Microfluidic Platforms for Yeast-Based Aging Studies

An integrative temperature-controlled microfluidic system for budding yeast heat shock response analysis at the single-cell level

Cells can respond and adapt to complex forms of environmental change. Budding yeast is widely used as a model system for these stress response studies. In these studies, the precise control of the environment with high temporal resolution is most important. However, there is a lack of single-cell research platforms that enable precise control of the temperature and form of cell growth. This has hindered our understanding of cellular coping strategies in the face of diverse forms of temperature change. Here, we developed a novel temperature-controlled microfluidic platform that integrates a microheater (using liquid metal) and a thermocouple (liquid metal vs. conductive PDMS) on a chip. Three forms of temperature changes (step, gradient, and periodical oscillations) were realized by automated equipment. The platform has the advantages of low cost and a simple fabrication process. Moreover, we investigated the nuclear entry and exit behaviors of the transcription factor Msn2 in yeast in response to heat stress (37 °C) with different heating modes. The feasibility of this temperature-controlled platform for studying the protein dynamic behavior of yeast cells was demonstrated.

Microfluidic Platforms for Real-Time In Situ Monitoring of Biomarkers for Cellular Processes

Cellular processes are mechanisms carried out at the cellular level that are aimed at guaranteeing the stability of the organism they comprise. The investigation of cellular processes is key to understanding cell fate, understanding pathogenic mechanisms, and developing new therapeutic technologies. Microfluidic platforms are thought to be the most powerful tools among all methodologies for investigating cellular processes because they can integrate almost all types of the existing intracellular and extracellular biomarker-sensing methods and observation approaches for cell behavior, combined with precisely controlled cell culture, manipulation, stimulation, and analysis. Most importantly, microfluidic platforms can realize real-time in situ detection of secreted proteins, exosomes, and other biomarkers produced during cell physiological processes, thereby providing the possibility to draw the whole picture for a cellular process. Owing to their advantages of high throughput, low sample consumption, and precise cell control, microfluidic platforms with real-time in situ monitoring characteristics are widely being used in cell analysis, disease diagnosis, pharmaceutical research, and biological production. This review focuses on the basic concepts, recent progress, and application prospects of microfluidic platforms for real-time in situ monitoring of biomarkers in cellular processes.

A computer vision and residual neural network (ResNet) combined method for automated and accurate yeast replicative aging analysis of high-throughput microfluidic single-cell images

With the rapid development of microfluidic platforms in high-throughput single-cell culturing, laborious operation to manipulate massive budding yeast cells (Saccharomyces cerevisiae) in replicative aging studies has been greatly simplified and automated. As a result, large datasets of microscopy images bring challenges to fast and accurately determine yeast replicative lifespan (RLS), which is the most important parameter to study cell aging. Based on our microfluidic diploid yeast long-term culturing (DYLC) chip that features 1100 traps to immobilize single cells and record their proliferation and aging via time-lapse imaging, herein, a dedicated algorithm combined with computer vision and residual neural network (ResNet) was presented to efficiently process tremendous micrographs in a high-throughput and automated manner. The image-processing algorithm includes following pivotal steps: (i) segmenting multi-trap micrographs into time-lapse single-trap sub-images, (ii) labeling 8 yeast budding features and training the 18-layer ResNet, (iii) converting the ResNet predictions in analog values into digital signals, (iv) recognizing cell dynamic events, and (v) determining yeast RLS and budding time interval (BTI) ultimately. The ResNet algorithm achieved high F1 scores (over 92%) demonstrating the effectiveness and accuracy in the recognition of yeast budding events, such as bud appearance, daughter dissection and cell death. Therefore, the results conduct that similar deep learning algorithms could be tailored to analyze high-throughput microscopy images and extract multiple cell behaviors in microfluidic single-cell analysis.

Fungi-on-a-Chip: microfluidic platforms for single-cell studies on fungi

This review highlights new advances in the emerging field of 'Fungi-on-a-Chip' microfluidics for single-cell studies on fungi and discusses several future frontiers, where we envisage microfluidic technology development to be instrumental in aiding our understanding of fungal biology. Fungi, with their enormous diversity, bear essential roles both in nature and our everyday lives. They inhabit a range of ecosystems, such as soil, where they are involved in organic matter degradation and bioremediation processes. More recently, fungi have been recognized as key components of the microbiome in other eukaryotes, such as humans, where they play a fundamental role not only in human pathogenesis, but also likely as commensals. In the food sector, fungi are used either directly or as fermenting agents and are often key players in the biotechnological industry, where they are responsible for the production of both bulk chemicals and antibiotics. Although the macroscopic fruiting bodies are immediately recognizable by most observers, the structure, function, and interactions of fungi with other microbes at the microscopic scale still remain largely hidden. Herein, we shed light on new advances in the emerging field of Fungi-on-a-Chip microfluidic technologies for single-cell studies on fungi. We discuss the development and application of microfluidic tools in the fields of medicine and biotechnology, as well as in-depth biological studies having significance for ecology and general natural processes. Finally, a future perspective is provided, highlighting new frontiers in which microfluidic technology can benefit this field.

A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2 s during ethanol fermentation automatically for over 168 h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.

Live cell microscopy: From image to insight

Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.

