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

Seiler, Spencer T.; Mantalas, Gary L.; Selberg, John; Cordero, Sergio; Torres-Montoya, Sebastian; Baudin, Pierre V.; Ly, Victoria T.; Amend, Finn; Tran, Liam; Hoffman, Ryan N.; Rolandi, Marco; Green, Edward; Haussler, David; Salama, Sofie R.; Teodorescu, Mircea

doi:10.1038/s41598-022-20096-9

Cited by 25 publications

(15 citation statements)

References 39 publications

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“…1C), and replaced by an equivalent volume of fresh media. Both types of media are perfused through flexible fluorinated ethylene propylene (FEP) tubing at 110 mm/s, which leads to low shear forces ( 30 ) (see Methods, Automated fluidics organoid culture ).…”

Section: Resultsmentioning

confidence: 99%

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

Voitiuk,

Seiler,

Pessoa de Melo

et al. 2024

Preprint

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The analysis of tissue cultures, particularly brain organoids, takes a high degree of coordination, measurement, and monitoring. We have developed an automated research platform enabling independent devices to achieve collaborative objectives for feedback-driven cell culture studies. Unified by an Internet of Things (IoT) architecture, our approach enables continuous, communicative interactions 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 and monitor their neuronal activity. The organoids are cultured in custom, 3D-printed chambers attached to commercial microelectrode arrays for electrophysiology monitoring. Periodic feeding is achieved using programmable microfluidic pumps. We developed computer vision fluid volume estimations of aspirated media, achieving high accuracy, and used feedback to rectify deviations in microfluidic perfusion during media feeding/aspiration cycles. We validated the system with a 7-day study of mouse cerebral cortex organoids, comparing manual and automated protocols. The automated experimental samples maintained robust neural activity throughout the experiment, comparable with the control samples. The automated system enabled hourly electrophysiology recordings that revealed dramatic temporal changes in neuron firing rates not observed in once-a-day recordings.

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Section: Resultsmentioning

confidence: 99%

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

Voitiuk,

Seiler,

Pessoa de Melo

et al. 2024

Preprint

Self Cite

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“…Yet, like many pluripotent stem cell-derived models, cortical organoids are affected by cell line variability and culture con-ditions that can affect the reproducibility of the protocols [76]. Moreover, transcriptomic analysis of cortical organoids has revealed strong signatures of cell stress [77, 78, 79], which can impair proper cell type specification [80]. In addition, in vitro conditions generate cell types of uncharacterized identity, that do not have an in vivo counterpart [78, 81].…”

Section: Resultsmentioning

confidence: 99%

Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis

Lehrer

Gonzalez-Ferrer

Haussler

et al. 2023

Preprint

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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 Machine 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 higher accuracy than state-of-the-art single cell classifiers. This accuracy is maintained in trans-sample label transfers of the adult human cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal subtypes, even in periods of fate refinement. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities in previously unclassified cells and to uncover genetic variations in the developmental trajectories of organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

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“…While the droplets insulate cells from contamination, perfluorocarbon oils provide air permeability and allow the cells to exchange air with the outside world. Bacteria, yeast, and mammalian cells ( Huebner et al, 2007 ; Köster et al, 2008 ; Pang et al, 2021 ; Seiler et al, 2022 ) have been proven able to survive encapsulated in droplets for a long time and retain good cell activity after resuscitation, which can be used for various biological measurements. Compared with traditional culture methods, fewer reagents are consumed, but vitality is slightly decreased ( Nakagawa et al, 2021 ).…”

Section: Single-cell Analysis Technique Based On Droplet Microfluidicsmentioning

confidence: 99%

Droplets microfluidics platform—A tool for single cell research

Cheng

et al. 2023

Front. Bioeng. Biotechnol.

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Cells are the most basic structural and functional units of living organisms. Studies of cell growth, differentiation, apoptosis, and cell-cell interactions can help scientists understand the mysteries of living systems. However, there is considerable heterogeneity among cells. Great differences between individuals can be found even within the same cell cluster. Cell heterogeneity can only be clearly expressed and distinguished at the level of single cells. The development of droplet microfluidics technology opens up a new chapter for single-cell analysis. Microfluidic chips can produce many nanoscale monodisperse droplets, which can be used as small isolated micro-laboratories for various high-throughput, precise single-cell analyses. Moreover, gel droplets with good biocompatibility can be used in single-cell cultures and coupled with biomolecules for various downstream analyses of cellular metabolites. The droplets are also maneuverable; through physical and chemical forces, droplets can be divided, fused, and sorted to realize single-cell screening and other related studies. This review describes the channel design, droplet generation, and control technology of droplet microfluidics and gives a detailed overview of the application of droplet microfluidics in single-cell culture, single-cell screening, single-cell detection, and other aspects. Moreover, we provide a recent review of the application of droplet microfluidics in tumor single-cell immunoassays, describe in detail the advantages of microfluidics in tumor research, and predict the development of droplet microfluidics at the single-cell level.

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

Cited by 25 publications

References 39 publications

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

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

Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis

Droplets microfluidics platform—A tool for single cell research

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