Single-cell imaging of CAR T cell activity in vivo reveals extensive functional and anatomical heterogeneity

Cazaux, Marine; Grandjean, Capucine L.; Lemaı̂tre, Fabrice; Garcia, Zacarias; Beck, Richard J.; Milo, Idan; Postat, Jérémy; Beltman, Joost B.; Cheadle, Eleanor J.; Bousso, Philippe

doi:10.1084/jem.20182375

Cited by 114 publications

(130 citation statements)

References 33 publications

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“…[24] Together, our in vitro data and the in vivo model confirm the importance of CAR T trafficking in promoting tumor-cell killing. Our results confirm that trafficking is a robust phenomenon that is independent of CAR T density.…”

Section: Discussionsupporting

confidence: 64%

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Dynamic Profiling of Antitumor Activity of CAR T Cells Using Micropatterned Tumor Arrays

et al. 2019

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Cancer immunotherapy based on the engineering of chimeric antigen receptors (CAR) on T cells has emerged as one of the most promising new therapies for patients with B‐cell malignancies. Preclinical assessments of essential CAR T cell functions such as trafficking and cytotoxicity are critical for accelerating the development of highly effective therapeutic candidates. However, current tools for evaluating CAR‐T functions lack sufficient precision. Here, a micropatterned tumor array (MiTA) is described that enables detailed and dynamic characterization of CAR T cell trafficking toward tumor‐cell islands and subsequent killing of tumor cells. It is shown that CAR T cells often merge into large clusters that envelop and kill the tumor cells with high efficiency. Significant differences are also measured between CAR T cells from different donors and between various CAR T cell constructs. Overall, the assay allows for multifaceted, dynamic, high‐content evaluation of CAR T trafficking, clustering, and killing and could eventually become a useful tool for immune‐oncology research and preclinical assessments of cell‐based immunotherapies.

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Section: Discussionsupporting

confidence: 64%

“…[24] Whether the cluster formation in vivo promotes tumor-cell killing or follows the same dynamics observed in vitro remains to be investigated. We found that the size and morphology of the CAR T cell clusters are correlated with the efficiency of clearance of the tumor cells on the island.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Profiling of Antitumor Activity of CAR T Cells Using Micropatterned Tumor Arrays

et al. 2019

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“…Cells were used on days 4-8, at which time >95% were CD8 + T cells. When indicated, T cells were retrovirally transduced to express mCherry as described 41 . Tumor-bearing mice were adoptively transferred with cultured T cells by intravenous injection of 20 × 10 6 cells.…”

Section: Methodsmentioning

confidence: 99%

“…Tumor-bearing mice were adoptively transferred with cultured T cells by intravenous injection of 20 × 10 6 cells. For experiments using CAR T cells, B cell lymphoma injection, conditioning and CAR T cell transfer were performed as previously described 41 .…”

Section: Methodsmentioning

confidence: 99%

Bystander IFN-γ activity promotes widespread and sustained cytokine signaling altering the tumor microenvironment

et al. 2020

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“…Extensive functional heterogeneity at the single-cell level has been observed in vivo for CAR T cell immunotherapy for B cell lymphoma in mice, with only approximately 20% of CAR T cell-B cell interactions leading to target killing 5 . The difference in killing efficiency is likely due to heterogeneity in cytotoxic potential among the CAR T cells 5 . Due to this T cell heterogeneity, T cell activation and function must be assessed at the single-cell level to fully characterize and optimize immunotherapy effects on T cells.…”

Section: Introductionmentioning

confidence: 99%

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

Wang

Walsh

Skala

et al. 2019

Preprint

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The importance of T cells in immunotherapy has motivated developing technologies to better characterize T cells and improve therapeutic efficacy. One specific 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 of individual cells in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust patterns of T cell activity is computationally challenging in the absence of exogenous labels or information-rich autofluorescence lifetime measurements. We demonstrate that advanced machine learning can accurately classify T cell activity from NAD(P)H intensity images and that those image-based signatures transfer across human donors. Using a dataset of 8,260 cropped single-cell images from six donors, we meticulously evaluate multiple machine learning models. These range from traditional models that represent images using summary statistics or extract image features with CellProfiler to deep 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. However, we observe that fine-tuning all layers of the pre-trained CNN does not provide a classification performance boost commensurate with the additional computational cost. Our software detailing our image processing and model training pipeline is available as Jupyter notebooks at https: //github.com/gitter-lab/t-cell-classification. lifetime imaging to identify macrophages within the tumor microenvironment in vivo 7 and classified activation state of T cells in vitro 6 . The fluorescence lifetime of NAD(P)H and FAD is highly sensitive to the microenvironment and binding of NAD(P)H and FAD. Thus, this fluorescence lifetime can be used to resolve metabolic differences between functional states of immune cells. However, fluorescence lifetime imaging requires specialized and expensive microscope components, limiting its use to particular labs. Although autofluorescence intensity images lack the depth of information provided by the fluorescence lifetime, intensity images can easily and quickly be acquired on almost any commercial fluorescence microscope, allowing widespread adoption and seamless integration of a new technique into existing protocols of live cell assessment. Here, we develop a computational framework that uses autofluorescence intensity images to assess T cell activation state at the single-cell level.Machine learning is promising for classifying cell subtypes from label-free images. For example, Pavillon et al. used regularized logistic regression to predict macrophage activation state 8 , and Yoo...

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Single-cell imaging of CAR T cell activity in vivo reveals extensive functional and anatomical heterogeneity

Abstract: Cazaux et al. use intravital imaging to dissect anti-CD19 CAR T cell activity. This study uncovers both anatomical and functional diversity in the outcome of anti-CD19 CAR T cell interactions with tumor cells impacting engraftment, killing dynamics, and tumor immunoediting.

Cited by 114 publications

References 33 publications

Dynamic Profiling of Antitumor Activity of CAR T Cells Using Micropatterned Tumor Arrays

Dynamic Profiling of Antitumor Activity of CAR T Cells Using Micropatterned Tumor Arrays

Bystander IFN-γ activity promotes widespread and sustained cytokine signaling altering the tumor microenvironment

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

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