scholarly journals Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data

2019 ◽  
Vol 35 (18) ◽  
pp. 3468-3475 ◽  
Author(s):  
Ismail M Khater ◽  
Fanrui Meng ◽  
Ivan Robert Nabi ◽  
Ghassan Hamarneh
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Cells ◽  
Single Molecule ◽  
Classification Accuracy ◽  
Supplementary Information ◽  
Prostate Cancer Cells ◽  
Localization Microscopy ◽  
Caveolin 1 ◽  
Single Molecule Localization Microscopy

Abstract Motivation Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distinct non-caveolar scaffolds (S1A, S1B and S2). To obtain further insight into low-level molecular interactions within these different structural domains, we now introduce graphlet decomposition over a range of proximity thresholds and show that frequency of different subgraph (k = 4 nodes) patterns for machine learning approaches (classification, identification, automatic labeling, etc.) effectively distinguishes caveolae and scaffold blobs. Results Caveolae formation requires both Cav1 and the adaptor protein CAVIN1 (also called PTRF). As a supervised learning approach, we applied a wide-field CAVIN1/PTRF mask to CAVIN1/PTRF-transfected PC3 prostate cancer cells and used the random forest classifier to classify blobs based on graphlet frequency distribution (GFD). GFD of CAVIN1/PTRF-positive (PTRF+) and -negative Cav1 clusters showed poor classification accuracy that was significantly improved by stratifying the PTRF+ clusters by either number of localizations or volume. Low classification accuracy (<50%) of large PTRF+ clusters and caveolae blobs identified by unsupervised learning suggests that their GFD is specific to caveolae. High classification accuracy for small PTRF+ clusters and caveolae blobs argues that CAVIN1/PTRF associates not only with caveolae but also non-caveolar scaffolds. At low proximity thresholds (50–100 nm), the caveolae groups showed reduced frequency of highly connected graphlets and increased frequency of completely disconnected graphlets. GFD analysis of single-molecule localization microscopy Cav1 clusters defines changes in structural organization in caveolae and scaffolds independent of association with CAVIN1/PTRF. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Effect of an angiotensin II type 1 receptor blocker on caveolin-1 expression in prostate cancer cells

2013 ◽  
Vol 4 ◽  
pp. 739-744 ◽  
Author(s):  
Agnieszka Wanda Piastowska-Ciesielska ◽  
Marcin Kozłowski ◽  
Waldemar Wagner ◽  
Kamila Domińska ◽  
Tomasz Ochędalski
Keyword(s):  
Prostate Cancer ◽  
Angiotensin Ii ◽  
Cancer Cells ◽  
Receptor Blocker ◽  
Prostate Cancer Cells ◽  
Caveolin 1

scholarly journals Distribution of Cholesterol and HER2 in Patient Breast Cancer Cells using Quantitative Single Molecule Localization Microscopy

2019 ◽  
Vol 116 (3) ◽  
pp. 440a
Author(s):  
Matthew S. Brehove ◽  
Steven J. Tobin ◽  
Devin L. Wakefield ◽  
Veronica Jones ◽  
Xueli Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Single Molecule ◽  
Breast Cancer Cells ◽  
Localization Microscopy ◽  
Single Molecule Localization Microscopy

scholarly journals RainbowSTORM: an open-source ImageJ plug-in for spectroscopic single-molecule localization microscopy (sSMLM) data analysis and image reconstruction

2020 ◽  
Vol 36 (19) ◽  
pp. 4972-4974
Author(s):  
Janel L Davis ◽  
Brian Soetikno ◽  
Ki-Hee Song ◽  
Yang Zhang ◽  
Cheng Sun ◽  
...  
Keyword(s):  
Open Source ◽  
Single Molecule ◽  
Spectroscopic Analysis ◽  
Emission Spectra ◽  
Supplementary Information ◽  
Localization Microscopy ◽  
Spatial Locations ◽  
Fluorescent Molecules ◽  
System Designs ◽  
Single Molecule Localization Microscopy

Abstract Summary Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously captures the spatial locations and full spectra of stochastically emitting fluorescent single molecules. It provides an optical platform to develop new multimolecular and functional imaging capabilities. While several open-source software suites provide subdiffraction localization of fluorescent molecules, software suites for spectroscopic analysis of sSMLM data remain unavailable. RainbowSTORM is an open-source ImageJ/FIJI plug-in for end-to-end spectroscopic analysis and visualization for sSMLM images. RainbowSTORM allows users to calibrate, preview and quantitatively analyze emission spectra acquired using different reported sSMLM system designs and fluorescent labels. Availability and implementation RainbowSTORM is a java plug-in for ImageJ (https://imagej.net)/FIJI (http://fiji.sc) freely available through: https://github.com/FOIL-NU/RainbowSTORM. RainbowSTORM has been tested with Windows and Mac operating systems and ImageJ/FIJI version 1.52. Supplementary information Supplementary data are available at Bioinformatics online.

