scholarly journals Simultaneous Droplet Generation with In-Series Droplet T-Junctions Induced by Gravity-Induced Flow

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1211
Author(s):  
Khashayar Bajgiran ◽  
Alejandro Cordova ◽  
Riad Elkhanoufi ◽  
James Dorman ◽  
Adam Melvin
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Droplet Diameter ◽  
Droplet Microfluidics ◽  
Droplet Generation ◽  
Single Experiment ◽  
Flow Rates ◽  
Wide Range ◽  
Significant Difference ◽  
Gravity Driven Flow

Droplet microfluidics offers a wide range of applications, including high-throughput drug screening and single-cell DNA amplification. However, these platforms are often limited to single-input conditions that prevent them from analyzing multiple input parameters (e.g., combined cellular treatments) in a single experiment. Droplet multiplexing will result in higher overall throughput, lowering cost of fabrication, and cutting down the hands-on time in number of applications such as single-cell analysis. Additionally, while lab-on-a-chip fabrication costs have decreased in recent years, the syringe pumps required for generating droplets of uniform shape and size remain cost-prohibitive for researchers interested in utilizing droplet microfluidics. This work investigates the potential of simultaneously generating droplets from a series of three in-line T-junctions utilizing gravity-driven flow to produce consistent, well-defined droplets. Implementing reservoirs with equal heights produced inconsistent flow rates that increased as a function of the distance between the aqueous inlets and the oil inlet. Optimizing the three reservoir heights identified that taller reservoirs were needed for aqueous inlets closer to the oil inlet. Studying the relationship between the ratio of oil-to-water flow rates () found that increasing resulted in smaller droplets and an enhanced droplet generation rate. An ANOVA was performed on droplet diameter to confirm no significant difference in droplet size from the three different aqueous inlets. The work described here offers an alternative approach to multiplexed droplet microfluidic devices allowing for the high-throughput interrogation of three sample conditions in a single device. It also has provided an alternative method to induce droplet formation that does not require multiple syringe pumps.

scholarly journals Negative Pressure Provides Simple and Stable Droplet Generation in a Flow-Focusing Microfluidic Device

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 662
Author(s):  
Nikita A. Filatov ◽  
Anatoly A. Evstrapov ◽  
Anton S. Bukatin
Keyword(s):  
Microfluidic Device ◽  
Negative Pressure ◽  
Low Cost ◽  
Control Valve ◽  
Droplet Microfluidics ◽  
Droplet Generation ◽  
Electric Signal ◽  
Absolute Pressure ◽  
Flow Focusing ◽  
Wide Range

Droplet microfluidics is an extremely useful and powerful tool for industrial, environmental, and biotechnological applications, due to advantages such as the small volume of reagents required, ultrahigh-throughput, precise control, and independent manipulations of each droplet. For the generation of monodisperse water-in-oil droplets, usually T-junction and flow-focusing microfluidic devices connected to syringe pumps or pressure controllers are used. Here, we investigated droplet-generation regimes in a flow-focusing microfluidic device induced by the negative pressure in the outlet reservoir, generated by a low-cost mini diaphragm vacuum pump. During the study, we compared two ways of adjusting the negative pressure using a compact electro-pneumatic regulator and a manual airflow control valve. The results showed that both types of regulators are suitable for the stable generation of monodisperse droplets for at least 4 h, with variations in diameter less than 1 µm. Droplet diameters at high levels of negative pressure were mainly determined by the hydrodynamic resistances of the inlet microchannels, although the absolute pressure value defined the generation frequency; however, the electro-pneumatic regulator is preferable and convenient for the accurate control of the pressure by an external electric signal, providing more stable pressure, and a wide range of droplet diameters and generation frequencies. The method of droplet generation suggested here is a simple, stable, reliable, and portable way of high-throughput production of relatively large volumes of monodisperse emulsions for biomedical applications.

scholarly journals Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ali Rohani ◽  
Jennifer A. Kashatus ◽  
Dane T. Sessions ◽  
Salma Sharmin ◽  
David F. Kashatus
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Data Science ◽  
Structural Information ◽  
Morphological Changes ◽  
Mitochondrial Morphology ◽  
Single Cell Level ◽  
Mitochondrial Network ◽  
Cell Level ◽  
Wide Range

