Cellular Metabolic Heterogeneity In Vivo Is Recapitulated in Tumor Organoids

Subclonal response heterogeneity to define cancer organoid therapeutic sensitivity

Tumor heterogeneity is predicted to confer inferior clinical outcomes with precision-based strategies, however, modeling heterogeneity in a manner that still represents the tumor of origin remains a formidable challenge. Sequencing technologies are limited in their ability to identify rare subclonal populations and predict response to treatments for patients. Patient-derived organotypic cultures have significantly improved the modeling of cancer biology by faithfully representing the molecular features of primary malignant tissues. Patient-derived cancer organoid (PCO) cultures contain subclonal populations with the potential to recapitulate heterogeneity, although treatment response assessments commonly ignore diversity in the molecular profile or treatment response. Here, we demonstrate the advantage of evaluating individual PCO heterogeneity to enhance the sensitivity of these assays for predicting clinical response. Additionally, organoid subcultures identify subclonal populations with altered treatment response. Finally, dose escalation studies of PCOs to targeted anti-EGFR therapy are utilized which reveal divergent pathway expression when compared to pretreatment cultures. Overall, these studies demonstrate the importance of population-based organoid response assessments, the use of PCOs to identify molecular heterogeneity not observed with bulk tumor sequencing, and PCO heterogeneity for understanding therapeutic resistance mechanisms.

A New Perspective on Precision Medicine: The Power of Digital Organoids

Precision medicine is a personalized medical model based on the individual's genome, phenotype, and lifestyle that provides tailored treatment plans for patients. In this context, tumor organoids, a 3-dimensional preclinical model based on patient-derived tumor cell self-organization, combined with digital analysis methods, such as high-throughput sequencing and image processing technology, can be used to analyze the genome, transcriptome, and cellular heterogeneity of tumors, so as to accurately track and assess the growth process, genetic characteristics, and drug responsiveness of tumor organoids, thereby facilitating the implementation of precision medicine. This interdisciplinary approach is expected to promote the innovation of cancer diagnosis and enhance personalized treatment. In this review, the characteristics and culture methods of tumor organoids are summarized, and the application of multi-omics, such as bioinformatics and artificial intelligence, and the digital methods of organoids in precision medicine research are discussed. Finally, this review explores the main causes and potential solutions for the bottleneck in the clinical translation of digital tumor organoids, proposes the prospects of multidisciplinary cooperation and clinical transformation to narrow the gap between laboratory and clinical settings, and provides references for research and development in this field.

Fluorescence Lifetime Imaging Techniques-A Review on Principles, Applications and Clinical Relevance

This article gives an overview of the most frequently used fluorescence-lifetime imaging (FLIM) techniques, their capabilities, and typical applications. Starting from a general introduction to fluorescence and phosphorescence lifetime, we will show that the fluorescence lifetime or, more accurately, the fluorescence decay function of a fluorophore is a direct indicator of the interaction with its molecular environment. FLIM is therefore more than a simple contrast technique in microscopy-it is a technique of molecular imaging. FLIM techniques can be classified into time-domain and frequency-domain techniques, analogue and photon counting techniques, and scanning and wide-field techniques. Starting from an overview of these general technical principles we will describe the features and peculiarities of the different FLIM techniques in use. An extended section is dedicated to TCSPC FLIM, addressing unique capabilities that make the technique especially interesting to FLIM of biological systems.

Discovery platforms for RNA therapeutics

RNA therapeutics are emerging as a unique opportunity to drug currently "undruggable" molecules and diseases. While their advantages over conventional, small molecule drugs, their therapeutic implications and the tools for their effective in vivo delivery have been extensively reviewed, little attention has been so far paid to the technological platforms exploited for the discovery of RNA therapeutics. Here, we provide an overview of the existing platforms and ex vivo assays for RNA discovery, their advantages and disadvantages, as well as their main fields of application, with specific focus on RNA therapies that have reached either phase 3 or market approval. LINKED ARTICLES: This article is part of a themed issue Non-coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc.

