Deciphering cell signaling networks with massively multiplexed biosensor barcoding

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

An optimized fluorescent biosensor for monitoring long-chain fatty acyl-CoAs metabolism in vivo

Long-chain fatty acyl-CoAs (LCACoAs) are intermediates in lipid metabolism that exert a wide range of cellular functions. However, our knowledge about the subcellular distribution and regulatory impacts of LCACoAs is limited by a lack of methods for detecting LCACoAs in living cells and tissues. Here, we report our development of LACSerHR, a genetically encoded fluorescent biosensor that enables precise measurement of subtle fluctuations in the levels of endogenous LCACoAs in vivo. LACSerHR significantly improve the fluorescent brightness and analyte affinity, in vitro and in vivo testing showcased LACSerHR's large dynamic range. We demonstrate LACSerHR's capacity for real-time evaluation of LCACoA levels in specific subcellular compartments, for example in response to disruption of ACSL enzyme function in HEK293T cells. Moreover, we show the application of LACSerHR for sensitive measurement of elevated LCACoA levels in the livers of mouse models for two common metabolic diseases (NAFLD and type 2 diabetes). Thus, our LACSerHR sensor is a powerful, broadly applicable tool for studying LCACoAs metabolism and disease.

Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change

Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here we develop data-driven models of single-cell phenotypic responses to extracellular stimuli by linking gene transcription levels to "morphodynamics" - changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals alter the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and provides a prediction of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data.

Reconstructing Signaling Networks Using Biosensor Barcoding

Understanding how signaling networks are regulated offers valuable insights into how cells and organisms react to internal and external stimuli and is crucial for developing novel strategies to treat diseases. To achieve this, it is necessary to delineate the intricate interactions between the nodes in the network, which can be accomplished by measuring the activities of individual nodes under perturbation conditions. To facilitate this, we have recently developed a biosensor barcoding technique that enables massively multiplexed tracking of numerous signaling activities in live cells using genetically encoded fluorescent biosensors. In this chapter, we detail how we employed this method to reconstruct the EGFR signaling network by systematically monitoring the activities of individual nodes under perturbations.

Bridging live-cell imaging and next-generation cancer treatment

By providing spatial, molecular and morphological data over time, live-cell imaging can provide a deeper understanding of the cellular and signalling events that determine cancer response to treatment. Understanding this dynamic response has the potential to enhance clinical outcome by identifying biomarkers or actionable targets to improve therapeutic efficacy. Here, we review recent applications of live-cell imaging for uncovering both tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. Given the increasing uses of T cell therapies, we discuss the unique opportunity of time-lapse imaging for capturing the interactivity and motility of immunotherapies. Although traditionally limited in the number of molecular features captured, novel developments in multidimensional imaging and multi-omics data integration offer strategies to connect single-cell dynamics to molecular phenotypes. We review the effect of these recent technological advances on our understanding of the cellular dynamics of tumour targeting and discuss their implication for next-generation precision medicine.

Fluorescent biosensors illuminate the spatial regulation of cell signaling across scales

As cell signaling research has advanced, it has become clearer that signal transduction has complex spatiotemporal regulation that goes beyond foundational linear transduction models. Several technologies have enabled these discoveries, including fluorescent biosensors designed to report live biochemical signaling events. As genetically encoded and live-cell compatible tools, fluorescent biosensors are well suited to address diverse cell signaling questions across different spatial scales of regulation. In this review, methods of examining spatial signaling regulation and the design of fluorescent biosensors are introduced. Then, recent biosensor developments that illuminate the importance of spatial regulation in cell signaling are highlighted at several scales, including membranes and organelles, molecular assemblies, and cell/tissue heterogeneity. In closing, perspectives on how fluorescent biosensors will continue enhancing cell signaling research are discussed.

