Three postdocs from MCB labs have been awarded fellowships that will support ambitious research projects. Kevin M. Dalton of the Hekstra Lab has received a Career Award at the Scientific Interface (CASI) from the Burroughs Wellcome Fund (BWF), Xiaotang Lu of the Lichtman Lab has received a K99/R00 Pathway to Independence Award from the NIH, and Sara Pinto dos Santos Matias of the Uchida Lab has received a Young Investigator Grant from the Brain & Behavior Research Foundation (BBRF).
More details on these postdocs and their research projects are below.
Kevin M. Dalton (Hekstra Lab)
Kevin Dalton is a structural biologist who has been part of the Hekstra Lab since its founding in 2017. “It has been a wild ride watching the Hekstra Lab grow from a room full of unpacked boxes into a medium-sized research group,” Dalton says.
Dalton has been chosen to receive a Career Award at the Scientific Interface (CASI) from the Burroughs Wellcome Fund (BWF). These awards provide up to $500,000 in funding over five years to enable the transition from advanced postdoctoral work to establishing a new lab as a primary investigator. “I am grateful to the Burroughs Wellcome Fund for supporting scientists like myself who strive to bring tools from other fields into biology,” Dalton says.
In his lab, Dalton plans to build on the expertise he has built working with x-ray data on proteins in the Hekstra Lab. “I have always found it amazing that we’re made out of the same elements as the inanimate matter in the universe,” he says. “That’s what initially inspired me to become a structural biologist. My career award will fund me as I build a research group focused on using machine learning to deepen the insights we can draw from structural biology experiments.”
Dalton’s long-term goal is to routinely record atomic-resolution movies of proteins in action. “I spent my postdoc trying to make a movie of a protein crystal using fast X-ray pulses,” he explains. “In the course of my work, I realized that these sorts of studies are often limited by the data processing algorithms as much as by the experimental apparatus and sample quality.” The CASI funding will enable Dalton to design new algorithms for time-resolved microscope data.
“In my next role, I aim to democratize time-resolved structural biology using concepts from modern machine learning so that more researchers can make clearer movies of more challenging experimental systems,” he adds.
Reflecting on his time as a postdoc at Harvard, Dalton says that joining a lab as it was getting off the ground has paid off. “It’s conventional wisdom that you shouldn’t join a young lab for your postdoc,” he says. “However, my experience has been that it provides useful opportunities to hone your mentoring skills. I feel much more prepared to start a group having worked closely with Doeke while he built his lab.”
Xiaotang Lu (Lichtman Lab)
“Connectomics is a nascent field of neuroscience that attempts to unravel the complexity of the brain by describing all its cellular constituents and their interconnections at synapses using serial section electron microscopy,” Lu says. “There are many technical obstacles that have slowed the progress of connectomics, such as staining a whole mouse brain with the heavy metals required for electron microscopic imaging or extracting molecular information from the same brain volume. To overcome those challenges often requires knowledge from chemistry and materials science.”
Lu’s work in the Lichtman Lab focuses on solving such problems and refining connectomics techniques. She was recently awarded a K99 grant from the National Institute of Neurological Disorders and Stroke (NINDS) and the BRAIN Initiative in support of this work. K99/R00s, also known as “Pathway to Independence Awards” enable postdocs to transition to becoming PIs by providing up to $90,000 per year during the postdoctoral phase and up to $249,000 funding per year during the primary investigator, or R00, phase of the award.
“I am very excited and thankful for receiving this BRAIN Initiative fellowship,” Lu says. “Personally, I feel this award gives me a lot of confidence in continuing the interdisciplinary research.”
“The fellowship will allow me to keep on solving bottleneck methodological problems for connectomic studies and hopefully also establish new paradigms for multimodal brain imaging,” she says. Specifically, Lu will develop a new staining technique for preparing large brain tissue samples (such as whole mouse brains) for electron microscopy and connectomic analysis and generate a library of nanobodies and miniaturized affinity binders that can label cells without disrupting the tissue structure. She also plans to use X-ray microscopes for multiplexed brain imaging.
“I want to thank my advisor Jeff Lichtman for his mentorship throughout my postdoc training and his support for my future scientific career,” she adds. “I also want to thank my collaborators. It would not be possible to carry out my research projects without their help.”
Sara Pinto dos Santos Matias (Uchida Lab)
of the Uchida Lab is studying how dopaminergic neurons in the brain encode possible future rewards. Understanding the brain’s reward circuits will likely lead to new insights into and treatments for mental health issues.
She will pursue this research under the auspices of a Young Investigator Grant from the Brain & Behavior Research Foundation (BBRF). The foundation’s mission is to further investigations into the underpinnings of mental health, and the Young Investigator program enables early career scientists, including postdoctoral fellows and new primary investigators, to collect pilot data.
“I am very happy to receive this fellowship!” Pinto dos Santos Matias says. “Until this point I have mostly implemented and used new techniques to record dopamine activity, so it is great that this effort has brought in funding to analyze my data under new theoretical frameworks.”
Pinto dos Santos Matias will use the grant funding to investigate how dopamine neurons represent reward distributions. “I am interested in this question because the activity of dopamine neurons has been traditionally explained as a reward prediction error used to update the representation of the mean expected reward that might be obtained in a particular context,” she says. “However, animals live in dynamic and probabilistic environments. Thus, having a representation of the full distribution of rewards available might be important for behavioral adaptation and accurate decision-making. I will use a new set of theoretical frameworks, including Distributional Reinforcement Learning, to investigate how distributions are represented in the dopamine system.”
She will be analyzing data she recorded from dopamine neurons when mice were presented with different reward distributions. “I am particularly looking forward to understanding how this distribution representation is learnt, and implemented in the brain, and if it can be manipulated to make subjects more pessimistic or more optimistic towards the future,” Matias says.
Matias adds, “I would like to thank my supervisor, Nao Uchida for his mentoring, and all of the lab members for support and useful discussions.”