Literature DB >> 29953863

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays.

Kevin Smith1, Filippo Piccinini2, Tamas Balassa3, Krisztian Koos3, Tivadar Danka3, Hossein Azizpour1, Peter Horvath4.   

Abstract

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.
Copyright © 2018 Elsevier Inc. All rights reserved.

Keywords:  cell classification; drug screening; freely available tools; high-content screening; machine learning; microscopy; oncology; phenomics; phenotypic image analysis; single-cell analysis

Mesh:

Year:  2018        PMID: 29953863     DOI: 10.1016/j.cels.2018.06.001

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   10.304


  26 in total

1.  Chrysalis: A New Method for High-Throughput Histo-Cytometry Analysis of Images and Movies.

Authors:  Dmitri I Kotov; Thomas Pengo; Jason S Mitchell; Matthew J Gastinger; Marc K Jenkins
Journal:  J Immunol       Date:  2018-12-03       Impact factor: 5.422

Review 2.  Towards systems tissue engineering: Elucidating the dynamics, spatial coordination, and individual cells driving emergent behaviors.

Authors:  Matthew S Hall; Joseph T Decker; Lonnie D Shea
Journal:  Biomaterials       Date:  2020-06-14       Impact factor: 12.479

3.  Systematic High-Content Screening of Fluorescently Tagged Yeast Double Mutant Strains.

Authors:  Harsha Garadi Suresh; Mojca Mattiazzi Usaj
Journal:  Methods Mol Biol       Date:  2021

4.  Defining host-pathogen interactions employing an artificial intelligence workflow.

Authors:  Daniel Fisch; Artur Yakimovich; Barbara Clough; Joseph Wright; Monique Bunyan; Michael Howell; Jason Mercer; Eva Frickel
Journal:  Elife       Date:  2019-02-12       Impact factor: 8.140

Review 5.  Deep learning for cellular image analysis.

Authors:  Erick Moen; Dylan Bannon; Takamasa Kudo; William Graf; Markus Covert; David Van Valen
Journal:  Nat Methods       Date:  2019-05-27       Impact factor: 28.547

6.  Regression plane concept for analysing continuous cellular processes with machine learning.

Authors:  Abel Szkalisity; Filippo Piccinini; Attila Beleon; Tamas Balassa; Istvan Gergely Varga; Ede Migh; Csaba Molnar; Lassi Paavolainen; Sanna Timonen; Indranil Banerjee; Elina Ikonen; Yohei Yamauchi; Istvan Ando; Jaakko Peltonen; Vilja Pietiäinen; Viktor Honti; Peter Horvath
Journal:  Nat Commun       Date:  2021-05-05       Impact factor: 14.919

Review 7.  Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations.

Authors:  Mojca Mattiazzi Usaj; Clarence Hue Lok Yeung; Helena Friesen; Charles Boone; Brenda J Andrews
Journal:  Cell Syst       Date:  2021-06-16       Impact factor: 11.091

8.  High-Throughput Imaging of Arrays of Fluorescently Tagged Yeast Mutant Strains.

Authors:  Mojca Mattiazzi Usaj; Dara S Lo; Ben T Grys; Brenda J Andrews
Journal:  Methods Mol Biol       Date:  2021

Review 9.  Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.

Authors:  Filippo Piccinini; Tamas Balassa; Antonella Carbonaro; Akos Diosdi; Timea Toth; Nikita Moshkov; Ervin A Tasnadi; Peter Horvath
Journal:  Comput Struct Biotechnol J       Date:  2020-06-03       Impact factor: 7.271

Review 10.  The palette of techniques for cell cycle analysis.

Authors:  Anna E Eastman; Shangqin Guo
Journal:  FEBS Lett       Date:  2020-05-22       Impact factor: 3.864

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