scholarly journals Dual Dean entrainment with volume ratio modulation for efficient co-encapsulation: Extreme single-cell indexing

2021 ◽  
Author(s):  
Jack Harrington ◽  
Luis Blay Esteban ◽  
Jonathan Butement ◽  
Andres F. Vallejo ◽  
Simon I. R. Lane ◽  
...  

AbstractThe future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular system. Current approaches are limited by the Poisson statistic, requiring dilute cell suspensions and associated losses in throughput. In this contribution, we apply Dean entrainment to both cell and bead inputs, defining different volume packets to effect efficient co-encapsulation. Volume ratio scaling was explored to identify optimal conditions. This enabled the co-encapsulation of single cells with reporter beads at rates of ~1 million cells/hour, while increasing assay signal-to-noise with cell multiplet rates of ~2.5% and capturing ~70% of cells. The method, called Pirouette-seq, extends our capacity to investigate biological systems.TOC AbstractPirouette-seq involves cell and reporter bead inertial ordering for efficient co-encapsulation, achieving a throughput of 1 million cells/hour, a 2.5% multiplet rate and a 70% cell capture efficiency.

Lab on a Chip ◽  
2016 ◽  
Vol 16 (13) ◽  
pp. 2440-2449 ◽  
Author(s):  
Soo Hyeon Kim ◽  
Teruo Fujii

The electroactive double well-array consists of trap-wells for highly efficient single-cell trapping using dielectrophoresis (cell capture efficiency of 96 ± 3%) and reaction-wells that confine cell lysates for analysis of intracellular materials from single cells.


Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3672 ◽  
Author(s):  
Yang Liu ◽  
Dahai Ren ◽  
Xixin Ling ◽  
Weibin Liang ◽  
Jing Li ◽  
...  

Single-cell capture plays an important role in single-cell manipulation and analysis. This paper presents a microfluidic device for deterministic single-cell trapping based on the hydrodynamic trapping mechanism. The device is composed of an S-shaped loop channel and thousands of aligned trap units. This arrayed structure enables each row of the device to be treated equally and independently, as it has row periodicity. A theoretical model was established and a simulation was conducted to optimize the key geometric parameters, and the performance was evaluated by conducting experiments on MCF-7 and Jurkat cells. The results showed improvements in single-cell trapping ability, including loading efficiency, capture speed, and the density of the patterned cells. The optimized device can achieve a capture efficiency of up to 100% and single-cell capture efficiency of up to 95%. This device offers 200 trap units in an area of 1 mm2, which enables 100 single cells to be observed simultaneously using a microscope with a 20× objective lens. One thousand cells can be trapped sequentially within 2 min; this is faster than the values obtained with previously reported devices. Furthermore, the cells can also be recovered by reversely infusing solutions. The structure can be easily extended to a large scale, and a patterned array with 32,000 trap sites was accomplished on a single chip. This device can be a powerful tool for high-throughput single-cell analysis, cell heterogeneity investigation, and drug screening.


Lab on a Chip ◽  
2021 ◽  
Author(s):  
Jack Harrington ◽  
Luis Blay Esteban ◽  
Jonathan Butement ◽  
Andres F. Vallejo ◽  
Simon Lane ◽  
...  

The future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular...


2019 ◽  
Vol 10 (5) ◽  
pp. 1506-1513 ◽  
Author(s):  
Min Li ◽  
Robbyn K. Anand

We present integration of selective single-cell capture at an array of wireless electrodes (bipolar electrodes, BPEs) with transfer into chambers, reagent exchange, fluidic isolation and rapid electrical lysis in a single platform, thus minimizing sample loss and manual intervention steps.


Author(s):  
Jacob Amontree ◽  
Kangfu Chen ◽  
Jose Varillas ◽  
Z. Hugh Fan

The characterization of single cells within heterogeneous populations has great impact on both biomedical sciences and cancer research. By investigating cellular compositions on a broad scale, pertinent outliers may be lost in the sample set. Alternatively, an investigation focused on the behavior of specific cells, such as circulating tumor cells (CTCs), will reveal genetic biomarkers or phenotypic characteristics associated with cancer and metastasis. On average, CTC concentration in peripheral blood is extremely low, as few as one to two per billion of healthy blood cells. Consequently, the critical element lacking in many methods of CTC detection is accurate cell capture efficiency at low concentrations. To simulate CTC isolation, researchers usually spike small amounts of tumor cells to healthy blood for separation. However, spiking tumor cells at extremely low concentrations is challenging in a standard laboratory setting. We report our study on an innovative apparatus and method designed for low-cost, precise, and replicable single-cell spiking (SCS). Our SCS method operates solely from capillary aspiration without the reliance on external laboratory equipment. To ensure that our method does not affect the viability of each cell, we investigated the effects of surface membrane tensions induced by aspiration. Finally, we performed affinity-based CTC isolation using human acute lymphoblastic leukemia cells (CCRF-CEM) spiked into healthy whole blood with the SCS technique. The results of the isolation experiments demonstrate the reliability of our method in generating low-concentration cell samples.


2020 ◽  
Author(s):  
Siamak Yousefi ◽  
Hao Chen ◽  
Jesse F. Ingels ◽  
Melinda S. McCarty ◽  
Arthur G. Centeno ◽  
...  

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.


2017 ◽  
Author(s):  
Junyue Cao ◽  
Jonathan S. Packer ◽  
Vijay Ramani ◽  
Darren A. Cusanovich ◽  
Chau Huynh ◽  
...  

AbstractConventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.


Lab on a Chip ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1370-1377 ◽  
Author(s):  
Cheng-Kun He ◽  
Ya-Wen Chen ◽  
Ssu-Han Wang ◽  
Chia-Hsien Hsu

A new microfluidics technique for high-efficiency paring and analyzing multiple single cells can facilitate cellular heterogeneity studies important for biological and biomedical research.


2013 ◽  
Vol 562-565 ◽  
pp. 589-593
Author(s):  
Shao Bo Du ◽  
Sheng Bo Sang ◽  
Wen Dong Zhang ◽  
Jie Hu ◽  
Peng Wei Li ◽  
...  

Here we demonstrate a microfluidic-based analysis system based on single cell capture array, which can physically trap individual cell using micrometer-sized structures. A stable and in vivo-like microenvironment was built with the novel structure at the single-cell detection level. The microfluidic-based design can decouple single cells from fluid flow with the help of micropillars. The size and geometry of the cell jails are designed in order to discriminate between mother and daughter cells. It provides an experimental platform to efficiently monitor individual cell state for a long period of time. Furthermore, the parallel microfluidic array can ensure accuracy. In addition, finite element method (FEM) was employed to predict fluid transport properties for the most optimal fluid microenvironment.


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