About

Sebastian Hernandez, M.D., is an Assistant Professor specializing in Emergency Medicine at UC Davis Medical Center. His clinical interests include emergency medicine and ultrasound. He completed his undergraduate studies with a B.S. in Molecular and Cellular Biology at the University of Arizona in 2014, earned his M.D. from Tufts University School of Medicine in 2020, and obtained a M.S. in Molecular and Cellular Biology from the University of Arizona in 2015. His residency in Emergency Medicine was completed at UC Davis in 2023, followed by a fellowship in Ultrasound at UC Davis in 2024. Dr. Hernandez is actively involved in various professional memberships, including UC Davis Diversity, Inclusion, and Equity in Medicine, the UC Davis Emergency Medicine Billing, EHR, and Documentation Committee, the UC Davis Emergency Medicine Intern Selection Committee, and the UC Davis Latinx Staff and Faculty Association. His honors include the GME High Value Competition in Sustainability Award in 2022, Resident MVP in 2023, and Golden Probie in 2023. His research contributions include publications on lysosome formation and function, notably in the context of Mucolipidosis Type IV and lysosome regulation in Caenorhabditis elegans.

Research topics

• Medicine
• Internal medicine
• Environmental health
• Demography
• Immunology

Selected publications

• Incubator-Free Organoid Culture in a Sealed Recirculatory System

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-05 · 3 citations

  preprintOpen access

  Organoids are powerful tools for studying development and disease, offering realistic organ-like human and animal tissues and facilitating experimental observation compared to live animal models. However, traditional organoid culture methods require a humidified incubator. This requirement complicates culture due to evaporative losses and restricted access to instrumentation, hindering the potential of organoids as physiologically accurate models easily subjected to detailed experimental observation. We introduce a compact, automated, sealed, incubator-free recirculatory organoid culture platform that replaces the air-liquid interface with a nonporous polymer gas exchanger and a liquid-phase gas buffer. This design prevents evaporation and stabilizes oxygen, pH, and osmolarity without feedback control. It enables single-actuator media exchange, simplifying automation. Dispensing with the incubator, we improve access for instruments such as live cell microscopes. We demonstrate compatibility with continuous multi-week live imaging of vascular organoids and show that brain organoids in this system maintain metabolic viability, structural fidelity, and electrophysiological activity comparable to traditional shaker-based cultures in an incubator.

  PublisherOA PDFDOI

• A feedback-driven brain organoid platform enables automated maintenance and high-resolution neural activity monitoring

  Internet of Things · 2025-07-07 · 10 citations

  articleOpen access

  The analysis of tissue cultures requires a sophisticated integration and coordination of multiple technologies for monitoring and measuring. We have developed an automated research platform enabling independent devices to achieve collaborative objectives for feedback-driven cell culture studies. Our approach enables continuous, communicative, non-invasive interactions within an Internet of Things (IoT) architecture among various sensing and actuation devices, achieving precisely timed control of in vitro biological experiments. The framework integrates microfluidics, electrophysiology, and imaging devices to maintain cerebral cortex organoids while measuring their neuronal activity. The organoids are cultured in custom, 3D-printed chambers affixed to commercial microelectrode arrays. Periodic feeding is achieved using programmable microfluidic pumps. We developed a computer vision fluid volume estimator used as feedback to rectify deviations in microfluidic perfusion during media feeding/aspiration cycles. We validated the system with a set of 7-day studies of mouse cerebral cortex organoids, comparing manual and automated protocols. It was shown that the automated protocols maintained robust neural activity throughout the experiment while enabling hourly electrophysiology recordings during the experiments. The median firing rates of neural units increased for each sample, and dynamic patterns of organoid firing rates were revealed by high-frequency recordings. Surprisingly, feeding did not affect the firing rate. Furthermore, media exchange during a recording did not show acute effects on firing rate, enabling the use of this automated platform for reagent screening studies. • Automated platform integrates IoT for brain organoid maintenance and monitoring. • IoT-enabled feedback system ensures precise microfluidic media handling. • High-frequency HD-MEA recordings reveal dynamic neural activity patterns. • IoT framework allows scalable, non-invasive, in-incubator organoid studies.

