scholarly journals Spatial regulation of clathrin-mediated endocytosis through position-dependent site maturation

2019 ◽  
Author(s):  
Ross TA Pedersen ◽  
Julian E Hassinger ◽  
Paul Marchando ◽  
David G Drubin
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Transition Point ◽  
Quantitative Imaging ◽  
Imaging Data ◽  
Cortical Actin ◽  
Initiation Phase ◽  
Spatial Regulation ◽  
Polarized Cells ◽  
Endocytosis Pathway ◽  
Dependent Site

AbstractDuring clathrin-mediated endocytosis (CME), over 50 different proteins assemble on the plasma membrane to reshape it into a cargo-laden vesicle. It has long been assumed that cargo triggers local CME site assembly in Saccharomyces cerevisiae based on the discovery that cortical actin patches clustered near exocytic sites are CME sites. Quantitative imaging data reported here lead to a radically different view of which CME steps are regulated and which steps are deterministic. We quantitatively and spatially describe progression through the CME pathway and pinpoint a cargo-sensitive regulatory transition point that governs progression from the initiation phase of CME to the internalization phase. Thus, site maturation, rather than site initiation, accounts for the previously observed polarized distribution of actin patches in this organism. While previous studies suggested that cargo ensures its own internalization by regulating either CME initiation rates or frequency of abortive events, our data instead identify maturation through a checkpoint in the pathway as the cargo-sensitive step.SummaryPedersen, Hassinger, et al. investigate steps of the clathrin-mediated endocytosis pathway that are subject to regulation. They report position-dependent differences in endocytic site maturation rates in polarized cells and suggest that cargo controls endocytic internalization through tuning site maturation rather than site initiation.

scholarly journals Spatial regulation of clathrin-mediated endocytosis through position-dependent site maturation

2020 ◽  
Vol 219 (11) ◽  
Author(s):  
Ross T.A. Pedersen ◽  
Julian E. Hassinger ◽  
Paul Marchando ◽  
David G. Drubin
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Plasma Membrane ◽  
Transition Point ◽  
Quantitative Imaging ◽  
Imaging Data ◽  
Cortical Actin ◽  
Initiation Phase ◽  
Spatial Regulation ◽  
Dependent Site

During clathrin-mediated endocytosis (CME), over 50 different proteins assemble on the plasma membrane to reshape it into a cargo-laden vesicle. It has long been assumed that cargo triggers local CME site assembly in Saccharomyces cerevisiae based on the discovery that cortical actin patches, which cluster near exocytic sites, are CME sites. Quantitative imaging data reported here lead to a radically different view of which CME steps are regulated and which steps are deterministic. We quantitatively and spatially describe progression through the CME pathway and pinpoint a cargo-sensitive regulatory transition point that governs progression from the initiation phase of CME to the internalization phase. Thus, site maturation, rather than site initiation, accounts for the previously observed polarized distribution of actin patches in this organism. While previous studies suggested that cargo ensures its own internalization by regulating either CME initiation rates or frequency of abortive events, our data instead identify maturation through a checkpoint in the pathway as the cargo-sensitive step.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals Radiomics in radiation oncology—basics, methods, and limitations

2020 ◽  
Vol 196 (10) ◽  
pp. 848-855
Author(s):  
Philipp Lohmann ◽  
Khaled Bousabarah ◽  
Mauritius Hoevels ◽  
Harald Treuer
Keyword(s):  
Mathematical Models ◽  
Quantitative Imaging ◽  
Human Perception ◽  
Image Features ◽  
Radiotherapy Planning ◽  
Large Field ◽  
Imaging Features ◽  
Imaging Data ◽  
Complete Evaluation ◽  
Vast Number

Abstract Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

scholarly journals Radiomics: from qualitative to quantitative imaging

2020 ◽  
Vol 93 (1108) ◽  
pp. 20190948 ◽  
Author(s):  
William Rogers ◽  
Sithin Thulasi Seetha ◽  
Turkey A. G. Refaee ◽  
Relinde I. Y. Lieverse ◽  
Renée W. Y. Granzier ◽  
...  
Keyword(s):  
Quantitative Imaging ◽  
Quantitative Information ◽  
Clinical Applications ◽  
Machine Learning Algorithms ◽  
Biological Information ◽  
Imaging Data ◽  
Multistage Process ◽  
Advantages And Disadvantages ◽  
Future Direction ◽  
Biological Outcomes

