Hancong Wu

Impedance-based cellular assays for regenerative medicine

Therapies based on regenerative techniques have the potential to radically improve healthcare in the coming years. As a result, there is an emerging need for non-destructive and label-free technologies to assess the quality of engineered tissues and cell-based products prior to their use in the clinic. In parallel, the emerging regenerative medicine industry that aims to produce stem cells and their progeny on a large scale will benefit from moving away from existing destructive biochemical assays towards data-driven automation and control at the industrial scale. Impedance-based cellular assays (IBCA) have emerged as an alternative approach to study stem-cell properties and cumulative studies, reviewed here, have shown their potential to monitor stem-cell renewal, differentiation and maturation. They offer a novel method to non-destructively assess and quality-control stem-cell cultures. In addition, when combined with in vitro disease models they provide complementary insights as label-free phenotypic assays. IBCA provide quantitative and very sensitive results that can easily be automated and up-scaled in multi-well format. When facing the emerging challenge of real-time monitoring of three-dimensional cell culture dielectric spectroscopy and electrical impedance tomography represent viable alternatives to two-dimensional impedance sensing. This article is part of the theme issue ‘Designer human tissue: coming to a lab near you’.

Image Reconstruction for Electrical Impedance Tomography Using Enhanced Adaptive Group Sparsity With Total Variation

A novel image reconstruction algorithm for electrical impedance tomography using enhanced adaptive group sparsity with total variation constraint is proposed in this paper. The new algorithm simultaneously utilizes the prior knowledge of regional structure feature and global characteristic of the conductivity distribution. The regional structure feature is encoded by using an enhanced adaptive group sparsity constraint. Meanwhile, the global characteristic of inclusion boundary is considered by imposing total variation constraint on the whole image. An enhanced adaptive pixel grouping algorithm is proposed based on Otsu’s thresholding method, which demonstrates good noise immunity. An accelerated alternating direction method of multipliers is utilized to solve the proposed problem for a faster convergence rate. The performance of the proposed algorithm is thoroughly evaluated by numerical simulation and experiments. Comparing with the state-of-the-art algorithms, such as the L1 regularization, total variation regularization, and our former work on adaptive group sparsity, the proposed method has demonstrated superior spatial resolution and better noise reduction performance. Combined with the total variation constraint, distinct boundary of inclusions has also been obtained.

Exploring the Potential of Electrical Impedance Tomography for Tissue Engineering Applications

In tissue engineering, cells are generally cultured in biomaterials to generate three-dimensional artificial tissues to repair or replace damaged parts and re-establish normal functions of the body. Characterizing cell growth and viability in these bioscaffolds is challenging, and is currently achieved by destructive end-point biological assays. In this study, we explore the potential to use electrical impedance tomography (EIT) as a label-free and non-destructive technology to assess cell growth and viability. The key challenge in the tissue engineering application is to detect the small change of conductivity associated with sparse cell distributions in regards to the size of the hosting scaffold, i.e., low volume fraction, until they assemble into a larger tissue-like structure. We show proof-of-principle data, measure cells within both a hydrogel and a microporous scaffold with an ad-hoc EIT equipment, and introduce the frequency difference technique to improve the reconstruction.