Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

9.6K
Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
9.6K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.1K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparative evaluation of gene selection approaches in transcriptomics: bias correction and visualization with TransPro.

GigaScience·2026
Same author

Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework.

Nature communications·2026
Same author

Cross-modality representation and multi-sample integration of spatially resolved omics data.

Briefings in bioinformatics·2026
Same author

A generic reference defined by consensus peaks for single-cell ATAC-seq data analysis.

Nature communications·2026
Same author

Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

A machine learning-based method to optimize the immunogenicity of human leukocyte antigen class I-restricted neoantigens.

Briefings in bioinformatics·2025

Related Experiment Video

Updated: Sep 13, 2025

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon
08:18

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

Published on: June 16, 2020

7.6K

Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning.

Chengwei Yan1, Yu Zhang2, Jiuxin Feng1

  • 1Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin, China.

Nature Communications
|July 27, 2025
PubMed
Summary

cpDistiller effectively corrects technical artifacts in Cell Painting imaging data, preserving biological signals for reliable gene function and drug discovery insights. This method enhances high-throughput biological research.

More Related Videos

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

693
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Related Experiment Videos

Last Updated: Sep 13, 2025

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon
08:18

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

Published on: June 16, 2020

7.6K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

693
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Area of Science:

  • Cellular imaging
  • High-throughput screening
  • Computational biology

Background:

  • Cell Painting (CP) is a high-throughput imaging technology providing morphological insights.
  • CP data is susceptible to technical artifacts, including batch and well-position effects (triple effects).
  • These artifacts obscure biological signals, necessitating robust correction methods for reliable analysis.

Purpose of the Study:

  • To develop and validate cpDistiller, a novel method for correcting triple effects in CP data.
  • To demonstrate cpDistiller's ability to preserve cellular heterogeneity while correcting technical variations.
  • To showcase cpDistiller's utility in inferring gene functions, interactions, and identifying drug targets.

Main Methods:

  • cpDistiller employs a pre-trained segmentation model.
  • It utilizes a semi-supervised Gaussian mixture variational autoencoder with contrastive and domain-adversarial learning.
  • The method was validated through extensive qualitative and quantitative experiments on diverse CP datasets.

Main Results:

  • cpDistiller effectively corrects triple effects, particularly well-position effects, in CP data.
  • The method successfully preserves crucial cellular heterogeneity.
  • cpDistiller accurately captures system-level phenotypic responses to genetic perturbations.
  • It reliably infers gene functions and interactions, even when integrated with scRNA-seq data.
  • The tool shows capability in identifying gene and compound targets.

Conclusions:

  • cpDistiller offers a powerful solution for mitigating technical artifacts in Cell Painting data.
  • This method enhances the reliability of morphological profiling for biological discovery.
  • cpDistiller holds significant potential for applications in drug discovery and systems biology research.