Biosensing and Lineage Tracking:
Expanding Transcriptional Recording in Bacteria
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2025
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Doctoral Thesis
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Abstract
Bacterial transcriptional recording platforms offer powerful tools to track gene expression in vivo, but their capacity to resolve environmental signals and cellular history remains limited. This thesis addresses two major gaps: the need for targeted, signal-specific biosensing in complex environments, and the need for strategies that integrate transcriptional memory with lineage and temporal context. In the first part, we develop biosensors for the short-chain fatty acids propionic acid and butyric acid using engineered transcription factors in E. coli. Using iterative library design, fluorescence-based selection, and in vivo validation, we construct metabolite-responsive circuits with high dynamic range and low background. These sensors are compatible with the Record-seq platform, enabling targeted transcriptional memory of metabolite exposure in the gut. In the second part, we extend Record-seq functionality through the development of selection-based and lineage-coupled architectures. We demonstrate that spacer acquisition on the CRISPR array of Record-seq can be linked to downstream gene expression, enabling positive and negative selection. Parallel efforts explore hypermutation and recombinase-based barcoding systems for bacterial lineage tracking, culminating in a design where spacer acquisition halts further diversification, coupling expression history to lineage identity. Together, these advances expand the functionality of bacterial memory systems, bridging transcriptional recording, environmental sensing, and clonal lineage resolution within a unified synthetic framework.
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ETH Zurich
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09580 - Platt, Randall / Platt, Randall