Dataset Information

Clus-DoC: a combined cluster detection and colocalization analysis for single-molecule localization microscopy data.

ABSTRACT: Advances in fluorescence microscopy are providing increasing evidence that the spatial organization of proteins in cell membranes may facilitate signal initiation and integration for appropriate cellular responses. Our understanding of how changes in spatial organization are linked to function has been hampered by the inability to directly measure signaling activity or protein association at the level of individual proteins in intact cells. Here we solve this measurement challenge by developing Clus-DoC, an analysis strategy that quantifies both the spatial distribution of a protein and its colocalization status. We apply this approach to the triggering of the T-cell receptor during T-cell activation, as well as to the functionality of focal adhesions in fibroblasts, thereby demonstrating an experimental and analytical workflow that can be used to quantify signaling activity and protein colocalization at the level of individual proteins.

SUBMITTER: Pageon SV

PROVIDER: S-EPMC5221594 | biostudies-other | 2016 Nov

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Clus-DoC: a combined cluster detection and colocalization analysis for single-molecule localization microscopy data.

Pageon Sophie V SV Nicovich Philip R PR Mollazade Mahdie M Tabarin Thibault T Gaus Katharina K

Molecular biology of the cell 20160831 22

Advances in fluorescence microscopy are providing increasing evidence that the spatial organization of proteins in cell membranes may facilitate signal initiation and integration for appropriate cellular responses. Our understanding of how changes in spatial organization are linked to function has been hampered by the inability to directly measure signaling activity or protein association at the level of individual proteins in intact cells. Here we solve this measurement challenge by developing ...[more]

PMID: 27582387

Similar Datasets

Project description:Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine learning approaches (including deep learning models) to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-crafted/designed features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand-designed features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches.

Project description:Super-resolved microscopy techniques have revolutionized the ability to study biological structures below the diffraction limit. Single molecule localization microscopy (SMLM) techniques are widely used because they are relatively straightforward to implement and can be realized at relatively low cost, e.g. compared to laser scanning microscopy techniques. However, while the data analysis can be readily undertaken using open source or other software tools, large SMLM data volumes and the complexity of the algorithms used often lead to long image data processing times that can hinder the iterative optimization of experiments. There is increasing interest in high throughput SMLM, but its further development and application is inhibited by the data processing challenges. We present here a widely applicable approach to accelerating SMLM data processing via a parallelized implementation of ThunderSTORM on a high-performance computing (HPC) cluster and quantify the speed advantage for a four-node cluster (with 24 cores and 128 GB RAM per node) compared to a high specification (28 cores, 128 GB RAM, SSD-enabled) desktop workstation. This data processing speed can be readily scaled by accessing more HPC resources. Our approach is not specific to ThunderSTORM and can be adapted for a wide range of SMLM software. LAY DESCRIPTION: Optical microscopy is now able to provide images with a resolution far beyond the diffraction limit thanks to relatively new super-resolved microscopy (SRM) techniques, which have revolutionized the ability to study biological structures. One approach to SRM is to randomly switch on and off the emission of fluorescent molecules in an otherwise conventional fluorescence microscope. If only a sparse subset of the fluorescent molecules labelling a sample can be switched on at a time, then each emitter will be, on average, spaced further apart than the diffraction-limited resolution of the conventional microscope and the separate bright spots in the image corresponding to each emitter can be localised to high precision by finding the centre of each feature using a computer program. Thus, a precise map of the emitter positions can be recorded by sequentially mapping the localisation of different subsets of emitters as they are switched on and others switched off. Typically, this approach, described as single molecule localisation microscopy (SMLM), results in large image data sets that can take many minutes to hours to process, depending on the size of the field of view and whether the SMLM analysis employs a computationally-intensive iterative algorithm. Such a slow workflow makes it difficult to optimise experiments and to analyse large numbers of samples. Faster SMLM experiments would be generally useful and automated high throughput SMLM studies of arrays of samples, such as cells, could be applied to drug discovery and other applications. However, the time required to process the resulting data would be prohibitive on a normal computer. To address this, we have developed a method to run standard SMLM data analysis software tools in parallel on a high-performance computing cluster (HPC). This can be used to accelerate the analysis of individual SMLM experiments or it can be scaled to analyse high throughput SMLM data by extending it to run on an arbitrary number of HPC processors in parallel. In this paper we outline the design of our parallelised SMLM software for HPC and quantify the speed advantage when implementing it on four HPC nodes compared to a powerful desktop computer.

Dataset Information

Clus-DoC: a combined cluster detection and colocalization analysis for single-molecule localization microscopy data.

Publications

Clus-DoC: a combined cluster detection and colocalization analysis for single-molecule localization microscopy data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets