Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors

Automated Analysis of Fluorescent Microscopic Images to Identify Protein-Protein Interactions

The identification and confirmation of protein interactions significantly challenges the field of systems biology and related bio-computational efforts. The identification of protein-protein interactions along with their spatial and temporal localization is useful for assigning functional information to proteins. Fluorescence microscopy is an ideal method for assessing protein localization and interactions as a number of techniques and reagents have been described. Historically, data sets obtained from fluorescence microscopy have been analyzed manually, a process that is both time consuming and tedious. The development of an automated system that can measure the location and dynamics of interacting proteins inside a live cell is of high priority. This paper describes an automated image analysis system used to identify an interaction between two proteins of interest. These proteins are fused to either Green Fluorescent Protein (GFP) or DivIVA, a bacterial cell division protein that localizes to the cell poles. Upon induction of the DivIVA fusion protein, the GFP-fusion protein is recruited to the cell poles if a positive interaction occurs. There were many problems that came into the picture during the development for an automated system to identify these positive interactions. There were basic segmentation and edge detection problems and the problems caused by inclusion bodies (will be discussed in the sections to follow). Different known procedures to obtain thresholds, and edges were evaluated and the apt ones for our analysis were implemented. A proper flow of advanced image processing and feature extraction algorithms was laid out. These steps were used to analyze the datasets of acquired images. Various methods applied are discussed in detail. The experiments conducted along with the results generated are discussed extensively. A statistical feature set used to quantify the image based information and to aid in the determination of a positive interaction is developed. Various image processing and feature extraction algorithms used to analyze fluorescence microscopic images were also applied to Atomic force microscopic images with a few modifications. There was a basic problem of uneven background noise and this was removed using a common procedure that is used to remove uneven illumination in DIC images. These AFM images were analyzed and quantized using numerical descriptors defined during the analysis of fluorescent microscopic images

Discovery of Stable and Selective Antibody Mimetics from Combinatorial Libraries of Polyvalent, Loop-Functionalized Peptoid Nanosheets.

The ability of antibodies to bind a wide variety of analytes with high specificity and high affinity makes them ideal candidates for therapeutic and diagnostic applications. However, the poor stability and high production cost of antibodies have prompted exploration of a variety of synthetic materials capable of specific molecular recognition. Unfortunately, it remains a fundamental challenge to create a chemically diverse population of protein-like, folded synthetic nanostructures with defined molecular conformations in water. Here we report the synthesis and screening of combinatorial libraries of sequence-defined peptoid polymers engineered to fold into ordered, supramolecular nanosheets displaying a high spatial density of diverse, conformationally constrained peptoid loops on their surface. These polyvalent, loop-functionalized nanosheets were screened using a homogeneous Förster resonance energy transfer (FRET) assay for binding to a variety of protein targets. Peptoid sequences were identified that bound to the heptameric protein, anthrax protective antigen, with high avidity and selectivity. These nanosheets were shown to be resistant to proteolytic degradation, and the binding was shown to be dependent on the loop display density. This work demonstrates that key aspects of antibody structure and function-the creation of multivalent, combinatorial chemical diversity within a well-defined folded structure-can be realized with completely synthetic materials. This approach enables the rapid discovery of biomimetic affinity reagents that combine the durability of synthetic materials with the specificity of biomolecular materials

Phosphorylation of nephrin induces phase separated domains that move through actomyosin contraction

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kim, S., Kalappurakkal, J. M., Mayor, S., & Rosen, M. K. Phosphorylation of nephrin induces phase separated domains that move through actomyosin contraction. Molecular Biology of the Cell, 30(24), (2019): 2996–3012, doi:10.1091/mbc.E18-12-0823.The plasma membrane of eukaryotic cells is organized into lipid and protein microdomains, whose assembly mechanisms and functions are incompletely understood. We demonstrate that proteins in the nephrin/Nck/N-WASP actin-regulatory pathway cluster into micron-scale domains at the basal plasma membrane upon triggered phosphorylation of transmembrane protein nephrin. The domains are persistent but readily exchange components with their surroundings, and their formation is dependent on the number of Nck SH3 domains, suggesting they are phase separated polymers assembled through multivalent interactions among the three proteins. The domains form independent of the actin cytoskeleton, but acto-myosin contractility induces their rapid lateral movement. Nephrin phosphorylation induces larger clusters at the cell periphery, which are associated with extensive actin assembly and dense filopodia. Our studies illustrate how multivalent interactions between proteins at the plasma membrane can produce micron-scale organization of signaling molecules, and how the resulting clusters can both respond to and control the actin cytoskeleton.We thank Hongtao Yu (University of Texas Southwestern Medical Center [UTSW]) for providing the HeLa cell line used in this work; Dan Billadeau and Timothy Gomez (Mayo Clinic) for providing antibodies; Nico Stuurman (University of California, San Francisco) for assistance with STORM imaging; Kate Luby-Phelps and Abhijit Bugde (UTSW Live Cell Imaging Core Facility) for their assistance in epifluorescence and spinning disk confocal experiments; Sudeep Banjade for advice on designing the S3, S2, S1 constructs; Khuloud Jaqaman (UTSW) for advice on cluster motility analysis; Salman Banani and Jonathan Ditlev (UTSW) for critical reading of the manuscript; and members of the Rosen lab and participants in the MBL/HHMI Summer Institutes for advice and helpful discussions. This work was supported by a Howard Hughes Medical Institute Collaborative Innovation Award; the Welch Foundation (I-1544 to M.K.R.); a J.C. Bose Fellowship from the Department of Science and Technology, government of India (to S.M.); a Margadarshi Fellowship from the Wellcome Trust—Department of Biotechnology, India Alliance (IA/M/15/1/502018 to S.M.). Research in the Rosen lab is supported by the Howard Hughes Medical Institute

