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1. Microfluidic Tissue Processing Platform for Single Cell Analysis and Primary Cell Isolation

   Lombardo, Jeremy; Aliaghaei, Marzieh; Haun, Jered (October 2020, IEEE Biomedical Engineering Society Annual Meeting (BMES 2020))

   null (Ed.)

   Tissues are highly complex ecosystems of different cell types. To capture this heterogeneity, high throughput single cell analysis methods are increasingly being employed. These methods, however, require that tissues first be dissociated into cellular suspensions, and this process represents a bottleneck hindering these efforts. Conventional protocols are inefficient, relying on manual, time-consuming, and variable steps. Advances in microfabricated technologies, however, hold exciting potential to carry out these procedures on-chip in a high throughput, repeatable, and automated fashion. Here, we present a microfluidic platform consisting of 3 different tissue-processing technologies that significantly improves the breakdown of diverse tissue types into high quality cell suspensions that are ready for downstream single cell analysis. 

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2. Optimization of Mechanical Tissue Dissociation Using an Integrated Microfluidic Device for Improved Generation of Single Cells Following Digestion

   https://doi.org/10.3389/fbioe.2022.841046  

   Aliaghaei, Marzieh; Haun, Jered B. (February 2022, Frontiers in Bioengineering and Biotechnology)

   The dissociation of tissue and cell aggregates into single cells is of high interest for single cell analysis studies, primary cultures, tissue engineering, and regenerative medicine. However, current methods are slow, poorly controlled, variable, and can introduce artifacts. We previously developed a microfluidic device that contains two separate dissociation modules, a branching channel array and nylon mesh filters, which was used as a polishing step after tissue processing with a microfluidic digestion device. Here, we employed the integrated disaggregation and filtration (IDF) device as a standalone method with both cell aggregates and traditionally digested tissue to perform a well-controlled and detailed study into the effect of mechanical forces on dissociation, including modulation of flow rate, device pass number, and even the mechanism. Using a strongly cohesive cell aggregate model, we found that single cell recovery was highest using flow rates exceeding 40 ml/min and multiple passes through the filter module, either with or without the channel module. For minced and digested kidney tissue, recovery of diverse cell types was maximal using multiple passes through the channel module and only a single pass through the filter module. Notably, we found that epithelial cell recovery from the optimized IDF device alone exceeded our previous efforts, and this result was maintained after reducing digestion time to 20 min. However, endothelial cells and leukocytes still required extended digestion time for maximal recover. These findings highlight the significance of parameter optimization to achieve the highest cell yield and viability based on tissue sample size, extracellular matrix content, and strength of cell-cell interactions. 

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3. Engineering anisotropic human stem cell-derived three-dimensional cardiac tissue on-a-chip

   https://doi.org/doi: 10.1016/j.biomaterials.2020.120195  

   Veldhuizen, Jaimeson; Cutts, Joshua; Brafman, David; Migrino, Raymond Q.; Nikkhah, Mehdi (October 2020, Biomaterials)

   Despite significant efforts in the study of cardiovascular diseases (CVDs), they persist as the leading cause of mortality worldwide. Considerable research into human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) has highlighted their immense potential in the development of in vitro human cardiac tissues for broad mechanistic, therapeutic, and patient-specific disease modeling studies in the pursuit of CVD research. However, the relatively immature state of hPSC-CMs remains an obstacle in enhancing clinical relevance ofengineered cardiac tissue models. In this study, we describe development of a microfluidic platform for 3D modeling of cardiac tissues, derived from both rat cells and hPSC-CMs, to better recapitulate the native myocardium through co-culture with interstitial cells (specifically cardiac fibroblasts), biomimetic collagen hydrogel encapsulation, and induction of highly anisotropic tissue architecture. The presented platform is precisely engineered through incorporation of surface topography in the form of staggered microposts to enable long-term culture and maturation of cardiac cells, resulting in formation of physiologically relevant cardiac tissues with anisotropy that mimics native myocardium. After two weeks of culture, hPSC-derived cardiac tissues exhibited well-defined sarcomeric striations, highly synchronous contractions, and upregulation of several maturation genes, including HCN1, KCNQ1, CAV1.2, CAV3.1, PLN, and RYR2. These findings demonstrate the ability of the proposed engineered platform to mature animal- as well as human stem cell-derived cardiac tissues over an extended period of culture, providing a novel microfluidic chip with the capability for cardiac disease modeling and therapeutic testing. 

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4. Deep Batch Integration and Denoise of Single‐Cell RNA‐Seq Data

   https://doi.org/10.1002/advs.202308934  

   Qin, Lu; Zhang, Guangya; Zhang, Shaoqiang; Chen, Yong (May 2024, Advanced Science)

   Abstract Numerous single‐cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single‐cell RNA sequencing (scRNA‐seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA‐seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning‐based method for batch effect correction, non‐linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial‐based autoencoder with dual Kullback–Leibler divergence loss functions, aligning cell points from different batches within a consistent low‐dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple‐batch scRNA‐seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA‐seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell‐specific differentially expressed genes. 

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5. Continuous Viscoelasticity Measurement of Cell Spheroids via Microfluidic Electrical Aspiration

   https://doi.org/10.1021/acssensors.4c01405  

   Chen, Heyi; Brown, Jacob; Urban, Aaron; Zhang, Ge; Zhe, Jiang (November 2024, ACS Sensors)

   Measurement of viscoelastic characteristics of cells cultured in 3D is critical to study many biological processes including tissue and organ growth. In this article, we present a unique electrical aspiration method to measure the viscoelastic properties of cell spheroids. A microfluidic sensor was created to demonstrate this method. Unlike the traditional optical aspiration method, the aspiration of the cell spheroids is monitored via monitoring the dynamic electrical resistance change of a symmetrical microfluidic aspiration channel. We first used the microfluidic device to measure the viscoelastic properties of two types of biological tissues derived from calfskin and porcine left ventricular myocardium. The equilibrium elastic modulus and creep time con-stants were measured to be 148.1±24.1 kPa and 76.7±3.5seconds and 64.5±7.7 kPa and 31.4±2.7 seconds respectively, which matched well with reported data. The test validated the principle of the electrical aspiration method. Next, we applied the method for measuring cell spheroids made of human mesenchymal stem cells at different culture stages. The equilibrium elastic modulus and apparent viscosity decreased with increasing culture time. Compared to optical aspiration methods, this microfluidic electrical aspiration method has no reliance on transparent materials and image processing, which thus allows simple set-up, fast data acquisition and analysis. The use of a symmetric aspiration channel and the linear half-space model enable measurements of a large number of viscoelastic properties via a single measurement with higher accuracy. This method will enable high throughput, continuous viscoelastic measurement of cell spheroids as well as other 3D cell culture models in flow conditions without the need for any optical measurements 

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