Epigenetic Landscape of DNA Methylation in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, characterized by its aggressive progression and dismal prognosis. Advances in epigenetic profiling, specifically DNA methylation analysis, have significantly deepened our understanding of PDAC pathogenesis. This review synthesizes findings from recent genome-wide DNA methylation studies, which have delineated a complex DNA methylation landscape differentiating between normal and cancerous pancreatic tissues, as well as across various stages and molecular subtypes of PDAC. These studies identified specific differentially methylated regions (DMRs) that not only enhance our grasp of the epigenetic drivers of PDAC but also offer potential biomarkers for early diagnosis and prognosis, enabling the customization of therapeutic approaches. The review further explores how DNA methylation profiling could facilitate the development of subtype-tailored therapies, potentially improving treatment outcomes based on precise molecular characterizations. Overall, leveraging DNA methylation alterations as functional biomarkers holds promise for advancing our understanding of disease progression and refining PDAC management strategies, which could lead to improved patient outcomes and a deeper comprehension of the disease's underlying biological mechanisms.

The Use of Patient-Derived Organoids in the Study of Molecular Metabolic Adaptation in Breast Cancer

Around 13% of women will likely develop breast cancer during their lifetime. Advances in cancer metabolism research have identified a range of metabolic reprogramming events, such as altered glucose and amino acid uptake, increased reliance on glycolysis, and interactions with the tumor microenvironment (TME), all of which present new opportunities for targeted therapies. However, studying these metabolic networks is challenging in traditional 2D cell cultures, which often fail to replicate the three-dimensional architecture and dynamic interactions of real tumors. To address this, organoid models have emerged as powerful tools. Tumor organoids are 3D cultures, often derived from patient tissue, that more accurately mimic the structural and functional properties of actual tumor tissues in vivo, offering a more realistic model for investigating cancer metabolism. This review explores the unique metabolic adaptations of breast cancer and discusses how organoid models can provide deeper insights into these processes. We evaluate the most advanced tools for studying cancer metabolism in three-dimensional culture models, including optical metabolic imaging (OMI), matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and recent advances in conventional techniques applied to 3D cultures. Finally, we explore the progress made in identifying and targeting potential therapeutic targets in breast cancer metabolism.

Evaluation of Cellpose segmentation with sequential thresholding for instance segmentation of cytoplasms within autofluorescence images

BACKGROUND

Autofluorescence imaging of the coenzyme, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), provides a label-free technique to assess cellular metabolism. Because NAD(P)H is localized in the cytosol and mitochondria, instance segmentation of cell cytoplasms from NAD(P)H images allows quantification of metabolism with cellular resolution. However, accurate cytoplasmic segmentation of autofluorescence images is difficult due to irregular cell shapes and cell clusters.

METHOD

Here, a cytoplasm segmentation method is presented and tested. First, autofluorescence images are segmented into cells via either hand-segmentation or Cellpose, a deep learning-based segmentation method. Then, a cytoplasmic post-processing algorithm (CPPA) is applied for cytoplasmic segmentation. CPPA uses a binarized segmentation image to remove non-segmented pixels from the NAD(P)H image and then applies an intensity-based threshold to identify nuclei regions. Errors at cell edges are removed using a distance transform algorithm. The nucleus mask is then subtracted from the cell segmented image to yield the cytoplasm mask image. CPPA was tested on five NAD(P)H images of three different cell samples, quiescent T cells, activated T cells, and MCF7 cells.

RESULTS

Using POSEA, an evaluation method tailored for instance segmentation, the CPPA yielded F-measure values of 0.89, 0.87, and 0.94 for quiescent T cells, activated T cells, and MCF7 cells, respectively, for cytoplasm identification of hand-segmented cells. CPPA achieved F-measure values of 0.84, 0.74, and 0.72 for Cellpose segmented cells.

CONCLUSION

These results exceed the F-measure value of a comparative cell segmentation method (CellProfiler, ∼0.50-0.60) and support the use of artificial intelligence and post-processing techniques for accurate segmentation of autofluorescence images for single-cell metabolic analyses.

