Understanding Neuronal Cell-Type Diversification From the Perspective of a Worm

From the outside looking in, the brain looks rather homogenous. It has folds and creases, some protruding lobes, but really only a handful of features that make it unique upon gross inspection. Taking a closer look (depending on the species) reveals hundreds to thousands, or even one hundred billion neurons that help make up the brain. Taking an even deeper dive, unfolds a richness of cell types that gives the brain an immense amount of diversity. The mechanisms governing cell-type diversity in the brain is poorly understood, but incredibly important. Understanding the genetic programs that make neurons different may help elucidate what went wrong when neurons (and brain structures) become pathologic.

Spearheading the research that investigates the molecular mechanisms governing neuronal cell-type diversity is Oliver Hobert. Professor Hobert has appointments in biological sciences and molecular biophysics at Columbia University, and has the privilege of being a Howard Hughes Medical Institute investigator. Dr. Hobert’s lab uses Caenorhabditis elegans (C. elegans) to take a “bottom-up” approach to elucidate the genetic programs responsible for cell type diversification in the brain. The “Bottom-up” approach is an attempt to define sequences of DNA (AKA the “gene battery”) that specify anatomical and functional properties of cells, and then dissects the regulatory elements governing the transcription of neighboring genes (AKA “cis-regulatory elements”).  Ultimately, C. elegans provides a genetically tractable model with well described neuroanatomy to test hypotheses regarding the development of different neuronal cell types. Hopefully, the molecular and analytical tools used in C. elegans, can be used to investigate these developmental mechanisms in other species.

Recently, the Hobert lab published a paper in Neuron titled: “Diversification of C. elegans Motor Neuron Identity via Selective Effector Gene Repressor”, which elucidates the mechanisms governing C. elegans motor neuron diversification. In the paper, they point out that C. elegans motor neurons are cholinergic and GABAergic, but can be further subdivided. For example, the cholinergic neurons can be divided into six classes based on their features (Fig 1a). In the end, they sought to uncover the mechanisms governing cholinergic motor neurons (MNs) diversification in the ventral nerve cord (VNC) of C. elegans.

Previous studies showed that 5/6 classes of MNs in the VNC shared the unc-3 transcription factor (Fig 1b). Additional studies showed that the loss of or misexpression of unc-3 lead to the loss of specific MN subtypes. Therefore, the unc-3 transcription factor was not only shared among 5/6 MNs, but also specified MN subtypes, which is a little paradoxical. How can a shared transcription factor also specify MN subtype? The Hobert lab sought to answer this question by testing two models. They hypothesized that either the unc-3 transcription factor requires class specific co-factors to activate class-specific features (co-activator model upper panels of Fig 1d) or unc-3 is capable of activating all features, including class-specific features, but is prevented from doing so via class-specific repressor proteins (repressor model lower panels of Fig 1d). If the activator model were true, loss of unc-3 would lead to loss of class-specific features. If the repressor model were true, loss of unc-3 would lead to ectopic expression of class-specific features.

To test the two models, they screened C. elegans mutants to identify alleles in which MN class-specific effector genes are either misexpressed (supporting repressor model) or lost in specific MN classes (supporting the co-activator model). An example of how the data was collected and analyzed is shown in figure 2. Using the genetic screen, the ot721 allele was found and unc-129 (an effector gene) was ectopically expressed. Figure 2a shows the expression pattern of unc-129 with GFP in both the wild-type and mutant ot721 C. elegans. In the mutant ot721 phenotype there is green protein found in VA and VB MNs, which indicates ectopic expression. Expression patterns for the mutants are shown in fig 2b. Further analysis showed that the mutant ot721 allele corresponded to a previously undescribed zinc finger transcription factor encoding gene bnc-1 (fig 2c). When bnc-1 is expressed in the mutant ot721 C. elegans, the phenotype was rescued.

In the end, they found that MN diversification was a result of class-specific repressor proteins that prevents unc-3 from activating subsets of class-specific effector genes. Furthermore, all the reported repressors are phylogenetically conserved. Therefore, the proposed mechanism for MN diversification in C. elegans may constitute a broadly applicable principle of neuronal identity diversification across species.

To learn more about the tools used to investigate the genetic programs that lead to neuronal diversification join Dr. Hobert and the rest of UCSD neurograduate program at 4pm Tuesday (05/29/18) at the CNCB Seminar Room at UCSD.


figure 1


figure 2_finalElischa Sanders is a first-year Neuroscience Ph.D. student, currently working in Eiman Azim’s lab


Combining Optogenetics and Drug Delivery: Remote-controlled dissection of neural circuits

Michael Bruchas, PhD, is an interdisciplinary scientist at Washington University in St. Louis, with departmental affiliations including Anesthesiology, Neuroscience, Psychiatry, and Biomedical Engineering. His work aims to dissect how G-protein coupled receptor (GPCR) systems function in the contexts of stress, depression, addiction, and pain. He and his collaborators were awarded 3.8 million dollars out of the 2013 White House BRAIN Initiative (https://medicine.wustl.edu/news/3-8-million-devoted-exploring-brains-circuitry-using-light/) which fostered the development of a novel wireless administration system that manipulates light and drug delivery (http://www.neurolux.org/). The techniques being innovated in his lab (http://www.bruchaslab.org/) allow for greater understanding of GPCR signaling in real time, within intact systems, and with respect to biologically relevant models of behavior.


