Glaucoma is a neurodegenerative disease that causes loss of retinal ganglion cells in the retina, resulting in incurable blindness. While current research efforts have largely focused on regenerating RGCs and other retinal neurons, it has been recently shown that inflammation resulting from immune responses to injury/degeneration can negatively impact retinal regeneration. Microglia are part of the innate immune system and are the only resident immune cell in the retina and brain; they are involved in inflammatory signaling, phagocytosis, and monitoring tissue for foreign pathogens. Previously, our lab has shown that microglia restrict regenerative capacity; microglia ablation improves neuronal replacement strategies in a mouse model. Here, we want to determine if we can develop a new method to ablate microglia from the retina via intraperitoneal (IP) injections, and observe if ablation affects invasion of the peripheral immune system (monocytes) during the inflammatory response post injury. To comprehensively ablate microglia from the retina, we test both dosage and treatment length (days) of IP injections of PLX5622, a drug which inhibits a receptor necessary for microglia survival. After specific treatment courses, mouse retinas are harvested and immunostained for microglia markers to determine the level of ablation. To determine the interaction between microglia presence and monocyte invasion in a glaucoma model, we use a CCR2-RFP mouse line to track monocytes. These mice will be injected intraocularly with NMDA, a toxin that kills RGCs. The number of invading monocytes at each time point post-NMDA-injury can be assessed with and without PLX5622 treatment to see if ablation of microglia impacts the invasion of monocytes. We expect monocyte recruitment to drastically decrease after microglia ablation. This work will allow us to investigate the role of microglia in developmental processes and identify immune targets that would be most amenable to immunomodulation interventions in future therapies for glaucoma.
 

Despite the brain being a 3D structure with a complex topography and spatial relationships, neuroscientists currently rely on 2D visualizations. These less informative visualizations obscure the distances between 3D regions, and hinder scientists’ ability to perceive functional correlations and anatomical connections. To provide a more decipherable method of exploring the structure and function of the brain, we built neuroscience tools specifically aimed toward exploratory 3D data visualization. I worked on the development of a Universal Renderer for Neuroscience (Urchin) that lets users plot their data in its original 3D anatomical context. Urchin can perform a variety of different functions such as displaying certain features (e.g. neurons, brain regions, or contextual objects such as probes), or interactively exploring the data within context of brain location via mouse and keyboard navigation. This not only paves the way for new methods of data analysis but also creates a deeper understanding of the structure and patterns found within the data. I worked on building Urchin within the Unity platform, implementing features to enhance data exploration and analysis via scripting in C. Some examples of functionality that I built include implementing 3D mesh rendering for brain regions, primitive models, and changing materials. I also established a proxy server that allows for secure communication between client side browser applications and python notebooks. Along with this, I developed a more intuitive and efficient python API that allows people with minimal coding experience to run the renderer visualizations with ease. Urchin enhances neuroscience research and education by providing a more interactive and immersive experience, allowing students and the public to directly engage with diverse data sets and investigate different aspects and features of the brain.

Using simple behavioral analysis (SimBA) and Deep Lab Cut (DLC), we can create predictive behavior classifiers using pose estimation (PE) data obtained through DLC. PE is a computerized technique to track and predict the location of mice by training the video dataset with labeled frames using specific regions of interest (ROIs). With this, we can create machine-learning (ML) predictive classifiers of complex social behavior in SimBA. Social behaviors and interactions are difficult to manually track due to their rapid successions. To overcome this, I plan to use ML classification using our SimBA pipeline for behavioral classification allowing us to exceed human performance and increase throughput and consistency. I plan to create accurate classifiers for social behaviors that I will use to analyze the behavioral motifs of mice undergoing an operant social stress procedure. First, we train male and female C57BL/6J mice to self-administer (SA) their same sex cage mate. Experimental mice are then subjected to either physical stress for males or witness stress for females. Following social stress, non-reinforced SA is used to assess social reward seeking. Next, social interaction (SI) tests are performed to document time spent approaching the familiar same-sex conspecific cage mates and the aggressive CD-1 mice. All behavior was recorded, and transferred to DLC, followed by frame extraction. Using these frames, we trained the operant behavioral dataset to track the orientation of the mice. Next, we evaluate the dataset for a low error margin as observed by a continuous plateau of iteration loss. Although not complete, I expect to create behavioral classifiers for mice during social decision making in a social reward context following social stress inclusive of sex differences. Providing descriptive statistics of both movement and probability of successive behaviors as they occur in real-time.