Insights into metabolic heterogeneity of colorectal cancer gained from fluorescence lifetime imaging

Heterogeneity of tumor metabolism is an important, but still poorly understood aspect of tumor biology. Present work is focused on the visualization and quantification of cellular metabolic heterogeneity of colorectal cancer using fluorescence lifetime imaging (FLIM) of redox cofactor NAD(P)H. FLIM-microscopy of NAD(P)H was performed in vitro in four cancer cell lines (HT29, HCT116, CaCo2 and CT26), in vivo in the four types of colorectal tumors in mice and ex vivo in patients' tumor samples. The dispersion and bimodality of the decay parameters were evaluated to quantify the intercellular metabolic heterogeneity. Our results demonstrate that patients' colorectal tumors have significantly higher heterogeneity of energy metabolism compared with cultured cells and tumor xenografts, which was displayed as a wider and frequently bimodal distribution of a contribution of a free (glycolytic) fraction of NAD(P)H within a sample. Among patients' tumors, the dispersion was larger in the high-grade and early stage ones, without, however, any association with bimodality. These results indicate that cell-level metabolic heterogeneity assessed from NAD(P)H FLIM has a potential to become a clinical prognostic factor.

Delineation of gastrointestinal tumors biopsies using a fluorescence lifetime imaging optical fiber probe

Autofluorescence spectroscopy has emerged in recent years as a powerful tool to report label-free contrast between normal and diseased tissues, both in vivo and ex-vivo. We report the application of an instrument employing an optical fiber probe and capable of performing real-time autofluorescence lifetime imaging at a macroscopic scale, under bright background conditions. We validate and demonstrate the practicality of this technology to discriminate healthy against neoplastic tissue in freshly excised tumor biopsies. The capability of delineating tumor margins through processing the fluorescence decays in the phasors domain was demonstrated on four different types of cancer, highlighting the broad range of potential clinical applications for the proposed approach. The presented results suggest that our autofluorescence lifetime imaging probe, together with phasor analysis, can offer a real-time tool to observe lifetime contrast on tissues and, thus, is a suitable candidate for improving in situ tissue diagnostics during surgery.

Imaging immunometabolism in situ in live animals

Immunometabolism is a rapidly developing field that holds great promise for diagnostic and therapeutic benefits to human diseases. The field has emerged based on seminal findings from in vitro and ex vivo studies that established the fundamental role of metabolism in immune cell effector functions. Currently, the field is acknowledging the necessity of investigating cellular metabolism within the natural context of biological processes. Examining cells in their native microenvironment is essential not only to reveal cell-intrinsic mechanisms but also to understand how cross-talk between neighboring cells regulates metabolism at the tissue level in a local niche. This necessity is driving innovation and advancement in multiple imaging-based technologies to enable analysis of dynamic intracellular metabolism at the single-cell level, with spatial and temporal resolution. In this review, we tally the currently available imaging-based technologies and explore the emerging methods of Raman and autofluorescence lifetime imaging microscopy, which hold significant potential and offer broad applications in the field of immunometabolism.

Optical imaging reveals chemotherapy-induced metabolic reprogramming of residual disease and recurrence

Fewer than 20% of triple-negative breast cancer patients experience long-term responses to mainstay chemotherapy. Resistant tumor subpopulations use alternative metabolic pathways to escape therapy, survive, and eventually recur. Here, we show in vivo, longitudinal metabolic reprogramming in residual disease and recurrence of triple-negative breast cancer xenografts with varying sensitivities to the chemotherapeutic drug paclitaxel. Optical imaging coupled with metabolomics reported an increase in non-glucose-driven mitochondrial metabolism and an increase in intratumoral metabolic heterogeneity during regression and residual disease in resistant MDA-MB-231 tumors. Conversely, sensitive HCC-1806 tumors were primarily reliant on glucose uptake and minimal changes in metabolism or heterogeneity were observed over the tumors' therapeutic life cycles. Further, day-matched resistant HCC-1806 tumors revealed a higher reliance on mitochondrial metabolism and elevated metabolic heterogeneity compared to sensitive HCC-1806 tumors. Together, metabolic flexibility, increased reliance on mitochondrial metabolism, and increased metabolic heterogeneity are defining characteristics of persistent residual disease, features that will inform the appropriate type and timing of therapies.

Preclinical Models of Anal Cancer Combined-Modality Therapy

INTRODUCTION

There have been no significant changes in anal cancer treatment options in 4 decades. In this study, we highlight two preclinical models designed to assess anal cancer treatments.

