Single-cell dispensing for isolation and analysis of individual mammalian and microbial cells

Media type: E-Book

Title: Single-cell dispensing for isolation and analysis of individual mammalian and microbial cells

Contributor: Riba, Julian [Verfasser]; Zengerle, Roland [Akademischer Betreuer]

Corporation: Albert-Ludwigs-Universität Freiburg, Fakultät für Angewandte Wissenschaften

imprint: Freiburg: Universität, 2020

Extent: Online-Ressource

Language: English

DOI: 10.6094/UNIFR/154844

Identifier:

Keywords: (local)doctoralThesis

Origination:

University thesis: Dissertation, Universität Freiburg, 2020

Footnote:

Description: Abstract: This thesis presents technological approaches and workflows for automated isolation and analysis of individual cells using single-cell dispensing. Single-cell dispensing is based on drop-on-demand noncontact liquid dispensing and cell detection. Focus of this thesis was to further develop the technology for isolation of smaller cells such as bacteria, the application of single-cell dispensing for single-cell genome analysis of clinical and environmental samples, and the use of machine-learning for cell classification and sorting. After a review of the state of the art and providing the fundamentals most relevant for this work, the technological and scientific results are presented in five sections. First, label-free isolation of single bacteria cells using single-cell dispensing is demonstrated. As will be reviewed in this work, the isolation and analysis of single prokaryotic cells down to 1 μm and less in size poses a special challenge since deterministic cell isolation relies on a method for detection of single cells. Here, a new printhead comprising an optical detection system for bacterial cells was developed. The magnification and contrast of the optical system for cell detection was increased, which was made possible by a redesign of the optical path enabling the use of a microscope objective with a working distance as low as 30 mm. New dispensing chips with a reduced nozzle size of 20 μm produce stable droplets with 35-60 pl in volume. It was shown that the new system allows for isolation of beads down to 1.3 μm in size with 95% efficiency. A single-cell isolation efficiency of 93.0±1.4% was obtained as shown by dispensing fluorescent labeled E. coli. Single cells from three different species were deposited into liquid growth medium for subsequent clonal cultivation with efficiencies ranging from 81% to 92% demonstrating high cell viability after single-cell dispensing. Using this prototype, a 96-well plate could be inocculated on average in about 8.1±1.8 minutes. It was further demonstrated, that individual bacterial cells from a heterogeneous sample of E.coli and E.faecalis can be isolated for clonal culturing directly on agar plates. Second, this thesis presents tools for precise deposition of single-cells and dosage of reagents in the sub-microliter range into PCR reaction vessels. A camera-based tool for measuring droplet impact positions is proposed. Using this tool, a system for automated dispenser offset correction was integrated into a single-cell printer. Combined with electrostatic neutralization of the well plates this enables robust deposition of single cells to the bottom of the wells of 96-well and 384-well PCR plates. Per fluorescence microscopy, 99.0% and 98.8% of beads were correctly delivered into the wells, respectively. Further, a concept for non-contact dosage of reagents for the assembly of single-cell whole genome amplification (WGA) reactions is presented. Using this system, WGA on mammalian cells and bacterial cells can be performed in reaction volumes as low as 2.5 μl in standard 384-well plates. This reduces the cost for WGA almost 20-fold compared to the reaction volumes proposed by the manufacturer of the WGA kit. Third, the advanced single-cell printer is used to study gene mutations in individual cancer cells. All 153 single cells that were isolated and subjected to WGA could be successfully amplified. On empty droplets, a PCR on repetitive sequences in the human genome (LINE1 retrotransposons) yielded no product after WGA, verifying the absence of free-floating DNA in the dispensed droplets. Representative gene variants identified in bulk specimens were sequenced in single-cell WGA DNA. In the osteosarcoma cell line U-2 OS, 22 of 25 cells yielded results for both an SLC34A2 and TET2 mutation site, including cells harboring the SLC34A2 but not the TET2 mutation. In the acute myeloid leukemia (AML) cell line Kasumi-1, 23 of 33 cells with data on both the KIT and TP53 mutation site harbored both mutations. The mutation status of one patient with AML with respect to TP53 confirmed a subclone with partial loss of chromosome 17p. In the sample of a second patient, the mutation status with respect to NRAS and WT1 demonstrated the sequential acquisition of mutations upon progression from myelodysplastic syndrome to AML.Fourth, the prototype instrument was used for taxonomic classification of single cells from different microbial samples based on 16s rRNA sequencing. For benchmarking, 72 E. coli cells were subjected to WGA and a subset of 10 cells were subjected to 16s rRNA sequencing. The success rates of each process step were determined resulting in a total workflow efficiency of 54% for E. coli. Next, single bacteria from a sputum, a faecal, and a wastewater sample were isolated and analyzed. In total, a WGA was performed on 384 wells, resulting in 98 high quality 16s rRNA Sanger sequences that were used for taxonomic classification at the genus level and calculation of phylogenetic distance trees. Across all three samples, 40 different operational taxonomic units (OTUs) were found. Among those, two cells belong to yet uncultivated families. In summary, this demonstrates that the presented method allows for genomic analysis of potentially uncultivable single bacteria from mixed microbial species of environmental and clinical origin. Finally, machine learning for classification of cell images is applied for ‘real-time’ cell viability sorting on a single-cell printer. It was shown that an extremely shallow convolutional neural network (CNN) for classification of low-complexity cell images outperforms more complex architectures. Datasets with hundreds of cell images from four different samples were used for training and validation of the CNNs. The clone recovery, i.e. the fraction of single-cells that grow to clonal colonies, is predicted to increase for all the samples investigated. Finally, a trained CNN was deployed on a c.sight™single-cell printer for ‘real-time’ sorting of a CHOK1 cells. On a sample with artificially damaged cells the clonal recovery could be increased from 27% to 73%, thereby resulting in a significantly faster and more efficient cloning. Depending on the classification threshold, the frequency at which viable cells are dispensed could be increased by up to 65%. This technology for image-based cell sorting is highly versatile and can be expected to enable cell sorting by computer vision with respect to different criteria in the future

Access State: Open Access

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