Microfluidics and AI for single-cell microbiology

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Karempudi, PraneethUppsala universitet,Molekylär systembiologi,Johan Elf (author)

Microfluidics and AI for single-cell microbiology

BookEnglish2023

Publisher, publication year, extent ...

Uppsala :Acta Universitatis Upsaliensis,2023
57 s.
electronicrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:uu-514317
ISBN:9789151319322
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-514317URI

Supplementary language notes

Language:English
Summary in:English &language:-1_t

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SwePubSwePub

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Subject category:vet swepub-contenttype
Subject category:dok swepub-publicationtype

Series

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology,1651-6214 ;2324

Notes

Most of the biological sciences deal with understanding the relationships between phenotypes and the underlying molecular mechanisms of organisms. This thesis is an engineering, computational, and experimental exercise in expanding the scope and scale of phenotype-genotype mapping techniques in single-cell microbiology using microscopy, microfluidics, and image processing. To this end, we use mother-machine-based microfluidic devices together with recently developed techniques in deep learning and optics. We use optical microscopes to observe cells of different genotypes, physically move cells, and image molecules inside them.We have designed a novel microfluidic device to expand the throughput of single-cell lineage tracing an order of magnitude compared to existing methods. We demonstrate the ability to isolate single cells from such a device using optical tweezers after phenotypic characterization in real time. We have developed analysis algorithms of various kinds with the prime intention of performing high-throughput real-time image processing in conjunction with experimental runs to identify interesting cells for further investigation.We have also developed an experimental protocol for bacterial species identification using fluorescence-in-situ hybridization (FISH) in microfluidic chips to complement an existing phenotype-based antibiotic-susceptibility test (AST). We apply this method together with deep-learning-based cell segmentation and tracking algorithms, and image classification methods to perform species-ID of up to 10 species in 2-3 hrs.Lastly, we have developed a 3D dot localization method to investigate how the chromosome structure changes during the E. coli cell cycle. Different loci on the E. coli chromosome were labeled using DNA-binding fluorescent proteins and imaged using an optical setup with an astigmatic point-spread-function. Mother-machine devices were used to constrain the movement of cells to the lateral plane during growth. A deep-learning-based single-molecule localization method was adapted for this application and used to map the chromosomal loci’s physical position in 3D as a function of cell size during the E. coli cell cycle.

Subject headings and genre

NATURVETENSKAP Biologi Biofysik hsv//swe
NATURAL SCIENCES Biological Sciences Biophysics hsv//eng
NATURVETENSKAP Biologi Bioinformatik och systembiologi hsv//swe
NATURAL SCIENCES Biological Sciences Bioinformatics and Systems Biology hsv//eng
NATURVETENSKAP Data- och informationsvetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences hsv//eng
Microfluidics
Artificial intelligence
Deep learning
Single-cell microbiology
Biology with specialization in Molecular Biotechnology
Biologi med inriktning mot molekylär bioteknik

Added entries (persons, corporate bodies, meetings, titles ...)

Elf, JohanUppsala universitet,Science for Life Laboratory, SciLifeLab,Molekylär systembiologi(Swepub:uu)johaande (thesis advisor)
Shechtman, Yoav,Associate ProfessorTechnion - Israel Institute of Technology, Israel (opponent)
Uppsala universitetMolekylär systembiologi (creator_code:org_t)

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