A versatile system to record cell-cell interactions

PUFFFIN: an ultra-bright, customisable, single-plasmid system for labelling cell neighbourhoods

Cell communication coordinates developmental processes, maintains homeostasis, and contributes to disease. Therefore, understanding the relationship between cells in a shared environment is crucial. Here we introduce Positive Ultra-bright Fluorescent Fusion For Identifying Neighbours (PUFFFIN), a cell neighbour-labelling system based upon secretion and uptake of positively supercharged fluorescent protein s36GFP. We fused s36GFP to mNeonGreen or to a HaloTag, facilitating ultra-bright, sensitive, colour-of-choice labelling. Secretor cells transfer PUFFFIN to neighbours while retaining nuclear mCherry, making identification, isolation, and investigation of live neighbours straightforward. PUFFFIN can be delivered to cells, tissues, or embryos on a customisable single-plasmid construct composed of interchangeable components with the option to incorporate any transgene. This versatility enables the manipulation of cell properties, while simultaneously labelling surrounding cells, in cell culture or in vivo. We use PUFFFIN to ask whether pluripotent cells adjust the pace of differentiation to synchronise with their neighbours during exit from naïve pluripotency. PUFFFIN offers a simple, sensitive, customisable approach to profile non-cell-autonomous responses to natural or induced changes in cell identity or behaviour.

Promotion and Detection of Cell-Cell Interactions through a Bioorthogonal Approach

Manipulation of cell-cell interactions via cell surface modification is crucial in tissue engineering and cell-based therapy. To be able to monitor intercellular interactions, it can also provide useful information for understanding how the cells interact and communicate. We report herein a facile bioorthogonal strategy to promote and monitor cell-cell interactions. It involves the use of a maleimide-appended tetrazine-caged boron dipyrromethene (BODIPY)-based fluorescent probe and a maleimide-substituted bicyclo[6.1.0]non-4-yne (BCN) to modify the membrane of macrophage (RAW 264.7) and cancer (HT29, HeLa, and A431) cells, respectively, via maleimide-thiol conjugation. After modification, the two kinds of cells interact strongly through inverse electron-demand Diels-Alder reaction of the surface tetrazine and BCN moieties. The coupling also disrupts the tetrazine quenching unit, restoring the fluorescence emission of the BODIPY core on the cell-cell interface, and promotes phagocytosis. Hence, this approach can promote and facilitate the detection of intercellular interactions, rendering it potentially useful for macrophage-based immunotherapy.

Multiple dyes applications for fluorescent convertible polymer capsules as macrophages tracking labels

Tracing individual cell pathways among the whole population is crucial for understanding their behavior, cell communication, migration dynamics, and fate. Optical labeling is one approach for tracing individual cells, but it typically requires genetic modification to induce the generation of photoconvertible proteins. Nevertheless, this approach has limitations and is not applicable to certain cell types. For instance, genetic modification often leads to the death of macrophages. This study aims to develop an alternative method for labeling macrophages by utilizing photoconvertible micron-sized capsules capable of easy internalization and prolonged retention within cells. Thermal treatment in a polyvinyl alcohol gel medium is employed for the scalable synthesis of capsules with a wide range of fluorescent dyes, including rhodamine 6G, pyronin B, fluorescein, acridine yellow, acridine orange, thiazine red, and previously reported rhodamine B. The fluorescence brightness, photostability, and photoconversion ability of the capsules are evaluated using confocal laser scanning microscopy. Viability, uptake, mobility, and photoconversion studies are conducted on RAW 264.7 and bone marrow-derived macrophages, serving as model cell lines. The production yield of the capsules is increased due to the use of polyvinyl alcohol gel, eliminating the need for conventional filtration steps. Capsules entrapping rhodamine B and rhodamine 6G meet all requirements for intracellular use in individual cell tracking. Mass spectrometry analysis reveals a sequence of deethylation steps that result in blue shifts in the dye spectra upon irradiation. Cellular studies on macrophages demonstrate robust uptake of the capsules. The capsules exhibit minimal cytotoxicity and have a negligible impact on cell motility. The successful photoconversion of RhB-containing capsules within cells highlights their potential as alternatives to photoconvertible proteins for individual cell labeling, with promising applications in personalized medicine.

