Differential detection photothermal spectroscopy: towards ultra-fast and sensitive label-free detection in picoliter &amp; femtoliter droplets

Maceiczyk, Richard M.; Hess, David; Chiu, Flora W. Y.; Stavrakis, Stavros; deMello, Andrew J.

doi:10.1039/c7lc00946a

Cited by 45 publications

(39 citation statements)

References 51 publications

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“…The presented system will not be able to compete with high-throughput sorting techniques such as fluorescent activated cell sorting as the full image takes longer to acquire and process, however, it can be used as a complementary technique when small cell populations need to be analysed or when labelling is undesired or when the image data presents a unique opportunity for sorting cell populations. The presented platform could be strengthened by additional sensing modules to aid in the decision making such as differential detection photothermal interferometry 83 , digital holographic microscopy 84 , whispering gallery mode resonators 85 , refractive index cytometry 86 or impedance cytometry 87 .…”

Section: Resultsmentioning

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: Resultsmentioning

confidence: 99%

Image-Based Single Cell Sorting Automation in Droplet Microfluidics

Şeşen

Whyte

2020

Sci Rep

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“…To address the limits of fluorescence based screens, recent work has expanded the analytical techniques that can be applied to the active sorting of droplets. UV/Vis absorbance, droplet imaging, and Raman spectroscopy have been employed as alternatives to fluorescence detection in droplets. Although these methods have widened the range of analytical techniques available for droplet sorting, they too have significant limitations in their implementation.…”

Section: Introductionmentioning

confidence: 99%

Mass Activated Droplet Sorting (MADS) Enables High‐Throughput Screening of Enzymatic Reactions at Nanoliter Scale

et al. 2020

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Microfluidic droplet sorting enables the high‐throughput screening and selection of water‐in‐oil microreactors at speeds and volumes unparalleled by traditional well‐plate approaches. Most such systems sort using fluorescent reporters on modified substrates or reactions that are rarely industrially relevant. We describe a microfluidic system for high‐throughput sorting of nanoliter droplets based on direct detection using electrospray ionization mass spectrometry (ESI‐MS). Droplets are split, one portion is analyzed by ESI‐MS, and the second portion is sorted based on the MS result. Throughput of 0.7 samples s−1 is achieved with 98 % accuracy using a self‐correcting and adaptive sorting algorithm. We use the system to screen ≈15 000 samples in 6 h and demonstrate its utility by sorting 25 nL droplets containing transaminase expressed in vitro. Label‐free ESI‐MS droplet screening expands the toolbox for droplet detection and recovery, improving the applicability of droplet sorting to protein engineering, drug discovery, and diagnostic workflows.

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“…[ 86 ] Furthermore, photothermal spectroscopy techniques, such as differential detection photothermal interferometry (DDPI), have been used in flow to determine plasmonic nanoparticle size distributions from electrostatic approximations and Mie theory calculations. [ 87 ]…”

Section: In‐flow Size and Size Distribution Control Of Colloidal Nanomentioning

confidence: 99%

Accelerated Development of Colloidal Nanomaterials Enabled by Modular Microfluidic Reactors: Toward Autonomous Robotic Experimentation

2020

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Solution-processed organic, [1-3] metallic, [4-6] and semiconductor [7,8] nanomaterials, possess unique size-related physicochemical, optical, magnetic, and electronic properties. These materials have enabled groundbreaking advancements in a variety of applications including catalysis, [9-11] drug delivery, [12,13] data storage, [14] and solar cells. [15] Different nucleation and growth models such as LaMer burst nucleation, [16] Ostwald ripening, [17] Finke-Watzky two-step mechanism, [18] orientated attachment, [19] and coalescence [20] have attempted to explain the mechanisms through which nanoparticles are formed in In recent years, microfluidic technologies have emerged as a powerful approach for the advanced synthesis and rapid optimization of various solution-processed nanomaterials, including semiconductor quantum dots and nanoplatelets, and metal plasmonic and reticular framework nanoparticles. These fluidic systems offer access to previously unattainable measurements and synthesis conditions at unparalleled efficiencies and sampling rates. Despite these advantages, microfluidic systems have yet to be extensively adopted by the colloidal nanomaterial community. To help bridge the gap, this progress report details the basic principles of microfluidic reactor design and performance, as well as the current state of online diagnostics and autonomous robotic experimentation strategies, toward the size, shape, and composition-controlled synthesis of various colloidal nanomaterials. By discussing the application of fluidic platforms in recent high-priority colloidal nanomaterial studies and their potential for integration with rapidly emerging artificial intelligence-based decision-making strategies, this report seeks to encourage interdisciplinary collaborations between microfluidic reactor engineers and colloidal nanomaterial chemists. Full convergence of these two research efforts offers significantly expedited and enhanced nanomaterial discovery, optimization, and manufacturing.

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Differential detection photothermal spectroscopy: towards ultra-fast and sensitive label-free detection in picoliter & femtoliter droplets

Cited by 45 publications

References 51 publications

Image-Based Single Cell Sorting Automation in Droplet Microfluidics

Image-Based Single Cell Sorting Automation in Droplet Microfluidics

Mass Activated Droplet Sorting (MADS) Enables High‐Throughput Screening of Enzymatic Reactions at Nanoliter Scale

Accelerated Development of Colloidal Nanomaterials Enabled by Modular Microfluidic Reactors: Toward Autonomous Robotic Experimentation

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