ArticleBioinformatics (Oxford, England)2019
TIMING 2.0: high-throughput single-cell profiling of dynamic cell-cell interactions by time-lapse imaging microscopy in nanowell grids.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions.eLife · 2026Article
- A Modular In-Incubator Microscope for Longitudinal Live Cell Microscopy.bioRxiv : the preprint server for biology · 2026Article
- Label-free TIMING: an efficient, reliable and scalable AI workflow for automated profiling of cell-cell interaction behaviors in nanowell arrays.Frontiers in bioinformatics · 2026Article
- Scalable intracellular delivery via microfluidic vortex shedding enhances the function of chimeric antigen receptor T-cells.Scientific reports · 2025Article
- Molecular dynamics at immune synapse lipid rafts influence the cytolytic behavior of CAR T cells.Science advances · 2025Article
- CD28 Costimulation Augments CAR Signaling in NK Cells via the LCK/CD3ζ/ZAP70 Signaling Axis.Cancer discovery · 2024Article
- Scalable intracellular delivery via microfluidic vortex shedding enhances the function of chimeric antigen receptor T-cells.Research square · 2024Article
- PostFocus: automated selective post-acquisition high-throughput focus restoration using diffusion model for label-free time-lapse microscopy.Bioinformatics (Oxford, England) · 2024Article
- Scalable intracellular delivery via microfluidic vortex shedding enhances the function of chimeric antigen receptor T-cells.bioRxiv : the preprint server for biology · 2024Article
- Decoding the mechanisms of chimeric antigen receptor (CAR) T cell-mediated killing of tumors: insights from granzyme and Fas inhibition.Cell death & disease · 2024Article
- Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.Bioinformatics (Oxford, England) · 2023Article
- Designed improvement to T-cell immunotherapy by multidimensional single cell profiling.Journal for immunotherapy of cancer · 2021Article
- Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images.Journal of clinical medicine · 2019Article
- Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
motivationAutomated profiling of cell-cell interactions from high-throughput time-lapse imaging microscopy data of cells in nanowell grids (TIMING) has led to fundamental insights into cell-cell interactions in immunotherapy. This application note aims to enable widespread adoption of TIMING by (i) enabling the computations to occur on a desktop computer with a graphical processing unit instead of a server; (ii) enabling image acquisition and analysis to occur in the laboratory avoiding network data transfers to/from a server and (iii) providing a comprehensive graphical user interface.
resultsOn a desktop computer, TIMING 2.0 takes 5 s/block/image frame, four times faster than our previous method on the same computer, and twice as fast as our previous method (TIMING) running on a Dell PowerEdge server. The cell segmentation accuracy (f-number = 0.993) is superior to our previous method (f-number = 0.821). A graphical user interface provides the ability to inspect the video analysis results, make corrective edits efficiently (one-click editing of an entire nanowell video sequence in 5-10 s) and display a summary of the cell killing efficacy measurements. AVAILABILITY AND IMPLEMENTATION: Open source Python software (GPL v3 license), instruction manual, sample data and sample results are included with the Supplement (https://github.com/RoysamLab/TIMING2). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.