News

Subcellular transcriptomes and proteomes of developing axon projections in the cerebral cortex

January 9, 2019

Poulopoulos A, Murphy AJ, Ozkan A, Davis P, Hatch J, Kirchner R, Macklis JD

The development of neural circuits relies on axon projections establishing diverse, yet well-defined, connections between areas of the nervous system. Each projection is formed by growth cones-subcellular specializations at the tips of growing axons, encompassing sets of molecules that control projection-specific growth, guidance, and target selection1. To investigate the set of molecules within native growth cones that form specific connections, here we developed growth cone sorting and subcellular RNA-proteome mapping, an approach that identifies and quantifies local transcriptomes and proteomes from labelled growth cones of single projections in vivo. Using this approach on the developing callosal projection of the mouse cerebral cortex, we mapped molecular enrichments in trans-hemispheric growth cones relative to their parent cell bodies, producing paired subcellular proteomes and transcriptomes from single neuron subtypes directly from the brain. These data provide generalizable proof-of-principle for this approach, and reveal molecular specializations of the growth cone, including accumulations of the growth-regulating kinase mTOR2, together with mRNAs that contain mTOR-dependent motifs3,4. These findings illuminate the relationships between subcellular distributions of RNA and protein in developing projection neurons, and provide a systems-level approach for the discovery of subtype- and stage-specific molecular substrates of circuit wiring, miswiring, and the potential for regeneration.

Nature

Computational Neuroscience course offered this semester

January 7, 2019

Computational Neuroscience
Prof. Haim Sompolinsky, Hebrew University/Harvard

HARVARD GSAS: MCB 131 / NEURO 131
(cross-listed in Physics and SEAS)
canvas.harvard.edu/courses/49249
Mondays and Wednesdays, 3PM-4:15PM in Northwest B104

Questions? Email: Haozhe Shan: hshan [at] g [dot] harvard [dot] edu
Nimrod Shaham: nshaham [at] fas [dot] harvard [dot] edu
Haim Sompolinsky: haim [at] fiz [dot] huji [dot] ac [dot] il

Description: Follows trends in modern brain theory, focusing on local neuronal circuits
and deep architectures. Explores the relation between network structure, dynamics, and
function. Introduces tools from information theory, dynamical systems, statistics, and
learning theory in the study of experience-dependent neural computation. Specific topics
include: computational principles of early sensory systems; unsupervised, supervised and
reinforcement learning; attractor computation and memory in recurrent cortical circuits;
noise, chaos, and coding in neuronal systems; learning and computation in deep networks
in the brain and in AI systems.

Prerequisite: Basic knowledge of multivariate calculus, differential equations, linear
algebra, and elementary probability theory. This course is aimed at graduate students and
advanced undergraduates.

Joshua Sanes Recieves Perl-UNC Neuroscience Prize

December 19, 2018

The award, which recognizes seminal discoveries in neuroscience, will be presented to Sanes for his work on cell-surface proteins that control circuit assembly in the visual system.

Sanes’ work has shed important new light on how the neural circuits in the retina work by marking circuits transgenically, mapping their connections and seeking recognition molecules that mediate their connectivity. Researchers in Sanes’ lab have also used genetic methods to manipulate those molecules, and assess the structural and functional consequences of removing or swapping them.

Sanes will share the prize with UCLA Biological Chemistry Professor Larry Zipursky. The two have worked on similar problems but in different systems – Sanes using mice and Zipursky using fruit flies.

“It’s a real thrill to join the incredible luminaries who have been honored by the Perl-UNC Prize over the years, and a special treat to win it with my friend, colleague, and collaborator, Larry Zipursky,” Sanes said. “I view the prize as recognizing the community of basic neuroscience researchers who use a variety of model systems to learn how the brain gets made. It’s a good time to be doing this, as evidence mounts that defects in the assembly of this ridiculously complex structure may underlie most psychiatric and many neurological disorders.”

Sanes and colleagues are also using the retina to address the thorny problem of neuronal classification, using high-throughput single cell RNA sequencing to profile tens of thousands of retinal cells, then apply bioinformatics methods to categorize them.

Established in 2000 and named for former UNC professor Edward Perl, who discovered that a specific type of sensory neuron responded to painful stimuli (nociceptor) and was the first president of the Society for Neuroscience, the prize is becoming increasingly well known among biomedical scientists, as six of its previous winners have gone on to win Nobel Prizes in Physiology or Medicine or in Chemistry. Three other recipients have also gone on to win the prestigious Kavli Prize.

Swartz Program at Harvard University has an opening for a postdoctoral fellow

December 13, 2018

The Swartz Program at Harvard University has an opening for a postdoctoral fellow in theoretical and computational neuroscience.

Based on a grant from the Swartz Foundation, Harvard University established the Swartz Program in Theoretical Neuroscience. The Swartz Program supports a program in computational neuroscience, to foster research collaborations between theorists and experimentalists.

Fellowships last for two years, and cover a stipend and support for travel to the annual Swartz meeting on computational neuroscience. Postdocs join a vibrant group of experimental and theoretical neuroscientists at Harvard's Center for Brain Science. Harvard's Swartz Program is led by its Director, Haim Sompolinsky.

The Center for Brain Science includes junior and senior faculty doing research on a wide variety of topics, including neural mechanisms of rodent learning, decision-making, and sex-specific and social behaviors; human motor control; behavioral and fMRI studies of human cognition; circuit mechanisms of learning and behavior in worms, larval flies, and larval zebrafish; circuit mechanisms of individual differences in flies and humans; rodent and fly olfaction; inhibitory circuit development; retinal circuits; and large-scale reconstruction of detailed brain circuitry.

Interested applicants should send a CV, statement of research interests, and arrange for three letters of reference to be sent to Haim Sompolinsky (haim [at] fiz [dot] huji [dot] ac [dot] il), Kenneth Blum (kenneth_blum [at] harvard [dot] edu), or our newest faculty member, Cengiz Pehlevan (cpehlevan [at] seas [dot] harvard [dot] edu; https://pehlevan.seas.harvard.edu/). Applications should have “Swartz Fellowship” in the subject line.

Harvard University is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions, or any other characteristic protected by law.

Brain-wide Organization of Neuronal Activity and Convergent Sensorimotor Transformations in Larval Zebrafish

November 21, 2018

Chen X, Mu Y, Hu Y, Kuan AT, Nikitchenko M, Randlett O, Chen AB, Gavornik JP, Sompolinsky H, Engert F, Ahrens MB

Simultaneous recordings of large populations of neurons in behaving animals allow detailed observation of high-dimensional, complex brain activity. However, experimental approaches often focus on singular behavioral paradigms or brain areas. Here, we recorded whole-brain neuronal activity of larval zebrafish presented with a battery of visual stimuli while recording fictive motor output. We identified neurons tuned to each stimulus type and motor output and discovered groups of neurons in the anterior hindbrain that respond to different stimuli eliciting similar behavioral responses. These convergent sensorimotor representations were only weakly correlated to instantaneous motor activity, suggesting that they critically inform, but do not directly generate, behavioral choices. To catalog brain-wide activity beyond explicit sensorimotor processing, we developed an unsupervised clustering technique that organizes neurons into functional groups. These analyses enabled a broad overview of the functional organization of the brain and revealed numerous brain nuclei whose neurons exhibit concerted activity patterns.

Neuron