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Farndale, L.

Publications and source records attributed to Farndale, L..

3 recordsLinked to original sources

CYCLOPS: an open end-to-end platform for cyclic multiplex imaging and single-cell phenotyping

Multiplex immunofluorescent imaging enables deep spatial profiling of protein expression in tissues but is often limited by reliance on proprietary reagents, dedicated hardware, and closed analysis ecosystems. Here we present CYCLOPS (Cyclic Open Platform for Spatial Proteomics), an end-to-end, open-source workflow for cyclic multiplex imaging and single-cell phenotyping using standard microscopy infrastructure. CYCLOPS integrates an Arduino-based automated fluidics system, an open-chamber stage insert, and antibody-oligonucleotide conjugation based entirely on published chemistries and off-the-shelf components. We demonstrate robust and reproducible antibody conjugation, high-quality multiplexed staining, and stable imaging across >10 cycles with minimal drift (<1 {micro}m) and consistent fluorescence retention with low signal carry-over. The system supports efficient buffer exchange and consistent performance across multiple markers and imaging rounds. Using confocal microscopy, the workflow is compatible with three-dimensional imaging, enabling multiplexed analysis of volumetric tissue structures. To enable quantitative analysis, we establish an open-source image processing and analysis pipeline for single-cell feature extraction and phenotypic classification, avoiding reliance on proprietary software or black-box workflows. This framework integrates image registration, segmentation, and supervised classification to generate biologically interpretable single-cell data. Together, CYCLOPS provides a flexible and accessible platform for cyclic multiplex imaging, lowering barriers to adoption and enabling broader use of spatial proteomics across diverse research settings. This accessible framework democratizes high-plex imaging by enabling any laboratory with a standard confocal microscope to perform iterative multiplexing without reliance on proprietary reagents or hardware.

Cell Biology↗

Morphospatial profiling of cancer-associated fibroblasts reveals architectural subtypes of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with an urgent need for biomarkers to predict prognosis and guide treatment. Understanding the complex spatial biology of pancreatic cancer-associated fibroblasts (CAFs) and the broader architecture of the PDAC tumour microenvironment is central to this challenge. Using a multi-omics approach across multiple spatial resolutions in a large human PDAC cohort, we integrate geometry and shape to define discrete morphological CAF subtypes, expanding CAF phenotyping beyond conventional proteomics. We then reveal an architectural and molecular axis of PDAC at tissue level, suggestive of epithelial-stromal co-evolution, with translational implications and prioritisation of stromal targets. Finally, we recapitulate this axis by introducing four unique, internally validated architectural subtypes of PDAC, each characterised by a common microenvironment and CAF enrichment profile. These archetypes outperform conventional pathology in prognostication, and predict response to adjuvant chemotherapy. Collectively, this study establishes a novel morphological paradigm for spatial biology, illuminates the architectural landscape of PDAC, and provides a framework for spatial biomarker discovery to close the translational gap in this devastating disease.

cancer biology↗

Self-Supervised AI Reveals a Hidden Landscape of Prognostic Spatial Patterns in Multiplex Immunofluorescence Images

Modern spatial proteomic methods, such as multiplex immunofluorescence (mIF) imaging, offer a data-rich view of spatial biology in intact tissues. However, interpreting its complexity is a major bottleneck, limiting its potential for biological discovery and clinical translation. Current computational methods often rely on segmentation-based approaches that discard crucial morphological information and are limited to testing pre-defined hypotheses. Here, we introduce a self-supervised learning (SSL) framework that enables hypothesis-agnostic, context-aware discovery of biomarkers directly from mIF images. Our approach extracts rich feature representations that capture holistic architectural patterns, which integrate cellular morphology, marker interactions, and microenvironmental context without human supervision. Applying this framework to over 7,000 mIF tissue images from over 1,800 patients in two distinct cancer types, we demonstrate superior prognostic performance over conventional segmentation analyses. The method autonomously identified previously unknown and potentially clinically actionable biological patterns. In lung adenocarcinoma, these include a Ki67-mediated immune evasion phenotype, a sub-cellular pattern of GLB1 expression which aligns with low-grade EGFR-driven tumours, and distinct modes of tumour-immune interaction in PD-L1+ patients. We also find a regulatory T-cell-mediated immunosupressive environment promoting tumour budding in colorectal carcinoma. Our work establishes SSL as a powerful, scalable, and unbiased platform to decode tissue ecosystems while being fully explainable without pre-defined hypotheses. This paradigm shift transforms high-plex imaging from a hypothesistesting tool into a hypothesis-generating engine that can accelerate the discovery of next-generation spatial biomarkers.

cancer biology↗