Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells

Label-free three-dimensional imaging and quantitative analysis of living fibroblasts and myofibroblasts by holotomographic microscopy

Holotomography (HT) is a cutting-edge fast live-cell quantitative label-free imaging technique. Based on the principle of quantitative phase imaging, it combines holography and tomography to record a three-dimensional map of the refractive index, used as intrinsic optical and quantitative imaging contrast parameter of biological samples, at a sub-micrometer spatial resolution. In this study HT has been employed for the first time to analyze the changes of fibroblasts differentiating towards myofibroblasts - recognized as the main cell player of fibrosis - when cultured in vitro with the pro-fibrotic factor, namely transforming growth factor-β1. In parallel, F-actin, vinculin, α-smooth muscle actin, phospho-myosin light chain 2, type-1 collagen, peroxisome proliferator-activated receptor-gamma coactivator-1α expression and mitochondria were evaluated by confocal laser scanning microscopy. Plasmamembrane passive properties and transient receptor potential canonical channels' currents were also recorded by whole-cell patch-clamp. The fluorescence images and electrophysiological results have been compared to the data obtained by HT and their congruence has been discussed. HT turned out to be a valid approach to morphologically distinguish fibroblasts from well differentiated myofibroblasts while obtaining objective measures concerning volume, surface area, projection area, surface index and dry mass (i.e., the mass of the non-aqueous content inside the cell including proteins and subcellular organelles) of the entire cell, nuclei and nucleoli with the major advantage to monitor outer and inner features in living cells in a non-invasive, rapid and label-free approach. HT might open up new research opportunities in the field of fibrotic diseases. RESEARCH HIGHLIGHTS: Holotomography (HT) is a label-free laser interferometric imaging technology exploiting the intrinsic optical property of cells namely refractive index (RI) to enable a direct imaging and analysis of whole cells or intracellular organelles. HT turned out a valid approach to distinguish morphological features of living unlabeled fibroblasts from differentiated myofibroblasts. HT provided quantitative information concerning volume, surface area, projection area, surface index and dry mass of the entire fibroblasts/myofibroblasts, nuclei and nucleoli.

3D live imaging and phenotyping of CAR-T cell mediated-cytotoxicity using high-throughput Bessel oblique plane microscopy

Clarification of the cytotoxic function of T cells is crucial for understanding human immune responses and immunotherapy procedures. Here, we report a high-throughput Bessel oblique plane microscopy (HBOPM) platform capable of 3D live imaging and phenotyping of chimeric antigen receptor (CAR)-modified T-cell cytotoxicity against cancer cells. The HBOPM platform has the following characteristics: an isotropic subcellular resolution of 320 nm, large-scale scouting over 400 interacting cell pairs, long-term observation across 5 hours, and quantitative analysis of the Terabyte-scale 3D, multichannel, time-lapse image datasets. Using this advanced microscopy platform, several key subcellular events in CAR-T cells are captured and comprehensively analyzed; these events include the instantaneous formation of immune synapses and the sustained changes in the microtubing morphology. Furthermore, we identify the actin retrograde flow speed, the actin depletion coefficient, the microtubule polarization and the contact area of the CAR-T/target cell conjugates as essential parameters strongly correlated with CAR-T-cell cytotoxic function. Our approach will be useful for establishing criteria for quantifying T-cell function in individual patients for all T-cell-based immunotherapies.

In silico labeling in cell biology: Potential and limitations

In silico labeling is the computational cross-modality image translation where the output modality is a subcellular marker that is not specifically encoded in the input image, for example, in silico localization of organelles from transmitted light images. In principle, in silico labeling has the potential to facilitate rapid live imaging of multiple organelles with reduced photobleaching and phototoxicity, a technology enabling a major leap toward understanding the cell as an integrated complex system. However, five years have passed since feasibility was attained, without any demonstration of using in silico labeling to uncover new biological insight. In here, we discuss the current state of in silico labeling, the limitations preventing it from becoming a practical tool, and how we can overcome these limitations to reach its full potential.

