Pedro M. Pereira

Universidade Nova de Lisboa

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Ricardo Henriques

MRC Laboratory for Molecular Cell Biology

Mariana G. Pinho

Universidade Nova de Lisboa

S J Culley

King's College London

Guillaume Jacquemet

Turku Centre for Computer Science

Romain F. Laine

MRC Laboratory for Molecular Cell Biology

Jason Mercer

University of Birmingham

Caron Jacobs

University of Cape Town

Raquel Portela

Universidade Nova de Lisboa

Christophe Leterrier

Institut de Neurophysiopathologie

Mark Marsh

MRC Laboratory for Molecular Cell Biology

Cooperative Institutions

Universidade Nova de Lisboa

Merck & Co., Inc., Rahway, NJ, USA (United States)

University of Lisbon

University College London

Instituto de Tecnología Química

MRC Laboratory for Molecular Cell Biology

Imperial College London

Rede de Química e Tecnologia

European Molecular Biology Organization

European Molecular Biology Laboratory

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Weekly tracking of stability of the flow of conversations into the subprocesses of the Last Planner System

Omar Zegarra Luís F. Alarcón Pedro M. Pereira Nuno Cachadinha

21st Annual Conference of the International Group for Lean Construction – IGLC 21 – Fortaleza, Brazil

Planner

Tracking (education)

Tracking system

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Citations (3)

DeepBacs – E. coli SIM prediction dataset and CARE model

Zenodo (CERN European Organization for Nuclear Research) (2021)

Pedro M. Pereira Mariana G. Pinho

Training and test images of live, membrane-labeled E. coli cells for prediction of SIM super-resolution images from widefield images, as well as a trained CARE model. Additional information can be found on this github wiki. The example image shows a widefield fluorescence image and SIM reconstruction of FM5-95 labelled, live E. coli cells. Training and test dataset Data type: Paired microscopy images (fluorescence) of low (widefield) and high resolution (SIM) Microscopy data type: Fluorescence microscopy (FM5-95) Microscope: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) Cell type: E. coli DH5α grown under agarose pads File format: .tif (16-bit for widefield images and 32-bit for SIM reconstructions) Image size: 1024 x 1024 px² (40 nm/px)
Image preprocessing: E. coli widefield images were scaled with a factor of 2 to match the SIM reconstruction pixel size. CARE model The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 300 epochs on 5500 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (80 x 80 px²), 100 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1.12). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.1), numpy (v1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 4 using rotation and flipping. Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin. Author(s): Pedro Matos Pereira^1,2, Mariana Pinho^1,3 Contact email: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt Affiliation: 1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2) ORCID: https://orcid.org/0000-0002-1426-9540 3) ORCID: https://orcid.org/0000-0002-7132-8842

10.5281/zenodo.5551152

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LongContinuous_UtrGFP

Zenodo (CERN European Organization for Nuclear Research) (2018)

S J Culley Kalina Tosheva Pedro M. Pereira Ricardo Henriques

Cos7 cells expressing Utrophin-GFP imaged as seen in Fig 2e of the paper 'SRRF: Universal live-cell super-resolution microscopy' with DOI:10.1016/j.biocel.2018.05.014 Details: Illumination = 5% 470nm LED Objective = Nikon Apo TIRF 100x Oil DIC N2 Set exposure time = 30ms Timestamp frame 59068 = 1800.333 --> mean exposure time = 30.5ms per frame Pixel size = 0.107 um (1.5x mag)

10.5281/zenodo.7572745

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Nanoscale Colocalization of NK Cell Activating and Inhibitory Receptors Controls Signal Integration

Frontiers in Immunology (2022)

David Tomaz Pedro M. Pereira Nadia Guerra Julian Dyson Keith G. Gould

Natural killer (NK) cell responses depend on the balance of signals from inhibitory and activating receptors. However, how the integration of antagonistic signals occurs upon NK cell–target cell interaction is not fully understood. Here we provide evidence that NK cell inhibition via the inhibitory receptor Ly49A is dependent on its relative colocalization at the nanometer scale with the activating receptor NKG2D upon immune synapse (IS) formation. NKG2D and Ly49A signal integration and colocalization were studied using NKG2D-GFP and Ly49A-RFP-expressing primary NK cells, forming ISs with NIH3T3 target cells, with or without the expression of single-chain trimer (SCT) H2-Dd and an extended form of SCT H2-Dd-CD4 MHC-I molecules. Nanoscale colocalization was assessed by Förster resonance energy transfer between NKG2D-GFP and Ly49A-RFP and measured for each synapse. In the presence of their respective cognate ligands, NKG2D and Ly49A colocalize at the nanometer scale, leading to NK cell inhibition. However, increasing the size of the Ly49A ligand reduced the nanoscale colocalization with NKG2D, consequently impairing Ly49A-mediated inhibition. Thus, our data shows that NK cell signal integration is critically dependent on the dimensions of NK cell ligand–receptor pairs by affecting their relative nanometer-scale colocalization at the IS. Our results together suggest that the balance of NK cell signals and NK cell responses is determined by the relative nanoscale colocalization of activating and inhibitory receptors in the immune synapse.

