A convolutional neural network for segmentation of yeast cells without manual training annotations

MOTIVATION: Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process. RESULTS: We developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism. AVAILABILITY AND IMPLEMENTATION: The synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Frontiers in microfluidics, a teaching resource review

This is a literature teaching resource review for biologically inspired microfluidics courses or exploring the diverse applications of microfluidics. The structure is around key papers and model organisms. While courses gradually change over time, a focus remains on understanding how microfluidics has developed as well as what it can and cannot do for researchers. As a primary starting point, we cover micro-fluid mechanics principles and microfabrication of devices. A variety of applications are discussed using model prokaryotic and eukaryotic organisms from the set of bacteria (Escherichia coli), trypanosomes (Trypanosoma brucei), yeast (Saccharomyces cerevisiae), slime molds (Physarum polycephalum), worms (Caenorhabditis elegans), flies (Drosophila melangoster), plants (Arabidopsis thaliana), and mouse immune cells (Mus musculus). Other engineering and biochemical methods discussed include biomimetics, organ on a chip, inkjet, droplet microfluidics, biotic games, and diagnostics. While we have not yet reached the end-all lab on a chip, microfluidics can still be used effectively for specific applications

Computational methods and software for the design of inertial microfluidic flow sculpting devices

The ability to sculpt inertially flowing fluid via bluff body obstacles has enormous promise for applications in bioengineering, chemistry, and manufacturing within microfluidic devices. However, the computational difficulty inherent to full scale 3-dimensional fluid flow simulations makes designing and optimizing such systems tedious, costly, and generally tasked to computational experts with access to high performance resources. The goal of this work is to construct efficient models for the design of inertial microfluidic flow sculpting devices, and implement these models in freely available, user-friendly software for the broader microfluidics community. Two software packages were developed to accomplish this: uFlow and FlowSculpt . uFlow solves the forward problem in flow sculpting, that of predicting the net deformation from an arbitrary sequence of obstacles (pillars), and includes estimations of transverse mass diffusion and particles formed by optical lithography. FlowSculpt solves the more difficult inverse problem in flow sculpting, which is to design a flow sculpting device which produces a target flow shape. Each piece of software uses efficient, experimentally validated forward models developed within this work, which are applied to deep learning techniques to explore other routes to solving the inverse problem. The models are also highly modular, capable of incorporating new microfluidic components and flow physics to the design process. It is anticipated that the microfluidics community will integrate the tools developed here into their own research, and bring new designs, components, and applications to the inertial flow sculpting platform

Recent microfluidic innovations for sperm sorting

Sperm selection is a clinical need for guided fertilization in men with low-quality semen. In this regard, microfluidics can provide an enabling platform for the precise manipulation and separation of high-quality sperm cells through applying various stimuli, including chemical agents, mechanical forces, and thermal gradients. In addition, microfluidic platforms can help to guide sperms and oocytes for controlled in vitro fertilization or sperm sorting using both passive and active methods. Herein, we present a detailed review of the use of various microfluidic methods for sorting and categorizing sperms for different applications. The advantages and disadvantages of each method are further discussed and future perspectives in the field are given

The Use of Microfluidics and Dielectrophoresis for Separation, Concentration, and Identification of Bacteria

Typical bacterial analysis involves culturing and visualizing colonies on an array of agar plates. The growth patterns and colors among the array are used to identify the bacteria. For fast growing bacteria such as Escherichia coli, analysis will take one to two days. However, slow growing bacteria such as mycobacteria can take weeks to identify. In addition, there are some species of bacteria that are viable but nonculturable. This lengthy analysis time is unacceptable for life-threatening infections and emergency situations. It is clear that to decrease the analysis of the bacteria, the culturing and growth steps must be avoided. The goal of this research is to design, build, and test a device that could decrease the analysis time of bacteria. Device design accommodates for the varied growth and environmental conditions of expected samples for bacterial analysis. Clinical samples containing bacteria come in a wide variety of forms including urine, saliva, sputum, blood, etc. Each medium will have associated debris and other contaminants that must be isolated from bacteria before identification. This process can be challenging as bacteria and debris can range in size from a fraction of a micrometer to tens of micrometers. In addition, a device must be equipped to accurately identify bacteria regardless of growth conditions. Thus, to decrease the analysis time of bacteria, a device must be capable of isolation, concentration, and identification at a micron level. In this dissertation, a device was designed, built, and tested that incorporates dielectrophoresis for cell sorting and Raman spectroscopy for identification. Using the device, bacteria (1 μm in length) were successfully isolated away from 5 μm polystyrene spheres and Raman spectra of the trapped bacteria were collected. The simultaneous isolation and identification of bacteria from a mixed sample indicates the capability for the cDEP-Raman device to decrease the analysis time of bacteria from clinical samples

Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry

Machine learning offers promising solutions for high-throughput single-particle analysis in label-free imaging microflow cytomtery. However, the throughput of online operations such as cell sorting is often limited by the large computational cost of the image analysis while offline operations may require the storage of an exceedingly large amount of data. Moreover, the training of machine learning systems can be easily biased by slight drifts of the measurement conditions, giving rise to a significant but difficult to detect degradation of the learned operations. We propose a simple and versatile machine learning approach to perform microparticle classification at an extremely low computational cost, showing good generalization over large variations in particle position. We present proof-of-principle classification of interference patterns projected by flowing transparent PMMA microbeads with diameters of 15.2 mu m and 18.6 mu m. To this end, a simple, cheap and compact label-free microflow cytometer is employed. We also discuss in detail the detection and prevention of machine learning bias in training and testing due to slight drifts of the measurement conditions. Moreover, we investigate the implications of modifying the projected particle pattern by means of a diffraction grating, in the context of optical extreme learning machine implementations

Droplet Microfluidics

Droplet microfluidics has dramatically developed in the past decade and has been established as a microfluidic technology that can translate into commercial products. Its rapid development and adoption have relied not only on an efficient stabilizing system (oil and surfactant), but also on a library of modules that can manipulate droplets at a high-throughput. Droplet microfluidics is a vibrant field that keeps evolving, with advances that span technology development and applications. Recent examples include innovative methods to generate droplets, to perform single-cell encapsulation, magnetic extraction, or sorting at an even higher throughput. The trend consists of improving parameters such as robustness, throughput, or ease of use. These developments rely on a firm understanding of the physics and chemistry involved in hydrodynamic flow at a small scale. Finally, droplet microfluidics has played a pivotal role in biological applications, such as single-cell genomics or high-throughput microbial screening, and chemical applications. This Special Issue will showcase all aspects of the exciting field of droplet microfluidics, including, but not limited to, technology development, applications, and open-source systems