  PublisherDOI

• Weighted Graph for Longitudinal Tracking of Neurons in Cortical Organoids on a High-Density Microelectrode Array

  2025-07-14

  article

  Tracking cortical neurons over several days can provide extensive data for their electrophysiology features, development trajectories, and connectivity with other neurons within neuronal circuits. However, reliably identifying the same neurons over time remains a challenge for high-density microelectrode array (HD-MEA) recordings. In this study, we developed a weighted graph-based tracking algorithm to classify trackable units and monitor their features over time. We applied this algorithm to 160 recordings of neural activity collected from a mouse cortical organoid every hour for seven days on an HD-MEA. Recordings were pre-processed to extract spiking waveforms, spike trains, and spatial locations. Waveforms were clustered for putative cell types. Our approach identified 46 trackable units, representing 53.5% of all recorded neural activity samples. Functional connectivity analysis revealed that trackable units had a higher tendency to connect with untrackable ones. This study demonstrates the effectiveness of a weighted graph-based method for tracking individual neurons over extended periods and provides insights into neuronal development and network dynamics in cortical organoids.

  PublisherDOI

• Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-02 · 7 citations

  preprintOpen access1st author

  SUMMARY The mouse cortex is a canonical model for studying how functional neural networks emerge, yet it remains unclear which topological features arise from intrinsic cellular organization versus external regional cues. Mouse forebrain organoids provide a powerful system to investigate these intrinsic mechanisms. We generated dorsal (DF) and ventral (VF) forebrain organoids from mouse pluripotent stem cells and tracked their development using longitudinal electrophysiology. DF organoids showed progressively stronger network-wide correlations, while VF organoids developed more refined activity patterns, enhanced small-world topology, and increased modular organization. These differences emerged without extrinsic inputs and may be driven by the increased generation of Pvalb + interneurons in VF organoids. Our findings demonstrate how variations in cellular composition influence the self-organization of neural circuits, establishing mouse forebrain organoids as a tractable platform to study how neuronal populations shape cortical network architecture.

  PublisherOA PDFDOI

• HIPPIE: A Multimodal Deep Learning Model for Electrophysiological Classification of Neurons

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-15 · 4 citations

  preprintOpen access

  Abstract Extracellular electrophysiological recordings present unique computational challenges for neuronal classification due to noise, technical variability, and batch effects across experimental systems. We introduce HIPPIE (High-dimensional Interpretation of Physiological Patterns In Extracellular recordings), a deep learning framework that combines self-supervised pretraining on unlabeled datasets with supervised fine-tuning to classify neurons from extracellular recordings. Using conditional convolutional joint autoencoders, HIPPIE learns robust, technology-adjusted representations of waveforms and spiking dynamics. This model can be applied to electrophysiological classification and clustering across diverse biological cultures and technologies. We validated HIPPIE on both in vivo mouse recordings and in vitro brain slices, where it demonstrated superior performance over other unsupervised methods in cell-type discrimination and aligned closely with anatomically defined classes. Its latent space organizes neurons along electrophysiological gradients, while enabling batch and individual corrected alignment of recordings across experiments. HIPPIE establishes a general framework for systematically decoding neuronal diversity in native and engineered systems.

  PublisherOA PDFDOI

• A Modular Platform for Automated Organoid Culture and Longitudinal Imaging

  Research Square · 2025-11-10

  preprintOpen access
  PublisherOA PDFDOI

• Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-11-14 · 5 citations

  preprintOpen access

  SUMMARY Electrophysiology offers a high-resolution method for real-time measurement of neural activity. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage and of substantial complexity for extracting neural features and network dynamics. Analysis is often demanding due to the need for multiple software tools with different runtime dependencies. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the services and algorithms to serve as scalable and flexible building blocks within the pipeline. In this paper, we applied this pipeline on two types of cultures, cortical organoids and ex vivo brain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.