Fluorescent biosensor imaging meets deterministic mathematical modelling: quantitative investigation of signalling compartmentalization

Cells execute specific responses to diverse environmental cues by encoding information in distinctly compartmentalized biochemical signalling reactions. Genetically encoded fluorescent biosensors enable the spatial and temporal monitoring of signalling events in live cells. Temporal and spatiotemporal computational models can be used to interpret biosensor experiments in complex biochemical networks and to explore hypotheses that are difficult to test experimentally. In this review, we first provide brief discussions of the experimental toolkit of fluorescent biosensors as well as computational basics with a focus on temporal and spatiotemporal deterministic models. We then describe how we used this combined approach to identify and investigate a protein kinase A (PKA) - cAMP - Ca2+ oscillatory circuit in MIN6 β cells, a mouse pancreatic β cell system. We describe the application of this combined approach to interrogate how this oscillatory circuit is differentially regulated in a nano-compartment formed at the plasma membrane by the scaffolding protein A kinase anchoring protein 79/150. We leveraged both temporal and spatiotemporal deterministic models to identify the key regulators of this oscillatory circuit, which we confirmed with further experiments. The powerful approach of combining live-cell biosensor imaging with quantitative modelling, as discussed here, should find widespread use in the investigation of spatiotemporal regulation of cell signalling.

Maximizing the performance of protein-based fluorescent biosensors

Fluorescent protein (FP)-based biosensors are genetically encoded tools that enable the imaging of biological processes in the context of cells, tissues, or live animals. Though widely used in biological research, practically all existing biosensors are far from ideal in terms of their performance, properties, and applicability for multiplexed imaging. These limitations have inspired researchers to explore an increasing number of innovative and creative ways to improve and maximize biosensor performance. Such strategies include new molecular biology methods to develop promising biosensor prototypes, high throughput microfluidics-based directed evolution screening strategies, and improved ways to perform multiplexed imaging. Yet another approach is to effectively replace components of biosensors with self-labeling proteins, such as HaloTag, that enable the biocompatible incorporation of synthetic fluorophores or other ligands in cells or tissues. This mini-review will summarize and highlight recent innovations and strategies for enhancing the performance of FP-based biosensors for multiplexed imaging to advance the frontiers of research.

Spatiotemporal control of root immune responses during microbial colonization

The entire evolutionary trajectory of plants towards large and complex multi-cellular organisms has been accompanied by incessant interactions with omnipresent unicellular microbes. This led to the evolution of highly complex microbial communities, whose members display the entire spectrum of pathogenic to mutualistic behaviors. Plant roots are dynamic, fractally growing organs and even small Arabidopsis roots harbor millions of individual microbes of diverse taxa. It is evident that microbes at different positions on a root surface could experience fundamentally different environments, which, moreover, rapidly change over time. Differences in spatial scales between microbes and roots compares to humans and the cities they inhabit. Such considerations make it evident that mechanisms of root-microbe interactions can only be understood if analyzed at relevant spatial and temporal scales. This review attempts to provide an overview of the rapid recent progress that has been made in mapping and manipulating plant damage and immune responses at cellular resolution, as well as in visualizing bacterial communities and their transcriptional activities. We further discuss the impact that such approaches will have for a more predictive understanding of root-microbe interactions.

Targeting Ras signaling excitability in cancer cells through combined inhibition of FAK and PI3K

The Ras/PI3K/ERK signaling network is frequently mutated in various human cancers including cervical cancer and pancreatic cancer. Previous studies showed that the Ras/PI3K/ERK signaling network displays features of excitable systems including propagation of activity waves, all-or-none responses, and refractoriness. Oncogenic mutations lead to enhanced excitability of the network. A positive feedback loop between Ras, PI3K, the cytoskeleton, and FAK was identified as a driver of excitability. In this study, we investigated the effectiveness of targeting signaling excitability by inhibiting both FAK and PI3K in cervical and pancreatic cancer cells. We found that the combination of FAK and PI3K inhibitors synergistically suppressed the growth of select cervical and pancreatic cancer cell lines through increased apoptosis and decreased mitosis. In particular, FAK inhibition caused downregulation of PI3K and ERK signaling in cervical cancer but not pancreatic cancer cells. Interestingly, PI3K inhibitors activated multiple receptor tyrosine kinases (RTKs), including insulin receptor and IGF-1R in cervical cancer cells, as well as EGFR, Her2, Her3, Axl, and EphA2 in pancreatic cancer cells. Our results highlight the potential of combining FAK and PI3K inhibition for treating cervical and pancreatic cancer, although appropriate biomarkers for drug sensitivity are needed, and concurrent targeting of RTKs may be required for resistant cells.