How to Perform a Microfluidic Cultivation Experiment—A Guideline to Success

As a result of the steadily ongoing development of microfluidic cultivation (MC) devices, a plethora of setups is used in biological laboratories for the cultivation and analysis of different organisms. Because of their biocompatibility and ease of fabrication, polydimethylsiloxane (PDMS)-glass-based devices are most prominent. Especially the successful and reproducible cultivation of cells in microfluidic systems, ranging from bacteria over algae and fungi to mammalians, is a fundamental step for further quantitative biological analysis. In combination with live-cell imaging, MC devices allow the cultivation of small cell clusters (or even single cells) under defined environmental conditions and with high spatio-temporal resolution. Yet, most setups in use are custom made and only few standardised setups are available, making trouble-free application and inter-laboratory transfer tricky. Therefore, we provide a guideline to overcome the most frequently occurring challenges during a MC experiment to allow untrained users to learn the application of continuous-flow-based MC devices. By giving a concise overview of the respective workflow, we give the reader a general understanding of the whole procedure and its most common pitfalls. Additionally, we complement the listing of challenges with solutions to overcome these hurdles. On selected case studies, covering successful and reproducible growth of cells in MC devices, we demonstrate detailed solutions to solve occurring challenges as a blueprint for further troubleshooting. Since developer and end-user of MC devices are often different persons, we believe that our guideline will help to enhance a broader applicability of MC in the field of life science and eventually promote the ongoing advancement of MC.

Investigation of daughter cell dissection coincidence of single budding yeast cells immobilized in microfluidic traps

Microfluidic methodologies allow for automatic and high-throughput replicative lifespan (RLS) determination of single budding yeast cells. However, the resulted RLS is highly impacted by the robustness of experimental conditions, especially the microfluidic yeast-trapping structures, which are designed for cell retention, growth, budding, and daughter cell dissection. In this work, four microfluidic yeast-trapping structures, which were commonly used to immobilize mother cells and remove daughter cells for entire lifespan of budding yeast, were systematically investigated by means of finite element modeling (FEM). The results from this analysis led us to propose an optimized design, the yeast rotation (YRot) trap, which is a "leaky bowl"-shaped structure composed of two mirrored microcolumns facing each other. The YRot trap enables stable retention of mother cells in its "bowl" and hydrodynamic rotation of buds into its "leaky orifice" such that matured progenies can be dissected in a coincident direction. We validated the functions of the YRot trap in terms of cell rotation and daughter dissection by both FEM simulations and experiments. With the integration of denser YRot traps in microchannels, the microfluidic platform with stable single-yeast immobilization, long-term cell culturing, and coincident daughter dissection could potentially improve the robustness of experimental conditions for precise RLS determination in yeast aging studies.

Cell organelles and yeast longevity: an intertwined regulation

Organelles are dynamic structures of a eukaryotic cell that compartmentalize various essential functions and regulate optimum functioning. On the other hand, ageing is an inevitable phenomenon that leads to irreversible cellular damage and affects optimum functioning of cells. Recent research shows compelling evidence that connects organelle dysfunction to ageing-related diseases/disorders. Studies in several model systems including yeast have led to seminal contributions to the field of ageing in uncovering novel pathways, proteins and their functions, identification of pro- and anti-ageing factors and so on. In this review, we present a comprehensive overview of findings that highlight the role of organelles in ageing and ageing-associated functions/pathways in yeast.

The yeast replicative aging model

It has been nearly three decades since the budding yeast Saccharomyces cerevisiae became a significant model organism for aging research and it has emerged as both simple and powerful. The replicative aging assay, which interrogates the number of times a "mother" cell can divide and produce "daughters", has been a stalwart in these studies, and genetic approaches have led to the identification of hundreds of genes impacting lifespan. More recently, cell biological and biochemical approaches have been developed to determine how cellular processes become altered with age. Together, the tools are in place to develop a holistic view of aging in this single-celled organism. Here, we summarize the current state of understanding of yeast replicative aging with a focus on the recent studies that shed new light on how aging pathways interact to modulate lifespan in yeast.

On-chip analysis of carbon dots effect on yeast replicative lifespan

Carbon dots (CDs) are promising nanomaterials for biosensing, bioimaging, and drug delivery due to their large surface area, excellent optical properties, and thermal and chemical stability. However, biosafety of CDs is still understudied, and there is not a generally accepted standard to evaluate the toxicity of CDs. We present a gradient network generator microfluidic device for dose-dependent testing of toxicity of CDs to a unicellular eukaryotic model organism, yeast Pichia pastoris. We fully characterized the microfluidic model and compare its performance with a conventional method. The gradient generator increased the contact area between the mixing species and enabled a high-throughput testing of CDs in a wide range of concentrations in cell chambers. Real time monitoring of yeast cell proliferation in the presence of CDs showed dose-dependent growth inhibition and various budding cell shape profiles. Comparing the result of microfluidic platform and conventional method revealed statistically significant differences in the proliferation rate of the cells between the two platforms. To understand the toxicity mechanism, we studied binding of CDs to P. pastoris and found increasing interactions of CDs with the cell surface at CDs larger concentrations. This study demonstrated the utility of the gradient generator microfluidic device as a convenient tool for toxicity assessment of nanomaterials at a cellular level.