Molecularly Distinct Clathrin-Coated Pits Differentially Impact EGFR Fate and Signaling

International audienc

Design and validation of a human brain endothelial microvessel-on-a-chip open microfluidic model enabling advanced optical imaging

We describe here the design and implementation of an in vitro microvascular open model system using human brain microvascular endothelial cells. The design has several advantages over other traditional closed microfluidic platforms: (1) it enables controlled unidirectional flow of media at physiological rates to support vascular function, (2) it allows for very small volumes which makes the device ideal for studies involving biotherapeutics, (3) it is amenable for multiple high resolution imaging modalities such as transmission electron microscopy (TEM), 3D live fluorescence imaging using traditional spinning disk confocal microscopy, and advanced lattice light sheet microscopy (LLSM). Importantly, we miniaturized the design, so it can fit within the physical constraints of LLSM, with the objective to study physiology in live cells at subcellular level. We validated barrier function of our brain microvessel-on-a-chip by measuring permeability of fluorescent dextran and a human monoclonal antibody. One potential application is to investigate mechanisms of transcytosis across the brain microvessel-like barrier of fluorescently-tagged biologics, viruses or nanoparticles

Seipin is required for converting nascent to mature lipid droplets

How proteins control the biogenesis of cellular lipid droplets (LDs) is poorly understood. Using Drosophila and human cells, we show here that seipin, an ER protein implicated in LD biology, mediates a discrete step in LD formation—the conversion of small, nascent LDs to larger, mature LDs. Seipin forms discrete and dynamic foci in the ER that interact with nascent LDs to enable their growth. In the absence of seipin, numerous small, nascent LDs accumulate near the ER and most often fail to grow. Those that do grow prematurely acquire lipid synthesis enzymes and undergo expansion, eventually leading to the giant LDs characteristic of seipin deficiency. Our studies identify a discrete step of LD formation, namely the conversion of nascent LDs to mature LDs, and define a molecular role for seipin in this process, most likely by acting at ER-LD contact sites to enable lipid transfer to nascent LDs. DOI: http://dx.doi.org/10.7554/eLife.16582.00

Fluorescence-Enhancement Based Detection and Identification of Bacterial Contaminants

Due to the considerable challenges for reliable and expedient detection of virulent vegetative bacterial, bacterial endospores and other hazardous biological agents, accidental exposure is a principal cause of fatality due to wound infection or slow diagnosis. Staining techniques, due to their simplicity, form a set of important tools for pathogen monitoring and control. The staining assays, such as immunoassays, however, provide only Boolean information: i.e., stain vs. does not stain. As a result, only the species being sought may have a chance to be identified, leaving key determining factors for the diagnosis quite susceptible to human error. Our principle motivation for this project is to expand the current analytical techniques beyond their Boolean nature, and to address the apparent need for simple, expedient and inexpensive assays. We describe the development of an assay system based on the dynamics of fluorophore uptake that will serve as a robust platform for detection of both vegetative and endospore bacterial contaminants. The kinetics of fluorescence staining depends on the coatings and protein content, which reflect the species phenotype. Hence, such kinetic measurements gain access to genetic information, in less than a minute, without conducting amplification procedures such as PCR. We have demonstrated for the first time that the kinetics of emission enhancement, caused by cell uptake of fluorophores, provides statistically significant discernibility between different and closely related vegetative bacteria and bacterial endospore species respectively. We observed that the time course of the fluorescence signal provides a unique species-specific signature that can prove indispensible for the identification of dangerous and life-threatening spores

Interfacial Interactions Pertinent to Single-Molecule and Solar-Energy Applications

