NEW YORK (GenomeWeb) – Using spatial transcriptomics, researchers have uncovered when and where various genes are expressed during amyotrophic lateral sclerosis (ALS) development and progression.
The neurodegenerative disorder affects hundreds of thousands of people around the world and leads to paralysis and death typically within a few years of diagnosis, but how the disease advances has been unclear.
A team of researchers led by the New York Genome Center's Hemali Phatnani used spatial transcriptomics to analyze gene expression within the spinal cords of mice taken during ALS progression as well as from postmortem human spinal cord samples. As they reported in Science today, the researchers identified when and where various genes are expressed as ALS-linked dysfunction progresses.
"Spatial transcriptomics allows us, for the first time, to gain important insights into gene expression in individual cell types while in their natural multicellular context," Phatnani said in a statement, adding that they "can now examine and explore specific pathways in ALS where things are going wrong; where and in which cell types dysfunction is first seen; and how this spreads through the spinal cord."
Other contributing authors included researchers from the Science for Life Laboratory in Stockholm, where spatial transcriptomics was developed. A company by the same name spun out of the lab and was acquired in December by 10x Genomics.
As part of the approach, the researchers captured polyadenylated RNA using spatially barcoded DNA capture probes for sequencing. They applied this method to profile tissue sections taken from the spinal cord of a mouse model of ALS before symptom onset, after symptoms arose, and at end stage disease. They likewise profiled gene expression in samples from spinal cord tissue of post-mortem ALS patients.
In all, the researchers took more than 76,000 spatial gene expression measurements (SGEMs) from about 1,200 spinal cord tissue sections from 67 mice and more than 60,000 such SGEMs from 80 spinal cord tissue sections from seven postmortem patients.
Using a probabilistic model, the researchers examined the spatial distribution of these genes in the mouse and human spinal cord samples. As expected, known ALS genes like Matr3, Kif5a, and Pfn1 had altered expression in the mouse model. Additionally, the researchers reported that their results further reflected known spatial and disease progression gene expression patterns.
The researchers in particular focused on the dynamics of microglial activation, as microglia are involved in removing damaged neurons through phagocytosis.
The expression of both TREM2 and TYROBP — which form a complex that can trigger phagocytosis — increases in the microglial cells of the spinal cords of affected mice. Additionally, though, the researchers found that the upregulation of TYROBP in white matter begins before that of TREM2 and prior to the mice exhibiting symptoms, suggesting it is involved in early disease processes.
The researchers also identified 31 different gene coexpression modules and additional expression submodules, noting that some modules consisted of genes preferentially expressed in certain cell types.
For instance, submodule 8.9, which includes Prdx6 and Gfap, and submodule 29.41, which includes Slc7a10 and Bcan, represent different sets of genes expressed by different subpopulations of astrocytes during disease progression.
Meanwhile, submodule 1.12, which includes Sall1, was increased in the white matter of control and presymptomatic ALS mice. The loss of the expression of this submodule in microglia leads to phagocytic, inflammatory phenotype, the researchers noted. They added that, by the end stage of ALS, this submodule infiltrates the gray matter and its expression appears to be coordinated with that of other submodules.
Similar expression patterns were observed among the samples from human ALS patients, the researchers noted. They uncovered 28 human expression modules, some of which were the same as those found in mice.
This suggested to the researchers that their dataset could serve as resource to fuel further studies of the nervous system and related diseases.