Salk Institute researchers have developed a new genomics technology to simultaneously analyze DNA, RNA and chromatin – a combination of DNA and protein – from a single cell. The method, which took five years to develop, is an important step forward for large collaborations where multiple teams are working simultaneously to classify thousands of new cell types. The new technology, published in Cell genomics on March 9, 2022, will help streamline analyses.
“This multimodal platform is going to be useful in providing a comprehensive database that can be used by groups trying to integrate their single-modal data,” said Joseph Ecker, director of Salk’s genomics analysis lab, chairman of the board. Salk International in Genetics and a researcher at the Howard Hughes Medical Institute. “This new information may also inform and guide the future classification of cell types.”
Ecker thinks this technology will be vital for large-scale efforts, such as the National Institutes of Health’s BRAIN Initiative Cellular Census Network, which he co-chairs. A major effort of the BRAIN initiative is to develop catalogs of mouse and human brain cell types. This information can then be used to better understand how the brain grows and develops, and the role that different cell types play in neurodegenerative diseases, such as Alzheimer’s disease.
Current single-cell technology works by extracting either DNA, RNA or chromatin from a cell’s nucleus, then analyzing its molecular structure for patterns. However, this method destroys the cell in the process, forcing researchers to rely on computational algorithms to analyze more than one of these components per cell or to compare results.
For the new method, called snmCAT-seq, scientists used biomarkers to label DNA, RNA and chromatin without removing them from the cell. This allowed the researchers to measure all three types of molecular information in the same cell. Scientists then used this method to identify 63 cell types in the frontal cortex region of the human brain and evaluated the effectiveness of computational methods for integrating multiple single-cell technologies. The team found that computational methods have high accuracy in characterizing broadly defined brain cell populations, but show significant ambiguity in analyzing finely defined cell types, suggesting the need to define cell types. by various measures for more accurate classification.
The technology could also be used to better understand how genes and cells interact to cause neurodegenerative diseases.
“These diseases can broadly affect many cell types. But some cell populations might be particularly vulnerable,” says co-first author Chongyuan Luo, assistant professor of human genetics at UCLA’s David Geffen School of Medicine. “Genetic research has identified regions of the genome that are relevant to diseases like Alzheimer’s disease. We provide another dimension of data and identify the cell types affected by these genomic regions.
As a next step, the team plans to use the new platform to study other areas of the brain and compare cells from healthy human brains with those from brains with Alzheimer’s disease and other neurodegenerative diseases. .
Other authors include Hanqing Liu, Bang-An Wang, Zhuzhu Zhang, Dong-Sung Lee, Jingtian Zhou, Sheng-Yong Niu, Rosa Castanon, Anna Bartlett, Angeline Rivkin, Jacinta Lucero, Joseph R. Nery, Jesse R. Dixon and Ms. Margarita Behrens of Salk; Fangming Xie, Ethan J. Armand, Wayne I. Doyle, Sebastian Preissl, and Eran A. Mukamel of the University of California, San Diego; Kimberly Siletti, Lijuan Hu and Sten Linnarsson of Karolinska Institutet in Sweden; Trygve E. Bakken, Rebecca D. Hodge, and Ed Lein of the Allen Institute for Brain Science in Seattle; Rongxin Fang, Xinxin Wang, and Bing Ren of the Ludwig Institute for Cancer Research in La Jolla, California; Tim Stuart and Rahul Satija of the New York Genome Center; and David A. Davis and Deborah C. Mash of the University of Miami.
The research was supported by the National Institutes of Health (5R21HG009274, 5R21MH112161, 5U19MH11483, R01MH125252, U01HG012079, 5T32MH020002, R01HG010634, and U01MH114812), Howard Hughes Medical Institute, and UC San Diego School of Medicine.