Summary: A new computational tool developed by researchers revealed evidence of a gene directly attributed to Alzheimer's disease in the loss of neurons – making neurons, helping to solve decades – a long mystery in dementia research. The algorithm, called sympathysimilar to genetic data on individual cell types, previous methods also highlight that specific brain cells – not just immune cells – are affected in alzheimer's.
By combining large-scale genetic and cellular data, the tool provides a clear picture of how genetic risk translates into risk. The researchers say the process could revolutionize the way scientists find disease-affected cells in every aspect of neurological and metabolic disorders.
Key facts:
- Novel algorithm: This page sympathy The tool combines genetic and cellular data to map which type of cell types Drive.
- Featured Link: He found genetic evidence that links Alzheimer's risk to memory-related neurons rather than immune cells.
- Wide usage: The method can be applied to other complex diseases such as Parkinson's, helping in early detection and drug guidance.
Source: Rice University
The number of people living with dementia worldwide is estimated to be 57 million by 2021 with approximately 10 million new cases recorded each year. In the US, dementia affects six million lives, and the number of new cases is expected to double in the next few decades, according to the 2025 study.
Despite advances in the field, a full understanding of disease-causing mechanisms is still lacking.
To address this gap, researchers at Boston University have developed a sequencing tool that can help identify which specific types of cells in the body are genetically linked to diseases such as Alzheimer's and Parkinson's.
Information known as the “single-cell study integration system for writing cellular elements,” or seismic, the tool helped the team to connect Alzheimer's cells to Alzheimer's cells memory cells to link them to the organization through the genetic connection between the disease and these nerve cells. The algorithm that heals existing tools to identify cell types suitable for complex diseases and is effective for disease states beyond dementia.
Research, published internally Natural Communicationhelps uncover a long-standing contradiction in Alzheimer's research: While traces of genes in patients' DNA point to infection-fighting cells in the brain, the patients' brains are actually a different story.
“As we age, some brain cells naturally shrink, but in Dementia ⎯ a disease of memory loss
“The fact that memory cells do the dying and not the brain cells that fight infection raises this puzzling puzzle where the DNA evidence and the brain evidence don't match.”
The group's investigation used computational methods to analyze existing genetic data in a new way. Their approach combines two types of large-scale environmental data at the molecular level.
Previous attempts to draw related insights from these types of data were difficult to measure and describe and produced strong sub-organizations due to two major weaknesses:
First, according to Scran-SEQ, the resolution of the cell type can be very broad and very high, very bad details such as the region of the brain where the cells are located; Second, according to GWAS, the genetic signal in large studies based on clinical trials tend to emphasize the inclusion of other cell types that are more affected, ie cells related to the protection of diseases.
“We developed our own seismic algorithm to analyze genetic information and fine-tune it to specific types of brain cells,” Lai said. “This allows us to create a more detailed picture of which cell types are affected by these genetic programs.”
The researchers tested the algorithm and found that it performed better than existing tools, identifying the most relevant disease signals.
“We think this work can help reconcile conflicting patterns in data related to Alzheimer's research,” said Vicky Yao, assistant professor of computer science and a member of the Ken Kennedy Institute at the Ren Kennedy Institute in Rice.
“Furthermore, the method is likely to be valuable more broadly to help us better understand which cell types are appropriate for different complex diseases.”
The research comes amid a renewed push across the country to advance brain health and brains through new public investment programs. Earlier this year, the Texas Legislature established the Dementia Virevention and Institute of Texas (DRIT) through Senate Bill 5, a BiPartisan measure designed to accelerate innovation in dementia prevention, treatment and care.
This NOVEMBER, TITLE 14 will appear on the national vote to fund $3 billion over the next decade, creating the nation's largest funded dementia research program. Confused behind the Center for Cancer Prevention and Research of Texas (CPrit), Drit aims to make Texas a global leader in the research of brain health and Neurodegenerative Chishes.
“We are at a time when advances in computing and data science are increasingly changing the way we study human disease,” said Yao, a CPrit student. “Now we have to maintain this momentum.”
Funding: This research is supported by the National Institutes of Health (R21AG54564, R21AG08564, R21AG085464), CPRIT (RR190065), the Alzheimer's Foundation and the Karen Toffler Charrable Trust. The contents of this press release are the work of the authors and do not represent the official views of organizations and financial institutions.
Important Questions Answered:
A: Scientists have developed a new computational tool, called sympathythose indicators are special cel cel cel elements that are genetically linked to complex diseases like Alzheimer's.
A: The tool reconciles a long-standing controversy in Alzheimer's research by linking genetic evidence to actual neurons in memory formation, rather than immune cells thought to be key events.
A: Sympathy It combines genetic data (GWAS) with single RNA sequencing to reveal how disease-related genetic changes affect individual cell types in different brain regions.
A: The method could revolutionize the way researchers identify disease-causing cells in conditions such as Alzheimer's and Parkinson's, paving the way for preventative and preventive treatment strategies.
About this course AI, genetics, and Alzheimer's Disease Research News
Author: Silvia cernea Cark
Source: Rice University
Contact: Silvia cernea Cark – Rice University
Image: This photo is posted in Neuroscience News
Actual research: Open access.
“Collapsed associations between complex features and cell types in seismic” by Qiliang Lai et al. Natural Communication
-Catshangwa
Confounding associations between complex signals and cell types in Seismic
Combining single-cell RNA sequencing with a Genome-variation Association Study (GWAS) can find cellular variants involved in complex traits and diseases. However, current methods often lack power, interpretation and robustness.
Introducing sympathya framework that combines a novel Score to capture the signal of communication and the evolution of cellular types and to introduce influential gene analysis, a method to identify the genes that drive each type of lobo-trait association.
Over 1000 Cell-Type Conversions in different granularities and 28 polygenic traits, sympathy Ensures known entities and appropriate cell groups are not included in other methods.
In Parkinson's and Alzheimer's, sympathy It reveals both cell- and brain-specific differences in pathology.
Analysis of pathology-based alzheimer's zwas with sympathy It enables the identification of vulnerable neurons and the molecular mechanisms involved in their neurodegeneration.
Usually, sympathy It is an efficient, powerful, and descriptive approach that describes the relationship between polygenic traits and species-specific expression, providing new insights into disease phenomena.
