I build human organoids — and the tools to read them.
Self-organizing tissue models combined with quantitative image analysis, providing experimental systems that generate reproducible drug-response data at the preclinical stage.
- Animal and human organoid models that recapitulate intestine, colon, and vasculature in vivo
- High-throughput image analysis that turns high-resolution microscopy into quantitative data
- New Approach Methodologies (NAMs) toward animal-free preclinical testing
Dissecting disease and testing therapeutics in in vitro models.
Many preclinical failures can be attributed to a behavioural gap between the model system and the target human tissue. This work addresses that gap by constructing organoids that recapitulate native tissue architecture: murine adult-stem-cell-derived intestinal and colonic epithelium, and human iPSC-derived blood vessels. These models are paired with computational pipelines that convert confocal image stacks into reproducible, quantitative measures of drug response.
The resulting model system is physiologically relevant and high-throughput compatible, consistent with the field's shift toward New Approach Methodologies (NAMs) that reduce reliance on animal testing. The work integrates wet-lab biology with quantitative analysis; the degree of integration between the two is a primary determinant of whether a translational organoid platform is viable. Such a platform can serve multiple roles: as a disease model for mechanistic investigation, as a high-throughput screening and safety-testing system for candidate compounds, and, when derived from patient cells, as a basis for personalised therapeutic strategies.
The transcriptional logic of collective invasion
Colorectal carcinoma cells frequently invade not as single migratory cells but as cohesive collectives undergoing a partial epithelial–mesenchymal transition (pEMT) — retaining cell–cell contacts while acquiring mesenchymal motility. Using oncogenically transformed murine intestinal organoids — derived from adult intestinal stem cells — as a tractable model of carcinogenesis, my doctoral work identified the transcription factor Sox11 as an essential node in this program.
Canonical TGF-β1 signaling redirects Sox11's gene-regulatory activity, coupling it to a PDGF signaling axis that drives pEMT and collective invasion at the organoid front.
The study combined single-cell and bulk RNA-sequencing, gene-regulatory-network inference (SCENIC), and more than twenty CRISPR-Cas9 loss- and gain-of-function lines for functional validation. It established a mechanistic framework (published as first author in Oncogenesis, 2025) describing how a single transcription factor is repurposed by the tumour microenvironment to promote invasion.
Perturbing and visualizing tissue in situ
Mechanistic claims require perturbation. I have generated more than twenty CRISPR-Cas9 knockouts and overexpression lines in organoids, coupled to functional readouts. Beyond loss- and gain-of-function, I apply CRISPR-HOT (homology-independent organoid transgenesis) to knock in fluorescent reporters, epitope tags, and degron cassettes — including mutant FKBP for conditional protein destabilization — directly at endogenous loci.
This makes it possible to visualize and acutely control transcription factors at the invasive front of living organoids, linking molecular intervention to morphological consequence in the same intact tissue.
Blood vessel organoids for drug screening
Human iPSC-derived blood vessel organoids self-assemble interconnected networks of endothelial cells and pericytes enclosed by a basement membrane, forming three-dimensional human vascular tissue. At Angios FlexCo, this work established high-throughput production pipelines across multiple hydrogel embedding conditions, scaling these organoids from research-scale preparation toward a screening-ready platform.
On top of production, the work established a quantitative vascular toxicity workflow comprising compound treatment, immunofluorescence staining, confocal acquisition, and automated readout of vascular morphology and signal intensity. The pipeline supports dose-response characterization of vascular-disrupting and anti-angiogenic agents, consistent with the regulatory and ethical basis for New Approach Methodologies (NAMs) that reduce reliance on animal models.
A methods contribution describing an animal-origin-free route to these organoids is co-authored and published in Scientific Reports (2026).
Quantitative readouts from confocal image stacks
The utility of a model depends on the quality of the readouts it produces. This work develops automated image-analysis pipelines in Python and ImageJ macros to reduce subjectivity in organoid phenotyping. For vascular organoids, this includes segmentation, skeletonization, and graph-based network analysis (via networkx) to quantify branch topology, together with morphometric descriptors such as average diameter derived from Feret measurements.
For marker localization, a dedicated macro quantifies radial intensity profiles of endothelial (CD31) and pericyte (PDGFRB) signals — capturing spatial organization that simple intensity averages would miss. Every pipeline is written to fixed, documented conventions so that results are reproducible across experimental runs and transferable to collaborators.