Abstract Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

Inertial-ordering-assisted droplet microfluidics for high-throughput single-cell RNA-sequencing

Lab on a Chip ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 775-784 ◽  
Author(s):  
Hui-Sung Moon ◽  
Kwanghwi Je ◽  
Jae-Woong Min ◽  
Donghyun Park ◽  
Kyung-Yeon Han ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Droplet Microfluidics ◽  
Single Cell Rna Sequencing ◽  
Spiral Microchannel

We developed a modified high-throughput droplet barcoding technique for single-cell Drop-Seq via introduction of hydrodynamic ordering in a spiral microchannel.

Droplet-based microfluidics in biomedical applications

2021 ◽  
Author(s):  
Leyla Amirifar ◽  
Mohsen Besanjideh ◽  
Rohollah Nasiri ◽  
Amir Shamloo ◽  
Fatemeh Nasrollahi ◽  
...  
Keyword(s):  
Drug Delivery ◽  
High Throughput ◽  
Paradigm Shift ◽  
Cell Biology ◽  
Biomedical Applications ◽  
Molecular Detection ◽  
Droplet Microfluidics ◽  
Droplet Generation ◽  
Microfluidic Platforms ◽  
Standard Methods

Abstract Droplet-based microfluidic systems have been employed to manipulate discrete fluid volumes with immiscible phases. Creating the fluid droplets at microscale has led to a paradigm shift in mixing, sorting, encapsulation, sensing, and designing high throughput devices for biomedical applications. Droplet microfluidics has opened many opportunities in microparticle synthesis, molecular detection, diagnostics, drug delivery, and cell biology. In the present review, we first introduce standard methods for droplet generation (i.e., passive and active methods) and discuss the latest examples of emulsification and particle synthesis approaches enabled by microfluidic platforms. Then, the applications of droplet-based microfluidics in different biomedical applications are detailed. Finally, a general overview of the latest trends along with the perspectives and future potentials in the field are provided.

scholarly journals Prediction of Droplet Production Speed by Measuring the Droplet Spacing Fluctuations in a Flow-Focusing Microdroplet Generator

Micromachines ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 812 ◽  
Author(s):  
Wen Zeng ◽  
Dong Xiang ◽  
Hai Fu
Keyword(s):  
Fluid Flow ◽  
Efficient Method ◽  
Immiscible Fluids ◽  
Droplet Generation ◽  
Flow Focusing ◽  
Flow Conditions ◽  
Flow Rates ◽  
Wide Range ◽  
Droplet Production ◽  
Monodisperse Droplets

In a flow-focusing microdroplet generator, by changing the flow rates of the two immiscible fluids, production speed can be increased from tens to thousands of droplets per second. However, because of the nonlinearity of the flow-focusing microdroplet generator, the production speed of droplets is difficult to quantitatively study for the typical flow-focusing geometry. In this paper, we demonstrate an efficient method that can precisely predict the droplet production speed for a wide range of fluid flow rates. While monodisperse droplets are formed in the flow-focusing microchannel, droplet spacing as a function of time was measured experimentally. We discovered that droplet spacing changes periodically with time during each process of droplet generation. By comparing the frequency of droplet spacing fluctuations with the droplet production speed, precise predictions of droplet production speed can be obtained for different flow conditions in the flow-focusing microdroplet generator.

scholarly journals Development of Droplet Microfluidics Enabling High-Throughput Single-Cell Analysis

Molecules ◽  
2016 ◽  
Vol 21 (7) ◽  
pp. 881 ◽  
Author(s):  
Na Wen ◽  
Zhan Zhao ◽  
Beiyuan Fan ◽  
Deyong Chen ◽  
Dong Men ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Droplet Microfluidics ◽  
Cell Analysis

scholarly journals Comparison of high-throughput single-cell RNA sequencing data processing pipelines

2020 ◽  
Author(s):  
Mingxuan Gao ◽  
Mingyi Ling ◽  
Xinwei Tang ◽  
Shun Wang ◽  
Xu Xiao ◽  
...  
Keyword(s):  
Data Processing ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Evaluation Framework ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Single Experiment ◽  
Single Cell Rna Sequencing

Abstract With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

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