Utilizing a Proximity Dependent Labeling Strategy to Study Cancer-Immune Intercellular Interactions In Vitro and In Vivo

Immune cells play a critical role in surveilling and defending against cancer, emphasizing the importance of understanding how they interact and communicate with cancer cells to determine cancer status, treatment response, and the formation of the tumor microenvironment (TME). To this end, we conducted a study demonstrating the effectiveness of an enzyme-mediated intercellular proximity labeling (EXCELL) method, which utilizes a modified version of the sortase A enzyme known as mgSrtA, in detecting and characterizing immune-tumor cell interactions. The mgSrtA enzyme is expressed on the membrane of tumor cells, which is able to label immune cells that interact with tumor cells in a proximity-dependent manner. Our research indicates that the EXCELL technique can detect and characterize immune-tumor cell interactions in a time- and concentration-dependent manner, both in vitro and in vivo, without requiring pre-engineering of the immune cells. We also highlight its ability to detect various types of immune cell subpopulations in vivo that have migrated out of the tumor into the spleen, providing insights into the role of peripheral T-cell recruitment in tumor progression. Overall, our findings suggest that the EXCELL method has great potential for improving our understanding of immune cell dynamics within the TME, ultimately leading to more potent pharmacological effects and cancer immunotherapy strategies. SIGNIFICANCE STATEMENT: The enzyme-mediated intercellular proximity labeling method holds promise for detecting immune cell interactions with cancer cells, both in vitro and in vivo. It has important implications for studying immune tumor cell dynamics and potentially uncovering novel subtypes of immune cells within the tumor microenvironment, both prior to and during immunotherapeutic interventions.

Design Parameters for a Mass Cytometry Detectable HaloTag Ligand

Mass cytometry permits the high dimensional analysis of complex biological samples; however, some techniques are not yet integrated into the mass cytometry workflow due to reagent availability. The use of self-labeling protein systems, such as HaloTag, are one such application. Here, we describe the design and implementation of the first mass cytometry ligands for use with HaloTag. "Click"-amenable HaloTag warheads were first conjugated onto poly(l-lysine) or poly(acrylic acid) polymers that were then functionalized with diethylenetriaminepentaacetic acid (DTPA) lutetium metal chelates. Kinetic analysis of the HaloTag labeling rates demonstrated that the structure appended to the 1-chlorohexyl warhead was key to success. A construct with a diethylene glycol spacer appended to a benzamide gave similar rates (kobs ∼ 102 M-1 s-1), regardless of the nature of the polymer. Comparison of the polymer with a small molecule chelate having rapid HaloTag labeling kinetics (kobs ∼ 104 M-1 s-1) suggests the polymers significantly reduced the HaloTag labeling rate. HEK293T cells expressing surface-exposed GFP-HaloTag fusions were labeled with the polymeric constructs and 175Lu content measured by cytometry by time-of-flight (CyTOF). Robust labeling was observed; however, significant nonspecific binding of the constructs to cells was also present. Heavily pegylated polymers demonstrated that nonspecific binding could be reduced to allow cells bearing the HaloTag protein to be distinguished from nonexpressing cells.

Enabling neighbour labelling: using synthetic biology to explore how cells influence their neighbours

Cell-cell interactions are central to development, but exploring how a change in any given cell relates to changes in the neighbour of that cell can be technically challenging. Here, we review recent developments in synthetic biology and image analysis that are helping overcome this problem. We highlight the opportunities presented by these advances and discuss opportunities and limitations in applying them to developmental model systems.

Characterization of an N-Allylglyoxylamide-Based Bioorthogonal Nitrone Trap

Aldehydes are attractive bioorthogonal coupling partners. The ease of manipulation of aldehydes and their orthogonality to other classes of bioorthogonal reactions have inspired the exploration of chemistries, which generate irreversible conjugates. Similarly, nitrones have been shown to be potent 1,3-dipoles in bioorthogonal reactions when paired with strained alkynes. Here, we combine the reactivity of nitrones with the simplicity of aldehydes using an N-allylglyoxylamide, in a cascade reaction with an N-alkylhydroxylamine to produce a bicyclic isoxazolidine. The reaction is found to be catalyzed by 5-methoxyanthranilic acid and proceeds at pH 7 with favorable kinetics. Using the HaloTag7 protein bearing an N-alkylhydroxylamine, we show the reaction to be bioorthogonal in a complex cell lysate and to proceed well at the surface of a HEK293 cell. Furthermore, the reaction is compatible with a typical strain-promoted alkyne-azide click reaction. The characteristics of this reaction suggest it will be a useful addition to the pallet of bioorthogonal reactions that have revolutionized chemical biology.

