Tissue-wide electrophysiology with single-cell and millisecond spatiotemporal resolution is critical for heart and brain studies. Issues arise, however, from the invasive, localized implantation of electronics that destroys well-connected cellular networks within matured organs. Here, we report the creation of cyborg organoids: the three-dimensional (3D) assembly of soft, stretchable mesh nanoelectronics across the entire organoid by the cell–cell attraction forces from 2D-to-3D tissue reconfiguration during organogenesis. We demonstrate that stretchable mesh nanoelectronics can migrate with and grow into the initial 2D cell layers to form the 3D organoid structure with minimal impact on tissue growth and differentiation. The intimate contact between the dispersed nanoelectronics and cells enables us to chronically and systematically observe the evolution, propagation, and synchronization of the bursting dynamics in human cardiac organoids through their entire organogenesis.
architecture, diversity, and electrophysiology of the human brain at early stages. [7,8] Brain organoids thus provide a reliable and easily accessible platform to study human brain development and neurodevelopmental diseases, [9][10][11][12] bridging the gap between animal research and human clinical study.However, long-term stable recording of single-cell electrophysiology in developing brain organoids is still a challenge. The recording technology not only needs to form minimally invasive and long-term stable electrical interfaces with individual neurons 3D distributed across brain organoids but also needs to accommodate the rapid volume change occurring during the organoid organogenesis and cortical expansion. Optical imaging coupled with fluorescence dyes [13] or calcium indicators [14] has been used to visualize the neuron activities in 3D. They, however, are limited by temporal resolution, penetration depth, and long-term signal stability. Electrical measurement techniques such as 2D multielectrode arrays (MEA) [15,16] and patch-clamp [17,18] have been applied to measure the functional development of brain organoids, but they can only capture the activities from the bottom surface of brain organoids [1,19,20] or assay one cell at a time with cell membrane disruption. The recent development of 3D bioelectronics enables 3D interfaces with brain organoids. [21][22][23][24][25][26][27] However, they either only contact organoids at the surface by flexible electronics, [21][22][23] where noncorrelated and 3D-distributed single-unit action potentials cannot be recorded, or penetrate organoids invasively by rigid probes, [25] which cannot further accommodate volume and morphological changes of brain organoids during development. It has also been shown that organoids can grow around a suspended array of electrodes, [26,27] but the electrodes cannot deform to adapt to the morphological changes of the organoid. To date, it is still a challenge to noninvasively probe neuron activity at single-cell, single-spike spatiotemporal resolution across the 3D volume of brain organoids, and over the time course of development. This constraint prevents further understanding of the functional development in brain organoids and standardizing culture conditions and protocols for brain organoid generation based on their electrical functions.Recently, we developed a cyborg organoid platform by integrating "tissue-like" stretchable mesh nanoelectronics with 2D stem cell sheets. Leveraging the 2D-to-3D reconfiguration Human induced pluripotent stem cell derived brain organoids have shown great potential for studies of human brain development and neurological disorders. However, quantifying the evolution of the electrical properties of brain organoids during development is currently limited by the measurement techniques, which cannot provide long-term stable 3D bioelectrical interfaces with developing brain organoids. Here, a cyborg brain organoid platform is reported, in which "tissue-like" stretchable mesh nanoelectronics are designed...
Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we introduce an unsupervised and annotation-free framework, termed ClusterMap, which incorporates the physical location and gene identity of RNAs, formulates the task as a point pattern analysis problem, and identifies biologically meaningful structures by density peak clustering (DPC). Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and performs consistently on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell niche, and tissue organization principles from images with high-dimensional transcriptomic profiles.
Recording the activity of the same neurons over the adult life of an animal is important to neuroscience research and biomedical applications. Current implantable devices cannot provide stable recording on this time scale. Here, we introduce a method to precisely implant nanoelectronics with an open, unfolded mesh structure across multiple brain regions in the mouse.The open mesh structure forms a stable interwoven structure with the neural network, preventing probe drifting and showing no immune response and neuron loss during the yearlong implantation.Using the implanted nanoelectronics, we can track single-unit action potentials from the same neurons over the entire adult life of mice. Leveraging the stable recordings, we build machine learning algorithms that enable automated spike sorting, noise rejection, stability validation, and generate pseudotime analysis, revealing aging-associated evolution of the single-neuron activities.
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