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Koh, Y. W. H.

Publications and source records attributed to Koh, Y. W. H..

2 recordsLinked to original sources

Macrophage migration-dependent retraction fibers and migrasomes - a structural basis for spatiotemporal cytokine secretion.

Migrating cells leave behind trails of matrix-bound retraction fibers and migrasomes, but their cell-specific physiological functions are largely unknown. We comprehensively characterize retraction fibers and migrasomes of migrating macrophages in mouse and zebrafish in vitro and in vivo models. These structures share common and macrophage-centric components of tetraspanin enriched microdomains (TEMs) including tetraspanin 4 (TSPAN4), the integrin CD11b and the pTRAP, SCIMP; like TSPAN4, we show that SCIMP expression modulates retraction fiber and migrasome formation. In immune-activated migrating macrophages, rear-positioned recycling endosomes regulate the secretory trafficking of newly synthesized inflammatory cytokines into retraction fibers and migrasomes. Transmembrane TNF is delivered via SNARE-mediated carriers to the cell surface and to the surfaces of both retraction fibers and migrasomes, from where it can be released. Retraction fibers are thus newly identified here as cytokine secretion sites, and along with migrasomes they allow migrating macrophages to secrete more cytokine compared to stationary cells. Whereas cytokine secretion has been viewed as a process of ad hoc diffusion, instead, we reveal a new mechanism of migration-dependent, localized deposition and spatiotemporal release of bioactive cytokines from RFs and migrasomes. Cytokine deployment is thus organized in a constrained manner facilitating direct influence on local inflammatory responses.

cell biology↗

LLAMA: a robust and scalable machine learning pipeline for analysis of cellsurface projections in large scale 4D microscopy data

We present LLAMA, a pipeline for systematic analysis of terabyte scale 4D microscopy datasets. Analysis of individual biological structures in imaging at this scale requires efficient and robust methods which do not require human micromanagement or editing of outputs. To meet this challenge, we use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, and the generation of detailed object level statistics. Advanced visualisation is a key element of LLAMA: we provide a specialised software tool which supports quality control and optimisation as well as visualisation of outputs. LLAMA was used in a quantitative analysis of macrophage surface membrane projections (filopodia, ruffles, tent-pole ruffles) examining the differential effects of two interventions: lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1). Distinct patterns of increased activity were identified. In addition, a continuity of behaviour was found between tent pole ruffling and wave-like ruffling, further defining the role of filopodia in ruffling.

cell biology↗