From Alzforum by Chelsea Weidman Burke
As some scientists create huge, new, single-nucleus RNA-sequencing datasets, others devise ways to better use existing ones. At the Alzheimer’s Association International Conference held last month in San Diego, California, Evan Macosko of the Broad Institute of Harvard and MIT in Cambridge, Massachusetts, presented his work combining small databanks into a large one. Akin to doing a meta-analysis, this approach boosts statistical power, allowing Macosko to test if transcriptome changes tied to AD progression are consistent across cohorts.
Macosko and colleagues first turned to a unique cohort by Ville Leinonen, Kuopio University Hospital, Finland. Leinonen and colleagues have collected prefrontal cortex biopsies taken from 700 adults with normal-pressure hydrocephalus, a condition where excess cerebrospinal fluid swells the brain ventricles. It is treated by placing a drainage shunt into the ventricles, and this procedure allows scientists to take a tissue sample during surgery. The tissue provides a snapshot into the living brain without the peri- or postmortem artifacts that can compromise RNA stability and skew transcriptional signatures….

Reshuffle by Cell Type. Algorithms align cell clusters from different datasets (colors) to generate one cluster for each cell type (symbols). [Courtesy of Evan Macosko, Broad Institute.]
From Alzforum by Chelsea Weidman Burke
As some scientists create huge, new, single-nucleus RNA-sequencing datasets, others devise ways to better use existing ones. At the Alzheimer’s Association International Conference held last month in San Diego, California, Evan Macosko of the Broad Institute of Harvard and MIT in Cambridge, Massachusetts, presented his work combining small databanks into a large one. Akin to doing a meta-analysis, this approach boosts statistical power, allowing Macosko to test if transcriptome changes tied to AD progression are consistent across cohorts.
Macosko and colleagues first turned to a unique cohort by Ville Leinonen, Kuopio University Hospital, Finland. Leinonen and colleagues have collected prefrontal cortex biopsies taken from 700 adults with normal-pressure hydrocephalus, a condition where excess cerebrospinal fluid swells the brain ventricles. It is treated by placing a drainage shunt into the ventricles, and this procedure allows scientists to take a tissue sample during surgery. The tissue provides a snapshot into the living brain without the peri- or postmortem artifacts that can compromise RNA stability and skew transcriptional signatures….
Reshuffle by Cell Type. Algorithms align cell clusters from different datasets (colors) to generate one cluster for each cell type (symbols). [Courtesy of Evan Macosko, Broad Institute.]
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From Alzforum by Chelsea Weidman Burke
As some scientists create huge, new, single-nucleus RNA-sequencing datasets, others devise ways to better use existing ones. At the Alzheimer’s Association International Conference held last month in San Diego, California, Evan Macosko of the Broad Institute of Harvard and MIT in Cambridge, Massachusetts, presented his work combining small databanks into a large one. Akin to doing a meta-analysis, this approach boosts statistical power, allowing Macosko to test if transcriptome changes tied to AD progression are consistent across cohorts.
Macosko and colleagues first turned to a unique cohort by Ville Leinonen, Kuopio University Hospital, Finland. Leinonen and colleagues have collected prefrontal cortex biopsies taken from 700 adults with normal-pressure hydrocephalus, a condition where excess cerebrospinal fluid swells the brain ventricles. It is treated by placing a drainage shunt into the ventricles, and this procedure allows scientists to take a tissue sample during surgery. The tissue provides a snapshot into the living brain without the peri- or postmortem artifacts that can compromise RNA stability and skew transcriptional signatures….
Reshuffle by Cell Type. Algorithms align cell clusters from different datasets (colors) to generate one cluster for each cell type (symbols). [Courtesy of Evan Macosko, Broad Institute.]
Related Posts
Avoiding a sticky situation: how cells stop messenger RNAs from clumping together
RNA Sequencing identifies new tick-borne virus that causes flu-like illness
Worm’s radical transformation shows metamorphosis can change the functions of cells
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
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