Hua Ye

Prof. Hua Ye

orcid

Research Focus & Metrics

Academic Career Length

23Years (2004-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 October 2018 in Journal of Membrane Science
Verified
Journal Article
Closed access
ZXZhi Xu
Zhi Xu
GLGuozhen Liu
Guozhen Liu
HYHua Ye
Hua Ye
WJWanqin Jin
Wanqin Jin
ZCZhan-Feng Cui
Zhan-Feng Cui

1 September 2020 in Biotechnology Advances
Verified
Journal Article
Open access
JGJulian George
Julian George
CHChia-Chen Hsu
Chia-Chen Hsu
LNLinh Thuy Ba Nguyen
Linh Thuy Ba Nguyen
HYHua Ye
Hua Ye
ZCZhan-Feng Cui
Zhan-Feng Cui
The development of techniques to create and use multiphase microstructured hydrogels (granular hydrogels or microgels) has enabled the generation of cultures with more biologically relevant architecture and use of structured hydrogels is especially pertinent to the development of new types of central nervous system (CNS) culture models and therapies. We review material choice and the customisation of hydrogel structure, as well as the use of hydrogels in developmental models. Combining the use of structured hydrogel techniques with developmentally relevant tissue culture approaches will enable the generation of more relevant models and treatments to repair damaged CNS tissue architecture.

21 July 2020 in Proceedings of the National Academy of Sciences
Verified
Journal Article
Open access
PDF file
CHChia-Chen Hsu
Chia-Chen Hsu
JXJiabao Xu
Jiabao Xu
BBBas Brinkhof
Bas Brinkhof
HWHui Wang
Hui Wang
ZCZhan-Feng Cui
Zhan-Feng Cui
HYHua Ye
Hua Ye
Significance: We developed a label-free and noninvasive single-cell Raman microspectroscopy (SCRM)-based platform to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). Through large-scale Raman spectral analysis, we can distinguish hiPSCs and hiPSC-derived neural cells using their intrinsic biochemical profile. We identified glycogen as a Raman biomarker for neuronal differentiation and validated the results using conventional glycogen detection assays. The parameters obtained from SCRM were processed by a novel machine learning method based on t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, enabling highly accurate and robust cell classification. The platform and the proposed biomarker should also be applicable to other cell types and can shed light on developmental biology and glycogen metabolism disorders.

1 October 2018 in Stem Cell Research
Verified
Journal Article
Open access
FBFabio Bianchi
Fabio Bianchi
MMMajid Malboubi
Majid Malboubi
YLYichen Li
Yichen Li
JGJulian H. George
Julian H. George
AJAntoine Jerusalem
Antoine Jerusalem
HYHua Ye
Hua Ye
Primary rodent neurons and immortalised cell lines have overwhelmingly been used for in vitro studies of traumatic injury to peripheral and central neurons, but have some limitations of physiological accuracy. Motor neurons (MN) derived from human induced pluripotent stem cells (iPSCs) enable the generation of cell models with features relevant to human physiology. To facilitate this, it is desirable that MN protocols both rapidly and efficiently differentiate human iPSCs into electrophysiologically active MNs. In this study, we present a simple, rapid protocol for differentiation of human iPSCs into functional spinal (lower) MNs, involving only adherent culture and use of small molecules for directed differentiation, with the ultimate aim of rapid production of electrophysiologically functional cells for short-term neural injury experiments. We show successful differentiation in two unrelated iPSC lines, by quantifying neural-specific marker expression, and by evaluating cell functionality at different maturation stages by calcium imaging and patch clamping. Differentiated neurons were shown to be electrophysiologically altered by uniaxial mechanical deformation. Spontaneous network activity decreased with applied stretch, indicating aberrant network connectivity. These results demonstrate the feasibility of this rapid, simple protocol for differentiating iPSC-derived MNs, suitable for in vitro neural injury studies focussing on electrophysiological alterations caused by mechanical deformation or trauma.

11 June 2019 in Medical Engineering & Physics
Verified
Journal Article
Closed access
BZBin Zhang
Bin Zhang
QXQian Xue
Qian Xue
JLJintao Li
Jintao Li
LMLiang Ma
Liang Ma
YYYufeng Yao
Yufeng Yao
HYHuayong Yang
Huayong Yang
Corneal disease is one of the most important causes of blindness worldwide. Currently, the dominating treatment of corneal blindness is corneal transplantation. However, the main source of cornea for transplantation is based on donations which is far from enough to meet the requirement (less than 1:70 of cases). The severe shortage of donor cornea promotes the studies of effective corneal alternatives. However, many problems remain and can't be solved in current researches, such as original geometry reconstruction and ocular optical function restoring. 3D bioprinting can be a promising approach for corneal substitution. The advantages of this technology in corneal regeneration enable personalized corneal implant and single or multi-layer corneal equivalents with controllable structure and designed refractive ability. In this review, the progress, applications and limitations of most influential works among current keratoprosthesis and tissue-engineering cornea researches are discussed. Then the applications of 3D bioprinting in manufacturing multi-layered structures and surface are mentioned. Further, the potential, advantages in current research of 3D bioprinting single or multi-layer corneal equivalents and alternatives are discussed. Finally, an insight into the technical challenges and prospective facing the future research of 3D bioprinting corneal alternatives in vivo and in vitro is provided.

1 October 2018 in Journal of Membrane Science
Verified
Journal Article
Closed access
ZXZhi Xu
Zhi Xu
YSYuqing Sun
Yuqing Sun
YZYongxiang Zhuang
Yongxiang Zhuang
WJWenheng Jing
Wenheng Jing
HYHua Ye
Hua Ye
ZCZhan-Feng Cui
Zhan-Feng Cui