Biology subjects

Gunawan, I.

Publications and source records attributed to Gunawan, I..

4 recordsLinked to original sources

bioRxiv2025

Deep multiplexed 50-marker imaging of circulating tumor cells expands actionable biomarker profiling for precision oncology

Liquid biopsy-derived circulating tumor cells (CTCs) offer a minimally invasive avenue for precision oncology by enabling longitudinal monitoring of actionable molecular and cell-phenotypic biomarkers. However, conventional immunofluorescence imaging-based CTC profiling captures just 4-5 markers per cell, restricting crucial insights into oncogenic and resistance drivers, as well as tumor cell heterogeneity. Here we present an integrated pipeline employing deep multiplexed imaging to profile up to 50 molecular markers per CTC, capturing expression, phosphorylation, and subcellular localization of diverse biomarkers. Validated in a multi-stage prostate cancer resistance model and applied to prostate cancer patient-derived CTCs, this approach enhances CTC classification and reveals inter- and intra-patient heterogeneity correlating with therapy response. Machine learning identified therapeutically actionable signatures, suggesting patient-specific treatment strategies. This deep multiplexed imaging pipeline advances the utility of CTCs in guiding personalized cancer therapy by providing comprehensive molecular and phenotypic insights through minimally invasive liquid biopsies. Highlights- Multiplexed immunofluorescence imaging of circulating tumor cells captures up to 50 molecular markers per cell with subcellular localisation. - Single-cell and subcellular analyses identify quantitative signatures correlating with patient outcome, as well as therapeutically actionable biomarkers. - This method transforms the utility of CTCs for future use in patient stratification and for guiding personalised therapies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/684075v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@a36ff4org.highwire.dtl.DTLVardef@b06c1org.highwire.dtl.DTLVardef@1e8a572org.highwire.dtl.DTLVardef@c60753_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

cancer biology↗

bioRxiv2025

Generative semantic multiplexing (SemaPlex) for accessible and scalable multiplexed fluorescence imaging

Multiplexed fluorescence imaging enhances spatially-resolved interrogation of complex, multi-molecular cell processes that are insufficiently sampled using standard 4-5 plex imaging. To improve accessibility and scalability for multiplexed imaging, we demonstrate generative Semantic Multiplexing (SemaPlex); a simple experimental and deep learning strategy for amplifying marker plexity several-fold by semantically unmixing multiple markers combined per imaging channel. We first characterise key determinants of SemaPlex performance, achieving precise computational multiplexing of 2-to-8 markers synthetically mixed in one channel, facilitating enhanced cell phenotype classification. We then demonstrate practical SemaPlex application, acquiring 10 markers over 4 channels (3*3-plex+1) to efficiently emulate real multiplexed labelling. This permitted accurate reconstruction of quantitative single-cell phenotypic manifolds delineating cell-cycle and mitotic dynamics, with internally validated error-detection. Finally, we exemplify use of semantic guides; additional input channels that significantly enhance multiplexing fidelity. SemaPlex makes multiple-fold increases in fluorescence imaging-plexity accessible, scalable and customisable; democratising multiplexed imaging-based interrogation of complex cell biology.

systems biology↗

bioRxiv2025

Image quality metrics fail to accurately represent biological information in fluorescence microscopy

Image processing methods offer the potential to improve the quality of fluorescence microscopy data, allowing for image acquisition at lower, less phototoxic illumination doses. The training and evaluation of such methods is informed and driven by full-reference image quality metrics (IQMs); however, these metrics derive from applications to natural scene images, not fluorescence microscopy images. Here we investigate the response of IQMs to common properties of fluorescence microscopy data and whether IQMs are capable of reporting the biological information content of images. We find that IQM scores are biased by image content for both raw and processed microscopy data, and that improvements in IQM values reported after processing are not reliably correlated with performance in downstream analysis tasks. As common IQMs are unreliable proxies for guiding image processing developments in biological fluorescence microscopy, image processing performance should be benchmarked according to downstream analysis success.

bioinformatics↗

bioRxiv2025

Epithelial-mesenchymal cell state heterogeneity predetermines differential phospho-signaling responses to epidermal growth factor stimulation

Understanding why isogenic cancer cells respond differently to equivalent oncogenic stimuli is vital for optimizing anticancer therapies. Emerging evidence suggests that pre-existing differences in cell state may modulate signaling responses to new stimuli, but the interplay of specific cell states and signals remains unclear. We investigated whether epithelial-mesenchymal (E/M) state, a major axis of cancer cell heterogeneity, influences signaling responses to epidermal growth factor (EGF), a critical oncogenic stimulus in non-small cell lung cancer (NSCLC). We imaged >64,000 A549 NSCLC cells labeled for DNA, F-actin and alternate signaling markers (p-AKT-S473, p-AKT-T308, p-ERK or p-S6) after acute stimulation. Quantitative single-cell morphological and spatial profiling defined a stimulus-invariant E/M state landscape over which EGF signaling responses were compared. This revealed state-dependent differences in signal-activation magnitudes, dynamics and subcellular routing. AKT responses exhibited phosphosite- and compartment-specific dynamics across states, with epithelial cells showing strong, transient membrane-localized S473 and higher internalized T308, whereas mesenchymal cells displayed weaker but sustained nuclear and ruffle-localized S473. Regression-based computational multiplexing concurrently inferred all signaling responses per cell, mapping state-dependent divergence in multi-molecular signaling trajectories. E/M state thus pre-determines distinctive spatiotemporal profiles of EGF-induced signaling, with implications for signaling functions and anti-signaling therapy responses across E/M state-diverse tumors.

cancer biology↗