Imaging-Based Efficacy Evaluation of Cancer Immunotherapy in Engineered Tumor Platforms and Tumor Organoids

Cancer immunotherapy is used to treat tumors by modulating the immune system. Although the anticancer efficacy of cancer immunotherapy has been evaluated prior to clinical trials, conventional in vivo animal and endpoint models inadequately replicate the intricate process of tumor elimination and reflect human-specific immune systems. Therefore, more sophisticated models that mimic the complex tumor-immune microenvironment must be employed to assess the effectiveness of immunotherapy. Additionally, using real-time imaging technology, a step-by-step evaluation can be applied, allowing for a more precise assessment of treatment efficacy. Here, an overview of the various imaging-based evaluation platforms recently developed for cancer immunotherapeutic applications is presented. Specifically, a fundamental technique is discussed for stably observing immune cell-based tumor cell killing using direct imaging, a microwell that reproduces a confined space for spatial observation, a droplet assay that facilitates cell-cell interactions, and a 3D microphysiological system that reconstructs the vascular environment. Furthermore, it is suggested that future evaluation platforms pursue more human-like immune systems.

Newer generations of multi-target CAR and STAb-T immunotherapeutics: NEXT CART Consortium as a cooperative effort to overcome current limitations

Adoptive T cellular immunotherapies have emerged as relevant approaches for treating cancer patients who have relapsed or become refractory (R/R) to traditional cancer treatments. Chimeric antigen receptor (CAR) T-cell therapy has improved survival in various hematological malignancies. However, significant limitations still impede the widespread adoption of these therapies in most cancers. To advance in this field, six research groups have created the "NEXT Generation CART MAD Consortium" (NEXT CART) in Madrid's Community, which aims to develop novel cell-based immunotherapies for R/R and poor prognosis cancers. At NEXT CART, various basic and translational research groups and hospitals in Madrid concur to share and synergize their basic expertise in immunotherapy, gene therapy, and immunological synapse, and clinical expertise in pediatric and adult oncology. NEXT CART goal is to develop new cell engineering approaches and treatments for R/R adult and pediatric neoplasms to evaluate in multicenter clinical trials. Here, we discuss the current limitations of T cell-based therapies and introduce our perspective on future developments. Advancement opportunities include developing allogeneic products, optimizing CAR signaling domains, combining cellular immunotherapies, multi-targeting strategies, and improving tumor-infiltrating lymphocytes (TILs)/T cell receptor (TCR) therapy. Furthermore, basic studies aim to identify novel tumor targets, tumor molecules in the tumor microenvironment that impact CAR efficacy, and strategies to enhance the efficiency of the immunological synapse between immune and tumor cells. Our perspective of current cellular immunotherapy underscores the potential of these treatments while acknowledging the existing hurdles that demand innovative solutions to develop their potential for cancer treatment fully.

High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy

A critical requirement for studying cell mechanics is three-dimensional assessment of cellular shapes and forces with high spatiotemporal resolution. Traction force microscopy with fluorescence imaging enables the measurement of cellular forces, but it is limited by photobleaching and a slow acquisition speed. Here, we present refractive-index traction force microscopy (RI-TFM), which simultaneously quantifies the volumetric morphology and traction force of cells using a high-speed illumination scheme with 0.5-Hz temporal resolution. Without labelling, our method enables quantitative analyses of dry-mass distributions and shear (in-plane) and normal (out-of-plane) tractions of single cells on the extracellular matrix. When combined with a constrained total variation-based deconvolution algorithm, it provides 0.55-Pa shear and 1.59-Pa normal traction sensitivity for a 1-kPa hydrogel substrate. We demonstrate its utility by assessing the effects of compromised intracellular stress and capturing the rapid dynamics of cellular junction formation in the spatiotemporal changes in non-planar traction components.

