A team of scientists has utilized new technologies for mapping gene expression that provide a fuller picture of the mechanisms that contribute to disease onset and progression in ALS patients.
The paper, published in the journal Science,describes how the spatiotemporal gene expression atlas uncovers early changes in ALS disease not observable using traditional sequencing methods. The scientists also developed novel computational approaches that reveal disease-driven changes in the activity of many signaling pathways across all cell types in the central nervous system and may offer target opportunities for designing therapeutics and diagnostics.
“Most prior methods for researching this affliction involved breaking tissues to get at cells,” explains Richard Bonneau, a professor of biology and data science at New York University and one of the study’s co-authors, which appears in the journal Science. “With this advance, we can now see cells as they exist naturally and in 3D. As a result, we now potentially have new ways to more closely examine ALS and without destroying tissue.”
The global collaboration also included scientists at the New York Genome Center (NYGC), the Simons Foundation’s Flatiron Institute, the Science for Life Laboratory, and KTH Royal Institute of Technology in Sweden.
Researchers at the NYGC’s Center for Genomics of Neurodegenerative Disease (CGND) used spatial transcriptomics, combined with a novel computational approach, to obtain gene expression measurements over time and space for close to 12,000 genes in the spinal cord. The result is a new, multi-dimensional gene expression atlas, providing unparalleled detail and scale and offering a previously unavailable view of disease progression in ALS.
The researchers believe that the study can provide a framework for further mapping of the central nervous system and its modes of dysfunction, possibly aiding research into not just ALS but also other neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease.
What makes this study different from previous transcriptome profiling research is the method used by the scientists, spatial transcriptomics, which generates RNAseq profiles at many locations in a tissue section simultaneously, with the team able to record precisely where in the tissue each profile came from. The researchers examined four time points along disease progression from earliest adulthood to end stage in a mouse model of ALS and applied the method to postmortem tissue samples from ALS patients.
“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,” explains Hemali Phatnani, director of NYGC’s CGND and senior author of the study. “It enables unprecedented interrogation of cell-to-cell interactions so that we 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.”
The scientists have made this multidimensional gene expression atlas available as a resource to the research community via the interactive data exploration portal https://als-st.nygenome.org/.
Bonneau, also group leader for systems biology at the Center for Computational Biology at the Simons Foundation’s Flatiron Institute, led the team in devising novel computational methods for analysis of atlas data.
He and his colleagues note that the ongoing insights to be gleaned from this comprehensive, spatiotemporal, transcriptome-wide gene expression dataset will be crucial to advancing understanding of ALS, a complex neurodegenerative disease with no clear cause or known cure. More than 200,000 people worldwide are living with ALS, also known as Lou Gehrig’s disease, which typically manifests at first in distal muscles of a single limb, and then spreads throughout the body, leading to total paralysis and death. The average life expectancy of a person with ALS is about two to five years from the time of diagnosis.
In their paper, the team collected 76,136 spatial gene expression measurements (SGEMs) from 1,165 mouse tissue sections and 61,031 SGEMs from 80 human tissue sections. By combining the data from many tissue sections, the researchers were able to interrogate the expression of close to 12,000 genes simultaneously across the tissue region under examination. This is the first time that such a spatially resolved approach has been used to study ALS at this depth and scale.
“Even though it is the motor neurons that are most vulnerable in ALS, the neighboring cells that surround the motor neurons also play a role in the disease,” observes co-first author Silas Maniatis, staff scientist at NYGC’s CGND. “Our research focuses on understanding how disease-causing mutations disrupt the function of both neuronal and non-neuronal cells, and how disrupted interactions between the various cell types of the nervous system drive motor neuron loss in ALS. Spatial Transcriptomics and the associated computational tools we developed in this study give us a superb view of these processes. Perhaps more importantly, this combination of technologies and experimental approaches provide a framework for understanding other diseases of the nervous system.”
Source – NYU