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Article ; Online: Preface.

Nabi, Ivan Robert

2020 Volume 39, Issue 2, Page(s) 335

MeSH term(s)	Caveolin 1/metabolism ; Cellular Senescence/physiology ; Humans ; Lipid Metabolism ; Membrane Microdomains/metabolism ; Neoplasms/metabolism ; Neoplasms/pathology
Chemical Substances	Caveolin 1
Language	English
Publishing date	2020-05-15
Publishing country	Netherlands
Document type	Editorial ; Introductory Journal Article
ZDB-ID	604857-2
ISSN	1573-7233 ; 0167-7659
ISSN (online)	1573-7233
ISSN	0167-7659
DOI	10.1007/s10555-020-09894-7
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Article ; Online: Biography-Ivan Robert Nabi.

Nabi, Ivan Robert

Cancer metastasis reviews

2020 Volume 39, Issue 2, Page(s) 333

MeSH term(s)	History, 20th Century ; History, 21st Century ; Humans ; Machine Learning/history ; Medical Oncology/history ; Microscopy, Confocal/history ; Neoplasms/pathology
Language	English
Publishing date	2020-06-13
Publishing country	Netherlands
Document type	Biography ; Historical Article ; Journal Article
ZDB-ID	604857-2
ISSN	1573-7233 ; 0167-7659
ISSN (online)	1573-7233
ISSN	0167-7659
DOI	10.1007/s10555-020-09893-8
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Article ; Online: DataCurator.jl: efficient, portable and reproducible validation, curation and transformation of large heterogeneous datasets using human-readable recipes compiled into machine-verifiable templates.

Cardoen, Ben / Ben Yedder, Hanene / Lee, Sieun / Nabi, Ivan Robert / Hamarneh, Ghassan

Bioinformatics advances

2023 Volume 3, Issue 1, Page(s) vbad068

Abstract: Large-scale processing of heterogeneous datasets in interdisciplinary research often requires time-consuming manual data curation. Ambiguity in the data layout and preprocessing conventions can easily compromise reproducibility and scientific discovery, ... ...

Abstract	Large-scale processing of heterogeneous datasets in interdisciplinary research often requires time-consuming manual data curation. Ambiguity in the data layout and preprocessing conventions can easily compromise reproducibility and scientific discovery, and even when detected, it requires time and effort to be corrected by domain experts. Poor data curation can also interrupt processing jobs on large computing clusters, causing frustration and delays. We introduce
Language	English
Publishing date	2023-06-01
Publishing country	England
Document type	Journal Article
ISSN	2635-0041
ISSN (online)	2635-0041
DOI	10.1093/bioadv/vbad068
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Article ; Online: A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods.

Khater, Ismail M / Nabi, Ivan Robert / Hamarneh, Ghassan

Patterns (New York, N.Y.)

2020 Volume 1, Issue 3, Page(s) 100038

Abstract: Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light ... ...

Abstract	Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10-20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.
Language	English
Publishing date	2020-06-12
Publishing country	United States
Document type	Journal Article ; Review
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2020.100038
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Article ; Online: Nanomolar anti-SARS-CoV-2 Omicron activity of the host-directed TMPRSS2 inhibitor N-0385 and synergistic action with direct-acting antivirals.

Pérez-Vargas, Jimena / Lemieux, Gabriel / Thompson, Connor A H / Désilets, Antoine / Ennis, Siobhan / Gao, Guang / Gordon, Danielle G / Schulz, Annika Lea / Niikura, Masahiro / Nabi, Ivan Robert / Krajden, Mel / Boudreault, Pierre-Luc / Leduc, Richard / Jean, François

Antiviral research

2024 Volume 225, Page(s) 105869

Abstract: SARS-CoV-2 Omicron subvariants with increased transmissibility and immune evasion are spreading globally with alarming persistence. Whether the mutations and evolution of spike (S) Omicron subvariants alter the viral hijacking of human TMPRSS2 for viral ... ...