Enhanced functionalities of immune cells separated by a microfluidic lattice: assessment based on holotomography

The isolation of white blood cells (WBCs) from whole blood constitutes a pivotal process for immunological studies, diagnosis of hematologic disorders, and the facilitation of immunotherapy. Despite the ubiquity of density gradient centrifugation in WBC isolation, its influence on WBC functionality remains inadequately understood. This research employs holotomography to explore the effects of two distinct WBC separation techniques, namely conventional centrifugation and microfluidic separation, on the functionality of the isolated cells. We utilize three-dimensional refractive index distribution and time-lapse dynamics to analyze individual WBCs in-depth, focusing on their morphology, motility, and phagocytic capabilities. Our observations highlight that centrifugal processes negatively impact WBC motility and phagocytic capacity, whereas microfluidic separation yields a more favorable outcome in preserving WBC functionality. These findings emphasize the potential of microfluidic separation techniques as a viable alternative to traditional centrifugation for WBC isolation, potentially enabling more precise analyses in immunology research and improving the accuracy of hematologic disorder diagnoses.

Artificial intelligence-enabled quantitative phase imaging methods for life sciences

Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.

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

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.

Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images

Three-dimensional, quantitative imaging of biological cells and their internal structures performed by optical diffraction tomography (ODT) is an important part of biomedical research. However, conducting quantitative analysis of ODT images requires performing 3D segmentation with high accuracy, often unattainable with available segmentation methods. Therefore, in this work, we present a new semi-automatic method, called ODT-SAS, which combines several non-machine-learning techniques to segment cells and 2 types of their organelles: nucleoli and lipid structures (LS). ODT-SAS has been compared with Cellpose and slice-by-slice manual segmentation, respectively, in cell segmentation and organelles segmentation. The comparison shows superiority of ODT-SAS over Cellpose and reveals the potential of our technique in detecting cells, nucleoli and LS.

Elaborating the potential of Artificial Intelligence in automated CAR-T cell manufacturing

This paper discusses the challenges of producing CAR-T cells for cancer treatment and the potential for Artificial Intelligence (AI) for its improvement. CAR-T cell therapy was approved in 2018 as the first Advanced Therapy Medicinal Product (ATMP) for treating acute leukemia and lymphoma. ATMPs are cell- and gene-based therapies that show great promise for treating various cancers and hereditary diseases. While some new ATMPs have been approved, ongoing clinical trials are expected to lead to the approval of many more. However, the production of CAR-T cells presents a significant challenge due to the high costs associated with the manufacturing process, making the therapy very expensive (approx. $400,000). Furthermore, autologous CAR-T therapy is limited to a make-to-order approach, which makes scaling economical production difficult. First attempts are being made to automate this multi-step manufacturing process, which will not only directly reduce the high manufacturing costs but will also enable comprehensive data collection. AI technologies have the ability to analyze this data and convert it into knowledge and insights. In order to exploit these opportunities, this paper analyses the data potential in the automated CAR-T production process and creates a mapping to the capabilities of AI applications. The paper explores the possible use of AI in analyzing the data generated during the automated process and its capabilities to further improve the efficiency and cost-effectiveness of CAR-T cell production.

Hybrid machine-learning framework for volumetric segmentation and quantification of vacuoles in individual yeast cells using holotomography

The precise, quantitative evaluation of intracellular organelles in three-dimensional (3D) imaging data poses a significant challenge due to the inherent constraints of traditional microscopy techniques, the requirements of the use of exogenous labeling agents, and existing computational methods. To counter these challenges, we present a hybrid machine-learning framework exploiting correlative imaging of 3D quantitative phase imaging with 3D fluorescence imaging of labeled cells. The algorithm, which synergistically integrates a random-forest classifier with a deep neural network, is trained using the correlative imaging data set, and the trained network is then applied to 3D quantitative phase imaging of cell data. We applied this method to live budding yeast cells. The results revealed precise segmentation of vacuoles inside individual yeast cells, and also provided quantitative evaluations of biophysical parameters, including volumes, concentration, and dry masses of automatically segmented vacuoles.