His paper published in Cell (Jeong et. al 2015, https://www.cell.com/cell/abstract/S0092-8674(15)00828-4) in collaboration with John Rogers’s lab highlights the utility of this novel wireless technology. This technology has since been made available through his company, NeuroLux. An interview with Dr. Bruchas about this remote-controlled drug delivery system can be found here: https://youtu.be/144ELXk-Ueo


Prior to the development of this technology, the existing neural interface technologies metal cannulas connected to external drug supplies for pharmacological infusions and tethered fiber optics for optogenetics. These are not ideal for minimally invasive, untethered studies on freely behaving animals. This paper introduces wireless optofluidic neural probes that combine ultrathin, soft microfluidic drug delivery with cellular-scale inorganic light-emitting diode (μ-ILED) arrays. These probes are orders of magnitude smaller than cannulas and allow wireless, programmed spatiotemporal control of fluid delivery and photostimulation.

The figures below demonstrate the probes and implantation technique.


This is what the animals look like after implantation and recovery from this relatively minimally invasive surgery- NO WIRES!


These implants transmit signal through an antenna tuner and control box, back to any laptop with NeuroLux software.


The paper then demonstrated these devices could modify gene expression (Figure 4), deliver peptide ligands (Figure 5, reproduced below), and provide concurrent photostimulation with antagonist drug delivery (Figure 6, reproduced below) to manipulate mesoaccumbens reward-related behavior in freely moving animals. 

Figure 5. Untethered Delivery of Mu-Opioids (w/ DAMGO) into the Ventral Tegmental Area Causes Stereotypical, Repeated Rotation Behavior Picture5

Figure 6. Wireless Dopamine Receptor D1 Antagonism (w/ SCH23390) in the Nucleus Accumbens Shell (NAcSh) Blocks Photostimulation-Induced Real-Time Preference of Freely Moving Animals


Tl;dr: Michael Bruchas’s group developed neural probes with ultrathin, soft microfluidic channels coupled to μ-ILEDs. These optofluidic probes minimize tissue damage and are suitable for chronic implants, with potential for broad application in biomedicial science, engineering, and medicine. Wireless in vivo fluid delivery of viruses, peptides, and small-molecule agents is possible, and when combined wireless optogenetics, can be invaluable for neural circuit dissection

Emily Ho is a first-year Neurosciences Ph.D. student in the MSTP, currently working in Dr. Pamela Mellon’s lab.

The Genetics of Alzheimer’s Disease

Dr. Rudolph E. Tanzi is a world-renowned scientist and professor of Neurology at Harvard University. Investigating the molecular and genetic basis of neurological disease since the 1980s, he co-discovered three of the first genes that can cause early-onset familial Alzheimer’s disease, including amyloid precursor protein (APP) and presenilin. In 1993, Dr. Tanzi discovered the gene responsible for the neurological disorder known as Wilson’s disease, and over the past 25 years, he has collaborated on studies identifying several other disease genes including those causing neurofibromatosis, amyotrophic lateral sclerosis, and autism.

Dr. Tanzi has published nearly 500 research papers and has received the highest awards in his field, including the Metropolitan Life Foundation Award and Potamkin Prize. He received the 2015 Smithsonian American Ingenuity Award and was named to the 2015 list of TIME100 Most Influential People in the World. He co-authored the popular trade books “Decoding Darkness”, New York Times Bestseller, “Super Brain”, and “Super Genes” He was named by GQ magazine as a Rock Star of Science, and in his spare time, has played keyboards with the band Aerosmith, guitarist, Joe Perry, and singer, Chris Mann.
Most recently, as director of the Alzheimer’s Genome Project, Dr.

Tanzi has used mutations in the same genes he helped identify decades earlier (APP and presenilin) to create a three- dimensional human stem cell-derived neural culture system that recapitulates both AD plaque and tangle pathology (Figure). This revolutionary development made great strides to overcome the limitations of Alzheimer’s disease models to date. Mouse models with familial Alzheimer’s mutations exhibit amyloid accumulation and memory deficits, but fail to recapitulate other features of AD pathology such as neurofibrillary tangle pathology. In contrast, human neurons derived from AD patients have shown elevated levels of both toxic amyloid species and phosphorylated tau, but failed to form both amyloid plaque and neurofibrillary tangle pathology.


Using this system, Dr. Tanzi is able to study the pathogenic mechanisms of Alzheimer’s disease, and test therapeutics for AD including gamma secretase modulators and metal chaperones to lower beta-amyloid and tangle burden in the brain.

