LifeCanvas Portraits: Kaitlyn Dorst

Kaitlyn Dorst is a Ph.D. candidate in the Ramirez lab at Boston University.

LifeCanvas (LC): What has been the focus of your dissertation work?

Kaitlyn Dorst (KD): The Ramirez lab broadly studies physical imprints of memory in the brain known as engrams – more specifically, we focus on hippocampal cell populations. In my project, I use optogenetics to manipulate the coordinated activity of a specific engram associated with a fearful memory. In order to do this, I first “tag” the engram by subjecting the mice to contextual fear conditioning with a series of mild shocks and labeling the cell populations that activate with blue-light-sensitive channelrhodopsin. This tag allows me to then use blue light to reactivate the fear memory later on and examine the subsequent behavioral effect on the mice.

I wanted to hone in on how this engram produces different behavioral phenotypes when I modify external variables, such as the animal’s environment size. Even though the mice all experience the same fear engram reactivation, they behave differently in different environment sizes. Another element of the project is using a brain-wide approach to see what is happening beyond the hippocampus during engram manipulation, and what brain regions may be interacting to produce a phenotype.

Experimental paradigm for tagging engram ensembles (cell populations) containing a fear memory (i.e. foot shocks) with blue-light sensitive channelrhodopsin-2 (ChR2). Courtesy of Kaitlyn Dorst, created with BioRender.

LC: Do mice respond differently to fear engram reactivation versus naturally recalling the fear memory?

KD: There is variability in their behavior during the initial fear conditioning. Some rodents become more fearful, while others are more resilient. However, with optogenetic manipulation, they produce a more consistent freezing phenotype within the same environment size. While naturalistic memory may engage the same populations of cells as optogenetic manipulation, it may not produce as strong of an effect – we’re really stepping on the gas pedal to hijack how these memory ensembles work.

We do have network data to analyze from LifeCanvas looking at differences between mice experiencing natural recall versus optogenetically-induced recall. These mice go through fear conditioning in certain chambers, and are placed back in those chambers 24 hours later.

c-FOS expression in mouse hippocampus.

LC: How have LifeCanvas’ tools and services helped you address your research questions?

KD: Before working with LifeCanvas, my approach consisted of looking at coronal sections with a confocal microscope. But for the second part of my project, examining brain-wide interactions upon artificial manipulation of hippocampal neurons, I really needed to take a brain-wide approach. I was very excited to send whole brains to LifeCanvas for endogenous c-FOS clearing, immunolabeling, light sheet imaging, and analysis.

"Without a whole-brain approach, we may have missed entire thalamic and hypothalamic areas that appear to act as hubs."

With the resulting data, specifically density metrics, we can perform a series of graph theory analyses to look at how interacting brain regions are functionally connected. We are working on identifying hub regions: specific nodes that are key to connecting other parts of the network. Without a whole-brain approach, we may have missed entire thalamic and hypothalamic areas that appear to act as hubs.
One key ongoing experiment consists of pairing optogenetic activation of fear engrams with chemogenetic silencing of identified hub regions. I want to see whether the fear memory will still function in a way that manifests behaviorally, or if disrupting a hub also disrupts memory retention or behavioral output.

LC: How do your analytical approaches provide insight into functional connectivity?

KD: We generate correlations across 8 animals and 147 brain areas to see how c-FOS levels are consistently or differentially altered. We then plot these correlation values on graphs, showing regions of the brain as nodes, and correlations between those regions as edges. We can then examine the structure of the graph at the mesoscale: what are the trends in all the brain regions and their connections, as well as between optogenetically-modified and control animals?

Courtesy of Kaitlyn Dorst, created with BioRender.

We can get more information on these trends by functionally segregating the data with a clustering algorithm, which essentially groups brain areas by correlation profile. These are regions that respond to reactivation similarly. Eventually, we also want to quantify differences in the topology of these networks, as well as zoom in on the contributions of individual brain areas. This can be done by running simulations to, for example, delete hub regions from the network and see how the edges potentially reshuffle.

LC: What are the translational implications of this research?

"... If we can find hub regions of the brain that are highly interconnected within these networks, we could potentially identify new therapeutic target areas."

KD: Literature shows that optogenetics can be used to bring a tagged memory back online in an Alzheimer’s mouse model, suggesting that this memory may not be erased, but is just somehow inaccessible. On the flip side, with a condition like PTSD, you could potentially quell an active memory.

Using a brain-wide approach, we can more clearly see how networks differ among these fear memory models. And if we can find hub regions of the brain that are highly interconnected within these networks, we could potentially identify new therapeutic target areas.

LC: What was one highlight of this year’s SfN conference for you?

KD: One of my favorite parts was learning about the research from Dr. Michael Brecht’s lab, where researchers essentially played hide-and-seek with rats. Besides being adorable, the study took an interesting approach to studying natural behavior across wild and domesticated animals.
I also enjoyed this workshop on culturally validated pedagogy and inclusivity in neuroscience. How do we build representation from diverse backgrounds into academia? As someone interested in potentially becoming a professor someday, it’s important to think about how I can put this into practice.

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