Mapping nanoscale topographic features in thick tissues with speckle diffraction tomography

Resolving three-dimensional morphological features in thick specimens remains a significant challenge for label-free imaging. We report a new speckle diffraction tomography (SDT) approach that can image thick biological specimens with ~500 nm lateral resolution and ~1 μm axial resolution in a reflection geometry. In SDT, multiple-scattering background is rejected through spatiotemporal gating provided by dynamic speckle-field interferometry, while depth-resolved refractive index maps are reconstructed by developing a comprehensive inverse-scattering model that also considers specimen-induced aberrations. Benefiting from the high-resolution and full-field quantitative imaging capabilities of SDT, we successfully imaged red blood cells and quantified their membrane fluctuations behind a turbid medium with a thickness of 2.8 scattering mean-free paths. Most importantly, we performed volumetric imaging of cornea inside an ex vivo rat eye and quantified its optical properties, including the mapping of nanoscale topographic features of Dua's and Descemet's membranes that had not been previously visualized.

Deep learning-based label-free hematology analysis framework using optical diffraction tomography

Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.

Targeted Enzyme Activity Imaging with Quantitative Phase Microscopy

Quantitative phase imaging (QPI) is a powerful optical imaging modality for label-free, rapid, and three-dimensional (3D) monitoring of cells and tissues. However, molecular imaging of important intracellular biomolecules such as enzymes remains a largely unexplored area for QPI. Herein, we introduce a fundamentally new approach by designing QPI contrast agents that allow sensitive detection of intracellular biomolecules. We report a new class of bio-orthogonal QPI-nanoprobes for in situ high-contrast refractive index (RI) imaging of enzyme activity. The nanoprobes feature silica nanoparticles (SiO2 NPs) having higher RI than endogenous cellular components and surface-anchored cyanobenzothiazole-cysteine (CBT-Cys) conjugated enzyme-responsive peptide sequences. The nanoprobes specifically aggregated in cells with target enzyme activity, increasing intracellular RI and enabling precise visualization of intracellular enzyme activity. We envision that this general design of QPI-nanoprobes could open doors for spatial-temporal mapping of enzyme activity with direct implications for disease diagnosis and evaluating the therapeutic efficacy.

Single-shot refractive index slice imaging using spectrally multiplexed optical transfer function reshaping

The refractive index (RI) of cells and tissues is crucial in pathophysiology as a noninvasive and quantitative imaging contrast. Although its measurements have been demonstrated using three-dimensional quantitative phase imaging methods, these methods often require bulky interferometric setups or multiple measurements, which limits the measurement sensitivity and speed. Here, we present a single-shot RI imaging method that visualizes the RI of the in-focus region of a sample. By exploiting spectral multiplexing and optical transfer function engineering, three color-coded intensity images of a sample with three optimized illuminations were simultaneously obtained in a single-shot measurement. The measured intensity images were then deconvoluted to obtain the RI image of the in-focus slice of the sample. As a proof of concept, a setup was built using Fresnel lenses and a liquid-crystal display. For validation purposes, we measured microspheres of known RI and cross-validated the results with simulated results. Various static and highly dynamic biological cells were imaged to demonstrate that the proposed method can conduct single-shot RI slice imaging of biological samples with subcellular resolution.

Optical trapping with holographically structured light for single-cell studies

A groundbreaking work in 1970 by Arthur Ashkin paved the way for developing various optical trapping techniques. Optical tweezers have become an established method for the manipulation of biological objects, due to their noninvasiveness and precise controllability. Recent innovations are accelerating and now enable single-cell manipulation through holographic light structuring. In this review, we provide an overview of recent advances in optical tweezer techniques for studies at the individual cell level. Our review focuses on holographic optical tweezers that utilize active spatial light modulators to noninvasively manipulate live cells. The versatility of the technology has led to valuable integrations with microscopy, microfluidics, and biotechnological techniques for various single-cell studies. We aim to recapitulate the basic principles of holographic optical tweezers, highlight trends in their biophysical applications, and discuss challenges and future prospects.

