“At the organ or body scale, there are many changes that occur over hours to weeks and cannot be tracked over time,” says Edward Boyden, the Y. Eva Tan Professor of Neurotechnology and Professor of Biological Engineering, Brain and Cognition. PhD at MIT, an investigator at the Howard Hughes Medical Institute, and a member of MIT’s McGovern Institute for Brain Research and the Koch Institute for Integrative Cancer Research.
If the technique could be scaled up to work over longer periods of time, it could also be used to study processes such as aging and disease development, the researchers say.
Advertising
Boyden is lead author of the study, which appears today in Nature Biotechnology. The lead author of the paper is Changyang Linghu, a former J. Douglas Tan postdoctoral fellow at the McGovern Institute who is now an assistant professor at the University of Michigan.
Cell History
Biological systems, such as organs, are made up of many different types of cells, all with different functions. One way to study these functions is to describe the proteins, RNA, or other molecules that tell cells what to do. However, most methods for doing this only provide a single snapshot or do not work well with very large cell populations.
“Biological systems are often made up of large numbers of different types of cells. For example, the human brain has 86 billion cells,” says Linghu. “To understand these kinds of biological systems, we need to observe physiological events over time in these large cell populations.”
To achieve this, the research team came up with the idea of recording cellular events as a series of protein subunits that are continuously added to the chain. To create their chains, the researchers used engineered protein subunits that can self-assemble into long filaments not normally found in living cells.
The researchers developed a genetically encoded system in which one of these subunits is continuously produced inside the cells, while the other is produced only when a certain event occurs. Each subunit also contains a very short peptide called an epitope tag—in this case, the researchers chose tags called HA and V5. Each of these tags can be bound to a different fluorescent antibody, making it easier to visualize the tags later and determine the sequence of the protein subunits.
For this study, the researchers produced a contingent of V5-containing subunits based on the activation of a gene called c-fos, which is involved in encoding new memories. HA-tagged subunits make up most of the chain, but when a V5 label appears on the chain, it means that c-fos is activated during that time.
“We hope to use this type of protein self-assembly to record activity in each cell,” says Linghu. “It’s not just a snapshot of time, but a record of past history, just like how tree rings can hold information permanently over time as a tree grows.”
In this study, the researchers first used their system to record c-fos activation in neurons grown in a lab dish. The c-fos gene was activated by chemical activation of neurons, which led to the addition of the V5 subunit to the protein chain.
To test whether this approach could work in animal brains, the researchers programmed the brain cells of mice to produce chains of proteins that the animals would detect when exposed to a particular drug. The researchers were then able to detect that exposure by preserving the tissue and analyzing it with a light microscope.
The researchers designed their system to be modular so that different epitope tags can be changed or different types of cellular events can be detected, including, in principle, cell division or the activation of enzymes called protein kinases that help control many cellular pathways. .
The researchers also hope to extend the recording time they can obtain. In this study, they recorded events for several days before imaging the tissue. Since the length of the protein chain is limited by the size of the cell, there is a trade-off between the amount of time that can be recorded and the time resolution or frequency of recording the event.
“The total amount of information it can store is fixed, but we can in principle slow down or increase the growth rate of the chain,” says Linghu. “If we want to record for a longer time, we can slow down the synthesis so that it reaches the size of the cell, say two weeks. That way we can record for longer but with less time resolution.”
The researchers are also working on engineering the system so that it can record multiple types of events in the same chain by increasing the number of different sub-units that can be included.
Source: Eurekalert
