Scientists map gene expression without microscopes

By Samantha Black, PhD, ScienceBoard editor in chief

April 21, 2021 -- A new framework called Tomographer, which uses sequencing data from tissues cut into thin strips in a way that allows them to be reconstructed, has been developed to spatially resolve gene expression data without the need for a microscope. The work was published in Nature Biotechnology on April 19.

Spatially resolved molecular atlases help scientists understand where different types of cells are located in the body and map their gene expression in specific locations in tissues and organs. However, many sequencing modalities lack spatial counterparts.

Therefore, the scientific community is in need of generalized approaches to converting existing sequencing protocols into spatially resolved techniques.

New technologies such as in situ hybridization can be used to map the expression of multiple genes on the same tissue sample and have accelerated the generation of new atlases. In situ hybridization allows for a target gene to be tagged ("hybridized") with a fluorescent marker within sections of a tissue ("in situ") and visualized under a specialized microscope.

Consecutive photographs of each section are compiled to generate a "spatial" map of the gene's location inside the given tissue. This technique requires preselection of target genes and sequencing methods with resolution limitations.

Alternatively, gene expression patterns can be resolved using laser capture microdissection (LCM), which samples tissues on a grid. And next-generation sequencing (NGS) libraries can be used to quantify spatial gene expression patterns that delineate between cell populations. However, LCM assays are limited by laser-induced degradation of nucleic acids and the technical complexity of preparing multiple libraries.

An imaging-free approach to spatial sequencing could be tomo-seq, in which a tissue sample is cryosectioned into thin slices that are used to prepare NGS libraries and generate a 1D transcriptomic pattern. 3D reconstructions of gene expression patterns are done using an iterative proportional fitting (IFP) algorithm. A limitation of this method is that IFP cannot reconstruct complex anatomical configurations.


In the current study, scientists at the Swiss Federal Institute of Technology Lausanne (EPFL) School of Life Sciences described how they have created a computational algorithm called Tomographer that can transform gene-sequencing data into spatially resolved data such as images, without using a microscope. The framework uses a tissue sampling strategy based on multiangle sectioning and an associated algorithm that enables the reconstruction of 2D spatial patterns.

The sampling technique involves cutting tissues into consecutive thin slices ("primary sections") that are subsequently further sliced along an orthogonal plane at predefined orientations ("secondary sections"), resulting in tissue strips spanning the entire tissue. Gene expression quantification of the sections is implemented using spatial transcriptomics by reoriented projections and sequencing (STRP-seq), a method that combines the sampling strategy presented above with a customized, low-input RNA-seq protocol based on single-cell tagged reverse transcription sequencing (STRT-seq) chemistry. The method produces parallel-slice projections for each gene by quantifying the reads that map to a transcript in each of the secondary sections.

"The Tomographer algorithm opens a promising and robust path to 'spatialize' different genomics measurement techniques," said senior author Gioele la Manno, PhD, a research fellow at EPFL, in a statement.

The Tomographer framework was benchmarked for the ability to reconstruct transcriptome-wide spatial expression patterns against the Allen Adult Mouse Brain in situ hybridization atlas. First, the team measured 3,880 genes in the mouse brain. Then, they compared 923 reconstructed genes to the in situ hybridization data from the mouse brain atlas using Pearson's correlation coefficient and found that the Tomographer workflow was more than twice as accurate as IPF.

They also compared Tomographer to the spatial reconstruction capabilities of IFP-based Tomo-seq. La Manno and his team found that the new sampling scheme/algorithm combination significantly outperformed previous in situ hybridization methods.

Furthermore, they applied the technique to the brain of the Australian bearded dragon, Pogona vitticeps and successfully reconstructed 8,183 annotated genes. With this exercise, the team confirmed that the genes enriched from the STRP-seq protocol were the same from previous in situ hybridization data.

Importantly, the authors noted that the quality of Tomographer's reconstructions depends on the balance between the number and width of the tissue strips sampled. They noted that four cutting angles provided results that are a fair compromise between the reconstruction quality and sample processing effort and cost. Also, the technique requires a distance of at least 1.15 times the secondary section width in order to discriminate between two distinct points of primary strips.

Regardless of these limitations, the team suggested that the framework can be used in the future to link single-cell resolved data to its spatial context. They hope that resolving multiomics data using molecular tomography on individual samples can be deployed, so that biologically identical replicates are not required.

"It was an exciting opportunity to develop an accessible and flexible computational method that has the potential to facilitate progress in the health sciences," added co-lead author, Halima Hannah Schede, a doctoral candidate at EPFL. "I am very much looking forward to seeing what other spatially resolved biological data forms will be brought to light with Tomographer."

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