pYtags enable spatiotemporal measurements of receptor tyrosine kinase signaling in living cells

Receptor tyrosine kinases (RTKs) are major signaling hubs in metazoans, playing crucial roles in cell proliferation, migration, and differentiation. However, few tools are available to measure the activity of a specific RTK in individual living cells. Here, we present pYtags, a modular approach for monitoring the activity of a user-defined RTK by live-cell microscopy. pYtags consist of an RTK modified with a tyrosine activation motif that, when phosphorylated, recruits a fluorescently labeled tandem SH2 domain with high specificity. We show that pYtags enable the monitoring of a specific RTK on seconds-to-minutes time scales and across subcellular and multicellular length scales. Using a pYtag biosensor for epidermal growth factor receptor (EGFR), we quantitatively characterize how signaling dynamics vary with the identity and dose of activating ligand. We show that orthogonal pYtags can be used to monitor the dynamics of EGFR and ErbB2 activity in the same cell, revealing distinct phases of activation for each RTK. The specificity and modularity of pYtags open the door to robust biosensors of multiple tyrosine kinases and may enable engineering of synthetic receptors with orthogonal response programs.

Morphodynamical cell state description via live-cell imaging trajectory embedding

Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories-that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

Long-term p21 and p53 dynamics regulate the frequency of mitosis events and cell cycle arrest following radiation damage

Radiation exposure of healthy cells can halt cell cycle temporarily or permanently. In this work, we analyze the time evolution of p21 and p53 from two single cell datasets of retinal pigment epithelial cells exposed to several levels of radiation, and in particular, the effect of radiation on cell cycle arrest. Employing various quantification methods from signal processing, we show how p21 levels, and to a lesser extent p53 levels, dictate whether the cells are arrested in their cell cycle and how frequently these mitosis events are likely to occur. We observed that single cells exposed to the same dose of DNA damage exhibit heterogeneity in cellular outcomes and that the frequency of cell division is a more accurate monitor of cell damage rather than just radiation level. Finally, we show how heterogeneity in DNA damage signaling is manifested early in the response to radiation exposure level and has potential to predict long-term fate.

Genetically encoded fluorescent sensors for imaging neuronal dynamics in vivo

The brain relies on many forms of dynamic activities in individual neurons, from synaptic transmission to electrical activity and intracellular signaling events. Monitoring these neuronal activities with high spatiotemporal resolution in the context of animal behavior is a necessary step to achieve a mechanistic understanding of brain function. With the rapid development and dissemination of highly optimized genetically encoded fluorescent sensors, a growing number of brain activities can now be visualized in vivo. To date, cellular calcium imaging, which has been largely used as a proxy for electrical activity, has become a mainstay in systems neuroscience. While challenges remain, voltage imaging of neural populations is now possible. In addition, it is becoming increasingly practical to image over half a dozen neurotransmitters, as well as certain intracellular signaling and metabolic activities. These new capabilities enable neuroscientists to test previously unattainable hypotheses and questions. This review summarizes recent progress in the development and delivery of genetically encoded fluorescent sensors, and highlights example applications in the context of in vivo imaging.

Seeing Neurodegeneration in a New Light Using Genetically Encoded Fluorescent Biosensors and iPSCs

Neurodegenerative diseases present a progressive loss of neuronal structure and function, leading to cell death and irrecoverable brain atrophy. Most have disease-modifying therapies, in part because the mechanisms of neurodegeneration are yet to be defined, preventing the development of targeted therapies. To overcome this, there is a need for tools that enable a quantitative assessment of how cellular mechanisms and diverse environmental conditions contribute to disease. One such tool is genetically encodable fluorescent biosensors (GEFBs), engineered constructs encoding proteins with novel functions capable of sensing spatiotemporal changes in specific pathways, enzyme functions, or metabolite levels. GEFB technology therefore presents a plethora of unique sensing capabilities that, when coupled with induced pluripotent stem cells (iPSCs), present a powerful tool for exploring disease mechanisms and identifying novel therapeutics. In this review, we discuss different GEFBs relevant to neurodegenerative disease and how they can be used with iPSCs to illuminate unresolved questions about causes and risks for neurodegenerative disease.

