Image-based closed-loop feedback for highly mono-dispersed microdroplet production

Crawford, Dean Frazer; Smith, Clive A.; Whyte, Graeme

doi:10.1038/s41598-017-11254-5

Cited by 31 publications

(25 citation statements)

References 31 publications

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“…Channel height was measured via cross-section microscopy as ≈30 μm. PDMS was cast onto the moulds, degassed, cured, peeled, ported and bonded onto glass slides or coverslips as described elsewhere 89 . The chips were heated to 150 C for indium gallium arsenide (InGaAs) solder wicking into the electrode channels via capillary action 90 .…”

Section: Methodsmentioning

confidence: 99%

Image-Based Single Cell Sorting Automation in Droplet Microfluidics

Şeşen

Whyte

2020

Sci Rep

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the recent boom in single-cell omics has brought researchers one step closer to understanding the biological mechanisms associated with cell heterogeneity. Rare cells that have historically been obscured by bulk measurement techniques are being studied by single cell analysis and providing valuable insight into cell function. To support this progress, novel upstream capabilities are required for single cell preparation for analysis. Presented here is a droplet microfluidic, image-based single-cell sorting technique that is flexible and programmable. The automated system performs real-time dualcamera imaging (brightfield & fluorescent), processing, decision making and sorting verification. To demonstrate capabilities, the system was used to overcome the Poisson loading problem by sorting for droplets containing a single red blood cell with 85% purity. Furthermore, fluorescent imaging and machine learning was used to load single K562 cells amongst clusters based on their instantaneous size and circularity. the presented system aspires to replace manual cell handling techniques by translating expert knowledge into cell sorting automation via machine learning algorithms. This powerful technique finds application in the enrichment of single cells based on their micrographs for further downstream processing and analysis. Cell populations are remarkably heterogeneous. Even considering a similarly tasked sub-population of cells; there exists significant variability in their morphological properties and more so in their active genes and regulations 1,2. The recent genome-scale downstream capabilities (DNA 3 , RNA 4,5) at the single-cell resolution provide unprecedented insight into cellular heterogeneity, especially in cases where rare cell populations are masked by bulk measurements. This calls for alternative upstream techniques of selecting single cells for analysis. Compared to conventional manual cell handling techniques, microfluidics offers significant reagent volume reduction and ease of automation with enhanced repeatability and detection accuracy on low-cost, disposable chips 6. In this work, a droplet microfluidic system is developed to isolate single cells by image-based sorting. The platform aims to provide researchers with the methods to capture single cells of interest and study some of the deeper mechanisms involved in cell regulation and function. There already exists well-established methods for the detection and isolation of single cells for analysis. Some of these techniques are label-free 7-12 while others rely on fluorescent 13-16 or magnetic labelling. Labelling might not be possible for some systems due to lack of biomarkers or potential cytotoxicity of the labels (e.g. DNA intercalators) 17. Additionally, labels might be undesired if they interfere with the study such as in stem cell differentiation 18. Well known techniques for single cell preparation are Fluorescence Activated Cell Sorting 19,20 , Magnetic Assisted Cell Sorting 21,22 , Fluorescence Activated Droplet Sorting 23 , Laser Capture Mi...

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Section: Methodsmentioning

confidence: 99%

Image-Based Single Cell Sorting Automation in Droplet Microfluidics

Şeşen

Whyte

2020

Sci Rep

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“…This is of great significance for subsequent clinical promotion. The detected rate of this design has yet to be improved, and some solutions have been reported by overwhelming the Poisson distribution of droplets into single-cell packages [31][32][33][34], which is beneficial to further improving the system's performance.…”

Section: Discussionmentioning

confidence: 99%

A Microfluidic Detection System for Bladder Cancer Tumor Cells

Zhao

et al. 2019

Micromachines

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The clinical characteristics of excreted tumor cells can be found in the urine of bladder cancer patients, meaning the identification of tumor cells in urine can assist in bladder cancer diagnosis. The presence of white blood cells and epithelial cells in the urine interferes with the recognition of tumor cells. In this paper, a technique for detecting cancer cells in urine based on microfluidics provides a novel approach to bladder cancer diagnosis. The bladder cancer cell line (T24) and MeT-5A were used as positive bladder tumor cells and non-tumor cells, respectively. The practicality of the tumor cell detection system based on microfluidic cell chip detection technology is discussed. The tumor cell (T24) concentration was around 1 × 10 4 to 300 × 10 4 cells/mL. When phosphate buffer saline (PBS) was the diluted solution, the tumor cell detected rate was 63-71% and the detection of tumor cell number stability (coefficient of variation, CV%) was 6.7-4.1%, while when urine was the diluted solution, the tumor cell detected rate was 64-72% and the detection of tumor cell number stability (CV%) was 6.3-3.9%. In addition, both PBS and urine are tumor cell dilution fluid solutions. The sample was analyzed at a speed of 750 microns per hour. Based on the above experiments, a system for detecting bladder cancer cells in urine by microfluidic analysis chip technology was reported. The rate of recognizing bladder cancer cells reached 68.4%, and the speed reached 2 mL/h.

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“…However, such precise control of inlet flow rates is typically difficult to achieve in microfluidics, without a method of eliminating the effect of short-term oscillations and/or the effect of long-term drift in the flow system(s). 22 Though, inertia microfluidics is limited to very high flow rates.…”

Section: Introductionmentioning

confidence: 99%