MATERIALS AND METHODS

Transgenic K14E6/E7 mice were treated with 7, 12-dimethylbenz(a)anthracene until anal tumors developed. Mice were treated with localized radiation in addition to chemotherapy (combined-modality therapy [CMT]) and compared to no treatment control (NTC). K14E6/E7 mouse anal spheroids with and without Pik3ca mutations were isolated and treated with vehicle, LY3023414 (LY3) (a drug previously shown to be effective in cancer prevention), CMT, or CMT + LY3.

RESULTS

In the in vivo model, there was a significant increase in survival in the CMT group compared to the NTC group (P = 0.0392). In the ex vivo model, there was a significant decrease in the mean diameter of CMT and CMT + LY3-treated spheroids compared to vehicle (P ≤ 0.0001). For LY3 alone compared to vehicle, there was a statistically significant decrease in spheroid size in the K14E6/E7 group without mutation (P = 0.0004).

CONCLUSIONS

We have provided proof of concept for two preclinical anal cancer treatment models that allow for the future testing of novel therapies for anal cancer.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Single cell metabolic imaging of tumor and immune cells in vivo in melanoma bearing mice

Introduction

Metabolic reprogramming of cancer and immune cells occurs during tumorigenesis and has a significant impact on cancer progression. Unfortunately, current techniques to measure tumor and immune cell metabolism require sample destruction and/or cell isolations that remove the spatial context. Two-photon fluorescence lifetime imaging microscopy (FLIM) of the autofluorescent metabolic coenzymes nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) provides in vivo images of cell metabolism at a single cell level.

Methods

Here, we report an immunocompetent mCherry reporter mouse model for immune cells that express CD4 either during differentiation or CD4 and/or CD8 in their mature state and perform in vivo imaging of immune and cancer cells within a syngeneic B78 melanoma model. We also report an algorithm for single cell segmentation of mCherry-expressing immune cells within in vivo images.

Results

We found that immune cells within B78 tumors exhibited decreased FAD mean lifetime and an increased proportion of bound FAD compared to immune cells within spleens. Tumor infiltrating immune cell size also increased compared to immune cells from spleens. These changes are consistent with a shift towards increased activation and proliferation in tumor infiltrating immune cells compared to immune cells from spleens. Tumor infiltrating immune cells exhibited increased FAD mean lifetime and increased protein-bound FAD lifetime compared to B78 tumor cells within the same tumor. Single cell metabolic heterogeneity was observed in both immune and tumor cells in vivo.

Discussion

This approach can be used to monitor single cell metabolic heterogeneity in tumor cells and immune cells to study promising treatments for cancer in the native in vivo context.

POSEA: A novel algorithm to evaluate the performance of multi-object instance image segmentation

Many techniques and software packages have been developed to segment individual cells within microscopy images, necessitating a robust method to evaluate images segmented into a large number of unique objects. Currently, segmented images are often compared with ground-truth images at a pixel level; however, this standard pixel-level approach fails to compute errors due to pixels incorrectly assigned to adjacent objects. Here, we define a per-object segmentation evaluation algorithm (POSEA) that calculates segmentation accuracy metrics for each segmented object relative to a ground truth segmented image. To demonstrate the performance of POSEA, precision, recall, and f-measure metrics are computed and compared with the standard pixel-level evaluation for simulated images and segmented fluorescence microscopy images of three different cell samples. POSEA yields lower accuracy metrics than the standard pixel-level evaluation due to correct accounting of misclassified pixels of adjacent objects. Therefore, POSEA provides accurate evaluation metrics for objects with pixels incorrectly assigned to adjacent objects and is robust for use across a variety of applications that require evaluation of the segmentation of unique adjacent objects.

Fabrication of lumen-forming colorectal cancer organoids using a newly designed laminin-derived bioink

160Three-dimensional (3D) bioprinting systems, which are the prominent tools for biofabrication, should evolve around the cutting-edge technologies of tissue engineering. This is the case with organoid technology, which requires a plethora of new materials to evolve, including extracellular matrices with specific mechanical and biochemical properties. For a bioprinting system to facilitate organoid growth, it must be able to recreate an organ-like environment within the 3D construct. In this study, a well-established, self-assembling peptide system was employed to generate a laminin-like bioink to provide signals of cell adhesion and lumen formation in cancer stem cells. One bioink formulation led to the formation of lumen with outperforming characteristics, which showed good stability of the printed construct.

