Identification of different cell types is an indispensable task of biomedical research and clinical application. During the last decades, much attention was given to molecular characterization, and many cell types can now be identified using established markers that bind to cell-specific antigens. The required staining process is a lengthy and costly treatment, which can cause alterations of cellular properties, contaminate the sample and therefore limit its subsequent use. For example, for photoreceptor transplantations, highly pure samples of photoreceptor cells are required, which can currently only be obtained using molecular labelling, rendering the resulting sample incompatible for clinical application. A promising alternative to molecular markers is the label-free identification of cells using mechanical or morphological features. Real-time deformability cytometry (RT DC) is a microfluidic technique, which allows capturing both types of information simultaneously for single cells at high-throughput. In this thesis, I present machine learning methods which allow identifying different cell types, based on bright-field images from RT DC. In particular, I introduce algorithms that are fast enough to be applied in real-time during the measurement (at >1000 cells/s), which can be used for image-based cell sorting. The performance of the algorithms is shown for the identification of rod precursor cells in retina-samples, indicating that image-based sorting based on those algorithms would allow enriching photoreceptors to a final concentration, applicable for transplantation purposes.:Contents
Abstract iii
Kurzfassung iv
List of figures viii
List of tables x
1. Introduction 1
1.1. Texture and mechanical properties: label-free markers 4
1.2. The retina, diseases and cure by photoreceptor transplantation 5
1.3. Technologies for label-free assessment of cells 8
2. Materials and Methods 10
2.1. Experimental setup 10
2.1.1. Chip design for RT DC and RT-FDC 10
2.1.2. Chip design for soRT-FDC 11
2.1.3. Chip fabrication 13
2.1.4. RT-DC, RT-FDC and soRT FDC Setup 14
2.1.5. Physics of surface acoustic wave mediated sorting 16
2.1.6. Measurement buffer (MB) for RT DC 17
2.2. Online parameters 18
2.3. Offline parameters 21
2.4. Linear mixed models (LMM) 28
2.5. Normality test using probability plots 31
2.6. Gaussian mixture model (GMM) and Bayesian information criterion (BIC) 32
2.7. Random forests 33
2.8. Confusion matrix 34
2.9. Deep learning 36
2.10. Preparation of retina samples 43
2.11. Preparation of blood samples 44
2.12. Staining of neutrophils and monocytes 45
3. Results 46
3.1. Meta-analysis of RT-DC data 46
3.1.1. Correlations of area and volume 47
3.1.2. Correlations of deformation and inertia ratio 48
3.1.3. Further screening of correlations 50
3.1.4. Shape of distributions 52
3.1.5. Discussion 54
3.2. Characterization of retina cells in RT-DC 57
3.2.1. Maturation of retina cells 57
3.2.2. Comparing retina cell types using statistical tests 61
3.2.3. Discussion 63
3.3. Classification of retina cells using supervised machine learning 66
3.3.1. The dataset 66
3.3.2. Cell classification using optimized area gating 72
3.3.3. Cell classification using random forests 74
3.3.4. Cell classification using deep neural nets 80
3.3.5. Improving DNN accuracy using image augmentation 85
3.3.6. Tuning of final models and classification performance 93
3.3.7. Visualization of model attention 98
3.3.8. Discussion 100
3.4. Software tools to train and apply deep neural nets for sorting 105
3.4.1. AIDeveloper 105
3.4.2. Sorting Software 113
3.4.3. Discussion 114
3.5. Sorting experiments 117
3.5.1. Sorting of rod precursor cells 117
3.5.2. Sorting of neutrophils 120
3.5.3. Discussion 125
4. Conclusion and outlook 128
A. Appendix 131
I. Comparison of dense and convolutional layer 131
Bibliography 133
Acronyms 148
Acknowledgements 150
Erklärung 152
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:71754 |
| Date | 28 August 2020 |
| Creators | Herbig, Maik |
| Contributors | Guck, Jochen, Schlierf, Michael, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1038/s41592-020-0831-y, 1548-7091, 10.1101/2020.03.03.975250 |
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