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Showing 1–9 of 9 results

Advanced filters: Author: Zuwan Lin Clear advanced filters

Brain implantation of soft bioelectronics via embryonic development

A soft mesh microelectrode array can seamlessly integrate in developing brains, enabling long-term, stable mapping of how single-neuron activity and population dynamics emerge and evolve during brain development.

Hao Sheng
Ren Liu
Jia Liu
Research11 Jun 2025
Nature

Volume: 642, P: 954-964
Author Correction: Tracking neural activity from the same cells during the entire adult life of mice

Siyuan Zhao
Xin Tang
Jia Liu
Amendments and Corrections21 Apr 2023
Nature Neuroscience

Volume: 26, P: 1129
Publisher Correction: Spatial atlas of the mouse central nervous system at molecular resolution

Hailing Shi
Yichun He
Xiao Wang
Amendments and CorrectionsOpen Access08 Dec 2023
Nature

Volume: 625, P: E6
Cyborg organoids integrated with stretchable nanoelectronics can be functionally mapped during development

The integration of stretchable mesh nanoelectronics with stem cells enables the creation of cyborg organoid tissue, which can be longitudinally characterized during development to improve organoid models.

Zuwan Lin
Wenbo Wang
Jia Liu
Protocols26 Mar 2025
Nature Protocols

Volume: 20, P: 2528-2559
Spatial atlas of the mouse central nervous system at molecular resolution

In situ spatial transcriptomic analysis of more than 1 million cells are used to create a 200-nm-resolution spatial molecular atlas of the adult mouse central nervous system and identify previously unknown tissue architectures.

Hailing Shi
Yichun He
Xiao Wang
ResearchOpen Access27 Sept 2023
Nature

Volume: 622, P: 552-561
ClusterMap for multi-scale clustering analysis of spatial gene expression

In situ transcriptomics maps RNA expression patterns across intact tissues taking our understanding of gene expression to a new level. Here, the authors present a computational method that uncovers gene expression, cell niche, and tissue region patterns from 2D and 3D spatial transcriptomics.

Yichun He
Xin Tang
Xiao Wang
ResearchOpen Access08 Oct 2021
Nature Communications

Volume: 12, P: 1-13
Explainable multi-task learning for multi-modality biological data analysis

Multimodal biological data is challenging to analyze. Here, the authors develop UnitedNet, an explainable deep neural network for analyzing single-cell multimodal biological data and estimating relationships between gene expression and other modalities with cell-type specificity.

Xin Tang
Jiawei Zhang
Jia Liu
ResearchOpen Access03 May 2023
Nature Communications

Volume: 14, P: 1-19
Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape

TEMPOmap combines pulse-chase metabolic labeling with multiplexed three-dimensional in situ sequencing to simultaneously profile the age and subcellular location of individual RNA molecules from thousands of genes to reveal RNA kinetic landscapes.

Jingyi Ren
Haowen Zhou
Xiao Wang
ResearchOpen Access10 Apr 2023
Nature Methods

Volume: 20, P: 695-705
Tracking neural activity from the same cells during the entire adult life of mice

The authors developed flexible, unfolded mesh electronics for implantation in multiple brain regions of mice. The probes show minimal immune response and electrode drift, enabling stable recording of single-unit action potentials from the same neurons during the adult life of animals.

Siyuan Zhao
Xin Tang
Jia Liu
Research20 Feb 2023
Nature Neuroscience

Volume: 26, P: 696-710