A Neural Circuit Linking Location to Surroundings

Most of the time, when you go into into a room you’ve been in before, you have no issue finding your way around. Even if you have never been in the room before, it is not too difficult to orient yourself. The door is on one side of the room, windows on the other, with some furniture placed tastefully throughout. Once you come back into the room a second time, you immediately feel a sense of relative familiarity wash over you. How did you initially learn the layout of this room? Why was your internal map specific to other rooms, without falsely instilling a sense of familiarity in your new environment?

Jeffrey C. Magee became a Professor of Neuroscience at the Baylor College of Medicine and a Howard Hughes Medical Institute investigator in 2017, after a successful decade running a lab at their Janelia Research Campus in Virginia. His research aims to discover how individual neurons and their respective microcircuits process and store information. To achieve this, he uses a wide range of methods, including a variety of optical and electrical recordings and manipulations, in both the hippocampus and the cortex.


Figure 1. CA1 neurons fire rhythmic plateaus at certain locations within an environment.

In 2015, Magee and his collaborators attempt to find how neurons in the CA1 field of the hippocampus compute information they receive from two distinct regions carrying their own distinct information in their paper “Conjunctive input processing drives feature selectivity in hippocampal CA1 neurons,” published in Nature Neuroscience. These two regions are the entorhinal cortex, which uses place cells to process location information, and the CA3 field of the hippocampus, which processes contextual information. While we do know CA1 neurons themselves associate context and location, creating maps of individual environments, we do not know how these neurons actually do it.

In these CA1 neurons, they identified a consistent series of repetitive electrical activity across multiple measures in dendrites at certain locations when a mouse ran laps on a treadmill, which they termed ‘plateaus.’ Interestingly, these plateaus occurred in a regular rhythm (known as ‘theta’) found in the hippocampus during learning and sleep. These rhythms were aligned with input from CA3, which reinforces the importance of CA3 in location-specific processing and in driving the activity of these neurons.

Screen Shot 2018-04-29 at 10.15.25 PM.png

Figure 2. Inputs from entorhinal cortex drive CA1 neuron plateau firing.

However, these CA1 neurons also receive input from the entorhinal cortex. When the entorhinal cortex was inhibited, these plateaus would be shorter and smaller than normal. On the other hand, when the entorhinal cortex was stimulated, plateaus in CA1 dendrites would become longer and larger than usual. CA3 stimulation did not do anything, though, which likely means that CA3 may prime CA1 neurons for firing, but the entorhinal cortex is needed to actually cause these neurons to fire.

Screen Shot 2018-04-29 at 10.13.02 PM

Figure 3. CA1 plateau activity induces location-specific firing in a given environment.

The logical question is: what do these plateaus do, exactly? When plateaus were seen spontaneously in silent neurons, these neurons then began to fire afterwards during each lap when at the location where the plateau occurred. It appeared that these plateaus were key to inducing new place fields for these CA1 neurons when creating new mental maps, combining contextual and location information, as expected.

To check whether these plateaus directly induced place fields, they repeatedly electrically stimulated CA1 in a plateau-like manner at the same location on the track. After these artificial plateaus, the stimulated cells did subsequently respond at the location where stimulation occurred, responding like they did when the plateaus were spontaneous. Interestingly, stimulation had to be similar to a plateau, or else the stimulated neurons would not subsequently respond, showing that plateaus set these fields, instead of activity in general.

In this experiment, Magee and his colleagues were able to show how we map specific locations mentally, matching location and context into a single computation. This occurs via CA3 neurons, which bear contextual information, preparing CA1 dendrites to receive location information from the entorhinal cortex, which creates a plateau of activity. This CA1 neuron will then fire at a specific location in that specific environment, linking the two together. This serves as a good example of how the brain biologically performs a required, fundamental computation many of us normally take for granted.


James R. Howe VI is a first-year Neurosciences Ph.D. student currently rotating in Dr. Cory Root’s lab.

Crucial Neural Circuits Underlying Memory Consolidation

Memory has fascinated human beings for a long time. The French philosopher Rene Descartes described memory as an imprint made in the brain by external experience. Nineteenth-century psychologists had divided memory into distinct steps including acquisition, storage, retrieval. In the past few decades, neuroscientists have gone deeper into the neurobiological basis of memory.

Susumu Tonegawa is the Picower Professor of Biology and Neuroscience at MIT, the director of the RIKEN-MIT Center for Neural Circuit Genetics at the Picower Institute for Learning and Memory, and HHMI Investigator. The main research interest in his lab is to decipher the molecular, cellular and neural circuit mechanisms that underlie learning and memory.

