Non-ionic Detergent Phase Extraction for the Quantitative Proteomic Analysis of Heart Membranes Proteins using Label-Free LC-MS
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q-Tof Premier
ORGANISM(S): Homo Sapiens (human)
TISSUE(S): Heart
SUBMITTER: 
Hans Vissers
LAB HEAD: Hans Vissers
PROVIDER: PRD000043 | Pride | 2008-08-21
REPOSITORIES: Pride
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| PRIDE_Exp_Complete_Ac_3229.pride.mztab.gz | Mztab | |||
| PRIDE_Exp_Complete_Ac_3229.xml.gz | Xml |
Items per page: 5 1 - 2 of 2 |
Similar Datasets
Project description:Despite their denaturing properties, detergents are used to purify and study membrane proteins. Herein, we demonstrated a polymer-based detergent-free extraction of the membrane protein cytochrome-b5 along with E. coli lipids. Nuclear magnetic resonance experiments revealed the suitability of using nanodiscs for high-resolution studies and revealed the types of native lipids associated with the protein.
| S-EPMC7337974 | biostudies-literature
Project description:Basement membranes are specialized extracellular matrices formed by highly insoluble structural proteins and extracellular matrix (ECM)-bound components that provide structural and signaling support to tissues and are dynamic during development. Here, we present a mass spectrometry-based label-free quantitative proteomics protocol to investigate basement membranes and define their composition using samples from human kidney organoids and mouse fetal kidneys. This protocol facilitates the study of basement membrane and other ECM components during development to improve our understanding of matrix regulation and function. For complete details on the use and execution of this protocol, please refer to Morais et al.1.
| S-EPMC10722381 | biostudies-literature
Project description:Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity. Within tumors, cells coexist in different mutable phenotypes that exhibit differential functional properties and drug responses. The definition of these phenotypic states has been challenging to rigorously define with conventional marker-based methods, and more high-parameter molecular methods are cell-destructive, labor-intensive, and can take days to weeks to obtain a readout. To overcome these technical and practical limitations, we utilized the Deepcell platform to perform real-time classification of unlabeled melanoma cells into Melanocytic and Mesenchymal phenotypes. We used 19 patient-derived cell lines with known Melanocytic or Mesenchymal transcription scores to develop the ‘Melanoma Phenotype Classifier’ to phenotype melanoma cells based on morphology alone. A Classifier accuracy of >88% was achieved, and morphology analysis of the images revealed distinct morphotypes for each phenotype, highlighting distinct morphological differences. To further link phenotypic state with multi-dimensional morphological profiles, we performed genetic and chemical perturbations known to shift the phenotypic state. The AI Classifier successfully predicted shifts in phenotype driven by the perturbations. These results further demonstrate how phenotype is linked to distinct morphological changes that are detectable by AI. Lastly, we applied the Melanoma Phenotype Classifier to dissociated biopsy samples, which revealed phenotypic heterogeneity that was confirmed by single cell RNASeq. This work establishes a link between morphology and Melanoma phenotype, and lays the groundwork for the use of morphology as a label-free method of phenotyping viable melanoma cells combined with additional analyses.
2025-07-25 | GSE273247 | GEO
Project description:The main challenge of bottom-up proteomic sample preparation is to extract proteomes in a manner that enables efficient protein digestion for subsequent mass spectrometric analysis. Today's sample preparation strategies are commonly conceptualized around the removal of detergents, which are essential for extraction but strongly interfere with digestion and LC-MS. These multi-step preparations contribute to a lack of reproducibility as they are prone to losses, biases and contaminations, while being time-consuming and labor-intensive. We report a detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which consists of three mandatory steps, acidification, neutralization and digestion. SPEED is a universal method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction by complete sample dissolution. The protocol is highly reproducible, virtually loss-less, enables very rapid sample processing and is superior to the detergent/chaotropic agent-based methods FASP, ISD-Urea and SP3 for quantitative proteomics. SPEED holds the potential to dramatically simplify and standardize sample preparation while improving the depth of proteome coverage especially for challenging samples.
