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Antimicrobial resistance (AMR) is a global health crisis driven significantly by the overuse of antibiotics. Most of this systemic misuse in human healthcare is a direct consequence of a diagnostic void created by the temporal gap between patient presentation and microbiological results. Traditional Antibiotic Susceptibility Testing (AST) relies on macroscopic biomass accumulation, a process constrained by bacterial generation times, and requires 48 to 72 hours. This delay forces clinicians to rely on empirical guidelines, treating patients based on probability rather than diagnostic certainty. Dependence on empirical therapy drives antibiotic overuse, fueling AMR. However, as resistance rates rise, the utility of empirical guidelines diminishes, creating a paradox where increasing resistance demands immediate diagnostic answers that current technology cannot provide.

This thesis demonstrates that the temporal constraints of AST are not immutable biological constants, but technological artifacts resulting from limited detection sensitivity. It establishes that bacterial cells respond to effective antibiotics within minutes, and that detecting these immediate physiological changes requires only a sufficiently sensitive method. This detection is enabled by a novel nanofluidic platform that actively captures, maintains, and images individual bacterial cells. By utilizing phase-contrast microscopy, the method quantifies instantaneous single-cell growth rates in real-time. Paper I establishes the "Fast Antibiotic Susceptibility Testing" (FASTest) method, demonstrating that susceptibility profiles for Escherichia coli against nine antibiotics can be determined in 30 minutes, and that it correctly distinguishes resistant bacteria from susceptible ones across 49 uropathogenic E. coli isolates. Paper II validates the platform's robustness through a complex "lab-on-a-chip" application involving a multi-step molecular biology protocol for in situ genotyping. Finally, Paper III shows clinical feasibility of the method for 30-minute AST for sepsis using either positive blood cultures or isolated colonies.

The research presented in this thesis has been successfully translated from an academic concept into a fully automated medical device, the PA-100 AST System, which was recently awarded the Longitude Prize on Antimicrobial Resistance. This technology demonstrates that shifting observation from the population to the single cell bridges the diagnostic void, enabling the transition from empirical prescribing to evidence-based precision medicine for urinary tract infections at the Point-of-Care.

Bacterial populations can use bet-hedging strategies to cope with rapidly changing environments. One example is non-growing cells in clonal bacterial populations that are able to persist antibiotic treatment. Previous studies suggest that persisters arise in bacterial populations either stochastically through variation in levels of global signalling molecules between individual cells, or in response to various stresses. Here, we show that toxins used in contact-dependent growth inhibition (CDI) create persisters upon direct contact with cells lacking sufficient levels of CdiI immunity protein, which would otherwise bind to and neutralize toxin activity. CDI-mediated persisters form through a feedforward cycle where the toxic activity of the CdiA toxin increases cellular (p)ppGpp levels, which results in Lon-mediated degradation of the immunity protein and more free toxin. Thus, CDI systems mediate a population density-dependent bet-hedging strategy, where the fraction of non-growing cells is increased only when there are many cells of the same genotype. This may be one of the mechanisms of how CDI systems increase the fitness of their hosts.

The emergence and spread of antibiotic-resistant bacteria are aggravated by incorrect prescription and use of antibiotics. A core problem is that there is no sufficiently fast diagnostic test to guide correct antibiotic prescription at the point of care. Here, we investigate if it is possible to develop a point-of-care susceptibility test for urinary tract infection, a disease that 100 million women suffer from annually and that exhibits widespread antibiotic resistance. We capture bacterial cells directly from samples with low bacterial counts (10(4) cfu/mL) using a custom-designed microfluidic chip and monitor their individual growth rates using microscopy. By averaging the growth rate response to an antibiotic over many individual cells, we can push the detection time to the biological response time of the bacteria. We find that it is possible to detect changes in growth rate in response to each of nine antibiotics that are used to treat urinary tract infections in minutes. In a test of 49 clinical uropathogenic Escherichia coli (UPEC) isolates, all were correctly classified as susceptible or resistant to ciprofloxacin in less than 10 min. The total time for antibiotic susceptibility testing, from loading of sample to diagnostic readout, is less than 30 min, which allows the development of a point-of-care test that can guide correct treatment of urinary tract infection.

In this work, we present a proof-of-principle experiment that extends advanced live cell microscopy to the scale of pool-generated strain libraries. We achieve this by identifying the genotypes for individual cells in situ after a detailed characterization of the phenotype. The principle is demonstrated by single-molecule fluorescence time-lapse imaging of Escherichia coli strains harboring barcoded plasmids that express a sgRNA which suppresses different genes in the E.coli genome through dCas9 interference. In general, the method solves the problem of characterizing complex dynamic phenotypes for diverse genetic libraries of cell strains. For example, it allows screens of how changes in regulatory or coding sequences impact the temporal expression, location, or function of a gene product, or how the altered expression of a set of genes impacts the intracellular dynamics of a labeled reporter.

In this paper, we have developed tools to analyze prokaryotic cells growing in monolayers in a microfluidic device. Individual bacterial cells are identified using a novel curvature based approach and tracked over time for several generations. The resulting tracks are thereafter assessed and filtered based on track quality for subsequent analysis of bacterial growth rates. The proposed method performs comparable to the state-of-the-art methods for segmenting phase contrast and fluorescent images, and we show a 10-fold increase in analysis speed.

Transcription factors mediate gene regulation by site-specific binding to chromosomal operators. It is commonly assumed that the level of repression is determined solely by the equilibrium binding of a repressor to its operator. However, this assumption has not been possible to test in living cells. Here we have developed a single-molecule chase assay to measure how long an individual transcription factor molecule remains bound at a specific chromosomal operator site. We find that the lac repressor dimer stays bound on average 5 min at the native lac operator in Escherichia coli and that a stronger operator results in a slower dissociation rate but a similar association rate. Our findings do not support the simple equilibrium model. The discrepancy with this model can, for example, be accounted for by considering that transcription initiation drives the system out of equilibrium. Such effects need to be considered when predicting gene activity from transcription factor binding strengths.