Quantum Biology and the Potential Role of Entanglement and Tunneling in Non-Targeted Effects of Ionizing Radiation: A Review and Proposed Model

It is well established that cells, tissues, and organisms exposed to low doses of ionizing radiation can induce effects in non-irradiated neighbors (non-targeted effects or NTE), but the mechanisms remain unclear. This is especially true of the initial steps leading to the release of signaling molecules contained in exosomes. Voltage-gated ion channels, photon emissions, and calcium fluxes are all involved but the precise sequence of events is not yet known. We identified what may be a quantum entanglement type of effect and this prompted us to consider whether aspects of quantum biology such as tunneling and entanglement may underlie the initial events leading to NTE. We review the field where it may be relevant to ionizing radiation processes. These include NTE, low-dose hyper-radiosensitivity, hormesis, and the adaptive response. Finally, we present a possible quantum biological-based model for NTE.

The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics

Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic depth. In this review, we discuss the rapidly growing array of computational tools to infer cell-cell interactions from non-spatial single-cell RNA-sequencing and the limited but growing number of methods for spatial transcriptomics data. Downstream analyses of these computational tools and applications to cancer studies are highlighted. We finish by suggesting several directions for further extensions that anticipate the increasing availability of multi-omics cancer data.

Decoding the Complexity of Immune-Cancer Cell Interactions: Empowering the Future of Cancer Immunotherapy

The tumor and tumor microenvironment (TME) consist of a complex network of cells, including malignant, immune, fibroblast, and vascular cells, which communicate with each other. Disruptions in cell-cell communication within the TME, caused by a multitude of extrinsic and intrinsic factors, can contribute to tumorigenesis, hinder the host immune system, and enable tumor evasion. Understanding and addressing intercellular miscommunications in the TME are vital for combating these processes. The effectiveness of immunotherapy and the heterogeneous response observed among patients can be attributed to the intricate cellular communication between immune cells and cancer cells. To unravel these interactions, various experimental, statistical, and computational techniques have been developed. These include ligand-receptor analysis, intercellular proximity labeling approaches, and imaging-based methods, which provide insights into the distorted cell-cell interactions within the TME. By characterizing these interactions, we can enhance the design of cancer immunotherapy strategies. In this review, we present recent advancements in the field of mapping intercellular communication, with a particular focus on immune-tumor cellular interactions. By modeling these interactions, we can identify critical factors and develop strategies to improve immunotherapy response and overcome treatment resistance.

A Comparison of Cell-Cell Interaction Prediction Tools Based on scRNA-seq Data

Computational prediction of cell-cell interactions (CCIs) is becoming increasingly important for understanding disease development and progression. We present a benchmark study of available CCI prediction tools based on single-cell RNA sequencing (scRNA-seq) data. By comparing prediction outputs with a manually curated gold standard for idiopathic pulmonary fibrosis (IPF), we evaluated prediction performance and processing time of several CCI prediction tools, including CCInx, CellChat, CellPhoneDB, iTALK, NATMI, scMLnet, SingleCellSignalR, and an ensemble of tools. According to our results, CellPhoneDB and NATMI are the best performer CCI prediction tools, among the ones analyzed, when we define a CCI as a source-target-ligand-receptor tetrad. In addition, we recommend specific tools according to different types of research projects and discuss the possible future paths in the field.

Engineering Biomimetic Trogocytosis with Farnesylated Chemically Self-Assembled Nanorings

Inspired by the natural intercellular material-transfer process of trans-endocytosis or trogocytosis, we proposed that targeted farnesylated chemically self-assembled nanorings (f-CSANs) could serve as a biomimetic trogocytosis vehicle for engineering directional cargo transfer between cells, thus allowing cell-cell interactions to be monitored and facilitating cell-cell communications. The membranes of sender cells were stably modified by hydrophobic insertion with the targeted f-CSANs, which were efficiently transferred to receiver cells expressing the appropriate receptors by endocytosis. CSAN-assisted cell-cell cargo transfer (C4T) was demonstrated to be receptor specific and dependent on direct cell-cell interactions, the rate of receptor internalization, and the level of receptor expression. In addition, C4T was shown to facilitate cell-to-cell delivery of an apoptosis inducing drug, as wells as antisense oligonucleotides. Taken together, the C4T approach is a potentially versatile biomimetic trogocytosis platform that can be deployed as a macro-chemical biological tool for monitoring cell-cell interactions and engineering cell-cell communications.

