About this Research Topic
In recent years, methods studying life at the single-cell level have enabled radical new insights into the biology of tissues and organisms in health and disease. Transcriptomic approaches at the single-cell level allowed the identification of novel cell types, states and developmental trajectories, which helped unravel tissue formation, composition and cellular heterogeneity at the functional level. Genomic approaches delineating somatic mutations in single cells enabled the study of tumor heterogeneity, somatic mutation rates, and cellular lineages. Epigenetic techniques facilitated the study of chromatin accessibility and conformation, DNA methylation and histone modification in single cells.
The combined use of these techniques started shedding light on how the different –omics layers are linked with one another and how they interact to define cellular states and cellular types. More recently, such approaches have been complemented and augmented with spatial information by adding dimensionality and context to these data types, with an ever-increasing resolution and number of genes that can be assayed simultaneously.
Furthermore, the development and deployment of novel or more sensitive experimental techniques result in the generation of large and complex datasets. Statistically, the complexity of such datasets can be extremely high and has driven the development of novel computational analysis tools aimed at tackling such problems and help guide the interpretation of the wealth of information that these data offer.
This progress in understanding the functional relevance of cellular heterogeneity, empowered by single-cell and spatial techniques, has not only a positive impact on basic research but also holds great promise in translational and personalized medicine.
In this Research Topic, we welcome submissions of papers focusing on both experimental and computational approaches to collect and interpret single-cell–omics data in a variety of biological systems. Fields of interest include neuroscience, developmental biology, reprogramming, regeneration, cancer, immunology, and metabolism. Manuscript types that will be considered are Original Research articles and Review articles.
Original research articles should focus on the presentation of novel methods for tissue and cell isolation and processing, library preparation and sequencing approaches, single-omics (transcriptomics, genomics or epigenetics), multi-omic or spatial transcriptomic studies. We also welcome submissions describing novel algorithms and pipelines for single-cell and/or spatial data analysis that can aid the extraction of biologically relevant information from big datasets, along with its interpretation. We will also consider reviews relevant to this topic.
Keywords: omics, single-cell, spatial, heterogeneity, tissue
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