From the 1950s, studies of the famous amnesiac patient Henry Molaison revealed that the hippocampus is essential for the initial formation of episodic memories but not required for long-term memory storage and retrieval. Scientists believe long-term episodic memories are stored in the neocortex, the brain region also responsible for cognitive functions such as attention and planning. How are memories transferred from short- to long-term memory (memory consolidation)?  The standard model proposes that short-term memories are initially formed and stored in the hippocampus only, and then gradually transferred to long-term storage in the neocortex and to long-term storage in the neocortex and disappearing from the hippocampus.

In the recent Science paper from Tonegawa lab, titled ‘Engrams and circuits crucial for systems consolidation of a memory’, the researchers proposed a novel model for the memory consolidation (Figure 1).

New Model

Figure 1: A New Model for Systems Consolidation of Memory

Firstly, they used the activity-dependent cell-labeling approach to label engram cells in mice during fear conditioning — a mild electric shock delivered when the mouse is in a particular chamber, in three brain regions: the hippocampus, the prefrontal cortex, and the basolateral amygdala (Figure 2). Then, they could use light to reactivate these engram cells at different times and see if that reactivation induced a freezing behavior.  The researchers could also determine which engram cells were active when the mice were placed in the chamber where the fear conditioning occurred, using in vivo calcium imaging.

Figure 2

Figure 2: Engram cell labeling in PFC, DG and BLA with H2B-GFP is DOX-dependent.

Just one day after the fear conditioning, the researchers found that memories of the event were being stored in engram cells in both hippocampus and the prefrontal cortex. However, the engram cells in the prefrontal cortex were ‘silent’— they could stimulate freezing behavior when artificially activated by light, but they did not fire during natural memory recall.  Over the next two weeks, the silent engram cells in the prefrontal cortex gradually matured, as reflected by changes in their anatomy and physiological activity, until the cells became necessary for the animals to naturally recall the event (Figure 3).  By the end of the same period, the hippocampal engram cells became silent and were no longer needed for natural recall. However, traces of the memory remained: reactivating those cells with light still provoked the animals to freeze.  While in the basolateral amygdala, once memories were formed, the engram cells remained unchanged throughout the course of the experiment. Those cells, which are necessary to evoke the emotions linked with particular memories, communicate with engram cells in both the hippocampus and the prefrontal cortex.

Figure 3Figure 3: PFC engram cells mature with time

These findings suggest that traditional theories of consolidation may not be accurate, because memories are formed rapidly and simultaneously in the prefrontal cortex and the hippocampus during the day of training. During the consolidation, the prefrontal cortex becomes stronger and the hippocampus becomes weaker.

This study also showed that communication between the prefrontal cortex and the hippocampus is critical, because blocking the circuit connection between these two regions prevented the cortical engram cells from maturing properly. It would be interesting to investigate the mechanism underlying the prefrontal cortex engram maturation process.

Another interesting question in memory is whether and how the hippocampal neurons represent the pure temporal order of episodic events.  For the lecture at CNCB Marilyn G. Farquhar Seminar Room on Tuesday afternoon 4pm, Susumu Tonegawa is going to tell us about a hippocampal code that learns and represents the pure, discrete, temporal order of events simultaneously.  Welcome to join the lecture!

Xiaochun Cai is a first-year neuroscience Ph.D. student, currently rotating in Dr. Xin Jin lab.

Uncovering a Neural Portrait of the Human Mind

People utilize nonverbal cues to communicate and interact with their environments all the time. One of the most nonverbal cues we recognize comes in the form of faces. Seeing a face, we learn about a person’s identity, mood, age, and overall disposition. These features influence our own behavior as we can modulate our behavior to align with our observations. Many neuroscientists have pointed to the fusiform face area (FFA) as the paramount region of the brain that allows us to process and identify faces. Many fMRI studies has shown a large signal in the FFA when the subject is viewing a face than when they are viewing any other object. Those with damaged FFAs might suffer from something called prosopagnosia – the inability to identify faces. This idea lends support to the domain-specific hypothesis of facial processing – that there is a specific region of the brain that performs a specific function.

Dr. Nancy Kanwisher is a professor in the Department of Brain and Cognitive Sciences and an investigator at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology. Her lab is focused on investigating the functional organization of human brain using fMRI methods. Her lab has played a paramount role in discovering the fusiform face area (FFA), a region of the brain that responds (mostly) specifically to faces. This has revived a long-standing debate among neuroscientists on the question of how domain-specific is the architecture of the brain – is the brain organized into specific modules with distinct functions and if so how do these modules interact with one another to give rise to high level cognition? While some regions seem to perform a specific task, other brain regions are involved in nearly all tasks. Dr. Kanwisher’s lab focuses on addressing the questions that arise from this fact about the architecture of the human brain.

