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Teodorescu, M.

Publications and source records attributed to Teodorescu, M..

36 records · Page 2Linked to original sources

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↗

Pathological microcircuits initiate epileptiform events in patient hippocampal slices

How seizures begin at the level of microscopic neuronal circuits remains unknown. Advancements in high-density CMOS-based microelectrode arrays can be harnessed to study neuronal network activity with unprecedented spatial and temporal resolution. We use high-density electrophysiology recordings to probe the network activity of human hippocampal brain slices from six patients with mesial temporal lobe epilepsy. Two slices from the dentate gyrus exhibited epileptiform activity in the presence of low magnesium media with kainic acid. Both slices exhibit network oscillations indicative of a reciprocally connected circuit, which is unexpected under normal physiological conditions. Future studies may apply this approach to elucidate the network signals that underlie seizure initiation.

neuroscience↗

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↗

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↗

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↗

Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function

Due to the complexity of neuronal networks and the nonlinear dynamics of individual neurons, it is challenging to develop a systems-level model which is accurate enough to be useful yet tractable enough to apply. Mean-field models which extrapolate from single-neuron descriptions to large-scale models can be derived from the neurons transfer function, which gives its firing rate as a function of its synaptic input. However, analytically derived transfer functions are applicable only to the neurons and noise models from which they were originally derived. In recent work, approximate transfer functions have been empirically derived by fitting a sigmoidal curve, which imposes a maximum firing rate and applies only in the diffusion limit, restricting applications. In this paper, we propose an approximate transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. Refractory SoftPlus activation functions allow the derivation of simple empirically approximated mean-field models using simulation results, which enables prediction of the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. These models also support an accurate approximate bifurcation analysis as a function of the level of recurrent input. Finally, the model works without assuming large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms. Author SummaryAs one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable approach by modeling the behavior of a single representative neuron and extending this to the population. However, most mean-field models work only in the limit of weak interactions between neurons, where synaptic input behaves more like a diffusion process than the sum of discrete synaptic events. This paper introduces a data-driven mean-field model, estimated by curve-fitting a simple transfer function, which works with larger interaction strengths. The resulting model can predict population firing rates and bifurcations of equilibria, as well as providing a simple dynamical model that can be the basis for further analysis.

neuroscience↗

Protosequences in human cortical organoids model intrinsic states in the developing cortex

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human and murine brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human and murine brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

neuroscience↗

Programmable delivery of fluoxetine via wearable bioelectronics for wound healing in vivo

The ability to deliver drugs with precise dosages at specific time points can significantly improve disease treatment while reducing side effects. Drug encapsulation for gradual delivery has opened up the doors for superior treatment regimen. To expand on this ability, programming bioelectronic devices to deliver small molecules enables ad-hoc personalized therapeutic profiles that are more complex than simple gradual release. Here, we introduce a wearable bioelectronic bandage with an integrated electrophoretic ion pump that affords on-demand drug delivery with precise dose control. Delivery of fluoxetine to wounds in mice resulted in a 27.2% decrease in the macrophage ratio (M1/M2) and a 39.9% increase in re-epithelialization, indicating a shorter inflammatory phase and faster overall healing. Programmable drug delivery using wearable bioelectronics in wounds introduces a broadly applicable strategy for the long-term delivery of a prescribed treatment regimen with minimal external intervention.

bioengineering↗

Delivering biochemicals with precision using bioelectronic devices enhanced with feedback control

Precision medicine tailors treatment in a way that accounts for variations in patient response. Treatment strategies can be determined based on factors such as genetic mutations, age, and diet. Another way of implementing precision medicine in a dynamic fashion is through bioelectronics equipped with real-time sensing and intelligent actuation. Bioelectronic devices such as ion pumps can be utilized to deliver therapeutic drugs. To be able to perform precision medicine, medical devices need to be able to deliver drugs with high precision. For this, closed-loop control is required to be able to change the treatment strategy as new information about the response and progression of the biological system is received. To this end, a sliding mode controller is utilized given its ability to perform satisfactory control actions when there is model uncertainty. The controller is used in an experiment with the goal of delivering a pre-determined dosage of fluoxetine throughout a period of time.

systems biology↗

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↗

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↗

Open-loop lab-on-a-chip technology enables remote computer science training in Latinx life sciences students

Despite many interventions, science education remains highly inequitable throughout the world. Among all life sciences fields, Bioinformatics and Computational Biology suffer from the strongest underrepresentation of racial and gender minorities. Internet-enabled project-based learning (PBL) has the potential to reach underserved communities and increase the diversity of the scientific workforce. Here, we demonstrate the use of lab-on-a-chip (LoC) technologies to train Latinx life science undergraduate students in concepts of computer programming by taking advantage of open-loop cloud-integrated LoCs. We developed a context-aware curriculum to train students at over 8,000 km from the experimental site. We showed that this approach was sufficient to develop programming skills and increase the interest of students in continuing careers in Bioinformatics. Altogether, we conclude that LoC-based Internet-enabled PBL can become a powerful tool to train Latinx students and increase the diversity in STEM.