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

scholarly journals Quantitative Imaging Informatics for Cancer Research

2020 ◽  
pp. 444-453 ◽  
Author(s):  
Andrey Fedorov ◽  
Reinhard Beichel ◽  
Jayashree Kalpathy-Cramer ◽  
David Clunie ◽  
Michael Onken ◽  
...  
Keyword(s):  
Cancer Research ◽  
Best Practices ◽  
Cancer Imaging ◽  
Quantitative Imaging ◽  
Imaging Informatics ◽  
Imaging Data ◽  
Communication Interface ◽  
3D Slicer ◽  
And Training ◽  
Data Commons

PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.

scholarly journals Dynamic Localization and Function of Bni1p at the Sites of Directed Growth in Saccharomyces cerevisiae

2001 ◽  
Vol 21 (3) ◽  
pp. 827-839 ◽  
Author(s):  
Kumi Ozaki-Kuroda ◽  
Yasunori Yamamoto ◽  
Hidenori Nohara ◽  
Makoto Kinoshita ◽  
Takeshi Fujiwara ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Fluorescent Protein ◽  
Imaging System ◽  
Cell Polarization ◽  
Actin Binding ◽  
Cortical Actin ◽  
Dynamic Localization ◽  
Directed Growth ◽  
And Function

ABSTRACT Formin homology (FH) proteins are implicated in cell polarization and cytokinesis through actin organization. There are two FH proteins in the yeast Saccharomyces cerevisiae, Bni1p and Bnr1p. Bni1p physically interacts with Rho family small G proteins (Rho1p and Cdc42p), actin, two actin-binding proteins (profilin and Bud6p), and a polarity protein (Spa2p). Here we analyzed the in vivo localization of Bni1p by using a time-lapse imaging system and investigated the regulatory mechanisms of Bni1p localization and function in relation to these interacting proteins. Bni1p fused with green fluorescent protein localized to the sites of cell growth throughout the cell cycle. In a small-budded cell, Bni1p moved along the bud cortex. This dynamic localization of Bni1p coincided with the apparent site of bud growth. Abni1-disrupted cell showed a defect in directed growth to the pre-bud site and to the bud tip (apical growth), causing its abnormally spherical cell shape and thick bud neck. Bni1p localization at the bud tips was absolutely dependent on Cdc42p, largely dependent on Spa2p and actin filaments, and partly dependent on Bud6p, but scarcely dependent on polarized cortical actin patches or Rho1p. These results indicate that Bni1p regulates polarized growth within the bud through its unique and dynamic pattern of localization, dependent on multiple factors, including Cdc42p, Spa2p, Bud6p, and the actin cytoskeleton.

Quantitative Imaging of FRET-Based Biosensors for Cell- and Organelle-Specific Analyses in Plants

2016 ◽  
Vol 22 (2) ◽  
pp. 300-310 ◽  
Author(s):  
Swayoma Banerjee ◽  
Luis Rene Garcia ◽  
Wayne K. Versaw
Keyword(s):  
High Precision ◽  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Dynamic Range ◽  
Quantitative Imaging ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
High Dynamic Range ◽  
Imaging Data ◽  
Protein Protein Interactions

AbstractGenetically encoded Förster resonance energy transfer (FRET)-based biosensors have been used to report relative concentrations of ions and small molecules, as well as changes in protein conformation, posttranslational modifications, and protein–protein interactions. Changes in FRET are typically quantified through ratiometric analysis of fluorescence intensities. Here we describe methods to evaluate ratiometric imaging data acquired through confocal microscopy of a FRET-based inorganic phosphate biosensor in different cells and subcellular compartments of Arabidopsis thaliana. Linear regression was applied to donor, acceptor, and FRET-derived acceptor fluorescence intensities obtained from images of multiple plants to estimate FRET ratios and associated location-specific spectral correction factors with high precision. FRET/donor ratios provided a combination of high dynamic range and precision for this biosensor when applied to the cytosol of both root and leaf cells, but lower precision when this ratiometric method was applied to chloroplasts. We attribute this effect to quenching of donor fluorescence because high precision was achieved with FRET/acceptor ratios and thus is the preferred ratiometric method for this organelle. A ligand-insensitive biosensor was also used to distinguish nonspecific changes in FRET ratios. These studies provide a useful guide for conducting quantitative ratiometric studies in live plants that is applicable to any FRET-based biosensor.

