The Spatial Revolution
Transcriptomics is a vital research tool, with both bulk and single cell methods contributing greatly to our understanding of gene expression patterns in normal and diseased cells. However, knowing where in a tissue certain genes are expressed can add an additional valuable layer of information. Combining an imaging element to determine where in a tissue certain genes are expressed is called spatial transcriptomics, and it is rapidly becoming a popular research tool. However, as with any new method or data type, new challenges for spatial transcriptomics analysis have also arisen.
Current Challenges in Cell Identification in Tumors
An integral method in spatial transcriptomics is the ability to measure the transcriptome in situ, meaning capturing the data within intact tissue using positional molecular “barcodes.” These techniques include Slide-seq and 10X Genomics’ Visium protocol. However, these methods use a size-based “spot” capturing technique which can include signals from a mixture of cells, making teasing apart cellular identities an important and challenging step in the process of analyzing spatial transcriptomic data.
Current methods for decomposing cell types are optimized for general spatial transcriptomic data and bulk methods, and thus have some limitations when applied to the unique challenge of spatial transcriptomic data from tumors. Existing methods can only create predictions of the fraction of cancerous cells based on robust reference profiles of malignant cells or by just measuring the fraction of known cell type signatures and assigning the rest as malignant/unknown.
SpaCET: A New Spatial Transcriptomics Method for Tumors
Recently published in Nature Communications, Ru et. al. detailed a new computational method for identifying cell types specifically in spatial transcriptomic data from tumors. Called the Spatial Cellular Estimator for Tumors (SpaCET), they addressed the common challenges that plague existing spatial transcriptomic analysis tools in cancer.
In contrast to the current tools described above, SpaCET estimates the abundance of cancer cells using signatures of genomic disturbances that are prevalent across many common cancer types. In addition, SpaCET addresses the heterogeneous cellular density found across tumors. Rather than normalizing the density of each spot to 1 like in current analysis techniques, SpaCET is calibrated to the local cellular density.
Finally, the authors created a pan-cancer single-cell reference of scRNA-seq data to integrate for more robust cell-type identification. They found that SpaCET could outperform existing methods to estimate malignant cells, stromal cells and some types of immune cells in particular. Methodological advances in spatial transcriptomic analysis pipelines will be vital to realize the full potential of this new data type for investigating biological questions.
Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help
The utility of spatial transcriptomics for investigating causes of human disease is rapidly coming to the forefront of research. Many of our clients at Bridge Informatics are pursuing their research questions with these kinds of sophisticated bioinformatics approaches. 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 cutting-edge 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 jennifer.martinez@old.bridgeinformatics.com or dan.ryder@old.bridgeinformatics.com.
