A cell that reveals its physiological status at any given instant is one thing. A cell that displays its physiological history is another. The information it shares has more context, and not just in the temporal sense. Large numbers of cells that record (and reveal) their histories simultaneously could accomplish a tissue-level version of Joe Gould’s Oral History, which was inspired by William Butler Yeats, who wrote, “The history of a nation is not in parliaments and battlefields, but in what the people say to each other on fair days and high days, and in how they farm, and quarrel, and go on pilgrimage.”
Gould’s literary project was ill-fated, but a somewhat analogous cell programming project by MIT scientists looks very promising. These scientists, led by synthetic neurobiologist Edward S. Boyden, PhD, are coaxing cells to inscribe the history of their everyday functions in a long protein chain that can be imaged using a light microscope. These functions include the activation of genes and cellular pathways.
“There are a lot of changes that happen at organ or body scale, over hours to weeks, which cannot be tracked over time,” said Boyden, who is the Y. Eva Tan professor in neurotechnology at MIT and a member of MIT’s McGovern Institute for Brain Research and Koch Institute for Integrative Cancer Research. It is this kind of tracking that the MIT scientists hope to accomplish with their reprogramming technique.
The technique could be used to reveal the steps that underlie processes such as memory formation, response to drug treatment, and gene expression. If the technique could be made to work over sufficiently long periods of time, it could even shed light on processes such as aging and disease progression.
Details about the technique appeared in Nature Biotechnology, in a paper titled, “Recording of cellular physiological histories along optically readable self-assembling protein chains.” (The paper’s senior author is Boyden. The lead author is Changyang Linghu, PhD, a former J. Douglas Tan postdoctoral fellow at the McGovern Institute who is now an assistant professor at the University of Michigan.)
“Here we describe expression recording islands—a fully genetically encoded approach that enables both continual digital recording of biological information within cells and subsequent high-throughput readout in fixed cells,” the article’s authors wrote. “The information is stored in growing intracellular protein chains made of self-assembling subunits, human-designed filament-forming proteins bearing different epitope tags that each correspond to a different cellular state or function (for example, gene expression downstream of neural activity or pharmacological exposure), allowing the physiological history to be read out along the ordered subunits of protein chains with conventional optical microscopy.”
The “recording islands” represent a new way to study biological systems such as organs. These systems contain many different kinds of cells, all of which have distinctive functions. To study these systems, scientists typically resort to methods for imaging proteins, RNA, or other molecules inside the cells. These methods can provide hints as to what the cells are doing. However, most of these methods offer only a glimpse of a single moment in time, or don’t work well with very large populations of cells.
“Biological systems are often composed of a large number of different types of cells. For example, the human brain has 86 billion cells,” Linghu pointed out. “To understand those kinds of biological systems, we need to observe physiological events over time in these large cell populations.”
In the current study, the researchers chose epitope tags called HA and V5. Each of these tags can bind to a different fluorescent antibody, making it easy to visualize the tags later on and determine the sequence of the protein subunits.
Production of the V5-containing subunit is contingent 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 whenever the V5 tag shows up in the chain, that means that c-fos was activated during that time.
“We’re hoping to use this kind of protein self-assembly to record activity in every single cell,” Linghu remarked. “It’s not only a snapshot in time, but also records past history, just like how tree rings can permanently store information over time as the wood grows.”
The researchers used their system to record activation of c-fos in neurons growing in a lab dish. The c-fos gene was activated by chemically induced activation of the neurons, which caused the V5 subunit to be added to the protein chain.
To explore whether this approach could work in the brains of animals, the researchers programmed brain cells of mice to generate protein chains that would reveal when the animals were exposed to a particular drug. Later, the researchers were 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 swapped in, or different types of cellular events can be detected, including, in principle, cell division or activation of enzymes called protein kinases, which help control many cellular pathways.
The researchers also hope to extend the recording period that they can achieve. In this study, they recorded events for several days before imaging the tissue. There is a tradeoff between the amount of time that can be recorded and the time resolution, or frequency of event recording, because the length of the protein chain is limited by the size of the cell.
“The total amount of information it could store is fixed, but we could in principle slow down or increase the speed of the growth of the chain,” Linghu asserted. “If we want to record for a longer time, we could slow down the synthesis so that it will reach the size of the cell within, let’s say two weeks. In that way, we could record 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 subunits that can be incorporated.