Functional Comparison between Endogenous and Synthetic Notch Systems

The Notch pathway converts receptor-ligand interactions at the cell surface into a transcriptional response in the receiver cell. In recent years, synthetic Notch systems (synNotch) that respond to different inputs and transduce different transcriptional responses have been engineered. One class of synNotch systems uses antibody-antigen interactions at the cell surface to induce the proteolytic cleavage cascade of the endogenous Notch autoregulatory core and the consequent release of a synNotch intracellular domain (ICD), converting surface antigen detection into a cellular response. While the activation of endogenous Notch requires ubiquitylation and subsequent endocytosis of the ligand ICD, these synNotch systems do not seem to have such a requirement because the synNotch ligands completely lack an ICD. This observation raises questions about existing models for the synNotch activation mechanism. Here, we test how different structural and biochemical factors affect the dependence of endogenous and synthetic Notch activation on ligand ICD. We compare the behavior of antibody-antigen synNotch (aa-synNotch) to that of endogenous Notch, and to a synNotch system that uses rapamycin induced dimerization of FK506 binding protein (FKBP) and FKBP rapamycin binding (FRB) domaindimerization domains (ff-synNotch), which still requires a ligand ICD. We found that differences in receptor-ligand affinity, in the identity of the transmembrane domain, or in the presence or absence of extracellular epidermal growth factor repeats cannot explain the differences in ligand ICD requirement that distinguishes aa-synNotch from endogenous Notch or ff-synNotch. We also found that unlike endogenous Notch and ff-synNotch, the aa-synNotch system does not exhibit trans-endocytosis of the receptor extracellular domain into the sender cell. These findings suggest that the aa-synNotch systems bypass the ligand ICD requirement because antigen-antibody pairs are able to promote other adhesive cell-cell interactions that provide the mechanical tension needed for ligand activation.

Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks

Robustness in developing and homeostatic tissues is supported by various types of spatiotemporal cell-to-cell interactions. Although live imaging and cell tracking are powerful in providing direct evidence of cell coordination rules, extracting and comparing these rules across many tissues with potentially different length and timescales of coordination requires a versatile framework of analysis. Here we demonstrate that graph neural network (GNN) models are suited for this purpose, by showing how they can be applied to predict cell fate in tissues and utilized to infer the cell interactions governing the multicellular dynamics. Analyzing the live mammalian epidermis data, where spatiotemporal graphs constructed from cell tracks and cell contacts are given as inputs, GNN discovers distinct neighbor cell fate coordination rules that depend on the region of the body. This approach demonstrates how the GNN framework is powerful in inferring general cell interaction rules from live data without prior knowledge of the signaling involved.

During development and homeostasis, cells coordinate with each other to grow, deform, and maintain the tissues. Even with the modern high-throughput cell profiling technologies and high-resolution microscopy, it is still challenging to infer how cell coordination affects the dynamics such as cell fate choice, due to the complexity of the problem and the limited methods to perform perturbation experiments. We here propose a versatile framework of analysis utilizing an interpretable machine learning method based on graph neural network (GNN) which infers the cell-to-cell interaction rules from live images of multicellular dynamics. From the spatiotemporal graphs generated from live images of skin stem cells, we identified previously unaddressed neighbor fate coupling as well as rules consistent with past findings. We further found distinct interaction rules in a different skin region of the body, indicating that our method is useful in probing the diverse mechanism behind the robustness and flexibility in multicellular systems. The GNN framework is applicable for interaction rule discovery for general multicellular dynamics as well as in a wide range of systems where modeling by stochastic interacting agents is effective.

