ArticleJournal of biophotonics2020
Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.
Article in Journal of biophotonics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Illuminating Immunity: A Systematic Review of Immune Cell Autofluorescence.Journal of biophotonics · 2025Pooled it
- Imaging the eye as a window to brain health: frontier approaches and future directions.Journal of neuroinflammation · 2024Review
- Evaluation of Cellpose segmentation with sequential thresholding for instance segmentation of cytoplasms within autofluorescence images.Computers in biology and medicine · 2024Article
- 3D convolutional neural networks predict cellular metabolic pathway use from fluorescence lifetime decay data.APL bioengineering · 2024Article
- Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding.Advanced drug delivery reviews · 2023Review
- Touch-free optical technologies to streamline the production of T cell therapies.Current opinion in biomedical engineering · 2023Article
- Label-free optical imaging of cell function and collagen structure for cell-based therapies.Current opinion in biomedical engineering · 2023Article
- Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts.Scientific reports · 2023Article
- Label-free spatially maintained measurements of metabolic phenotypes in cells.Frontiers in bioengineering and biotechnology · 2023Article
- Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning.PloS one · 2023Article
- An approachable, flexible and practical machine learning workshop for biologists.Bioinformatics (Oxford, England) · 2022Article
- Identification of rare cell populations in autofluorescence lifetime image data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
- Disease-Relevant Single Cell Photonic Signatures Identify S100β Stem Cells and their Myogenic Progeny in Vascular Lesions.Stem cell reviews and reports · 2021Article
- Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications.Journal of biomedical optics · 2020Review
- Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.Journal of biophotonics · 2020Article
- An Analysis of Monitoring Solutions for CAR T Cell Production.Healthcare technology lettersReview
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single-cell images from six donors, we evaluate classifiers ranging from traditional models that use previously-extracted image features to convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter-lab/t-cell-classification.
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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.