In her paper, “The Quest for the FFA and Where It Led”, Dr. Kanwisher outlines the approach she took to discovering the FFA and the response to its discovery from the scientific community. Before Dr. Kanwisher started this research, it was known that the FFA was activated by facial stimuli. However, it was not known if the FFA was activated specifically by faces or if was just activated by a complex visual shapes in general. As she recruited more and more subjects, she got more support for the idea that the FFA is selectively involved in face processing. She reduced her hypothesis space by considering if the FFA might be responding to any biological body part, or that it might be specialized for the processing of any visual stimulus of which an individual might gain expertise. Throughout the years, there has been a lot of discussion about the specificity of the fusiform face area. However, Dr. Kanwisher’s hypothesis that it is in fact domain-specific has remained while other hypotheses have been ruled out.

One problem that Dr. Kanwisher sees with her approach is that what we really want to examine is not the response of the region to different stimuli or the information content of the neural response, but how the neural response of the region causally influences behavior. Another is a more theoretical argument against the domain-specificity hypothesis. Why would some categories of stimuli be represented in specific regions of the brain while others seem to be represented as a distributed neural response across brain regions. Similarly, some brain regions are not innate – the “visual word form area” is specific to visually presented letter strings only in subjects who have learned how to read. These questions led researchers to focus on primate studies. The discovery of the FFA in monkeys enabled scientists to look at the underlying neural response more directly and this garnered more evidence for the domain-specificity hypothesis of face processing. Although there are still objects to this hypothesis, Dr. Kanwisher and her colleagues have paved the way forward for cognitive scientists answering important questions while also opening up new lines of investigation (as can be seen in the picture below).


This figure illustrates the advancement of our understanding of the architecture of the human brain. In the 1990s, our understanding of domain-specific processing came mostly from people with focal brain damage. More recently, fMRI studies have advanced our knowledge of the precise location of brain regions involved in specific functions.


Tim Tadros is a first year PhD candidate in the neurosciences program currently rotating in Dr. Tanya Sharpee’s lab.

Rhythmic Sampling within and between Objects despite Sustained Attention at a Cued Location

Sabine Kastner studies the neural basis of visual perception, attention, and awareness using a translational approach that combines neuroimaging in humans and monkeys, monkey electrophysiology and studies in patients with brain lesions. The goal of her researches is to better understand how large-scale networks operate during cognition, using the visual attention network as a model network. Her work aims to answer the question of how large-scale networks set up efficient communication and which neural code is used in different network nodes to drive behavior. Dr. Kastner has served as the Scientific Director of Princeton’s neuroimaging facility since 2005. Her contributions to the field of cognitive neuroscience were recognized with the Young Investigator Award from the Cognitive Neuroscience Society in 2005.

In 2013, Dr. Kastner published a paper in Journal of Current Biology titled “Rhythmic Sampling within and between Objects despite Sustained Attention at a Cued Location”. This paper investigated the relationship between space- and object-based selections, attentional mechanisms that are believed to play a role in directing the brain’s limited processing resources (previous evidence has demonstrated that preferential processing resulting from a spatial cue (i.e., space-based selection) spreads to uncued locations if those locations are part of the same object (i.e., resulting in object-based selection)). Up to the date of publication of this paper, it was unclear whether a single set of neuronal processes is engaged in these two selection mechanisms, or separable neural processes are involved.

The method used in the paper to resolve this question is to look at the temporal dynamics of visual-target detection under conditions of space-based selection and object-based selection, based on the facts that the nature of attentional deployment causes specific changes in synchronization of local field potentials within and between neural ensembles, and a relationship between the pre-stimulus phase of theta oscillations (at 7 Hz) and the likelihood of visual-target detection under conditions of space-based selection was previously reported.

The experimental design of this paper is shown in Figure 1. Participants (n = 14) maintained central fixation and reported the occurrence of a near-threshold change in contrast (i.e., a visual target) at the end of one of two bar-shaped objects. A spatial cue indicated the location where the visual target was most likely to occur (with 75% cue validity). Following the cue, a valid or invalid target was presented during a randomly sampled 300–1100 ms cue-to-target interval. The spatial cue was used both to guide the deployment of space-based selection and to reset the phase of ongoing neural oscillations, causing the timing of high- and low-excitability states to align across trials.

f1Fig 1. The Experimental Design

Figure 2A shows the time course of visual-target detection, followed by detrending in Figure 2B to more clearly reveal the periodic nature of the dependence of detection rates on the timing of target onset (cue-target interval). Figure 2C is the power spectrum of Figure 2B computed using the fast Fourier transform (FFT) algorithm. Statistical tests revealed significant peaks at approximately 8 Hz under conditions of space- (i.e., at the cued location) and object-based selection (i.e., at the same object location). This indicates a relationship between the phase of theta-band oscillations (at 7 Hz) at the time of detection and the likelihood of visual-target detection is not limited to the cued location (previously shown) but rather spreads to uncued locations that are part of the same object (i.e., share visual boundaries with the cued location). The result also suggests that attention-dependent increases in synchronization at the cued and same-object locations seem to arise from common underlying neural processes, operating at a frequency of approximately 8 Hz. FFT results also showed a consistent phase offset of approximately 90 degrees between traces of visual-target detection at the cued and same-object locations across subjects (Figure 2D), which suggests that brain regions representing different locations within the attended object are synchronized at a common frequency but have location-specific phases.f2Figure 2. Visual-Target Detection under Conditions of Both Space- and Object-Based Selection Reflects Increased Theta-Band Synchronization. Color coding: cued location (black line), same-object (orange line), and different-object (blue line).