Maladaptive aggression characterizes - or is comorbid with - many neuropsychiatric illnesses, and can have devastating effects on individuals, their caretakers, and healthcare professionals. Human aggression is typically demarcated as exhibiting either reactive (defensive) or appetitive (rewarding) components. Despite a significant clinical awareness of the differences between these aggression presentations, preclinical characterization of their relative circuitry and associated neuronal mechanisms are absent. Using recently established protocols within our lab, we are able to study and compare these aggression phenotypes in outbred male mice in a high throughput manner. Briefly, for appetitive aggression, we train mice to self-administer a novel subordinate intruder over 7 days using a trial design. In the reactive condition, we non-contingently administered intruders with the same frequency distribution as the appetitive mice. In the current experiment, we used CD1xVgat-Cre or CD1xVglut1-Cre mice injected with pGP-AAV-syn-FLEX-jGCaMP7s in the lateral septum (LS) to examine cell-type specific activity via fiber photometry. GABAergic activity in the lateral septum has historically been implicated in the control of reactive aggression, but little is known about the role of excitatory activity in the LS in reactive or appetitive aggression. My roles in this project have included behavioral testing and filming of the mice, as well as scoring these videos for first attacks following intruder presentation. Using these timestamps, I will next analyze the changes in population level dynamics across different time points of aggression motivation, seeking, and consumption using the open source photometry analysis program guPPY. We expect that the photometry results for mice in reactive and appetitive environments will show different patterns of activity, with more glutamatergic activity in the appetitive group, and more GABAergic activity in the reactive groups. I hope to help understand and prevent unnecessary aggression through this research.

Electrophysiology experiments targeting deep brain structures require extensive training and expertise. However, even experienced researchers face challenges in placing electrodes precisely within a target location, particularly when using multiple electrodes simultaneously. On average, there is a 400-um (standard deviation) of human error when targeting Bregma and navigating to insertion coordinates. Slow setup time and human error can lead to unnecessary stress in experimental animals and prevent scientists from focusing on data collection. Our laboratory developed an experiment planning tool called Pinpoint to address these challenges. However, even with interactive tools, a typical two-probe experiment setup can take over an hour, increasing as more probes are added in complex experiments. To reduce time inefficiencies and lower the risk of human error, we developed an electrode manipulator automation platform for Pinpoint. Our platform consists of a server application called Ephys Link, which unifies communication between Pinpoint and various electrode manipulator platforms. With Ephys Link, scientists can view the electrodes they are using in their experiment live inside the virtual brain and pre-plan insertions for multiple probes. They can then simply press a button to have their probes automatically move to their chosen targets. We expect our automation pipeline to make multi-probe electrophysiology an easier and more accessible task for researchers, enabling them to focus on gathering high-quality data rather than managing the geometry of their experiments. To measure the impact of our automation platform, we plan to use positional logging, timed recordings, and researcher feedback to evaluate the efficacy of the pipeline in speeding up electrophysiology experiments. We expect to see increased targeting precision, reduced time setup time, and overall productivity boosts for researchers. By reducing electrophysiology's difficulty and time-consuming nature, our automation pipeline helps researchers alleviate cumbersome experiment setups and prevent unnecessary stress on experimental animals.

A major technical limitation in the study of complex social behavior of freely moving rodents is the manual annotation of behavior because it is subjective, extremely time-intensive, and prone to observer drift. Simple Behavioral Analysis (SimBA) utilizes machine learning (ML) applications to automate behavioral analysis by using pose estimation to create supervised ML predictive classifiers of rodent social behavior. In a single project, thousands of videos need to be preprocessed, which includes locating individual trials, identifying behavioral events within each trial, and choosing trimming points to focus on specific outcomes or the presence of multiple animals. Manual editing amounts to thousands of hours, often yielding clips with inaccuracies in timing or content, and leads to inaccurate ML predictive classifiers. To address this problem, my research is centered around integrating behavioral metadata into a tool that can automate preprocessing steps with high precision to improve the quality of resulting classifiers. I attempt this via two major improvements. First, to eliminate the reliance on manual record-keeping, I will implement a function that utilizes metadata to link relevant time-stamped events to their corresponding behavioral experiment video. Second, I will leverage ML-based object detection, such as YOLO4, to determine time points when two animals are present, which often indicates that a social reward has been obtained. Through this project, when compared to manual scoring, I expect that: (1) there will be a significant cut in the time necessary to preprocess videos, (2) the yield of usable trials for SimBA will increase, and (3) that there will be an improved accuracy of pose estimation and classifier performance. Overall, these additions will greatly enhance the ease and flexibility of data preparation for highly specific behavioral analyses during the task, enhancing the efficiency of ML procedures to yield powerful behavioral classifiers.
 

