells, but also other elements, and even from the same cell. For example, ATAC-seq, a genome-wide assay to investigate the chromatin states in the genome, is now able to be performed at the cell level. Compared to the bulk ATAC-seq, single-cell ATAC-seq is capable of differentiating cell types or stages. Single-cell TCR-seq, another advancing single-cell level sequencing technique, characterizes T-cell receptor (TCR) repertoires that can be served as immune response markers. By integrating these different types of single-cell omics data, we can reveal the in-depth biological mechanisms. For example, with the availability of sc-TCR and sc-ATAC sequencing data, we can uncover the epigenetic signatures of T cell clones.
The goals of the related research include in-depth biological mechanism study, drug discovery, and individualized medicine, with the aid of single-cell sequencing data integrative analysis. To perform an in-depth biological mechanism study, the single-cell sequencing data provides cell-level information hence it should enhance the biological mechanism discovered by bulk sequencing data analysis. For example, integrating RNA-seq and ATAC-seq demonstrates a general view of the gene regulatory network (GRN). While with cell-level data, we can construct clone-level, cell-type-specific, or cell-stage specific GRN and consequently determine the key regulators to drive the clones, cell types, or cell stages. This also leads to more efficient drug discovery since we have more specific regulators or genes to target. Chimeric antigen receptor (CAR) T-cell therapy is a good example that we can enhance individualized medicine with single-cell sequencing data. With CAR-T therapy, T cells are extracted from blood and engineered in the lab so that they can better target the tumor cells. However, different T cell clones may fight against the tumor cells with different efficacy. Sc-TCR and sc-RNA sequencing data analysis can help to improve the efficiency of the therapy by identifying T-cell clones that show a better response to tumor cells individually.
We are looking for manuscripts on the following areas:
1. New single-cell sequencing techniques that help to enhance biological mechanism study, drug discovery, and individualized medicine.
2. New computational pipelines or tools to perform the integrative analysis with single-cell multiomics data. They could be new tools that are built from the ground up or pipelines that integrate other tools but provide novel angles to interpret the results.
3. New statistical, machine learning, or deep learning models to perform the integrative analysis with single-cell multiomics data.
4. Reviews about the advanced single-cell sequencing techniques.
5. Reviews of the pipelines and tools for integrative analysis with single-cell multiomics data
