- Award ID(s):
- 1708706
- NSF-PAR ID:
- 10108056
- Date Published:
- Journal Name:
- Lab on a Chip
- Volume:
- 19
- Issue:
- 14
- ISSN:
- 1473-0197
- Page Range / eLocation ID:
- 2425 to 2434
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Droplet microfluidics has become an indispensable tool for biomedical research and lab-on-a-chip applications owing to its unprecedented throughput, precision, and cost-effectiveness. Although droplets can be generated and screened in a high-throughput manner, the inability to label the inordinate amounts of droplets hinders identifying the individual droplets after generation. Herein, we demonstrate an acoustofluidic platform that enables on-demand, real-time dispensing, and deterministic coding of droplets based on their volumes. By dynamically splitting the aqueous flow using an oil jet triggered by focused traveling surface acoustic waves, a sequence of droplets with deterministic volumes can be continuously dispensed at a throughput of 100 Hz. These sequences encode barcoding information through the combination of various droplet lengths. As a proof-of-concept, we encoded droplet sequences into end-to-end packages ( e.g. , a series of 50 droplets), which consisted of an address barcode with binary volumetric combinations and a sample package with consistent volumes for hosting analytes. This acoustofluidics-based, deterministic droplet coding technique enables the tagging of droplets with high capacity and high error-tolerance, and can potentially benefit various applications involving single cell phenotyping and multiplexed screening.more » « less
-
In drop-based microfluidics, an aqueous sample is partitioned into drops using individual pump sources that drive water and oil into a drop-making device. Parallelization of drop-making devices is necessary to achieve high-throughput screening of multiple experimental conditions, especially in time-sensitive studies. Here, we present the plate-interfacing parallel encapsulation (PIPE) chip, a microfluidic chip designed to generate 50 to 90 μm diameter drops of up to 96 different conditions in parallel by interfacing individual drop makers with a standard 384-well microtiter plate. The PIPE chip is used to generate two types of optically barcoded drop libraries consisting of two-color fluorescent particle combinations: a library of 24 microbead barcodes and a library of 192 quantum dot barcodes. Barcoded combinations in the drop libraries are rapidly measured within a microfluidic device using fluorescence detection and distinct barcoded populations in the fluorescence drop data are identified using DBSCAN data clustering. Signal analysis reveals that particle size defines the source of dominant noise present in the fluorescence intensity distributions of the barcoded drop populations, arising from Poisson loading for microbeads and shot noise for quantum dots. A barcoded population from a drop library is isolated using fluorescence-activated drop sorting, enabling downstream analysis of drop contents. The PIPE chip can improve multiplexed high-throughput assays by enabling simultaneous encapsulation of barcoded samples stored in a microtiter plate and reducing sample preparation time.more » « less
-
Abstract Microfluidic fluorescence‐activated cell sorters (μFACS) have attracted considerable interest because of their ability to identify and separate cells in inexpensive and biosafe ways. Here a high‐performance μFACS is presented by integrating a standing surface acoustic wave (SSAW)‐based, 3D cell‐focusing unit, an in‐plane fluorescent detection unit, and an SSAW‐based cell‐deflection unit on a single chip. Without using sheath flow or precise flow rate control, the SSAW‐based cell‐focusing technique can focus cells into a single file at a designated position. The tight focusing of cells enables an in‐plane‐integrated optical detection system to accurately distinguish individual cells of interest. In the acoustic‐based cell‐deflection unit, a focused interdigital transducer design is utilized to deflect cells from the focused stream within a minimized area, resulting in a high‐throughput sorting ability. Each unit is experimentally characterized, respectively, and the integrated SSAW‐based FACS is used to sort mammalian cells (HeLa) at different throughputs. A sorting purity of greater than 90% is achieved at a throughput of 2500 events s−1. The SSAW‐based FACS is efficient, fast, biosafe, biocompatible and has a small footprint, making it a competitive alternative to more expensive, bulkier traditional FACS.
-
Abstract Mosquito bites transmit a number of pathogens via salivary droplets deposited during blood-feeding, resulting in potentially fatal diseases. Little is known about the genomic content of these nanodroplets, including the transmission dynamics of live pathogens. Here we introduce Vectorchip, a low-cost, scalable microfluidic platform enabling high-throughput molecular interrogation of individual mosquito bites. We introduce an ultra-thin PDMS membrane which acts as a biting interface to arrays of micro-wells. Freely-behaving mosquitoes deposit saliva droplets by biting into these micro-wells. By modulating membrane thickness, we observe species-dependent differences in mosquito biting capacity, utilizable for selective sample collection. We demonstrate RT-PCR and focus-forming assays on-chip to detect mosquito DNA, Zika virus RNA, as well as quantify infectious Mayaro virus particles transmitted from single mosquito bites. The Vectorchip presents a promising approach for single-bite-resolution laboratory and field characterization of vector-pathogen communities, and could serve as a powerful early warning sentinel for mosquito-borne diseases.
-
Abstract Cleavage under targets and release using nuclease (CUT&RUN) is a recently developed chromatin profiling technique that uses a targeted micrococcal nuclease cleavage strategy to obtain high‐resolution binding profiles of protein factors or to map histones with specific post‐translational modifications. Due to its high sensitivity, CUT&RUN allows quality binding profiles to be obtained with only a fraction of the starting material and sequencing depth typically required for other chromatin profiling techniques such as chromatin immunoprecipitation. Although CUT&RUN has been widely adopted in multiple model systems, it has rarely been utilized in
Caenorhabditis elegans , a model system of great importance to genomic research. Cell dissociation techniques, which are required for this approach, can be challenging inC. elegans due to the toughness of the worm's cuticle and the sensitivity of the cells themselves. Here, we describe a robust CUT&RUN protocol for use inC. elegans to determine the genome‐wide localization of protein factors and specific histone marks. With a simple protocol utilizing live, uncrosslinked tissue as the starting material, performing CUT&RUN in worms has the potential to produce physiologically relevant data at a higher resolution than chromatin immunoprecipitation. This protocol involves a simple dissociation step to uniformly permeabilize worms while avoiding sample loss or cell damage, resulting in high‐quality CUT&RUN profiles with as few as 100 worms and detectable signal with as few as 10 worms. This represents a significant advancement over chromatin immunoprecipitation, which typically uses thousands or hundreds of thousands of worms for a single experiment. The protocols presented here provide a detailed description of worm growth, sample preparation, CUT&RUN workflow, library preparation for high‐throughput sequencing, and a basic overview of data analysis, making CUT&RUN simple and accessible for any worm lab. © 2022 Wiley Periodicals LLC.Basic Protocol 1 : Growth and synchronization ofC. elegans Basic Protocol 2 : Worm dissociation, sample preparation, and optimizationBasic Protocol 3 : CUT&RUN chromatin profilingAlternate Protocol : Improving CUT&RUN signal using a secondary antibodyBasic Protocol 4 : CUT&RUN library preparation for Illumina high‐throughput sequencingBasic Protocol 5 : Basic data analysis using Linux