The Neuropixels (NP) probe is a multielectrode array that can record from large populations of neurons with high temporal and spatial resolution, along a shank spanning multiple brain regions. Identifying specific neural populations recorded along the shank is critical for later determining their structural connectivity, adding further insight into their behavioral function. Due to the shank’s material, fluorescent dyes cannot be used for this purpose as the dye will disperse broadly. To solve this, we will use silk fibroin, a biocompatible molecule derived from the cocoon of Bombyx mori to encapsulate a fluorescent protein-encoding viral vector in a silk film that degrades after a controllable period of time. Viral approaches allow for genetic isolation of specific cell-types and circuits. We will combine herpes simplex virus with the silk film and apply it to discrete sections along the shank before insertion, to induce expression of green fluorescent protein (HSV-GFP) in nearby recorded neurons for visualization. First we will test a range of fibroin/HSV-GFP solutions to optimize targeted expression for acute recording applications. Following optimization, we will test the silk/HSV-GFP solution while recording from the mouse amygdala. Then, we will image the brain to visualize the neural populations that were recorded. We predict that the chosen silk/HSV-GFP solution will yield high expression of HSV-GFP in a localized region of the brain that corresponds to the coated subsection of the probe. In future experiments, we will combine the optimal solution with anterograde and retrograde viral tracers along the probes, allowing us to dissect the connectivity patterns of recorded neuronal populations. These experiments will integrate structure and function to derive greater insight from neurophysiological experiments during behavior in mice.

Current models of pain research involve restrictive forms of resident-intruder pairing where experimental mice are involuntarily placed in social situations. These methods have limited application as the research does not account for individual variability and the dynamic social decision-making characteristic of humans. Our research uses a novel volitional social procedure that more accurately represents human behavior in the context of pain. I conducted social self-administration protocols on C57 strain mus musculus to quantify changes in voluntary social interaction before and after neuropathic pain has been induced via spared nerve injury. In addition, I utilized a Von Frey filament test to measure changes in pain sensitivity over this time period. Two social self-administration (SA) experiments were conducted on separate cohorts of C57 mice. In Experiment I, SA was run intermittently at 3-day intervals following neuropathic injury, providing lapses between voluntary social engagement. In Experiment II, SA was run continuously following neuropathic injury. We found that the continuously run SA group experienced a rebound in social interaction to levels matching their pre-surgery states and sham controls, whereas the intermittent group displayed a stark decline in voluntary social interaction that reached statistical significance from sham controls on day 8. Interestingly, tests of allodynia that were conducted to determine prolonged mechanical sensitivity typical of chronic neuropathic pain showed that both groups were experiencing equal levels of increased pain sensitivity throughout behavioral testing. Our results show promise in revealing the dynamic connection between social interaction and pain perception. Research has already identified key areas of interest such as the medial prefrontal cortex (mPFC) and nucleus accumbens (NAc) as hubs responsible for regulating social behavior. We aim to further examine the physiological changes that occur in these areas as a result of persistent pain using a variety of sophisticated analytical techniques.

Single cell neural activity mapping is a novel experimental approach used to understand the relationship between neural activity and behavior/thought across the intact brain. The current approach utilizes immunodetection of Fos, a reliable and endogenous protein marker for neuronal activity that has unique induction and decay properties. This method combines whole-mount brain tissue clearing, IHC staining, and high speed volumetric imaging. However, whole-mount IHC is incredibly challenging due to many factors including variable antibody lots, lengthy processing protocols, and inconsistent timing. To overcome these limitations, various genetic methods including direct gene modification and replacement have been produced. However, these methods limit brain-wide expression profiles, display inaccurate signal to noise ratios, and yield low signal expression levels. Here, we have developed a novel activity-dependent tool that allows viral vector-compatible Fos-like reporting of neuronal activity. Our strategy relies upon non-promoter-based regulatory sequences that endows downstream genes with Fos-like induction profiles. Here, we present its effectiveness in ectopically labeling Fos+ cells in the mouse brain in-vivo, and report its comparison to other conventional genetic strategies. Further, we extend its range of use with the creation of multiple versions that enables a range of activity level reporting and through multiple wavelengths of fluorescence. In summary, this novel genetic tool can be used to ectopically map single cell neural activity more effectively in order to better understand the anatomical basis of neural coding driven by specific cell-types distributed across the entire brain.

