Integration of silicon chip microstructures for in-line microbial cell lysis in soft microfluidics

The paper presents fabrication methodologies that integrate silicon components into soft microfluidic devices to perform microbial cell lysis for biological applications. The integration methodology consists of a silicon chip that is fabricated with microstructure arrays and embedded in a microfluidic device, which is driven by piezoelectric actuation to perform cell lysis by physically breaking microbial cell walls via micromechanical impaction. We present different silicon microarray geometries, their fabrication techniques, integration of said micropatterned silicon impactor chips into microfluidic devices, and device operation and testing on synthetic microbeads and two yeast species (S. cerevisiae and C. albicans) to evaluate their efficacy. The generalized strategy developed for integration of the micropatterned silicon impactor chip into soft microfluidic devices can serve as an important process step for a new class of hybrid silicon-polymeric devices for future cellular processing applications. The proposed integration methodology can be scalable and integrated as an in-line cell lysis tool with existing microfluidics assays

Critical thinking for 21st-century education: A cyber-tooth curriculum?

It is often assumed that the advent of digital technologies requires fundamental change to the curriculum and to the teaching and learning approaches used in schools around the world to educate this generation of “digital natives” or the “net generation”. This article analyses the concepts of 21st-century skills and critical thinking, to understand how these aspects of learning might contribute to a 21st-century education. The author argues that, although both critical thinking and 21st-century skills are indeed necessary in a curriculum for a 21st-century education, they are not sufficient, even in combination. The role of knowledge and an understanding of differing cultural perspectives and values indicate that education should also fit local contexts in a global world and meet the specific needs of students in diverse cultures. It should also fit the particular technical and historical demands of the 21st century in relation to digital skills

Investigating Cellular Variability in Fungal Pathogens by Developing mDrop-seq, a High Throughput, Single Cell RNAseq Technology for Yeast Species

The rise of high throughput single-cell RNA sequencing increased our understanding of cellular population dynamics and the heterogeneity and stochasticity between individual cells. These gains have thus far been lost on microbial cells due to complicating factors that rendered microbes incompatible with technologies developed on mammalian cells. However, not all these drawbacks are present within Eukaryotic yeast cells, making them an ideal target microbe for technological development. In this dissertation, the development of mDrop-seq, a high throughput scRNA-seq for yeast species, is displayed through the processing of thousands of cells of two yeast species. In the first chapter, we use the model organism S. cerevisiae for initial development, testing, and profiling of 35,109 total yeast cells. In doing so, we test appropriate lysis conditions and time to allow for droplet microfluidic compatible cell lysis. S. cerevisiae cells are subjected to a 42°C heat shock in order to determine mDrop-seq capability to detect a large scale stress response at single cell resolution. Analytical pipelines for single-cell analysis that were developed for mammalian data are shown to work with yeast libraries, allowing for differential gene expression (DGE), clustering analysis, cell cycle assignment, and pseudo-time trajectory analysis. In the second chapter, we described further modifying and testing mDrop-seq on the clinically relevant species Candida albicans. Despite challenges such as thicker cell walls, we display mDrop-seq’s ability to process C. albicans cells using exposure to the antifungal drug Fluconazole. The final chapter of this dissertation uses mDrop-seq to search for sources of variation and batch effects within our data. We show that the activation of stress response pathways causes a reduction in transcriptomic variation between C. albicans cells. In total, the chapters of this dissertation show mDrop-seq’s value as a low cost, scalable scRNA-seq technology for yeast species

Supplying Power to Chimborazo, Ecuador

https://digitalcommons.wpi.edu/gps-posters/1375/thumbnail.jp

mDrop-Seq: Massively Parallel Single-Cell RNA-Seq of <i>Saccharomyces cerevisiae</i> and <i>Candida albicans</i>

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have been limited by technical challenges such as tough cell walls and low RNA quantity that prevent transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is demonstrated on two yeast species, Saccharomyces cerevisiae, a popular model organism, and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to S. cerevisiae. Single-cell transcriptomes in 39,705 C. albicans cells were characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity when treated with fluconazole. We demonstrate mDrop-seq to be an affordable and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to a better understanding of genetic variation in pathogens in response to stimuli and find immediate applications in investigating drug resistance, infection outcome and developing new drugs and treatment strategies