Colocalization

NKG2D

Immunological synapse

10.3389/fimmu.2022.868496

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Citations (2)

Quantitative mapping and minimization of super-resolution optical imaging artifacts

Nature Methods (2018)

S J Culley David Albrecht Caron Jacobs Pedro M. Pereira Christophe Leterrier

10.1038/nmeth.4605

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Citations (329)

Teichoic acids are temporal and spatial regulators of peptidoglycan cross-linking in Staphylococcus aureus

Proceedings of the National Academy of Sciences (2010)

Magda L. Atilano Pedro M. Pereira James R. Yates Patricia Reed Helena Veiga

The cell wall of Staphylococcus aureus is characterized by an extremely high degree of cross-linking within its peptidoglycan (PGN). Penicillin-binding protein 4 (PBP4) is required for the synthesis of this highly cross-linked peptidoglycan. We found that wall teichoic acids, glycopolymers attached to the peptidoglycan and important for virulence in Gram-positive bacteria, act as temporal and spatial regulators of PGN metabolism, controlling the level of cross-linking by regulating PBP4 localization. PBP4 normally localizes at the division septum, but in the absence of wall teichoic acids synthesis, it becomes dispersed throughout the entire cell membrane and is unable to function normally. As a consequence, the peptidoglycan of TagO null mutants, impaired in wall teichoic acid biosynthesis, has a decreased degree of cross-linking, which renders it more susceptible to the action of lysozyme, an enzyme produced by different host organisms as an initial defense against bacterial infection.

Teichoic acid

Penicillin binding proteins

Lipoteichoic acid

Cell envelope

10.1073/pnas.1004304107

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Citations (252)

Nuclear pores as versatile reference standards for quantitative superresolution microscopy

Nature Methods (2019)

Jervis Vermal Thevathasan Maurice Kahnwald Konstanty Cieśliński Philipp Hoess Sudheer Kumar Peneti

Nuclear pore

10.1038/s41592-019-0574-9

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Citations (306)

NanoPyx: super-fast bioimage analysis powered by adaptive machine learning

bioRxiv (Cold Spring Harbor Laboratory) (2023)

Bruno M. Saraiva Inês Cunha António D. Brito Gautier Follain Raquel Portela

To overcome the challenges posed by large and complex microscopy datasets, we have developed NanoPyx, an adaptive bioimage analysis framework designed for high-speed processing. At the core of NanoPyx is the Liquid Engine, an agent-based machine-learning system that predicts acceleration strategies for image analysis tasks. Unlike traditional single-algorithm methods, the Liquid Engine generates multiple CPU and GPU code variations using a meta-programming system, creating a competitive environment where different algorithms are benchmarked against each other to achieve optimal performance under the user”s computational environment. In initial experiments focusing on super-resolution analysis methods, the Liquid Engine demonstrated an over 10-fold computational speed improvement by accurately predicting the ideal scenarios to switch between algorithmic implementations. NanoPyx is accessible to users through a Python library, code-free Jupyter notebooks, and a napari plugin, making it suitable for individuals regardless of their coding proficiency. Furthermore, the optimisation principles embodied by the Liquid Engine have broader implications, extending their applicability to various high-performance computing fields.

Python

Plug-in

Implementation

10.1101/2023.08.13.553080

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Citations (6)

Peptidoglycan assembly machines: The Staphylococcus aureus Penicillin-Binding Proteins

Pedro M. Pereira

Penicillin binding proteins

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TMEM16F activation by Ca2+ triggers plasma membrane expansion and directs PD-1 trafficking

Scientific Reports (2019)

Christopher Bricogne Michael Fine Pedro M. Pereira Julia Sung Maha Tijani

TMEM16F is a Ca2+ -gated ion channel that is required for Ca2+ -activated phosphatidylserine exposure on the surface of many eukaryotic cells. TMEM16F is widely expressed and has roles in platelet activation during blood clotting, bone formation and T cell activation. By combining microscopy and patch clamp recording we demonstrate that activation of TMEM16F by Ca2+ ionophores in Jurkat T cells triggers large-scale surface membrane expansion in parallel with phospholipid scrambling. With continued ionophore application,TMEM16F-expressing cells then undergo extensive shedding of ectosomes. The T cell co-receptor PD-1 is selectively incorporated into ectosomes. This selectivity depends on its transmembrane sequence. Surprisingly, cells lacking TMEM16F not only fail to expand surface membrane in response to elevated cytoplasmic Ca2+, but instead undergo rapid massive endocytosis with PD-1 internalisation. These results establish a new role for TMEM16F as a regulator of Ca2+ activated membrane trafficking.

10.1038/s41598-018-37056-x

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Citations (49)