Biology subjects

Schweiger, H. E.

Publications and source records attributed to Schweiger, H. E..

10 recordsLinked to original sources

bioRxiv2026

A foundation model for brain organoid network dynamics uncovers intrinsic computation and convergent disease phenotypes

Brain organoids develop self-organizing neural networks that can be used to model neurodevelopment and disease. Here we introduce PULSE, a foundation model developed using over 900 hours of high-density microelectrode array recordings of in vitro and ex vivo neural tissue. We show that this single model can address two open questions: how disease-associated variants alter network development, and how naive circuits encode information. PULSE revealed accelerated network maturation in held-out human 16p11.2-deletion organoids. It also revealed a change in network coordination shared with SHANK3 deficiency. Because organoids develop without sensory input, they let us ask how circuits encode information before any representation is learned. Closed-loop control of a virtual inverted pendulum engaged existing spontaneous activity patterns rather than creating new ones. PULSE provides a common reference frame for neural circuits across genotypes, protocols and tasks.

neuroscience↗

bioRxiv2026

Cloud-Connected Pluripotent Stem Cell Platform Enhances Scientific Identity in Underrepresented Students

Stem cell research offers unique opportunities for authentic scientific engagement, yet infrastructure requirements have confined participation to elite institutions, perpetuating workforce disparities. We developed an integrated framework combining engineered biology, cloud-connected microscopy, and validated psychometric assessment to make pluripotent stem cell (PSC) experimentation widely accessible. The framework comprises three components: a doxycycline-inducible NGN2 mouse embryonic stem cell line for rapid neuronal specification, low-cost cloud microscopy for remote observation, and the validated Stem Cell Research Identity Scale (SCRIS) for quantifying educational outcomes. Implementation across a Title I high school and urban community college demonstrated significant increases in scientific identity. Students using differentiating PSCs showed broader science identity development than those using neuroblastoma cells, particularly in competence, research readiness, and recognition. High school students showed enhanced research competence gains compared to community college students despite equivalent intervention duration. Demographic analyses revealed enhanced effectiveness for Hispanic and first-generation college students. This framework provides a scalable model for broadening participation in advanced biomedical research.

scientific communication and education↗

bioRxiv2025

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

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.

neuroscience↗

bioRxiv2025

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

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.

neuroscience↗

bioRxiv2024

Goal-Directed Learning in Cortical Organoids

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.

neuroscience↗

bioRxiv2024

Multiscale Cloud-based Pipeline for Neuronal Electrophysiology Analysis and Visualization

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.

neuroscience↗

bioRxiv2024

Fate plasticity of interneuron specification

The generation of neuronal subtypes in the mammalian central nervous system is driven by competing genetic programs. The medial ganglionic eminence (MGE) gives rise to two major cortical interneuron (cIN) populations, marked by Somatostatin (Sst) and Parvalbumin (Pvalb), which develop on different timelines. The extent to which external signals influence these identities remains poorly understood. Pvalb-positive cINs are particularly important for regulating cortical circuits through strong perisomatic inhibition, yet they have been difficult to model in vitro. Here we investigated the role of the environment in shaping and maintaining Pvalb cINs. We grafted mouse MGE progenitors into a variety of 2D and 3D co-culture models, including mouse and human cortical, MGE, and thalamic systems with dissociated cells, organoids, organotypic cultures, and conditioned media. Across models, we observed distinct proportions of Sst- and Pvalb-positive cIN descendants. Strikingly, grafting MGE progenitors into 3D human, but not mouse, corticogenesis models led to efficient, non-autonomous differentiation of Pvalb-positive cINs. This differentiation was characterized by upregulation of Pvalb maturation markers, downregulation of Sst-specific markers, and the formation of perineuronal nets. Furthermore, lineage-traced postmitotic Sst-positive cINs, when grafted onto human cortical models, also upregulated Pvalb expression. These results reveal an unexpected level of fate plasticity in MGE-derived cINs, demonstrating that their identities can be dynamically shaped by the surrounding environment.

neuroscience↗

bioRxiv2024

A feedback-driven IoT microfluidic, electrophysiology, and imaging platform for brain organoid studies

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.

neuroscience↗

bioRxiv2023

Internet-connected cortical organoids for project-based stem cell and neuroscience education

The introduction of internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell-derived cortical organoids in two different settings: Using microscopy to monitor organoid growth in an introductory tissue culture course, and using high density multielectrode arrays to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training. HIGHLIGHTS- Development of cortical organoids as pedagogical tools for undergraduate education. - Organoids implemented in a tissue culture course through cloud-enabled microscopy. - Multielectrode arrays allow for live organoid manipulation in a mathematics course. - Students self-report increased interest in neuroscience and stem cells topics.

scientific communication and education↗

bioRxiv2023

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

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 {gamma}-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.

bioengineering↗