Smart genetically-encoded biosensors for the chemical monitoring of living systems

Genetically-encoded biosensors provide the all-optical and non-invasive visualization of dynamic biochemical events within living systems, which has allowed the discovery of profound new insights. Twenty-five years of biosensor development has steadily improved their performance and has provided us with an ever increasing biosensor repertoire. In this feature article, we present recent advances made in biosensor development and provide a perspective on the future direction of the field.

NF-kB in Signaling Patterns and Its Temporal Dynamics Encode/Decode Human Diseases

Defects in signaling pathways are the root cause of many disorders. These malformations come in a wide variety of types, and their causes are also very diverse. Some of these flaws can be brought on by pathogenic organisms and viruses, many of which can obstruct signaling processes. Other illnesses are linked to malfunctions in the way that cell signaling pathways work. When thinking about how errors in signaling pathways might cause disease, the idea of signalosome remodeling is helpful. The signalosome may be conveniently divided into two types of defects: phenotypic remodeling and genotypic remodeling. The majority of significant illnesses that affect people, including high blood pressure, heart disease, diabetes, and many types of mental illness, appear to be caused by minute phenotypic changes in signaling pathways. Such phenotypic remodeling modifies cell behavior and subverts normal cellular processes, resulting in illness. There has not been much progress in creating efficient therapies since it has been challenging to definitively confirm this connection between signalosome remodeling and illness. The considerable redundancy included into cell signaling systems presents several potential for developing novel treatments for various disease conditions. One of the most important pathways, NF-κB, controls several aspects of innate and adaptive immune responses, is a key modulator of inflammatory reactions, and has been widely studied both from experimental and theoretical perspectives. NF-κB contributes to the control of inflammasomes and stimulates the expression of a number of pro-inflammatory genes, including those that produce cytokines and chemokines. Additionally, NF-κB is essential for controlling innate immune cells and inflammatory T cells' survival, activation, and differentiation. As a result, aberrant NF-κB activation plays a role in the pathogenesis of several inflammatory illnesses. The activation and function of NF-κB in relation to inflammatory illnesses was covered here, and the advancement of treatment approaches based on NF-κB inhibition will be highlighted. This review presents the temporal behavior of NF-κB and its potential relevance in different human diseases which will be helpful not only for theoretical but also for experimental perspectives.

Multiphoton intravital microscopy of rodents

Tissues are heterogeneous with respect to cellular and non-cellular components and in the dynamic interactions between these elements. To study the behaviour and fate of individual cells in these complex tissues, intravital microscopy (IVM) techniques such as multiphoton microscopy have been developed to visualize intact and live tissues at cellular and subcellular resolution. IVM experiments have revealed unique insights into the dynamic interplay between different cell types and their local environment, and how this drives morphogenesis and homeostasis of tissues, inflammation and immune responses, and the development of various diseases. This Primer introduces researchers to IVM technologies, with a focus on multiphoton microscopy of rodents, and discusses challenges, solutions and practical tips on how to perform IVM. To illustrate the unique potential of IVM, several examples of results are highlighted. Finally, we discuss data reproducibility and how to handle big imaging data sets.

Spectrally Tunable Förster Resonance Energy Transfer-Based Biosensors Using Organic Dye Grafting

Biosensors based on Förster resonance energy transfer (FRET) have revolutionized cellular biology by allowing the direct measurement of biochemical processes in situ. Many genetically encoded sensors make use of fluorescent proteins that are limited in spectral versatility and that allow few ways to change the spectral properties once the construct has been created. In this work, we developed genetically encoded FRET biosensors based on the chemigenetic SNAP and HaloTag domains combined with matching organic fluorophores. We found that the resulting constructs can display comparable responses, kinetics, and reversibility compared to their fluorescent protein-based ancestors, but with the added advantage of spectral versatility, including the availability of red-shifted dye pairs. However, we also find that the introduction of these tags can alter the sensor readout, showing that careful validation is required before applying such constructs in practice. Overall, our approach delivers an innovative methodology that can readily expand the spectral variety and versatility of FRET-based biosensors.

