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Starfish: A Tool for Processing Spatial Transcriptomics Data
Apr 28, 2020
Spatial, or in situ transcriptomics, reveals a wealth of information about the relationships between cells but presents a significant analysis challenge. This growing field has sparked the development of a variety of technologies that enable scientists to study gene expression in cells and their local tissues. By studying gene expression, researchers can understand how the spatial distributions of unique or diseased cells impact health and disease — for example, learning why some tumors naturally evade immune cell recognition.
To compare which single-cell transcriptomics technologies are most effective, CZI and researchers at the Allen Institute and other organizations formed the SpaceTx (“spatial transcriptomics”) Consortium in 2017. The consortium developed what became known as starfish — a Python library that lets scientists build scalable and modular image processing pipelines for their experiments, extracting gene expression levels for cells in tissue. This enables comparison of image-based transcriptomics data and integration of these data with other single-cell transcriptomics.
As profiled in Nature, this open source software can read image files, register and remove the noise from pictures, find spots and identify the RNA molecules that they represent in several different experimental strategies. Starfish provides the single-cell biology community with a resource for learning how image-based transcriptomics data quality is influenced by factors such as assay chemistry and data processing decisions, with comprehensive documentation and data processing examples.
Read how CZI Science engineer Shannon Axelrod’s work on starfish makes it easier for researchers to unlock new discoveries.