Research Topic

Deep Spatial Profiling of Omics Data

About this Research Topic

Advances in multi-omics technologies allow sequencing of spatially identified cells which is a key in understanding cell type identity in a multicellular organism. However, more efforts are needed to characterize these complex tissues in order to fully measure genes activity in a tissue sample and map where the activity is occurring.
Inferring spatial and signaling relationships between cells to predict gene patterns enable analyses of gene expression heterogeneities in individual cells, classify subpopulations, tissues, and their communications. Spatial -omics is critical for understanding cell identity and function in the context of tissues. By including spatial information, researchers can measure all the gene activity in a tissue sample and map where the activity is occurring. It can lead to new discoveries that will prove instrumental in helping scientists gain a better understanding of biological processes and disease.
We welcome researchers in the field of genetics and genomics to contribute their high-quality Original Research, Brief Research Reports, Mini Reviews, and Review articles. Potential subtopics include, but are not limited to:

- Spatial Omics Methods, to distinguish subpopulations with the specificity of current -omic approaches by overlaying -omic level data onto tissue images.
- Spatial Proteome Profiling, the localizations of proteins and their dynamics at the subcellular level which is essential for a complete understanding of proteomics cell biology and as a discovery tool to unravel disease mechanisms
- Spatial Transcriptomics, that aim to characterize gene expression profiles while retaining information of the spatial tissue context, genes with spatially patterned gene expression as known and novel markers of different cell types
- Spatial Genome Organization and Architecture, to understand genome organization in greater detail, and provide insights into the mechanisms underlying structure formation and functions of nucleome.


Keywords: Spatial Profiling, Deep Learning, Multi-Omics Data, Spatial Transcriptomics, Spatial Genome, Spatial Network Analysis, Spatial Proteome Profiling, Spatial Omics Methods


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Advances in multi-omics technologies allow sequencing of spatially identified cells which is a key in understanding cell type identity in a multicellular organism. However, more efforts are needed to characterize these complex tissues in order to fully measure genes activity in a tissue sample and map where the activity is occurring.
Inferring spatial and signaling relationships between cells to predict gene patterns enable analyses of gene expression heterogeneities in individual cells, classify subpopulations, tissues, and their communications. Spatial -omics is critical for understanding cell identity and function in the context of tissues. By including spatial information, researchers can measure all the gene activity in a tissue sample and map where the activity is occurring. It can lead to new discoveries that will prove instrumental in helping scientists gain a better understanding of biological processes and disease.
We welcome researchers in the field of genetics and genomics to contribute their high-quality Original Research, Brief Research Reports, Mini Reviews, and Review articles. Potential subtopics include, but are not limited to:

- Spatial Omics Methods, to distinguish subpopulations with the specificity of current -omic approaches by overlaying -omic level data onto tissue images.
- Spatial Proteome Profiling, the localizations of proteins and their dynamics at the subcellular level which is essential for a complete understanding of proteomics cell biology and as a discovery tool to unravel disease mechanisms
- Spatial Transcriptomics, that aim to characterize gene expression profiles while retaining information of the spatial tissue context, genes with spatially patterned gene expression as known and novel markers of different cell types
- Spatial Genome Organization and Architecture, to understand genome organization in greater detail, and provide insights into the mechanisms underlying structure formation and functions of nucleome.


Keywords: Spatial Profiling, Deep Learning, Multi-Omics Data, Spatial Transcriptomics, Spatial Genome, Spatial Network Analysis, Spatial Proteome Profiling, Spatial Omics Methods


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

03 May 2021 Manuscript

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Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

03 May 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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