There are certainly plenty of insights that are not even alluded to in this short blog due to word limit, to learn more about the beauty of them, please join us on Tuesday 3/20/2018 at 4pm at Marilyn G. Farquhar Seminar Room at CNCB.

Huanqiu Zhang is a first-year neuroscience Ph.D. student currently rotating in Dr. Maxim Bazhenov’s laboratory.

The origin of the human brain as told by divergent spatiotemporal gene expression patterns

The evolution of complex cognitive, emotional and motor abilities can be attributed to the arrival and growth of the mammalian neocortex. Amongst these mammals, humans believe their mental capacities are second to none. Amongst mammals, human brains are bigger, therefore, humans would obviously be the “dominant species”. Despite popular belief and innuendo… size isn’t everything. Neither brain size nor total neuron number can account for the functional differences between humans and other species. Furthermore, various genetic conditions in humans, lead to macrocephaly where patients have larger brains but significantly reduced cognitive functions. Ultimately, it’s not the size of the brain but the connections of distinct neurons. Neuronal connections are genetically determined and processed during development. Therefore, to understand what makes each human unique, and endows us with sentience, we need to elucidate the genetic programs that determine neuronal connections in the neocortex and how they differ amongst our evolutionary predecessors.

Dr. Sestan is a professor with several appointments including neuroscience, genetics, and psychiatry, and is the executive director of the “Genome Editing Center” at Yale School of Medicine. In general, Dr. Sestan’s lab is interested in understanding the genetic programs responsible for dictating neocortical neural connections during development. To aid this understanding Dr. Sestan uses the genetic tools available in mouse models, and also does genetic analysis of non-human primates. Ultimately, the lab wants to understand how the genes in specific genetic programs contribute to neuron identity, neocortical layer formation, and the regulatory mechanisms by which these programs may have evolved.

Recently in a Science paper titled “Molecular and cellular reorganization of neural circuits in the human lineage” (André M. M. Sousa et al. Science 2017;358:1027-1032), the Sestan lab performed transcriptome sequencing of 16 regions of adult human, chimpanzee, and macaque brains to better understand the molecular and cellular differences in brain organization between human and nonhuman primates. Additionally, they used single-cell transcriptomic analysis, which revealed global, regional, and cell-type–specific species expression differences in genes representing distinct functional categories. Finally, they were able to use these methods to show species-specific expression patterns of genes involved in dopamine biosynthesis and signaling, which elucidated a subpopulation of interneurons that are upregulated in humans but absent in some non-human primates.

The figures below are from 247 tissue samples that belonged to six humans, five chimps, and five macaque monkeys that resulted in an annotation set of 26,514 mRNAs, and 1,485 miRNAs. The details of each figure can be found in the legend, but the major takeaways will be discussed here.

Figure 1a shows that about 12% (3,154) of the total mRNA genes show human specific differential expression in distinct brain regions, when compared to macaque and chimpanzee expression. Figure 1b shows that 13% (202) of the miRNAs exhibit differential expression that is specific to humans.  Lastly, figure 1c is an example of how they broke down the 3,154 mRNAs that demonstrate human specific expression patterns, and analyzed what the genes are coding for, and which genes were up- or down-regulated and in which brain regions.

JC fig 1

Figure 2 (figure 3 in the paper), was generated by integrating their transcriptome data from figure 1 with single-cell RNA sequencing (RNA-seq) data generated from the human neocortex. They could now evaluate gene expression at the cellular level. Overall, the researchers found genes displaying species- and/or region-specific expression patterns also exhibited cell-type—specific expression patterns. Figure 2a and 2b are showing radar plots where each column shows a specific cell-type, and only genes expressed in the respective cell types are plotted. This allowed researchers to see how genes were expressed in cells and if they were implicated in i) neuropsychiatric disease, ii) neurotransmitter processing or trafficking, or iii) encoded ion channels. They used this information for follow-up experiments investigating the molecular profiling, developmental origin in humans, and in-vitro characterization of TH+ interneurons. TH+ interneurons are found throughout the cortex in humans and involved in the biosynthesis of dopamine.

JC fig 2

In the end, these researchers were able to show that by analyzing gene expression patterns in various brain regions of the neocortex, they could elucidate evolutionary modifications in genetic programs and neuron distribution associated with neuromodulatory systems that may underlie cognitive and behavioral differences between species.