Untangling the web of intratumour heterogeneity

Intratumour heterogeneity (ITH) is a hallmark of cancer that drives tumour evolution and disease progression. Technological and computational advances have enabled us to assess ITH at unprecedented depths, yet this accumulating knowledge has not had a substantial clinical impact. This is in part due to a limited understanding of the functional relevance of ITH and the inadequacy of preclinical experimental models to reproduce it. Here, we discuss progress made in these areas and illuminate future directions.

SyNPL: Synthetic Notch pluripotent cell lines to monitor and manipulate cell interactions in vitro and in vivo

Cell-cell interactions govern differentiation and cell competition in pluripotent cells during early development, but the investigation of such processes is hindered by a lack of efficient analysis tools. Here, we introduce SyNPL: clonal pluripotent stem cell lines that employ optimised Synthetic Notch (SynNotch) technology to report cell-cell interactions between engineered ‘sender’ and ‘receiver’ cells in cultured pluripotent cells and chimaeric mouse embryos. A modular design makes it straightforward to adapt the system for programming differentiation decisions non-cell-autonomously in receiver cells in response to direct contact with sender cells. We demonstrate the utility of this system by enforcing neuronal differentiation at the boundary between two cell populations. In summary, we provide a new adaptation of SynNotch technology that could be used to identify cell interactions and to profile changes in gene or protein expression that result from direct cell-cell contact with defined cell populations in culture and in early embryos, and that can be customised to generate synthetic patterning of cell fate decisions.

Summary: Optimised Synthetic Notch circuitry in mouse pluripotent stem cells provides a modular tool with which to monitor cell-cell interactions and program synthetic patterning of cell fates in culture and in embryos.

Listening in on Multicellular Communication in Human Tissue Immunology

Immune responses in human tissues rely on the concerted action of different cell types. Inter-cellular communication shapes both the function of the multicellular interaction networks and the fate of the individual cells that comprise them. With the advent of new methods to profile and experimentally perturb primary human tissues, we are now in a position to systematically identify and mechanistically dissect these cell-cell interactions and their modulators. Here, we introduce the concept of multicellular hubs, functional modules of immune responses in tissues. We outline a roadmap to discover multicellular hubs in human tissues and discuss how emerging technologies may further accelerate progress in this field.

A light-gated transcriptional recorder for detecting cell-cell contacts

Technologies for detecting cell-cell contacts are powerful tools for studying a wide range of biological processes, from neuronal signaling to cancer-immune interactions within the tumor microenvironment. Here, we report TRACC (Transcriptional Readout Activated by Cell-cell Contacts), a GPCR-based transcriptional recorder of cellular contacts, which converts contact events into stable transgene expression. TRACC is derived from our previous protein-protein interaction recorders, SPARK (Kim et al., 2017) and SPARK2 (Kim et al., 2019), reported in this journal. TRACC incorporates light gating via the light-oxygen-voltage-sensing (LOV) domain, which provides user-defined temporal control of tool activation and reduces background. We show that TRACC detects cell-cell contacts with high specificity and sensitivity in mammalian cell culture and that it can be used to interrogate interactions between neurons and glioma, a form of brain cancer.

Shifting the focus of zebrafish toward a model of the tumor microenvironment

Cancer cells exist in a complex ecosystem with numerous other cell types in the tumor microenvironment (TME). The composition of this tumor/TME ecosystem will vary at each anatomic site and affects phenotypes such as initiation, metastasis, and drug resistance. A mechanistic understanding of the large number of cell-cell interactions between tumor and TME requires models that allow us to both characterize as well as genetically perturb this complexity. Zebrafish are a model system optimized for this problem, because of the large number of existing cell-type-specific drivers that can label nearly any cell in the TME. These include stromal cells, immune cells, and tissue resident normal cells. These cell-type-specific promoters/enhancers can be used to drive fluorophores to facilitate imaging and also CRISPR cassettes to facilitate perturbations. A major advantage of the zebrafish is the ease by which large numbers of TME cell types can be studied at once, within the same animal. While these features make the zebrafish well suited to investigate the TME, the model has important limitations, which we also discuss. In this review, we describe the existing toolset for studying the TME using zebrafish models of cancer and highlight unique biological insights that can be gained by leveraging this powerful resource.