Visualizing the dynamics of nanometer-sized macromolecules presents considerable challenges that stem from non-specific interfacial interactions between the micrometer-sized probes used in those visualizations; as a result, advances in single-molecule protein interaction studies have not been extensively explored. The first part of my doctoral research sought to address this limitation by determining the capability of different surface coatings of polyethylene glycol to suppress non-specific interfacial interactions. Concurrently, we developed a magnetic puller setup capable of attaining forces between hundreds of femto-newtons and approximately one hundred pico-newtons. Magnetic pullers allow for probing thousands of single-molecule events simultaneously, providing considerable advantages over traditional magnetic tweezers. The design and application of thermally-regulated electromagnetic pullers, capable of attaining forces in the fN-to-nN dynamic range, is essential for single-molecule proteomic studies.By applying relatively weak pulling forces (e.g., ~1.2 pN), we examined the efficacy of removing polystyrene microbeads from glass surfaces. When either the glass or the beads were not PEGylated, the adhesion between them was substantial. Furthermore, when the PEG polymers were too short or too long, we still observed substantial adhesion of the beads to the glass surfaces. Coatings of PEG with molecular weights ranging between 3 and 10 kDa proved critical for suppressing the adhesion. My research also focused on investigating anthranilamide derivatives, as bioinspired electrets, for improving the efficiency of interfacial charge transfers that are essential for solar-energy applications. A substantial portion of these studies were directed toward understanding the fundamental electrostatic properties of amides, with a focus on carboxyamides. Carboxyamides are small polar groups that, as peptide bonds, constitute the principle structural components of proteins. The electric fields from the amide dipoles govern the electrostatic properties and activity of proteins. Therefore, we undertook a detailed study of the medium dependence of the molar polarization and of the permanent dipole moments of amides with different states of alkylation. The experimentally-measured and theoretically-calculated dipole moments of the solvated amides both manifested a dependence on the media polarity. Specifically, an increase in solvent polarity led to a subsequent increase in both the measured and calculated permanent dipole moments of the solutes. We attributed the observed enhancement of the amide dipoles to the reaction fields in the solvated cavities. Our bioinspired approach and usage of amide dipoles as a principal field source allowed us to develop molecular electrets based on oligo-anthranilamides. Electrets, and specifically, dipole-polarization electrets, are the electrostatic analogues of magnets, i.e., they are systems with codirectionally ordered permanent electric dipoles. The de novo designed anthranilamides are bioinspired in the sense that, similar to protein helices, they possess permanent intrinsic dipoles resultant from the ordered orientation of amide and hydrogen bonds. Unlike the helices, however, these bioinspired oligomers have the redox properties necessary to mediating long-range charge transfer along their backbones. Overall, the most significant contributions from my doctoral research are: (1) the optimization of polyethylene glycol surface coatings for suppressing non-specific interfacial interactions; (2) the development of an electromagnetic puller setup with a wide dynamic force range capable of simultaneously probing thousands of single-molecule protein interactions; (3) the characterization of the effects of solvent polarity on the dipole moments of amides; and (4) the demonstration of the ability of organic materials with dipole moments to rectify photoinduced charge transfer

Single-Particle Detection of Transcription following Rotavirus Entry

ABSTRACT Infectious rotavirus particles are triple-layered, icosahedral assemblies. The outer layer proteins, VP4 (cleaved to VP8* and VP5*) and VP7, surround a transcriptionally competent, double-layer particle (DLP), which they deliver into the cytosol. During entry of rhesus rotavirus, VP8* interacts with cell surface gangliosides, allowing engulfment into a membrane vesicle by a clathrin-independent process. Escape into the cytosol and outer-layer shedding depend on interaction of a hydrophobic surface on VP5* with the membrane bilayer and on a large-scale conformational change. We report here experiments that detect the fate of released DLPs and their efficiency in initiating RNA synthesis. By replacing the outer layer with fluorescently tagged, recombinant proteins and also tagging the DLP, we distinguished particles that have lost their outer layer and entered the cytosol (uncoated) from those still within membrane vesicles. We used fluorescent in situ hybridization with probes for nascent transcripts to determine how soon after uncoating transcription began and what fraction of the uncoated particles were active in initiating RNA synthesis. We detected RNA synthesis by uncoated particles as early as 15 min after adding virus. The uncoating efficiency was 20 to 50%; of the uncoated particles, about 10 to 15% synthesized detectable RNA. In the format of our experiments, about 10% of the added particles attached to the cell surface, giving an overall ratio of added particles to RNA-synthesizing particles of between 250:1 and 500:1, in good agreement with the ratio of particles to focus-forming units determined by infectivity assays. Thus, RNA synthesis by even a single, uncoated particle can initiate infection in a cell. IMPORTANCE: The pathways by which a virus enters a cell transform its packaged genome into an active one. Contemporary fluorescence microscopy can detect individual virus particles as they enter cells, allowing us to map their multistep entry pathways. Rotaviruses, like most viruses that lack membranes of their own, disrupt or perforate the intracellular, membrane-enclosed compartment into which they become engulfed following attachment to a cell surface, in order to gain access to the cell interior. The properties of rotavirus particles make it possible to determine molecular mechanisms for these entry steps. In the work described here, we have asked the following question: what fraction of the rotavirus particles that penetrate into the cell make new viral RNA? We find that of the cell-attached particles, between 20 and 50% ultimately penetrate, and of these, about 10% make RNA. RNA synthesis by even a single virus particle can initiate a productive infection

Adaptive wavelet distillation from neural networks through interpretations

Recent deep-learning models have achieved impressive prediction performance, but often sacrifice interpretability and computational efficiency. Interpretability is crucial in many disciplines, such as science and medicine, where models must be carefully vetted or where interpretation is the goal itself. Moreover, interpretable models are concise and often yield computational efficiency. Here, we propose adaptive wavelet distillation (AWD), a method which aims to distill information from a trained neural network into a wavelet transform. Specifically, AWD penalizes feature attributions of a neural network in the wavelet domain to learn an effective multi-resolution wavelet transform. The resulting model is highly predictive, concise, computationally efficient, and has properties (such as a multi-scale structure) which make it easy to interpret. In close collaboration with domain experts, we showcase how AWD addresses challenges in two real-world settings: cosmological parameter inference and molecular-partner prediction. In both cases, AWD yields a scientifically interpretable and concise model which gives predictive performance better than state-of-the-art neural networks. Moreover, AWD identifies predictive features that are scientifically meaningful in the context of respective domains. All code and models are released in a full-fledged package available on Github (https://github.com/Yu-Group/adaptive-wavelets)