Isocyanide Substitution in Acridine Orange Shifts DNA Damage-Mediated Phototoxicity to Permeabilization of the Lysosomal Membrane in Cancer Cells

In cancer therapy, immunogenic cell death eliminates tumor cells more efficiently than conventional apoptosis. During photodynamic therapy (PDT), some photosensitizer (PS) targeting lysosomes divert apoptosis to the immunologically more relevant necrosis-like cell death. Acridine orange (AO) is a PS targeting lysosome. We synthesized a new compound, 3-N,N-dimethylamino-6-isocyanoacridine (DM), a modified AO, aiming to target lysosomes better. To compare DM and AO, we studied optical properties, toxicity, cell internalization, and phototoxicity. In addition, light-mediated effects were monitored by the recently developed QUINESIn method on nuclei, and membrane stability, morphology, and function of lysosomes utilizing fluorescent probes by imaging cytometry in single cells. DM proved to be a better lysosomal marker at 405 nm excitation and lysed lysosomes more efficiently. AO injured DNA and histones more extensively than DM. Remarkably, DM’s optical properties helped visualize shockwaves of nuclear DNA released from cells during the PDT. The asymmetric polar modification of the AO leads to a new compound, DM, which has increased efficacy in targeting and disrupting lysosomes. Suitable AO modification may boost adaptive immune response making PDT more efficient.

Spatial analysis for highly multiplexed imaging data to identify tissue microenvironments

Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organisation of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarising the information-rich data generated from these technologies.

CD30-Positive Extracellular Vesicles Enable the Targeting of CD30-Negative DLBCL Cells by the CD30 Antibody-Drug Conjugate Brentuximab Vedotin

CD30, a member of the TNF receptor superfamily, is selectively expressed on a subset of activated lymphocytes and on malignant cells of certain lymphomas, such as classical Hodgkin Lymphoma (cHL), where it activates critical bystander cells in the tumor microenvironment. Therefore, it is not surprising that the CD30 antibody-drug conjugate Brentuximab Vedotin (BV) represents a powerful, FDA-approved treatment option for CD30+ hematological malignancies. However, BV also exerts a strong anti-cancer efficacy in many cases of diffuse large B cell lymphoma (DLBCL) with poor CD30 expression, even when lacking detectable CD30+ tumor cells. The mechanism remains enigmatic. Because CD30 is released on extracellular vesicles (EVs) from both, malignant and activated lymphocytes, we studied whether EV-associated CD30 might end up in CD30– tumor cells to provide binding sites for BV. Notably, CD30+ EVs bind to various DLBCL cell lines as well as to the FITC-labeled variant of the antibody-drug conjugate BV, thus potentially conferring the BV binding also to CD30– cells. Confocal microscopy and imaging cytometry studies revealed that BV binding and uptake depend on CD30+ EVs. Since BV is only toxic toward CD30– DLBCL cells when CD30+ EVs support its uptake, we conclude that EVs not only communicate within the tumor microenvironment but also influence cancer treatment. Ultimately, the CD30-based BV not only targets CD30+ tumor cell but also CD30– DLBCL cells in the presence of CD30+ EVs. Our study thus provides a feasible explanation for the clinical impact of BV in CD30– DLBCL and warrants confirming studies in animal models.

Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy

Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput by the labeling process. We develop a label-free technique that alleviates the physical staining and provides multiplexed readouts via a deep learning–augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that single cell–level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.

Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy

Traditional imaging cytometry uses fluorescence markers to identify specific structures, but is limited in throughput by the labeling process. Here we develop a label-free technique that alleviates the physical staining and provides highly multiplexed readouts via a deep learning-augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts highly accurate subcellular features after training on immunofluorescence images. We demonstrate up to 3× improvement in the prediction accuracy over the state-of-the-art. Beyond fluorescence prediction, we demonstrate that single-cell level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis and DNA synthesis. We further show that the multiplexed readouts enable accurate multi-parametric single-cell profiling across a large cell population. Our method can dramatically improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.