Meaningful connections: Interrogating the role of physical fibroblast cell-cell communication in cancer

As part of the connective tissue, activated fibroblasts play an important role in development and disease pathogenesis, while quiescent resident fibroblasts are responsible for sustaining tissue homeostasis. Fibroblastic activation is particularly evident in the tumor microenvironment where fibroblasts transition into tumor-supporting cancer-associated fibroblasts (CAFs), with some CAFs maintaining tumor-suppressive functions. While the tumor-supporting features of CAFs and their fibroblast-like precursors predominantly function through paracrine chemical communication (e.g., secretion of cytokine, chemokine, and more), the direct cell-cell communication that occurs between fibroblasts and other cells, and the effect that the remodeled CAF-generated interstitial extracellular matrix has in these types of cellular communications, remain poorly understood. Here, we explore the reported roles fibroblastic cell-cell communication play within the cancer stroma context and highlight insights we can gain from other disciplines.

High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers

INTRODUCTION

The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction).

AREA COVERED

In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions.

EXPERT OPINION

The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.

Prospect of cell penetrating peptides in stem cell tracking

Stem cell therapy has shown great efficacy in many diseases. However, the treatment mechanism is still unclear, which is a big obstacle for promoting clinical research. Therefore, it is particularly important to track transplanted stem cells in vivo, find out the distribution and condition of the stem cells, and furthermore reveal the treatment mechanism. Many tracking methods have been developed, including magnetic resonance imaging (MRI), fluorescence imaging, and ultrasound imaging (UI). Among them, MRI and UI techniques have been used in clinical. In stem cell tracking, a major drawback of these technologies is that the imaging signal is not strong enough, mainly due to the low cell penetration efficiency of imaging particles. Cell penetrating peptides (CPPs) have been widely used for cargo delivery due to its high efficacy, good safety properties, and wide delivery of various cargoes. However, there are few reports on the application of CPPs in current stem cell tracking methods. In this review, we systematically introduced the mechanism of CPPs into cell membranes and their advantages in stem cell tracking, discussed the clinical applications and limitations of CPPs, and finally we summarized several commonly used CPPs and their specific applications in stem cell tracking. Although it is not an innovation of tracer materials, CPPs as a powerful tool have broad prospects in stem cell tracking.

Caged Cumate Enables Proximity-Dependent Control Over Gene Expression

Cell-cell interactions underlie diverse physiological processes yet remain challenging to examine with conventional imaging tools. Here we report a novel strategy to illuminate cell proximity using transcriptional activators. We repurposed cumate, a small molecule inducer of gene expression, by caging its key carboxylate group with a nitrile. Nitrilase-expressing activator cells released the cage, liberating cumate for consumption by reporter cells. Reporter cells comprising a cumate-responsive switch expressed a target gene when in close proximity to the activator cells. Overall, this strategy provides a versatile platform to image and potentially manipulate cellular interactions over time.

Recent advances in intravital microscopy for preclinical research

Intravital microscopy (IVM) has revolutionized our understanding of single-cell behavior in complex tissues by enabling real-time observation of molecular and cellular processes in their natural environment. In preclinical research, IVM has emerged as a standard tool for mechanistic studies of therapy response and the rational design of new treatment strategies. Technological developments keep expanding the imaging depth and quality that can be achieved in living tissue, and the maturation of imaging modalities such as fluorescence and phosphorescence lifetime imaging facilitates co-registration of individual cell dynamics with metabolic tissue states. Correlation of IVM with mesoscopic and macroscopic imaging modalities further promotes the translation of mechanistic insights gained by IVM into clinically relevant information. This review highlights some of the recent advances in IVM that have made the transition from experimental optical techniques to practical applications in basic and preclinical research.

Strategies for monitoring cell-cell interactions

Multicellular organisms depend on physical cell-cell interactions to control physiological processes such as tissue formation, neurotransmission and immune response. These intercellular binding events can be both highly dynamic in their duration and complex in their composition, involving the participation of many different surface and intracellular biomolecules. Untangling the intricacy of these interactions and the signaling pathways they modulate has greatly improved insight into the biological processes that ensue upon cell-cell engagement and has led to the development of protein- and cell-based therapeutics. The importance of monitoring physical cell-cell interactions has inspired the development of several emerging approaches that effectively interrogate cell-cell interfaces with molecular-level detail. Specifically, the merging of chemistry- and biology-based technologies to deconstruct the complexity of cell-cell interactions has provided new avenues for understanding cell-cell interaction biology and opened opportunities for therapeutic development.