On-chip cell analysis platform: Implementation of contact fluorescence microscopy in microfluidic chips

Application of optically superior, tunable fluorescent nanotechnologies have long been demonstrated throughout many chemical and biological sensing applications. Combined with microfluidics technologies, i.e. on lab-on-a-chip platforms, such fluorescent nanotechnologies have often enabled extreme sensitivity, sometimes down to single molecule level. Within recent years there has been a peak interest in translating fluorescent nanotechnology onto paper-based platforms for chemical and biological sensing, as a simple, low-cost, disposable alternative to conventional silicone-based microfluidic substrates. On the other hand, smartphone integration as an optical detection system as well as user interface and data processing component has been widely attempted, serving as a gateway to on-board quantitative processing, enhanced mobility, and interconnectivity with informational networks. Smartphone sensing can be integrated to these paper-based fluorogenic assays towards demonstrating extreme sensitivity as well as ease-of-use and low-cost. However, with these emerging technologies there are always technical limitations that must be addressed; for example, paper’s autofluorescence that perturbs fluorogenic sensing; smartphone flash’s limitations in fluorescent excitation; smartphone camera’s limitations in detecting narrow-band fluorescent emission, etc. In this review, physical optical setups, digital enhancement algorithms, and various fluorescent measurement techniques are discussed and pinpointed as areas of opportunities to further improve paper-based fluorogenic optical sensing with smartphones.

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“…Such systems have been demonstrated to resolve highly comparable fluorescent images of live cells [ 102 – 104 ] (Fig. 9 ).…”

Section: Future Outlook: Contact Cmos Fluorescence Imagermentioning

confidence: 99%

Challenges in paper-based fluorogenic optical sensing with smartphones

Ulep

Yoon

2018

Nano Convergence

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“…A nicer alternative would be on-chip fluorescence microscopy. 4,8,17,18 Huang et al 17 demonstrated on-chip detection of targeted oligo-DNA (21-bp) binding using Qdot-655 fluorophores with detection limits down to ∼ 20 fluorophores/µm 2 for a 50 × 50 µm pixel size in 2009. Better performance is expected now, with detection limits down to ∼ 1 fluorophore/µm 2 predicted.…”

Section: Look At the Pretty Lightsmentioning

confidence: 99%

“…24 Hence the final device structure (Fig. 1f) would be: (from bottom) CMOS backplane, 8 thin glass floor, 8 PMMA nanochannel layer, 7 polycarbonate membrane, 5 and microfluidics layers. 9 7.…”

Section: Look At the Pretty Lightsmentioning

confidence: 99%

Use DNA origami as a scaffold for self-assembly of optical metamolecules

Hwang

Wickham

Perrault

et al. 2015

2015 11th Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR)

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The idea is a nanoscale extension of a sporting event called a 'color run'. 1 The filament is wrapped with a DNA origami 'barrel', 2 which acts as the nanoscale equivalent of a white t-shirt. The DNA t-shirt has dangling ss-DNA 'handles' that bind 'tags' consisting of an 'anti-handle' oligo bound to a fluorophore. The tagging methodology exploits the recent development of DNA-based 'metafluorophores' 3 with digitally tunable optical properties based on a collection of fluorophores bound to a single DNA origami structure. In this idea, the filament collects different color tags at 'color-stations' as it travels through the network (Fig. 1). These contribute to a final DNA t-shirt color that represents of the path taken. Readout is implemented through lenseless on-chip imaging 4 via pixels in a CMOS backplane located at read-out points in the network. Color-stations are implemented via microfluidic channels on a dialysis membrane, 5 which feed tags into the network in localised areas. A chemical countermeasure to prevent false tagging by stray tags that have drifted in from other stations by exploiting strand-displacement techniques 6 is also proposed.

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“…Development of fluorescence and image-based single cell technologies has enabled systematic investigation of cellular heterogeneity in a wide range of diseased tissues and cellular populations [1, 2]. While conventional single cell analytical tools like flow cytometry (and Fluorescence Activated Cell Sorting, Image Flow Cytometry) can detect, sort and collect cells with desired properties, these techniques do not permit dynamic monitoring of cell responses as the data is collected at a single time point [3].…”

Section: Introductionmentioning

confidence: 99%

FluoroCellTrack: An algorithm for automated analysis of high-throughput droplet microfluidic data

2019

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High-throughput droplet microfluidic devices with fluorescence detection systems provide several advantages over conventional end-point cytometric techniques due to their ability to isolate single cells and investigate complex intracellular dynamics. While there have been significant advances in the field of experimental droplet microfluidics, the development of complementary software tools has lagged. Existing quantification tools have limitations including interdependent hardware platforms or challenges analyzing a wide range of high-throughput droplet microfluidic data using a single algorithm. To address these issues, an all-in-one Python algorithm called FluoroCellTrack was developed and its wide-range utility was tested on three different applications including quantification of cellular response to drugs, droplet tracking, and intracellular fluorescence. The algorithm imports all images collected using bright field and fluorescence microscopy and analyzes them to extract useful information. Two parallel steps are performed where droplets are detected using a mathematical Circular Hough Transform (CHT) while single cells (or other contours) are detected by a series of steps defining respective color boundaries involving edge detection, dilation, and erosion. These feature detection steps are strengthened by segmentation and radius/area thresholding for precise detection and removal of false positives. Individually detected droplet and contour center maps are overlaid to obtain encapsulation information for further analyses. FluoroCellTrack demonstrates an average of a ~92–99% similarity with manual analysis and exhibits a significant reduction in analysis time of 30 min to analyze an entire cohort compared to 20 h required for manual quantification.

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On-chip cell analysis platform: Implementation of contact fluorescence microscopy in microfluidic chips

Cited by 27 publications

References 37 publications

Challenges in paper-based fluorogenic optical sensing with smartphones

Challenges in paper-based fluorogenic optical sensing with smartphones

Use DNA origami as a scaffold for self-assembly of optical metamolecules

FluoroCellTrack: An algorithm for automated analysis of high-throughput droplet microfluidic data

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