scholarly journals Control of Mitotic Spindle Position by the Saccharomyces cerevisiae Formin Bni1p

1999 ◽  
Vol 144 (5) ◽  
pp. 947-961 ◽  
Author(s):  
Laifong Lee ◽  
Saskia K. Klee ◽  
Marie Evangelista ◽  
Charles Boone ◽  
David Pellman
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Mitotic Spindle ◽  
Cortical Microtubule ◽  
Spindle Pole Body ◽  
Spindle Pole ◽  
Spindle Orientation ◽  
Cell Cortex ◽  
Cortical Actin ◽  
Bud Site ◽  
Spindle Position

Alignment of the mitotic spindle with the axis of cell division is an essential process in Saccharomyces cerevisiae that is mediated by interactions between cytoplasmic microtubules and the cell cortex. We found that a cortical protein, the yeast formin Bni1p, was required for spindle orientation. Two striking abnormalities were observed in bni1Δ cells. First, the initial movement of the spindle pole body (SPB) toward the emerging bud was defective. This phenotype is similar to that previously observed in cells lacking the kinesin Kip3p and, in fact, BNI1 and KIP3 were found to be in the same genetic pathway. Second, abnormal pulling interactions between microtubules and the cortex appeared to cause preanaphase spindles in bni1Δ cells to transit back and forth between the mother and the bud. We therefore propose that Bni1p may localize or alter the function of cortical microtubule-binding sites in the bud. Additionally, we present evidence that other bipolar bud site determinants together with cortical actin are also required for spindle orientation.

scholarly journals Bni1p Regulates Microtubule-Dependent Nuclear Migration through the Actin Cytoskeleton in Saccharomyces cerevisiae

1999 ◽  
Vol 19 (12) ◽  
pp. 8016-8027 ◽  
Author(s):  
Takeshi Fujiwara ◽  
Kazuma Tanaka ◽  
Eiji Inoue ◽  
Mitsuhiro Kikyo ◽  
Yoshimi Takai
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Actin Cytoskeleton ◽  
Nuclear Migration ◽  
Growth Defect ◽  
Temperature Sensitive ◽  
Cortical Actin ◽  
Synthetic Lethal ◽  
Downstream Target ◽  
Synthetically Lethal ◽  
Mother Cells

ABSTRACT The RHO1 gene encodes a yeast homolog of the mammalian RhoA protein. Rho1p is localized to the growth sites and is required for bud formation. We have recently shown that Bni1p is one of the potential downstream target molecules of Rho1p. The BNI1gene is implicated in cytokinesis and the establishment of cell polarity in Saccharomyces cerevisiae but is not essential for cell viability. In this study, we screened for mutations that were synthetically lethal in combination with a bni1 mutation and isolated two genes. They were the previously identifiedPAC1 and NIP100 genes, both of which are implicated in nuclear migration in S. cerevisiae. Pac1p is a homolog of human LIS1, which is required for brain development, whereas Nip100p is a homolog of rat p150Glued, a component of the dynein-activated dynactin complex. Disruption ofBNI1 in either the pac1 or nip100mutant resulted in an enhanced defect in nuclear migration, leading to the formation of binucleate mother cells. The arp1 bni1mutant showed a synthetic lethal phenotype while the cin8 bni1 mutant did not, suggesting that Bni1p functions in a kinesin pathway but not in the dynein pathway. Cells of the pac1 bni1 and nip100 bni1 mutants exhibited a random distribution of cortical actin patches. Cells of the pac1 act1-4 mutant showed temperature-sensitive growth and a nuclear migration defect. These results indicate that Bni1p regulates microtubule-dependent nuclear migration through the actin cytoskeleton. Bni1p lacking the Rho-binding region did not suppress the pac1 bni1 growth defect, suggesting a requirement for the Rho1p-Bni1p interaction in microtubule function.

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