SPICEMIX Sees Spatial Transcriptomics Move Forward

Why Spatial Transcriptomics?

What determines the identity of a cell? Early in embryogenesis, location is one of the most important factors for determining cell fate. Cells are exposed to different concentrations of key transcription factors that initiate location-specific changes in gene expression to begin the process of cell differentiation and specialization.

Even in mature cells, it is a series of complex interactions between intrinsic genetic factors, spatial factors and temporal factors that determine the relative makeup of mammalian cell types. Even though pure transcriptomics is a vital research tool, combining an imaging element to determine where in a tissue certain genes are expressed can be extremely valuable. This is called spatial transcriptomics, and it is rapidly becoming a popular research tool.

Challenges in Data Integration

Computational methods have been developed to harness the unique properties of spatial transcriptomic data, including methods to determine spatial domains/cell types in tissues, look at the spatial differences in gene expression and align single cell RNA-seq data with spatial data.

However, some of these existing models can be prone to overfitting, and most do not integrate the contributions of genes to cell identity with their differential spatial expression patterns.

SPICEMIX: A Cell Identity-Focused Spatial Transcriptomics Method

In a recent paper in Nature Genetics, Chidester et. al. from Carnegie Mellon’s Computational Biology Department published a new method for spatial transcriptomics analysis. Spatial identification of cells using matrix factorization, or SPICEMIX, integrates a linear latent variable model of gene expression with a graphical model of cell spatial organization.

This approach is able to produce more robust “pictures” of complex tissues, their organization and the differential expression patterns of genes throughout them. The authors applied SPICEMIX to data from the visual cortex region of the mouse brain, where it reliably identified the spatial variability of expression of functionally grouped genes (metagenes). This method advances the cutting edge of spatial transcriptomics data, allowing for more effective interpretation of the data and its biological significance.

If you’re looking for a more effective way to interpret spatial transcriptomics data, SPICEMIX may be an option, depending on your tissue of interest. This approach integrates linear latent variable models of gene expression with graphical models of cell organization, producing robust images of complex tissues and their differential expression patterns. With SPICEMIX, you can reliably identify the spatial variability of expression of functionally grouped genes, produce “pictures” that are more accurate than traditional methods, and gain insight into biological significance from your data.

Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help

Many of our clients at Bridge Informatics are at the cutting edge of research, using new tools like spatial transcriptomics to tackle their research questions. From pipeline development and software engineering to deploying existing bioinformatics tools, Bridge Informatics can help you on every step of your research journey.As experts across data types from leading sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Bridge Informatics’ bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis. Click here to schedule a free introductory call with a member of our team.

Jane Cook, Biochemist & Content Writer, Bridge Informatics

Jane Cook, leading Content Writer for Bridge Informatics, has written over 100 articles on the latest topics and trends for the bioinformatics community. Jane’s broad and deep interdisciplinary molecular biology experience spans developing biochemistry assays to genomics. Prior to joining Bridge, Jane held research assistant roles in biochemistry research labs across a variety of therapeutic areas. While obtaining her B.A. in Biochemistry from Trinity College in Dublin, Ireland, Jane also studied journalism at New York University’s Arthur L. Carter Journalism Institute. As a native Texan, she embraces any challenge that comes her way. Jane hails from Dallas but returns to Ireland any and every chance she gets. If you’re interested in reaching out, please email daniel.dacey@old.bridgeinformatics.com or dan.ryder@old.bridgeinformatics.com.

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