Imaging and analysis for simultaneous tracking of fluorescent biosensors in barcoded cells

We recently developed a biosensor barcoding approach for highly multiplexed tracking of molecular activities in live cells. In this protocol, we detail the labeling of cells expressing different genetically encoded fluorescent biosensors with a pair of barcoding proteins and parallel imaging. Signals from cells with the same barcodes are then pooled together to obtain the dynamics of the corresponding biosensor activity. We describe the steps involved in cell barcoding, image acquisition, and analysis by deep learning models.

For complete details on the use and execution of this protocol, please refer to Yang et al. (2021).

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Cells expressing different biosensors can be barcoded for simultaneous imaging

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Spectral imaging is used to resolve multiple red barcoding proteins

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Deep learning models accelerate barcode reading during image analysis

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Putting in the Erk: Growth factor signaling and mesoderm morphogenesis

It has long been known that FGF signaling contributes to mesoderm formation, a germ layer found in triploblasts that is composed of highly migratory cells that give rise to muscles and to the skeletal structures of vertebrates. FGF signaling activates several pathways in the developing mesoderm, including transient activation of the Erk pathway, which triggers mesodermal fate specification through the induction of the gene brachyury and activates morphogenetic programs that allow mesodermal cells to position themselves in the embryo. In this review, we discuss what is known about the generation and interpretation of transient Erk signaling in mesodermal tissues across species. We focus specifically on mechanisms that translate the level and duration of Erk signaling into cell fate and cell movement instructions and discuss strategies for further interrogating the role that Erk signaling dynamics play in mesodermal gastrulation and morphogenesis.

Challenges and the Evolving Landscape of Assessing Blood-Based PD-L1 Expression as a Biomarker for Anti-PD-(L)1 Immunotherapy

While promising, PD-L1 expression on tumor tissues as assessed by immunohistochemistry has been shown to be an imperfect biomarker that only applies to a limited number of cancers, whereas many patients with PD-L1-negative tumors still respond to anti-PD-(L)1 immunotherapy. Recent studies using patient blood samples to assess immunotherapeutic responsiveness suggests a promising approach to the identification of novel and/or improved biomarkers for anti-PD-(L)1 immunotherapy. In this review, we discuss the advances in our evolving understanding of the regulation and function of PD-L1 expression, which is the foundation for developing blood-based PD-L1 as a biomarker for anti-PD-(L)1 immunotherapy. We further discuss current knowledge and clinical study results for biomarker identification using PD-L1 expression on tumor and immune cells, exosomes, and soluble forms of PD-L1 in the peripheral blood. Finally, we discuss key challenges for the successful development of the potential use of blood-based PD-L1 as a biomarker for anti-PD-(L)1 immunotherapy.

Biomarkers of Cancer Stem Cells for Experimental Research and Clinical Application

The use of biomarkers in cancer diagnosis, therapy, and prognosis has been highly effective over several decades. Studies of biomarkers in cancer patients pre- and post-treatment and during cancer progression have helped identify cancer stem cells (CSCs) and their related microenvironments. These analyses are critical for the therapeutic application of drugs and the efficient targeting and prevention of cancer progression, as well as the investigation of the mechanism of the cancer development. Biomarkers that characterize CSCs have thus been identified and correlated to diagnosis, therapy, and prognosis. However, CSCs demonstrate elevated levels of plasticity, which alters their functional phenotype and appearance by interacting with their microenvironments, in response to chemotherapy and radiotherapeutics. In turn, these changes induce different metabolic adaptations of CSCs. This article provides a review of the most frequently used CSCs and stem cell markers.

Live-cell fluorescence spectral imaging as a data science challenge

Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.