We recently introduced operant social stress (OSS), a new operant procedure that classifies social motivation in mice as they lever press for volitional social interactions with a familiar partner before, during, and after social stress exposure. For social stress exposures, male mice underwent social defeat and female mice underwent witness defeat. In social defeat procedures, mice are repeatedly exposed to physical antagonistic interactions by an aggressive, larger mouse, while mice exposed to witness defeat mice observe these interactions from across a perforated barrier. Consistent with the literature, male mice exposed to social defeat exhibited reduced social motivation, while female mice exposed to witness defeat displayed an increase in social motivation. These opposing observations suggest different underlying mechanisms, but it remains unclear whether lever pressing during the task truly represents a motivation for affiliative reward. Thus, to rule out the contribution of non-social factors impacting social self-administration, we performed two control experiments to rule out alternative interpretations in this new operant method. In Experiment 1, we removed the familiar partner from the waiting chamber, in turn removing the social aspect of the reward. In Experiment 2, we unpaired the social rewards from the contingent lever, instead randomly delivering them during each trial. We hypothesized, and observed, that these manipulations would prevent mice from acquiring operant responses in the task and rule out non-social factors in driving operant responding. With the inclusion of these control experiments, we can now directly assess the social motivation of both male and female mice, facilitating deeper investigations into the underlying mechanisms in future studies.

 Within the field of neuroscience, optogenetics is an established experimental tool which can be used to alter specific cell function or trigger enzymatic reactions under millisecond time-scale precision. The high temporal precision of optogenetic recombinases allows for precise identification of cell populations which causally regulate specific behaviors in health and disease. Furthermore, the inducible optogenetic control specifically of recombinase activity for cell-type targeting eliminates the use of other inducible methods including exogenous chemicals that operate on lower time-scales and notoriously cause non-specific effects. Currently available optogenetic recombinases are driven by low wavelengths of light (e.g., Yao et al, 2020) which limit their in-vivo use to local and/or superficial areas of the brain in an invasive manner. Our recently engineered optogenetics-based protein pair, NOC (Near-IR Optogenetic Cre recombinase) induces Cre recombinase activity with near-infrared (NIR) light through dimerization of split-Cre fragments. We have previously demonstrated NOC’s capability for functional Cre recombinase activity under 650 nm light administration, and now aim to optimize the inducibility profile of NOC through two additional modifications. These include modifications to Cre split-site locations based off of previously designed blue light-inducible recombinases (Yao et. al, 2020), and a novel 660 nm inducible photoreceptor pair (Zhou et. al, 2022). The inducibility of these new configurations will be tested using our in-vitro fiber optic system. The optimal configuration of NOC will lay the groundwork for NOGen (NIR OptoGenetic ensemble capture), an alternative version of NOC which will include a calcium-sensing domain on one of the protein fragments. This will limit NOGen’s activity to active neurons only, offering greater precision in identifying specific cell-populations which drive particular behaviors. These improved molecular tools can be used to further our understanding of brain anatomy and function, which serve as an important catalyst for the development of improved brain disorder treatment.

In mammals, hair cell loss or damage leads to permanent loss of auditory and vestibular function due to the inability of mammals to regenerate these cells. The primary function of hair cells is to respond to auditory and vestibular stimuli to facilitate perception of sound, head movement, and gravity. Hair cells are particularly sensitive to changes in their mitochondria, a membrane enclosed organelle that provides ATP in all eukaryotic cells. Disturbances or damages to the mitochondria can be caused by mitochondrial deafness genes, aminoglycoside-induced death, or senescence. Currently, there is very little known about the biology of hair cell mitochondria.The zebrafish neuromasts within the lateral line can serve as a functional model for cochlear hair cells because of its homology with the mammalian inner ear at genetic and structural levels. We use the zebrafish lateral line system in conjunction with the serial-block face scanning electron microscopy (SBFSEM) to produce three dimensional imaging of hair cell mitochondrial morphology. SBFSEM allows us to measure and quantify mitochondrial phenotypes. We have identified structural characteristics of mitochondrial networks adjacent to post-synaptic release sites that interact with afferent neurons and regulate synaptic transmission. The information gained from SBFSEM on hair cell mitochondrial morphologies will provide valuable insight on protective intracellular mechanisms that can prevent synaptopathy and protect against hearing loss. In our future work, we will quantify this mitochondrial networking morphology. These data will further inform functional experiments regarding the role of mitochondria at these synapses.

