From Alzforum by Chelsea Weidman Burke
As the mountain of single-nucleus RNA-sequencing data grows taller, how can scientists extract meaning from it? One way is pseudotime analysis. In essence, this algorithm orders cells on a virtual timeline based on the similarity of their gene-expression patterns. “Cells that are alike are placed near each other along the spectrum of transcriptional changes,” explained Laura Heath of Sage Bionetworks in Seattle. Heath presented one of several pseudotime analyses currently being done in the Alzheimer’s field at the Alzheimer’s Association International Conference, held last month in San Diego, California.
The resulting diagrams look like trees. Scientists call branches healthy or diseased based on their expression of known markers. This, in turn, places each cell along the health to disease trajectory, exposing sequential gene expression patterns.
Pseudotime analysis allows scientists to turn cross-sectional data into “faux” longitudinal data to understand how cells change over time. This is important for Alzheimer’s, a disease that unfolds over the course of 30 years. Postmortem tissue offers but a snapshot of one time point, making it hard to discern when and how disease markers develop. Most brain transcriptomic data come from postmortem samples and are likewise difficult to interpret because it is hard to know if gene-expression changes are due to AD pathogenesis or organ damage associated with the end of life.
From Alzforum by Chelsea Weidman Burke
As the mountain of single-nucleus RNA-sequencing data grows taller, how can scientists extract meaning from it? One way is pseudotime analysis. In essence, this algorithm orders cells on a virtual timeline based on the similarity of their gene-expression patterns. “Cells that are alike are placed near each other along the spectrum of transcriptional changes,” explained Laura Heath of Sage Bionetworks in Seattle. Heath presented one of several pseudotime analyses currently being done in the Alzheimer’s field at the Alzheimer’s Association International Conference, held last month in San Diego, California.
The resulting diagrams look like trees. Scientists call branches healthy or diseased based on their expression of known markers. This, in turn, places each cell along the health to disease trajectory, exposing sequential gene expression patterns.
Pseudotime analysis allows scientists to turn cross-sectional data into “faux” longitudinal data to understand how cells change over time. This is important for Alzheimer’s, a disease that unfolds over the course of 30 years. Postmortem tissue offers but a snapshot of one time point, making it hard to discern when and how disease markers develop. Most brain transcriptomic data come from postmortem samples and are likewise difficult to interpret because it is hard to know if gene-expression changes are due to AD pathogenesis or organ damage associated with the end of life.
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From Alzforum by Chelsea Weidman Burke
As the mountain of single-nucleus RNA-sequencing data grows taller, how can scientists extract meaning from it? One way is pseudotime analysis. In essence, this algorithm orders cells on a virtual timeline based on the similarity of their gene-expression patterns. “Cells that are alike are placed near each other along the spectrum of transcriptional changes,” explained Laura Heath of Sage Bionetworks in Seattle. Heath presented one of several pseudotime analyses currently being done in the Alzheimer’s field at the Alzheimer’s Association International Conference, held last month in San Diego, California.
The resulting diagrams look like trees. Scientists call branches healthy or diseased based on their expression of known markers. This, in turn, places each cell along the health to disease trajectory, exposing sequential gene expression patterns.
Pseudotime analysis allows scientists to turn cross-sectional data into “faux” longitudinal data to understand how cells change over time. This is important for Alzheimer’s, a disease that unfolds over the course of 30 years. Postmortem tissue offers but a snapshot of one time point, making it hard to discern when and how disease markers develop. Most brain transcriptomic data come from postmortem samples and are likewise difficult to interpret because it is hard to know if gene-expression changes are due to AD pathogenesis or organ damage associated with the end of life.
Related Posts
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Atlas of the brain’s striatum could guide researchers to new drug treatments
Immune cells offer insights on billion-dollar virus
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
Unlocking the past – new method helps gain insights into old tissue
Novel AI model trained on RNA-Seq data accurately detects key gene mutations and predicts biomarkers across 32 cancer types
Transcriptomic aging clock reveals age-related molecular patterns in opioid dependence
RNA sequencing helps predict stem cell transplant benefit in pediatric AML
Protein ‘switch’ determines whether liposarcoma cells will become aggressive
Precursor tRNAs sense temperature changes: heat stress-induced capped pre-tRNAs suppress protein synthesis
Ketamine increases neuroplasticity in female mice but not in males
Somatic mutations linked to vascular damage in progeria
Scientists map dormant cancer cells’ hideouts, opening new targets for treatment
Soluble signals released by neighboring cells direct how the human kidney is built
Genetics influence how cancer arises – and how it evolves
RNA-based testing uncovers extraordinary diversity in mutations driving lung cancer
Study offers new insights into why ex-smokers remain at elevated risk of lung disease
Learning the grammar of gene regulation
New findings could transform new treatment for rare brain tumor astroblastoma
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