The human immune system comprises several cell types that play key roles in determining how a tumor responds to immune checkpoint blockade (ICB) and all phases of the tumor, e.g., genesis, progress, metastasis. Recently, the great progress in single-cell genomics, proteomics, and epigenomics provides an opportunity to better understand the composition of immune system and the mechanisms that underlie response to ICB.
The aim of this Research Topic is to promote the research advances in data generation, new bioinformatic algorithms/methods/resources, and other related approaches to improve research efficacy, dissect the immune system and tumor microenvironment, and propose new immunotherapeutic target candidates. This will lead to a better practice of single cell sequencing data analysis, benchmark existing methods, and provide guidelines for future practices to mine the knowledge hidden in the data.
We especially aim to highlight the tumor microenvironments—including the cellular composition, heterogeneity or spatial organization, and cell-cell interactions during disease development and treatment—as well as to understand and predict responses to ICB.
We therefore encourage Original Research (or Brief Research Report) as well as Review (or Mini Review) articles that have made advances in single cell sequencing techniques and data analysis methods, as well as their clinical applications (especially in tumor immunology). Potential topics include but are not limited to the following:
-Advances in single-cell sequencing technologies, including single-cell epigenomics, combining CRISPR genetics screens, and single-cell sequencing;
-Valuable datasets related to critical tumor immunological aspects;
-Pioneering exploration of new immunotherapeutic strategies, methods, targets;
-Bioinformatics methods for single-cell data quality control;
-Algorithms for dropout imputation of single-cell data;
-Methods/tools for single-cell data normalization, especially for batch effect removal;
-Advanced analysis of single-cell data including clustering, differential and highly variable gene calling, data alignment and integration, and so on;
-Single-cell dynamics and interactions inference;
-Methods/tools for the integration of single-cell sequencing data with other types of data;
-Single-cell data visualization;
-Immunotherapy response prediction.
The human immune system comprises several cell types that play key roles in determining how a tumor responds to immune checkpoint blockade (ICB) and all phases of the tumor, e.g., genesis, progress, metastasis. Recently, the great progress in single-cell genomics, proteomics, and epigenomics provides an opportunity to better understand the composition of immune system and the mechanisms that underlie response to ICB.
The aim of this Research Topic is to promote the research advances in data generation, new bioinformatic algorithms/methods/resources, and other related approaches to improve research efficacy, dissect the immune system and tumor microenvironment, and propose new immunotherapeutic target candidates. This will lead to a better practice of single cell sequencing data analysis, benchmark existing methods, and provide guidelines for future practices to mine the knowledge hidden in the data.
We especially aim to highlight the tumor microenvironments—including the cellular composition, heterogeneity or spatial organization, and cell-cell interactions during disease development and treatment—as well as to understand and predict responses to ICB.
We therefore encourage Original Research (or Brief Research Report) as well as Review (or Mini Review) articles that have made advances in single cell sequencing techniques and data analysis methods, as well as their clinical applications (especially in tumor immunology). Potential topics include but are not limited to the following:
-Advances in single-cell sequencing technologies, including single-cell epigenomics, combining CRISPR genetics screens, and single-cell sequencing;
-Valuable datasets related to critical tumor immunological aspects;
-Pioneering exploration of new immunotherapeutic strategies, methods, targets;
-Bioinformatics methods for single-cell data quality control;
-Algorithms for dropout imputation of single-cell data;
-Methods/tools for single-cell data normalization, especially for batch effect removal;
-Advanced analysis of single-cell data including clustering, differential and highly variable gene calling, data alignment and integration, and so on;
-Single-cell dynamics and interactions inference;
-Methods/tools for the integration of single-cell sequencing data with other types of data;
-Single-cell data visualization;
-Immunotherapy response prediction.