Enabling systems biology approaches through microfabricated systems

Fly Me to the Micron: Microtechnologies for Drosophila Research

Multicellular model organisms, such as Drosophila melanogaster (fruit fly), are frequently used in a myriad of biological research studies due to their biological significance and global standardization. However, traditional tools used in these studies generally require manual handling, subjective phenotyping, and bulk treatment of the organisms, resulting in laborious experimental protocols with limited accuracy. Advancements in microtechnology over the course of the last two decades have allowed researchers to develop automated, high-throughput, and multifunctional experimental tools that enable novel experimental paradigms that would not be possible otherwise. We discuss recent advances in microtechnological systems developed for small model organisms using D. melanogaster as an example. We critically analyze the state of the field by comparing the systems produced for different applications. Additionally, we suggest design guidelines, operational tips, and new research directions based on the technical and knowledge gaps in the literature. This review aims to foster interdisciplinary work by helping engineers to familiarize themselves with model organisms while presenting the most recent advances in microengineering strategies to biologists.

Compartmentalized Platforms for Neuro-Pharmacological Research

Dissociated primary neuronal cell culture remains an indispensable approach for neurobiology research in order to investigate basic mechanisms underlying diverse neuronal functions, drug screening and pharmacological investigation. Compartmentalization, a widely adopted technique since its emergence in 1970s enables spatial segregation of neuronal segments and detailed investigation that is otherwise limited with traditional culture methods. Although these compartmental chambers (e.g. Campenot chamber) have been proven valuable for the investigation of Peripheral Nervous System (PNS) neurons and to some extent within Central Nervous System (CNS) neurons, their utility has remained limited given the arduous manufacturing process, incompatibility with high-resolution optical imaging and limited throughput. The development in the area of microfabrication and microfluidics has enabled creation of next generation compartmentalized devices that are cheap, easy to manufacture, require reduced sample volumes, enable precise control over the cellular microenvironment both spatially as well as temporally, and permit highthroughput testing. In this review we briefly evaluate the various compartmentalization tools used for neurobiological research, and highlight application of the emerging microfluidic platforms towards in vitro single cell neurobiology.

High-throughput screening approaches and combinatorial development of biomaterials using microfluidics

From the first microfluidic devices used for analysis of single metabolic by-products to highly complex multicompartmental co-culture organ-on-chip platforms, efforts of many multidisciplinary teams around the world have been invested in overcoming the limitations of conventional research methods in the biomedical field. Close spatial and temporal control over fluids and physical parameters, integration of sensors for direct read-out as well as the possibility to increase throughput of screening through parallelization, multiplexing and automation are some of the advantages of microfluidic over conventional, 2D tissue culture in vitro systems. Moreover, small volumes and relatively small cell numbers used in experimental set-ups involving microfluidics, can potentially decrease research cost. On the other hand, these small volumes and numbers of cells also mean that many of the conventional molecular biology or biochemistry assays cannot be directly applied to experiments that are performed in microfluidic platforms. Development of different types of assays and evidence that such assays are indeed a suitable alternative to conventional ones is a step that needs to be taken in order to have microfluidics-based platforms fully adopted in biomedical research. In this review, rather than providing a comprehensive overview of the literature on microfluidics, we aim to discuss developments in the field of microfluidics that can aid advancement of biomedical research, with emphasis on the field of biomaterials. Three important topics will be discussed, being: screening, in particular high-throughput and combinatorial screening; mimicking of natural microenvironment ranging from 3D hydrogel-based cellular niches to organ-on-chip devices; and production of biomaterials with closely controlled properties. While important technical aspects of various platforms will be discussed, the focus is mainly on their applications, including the state-of-the-art, future perspectives and challenges.

STATEMENT OF SIGNIFICANCE

Microfluidics, being a technology characterized by the engineered manipulation of fluids at the submillimeter scale, offers some interesting tools that can advance biomedical research and development. Screening platforms based on microfluidic technologies that allow high-throughput and combinatorial screening may lead to breakthrough discoveries not only in basic research but also relevant to clinical application. This is further strengthened by the fact that reliability of such screens may improve, since microfluidic systems allow close mimicking of physiological conditions. Finally, microfluidic systems are also very promising as micro factories of a new generation of natural or synthetic biomaterials and constructs, with finely controlled properties.

Microfluidic-based biomimetic models for life science research

The advances in microfluidic technology have recently generated various microfluidic-based biomimetic models as novel 3D models for life science research, offering some great advantages over conventional models.