scientific communication and education↗

A data-efficient deep learning tool for scRNA-Seq label transfer in neuroscience

Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Ma-chine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult hu-man cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal sub-types, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

bioinformatics↗

Cloud-controlled microscopy enables remote project-based biology education in Latinx communities in the United States and Latin America

Project-based learning (PBL) has long been recognized as an effective way to teach complex biology concepts. However, not all institutions have the resources to facilitate effective project-based coursework for students. We have developed a framework for facilitating PBL using remote-controlled internet-connected microscopes. Through this approach, one lab facility can host an experiment allowing simultaneous interaction by many students worldwide. Experiments on this platform can be run on long timescales and with materials that are typically unavailable to high school classrooms. This allows students to perform novel research projects rather than just repeat standard classroom experiments. To investigate the impact of this program, we designed and ran six user studies with students worldwide. All experiments were executed in Santa Cruz and San Francisco, California, with observations and decisions made remotely by the students using their personal computers and cellphones. In surveys gathered after the experiments conclusion, students reported increased excitement for science and a greater desire to pursue a career in STEM. This framework represents a novel, scalable, and effective PBL approach that has the potential to democratize biology and STEM education around the world.

scientific communication and education↗

Modular automated microfluidic cell culture platform reduces glycolytic stress in cerebral cortex organoids

Organ-on-a-chip systems combine microfluidics, cell biology, and tissue engineering to culture 3D organ-specific in vitro models that recapitulate the biology and physiology of their in vivo counterparts. Here, we have developed a multiplex platform that automates the culture of individual organoids in isolated microenvironments at user-defined media flow rates. Programmable workflows allow the use of multiple reagent reservoirs that may be applied to direct differentiation, study temporal variables, and grow cultures long term. Novel techniques in polydimethylsiloxane (PDMS) chip fabrication are described here that enable features on the upper and lower planes of a single PDMS substrate. RNA sequencing (RNA-seq) analysis of automated cerebral cortex organoid cultures shows benefits in reducing glycolytic and endoplasmic reticulum stress compared to conventional in vitro cell cultures.

bioengineering↗

Internet of Things Architecture for High Throughput Biology

The Internet of Things (IoT) provides a simple framework to easily control online devices. IoT is now a commonplace tool used by technology companies, but it is rarely used in biology experiments. IoT can benefit research through alarm notifications, automation, and the real-time monitoring of experiments. We developed and implemented an IoT architecture to control biological devices used in experiments. We developed our own electrophysiology, microscopy, and microfluidic devices so that may be controlled through a unified IoT architecture. The system allows each device to be monitored and controlled through an online web tool. We present our IoT architecture so other labs may replicate it for their own experiments.

bioengineering↗

Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments

ObjectiveNeural activity represents a functional readout of neurons that is increasingly important to monitor in a wide range of experiments. Extracellular recordings have emerged as a powerful technique for measuring neural activity because these methods do not lead to the destruction or degradation of the cells being measured. Current approaches to electrophysiology have a low throughput of experiments due to manual supervision and expensive equipment. This bottleneck limits broader inferences that can be achieved with numerous long-term recorded samples. ApproachWe developed Piphys, an inexpensive open source neurophysiological recording platform that consists both hardware and software. It is easily accessed and controlled via a standard web interface through Internet of Things (IoT) protocols. Main ResultsWe used a Raspberry Pi as the primary processing device and Intan bioamplifier. We designed a hardware expansion circuit board and software to enable voltage sampling and user interaction. This standalone system was validated with primary human neurons, showing reliability in collecting real-time neural activity. SignificanceThe hardware modules and cloud software allow for remote control of neural recording experiments as well as horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale.

bioengineering↗

Development of a Low-Cost System for Simultaneous Longitudinal Biological Imaging

Simultaneous longitudinal imaging across multiple conditions and replicates has been crucial for scientific studies aiming to understand biological processes and disease. Yet, imaging systems capable of accomplishing these tasks are economically unattainable for most academic and teaching laboratories around the world. Here we propose the Picroscope, which is the first low cost system for simultaneous longitudinal biological imaging made primarily using off-the-shelf and 3D-printed materials. The Picroscope is compatible with standard 24-well cell culture plates and captures 3D z-stack image data. The Picroscope can be controlled remotely, allowing for automatic imaging with minimal intervention from the investigator. Here we use this system in a range of applications. We gathered longitudinal whole organism image data for frogs, zebrafish and planaria worms. We also gathered image data inside an incubator to observe 2D monolayers and 3D mammalian tissue culture models. Using this tool, we can measure the behavior of entire organisms or individual cells over long time periods.

bioengineering↗