Does the picture of a bowl of fruit salad juxtaposed with that of a fruit smoothie ring a bell? If you’re a computational biologist, it probably does. It’s the most common analogy used to emphasize the difference between single-cell- and bulk RNA sequencing technologies.
Single cell-RNA sequencing (scRNA-seq) revolutionized biomedical research by providing unprecedented granularity to gene expression data. Thanks to scRNA-seq, it is now possible to obtain the expression profile of individual cells from large cell populations, just like it’s possible to distinguish between the features of each piece of fruit in a bowl of fruit salad. This has helped gain a better understanding of the role of cellular heterogeneity in physiology and pathology. However, sc-RNA seq does not provide any positional and spatial information about cells.
Spatially resolved transcriptomics was originally invented in 2016 by Lundeberg, Frisen, and Stahl at KTH, Sweden. This technology provides gene expression data for large numbers of cells, while simultaneously adding another dimension to the data - positional information. Imaging techniques provide localization information but are not easily scalable. Combining molecular characteristics with positional information enables researchers to examine the tissue microenvironment in a holistic manner. Going back to the analogy of the fruit, spatial transcriptomics will tell us where each piece of fruit is present in the bowl, in addition to identifying the unique features of each fruit.
A typical workflow starts with isolating and staining tissue sections of interest, followed by visualization. The sections are then placed in physical contact with an array that holds RNA-binding capture probes. Permeabilizing the tissue sections frees RNAs, which bind to the capture probes on the array. The bound RNA is used to synthesize cDNA and subsequently generate sequencing libraries. Sequencing the libraries and visualizing the data tells us where our genes of interest are expressed and in which parts of the tissue sections.
Spatial reconstruction of tissues has seen varied applications, from neuroscience and developmental biology to tumor heterogeneity in oncology. A huge challenge though is managing the vast amounts of data produced with each experiment. The high dimensionality of the data also slows down the process of data analysis considerably. Working through these challenges in the years to follow could lead to the development of scalable techniques that harness the potential of integrating imaging and transcriptomics methods.
Get the latest insights on Biomolecular data and ML