Microscopy-Based High-Content Screening.

Cell

Division Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Department of Cell and Molecular Biology, Heidelberg University, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.

Published: December 2015

Image-based screening is used to measure a variety of phenotypes in cells and whole organisms. Combined with perturbations such as RNA interference, small molecules, and mutations, such screens are a powerful method for gaining systematic insights into biological processes. Screens have been applied to study diverse processes, such as protein-localization changes, cancer cell vulnerabilities, and complex organismal phenotypes. Recently, advances in imaging and image-analysis methodologies have accelerated large-scale perturbation screens. Here, we describe the state of the art for image-based screening experiments and delineate experimental approaches and image-analysis approaches as well as discussing challenges and future directions, including leveraging CRISPR/Cas9-mediated genome engineering.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cell.2015.11.007DOI Listing

Publication Analysis

Top Keywords

image-based screening
8
microscopy-based high-content
4
high-content screening
4
screening image-based
4
screening measure
4
measure variety
4
variety phenotypes
4
phenotypes cells
4
cells organisms
4
organisms combined
4

Similar Publications

Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification.

View Article and Find Full Text PDF

Background/objectives: In recent years, numerous studies have been published on determining the WHO grade of central nervous system (CNS) tumors using machine learning algorithms. These studies are usually based on magnetic resonance imaging (MRI) and sometimes also on positron emission tomography (PET) images. To date, however, there are virtually no corresponding studies based on routinely generated computed tomography (CT) images.

View Article and Find Full Text PDF

Background: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD.

Methods: We evaluated the potential of an automated ILD quantification system (icolung) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD.

View Article and Find Full Text PDF

Brain imaging data is one of the primary predictors for assessing the risk of Alzheimer's disease (AD). This study aims to extract image-based features associated with the possibly right-censored time-to-event outcomes and to improve predictive performance. While the functional proportional hazards model is well-studied in the literature, these studies often do not consider the existence of patients who have a very low risk and are approximately insusceptible to AD.

View Article and Find Full Text PDF

Left Ventricular Hemodynamic Forces Changes in Fabry Disease: A Cardiac Magnetic Resonance Study.

J Magn Reson Imaging

January 2025

Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Background: Hemodynamic force (HDF) from cardiac MRI can indicate subclinical myocardial dysfunction, and help identify early cardiac changes in patients with Fabry disease (FD). The hemodynamic change in FD patients remains unclear.

Purpose: To explore HDF changes in FD and the potential of HDF measurements as diagnostic markers indicating early cardiac changes in FD.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!