Elischa Sanders is a first year Neuroscience Ph.D. student, currently working in Eiman Azim’s lab

Expanding on our imaging capabilities: Wide field-of-view calcium imaging between brain areas

The brain is made up of billions of neurons, cells exquisitely specialized for communication with other local neurons and with cells at quite a distance between brain areas. Calcium imaging, in which a fluorescent protein or molecule detects the calcium ions involved in synaptic transmission, has given neuroscientists the ability to visualize the activity of individual neurons in mice and other species. However, our ability to visualize the fluorescent responses of individual neurons under a microscope has long been limited to small fields of view, restricting the number of neurons and the brain areas that can be imaged simultaneously, and the correlations between these brain areas.

Dr. Spencer L. Smith is an Associate Professor in the Department of Cell Biology and Physiology and Investigator at the Carolina Institute for Developmental Disabilities at the University of North Carolina, Chapel Hill. Dr. Smith is primarily focused on population dynamics and circuit architecture in the primary and higher visual cortical areas in mice. To address the discussed limitations in calcium imaging and explore activity correlations between primary visual cortex V1 and higher visual areas during visually evoked activity in mice, Dr. Smith’s lab has also developed a novel multiphoton imaging technique for wide-field calcium imaging between cortical brain regions.

In “Wide field-of-view, multi-region, two-photon imaging of neuronal activity in the mammalian brain” (2016, Nature Biotechnology), Dr. Smith and colleagues describe the development of their Trepan2p microscope imaging system for use in ultra-wide field of view imaging and dual region imaging with offset in the XY and/or Z planes without the need to reposition the imaged specimen. To accomplish this, laser light from an 80MHz laser is split into two beams, one of which is sent through a delay path to delay the pulses by 6.25ns (or half of a period). Using custom steering mirrors and electronically tunable lenses (ETL), each light path can be individually positioned in the X, Y, and Z planes.

Scope schematic

Schematic of Trepan2p system. λ/2: half-wave plate, PBS: polarizing beam splitting cube, M: mirror, BB: beam block, BE: beam expander, ETL: electrically tunable lens, SM1/2: steering mirror, GS: galvanometer scanners, SL: scan lens, TL: tube lens, Obj: objective, DM: dichroic mirror, CL1/2: collection lens, PMT: photomultiplier tube


Dr. Smith and colleagues used the Trepan2p microscope to image cortical areas of interest for higher order visual processing in mice. Using a wide, 3.5mm field of view, the authors imaged GCaMP6s fluorescent calcium transients from individual neurons in primary visual cortex V1 and up to six higher order visual areas simultaneously using a naturalistic movie stimulus.

Ca2+ signal.png

(a) The Trepan2p system allows for wide field viewing of multiple visual cortical areas. (b) Intrinsic signal imaging was used to map out visual cortical areas (yellow). (c) 5,361 individual neurons expressing genetically encoded GCaMP6s can be individually resolved in this field of view. (d) Visually evoked calcium transients defined as increases in fluorescent signal (deltaF/F) were recorded over time during a naturalistic movie stimulus for all 5,361 neurons. (e) Calcium transient responses for the first 50 neurons.


Finally, Dr. Smith and his team used the independently controllable light paths of the Trepan2p system to image calcium transients with high temporal resolution in the V1 and higher order visual areas AM and PM in response to stimuli of visual gratings and naturalistic movie presentation. Interestingly, using the imaged calcium activity to infer action potential timing indicated an increase in correlated activity between V1 neurons and AM/PM neurons during presentation of the naturalistic movie stimulus, but not the visual grating stimulus. Overall, Dr. Smith and colleagues have engineered a new two-photon imaging system that alleviates previously restricted fields of view and allows for high temporal and cellular resolution for imaging correlated activity in multiple brain regions.

Gratings v naturalistic.png

Genetically encoded GCaMP6s calcium transients were recorded from V1 and higher visual areas AM and PM in response to naturalistic video and visual grating stimuli. Calcium transients were used to infer action potential timing. Activity correlations between pairs of cells (1 V1 neuron and 1 AM or PM neuron) increased in response to the naturalistic movie stimulus.


Please join us at 4:00 pm on Tuesday February 20th for Dr. Smith’s seminar entitled “Next generation multiphoton imaging reveals visual cortical areas acting in concert”


Susan Lubejko is a first year Neuroscience Ph.D. student, currently rotating in Dr. Jeff Isaacson’s lab

Neuronal Diversity in the Basal Ganglia Output Region

Each animal must use information about past experience, its sensory environment, and its internal motivation state to decide what action to carry out. Specialized brain circuits in the basal ganglia are fundamental to this process of “action selection” and reinforcement learning. Disruptions in the basal ganglia directly cause severe human neuropsychiatric disorders such as Parkinson’s and Huntington’s disease, and contribute to obsessive-compulsive disorder, Tourette’s syndrome, schizophrenia, as well as drug addiction. The circuitry of the basal ganglia is complex and, despite many box and arrow diagrams in textbooks, poorly understood (Figure 1).

Picture1.pngFigure1: Connectivity of the Basal Ganglia (from S. Ikemoto et al., Behavioural Brain Research, 2015).