Abstract	SARS-CoV-2 Omicron subvariants with increased transmissibility and immune evasion are spreading globally with alarming persistence. Whether the mutations and evolution of spike (S) Omicron subvariants alter the viral hijacking of human TMPRSS2 for viral entry remains to be elucidated. This is particularly important to investigate because of the large number and diversity of mutations of S Omicron subvariants reported since the emergence of BA.1. Here we report that human TMPRSS2 is a molecular determinant of viral entry for all the Omicron clinical isolates tested in human lung cells, including ancestral Omicron subvariants (BA.1, BA.2, BA.5), contemporary Omicron subvariants (BQ.1.1, XBB.1.5, EG.5.1) and currently circulating Omicron BA.2.86. First, we used a co-transfection assay to demonstrate the endoproteolytic cleavage by TMPRSS2 of spike Omicron subvariants. Second, we found that N-0385, a highly potent TMPRSS2 inhibitor, is a robust entry inhibitor of virus-like particles harbouring the S protein of Omicron subvariants. Third, we show that N-0385 exhibits nanomolar broad-spectrum antiviral activity against live Omicron subvariants in human Calu-3 lung cells and primary patient-derived bronchial epithelial cells. Interestingly, we found that N-0385 is 10-20 times more potent than the repositioned TMPRSS2 inhibitor, camostat, against BA.5, EG.5.1, and BA.2.86. We further found that N-0385 shows broad synergistic activity with clinically approved direct-acting antivirals (DAAs), i.e., remdesivir and nirmatrelvir, against Omicron subvariants, demonstrating the potential therapeutic benefits of a multi-targeted treatment based on N-0385 and DAAs.
MeSH term(s)	Humans ; Antiviral Agents ; COVID-19 ; Hepatitis C, Chronic ; SARS-CoV-2 ; Antibodies, Neutralizing ; Antibodies, Viral ; Serine Endopeptidases ; Sulfonamides ; Benzothiazoles
Chemical Substances	(2S)-N-((2S)-1-(((2S)-1-(1,3-benzothiazol-2-yl)-5-(diaminomethylideneamino)-1-oxopentan-2-yl)amino)-1-oxo-3-phenylpropan-2-yl)-2-(methanesulfonamido)pentanediamide) ; Antiviral Agents ; Antibodies, Neutralizing ; Antibodies, Viral ; TMPRSS2 protein, human (EC 3.4.21.-) ; Serine Endopeptidases (EC 3.4.21.-) ; Sulfonamides ; Benzothiazoles
Language	English
Publishing date	2024-03-26
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	306628-9
ISSN	1872-9096 ; 0166-3542
ISSN (online)	1872-9096
ISSN	0166-3542
DOI	10.1016/j.antiviral.2024.105869
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Article ; Online: Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data.

Khater, Ismail M / Meng, Fanrui / Nabi, Ivan Robert / Hamarneh, Ghassan

Bioinformatics (Oxford, England)

2019 Volume 35, Issue 18, Page(s) 3468–3475

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 ... ...

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.
MeSH term(s)	Caveolae ; Caveolin 1 ; Humans ; Machine Learning ; Male ; Prostatic Neoplasms ; RNA-Binding Proteins
Chemical Substances	Caveolin 1 ; RNA-Binding Proteins
Language	English
Publishing date	2019-03-13
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz113
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Zs.A 2374: Show issues

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Article ; Online: Caveolae and scaffold detection from single molecule localization microscopy data using deep learning.

Khater, Ismail M / Aroca-Ouellette, Stephane T / Meng, Fanrui / Nabi, Ivan Robert / Hamarneh, Ghassan

PloS one

2019 Volume 14, Issue 8, Page(s) e0211659

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Caveolae/metabolism ; Deep Learning ; Humans ; Image Processing, Computer-Assisted/methods ; PC-3 Cells ; Single Molecule Imaging
Language	English
Publishing date	2019-08-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0211659
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Article ; Online: ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy.

Cardoen, Ben / Yedder, Hanene Ben / Sharma, Anmol / Chou, Keng C / Nabi, Ivan Robert / Hamarneh, Ghassan

IEEE transactions on medical imaging

2019 Volume 39, Issue 6, Page(s) 1942–1956

Abstract: Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the forefront ... ...

Abstract	Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the forefront of scientific discovery in cancer, infectious, and degenerative diseases. By stochastic temporal and spatial separation of light emissions from fluorescent labelled proteins, SMLM is capable of nanometer scale reconstruction of cellular structures. Precise localization of proteins in 3D astigmatic SMLM is dependent on parameter sensitive preprocessing steps to select regions of interest. With SMLM acquisition highly variable over time, it is non-trivial to find an optimal static parameter configuration. The high emitter density required for reconstruction of complex protein structures can compromise accuracy and introduce artifacts. To address these problems, we introduce two modular auto-tuning pre-processing methods: adaptive signal detection and learned recurrent signal density estimation that can leverage the information stored in the sequence of frames that compose the SMLM acquisition process. We show empirically that our contributions improve accuracy, precision and recall with respect to the state of the art. Both modules auto-tune their hyper-parameters to reduce the parameter space for practitioners, improve robustness and reproducibility, and are validated on a reference in silico dataset. Adaptive signal detection and density prediction can offer a practitioner, in addition to informed localization, a tool to tune acquisition parameters ensuring improved reconstruction of the underlying protein complex. We illustrate the challenges faced by practitioners in applying SMLM algorithms on real world data markedly different from the data used in development and show how ERGO can be run on new datasets without retraining while motivating the need for robust transfer learning in SMLM.
MeSH term(s)	Algorithms ; Artifacts ; Microscopy ; Reproducibility of Results ; Single Molecule Imaging
Language	English
Publishing date	2019-12-25
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2019.2962361
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Zs.A 2546: Show issues

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Article ; Online: Super-resolution modularity analysis shows polyhedral caveolin-1 oligomers combine to form scaffolds and caveolae.

