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Droplet-Based Microfluidic Platform Employing Sorting and Downstream Merging for Single-Cell Analysis

dc.contributor.authorChung, Meng Ting
dc.date.accessioned2019-10-01T18:22:58Z
dc.date.availableNO_RESTRICTION
dc.date.available2019-10-01T18:22:58Z
dc.date.issued2019
dc.date.submitted2019
dc.identifier.urihttps://hdl.handle.net/2027.42/151397
dc.description.abstractSingle-cell analysis techniques have emerged to overcome the limitation of bulk analysis in which the heterogeneous gene expression information from individual cells is lost. Droplet-based techniques appear to be one of the most promising approaches as they compartmentalize single cells in an immiscible two-phase flow and enable high-throughput analysis while preserving the characteristics of each cell in a small-volume droplet. One of the challenges in droplet microfluidics is the manipulation of droplets usually requires the sequential use of many custom-made microfluidic devices, and one device can only address one specific task. This makes it challenging to generate robust experimental pipelines for complicated tasks. This thesis introduces our new working pipeline for droplet-based microfluidics and its applications in transcriptomic analysis. In the first part of this thesis, we present a droplet-based microfluidic platform (Sort-N-Merge) integrating several droplet operational units to achieve fully on-chip processing. This is the first droplet-based workflow that enables reconfigurable droplet sorting and downstream merging. With this platform, single-cells can be encapsulated, fluorescence-activated sorted, and one-to-one merge with other-sorted droplets containing necessary cells, reagents, or microparticles. Such an operational procedure is similar to using traditional pipettes and microtiter plates, making it adaptable to many well-developed biological assays with smaller reaction volume and higher throughput. In the second part, we demonstrate the use of this system for profiling transcriptomes of rare neuron stem cells from single Drosophila’s brain. The conventional droplet-based single-cell mRNA-sequencing approach by pairing a single barcoded primer-associated bead and a single cell in a droplet based on the stochastic Poisson process only allows less than 10% of cells in the sample to be effectively sequenced. Rare cells could be lost not only during harsh FACS sorting but also such inefficient sequencing processes. Our Sort-N-Merge workflow deterministically sorts target cells and barcoded beads into single droplets, thus making mRNA-sequencing of rare cells from a large population possible. In the last part, single-cell mRNA detection using reverse-transcription loop-mediated isothermal-amplification (RT-LAMP) was demonstrated by our system. By sequentially adding lysis buffer and reactant mixtures to nanoliter-sized reactors, human hydroxymethylbilane synthase (HMBS) gene expressions from hundreds of cells were detected within one hour. The fully on-chip workflow including cell isolation, sorting, lysing, and RNA detection provides a robust experimental pipeline for a wide variety of physiological studies. The demonstrated applications prove our microfluidic work flow could be adapted to a wide variety of single-cell assays. Furthermore, the fully on-chip processing gets rid of laborious hands-on operations and potentially leads to automation of the whole process in the future.
dc.language.isoen_US
dc.subjectDroplet microfluidics
dc.subjectSingle-cell analysis
dc.subjectSingle-cell mRNA sequencing
dc.titleDroplet-Based Microfluidic Platform Employing Sorting and Downstream Merging for Single-Cell Analysis
dc.typeThesis
dc.description.thesisdegreenamePhDen_US
dc.description.thesisdegreedisciplineMechanical Engineering
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studies
dc.contributor.committeememberKurabayashi, Katsuo
dc.contributor.committeememberCai, Dawen
dc.contributor.committeememberFu, Jianping
dc.contributor.committeememberLiu, Allen Po-Chih
dc.subject.hlbsecondlevelBiomedical Engineering
dc.subject.hlbsecondlevelMechanical Engineering
dc.subject.hlbtoplevelEngineering
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/151397/1/mengting_1.pdf
dc.identifier.orcid0000-0001-7539-5893
dc.identifier.name-orcidChung, Meng Ting; 0000-0001-7539-5893en_US
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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