ArticleBioinformatics (Oxford, England)2015
Automated profiling of individual cell-cell interactions from high-throughput time-lapse imaging microscopy in nanowell grids (TIMING).
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 57 citations in OpenAlex.
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- 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
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- Scalable intracellular delivery via microfluidic vortex shedding enhances the function of chimeric antigen receptor T-cells.Research square · 2024Article
- Scalable intracellular delivery via microfluidic vortex shedding enhances the function of chimeric antigen receptor T-cells.bioRxiv : the preprint server for biology · 2024Article
- Multidimensional analysis reveals predictive markers for CAR-T efficacy.Nature cancer · 2024Article
- Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data.BMC bioinformatics · 2023Article
- 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
- Microraft arrays for serial-killer CD19 chimeric antigen receptor T cells and single cell isolation.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2023Article
- KIR-based inhibitory CARs overcome CAR-NK cell trogocytosis-mediated fratricide and tumor escape.Nature medicine · 2022Article
- AutoCellANLS: An Automated Analysis System for Mycobacteria-Infected Cells Based on Unstained Micrograph.Biomolecules · 2022Article
- Going with the Flow: Modeling the Tumor Microenvironment Using Microfluidic Technology.Cancers · 2021Review
- The future of microfluidics in immune checkpoint blockade.Cancer gene therapy · 2021Review
- Review
- Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer.Science advances · 2021Article
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- Single-cell transcriptomics identifies multiple pathways underlying antitumor function of TCR- and CD8αβ-engineered human CD4Science advances · 2020Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
motivationThere is a need for effective automated methods for profiling dynamic cell-cell interactions with single-cell resolution from high-throughput time-lapse imaging data, especially, the interactions between immune effector cells and tumor cells in adoptive immunotherapy.
resultsFluorescently labeled human T cells, natural killer cells (NK), and various target cells (NALM6, K562, EL4) were co-incubated on polydimethylsiloxane arrays of sub-nanoliter wells (nanowells), and imaged using multi-channel time-lapse microscopy. The proposed cell segmentation and tracking algorithms account for cell variability and exploit the nanowell confinement property to increase the yield of correctly analyzed nanowells from 45% (existing algorithms) to 98% for wells containing one effector and a single target, enabling automated quantification of cell locations, morphologies, movements, interactions, and deaths without the need for manual proofreading. Automated analysis of recordings from 12 different experiments demonstrated automated nanowell delineation accuracy >99%, automated cell segmentation accuracy >95%, and automated cell tracking accuracy of 90%, with default parameters, despite variations in illumination, staining, imaging noise, cell morphology, and cell clustering. An example analysis revealed that NK cells efficiently discriminate between live and dead targets by altering the duration of conjugation. The data also demonstrated that cytotoxic cells display higher motility than non-killers, both before and during contact. CONTACT: broysam@central.uh.edu or nvaradar@central.uh.edu 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.