Rigorous ethological observation via machine learning techniques, termed computational neuroethology, is a rapidly expanding field. Our lab has created an open-source pipeline for automated behavioral analysis using supervised machine learning called Simple Behavioral Analysis (SimBA), to aid in the high throughput analysis of social behavior. Using pose estimation data of socially interacting animals obtained through open source pipelines such as SLEAP or DeepLabCut, we are able to create large training sets of video frames that are hand scored as positive or negative for a behavior, which we then feed into supervised random forest algorithms. These algorithms then build classifiers which can detect the behaviors in novel videos. My work has focused on building and titrating classifiers for two important social behaviors: face and body sniffing by a dominant mouse toward a subordinate. So far, I have hand-scored a large dataset of social interaction videos to create a sizable training set. I have begun the initial phases of training my classifiers, which involves finding appropriate hyperparameters for the random forest algorithms so that they can differentiate positive and negative behaviors, and refrain from overfitting to our training datasets. Using both machine learning performance metrics as well as hand versus machine comparisons, I am able to understand the generalizability and accuracy of my classifiers. As I continue with this project, I will selectively add more positive and negative examples to correct false positives and boost the confidence of the classifiers through subsequent iterations. This work allows me to gain an understanding of the principles of machine learning techniques, and create classifiers that we openly provide to behavioral neuroscience labs across the world. We expect that the pooling of these classifiers with outside labs will promote a high level of standardization of behavioral definitions in behavioral neuroscience, ultimately increasing reliability and reproducibility.

Neuropsychiatric disorders pose a difficult challenge for healthcare providers. Treatments for such disorders vary in efficacy and come with detrimental costs for patients and their communities. Historically, preclinical animal models have failed to incorporate the nuances of volitional human social behavior. This project used chronic social defeat stress to induce depression-like behaviors in male and female mice, this was followed by self-administered social interactions within an operant chamber in which lever presses were reinforced by social contact. The goal is to develop preclinical animal models that can be assessed to identify mechanisms responsible for stress-induced social motivation. The mice will be injected with a nuclear localized tag (oNLS) and viral retrograde tracer rAAV2-retro-GFP. Male and female mice will train to self-administer social interaction with a sex and age-matched housing partner over the course of ten 12-trial sessions. Next, experimental male and female mice will be subjected to physical and witness defeats followed by operant social self-administration. Before and after the 10-day operant social stress sessions, we will test social reward seeking via non-reinforced self-administration of social reward followed by a progressive ratio test. Brain tissue will be collected and prepared for immunohistochemistry and iDISCO+ whole-brain clearing for cfos labelling. We predict results will show differential cfos activity in sexually dimorphic brain regions such as the hippocampus, prefrontal cortex, amygdala and the bed nucleus of stria terminalis. We determine that operant social stress can be used to discern differences in social motivation in male and female mice as a result of stress-induced factors. There is great potential in using whole-brain activity mapping to identify brain structures activated during social reward following social stress, as this can also serve as a technical resource for the field by identifying relevant non-canonical brain regions and circuits that govern such behaviors.

The retina is made of mostly neurons and glia. In mammals when neurons degenerate in the brain or retina, they are not replaced. Studying other species, the Reh lab discovered a way to stimulate Muller glia in the retina, using the pro-neural Ascl1 transcription factor, to regenerate retinal neurons. My project focuses on another glial cell in the human retina: the astrocyte. Retinal astrocytes come from different progenitors in the brain and migrate, through the optic nerve, to the retina during development. There are two questions about astrocytes that are addressed in this project. When do astrocytes enter the retina? And can human retinal astrocytes be reprogrammed into neurons using the Ascl1 transcription factor? For the first question, based on immunohistochemistry(IHC) stainings done on sections of the human fetal retina I conclude that astrocytes enter the retina between 62-72 days of fetal development. For the second question, I hypothesize the Ascl1 transcription factor can reprogram human retinal astrocytes and the unique development and migration will affect the types of neurons they regenerate. My IHC stainings on cultures of retinal cells confirms that a lenti-virus I added to the culture with an astrocyte marker(Pax2) sufficiently targets astrocytes. I am currently working on isolating astrocytes from other cells in culture. Afterwards a lentivirus with the pro-neural Ascl1 transcription factor will be added to the astrocytes to begin reprogramming trials. Human retinal astrocytes have not been reprogrammed successfully before. If we can reprogram retinal astrocytes into neurons, it would potentially have implications for neural replacement in the retina. This would contribute to research in gene therapies for neurodegenerative retinal diseases such as: glaucoma, AMD and retinitis pigmentosa.

