1932

Abstract

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: () next-generation sequencing–based assays, () imaging-based spatially resolved transcriptomics RNA approaches including in situ hybridization/in situ sequencing, and () imaging-based proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-102523-103640
2024-05-20
2024-06-17
Loading full text...

Full text loading...

/content/journals/10.1146/annurev-biodatasci-102523-103640
Loading

Supplemental Table 1

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error