Dataset Information

Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins.

ABSTRACT: The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the localization behavior of proteins across multiple experiments as a quantitative pattern. By applying this approach to 24 experimental screens consisting of nearly 400,000 images, we differentiated specific responses from more generalized ones, discovered nuance in the localization behavior of stress-responsive proteins, and formed hypotheses by clustering proteins that have similar patterns. Previous approaches aim to capture all localization changes for a single screen as accurately as possible, whereas our work aims to integrate large amounts of imaging data to find unexpected new cell biology.

SUBMITTER: Lu AX

PROVIDER: S-EPMC5935485 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins.

Lu Alex X AX Chong Yolanda T YT Hsu Ian Shen IS Strome Bob B Handfield Louis-Francois LF Kraus Oren O Andrews Brenda J BJ Moses Alan M AM

eLife 20180405

The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the ...[more]

PMID: 29620521

Similar Datasets

Project description:Annexin 1 (ANN1) is the most abundant member of the evolutionary conserved multigene protein superfamily of annexins in plants. Generally, annexins participate in diverse cellular processes, such as cell growth, differentiation, vesicle trafficking, and stress responses. The expression of annexins is developmentally regulated, and it is sensitive to the external environment. ANN1 is expressed in almost all Arabidopsis tissues, while the most abundant is in the root, root hairs, and in the hypocotyl epidermal cells. Annexins were also occasionally proposed to associate with cytoskeleton and vesicles, but they were never developmentally localized at the subcellular level in diverse plant tissues and organs. Using advanced light-sheet fluorescence microscopy (LSFM), we followed the developmental and subcellular localization of GFP-tagged ANN1 in post-embryonic Arabidopsis organs. By contrast to conventional microscopy, LSFM allowed long-term imaging of ANN1-GFP in Arabidopsis plants at near-environmental conditions without affecting plant viability. We studied developmental regulation of ANN1-GFP expression and localization in growing Arabidopsis roots: strong accumulation was found in the root cap and epidermal cells (preferentially in elongating trichoblasts), but it was depleted in dividing cells localized in deeper layers of the root meristem. During root hair development, ANN1-GFP accumulated at the tips of emerging and growing root hairs, which was accompanied by decreased abundance in the trichoblasts. In aerial plant parts, ANN1-GFP was localized mainly in the cortical cytoplasm of trichomes and epidermal cells of hypocotyls, cotyledons, true leaves, and their petioles. At the subcellular level, ANN1-GFP was enriched at the plasma membrane (PM) and vesicles of non-dividing cells and in mitotic and cytokinetic microtubular arrays of dividing cells. Additionally, an independent immunolocalization method confirmed ANN1-GFP association with mitotic and cytokinetic microtubules (PPBs and phragmoplasts) in dividing cells of the lateral root cap. Lattice LSFM revealed subcellular accumulation of ANN1-GFP around the nuclear envelope of elongating trichoblasts. Massive relocation and accumulation of ANN1-GFP at the PM and in Hechtian strands and reticulum in plasmolyzed cells suggest a possible osmoprotective role of ANN1-GFP during plasmolysis/deplasmolysis cycle. This study shows complex developmental and subcellular localization patterns of ANN1 in living Arabidopsis plants.

Project description:Motivation:Fluorescence localization microscopy is extensively used to study the details of spatial architecture of subcellular compartments. This modality relies on determination of spatial positions of fluorophores, labeling an extended biological structure, with precision exceeding the diffraction limit. Several established models describe influence of pixel size, signal-to-noise ratio and optical resolution on the localization precision. The labeling density has been also recognized as important factor affecting reconstruction fidelity of the imaged biological structure. However, quantitative data on combined influence of sampling and localization errors on the fidelity of reconstruction are scarce. It should be noted that processing localization microscopy data is similar to reconstruction of a continuous (extended) non-periodic signal from a non-uniform, noisy point samples. In two dimensions the problem may be formulated within the framework of matrix completion. However, no systematic approach has been adopted in microscopy, where images are typically rendered by representing localized molecules with Gaussian distributions (widths determined by localization precision). Results:We analyze the process of two-dimensional reconstruction of extended biological structures as a function of the density of registered emitters, localization precision and the area occupied by the rendered localized molecule. We quantify overall reconstruction fidelity with different established image similarity measures. Furthermore, we analyze the recovered similarity measure in the frequency space for different reconstruction protocols. We compare the cut-off frequency to the limiting sampling frequency, as determined by labeling density. Availability and implementation:The source code used in the simulations along with test images is available at https://github.com/blazi13/qbioimages. Contact:bruszczy@nencki.gov.pl or t.bernas@nencki.gov.pl. Supplementary information:Supplementary data are available at Bioinformatics online.

Project description:BackgroundThe computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins.ResultsA final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out.ConclusionBoth subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model.

Dataset Information

Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins.

Publications

Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets