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  • 1. Brasko, Csilla
    et al.
    Smith, Kevin
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Molnar, Csaba
    Farago, Nora
    Hegedus, Lili
    Balind, Arpad
    Balassa, Tamas
    Szkalisity, Abel
    Sukosd, Farkas
    Kocsis, Katalin
    Balint, Balazs
    Paavolainen, Lassi
    Enyedi, Marton Z.
    Nagy, Istvan
    Puskas, Laszlo G.
    Haracska, Lajos
    Tamas, Gabor
    Horvath, Peter
    Intelligent image-based in situ single-cell isolation2018In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, article id 226Article in journal (Refereed)
    Abstract [en]

    Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.

  • 2. Fusco, Ludovico
    et al.
    Lefort, Riwal
    Smith, Kevin
    École Polytechnique Fédérale de Lausanne.
    Benmansour, Fethallah
    Gonzalez, German
    Barillari, C
    Rinn, Bernd
    Fleuret, Francois
    Fua, Pascal
    Pertz, Olivier
    Computer vision profiling of neurite outgrowth dynamics reveals spatio-temporal modularity of Rho GTPase signaling2016In: Journal of Cell Biology, ISSN 0021-9525, E-ISSN 1540-8140, Vol. 212, no 1, p. 91-111Article in journal (Refereed)
    Abstract [en]

    Rho guanosine triphosphatases (GTPases) control the cytoskeletal dynamics that power neurite outgrowth. This process consists of dynamic neuriteinitiation, elongation, retraction, and branching cycles that are likely to be regulated by specific spatiotemporal signaling networks, which cannot be resolved with static, steady-state assays. We present Neurite-Tracker, a computer-vision approach to automatically segment and track neuronal morphodynamics in time-lapse datasets. Feature extraction then quantifies dynamic neurite outgrowth phenotypes. We identify a set of stereotypic neurite outgrowth morphodynamic behaviors in a cultured neuronal cell system. Systematic RNA interference perturbation of a Rho GTPase interactome consisting of 219 proteins reveals a limited set of morphodynamic phenotypes. As proof of concept, we show that loss of function of two distinct RhoA-specific GTPase-activating proteins (GAPs) leads to opposite neurite outgrowth phenotypes. Imaging of RhoA activation dynamics indicates that both GAPs regulate different spatiotemporal Rho GTPase pools, with distinct functions. Our results provide a starting point to dissect spatiotemporal Rho GTPase signaling networks that regulate neurite outgrowth.

  • 3. Piccinini, Filippo
    et al.
    Balassa, Tamas
    Szkalisity, Abel
    Molnar, Csaba
    Paavolainen, Lassi
    Kujala, Kaisa
    Buzas, Krisztina
    Sarazova, Marie
    Pietiainen, Vilja
    Kutay, Ulrike
    Smith, Kevin
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Horvath, Peter
    Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data2017In: CELL SYSTEMS, ISSN 2405-4712, Vol. 4, no 6, p. 651-+Article in journal (Refereed)
    Abstract [en]

    High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.

  • 4. Robertson, Stephanie
    et al.
    Azizpour, Hossein
    KTH, School of Computer Science and Communication (CSC).
    Smith, Kevin
    KTH, School of Computer Science and Communication (CSC).
    Hartman, Johan
    Digital image analysis in breast pathology-from image processing techniques to artificial intelligence2018In: Translational Research: The Journal of Laboratory and Clinical Medicine, ISSN 1931-5244, E-ISSN 1878-1810, Vol. 194, p. 19-35Article, review/survey (Refereed)
    Abstract [en]

    Breast cancer is the most common malignant disease in women worldwide. In recent decades, earlier diagnosis and better adjuvant therapy have substantially improved patient outcome. Diagnosis by histopathology has proven to be instrumental to guide breast cancer treatment, but new challenges have emerged as our increasing understanding of cancer over the years has revealed its complex nature. As patient demand for personalized breast cancer therapy grows, we face an urgent need for more precise biomarker assessment and more accurate histopathologic breast cancer diagnosis to make better therapy decisions. The digitization of pathology data has opened the door to faster, more reproducible, and more precise diagnoses through computerized image analysis. Software to assist diagnostic breast pathology through image processing techniques have been around for years. But recent breakthroughs in artificial intelligence (AI) promise to fundamentally change the way we detect and treat breast cancer in the near future. Machine learning, a subfield of AI that applies statistical methods to learn from data, has seen an explosion of interest in recent years because of its ability to recognize patterns in data with less need for human instruction. One technique in particular, known as deep learning, has produced groundbreaking results in many important problems including image classification and speech recognition. In this review, we will cover the use of AI and deep learning in diagnostic breast pathology, and other recent developments in digital image analysis.

  • 5.
    Smith, Kevin
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Piccinini, Filippo
    IRCCS, Ist Sci Romagnolo Studio & Cura Tumori IRST, Via P Maroncelli 40, I-47014 Meldola, FC, Italy..
    Balassa, Tamas
    Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary..
    Koos, Krisztian
    Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary..
    Danka, Tivadar
    Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary..
    Azizpour, Hossein
    KTH, School of Electrical Engineering and Computer Science (EECS). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Horvath, Peter
    Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary.;Univ Helsinki, Inst Mol Med Finland, Tukholmankatu 8, FIN-00014 Helsinki, Finland..
    Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays2018In: CELL SYSTEMS, ISSN 2405-4712, Vol. 6, no 6, p. 636-653Article, review/survey (Refereed)
    Abstract [en]

    Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

  • 6.
    Sullivan, Devin P.
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Winsnes, Casper F.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Åkesson, Lovisa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hjelmare, Martin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Wiking, Mikaela
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schutten, Rutger
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Campbell, Linzi
    CCP Hf, Reyjkavik, Iceland..
    Leifsson, Hjalti
    CCP Hf, Reyjkavik, Iceland..
    Rhodes, Scott
    CCP Hf, Reyjkavik, Iceland..
    Nordgren, Andie
    CCP Hf, Reyjkavik, Iceland..
    Smith, Kevin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Revaz, Bernard
    MMOS Sarl, Monthey, Switzerland..
    Finnbogason, Bergur
    CCP Hf, Reyjkavik, Iceland..
    Szantner, Attila
    MMOS Sarl, Monthey, Switzerland..
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Deep learning is combined with massive-scale citizen science to improve large-scale image classification2018In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 36, no 9, p. 820-+Article in journal (Refereed)
    Abstract [en]

    Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.

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