The retina is a neuronal tissue located on the back of the eye that receives light information, and relays this information to the brain, allowing us to see. The light is received by cells called photoreceptors, which send information through a series of inner-retinal neurons before being relayed to the brain via ganglion cells. My project is investigating the effect of a protein called DICER on retinal cell fate in the developing human retina. DICER is important for the maturation of microRNAs, which influence gene expression of retinal precursors that play a crucial role in the progression from early cell types in the mouse, such as ganglion cells, to later cell types, such as photoreceptors and inner retinal neurons. To understand if DICER is also necessary in human development, I am culturing human retinal organoids derived from embryonic stem cells (ESCs), which recapitulate human development in gene expression and developmental timing. I then preferentially knock out (KO) DICER at different developmental time points depending on the timing of infection. I have knocked out DICER in retinal organoids at an early time point and observed that, like mouse studies, cells without DICER more frequently become the early born cell type, ganglion cells. I am now investigating the necessity of DICER in retinal development at later time points. If DICER plays a similar role in cell fate progression, I expect to see an enrichment of later born cell types in the DICER KO cells. By better understanding human retinal development, we will advance future research of regenerative therapies to mitigate the impacts of retinal degenerative diseases leading to vision loss, such as macular degeneration and glaucoma, which affect photoreceptor cells and ganglion cells, respectively.

Inherited retinal diseases (IRDs) are a diverse family of disorders which cause vision loss and retinal degeneration. With only 1-2% of the genome being protein-encoding, genetic variation within the expansive noncoding genome is critical to the development of disease phenotypes in the retina. Macular Telangiectasia Type II (MacTel) is an IRD resulting in disruption of central vision and greatly impacting vision-related quality of life. MacTel has an estimated prevalence of 1 in 1000 individuals, affecting approximately two million people globally. Though MacTel etiology largely remains unknown, accumulation of improperly degraded lipids within the retina is a leading hypothesis in its pathogenesis. Additionally, genome-wide association studies have implicated numerous loci in the development of MacTel, including the novel gene locus ceramide synthase 4 (CERS4). As CERS4 plays a critical role in the synthesis of lipid precursors and is highly expressed in the retina, it stands as a promising candidate for influencing MacTel development. We hypothesize that cis-regulatory element (CRE) mutations are central to the genetic frameworks underlying MacTel. We aim to characterize the sufficiency of putative enhancer regions to drive gene expression. We have identified potential CERS4 enhancer regions through a machine learning approach using adult human retina ATAC sequencing datasets. Sufficiency of candidate enhancer regions will be evaluated by insertion to a barcoded reporter library and electroporation into mouse retinas. Following proof of sufficiency, we will perform saturation mutagenesis on identified enhancers to investigate the impact of all possible single nucleotide variants (SNVs) within these regions. The results of our investigation will aid in identifying SNVs of interest within the CERS4 locus, potentially implicating specific mutations towards the development of MacTel. Greater understanding of CRE mutations will improve early clinical diagnosis and inform future therapies for patients with MacTel.

Macular telangiectasia type II is a late-onset retinal degeneration disease which causes loss of central vision and a disruption in cell class proportions in the retina. Genome-wide association studies have identified a point mutation in the 5q14.3 enhancer as associated with MacTel. This enhancer has been shown to regulate the activity of microRNA 9-2. To elucidate the function of this particular enhancer on retinal health and cell class composition, both enhancer knockout and miR9-2 mice models were generated. In adult enhancer knockout mice, there were no significant changes in cell class composition compared with wild-type mice. In the miR9-2 knockout mouse model, it was found that at the 5 week time point, there was a significant increase in müller glial cells. Müller glial cell loss has been observed in Mactel patients, and these cells have been shown to play a crucial role in maintaining proper vascular networks. Future experiments to determine the effects of enhancer and miR9-2 loss on vasculature in the retina would help further identify the role of the 5q14.3 enhancer and its targets on retinal health.