About this Research Topic
Single-cell methods have revolutionized the study of developmental systems both in vivo and in vitro. Most prominently, single-cell profiling has been used to catalogue the cell types in a large array of fetal and adult tissues. These single-cell atlases can, for example, serve as benchmarks for in vitro-derived cells, whose homogeneity, fidelity and maturity with respect to their in vivo counterparts is often lacking.
Going beyond the characterization of cell type diversity, trajectory inference has revealed lineage hierarchies and developmental dynamics. In conjunction with signaling pathway analysis, trajectory inference has the power to identify regulatory mechanism and their time of action, which could suggest regenerative pharmacological interventions or improvements of differentiation protocols. Integrating single-cell gene expression with single-cell chromatin accessibility can provide additional insights into gene regulatory mechanisms, for example, by discovering enriched transcription factor binding motifs. Both for tissue development in vivo as well as 3D in vitro systems, such as organoids, cell-cell interactions are crucially important. Such interactions are increasingly studied with spatial transcriptomics.
Given its currently limited resolution, spatial transcriptomics data is often complemented with single-cell profiling, which has spawned a range of algorithms for the integration of these data types. Including also genomic and epigenomic information allows us to study how genetic and epigenetic abnormalities are manifested in specific cell types, which is crucially important for the understanding of various diseases.
Despite the rapidly growing number of computational methods focusing on single-cell measurements, major bioinformatic challenges remain. Two prominent examples are the meaningful integration of data from different modalities (genomics, transcriptomics, chromatin accessibility etc.) or conditions (in vivo / in vitro, healthy/ diseased) and causal inference of cell type-specific regulatory models that can inform regenerative medicine or disease modeling. We need data integration methods that explicitly consider the biological relationships within and between data sets and thereby allow us to interpret the observed differences between, for example, in vitro and in vivo characteristics. While we will continue to learn much from integrating and correlating various sources of molecular information, the final frontier of single cell bioinformatics is the inference of causal relationships. Massively parallel perturbation studies, now often using the CRISPR/CAS system, produce a wealth of new information about regulatory interactions. Innovative computational tools are needed to combine biologically meaningful data integration with perturbation studies. Such tools might finally allow us to infer cell-type specific gene regulatory networks that do not only explain the development of embryonic tissues and regeneration of adult organs but also predict the impact of abnormalities occurring in disease.
This research topic is dedicated to computational work that aims to characterize, understand and predict embryonic development, tissue regeneration or in vitro differentiation at the single-cell level. We are interested in papers describing novel algorithms, tools and data bases; computational research with existing methods leading to new biological insights; benchmark studies as well as perspectives and reviews. We hope to not only provide a platform for computational scientists to share their latest tools, but also provide an overview of the state-of-the-art for experimentalists.
Going beyond the characterization of cell type diversity, trajectory inference has revealed lineage hierarchies and developmental dynamics. In conjunction with signaling pathway analysis, trajectory inference has the power to identify regulatory mechanism and their time of action, which could suggest regenerative pharmacological interventions or improvements of differentiation protocols. Integrating single-cell gene expression with single-cell chromatin accessibility can provide additional insights into gene regulatory mechanisms, for example, by discovering enriched transcription factor binding motifs. Both for tissue development in vivo as well as 3D in vitro systems, such as organoids, cell-cell interactions are crucially important. Such interactions are increasingly studied with spatial transcriptomics.
Given its currently limited resolution, spatial transcriptomics data is often complemented with single-cell profiling, which has spawned a range of algorithms for the integration of these data types. Including also genomic and epigenomic information allows us to study how genetic and epigenetic abnormalities are manifested in specific cell types, which is crucially important for the understanding of various diseases.
Despite the rapidly growing number of computational methods focusing on single-cell measurements, major bioinformatic challenges remain. Two prominent examples are the meaningful integration of data from different modalities (genomics, transcriptomics, chromatin accessibility etc.) or conditions (in vivo / in vitro, healthy/ diseased) and causal inference of cell type-specific regulatory models that can inform regenerative medicine or disease modeling. We need data integration methods that explicitly consider the biological relationships within and between data sets and thereby allow us to interpret the observed differences between, for example, in vitro and in vivo characteristics. While we will continue to learn much from integrating and correlating various sources of molecular information, the final frontier of single cell bioinformatics is the inference of causal relationships. Massively parallel perturbation studies, now often using the CRISPR/CAS system, produce a wealth of new information about regulatory interactions. Innovative computational tools are needed to combine biologically meaningful data integration with perturbation studies. Such tools might finally allow us to infer cell-type specific gene regulatory networks that do not only explain the development of embryonic tissues and regeneration of adult organs but also predict the impact of abnormalities occurring in disease.
This research topic is dedicated to computational work that aims to characterize, understand and predict embryonic development, tissue regeneration or in vitro differentiation at the single-cell level. We are interested in papers describing novel algorithms, tools and data bases; computational research with existing methods leading to new biological insights; benchmark studies as well as perspectives and reviews. We hope to not only provide a platform for computational scientists to share their latest tools, but also provide an overview of the state-of-the-art for experimentalists.
Keywords: single-cell methods, computational biology, embryonic development, tissue regeneration, stem cell differentiation, data integration, causal models, network inference
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