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Automated analysis of dynamic behavior of single cells in picoliter droplets
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0003-3618-9944
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-5232-0805
Show others and affiliations
2014 (English)In: Lab on a Chip, ISSN 1473-0197, E-ISSN 1473-0189, Vol. 14, no 5, 931-937 p.Article in journal (Refereed) Published
Abstract [en]

We present a droplet-based microfluidic platform to automatically track and characterize the behavior of single cells over time. This high-throughput assay allows encapsulation of single cells in micro-droplets and traps intact droplets in arrays of miniature wells on a PDMS-glass chip. Automated time-lapse fluorescence imaging and image analysis of the incubated droplets on the chip allows the determination of the viability of individual cells over time. In order to automatically track the droplets containing cells, we developed a simple method based on circular Hough transform to identify droplets in images and quantify the number of live and dead cells in each droplet. Here, we studied the viability of several hundred single isolated HEK293T cells over time and demonstrated a high survival rate of the encapsulated cells for up to 11 hours. The presented platform has a wide range of potential applications for single cell analysis, e.g. monitoring heterogeneity of drug action over time and rapidly assessing the transient behavior of single cells under various conditions and treatments in vitro.

Place, publisher, year, edition, pages
2014. Vol. 14, no 5, 931-937 p.
Keyword [en]
Microfluidic System, Flow-Cytometry, In-Vitro, Expression, Time, Heterogeneity, Device, Encapsulation, Tracking, Kinetics
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-129290DOI: 10.1039/c3lc51136gISI: 000330784400013Scopus ID: 2-s2.0-84893475740OAI: oai:DiVA.org:kth-129290DiVA: diva2:651483
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceFormasSwedish Research Council
Note

QC 20140228. Updated from manuscript to article in journal.

Available from: 2013-09-26 Created: 2013-09-25 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Live Single Cell Imaging and Analysis Using Microfluidic Devices
Open this publication in new window or tab >>Live Single Cell Imaging and Analysis Using Microfluidic Devices
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Today many cell biological techniques study large cell populations where an average estimate of individual cells’ behavior is observed. On the other hand, single cell analysis is required for studying functional heterogeneities between cells within populations. This thesis presents work that combines the use of microfluidic devices, optical microscopy and automated image analysis to design various cell biological assays with single cell resolution including cell proliferation, clonal expansion, cell migration, cell-cell interaction and cell viability tracking. In fact, automated high throughput single cell techniques enable new studies in cell biology which are not possible with conventional techniques.

In order to automatically track dynamic behavior of single cells, we developed a microwell based device as well as a droplet microfluidic platform. These high throughput microfluidic assays allow automated time-lapse imaging of encapsulated single cells in micro droplets or confined cells inside microwells. Algorithms for automatic quantification of cells in individual microwells and micro droplets are developed and used for the analysis of cell viability and clonal expansion. The automatic counting protocols include several image analysis steps, e.g. segmentation, feature extraction and classification. The automatic quantification results were evaluated by comparing with manual counting and revealed a high success rate. In combination these automatic cell counting protocols and our microfluidic platforms can provide statistical information to better understand behavior of cells at the individual level under various conditions or treatments in vitro exemplified by the analysis of function and regulation of immune cells. Thus, together these tools can be used for developing new cellular imaging assays with resolution at the single cell level.

To automatically characterize transient migration behavior of natural killer (NK) cells compartmentalized in microwells, we developed a method for single cell tracking. Time-lapse imaging showed that the NK cells often exhibited periods of high motility, interrupted with periods of slow migration or complete arrest. These transient migration arrest periods (TMAPs) often overlapped with periods of conjugations between NK cells and target cells. Such conjugation periods sometimes led to cell-mediated killing of target cells. Analysis of cytotoxic response of NK cells revealed that a small sub-class of NK cells called serial killers was able to kill several target cells. In order to determine a starting time point for cell-cell interaction, a novel technique based on ultrasound was developed to aggregate NK and target cells into the center of the microwells. Therefore, these assays can be used to automatically and rapidly assess functional and migration behavior of cells to detect differences between health and disease or the influence of drugs.

The work presented in this thesis gives good examples of how microfluidic devices combined with automated imaging and image analysis can be helpful to address cell biological questions where single cell resolution is necessary. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. vi, 53 p.
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2013:14
Keyword
Single cell analysis, time-lapse fluorescence imaging, automated image analysis, microwell, droplet microfluidics, NK cells, single cell tracking, migration behavior analysis, cell-cell interaction, optical microscopy, image analysis, image processing, microfluidics, immune cells, tracking, counting, morphology analysis
National Category
Other Medical Biotechnology Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:kth:diva-129278 (URN)978-91-7501-846-1 (ISBN)
Public defence
2013-10-18, Gardaulan, Smittskyddsinstitutet, Nobels väg 18, Karolinska Institutet, Solna, 13:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 70784
Note

QC 20130927

Available from: 2013-09-26 Created: 2013-09-25 Last updated: 2014-02-28Bibliographically approved
2. Droplet microfluidics for single cell and nucleic acid analysis
Open this publication in new window or tab >>Droplet microfluidics for single cell and nucleic acid analysis
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Droplet microfluidics is an emerging technology for analysis of single cells and biomolecules at high throughput. The controlled encapsulation of particles along with the surrounding microenvironment in discrete droplets, which acts as miniaturized reaction vessels, allows millions of particles to be screened in parallel. By utilizing the unit operations developed to generate, manipulate and analyze droplets, this technology platform has been used to miniaturize a wide range of complex biological assays including, but not limited to, directed evolution, rare cell detection, single cell transcriptomics, rare mutation detection and drug screening.

The aim of this thesis is to develop droplet microfluidics based methods for analysis of single cells and nucleic acids. In Paper I, a method for time-series analysis of mammalian cells, using automated fluorescence microscopy and image analysis technique is presented. The cell-containing droplets were trapped on-chip and imaged continuously to assess the viability of hundreds of isolated individual cells over time. This method can be used for studying the dynamic behavior of cells. In Paper II, the influence of droplet size on cell division and viability of mammalian cell factories during cultivation in droplets is presented. The ability to achieve continuous cell division in droplets will enable development of mammalian cell factory screening assays in droplets. In Paper III, a workflow for detecting the outcome of droplet PCR assay using fluorescently color-coded beads is presented. This workflow was used to detect the presence of DNA biomarkers associated with poultry pathogens in a sample. The use of color-coded detection beads will help to improve the scalability of the detection panel, to detect multiple targets in a sample. In Paper IV, a novel unit operation for label-free enrichment of particles in droplets using acoustophoresis is presented. This technique will be useful for developing droplet-based assays that require label-free enrichment of cells/particles and removal of droplet content. In general, droplet microfluidics has proven to be a versatile tool for biological analysis. In the years to come, droplet microfluidics could potentially be used to improve clinical diagnostics and bio-based production processes.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 58 p.
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2016:17
Keyword
Acoustophoresis, Biomarker detection, Cell behavior analysis, Cell factories, Droplet microfluidics, Droplet PCR, High throughput biology, Label-free enrichment, Single cell analysis
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-192668 (URN)978-91-7729-125-1 (ISBN)
Public defence
2016-10-21, Gard-aulan, Nobels väg 18, Solna, 10:00 (English)
Opponent
Supervisors
Note

QC 20160926

Available from: 2016-09-26 Created: 2016-09-19 Last updated: 2016-09-26Bibliographically approved

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Periyannan Rajeswari, Prem KumarJönsson, Håkan N.

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