TIMING 2.0: high-throughput single-cell profiling of dynamic cell–cell interactions by time-lapse imaging microscopy in nanowell grids

Lu, Hengyang; Li, Jiabing; Martínez-Paniagua, Melisa; Bandey, Irfan N; Amritkar, Amit; Singh, Harjeet; Mayerich, David; Varadarajan, Navin; Roysam, Badrinath

doi:10.1093/bioinformatics/bty676

Cited by 12 publications

(14 citation statements)

References 3 publications

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“…Image processing, cell segmentation, and tracking, and data analytics Image analysis and cell segmentation/tracking were performed as described by us previously. 51 Only the nanowells containing the T cells that were selected for single-cell reverse transcription (RT)-qPCR were processed for image processing and cell segmentation, offline and after the experiment, and spatio-temporal measurement of cell interactions were extracted.…”

Section: Open Accessmentioning

confidence: 99%

Designed improvement to T-cell immunotherapy by multidimensional single cell profiling

Bandey

Adolacion

Romain

et al. 2021

J Immunother Cancer

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BackgroundAdoptive cell therapy based on the infusion of chimeric antigen receptor (CAR) T cells has shown remarkable efficacy for the treatment of hematologic malignancies. The primary mechanism of action of these infused T cells is the direct killing of tumor cells expressing the cognate antigen. However, understanding why only some T cells are capable of killing, and identifying mechanisms that can improve killing has remained elusive.MethodsTo identify molecular and cellular mechanisms that can improve T-cell killing, we utilized integrated high-throughput single-cell functional profiling by microscopy, followed by robotic retrieval and transcriptional profiling.ResultsWith the aid of mathematical modeling we demonstrate that non-killer CAR T cells comprise a heterogeneous population that arise from failure in each of the discrete steps leading to the killing. Differential transcriptional single-cell profiling of killers and non-killers identified CD137 as an inducible costimulatory molecule upregulated on killer T cells. Our single-cell profiling results directly demonstrate that inducible CD137 is feature of killer (and serial killer) T cells and this marks a different subset compared with the CD107apos (degranulating) subset of CAR T cells. Ligation of the induced CD137 with CD137 ligand (CD137L) leads to younger CD19 CAR T cells with sustained killing and lower exhaustion. We genetically modified CAR T cells to co-express CD137L, in trans, and this lead to a profound improvement in anti-tumor efficacy in leukemia and refractory ovarian cancer models in mice.ConclusionsBroadly, our results illustrate that while non-killer T cells are reflective of population heterogeneity, integrated single-cell profiling can enable identification of mechanisms that can enhance the function/proliferation of killer T cells leading to direct anti-tumor benefit.

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Section: Open Accessmentioning

confidence: 99%

Designed improvement to T-cell immunotherapy by multidimensional single cell profiling

Bandey

Adolacion

Romain

et al. 2021

J Immunother Cancer

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“…It is essential to be able to profile the functionality of the genetically modified cells prior to infusion and this, in turn, implies the ability to study the many different aspects of dynamic T-cell function including cytokine secretion and cytotoxicity [33,34]. One of the major challenges to studying cellular function in a dynamic manner using timelapse imaging is the ability to have robust algorithms that are able to segment and track the cells accurately [35]. Our paper demonstrates a critical advance in this domain by enabling the detection of not just cellular boundaries but also nuclear boundaries within individual cells.…”

Section: Discussionmentioning

confidence: 99%

Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images

Yuan

Rezvan

et al. 2019

JCM

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Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling.

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“…After loading the cells to the nanowell chips at the concentration of 2 million effector cells and 1 million target cells/ml, we applied a fluorescent apoptosis marker by immersing the entire chip into phenol red-free cell-culture media containing Annexin-V, conjugated to fluorophore Alexa Fluor 647 (from Life Technologies), at a dilution of 1:60. Our imaging system, Time-lapse Imaging Microscopy In Nanowell Grids (TIMING) ( Merouane et al 2015 , Lu et al 2019 ), is a high-throughput, multi-channel profiling approach to time-lapse images at the single-cell level. An Axio fluorescent microscope (Carl Zeiss), equipped with 20× 0.8 NA objective and a scientific CMOS camera (Orca Flash 4.0), took images of the chip every 5 min in a humidity/CO 2 controlled chamber.…”

Section: Methodsmentioning

confidence: 99%

“…This success has sparked a strong interest in evaluating therapeutics for other types of cancer (including solid tumors) and personalizing treatments ( Marofi et al 2021 ). In this context, the advancement of high-throughput assays is crucial for detailed profiling of cellular activities, especially cell–cell interactions and killing events, for advancing cancer immunotherapy ( Ramm 1999 , Merouane et al 2015 , Lu et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks

Wu,

Martinez-Paniagua,

Reichel

et al. 2023

Bioinformatics

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Motivation Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell–cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner. Results Our trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining. Availability and implementation Open-source code and sample data provided at https://github.com/kwu14victor/ApoBDproject.

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TIMING 2.0: high-throughput single-cell profiling of dynamic cell–cell interactions by time-lapse imaging microscopy in nanowell grids

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 12 publications

References 3 publications

Designed improvement to T-cell immunotherapy by multidimensional single cell profiling

Designed improvement to T-cell immunotherapy by multidimensional single cell profiling

Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks

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