New views on phototransduction from atomic force microscopy and single molecule force spectroscopy on native rods

Unfolding and identification of membrane proteins in situ

Single-molecule force spectroscopy (SMFS) uses the cantilever tip of an AFM to apply a force able to unfold a single protein. The obtained force-distance curve encodes the unfolding pathway, and from its analysis it is possible to characterize the folded domains. SMFS has been mostly used to study the unfolding of purified proteins, in solution or reconstituted in a lipid bilayer. Here, we describe a pipeline for analyzing membrane proteins based on SMFS, that involves the isolation of the plasma membrane of single cells and the harvesting of force-distance curves directly from it. We characterized and identified the embedded membrane proteins combining, within a Bayesian framework, the information of the shape of the obtained curves, with the information from Mass Spectrometry and proteomic databases. The pipeline was tested with purified/reconstituted proteins and applied to five cell types where we classified the unfolding of their most abundant membrane proteins. We validated our pipeline by overexpressing 4 constructs, and this allowed us to gather structural insights of the identified proteins, revealing variable elements in the loop regions. Our results set the basis for the investigation of the unfolding of membrane proteins in situ, and for performing proteomics from a membrane fragment.

Atomic force microscopy-single-molecule force spectroscopy unveils GPCR cell surface architecture

G protein-coupled receptors (GPCRs) form the largest family of cell surface receptors. Despite considerable insights into their pharmacology, the GPCR architecture at the cell surface still remains largely unexplored. Herein, we present the specific unfolding of different GPCRs at the surface of living mammalian cells by atomic force microscopy-based single molecule force spectroscopy (AFM-SMFS). Mathematical analysis of the GPCR unfolding distances at resting state revealed the presence of different receptor populations relying on distinct oligomeric states which are receptor-specific and receptor expression-dependent. Moreover, we show that the oligomer size dictates the receptor spatial organization with nanoclusters of high-order oligomers while lower-order complexes spread over the whole cell surface. Finally, the receptor activity reshapes both the oligomeric populations and their spatial arrangement. These results add an additional level of complexity to the GPCR pharmacology until now considered to arise from a single receptor population at the cell surface.

Atomic force microscopy-based single molecule force spectroscopy reveals the unfolding of G-protein coupled receptors on the surface of living mammalian cells.

Automatic classification of single-molecule force spectroscopy traces from heterogeneous samples

MOTIVATION

Single-molecule force spectroscopy (SMFS) experiments pose the challenge of analysing protein unfolding data (traces) coming from preparations with heterogeneous composition (e.g. where different proteins are present in the sample). An automatic procedure able to distinguish the unfolding patterns of the proteins is needed. Here, we introduce a data analysis pipeline able to recognize in such datasets traces with recurrent patterns (clusters).

RESULTS

We illustrate the performance of our method on two prototypical datasets: ∼50 000 traces from a sample containing tandem GB1 and ∼400 000 traces from a native rod membrane. Despite a daunting signal-to-noise ratio in the data, we are able to identify several unfolding clusters. This work demonstrates how an automatic pattern classification can extract relevant information from SMFS traces from heterogeneous samples without prior knowledge of the sample composition.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ninailieva/SMFS_clustering.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.