  PublisherOA PDFDOI

• A feedback-driven brain organoid platform enables automated maintenance and high-resolution neural activity monitoring

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-03-17 · 6 citations

  preprintOpen access

  Abstract The analysis of tissue cultures, particularly brain organoids, requires a sophisticated integration and coordination of multiple technologies for monitoring and measuring. We have developed an automated research platform enabling independent devices to achieve collaborative objectives for feedback-driven cell culture studies. Our approach enables continuous, communicative, non-invasive interactions within an Internet of Things (IoT) architecture among various sensing and actuation devices, achieving precisely timed control of in vitro biological experiments. The framework integrates microfluidics, electrophysiology, and imaging devices to maintain cerebral cortex organoids while measuring their neuronal activity. The organoids are cultured in custom, 3D-printed chambers affixed to commercial microelectrode arrays. Periodic feeding is achieved using programmable microfluidic pumps. We developed a computer vision fluid volume estimator used as feedback to rectify deviations in microfluidic perfusion during media feeding/aspiration cycles. We validated the system with a set of 7-day studies of mouse cerebral cortex organoids, comparing manual and automated protocols. The automated protocols were validated in maintaining robust neural activity throughout the experiment. The automated system enabled hourly electrophysiology recordings for the 7-day studies. Median neural unit firing rates increased for every sample and dynamic patterns of organoid firing rates were revealed by high-frequency recordings. Surprisingly, feeding did not affect firing rate. Furthermore, performing media exchange during a recording showed no acute effects on firing rate, enabling the use of this automated platform for reagent screening studies.

  PublisherOA PDFDOI

• Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters

  Cell Reports Methods · 2024-01-01 · 21 citations

  articleOpen access

  Precise modulation of brain activity is fundamental for the proper establishment and maturation of the cerebral cortex. To this end, cortical organoids are promising tools to study circuit formation and the underpinnings of neurodevelopmental disease. However, the ability to manipulate neuronal activity with high temporal resolution in brain organoids remains limited. To overcome this challenge, we introduce a bioelectronic approach to control cortical organoid activity with the selective delivery of ions and neurotransmitters. Using this approach, we sequentially increased and decreased neuronal activity in brain organoids with the bioelectronic delivery of potassium ions (K+) and γ-aminobutyric acid (GABA), respectively, while simultaneously monitoring network activity. This works highlights bioelectronic ion pumps as tools for high-resolution temporal control of brain organoid activity toward precise pharmacological studies that can improve our understanding of neuronal function.

  PublisherOA PDFDOI

• Goal-Directed Learning in Cortical Organoids

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-12-12 · 10 citations

  preprintOpen access

  Abstract Experimental neuroscience techniques are advancing rapidly, with major recent developments in high-density electrophysiology and targeted electrical stimulation. In combination with these techniques, cortical organoids derived from pluripotent stem cells show great promise as in vitro models of brain development and function. Although sensory input is vital to neurodevelopment in vivo , few studies have explored the effect of meaningful input to in vitro neural cultures over time. In this work, we demonstrate the first example of goal-directed learning in brain organoids. We developed a closed-loop electrophysiology framework to embody mouse cortical organoids into a simulated dynamical task (the inverted pendulum problem known as ‘Cartpole’) and evaluate learning through high-frequency training signals. Longitudinal experiments enabled by this framework illuminate how different methods of selecting training signals enable improvement on the tasks. We found that for most organoids, training signals chosen by artificial reinforcement learning yield better performance on the task than randomly chosen training signals or the absence of a training signal. This systematic approach to studying learning mechanisms in vitro opens new possibilities for therapeutic interventions and biological computation.

  PublisherOA PDFDOI

Frequent coauthors

• Eva Harris

  University of California, Berkeley

  45 shared

• Ana M. Mora

  University of California, Berkeley

  44 shared

• Marcus P. Wong

  44 shared

• Nicholas P. Jewell

  London School of Hygiene & Tropical Medicine

  42 shared

• Katherine Kogut

  Center for Environmental Health

  41 shared

• Karen Huen

  University of California, Berkeley

  41 shared

• Stephen Rauch

  University of California, Berkeley

  41 shared

• Brenda Eskenazi

  Center for Environmental Health

  41 shared

Education

• Bs., Chemistry

  Universidad de Costa Rica

• Master, Chemistry

  Universidad de Costa Rica Escuela de Química

Awards & honors

• GME High Value Competition in Sustainability Award (2022)
• Resident MVP (2023)
• Golden Probie (2023)