Robust visualization and discrimination of nanoparticles by interferometric imaging

Single-molecule and single-nanoparticle biosensors are a growing frontier in diagnostics. Digital biosensors are those which enumerate all specifically immobilized biomolecules or biological nanoparticles, and thereby achieve limits of detection usually beyond the reach of ensemble measurements. Here we review modern optical techniques for single nanoparticle detection and describe the single-particle interferometric reflectance imaging sensor (SP-IRIS). We present challenges associated with reliably detecting faint nanoparticles with SP-IRIS, and describe image acquisition processes and software modifications to address them. Specifically, we describe a image acquisition processing method for the discrimination and accurate counting of nanoparticles that greatly reduces both the number of false positives and false negatives. These engineering improvements are critical steps in the translation of SP-IRIS towards applications in medical diagnostics.R01 AI096159 - NIAID NIH HHSFirst author draf

Combined flow cytometry and high-throughput image analysis for the study of essential genes in Caenorhabditis elegans

Background: Advances in automated image-based microscopy platforms coupled with high-throughput liquid workflows have facilitated the design of large-scale screens utilising multicellular model organisms such as Caenorhabditis elegans to identify genetic interactions, therapeutic drugs or disease modifiers. However, the analysis of essential genes has lagged behind because lethal or sterile mutations pose a bottleneck for high-throughput approaches, and a systematic way to analyse genetic interactions of essential genes in multicellular organisms has been lacking. Results: In C. elegans, non-conditional lethal mutations can be maintained in heterozygosity using chromosome balancers, commonly expressing green fluorescent protein (GFP) in the pharynx. However, gene expression or function is typically monitored by the use of fluorescent reporters marked with the same fluorophore, presenting a challenge to sort worm populations of interest, particularly at early larval stages. Here, we develop a sorting strategy capable of selecting homozygous mutants carrying a GFP stress reporter from GFP-balanced animals at the second larval stage. Because sorting is not completely error-free, we develop an automated high-throughput image analysis protocol that identifies and discards animals carrying the chromosome balancer. We demonstrate the experimental usefulness of combining sorting of homozygous lethal mutants and automated image analysis in a functional genomic RNA interference (RNAi) screen for genes that genetically interact with mitochondrial prohibitin (PHB). Lack of PHB results in embryonic lethality, while homozygous PHB deletion mutants develop into sterile adults due to maternal contribution and strongly induce the mitochondrial unfolded protein response (UPR mt ). In a chromosome-wide RNAi screen for C. elegans genes having human orthologues, we uncover both known and new PHB genetic interactors affecting the UPR mt and growth. Conclusions: The method presented here allows the study of balanced lethal mutations in a high-throughput manner. It can be easily adapted depending on the user's requirements and should serve as a useful resource for the C. elegans community for probing new biological aspects of essential nematode genes as well as the generation of more comprehensive genetic networks.European Research Council ERC-2011-StG-281691Ministerio de Economía y Competitividad BFU2012–3550

PML isoforms in response to arsenic: high-resolution analysis of PML body structure and degradation

Arsenic is a clinically effective treatment for acute promyelocytic leukaemia (APL) in which the promyelocytic leukaemia (PML) protein is fused to retinoic receptor alpha (RARα). PML-RARα is degraded by the proteasome by a SUMO-dependent, ubiquitin-mediated pathway in response to arsenic treatment, curing the disease. Six major PML isoforms are expressed as a result of alternative splicing, each of which encodes a unique C-terminal region. Using a system in which only a single EYFP-linked PML isoform is expressed, we demonstrate that PMLI, PMLII and PMLVI accumulate in the cytoplasm following arsenic treatment, whereas PMLIII, PMLIV and PMLV do not. 3D structured illumination was used to obtain super-resolution images of PML bodies, revealing spherical shells of PML along with associated SUMO. Arsenic treatment results in dramatic isoform-specific changes to PML body ultrastructure. After extended arsenic treatment most PML isoforms are degraded, leaving SUMO at the core of the nuclear bodies. A high-content imaging assay identifies PMLV as the isoform most readily degraded following arsenic treatment, and PMLIV as relatively resistant to degradation. Immunoprecipitation analysis demonstrates that all PML isoforms are modified by SUMO and ubiquitin after arsenic treatment, and by using siRNA, we demonstrate that arsenic-induced degradation of all PML isoforms is dependent on the ubiquitin E3 ligase RNF4. Intriguingly, depletion of RNF4 results in marked accumulation of PMLV, suggesting that this isoform is an optimal substrate for RNF4. Thus the variable C-terminal domain influences the rate and location of degradation of PML isoforms following arsenic treatment

Mining Images in Biomedical Publications: Detection and Analysis of Gel Diagrams

Authors of biomedical publications use gel images to report experimental results such as protein-protein interactions or protein expressions under different conditions. Gel images offer a concise way to communicate such findings, not all of which need to be explicitly discussed in the article text. This fact together with the abundance of gel images and their shared common patterns makes them prime candidates for automated image mining and parsing. We introduce an approach for the detection of gel images, and present a workflow to analyze them. We are able to detect gel segments and panels at high accuracy, and present preliminary results for the identification of gene names in these images. While we cannot provide a complete solution at this point, we present evidence that this kind of image mining is feasible.Comment: arXiv admin note: substantial text overlap with arXiv:1209.148