Triptolide inhibits the migration and invasion of human prostate cancer cells via Caveolin-1/CD147/MMPs pathway

2016 ◽  
Vol 84 ◽  
pp. 1776-1782 ◽  
Author(s):  
Shiqi Yuan ◽  
Liping Wang ◽  
Xixi Chen ◽  
Bo Fan ◽  
Qingmin Yuan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Cells ◽  
Human Prostate ◽  
Migration And Invasion ◽  
Human Prostate Cancer ◽  
Prostate Cancer Cells ◽  
Caveolin 1

scholarly journals Caveolin-1 Promotes Autoregulatory, Akt-Mediated Induction of Cancer-Promoting Growth Factors in Prostate Cancer Cells

2009 ◽  
Vol 7 (11) ◽  
pp. 1781-1791 ◽  
Author(s):  
Likun Li ◽  
Chengzhen Ren ◽  
Guang Yang ◽  
Alexei A. Goltsov ◽  
Ken-ichi Tabata ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Growth Factors ◽  
Cancer Cells ◽  
Prostate Cancer Cells ◽  
Caveolin 1

scholarly journals An inverted Caveolin-1 topology defines a novel exosome secreted from prostate cancer cells

2020 ◽  
Author(s):  
Nicholas Ariotti ◽  
Yeping Wu ◽  
Satomi Okano ◽  
Yann Gambin ◽  
Jordan Follett ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Disease Progression ◽  
Single Molecule ◽  
Fluorescent Microscopy ◽  
Prostate Cancer Cells ◽  
Caveolin 1 ◽  
Autophagy Pathway ◽  
Secretory Autophagy ◽  
Cav1 Expression

ABSTRACTCaveolin-1 (Cav1) expression and secretion is associated with prostate cancer (PCa) disease progression but the mechanisms underpinning Cav1 release remain poorly understood. Numerous studies have shown Cav1 can be secreted within exosome-like vesicles, but antibody-mediated neutralization can mitigate PCa progression; this is suggestive of an inverted (non-exosomal) Cav1 topology. Here we show that Cav1 can be secreted from specific PCa types in an inverted vesicle-associated form consistent with the features of bioactive Cav1 secretion. Characterization of the isolated vesicles by electron microscopy, single molecule fluorescent microscopy and proteomics reveals they represent a novel class of exosomes ∼40 nm in diameter containing ∼50-60 copies of Cav1 and strikingly, are released via a non-canonical secretory autophagy pathway. This study provides novel insights into a mechanism whereby Cav1 translocates from a normal plasma membrane distribution to an inverted secreted form implicated in PCa disease progression.

scholarly journals Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianpeng Xue ◽  
Yang Pu ◽  
Jason Smith ◽  
Xin Gao ◽  
Chun Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Prostate Cancer Cell ◽  
Cancer Cell Lines ◽  
Prostate Cancer Cells ◽  
Prostate Cancer Cell Lines ◽  
Native Fluorescence

AbstractMetastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular components within tissues and cells using recent optical spectroscopic techniques. The aim of this study was to develop a noninvasive label-free optical biopsy technique to retrieve the characteristic molecular information for detecting different metastatic potentials of prostate cancer cells. Herein we report using native fluorescence (NFL) spectroscopy along with machine learning (ML) to differentiate prostate cancer cells with different metastatic abilities. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key biomolecules that are correlated with metastatic potentials. The relative concentrations of the molecular spectral components were retrieved and used to classify the cancer cells with different metastatic potentials. A multi-class classification was performed using support vector machines (SVMs). The NFL spectral data were collected from three prostate cancer cell lines with different levels of metastatic potentials. The key biomolecules in the prostate cancer cells were identified to be tryptophan, reduced nicotinamide adenine dinucleotide (NADH) and hypothetically lactate as well. The cancer cells with different metastatic potentials were classified with high accuracy using the relative concentrations of the key molecular components. The results suggest that the changes in the relative concentrations of these key fluorophores retrieved from NFL spectra may present potential criteria for detecting prostate cancer cells of different metastatic abilities.

scholarly journals Caveolae and scaffold detection from single molecule localization microscopy data using deep learning

2019 ◽  
Author(s):  
Ismail M. Khater ◽  
Stephane T. Aroca-Ouellette ◽  
Fanrui Meng ◽  
Ivan Robert Nabi ◽  
Ghassan Hamarneh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Single Molecule ◽  
Adaptor Protein ◽  
Point Clouds ◽  
Learning Models ◽  
Localization Microscopy ◽  
Biological Structures ◽  
Single Molecule Localization Microscopy

AbstractCaveolae 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.

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