Combinatorial RNA barcodes for spatial clonal tracing with multi-platform phenotyping to study cancer evolution
File(s)
Author(s)
Flittner, Elizabeth
Type
Thesis or dissertation
Abstract
Cancer progression is driven by dynamic clonal evolution and interactions across molecular scales, yet experimental dissection of these processes is often limited to a single modality. Comprehensive investigation of tumour evolution requires lineage-tracing systems that are scalable, modality-flexible, and compatible with phenotypic and spatial analysis. This thesis describes the development and validation of X-CODE, a modular RNA barcoding framework that anchors lineage with defined, probe-detectable sequences, enabling integration across sequencing-based, cytometric, and spatial imaging platforms.
X-CODE combines an RNA barcode, detectable by probe hybridisation, with a matching short sequencing barcode to support clonal tracking within a unified experimental architecture without constraining downstream analytical modalities. This thesis shows combinatorial generation of high-diversity X-CODE libraries, enabling tracking of large populations. Isotope-based detection of barcodes via in-suspension time-of-flight mass cytometry is demonstrated across library scales, preserving compatibility with high-dimensional antibody phenotyping.
To extend lineage resolution into intact tissues, imaging-based X-CODE detection was implemented using a repurposed Akoya PhenoCycler-Fusion workflow, enabling spatially resolved clonal mapping. Detection of X-CODE by imaging mass cytometry was also established, broadening compatibility across imaging platforms. Together, this establishes a sequencing-independent route for clonal analysis within cytometry and imaging workflows. Furthermore, MALDI mass spectrometry imaging with sequential, same-section X-CODE detection was demonstrated to support integrative, unbiased molecular phenotyping with linked clonal identity.
Applied to an in vivo model of androgen deprivation in prostate cancer, X-CODE resolves clonal architecture and selection dynamics underpinning castration resistance with spatial resolution, revealing clone-specific phenotypic and metabolic plasticity within structured tumour ecosystems. Collectively, this work establishes X-CODE as a scalable, experimentally accessible lineage-tracing platform that integrates cytometric, spatial, metabolic, and sequencing readouts, enabling unified analysis of clonal dynamics across molecular and spatial dimensions.
X-CODE combines an RNA barcode, detectable by probe hybridisation, with a matching short sequencing barcode to support clonal tracking within a unified experimental architecture without constraining downstream analytical modalities. This thesis shows combinatorial generation of high-diversity X-CODE libraries, enabling tracking of large populations. Isotope-based detection of barcodes via in-suspension time-of-flight mass cytometry is demonstrated across library scales, preserving compatibility with high-dimensional antibody phenotyping.
To extend lineage resolution into intact tissues, imaging-based X-CODE detection was implemented using a repurposed Akoya PhenoCycler-Fusion workflow, enabling spatially resolved clonal mapping. Detection of X-CODE by imaging mass cytometry was also established, broadening compatibility across imaging platforms. Together, this establishes a sequencing-independent route for clonal analysis within cytometry and imaging workflows. Furthermore, MALDI mass spectrometry imaging with sequential, same-section X-CODE detection was demonstrated to support integrative, unbiased molecular phenotyping with linked clonal identity.
Applied to an in vivo model of androgen deprivation in prostate cancer, X-CODE resolves clonal architecture and selection dynamics underpinning castration resistance with spatial resolution, revealing clone-specific phenotypic and metabolic plasticity within structured tumour ecosystems. Collectively, this work establishes X-CODE as a scalable, experimentally accessible lineage-tracing platform that integrates cytometric, spatial, metabolic, and sequencing readouts, enabling unified analysis of clonal dynamics across molecular and spatial dimensions.
Version
Open Access
Date Issued
2026-02-21
Date Awarded
2026-04-01
Copyright Statement
Attribution-NonCommercial 4.0 International Licence (CC BY-NC)
License URL
Advisor
Takats, Zoltan
Sponsor
Cancer Research UK
Grant Number
CANTAC721\100024
Publisher Department
Department of Metabolism, Digestion and Reproduction
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)