Artificial confocal microscopy for deep label-free imaging

Widefield microscopy of optically thick specimens typically features reduced contrast due to "spatial crosstalk", in which the signal at each point in the field of view is the result of a superposition from neighbouring points that are simultaneously illuminated. In 1955, Marvin Minsky proposed confocal microscopy as a solution to this problem. Today, laser scanning confocal fluorescence microscopy is broadly used due to its high depth resolution and sensitivity, but comes at the price of photobleaching, chemical, and photo-toxicity. Here, we present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity, on unlabeled specimens, nondestructively. We equipped a commercial laser scanning confocal instrument with a quantitative phase imaging module, which provides optical path-length maps of the specimen in the same field of view as the fluorescence channel. Using pairs of phase and fluorescence images, we trained a convolution neural network to translate the former into the latter. The training to infer a new tag is very practical as the input and ground truth data are intrinsically registered, and the data acquisition is automated. The ACM images present significantly stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and 3D liver cancer spheroids. By training on nucleus-specific tags, ACM allows for segmenting individual nuclei within dense spheroids for both cell counting and volume measurements. In summary, ACM can provide quantitative, dynamic data, nondestructively from thick samples, while chemical specificity is recovered computationally.

Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D

Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study's objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows.

Optical diffraction tomography and image reconstruction to measure host cell alterations caused by divergent Plasmodium species

Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium. Understanding the biological features of various parasite forms is important for the optical diagnosis and defining pathological states, which are often constrained by the lack of ambient visualization approaches. Here, we employ a label-free tomographic technique to visualize the host red blood cell (RBC) remodeling process and quantify changes in biochemical properties arising from parasitization. Through this, we provide a quantitative body of information pertaining to the influence of host cell environment on growth, survival, and replication of P. falciparum and P. vivax in their respective host cells: mature erythrocytes and young reticulocytes. These exquisite three-dimensional measurements of infected red cells demonstrats the potential of evolving 3D imaging to advance our understanding of Plasmodium biology and host-parasite interactions.

High-fidelity optical diffraction tomography of live organisms using iodixanol refractive index matching

Optical diffraction tomography (ODT) enables the three-dimensional (3D) refractive index (RI) reconstruction. However, when the RI difference between a sample and a medium increases, the effects of light scattering become significant, preventing the acquisition of high-quality and accurate RI reconstructions. Herein, we present a method for high-fidelity ODT by introducing non-toxic RI matching media. Optimally reducing the RI contrast enhances the fidelity and accuracy of 3D RI reconstruction, enabling visualization of the morphology and intra-organization of live biological samples without producing toxic effects. We validate our method using various biological organisms, including C. albicans and C. elegans.

Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging

Complex intracellular organizations are commonly represented by dividing the metabolic process of cells into different organelles. Therefore, identifying sub-cellular organelle architecture is significant for understanding intracellular structural properties, specific functions, and biological processes in cells. However, the discrimination of these structures in the natural organizational environment and their functional consequences are not clear. In this article, we propose a new pixel-level multimodal fusion (PLMF) deep network which can be used to predict the location of cellular organelle using label-free cell optical microscopy images followed by deep-learning-based automated image denoising. It provides valuable insights that can be of tremendous help in improving the specificity of label-free cell optical microscopy by using the Transformer-Unet network to predict the ground truth imaging which corresponds to different sub-cellular organelle architectures. The new prediction method proposed in this article combines the advantages of a transformer's global prediction and CNN's local detail analytic ability of background features for label-free cell optical microscopy images, so as to improve the prediction accuracy. Our experimental results showed that the PLMF network can achieve over 0.91 Pearson's correlation coefficient (PCC) correlation between estimated and true fractions on lung cancer cell-imaging datasets. In addition, we applied the PLMF network method on the cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new way for the time-resolved study of subcellular components in different cells, especially for cancer cells.

Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

A Novel Peptide-MHC Targeted Chimeric Antigen Receptor T Cell Forms a T Cell-like Immune Synapse

Chimeric Antigen Receptor (CAR) T cell therapy is a promising form of adoptive cell therapy that re-engineers patient-derived T cells to express a hybrid receptor specific to a tumour-specific antigen of choice. Many well-characterised tumour antigens are intracellular and therefore not accessible to antibodies at the cell surface. Therefore, the ability to target peptide-MHC tumour targets with antibodies is key for wider applicability of CAR T cell therapy in cancer. One way to evaluate the effectiveness and efficiency of ligating tumour target cells is studying the immune synapse. Here we generated a second-generation CAR to targeting the HLA-A*02:01 restricted H3.3K27M epitope, identified as a possible therapeutic target in ~75% of diffuse midline gliomas, used as a model antigen to study the immune synapse. The pMHCI-specific CAR demonstrated specificity, potent activation, cytokine secretion and cytotoxic function. Furthermore, we characterised killing kinetics using live cell imaging as well as CAR synapse confocal imaging. Here we provide evidence of robust CAR targeting of a model peptide-MHC antigen and that, in contrast to protein-specific CARs, these CARs form a TCR-like immune synapse which facilitates TCR-like killing kinetics.

Single Cell Technologies to Dissect Heterogenous Immune Cell Therapy Products

Single cell tools have dramatically transformed the life sciences; concurrently, autologous and allogeneic immune cell therapies have recently entered the clinic. Here we discuss methods, applications, and considerations for single cell technologies in the context of immune cell manufacturing. Molecular heterogeneity can be profiled at the level of the genome, epigenome, transcriptome, proteome, metabolome, and antigen receptor repertoire, in isolation or in tandem through multi-omic approaches. Such data inform heterogeneity within cell products and can be linked to potency readouts and clinical data, with the ultimate goal of identifying Critical Quality Attributes to predict patient outcomes. Non-destructive approaches hold promise for monitoring cell state and analyzing the impacts of gene edits within engineered products. Destructive omics approaches could be combined with non-destructive technologies to predict therapeutic potency. These technologies are poised to redefine cell manufacturing toward rapid, cost-effective, and high-throughput methods to detect and respond to dynamic cell states.

Roadmap on Digital Holography-Based Quantitative Phase Imaging

Quantitative Phase Imaging (QPI) provides unique means for the imaging of biological or technical microstructures, merging beneficial features identified with microscopy, interferometry, holography, and numerical computations. This roadmap article reviews several digital holography-based QPI approaches developed by prominent research groups. It also briefly discusses the present and future perspectives of 2D and 3D QPI research based on digital holographic microscopy, holographic tomography, and their applications.

Label-free monitoring of 3D cortical neuronal growth in vitro using optical diffraction tomography

The highly complex central nervous systems of mammals are often studied using three-dimensional (3D) in vitro primary neuronal cultures. A coupled confocal microscopy and immunofluorescence labeling are widely utilized for visualizing the 3D structures of neurons. However, this requires fixation of the neurons and is not suitable for monitoring an identical sample at multiple time points. Thus, we propose a label-free monitoring method for 3D neuronal growth based on refractive index tomograms obtained by optical diffraction tomography. The 3D morphology of the neurons was clearly visualized, and the developmental processes of neurite outgrowth in 3D spaces were analyzed for individual neurons.

Fly for ALS: Drosophila modeling on the route to amyotrophic lateral sclerosis modifiers

Amyotrophic lateral sclerosis (ALS) is a rare, devastating disease, causing movement impairment, respiratory failure and ultimate death. A plethora of genetic, cellular and molecular mechanisms are involved in ALS signature, although the initiating causes and progressive pathological events are far from being understood. Drosophila research has produced seminal discoveries for more than a century and has been successfully used in the past 25 years to untangle the process of ALS pathogenesis, and recognize potential markers and novel strategies for therapeutic solutions. This review will provide an updated view of several ALS modifiers validated in C9ORF72, SOD1, FUS, TDP-43 and Ataxin-2 Drosophila models. We will discuss basic and preclinical findings, illustrating recent developments and novel breakthroughs, also depicting unsettled challenges and limitations in the Drosophila-ALS field. We intend to stimulate a renewed debate on Drosophila as a screening route to identify more successful disease modifiers and neuroprotective agents.

