Biology subjects

Li, Y. L.

Publications and source records attributed to Li, Y. L..

5 recordsLinked to original sources

bioRxiv2025

Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy

Single-molecule localization microscopy (SMLM) achieves nanoscale imaging of complex protein structures in the cell. However, the ability to capture structural variability across cell conditions (cell lines, gene expression, treatment) from 3D point cloud SMLM data remains limited. We present siMILe, a weakly-supervised multiple instance learning (MIL) machine learning method to close this gap in interpretable subcellular discovery. siMILe identifies condition-specific changes in protein assemblies by leveraging their shape and network features, without requiring structure-level supervision. siMILe improves structure classification by extending embedded instance selection (MILES) through adversarial erasing and a symmetric classifier. We validated siMILe by detecting caveolae from caveolin-1 (Cav1) labeled PC3 prostate cancer cells differentially expressing cavin-1. In PC3-CAVIN1 cells, cavin-1 closely associates with siMILe-identified caveolae, to a lesser extent with higher-order non-caveolar Cav1 scaffolds, but not small Cav1 oligomers corresponding to 8S complexes, supporting a role for progressive cavin-1 interaction in 8S complex oligomerization. We also validated siMILe on simulated SMLM data and in detecting inhibitor-induced structural variations within clathrin-coated pit data. These results highlight siMILes potential to identify differential molecular structures in distinct cell conditions. siMILe extends the SuperResNET SMLM software platform with the ability to detect interpretable structural differences across conditions.

bioinformatics↗

bioRxiv2025

Logic-gating the HaloTag system with Conditional-Halo-ligator 'CHalo' reagents

HaloTag proteins spontaneously ligate onto any chemical reagent featuring a chloroalkane motif (CA). We introduce the conditional CHalo motif, which ligates to HaloTag only after uncaging by light or enzymes. (1) Photo-triggered CHalo fluorogenic reagents allow spatiotemporally-specific labeling; (2) photo-triggered CHalo heterodimerisers can photocontrol protein recruitment; and (3) enzyme-triggered CHalo reagents can durably record diverse enzyme activities, and multiplexing them should allow quantitative ratiometric recording of multiple activities in parallel. CHalo thus permits manifold extensions to the HaloTag technology. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/676741v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@d18cd6org.highwire.dtl.DTLVardef@175694forg.highwire.dtl.DTLVardef@154ffd5org.highwire.dtl.DTLVardef@167fec2_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

CLIC-dependent internalization of caveolin-1 to lysosomal vacuoles in response to osmotic regulation

Originally thought to be a major endocytic portal, caveolae are now considered to function as a membrane buffer, whereby caveolae flattening protects the plasma membrane from rupture under mechanical stress, such as hypotonic shock. However, the fate of the caveolae coat protein caveolin-1 in response to hypotonic shock is not known. Here, we show that extended hypotonic shock induces ubiquitin-independent, CLIC-dependent endocytosis of caveolin-1 to large, intracellular, CD44-positive, pH-neutral lysosomal vacuoles negative for multivesicular body markers. Caveolin-1 recycles from these vacuoles to the plasma membrane upon return to isotonic conditions. Caveolin-1 internalizes not in response to initial cell expansion upon hypotonic shock but rather subsequent reduction of cell volume, and also in low-tension cells grown on reduced stiffness hydrogels. Upon hypertonic shock, caveolin-1 internalization occurs selectively in PC3 cells, lacking cavin-1, required for caveolae formation, and is inhibited upon cavin-1 reintroduction. CLIC endocytosis of non-caveolar caveolin-1 to neutral pH lysosomal vacuoles in response to osmotic regulation defines a novel recycling pathway enabling caveolae membrane buffering.

cell biology↗

bioRxiv2024

SuperResNET single molecule localization microscopy model-free network analysis of Nup96 achieves molecular resolution

SuperResNET is an integrated machine learning-based analysis software for visualizing and quantifying 3D point cloud data acquired by single molecule localization microscopy (SMLM). The computational modules of SuperResNET include correction for multiple blinking of a single fluorophore, denoising, segmentation (clustering), and feature extraction, which are then used for cluster group identification, modularity analysis, blob retrieval and visualization in 2D and 3D. Using publicly available dSTORM data, we apply a graphical user interface (GUI) version of SuperResNET to nucleoporin Nup96 structures, that present a highly organized octagon structure comprised of eight corners. SuperResNET GUI effectively segments nuclear pores and Nup96 corners based on differential proximity threshold analysis. SuperResNET GUI quantitatively analyzes features from segmented nuclear pore structures, including complete structures with 8-fold symmetry, and from segmented corners. SuperResNET GUI modularity analysis of segmented corners distinguishes two modules at 11.1 nm distance, corresponding to two individual Nup96 molecules. SuperResNET GUI is therefore a model-free tool that can reconstruct network architecture and molecular distribution of subcellular structures without the bias of a specified prior model, attaining molecular resolution from dSTORM data. SuperResNET GUI provides flexibility to report on structural diversity in situ within the cell without model-fitting, providing opportunities for biological discovery.

cell biology↗

bioRxiv2024

Super-resolution single molecule network analysis (SuperResNET) detects changes to clathrin structure by small molecule inhibitors

Specificity of small molecules for their target molecule in the cell is critical to determine their effective use as biologics and therapeutics. Small molecule inhibitors of clathrin endocytosis, Pitstop 2, and the dynamin inhibitor Dynasore, have off-target effects and their specificity has been challenged. Here, we used SuperResNET to apply network analysis to 20 nm resolution dSTORM single-molecule localization microscopy (SMLM) to test whether Pitstop 2 and Dynasore alter the morphology of clathrin coated pits in intact cells. SuperResNET analysis of dSTORM data from HeLa and Cos7 cells identifies three classes of clathrin structures: small oligomers (Class I); pits and vesicles (Class II); and larger clusters corresponding to fused clathrin pits and clathrin plaques (Class III). SuperResNET analysis of high resolution MinFlux imaging identifies Class 1 oligomers as well as Class 2 structures including morphologically identifiable clathrin pits and vesicles. SuperResNET feature analysis of dSTORM data shows that Pitstop 2 and Dynasore induce the formation of distinct homogenous populations of clathrin structures in HeLa cells. Pitstop 2 blobs are smaller and more elongated than those induced by Dynasore, indicating that these two clathrin inhibitors arrest clathrin endocytosis at distinct stages. Pitstop 2 and Dynasore are not impacting clathrin structure via actin depolymerization as the actin depolymerizing agent latrunculin A (LatA) induced larger heterogeneous clathrin structures. Ternary analysis of SuperResNET shape features presents a distinct profile for Pitstop 2 Class II structures. The most representative Pitstop blobs align with and resemble MinFlux clathrin pits while control structures resemble Minflux clathrin vesicles. SuperResNET analysis of SMLM data is therefore a highly sensitive approach to detect the effect of small molecules on target molecule structure in situ in the cell.

cell biology↗