Khater, Ismail M / Liu, Qian / Chou, Keng C / Hamarneh, Ghassan / Nabi, Ivan Robert

Scientific reports

2019 Volume 9, Issue 1, Page(s) 9888

Abstract: Caveolin-1 (Cav1), the coat protein for caveolae, also forms non-caveolar Cav1 scaffolds. Single molecule Cav1 super-resolution microscopy analysis previously identified caveolae and three distinct scaffold domains: smaller S1A and S2B scaffolds and ... ...

Abstract	Caveolin-1 (Cav1), the coat protein for caveolae, also forms non-caveolar Cav1 scaffolds. Single molecule Cav1 super-resolution microscopy analysis previously identified caveolae and three distinct scaffold domains: smaller S1A and S2B scaffolds and larger hemispherical S2 scaffolds. Application here of network modularity analysis of SMLM data for endogenous Cav1 labeling in HeLa cells shows that small scaffolds combine to form larger scaffolds and caveolae. We find modules within Cav1 blobs by maximizing the intra-connectivity between Cav1 molecules within a module and minimizing the inter-connectivity between Cav1 molecules across modules, which is achieved via spectral decomposition of the localizations adjacency matrix. Features of modules are then matched with intact blobs to find the similarity between the module-blob pairs of group centers. Our results show that smaller S1A and S1B scaffolds are made up of small polygons, that S1B scaffolds correspond to S1A scaffold dimers and that caveolae and hemispherical S2 scaffolds are complex, modular structures formed from S1B and S1A scaffolds, respectively. Polyhedral interactions of Cav1 oligomers, therefore, leads progressively to the formation of larger and more complex scaffold domains and the biogenesis of caveolae.
MeSH term(s)	Caveolae/metabolism ; Caveolin 1/metabolism ; Cell Line, Tumor ; Cell Membrane/metabolism ; HeLa Cells ; Humans ; Microscopy/methods ; Single Molecule Imaging/methods
Chemical Substances	CAV1 protein, human ; Caveolin 1
Language	English
Publishing date	2019-07-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-019-46174-z
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Article: Caveolin-1: role in cell signaling.

Boscher, Cécile / Nabi, Ivan Robert

Advances in experimental medicine and biology

2012 Volume 729, Page(s) 29–50

Abstract: Caveolins (Cavs) are integrated plasma membrane proteins that are complex signaling regulators with numerous partners and whose activity is highly dependent on cellular context. Cavs are both positive and negative regulators of cell signaling in and/or ... ...

Abstract	Caveolins (Cavs) are integrated plasma membrane proteins that are complex signaling regulators with numerous partners and whose activity is highly dependent on cellular context. Cavs are both positive and negative regulators of cell signaling in and/or out of caveolae, invaginated lipid raft domains whose formation is caveolin expression dependent. Caveolins and rafts have been implicated in membrane compartmentalization; proteins and lipids accumulate in these membrane microdomains where they transmit fast, amplified and specific signaling cascades. The concept of plasma membrane organization within functional rafts is still in exploration and sometimes questioned. In this chapter, we discuss the opposing functions of caveolin in cell signaling regulation focusing on the role of caveolin both as a promoter and inhibitor of different signaling pathways and on the impact of membrane domain localization on caveolin functionality in cell proliferation, survival, apoptosis and migration.
MeSH term(s)	Animals ; Caveolae/metabolism ; Caveolin 1/chemistry ; Caveolin 1/metabolism ; Cell Line ; Humans ; Protein Structure, Tertiary ; Receptor Protein-Tyrosine Kinases/metabolism ; Signal Transduction
Chemical Substances	Caveolin 1 ; Receptor Protein-Tyrosine Kinases (EC 2.7.10.1)
Language	English
Publishing date	2012
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ISSN	2214-8019 ; 0065-2598
ISSN (online)	2214-8019
ISSN	0065-2598
DOI	10.1007/978-1-4614-1222-9_3
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