Next-Generation Implementation of Chimeric Antigen Receptor T-Cell Therapy Using Digital Health

Chimeric antigen receptor T-cell (CAR-T) therapy is a paradigm-shifting immunotherapy modality in oncology; however, unique toxicities such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome limit its ability to be implemented more widely in the outpatient setting or at smaller-volume centers. Three operational challenges with CAR-T therapy include the following: (1) the logistics of toxicity monitoring, ie, with frequent vital sign checks and neurologic assessments; (2) the specialized knowledge required for toxicity management, particularly with regard to CRS and immune effector cell-associated neurotoxicity syndrome; and (3) the need for high-quality symptomatic and supportive care during this intensive period. In this review, we explore potential niches for digital innovations that can improve the implementation of CAR-T therapy in each of these domains. These tools include patient-facing technologies and provider-facing platforms: for example, wearable devices and mobile health apps to screen for fevers and encephalopathy, electronic patient-reported outcome assessments-based workflows to assist with symptom management, machine learning algorithms to predict emerging CRS in real time, clinical decision support systems to assist with toxicity management, and digital coaching to help maintain wellness. Televisits, which have grown in prominence since the novel coronavirus pandemic, will continue to play a key role in the monitoring and management of CAR-T-related toxicities as well. Limitations of these strategies include the need to ensure care equity and stakeholder buy-in, both operationally and financially. Nevertheless, once developed and validated, the next-generation implementation of CAR-T therapy using these digital tools may improve both its safety and accessibility.

Isotropically resolved label-free tomographic imaging based on tomographic moulds for optical trapping

A major challenge in three-dimensional (3D) microscopy is to obtain accurate spatial information while simultaneously keeping the microscopic samples in their native states. In conventional 3D microscopy, axial resolution is inferior to spatial resolution due to the inaccessibility to side scattering signals. In this study, we demonstrate the isotropic microtomography of free-floating samples by optically rotating a sample. Contrary to previous approaches using optical tweezers with multiple foci which are only applicable to simple shapes, we exploited 3D structured light traps that can stably rotate freestanding complex-shaped microscopic specimens, and side scattering information is measured at various sample orientations to achieve isotropic resolution. The proposed method yields an isotropic resolution of 230 nm and captures structural details of colloidal multimers and live red blood cells, which are inaccessible using conventional tomographic microscopy. We envision that the proposed approach can be deployed for solving diverse imaging problems that are beyond the examples shown here.

Interleukin-18 and -10 may be associated with lymph node metastasis in breast cancer

Reports on the expression of interleukin (IL)-10 in breast cancer are rare. The present study investigated the correlation between IL-18 and −10 in breast cancer, and assessed their clinical significance. Breast cancer (n=104) and breast fibroadenoma (n=31) tissues that were surgically removed and pathologically confirmed at Jinan Central Hospital Affiliated to Shandong University (Jinan, China) between November 2016 and January 2019 were collected. The expression of IL-18 and −10 was observed via immunohistochemistry. Breast cancer tissues were positive for IL-18 expression, which was primarily located in the cell membrane and cytoplasm. A significant difference in IL-18 expression was observed between breast cancer and fibroadenoma tissues (75.0 vs. 19.4%; P<0.001). IL-10 was expressed in breast cancer tissues and primarily located in the cytoplasm. Breast cancer tissues showed a significantly higher level of IL-10 expression compared with breast fibroadenoma tissues (78.8 vs. 22.6%; P<0.001). The regions of positive IL-18 and −10 expression were consistent. Tissues with positive expression of IL-18 and/or −10 had a significantly higher rate of lymph node metastasis than those with negative expression (IL-18: 67.9 vs. 42.3%; P=0.035; and IL-10: 67.1 vs. 40.9%; P=0.047). In conclusion, IL-18 is highly expressed in breast cancer and correlates positively with IL-10. Both IL-18 and −10 may correlate positively with lymph node metastasis in breast cancer.