| S-EPMC6944244 | biostudies-literature
Project description:Dextran hydrolysis-mediated conversion of polyethylene glycol (PEG)-dextran (DEX) aqueous two-phase system droplets to a single phase was used to directly visualize Dextranase activity. DEX droplets were formed either by manual micropipetting or within a continuous PEG phase by computer controlled actuation of an orifice connecting rounded channels formed by backside diffused light lithography. The time required for the two-phase to one-phase transition was dependent on the Dextranase concentration, pH of the medium, and temperature. The apparent Michaelis constants for Dextranase were estimated based on previously reported catalytic constants, the binodal polymer concentration curves for PEG-DEX phase transition for each temperature, and pH condition. The combination of a microfluidic droplet system and phase transition observation provides a new method for label-free direct measurement of enzyme activity.
| S-EPMC4004187 | biostudies-literature
Project description:Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A virus), HAdV (adenovirus), and ZIKV (Zika virus). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic-processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically.
| S-EPMC8408039 | biostudies-literature
Project description:Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining), we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin, kidney, and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones' stain, and Masson's trichrome stain, respectively. This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general, by eliminating the need for histological staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning.
| S-EPMC6363787 | biostudies-literature
Project description:Hybrid vesicles (HVs) that consist of mixtures of block copolymers and lipids are robust biomimetics of liposomes, providing a valuable building block in bionanotechnology, catalysis, and synthetic biology. However, functionalization of HVs with membrane proteins remains laborious and expensive, creating a significant current challenge in the field. Here, using a new approach of extraction with styrene-maleic acid (SMA), we show that a membrane protein (cytochrome bo 3) directly transfers into HVs with an efficiency of 73.9 ± 13.5% without the requirement of detergent, long incubation times, or mechanical disruption. Direct transfer of membrane proteins using this approach was not possible into liposomes, suggesting that HVs are more amenable than liposomes to membrane protein incorporation from a SMA lipid particle system. Finally, we show that this transfer method is not limited to cytochrome bo 3 and can also be performed with complex membrane protein mixtures.
| S-EPMC9097535 | biostudies-literature
Project description:In the past few years there has been a growth in the use of nano-particles for stabilizing lipid membranes with embedded proteins. These bionanoparticles provide a solution to the challenging problem of membrane protein isolation by maintaining a lipid bilayer essential to protein integrity and activity. We have described the use of an amphipathic polymer (Poly(styrene-co-maleic acid); SMA) to produce discoidal nanoparticles that contain a lipid bilayer with embedded protein. However the structure of the nanoparticle itself has not yet been determined. This leaves a major gap in understanding how the SMA stabilizes the encapsulated bilayer and how the bilayer relates physically and structurally to an unecapsulated lipid bilayer. In this paper we address this issue by describing the structure of the SMA Lipid Particle (SMALP) using data from small angle neutron scattering (SANS), electron microscopy (EM), attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), differential scanning calorimetry (DSC) and nuclear magnetic resonance spectroscopy (NMR). We show that the particle is disc shaped containing a polymer "bracelet" encircling the lipid bilayer. The structure and orientation of the individual components within the bilayer and polymer are determined showing that styrene moieties within SMA intercalate between the lipid acyl chains. The dimensions of the encapsulated bilayer are also determined and match those measured for a natural membrane. Taken together, the description of structure of the SMALP forms the foundation of future development and applications of SMALPs in membrane protein production and analysis.
| S-EPMC6485620 | biostudies-literature
Project description:The vast majority of chemistry and biology occurs in solution, and new label-free analytical techniques that can help resolve solution-phase complexity at the single-molecule level can provide new microscopic perspectives of unprecedented detail. Here, we use the increased light-molecule interactions in high-finesse fiber Fabry-Pérot microcavities to detect individual biomolecules as small as 1.2 kDa with signal-to-noise ratios >100, even as the molecules are freely diffusing in solution. Our method delivers 2D intensity and temporal profiles, enabling the distinction of sub-populations in mixed samples. Strikingly, we observe a linear relationship between passage time and molecular radius, unlocking the potential to gather crucial information about diffusion and solution-phase conformation. Furthermore, mixtures of biomolecule isomers of the same molecular weight can also be resolved. Detection is based on a novel molecular velocity filtering and dynamic thermal priming mechanism leveraging both photo-thermal bistability and Pound-Drever-Hall cavity locking. This technology holds broad potential for applications in life and chemical sciences and represents a major advancement in label-free in vitro single-molecule techniques.
| S-EPMC10055403 | biostudies-literature