ArticleCell death discovery2021
Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis.
Article in Cell death discovery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 58 citations in OpenAlex.
- Multi-cell, non-invasive, online monitoring of PLA-coated magnetic nanoparticles uptake by MCF-7 cells using digital holographic microscopy.Bioactive materials · 2026Article
- AI-driven dual-mode phase and label-free fluorescence imaging platform using a single-shot gabor hologram.Microsystems & nanoengineering · 2026Article
- Structural, Compositional, and Dielectric State Profiling in Label-Free Single-Cell Monitoring.Small methods · 2026Review
- Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview.Journal of pharmaceutical analysis · 2026Review
- AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms.Biomedical optics express · 2026Review
- Modeling glioblastoma relapse in vitro: a critical journey from 2D models to organ-on-chip alternatives.NPJ precision oncology · 2026Review
- Regularized Single-Cell Imaging Enables Generalizable AI Models for Stain-Free Cell Viability Screening.Small methods · 2026Article
- Morphological and Optical Characterization of NRAS-Mutant Melanoma Cells and Primary Melanocytes via Quantitative Phase Imaging With Digital Holographic Microscopy.Cell biochemistry and function · 2026Article
- Deep transfer learning approach for automated cell death classification reveals novel ferroptosis-inducing agents in subsets of B-ALL.Cell death & disease · 2025Article
- An in vivo microscopy dataset for the characterization of leukocyte death.Scientific data · 2025Article
- CeDaD-a novel assay for simultaneous tracking of cell death and division in a single population.Cell death discovery · 2025Article
- Deep learning-based image classification reveals heterogeneous execution of cell death fates during viral infection.Molecular biology of the cell · 2025Article
- Machine learning with label-free Raman microscopy to investigate ferroptosis in comparison with apoptosis and necroptosis.Communications biology · 2025Article
- Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.eLife · 2024Article
- Current trends in luminescence-based assessment of apoptosis.RSC advances · 2023Review
- Quantitative phase imaging for characterization of single cell growth dynamics.Lasers in medical science · 2023Review
- CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting.Cell death discovery · 2023Article
- High-throughput mechano-cytometry as a method to detect apoptosis, necroptosis, and ferroptosis.Cell proliferation · 2023Article
- Article
- Machine learning-based detection of label-free cancer stem-like cell fate.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Regulated cell death modalities such as apoptosis and necroptosis play an important role in regulating different cellular processes. Currently, regulated cell death is identified using the golden standard techniques such as fluorescence microscopy and flow cytometry. However, they require fluorescent labels, which are potentially phototoxic. Therefore, there is a need for the development of new label-free methods. In this work, we apply Digital Holographic Microscopy (DHM) coupled with a deep learning algorithm to distinguish between alive, apoptotic and necroptotic cells in murine cancer cells. This method is solely based on label-free quantitative phase images, where the phase delay of light by cells is quantified and is used to calculate their topography. We show that a combination of label-free DHM in a high-throughput set-up (~10,000 cells per condition) can discriminate between apoptosis, necroptosis and alive cells in the L929sAhFas cell line with a precision of over 85%. To the best of our knowledge, this is the first time deep learning in the form of convolutional neural networks is applied to distinguish-with a high accuracy-apoptosis and necroptosis and alive cancer cells from each other in a label-free manner. It is expected that the approach described here will have a profound impact on research in regulated cell death, biomedicine and the field of (cancer) cell biology in general.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.