Cyborg Organoids: Implantation of Nanoelectronics via Organogenesis for Tissue-Wide Electrophysiology

Li, Qiang; Nan, Kewang; Floch, Paul Le; Lin, Zuwan; Sheng, Hao; Liu, Jia

doi:10.1101/697664

“…In this regard, exploring ClusterMap’s ability to analyze 3D in situ transcriptomics is particularly desired. We applied ClusterMap to two 3D thick-tissue samples: STARmap cardiac organoid 8-gene dataset 25 and STARmap mouse V1 28-gene dataset 6 (Supplementary Table 1). We analyzed the 3D data following the sample protocol described in Fig.…”

Section: Resultsmentioning

confidence: 99%

ClusterMap: multi-scale clustering analysis of spatial gene expression

He

¹

,

Tang

²

,

Huang

³

et al. 2021

Preprint

Self Cite

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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 present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs 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-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.

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“…Electronic compounds and devices are generally pre‐assembled before their incorporation with biological structures, hindering the interface between both systems due to poor integration (Q. Li et al, 2019). This integration can be improved following bionic manufacturing approaches based on functionalization of biological surfaces with nanomaterials to fabricate “cyborg cells” and bioelectronic interfaces (S. Konnova et al, 2015).…”

Section: Wireless Nanobioelectronic Toolsmentioning

confidence: 99%

Toward nanobioelectronic medicine: Unlocking new applications using nanotechnology

Gibney

¹

,

Hicks

²

,

Robinson

³

et al. 2021

WIREs Nanomed Nanobiotechnol

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Bioelectronic medicine aims to interface electronic technology with biological components and design more effective therapeutic and diagnostic tools. Advances in nanotechnology have moved the field forward improving the seamless interaction between biological and electronic components. In the lab many of these nanobioelectronic devices have the potential to improve current treatment approaches, including those for cancer, cardiovascular disorders, and disease underpinned by malfunctions in neuronal electrical communication. While promising, many of these devices and technologies require further development before they can be successfully applied in a clinical setting. Here, we highlight recent work which is close to achieving this goal, including discussion of nanoparticles, carbon nanotubes, and nanowires for medical applications. We also look forward toward the next decade to determine how current developments in nanotechnology could shape the growing field of bioelectronic medicine. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Diagnostic Tools > Biosensing

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“…In this regard, exploring ClusterMap's ability to analyze 3D in situ transcriptomics is particularly desired. We applied ClusterMap to two 3D thick-tissue samples: STARmap cardiac organoid 8gene dataset 25 and STARmap mouse V1 28-gene dataset 6 (Supplementary Table 1). We analyzed the 3D data following the sample protocol described in Fig.…”

Section: Clustermap Analyses In Thick Tissue Blocksmentioning

confidence: 99%

ClusterMap: multi-scale clustering analysis of spatial gene expression

He

¹

,

Tang

²

,

Huang

³

et al. 2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

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 present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs 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-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.

show abstract

Cyborg Organoids: Implantation of Nanoelectronics via Organogenesis for Tissue-Wide Electrophysiology

Cited by 20 publications

References 33 publications

ClusterMap: multi-scale clustering analysis of spatial gene expression

ClusterMap: multi-scale clustering analysis of spatial gene expression

Toward nanobioelectronic medicine: Unlocking new applications using nanotechnology

ClusterMap: multi-scale clustering analysis of spatial gene expression

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