Solving the mysteries of bioscience
We are an independent nonprofit bioscience research institute aimed at unlocking the mysteries of human biology through foundational science.
Foundational Science Fuels Breakthroughs
We are leaders in large-scale research that transforms our understanding of human health and disease and shapes how science is conducted worldwide.
Inspiring Next-Generation Scientists
To us, open science extends to inspiring the next generation of scientists by supporting access to science resources, research, and experiences.
Method that captures 3 different characteristics of individual human cells at once can shed light on aging and disease
Featuring Alexandra Basford
3 min read
Researchers at the Allen Institute are on a quest to understand everything they can about the microscopic defense system coursing through our blood — our immune cells.
The human immune system touches all aspects of health and disease, as the last year’s global pandemic has all too painfully highlighted. But many characteristics of the system remain poorly understood, especially when you get down to the level of individual cells.
How many different “types” of immune cells do we have? What suite of changes do those cells undergo when faced with a foreign invader? Which molecular differences signal a healthy — or misfunctioning — immune system?
These questions are at best only partially answered by existing data. A new method developed by scientists at the Allen Institute for Immunology, a division of the Allen Institute, aims to address some of these unknowns.
The method, described in a study published in the journal eLife last month, captures three types of data simultaneously from individual human immune cells: proteins present on the cells’ surfaces, which can signal an immune cell’s specific job in the body; the set of genes switched on or expressed in each cell; and the cell’s “epigenetic” landscape, which gives clues as to how its genes are regulated.
The research team is using the technique to probe how the immune system changes or goes wrong in patients with advanced multiple myeloma, a blood cancer, in partnership with clinical researchers at Fred Hutchinson Cancer Research Center. They’re also launching a new study with the method to understand the details of immune changes as we age, comparing immune cells from healthy children and older adults in collaboration with researchers at the University of Pennsylvania.
The technique joins a growing set of methods that fall under the rubric of “multi-omics,” techniques that capture multiple, complete sets of different kinds of data. The -ome in multi-omics harkens from terms like genome and proteome, the complete set of a cell’s DNA or proteins. The researchers dubbed their technique TEA-seq, which stands for transcription (another term for gene expression), epitopes (the immune cells’ surface markers), and accessibility of the DNA (the gene regulation).
Many methods exist to capture these data from single cells separately, and the Allen Institute researchers are using some of those methods in their research.
“These separate measurements are valuable and interesting, but what we really needed was a way to connect all three,” said Lucas Graybuck, Ph.D., Senior Scientist at the Allen Institute for Immunology. Graybuck, Elliott Swanson, Methods Development Specialist at the Allen Institute for Immunology, and Peter Skene, Ph.D., Director of Molecular Biology and Biochemistry, led the eLife publication.
Running separate experiments to capture the three kinds of data means there’s no way to know which cells match up across the datasets — it takes an educated guess guided by computational analysis. With multi-omics approaches, researchers can link those data points directly, no guessing required.
Linking different kinds of changes that happen to immune cells in disease — from changes in how genes are regulated to proteins that dot the surface of immune cells — will help the scientists get a more complete picture of the immune system as a whole, in health and in disease. Their ultimate goal is to use the resulting datasets as a starting point to find better treatments for the diseases they study.
“The more we understand the complete system, the more points in that system we could potentially target for better therapies,” Skene said. — written by Rachel Tompa, Ph.D.