How the work gets done.
The experimental and computational toolkit behind my doctoral work: build a faithful disease model, perturb it with genome engineering, read it out by imaging and sequencing, then anchor every finding back to patients.
TKA Organoid Model
A mouse intestinal organoid carrying the three most common colorectal-cancer driver mutations — Apc, KrasG12D, Trp53R172H — switchable on demand.
CRISPR Knockout · Clonal Lines
Deleting a gene cleanly from an organoid and growing single-cell-derived clones to ask what that gene was for.
CRISPR-HOT Endogenous Tagging
Marking a protein at its own gene so it can be seen and degraded, when no usable antibody exists.
Inducible Overexpression
Switching a gene on with a drug to test whether it is sufficient — the gain-of-function counterpart to knockout.
Transwell Invasion Assay
Turning collective invasion into a number, so that genotypes can be compared quantitatively.
Whole-Mount Immunofluorescence
Imaging proteins in an intact 3D organoid by confocal microscopy, to see not just how much but where.
Single-Cell RNA-seq + Trajectory
Reading every cell in a treated organoid separately, then ordering them along the path from epithelial to invasive.
Regulon & Leader-Cell Analysis
Inferring which transcription factor controls the invasive cells, and scoring which cells are leaders.
Bulk RNA-seq
Deep population-level transcriptomes to validate the single-cell findings and define gene modules.
CRC Patient Data Mining
Testing whether an organoid finding holds in real patients, using public cancer-genomics and survival data.
iPSC Vascular Organoid · HTP Generation
Differentiating human iPSCs into self-organizing blood-vessel organoids — endothelium and pericytes — and scaling the protocol into high-throughput plate formats for screening.
Drug Testing · IF, Confocal & Network Analysis
Treating vascular organoids with compounds, then reading structural damage by immunofluorescence, confocal imaging, and quantitative vascular-network analysis (CD31 / PDGFRB; length, branching, diameter) to score vascular toxicity.
A researcher integrating experimental biology and computation.
Seven years across academia and industry developing advanced in vitro models, ranging from the molecular mechanisms of cancer to screening-ready human tissue platforms.
I am a PhD scientist working on organoids (three-dimensional tissue models spanning murine adult-stem-cell-derived intestinal and colonic epithelium and human iPSC-derived blood vessels) and the computational analytics required to turn them into reliable experimental systems. My trajectory has moved from fundamental discovery toward translational application, since the most useful organoid platforms are generally built where rigorous biology meets reproducible quantification.
My doctoral research at the University of Freiburg, in the Andreas Hecht lab, examined how colorectal cancer cells invade collectively. I identified the transcription factor Sox11 as an essential regulator of partial EMT, integrating single-cell transcriptomics, gene-regulatory-network inference, and extensive CRISPR perturbation. This work established an approach of pairing mechanistic hypotheses with the analytical infrastructure required to test them at scale.
A model that cannot be quantified reproducibly is insufficient as a reliable experimental system.
I currently develop high-throughput vascular organoid platforms and drug-toxicity workflows at Angios FlexCo in Innsbruck; this direction is consistent with the field's move toward New Approach Methodologies (NAMs).
Presented as invited talks and posters in Basel, Lyon, Freiburg and Boston (2019–2025). Funded by the MeInBio doctoral program (DFG), the Wilhelm Sander-Stiftung, DAAD RISE, and a BaCell3D fellowship in Basel.
Outside the lab I am a constant traveler — more than twenty countries across Asia and Europe — a senior badminton player of eight years, and an alpine hiker who logs over twenty trails a year in the Alps and the Black Forest. In Europe, I cook seriously in the Chinese and Japanese traditions. These habits stem from my attention to detail, endurance, and adaptability.
Let's build a better model.
Open to collaborations in organoid platform development, preclinical screening, NAMs, and quantitative image analysis across academic, CRO, and pharmaceutical settings.
Scientist at Angios FlexCo, Innsbruck, developing high-throughput vascular organoid and drug-toxicity platforms.
Platform Development
Standing up iPSC-derived organoid systems and high-throughput culture workflows from protocol to reproducible pipeline.
Screening & NAMs
Designing drug-response and toxicity assays on human organoid models aligned with New Approach Methodologies.
Image Analytics
Automated quantification of organoid morphometry, marker intensity, and vascular network topology in Python & ImageJ.