Dr. Bernardo Sabatini is a Professor of Neurobiology at Harvard Medical School and a Howard Hughes Medical Institute Investigator, whose lab focuses on studying the synapses and circuits of the basal ganglia. His lab tries to answer questions such as how the circuitry of the basal ganglia is established and what are the dynamic interactions among nuclei of the basal ganglia and with other brain structures that mediate the selection and triggering of a motor action, utilizing a combination of viral tracing, optogenetics, imaging, single-cell sequencing and electrophysiological approaches.

In the recent Neuron paper titled “Genetically Distinct Parallel Pathways in the Entopeduncular Nucleus for Limbic and Sensorimotor Output of the Basal Ganglia”, his lab defined three neuron subtypes in the entopeduncular nucleus (EP), which release different neurotransmitters and have distinct targets.

EP is a major basal ganglia output nucleus. Most circuit-level schemes of basal ganglia organization describe the EP as a homogeneous group of neurons (Figure 1), while some studies indicate the existence of cellular heterogeneity. Anatomically, the EP lies posterior to the globus pallidus externus(GPe) and anterior to the subthalamic nucleus(STN). Immunostaining of this region indicate that there are at least two types of distinct neurons in the EP, with Somatostatin (Sst) expressed neurons in the anterior EP and Parvalbumin (Pvalb) expressed neruons in the posterior region. To further characterize their transcriptional difference, they performed single-cell mRNA sequencing (“Drop-seq”) on cell suspensions from the EP and surrounding areas, and defined two neuronal populations intrinsic to the EP (cluster 5 and 6 in figure 2D). Using differential expression analysis, they found genes enriched in cluster 5 were Kcnc3, Lypd1, Pvalb and Snc4b, whereas cluster 6 was enriched for Sst, Slc17a6(encoding VGlut2),Tbr1, Meis2 and Nrn1. These two clusters both expressed high levels of Slc32a1 (encoding Vgat), Gad1 and Gad2 (encoding the GABAergic synthetic enzymes GAD67 and GAD65). There was also a third minority EP neuron identified by RNA fluorescent in situ hybridization (FISH), which expressed Pvalb and Slc17a6 but not Gad. Based on the imaging, Drop-seq and FISH results, the EP neurons were mainly categorized into three classes: (1) Sst+/Tbr1+/Gad+/Slc17a6+ as GABA/glutamate dual-transmitter neurons, (2) Pvalb+/Gad+/Lypd1+ as purely GABAergic neurons, and (3) Pvalb+/Slc17a6+/Lypd1 as purely glutamatergic neurons.

Picture2.pngFigure 2: Single-Cell RNA Sequencing Defines Two Neuronal Populations in EP.

Next step, they genetically targeted EP subpopulations for electrophysiological characterization, anatomical tracing and functional mapping of outputs and retrograde viral tracing of inputs to show their involvement in different microcircuits.

For electrophysiological characterization, they found that Sst+ neurons had a smaller capacitance, wider action potentials and smaller after-hyperpolarization (AHP) than Pvalb+ neurons, which demonstrated that Sst+ neurons are more excitable (Figure 3).

Picture3.pngFigure 3: Differential Electrophysiological Properties of Sst+ and Pvalb+ Expressing EP Neurons

Then, to map the output of these neuron subtypes, they applied anatomical tracing by injecting Cre-dependent AAV encoding Synaptophysin-mCherry into the EP, and showed that Sst-Cre+ axons mainly projected to the lateral portion of LHb, while Pvalb-Cre+ axons projected to oval nucleus of the LHb and other brain regions such as ventro-anterior lateral thalamus (VAL), ventro-medial thalamus (VM), anteriordorsal thalamus (AD), PF, and brainstem (Figure 4). Apart from distinct outputs for these different EP neuronal subtypes, they also have different inputs and are involved in distinct microcircuits within the basal ganglia. Actually, monosynaptic retrograde tracing placed the three neuron types in one of two basal ganglia circuit. Sst+ and Pvalb+ neurons that project to LHb receive input biased toward limbic-associated regions of striatum, while Pvalb+ neurons that project to motor thalamus received input mainly from sensorimotor regions of striatum.

Picture4.pngFigure 4: Sst+ EP Neurons Target LHb, and Pvalb+ Neurons Target LHb and Motor Thalamus.

To summarize, the genetically defined three neuron subtypes in the EP has distinct electrophysiological properties, and the map of the input-output relationships of three classes of EP neurons may indicate distinct microcircuits within the general basal ganglia framework. The basal ganglia have a complex connectivity, with more circuits awaiting to be further explored.

To learn more about most recent research from Dr. Bernardo Sabatini lab, please join the seminar at 4:00pm, Tuesday at Marilyn G. Farquhar Seminar Room.

Xiaochun Cai is a first-year neuroscience Ph.D. student, currently rotating in the laboratory of Dr. Rusty Gage.