Simultaneous monitoring of Staphylococcus aureus growth in a multi-parametric microfluidic platform using microscopy and impedance spectroscopy

We describe the design, construction, and characterization of a scalable microfluidic platform that allows continuous monitoring of biofilm proliferation under shear stress conditions. Compared to other previous end-point assay studies, our platform offers the advantages of integration into multiple environments allowing simultaneous optical microscopy and impedance spectroscopy measurements. In this work we report a multi-parametric sensor that can monitor the growth and activity of a biofilm. This was possible by combining two interdigitated microelectrodes (IDuEs), and punctual electrodes to measure dissolved oxygen, K+, Na+ and pH. The IDuE has been optimized to permit sensitive and reliable impedance monitoring of Staphylococcus aureus V329 growth with two- and four-electrode measurements. We distinguished structural and morphological changes on intact cellular specimens using four-electrode data modeling. We also detected antibiotic mediated effects using impedance. Results were confirmed by scanning electrode microscopy and fluorescence microscopy after live/dead cell staining. The bacitracin mediated effects detected with impedance prove that the approach described can be used for guiding the development of novel anti-biofilm agents to better address bacterial infection.

Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans

Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision the broad utility of the framework for diverse problems across different length scales and imaging methods.

New technologies have increased the size and content-richness of biological imaging datasets. As a result, automated image processing is increasingly necessary to extract relevant data in an objective, consistent and time-efficient manner. While image processing tools have been developed for general problems that affect large communities of biologists, the diversity of biological research questions and experimental techniques have left many problems unaddressed. Moreover, there is no clear way in which non-computer scientists can immediately apply a large body of computer vision and image processing techniques to address their specific problems or adapt existing tools to their needs. Here, we address this need by demonstrating an adaptable framework for image processing that is capable of accommodating a large range of biological problems with both high accuracy and computational efficiency. Moreover, we demonstrate the utilization of this framework for disparate problems by solving two specific image processing challenges in the model organism Caenorhabditis elegans. In addition to contributions to the C. elegans community, the solutions developed here provide both useful concepts and adaptable image-processing modules for other biological problems.

On-demand control of microfluidic flow via capillary-tuned solenoid microvalve suction

A simple, low-cost and on-demand microfluidic flow controlling platform was developed based on a unique capillary-tuned solenoid microvalve suction effect without any outer pressure source. The suction effect was innovatively employed as a stable and controllable driving force for the manipulation of the microfluidic system by connecting a piece of capillary between the microvalve and the microfluidic chip, which caused significant hydrodynamic resistance differences among the solenoid valve ports and changed the flowing mode inside the valve. The volume of sucked liquid could be controlled from microliters even down to picoliters either by decreasing the valve energized duration (from a maximum energized duration to the valve response time of 20 ms) or by increasing the inserted capillary length (i.e., its hydrodynamic resistance). Several important microfluidic unit operations such as cell/droplet sorting and on-demand size-controllable droplet generation have been demonstrated on the developed platform and both simulations and experiments confirmed that this platform has good controllability and stability.

Shape-controlled synthesis of hybrid nanomaterials via three-dimensional hydrodynamic focusing

Shape-controlled synthesis of nanomaterials through a simple, continuous, and low-cost method is essential to nanomaterials research toward practical applications. Hydrodynamic focusing, with its advantages of simplicity, low-cost, and precise control over reaction conditions, has been used for nanomaterial synthesis. While most studies have focused on improving the uniformity and size control, few have addressed the potential of tuning the shape of the synthesized nanomaterials. Here we demonstrate a facile method to synthesize hybrid materials by three-dimensional hydrodynamic focusing (3D-HF). While keeping the flow rates of the reagents constant and changing only the flow rate of the buffer solution, the molar ratio of two reactants (i.e., tetrathiafulvalene (TTF) and HAuCl4) within the reaction zone varies. The synthesized TTF-Au hybrid materials possess very different and predictable morphologies. The reaction conditions at different buffer flow rates are studied through computational simulation, and the formation mechanisms of different structures are discussed. This simple one-step method to achieve continuous shape-tunable synthesis highlights the potential of 3D-HF in nanomaterials research.

Advances in microfluidic cell separation and manipulation

Cellular separations are required in many contexts in biochemical and biomedical applications for the identification, isolation, and analysis of phenotypes or samples of interest. Microfluidics is uniquely suited for handling biological samples, and emerging technologies have become increasingly accessible tools for researchers and clinicians. Here, we review advances in the last few years in techniques for microfluidic cell separation and manipulation. Applications such as high-throughput cell and organism phenotypic screening, purification of heterogeneous stem cell populations, separation of blood components, and isolation of rare cells in patients highlight some of the areas in which these technologies show great potential. Continued advances in separation mechanisms and understanding of cellular systems will yield further improvements in the throughput, resolution, and robustness of techniques.