Label-free metabolic imaging for sensitive and robust monitoring of anti-CD47 immunotherapy response in triple-negative breast cancer

Background

Immunotherapy is revolutionizing cancer treatment from conventional radiotherapies and chemotherapies to immune checkpoint inhibitors which use patients’ immune system to recognize and attack cancer cells. Despite the huge clinical success and vigorous development of immunotherapies, there is a significant unmet need for a robust tool to identify responders to specific immunotherapy. Early and accurate monitoring of immunotherapy response is indispensable for personalized treatment and effective drug development.

Methods

We established a label-free metabolic intravital imaging (LMII) technique to detect two-photon excited autofluorescence signals from two coenzymes, NAD(P)H (reduced nicotinamide adenine dinucleotide (phosphate) hydrogen) and FAD (flavin adenine dinucleotide) as robust imaging markers to monitor metabolic responses to immunotherapy. Murine models of triple-negative breast cancer (TNBC) were established and tested with different therapeutic regimens including anti-cluster of differentiation 47 (CD47) immunotherapy to monitor time-course treatment responses using the developed metabolic imaging technique.

Results

We first imaged the mechanisms of the CD47-signal regulatory protein alpha pathway in vivo, which unravels macrophage-mediated antibody-dependent cellular phagocytosis and illustrates the metabolism of TNBC cells and macrophages. We further visualized the autofluorescence of NAD(P)H and FAD and found a significant increase during tumor growth. Following anti-CD47 immunotherapy, the imaging signal was dramatically decreased demonstrating the sensitive monitoring capability of NAD(P)H and FAD imaging for therapeutic response. NAD(P)H and FAD intravital imaging also showed a marked decrease after chemotherapy and radiotherapy. A comparative study with conventional whole-body bioluminescence and fluorescent glucose imaging demonstrated superior sensitivity of metabolic imaging. Flow cytometry validated metabolic imaging results. In vivo immunofluorescent staining revealed the targeting ability of NAD(P)H imaging mainly for tumor cells and a small portion of immune-active cells and that of FAD imaging mainly for immunosuppressive cells such as M2-like tumor-associated macrophages.

Conclusions

Collectively, this study showcases the potential of the LMII technique as a powerful tool to visualize dynamic changes of heterogeneous cell metabolism of cancer cells and immune infiltrates in response to immunotherapy thus providing sensitive and complete monitoring. Leveraged on ability to differentiate cancer cells and immunosuppressive macrophages, the presented imaging approach provides particularly useful imaging biomarkers for emerged innate immune checkpoint inhibitors such as anti-CD47 therapy.

Technologies to Assess Drug Response and Heterogeneity in Patient-Derived Cancer Organoids

Patient-derived cancer organoids (PDCOs) are organotypic 3D cultures grown from patient tumor samples. PDCOs provide an exciting opportunity to study drug response and heterogeneity within and between patients. This research can guide new drug development and inform clinical treatment planning. We review technologies to assess PDCO drug response and heterogeneity, discuss best practices for clinically relevant drug screens, and assert the importance of quantifying single-cell and organoid heterogeneity to characterize response. Autofluorescence imaging of PDCO growth and metabolic activity is highlighted as a compelling method to monitor single-cell and single-organoid response robustly and reproducibly. We also speculate on the future of PDCOs in clinical practice and drug discovery.Future development will require standardization of assessment methods for both morphology and function in PDCOs, increased throughput for new drug development, prospective validation with patient outcomes, and robust classification algorithms.

The impact of microfluidics in high-throughput drug-screening applications

Drug discovery is an expensive and lengthy process. Among the different phases, drug discovery and preclinical trials play an important role as only 5-10 of all drugs that begin preclinical tests proceed to clinical trials. Indeed, current high-throughput screening technologies are very expensive, as they are unable to dispense small liquid volumes in an accurate and quick way. Moreover, despite being simple and fast, drug screening assays are usually performed under static conditions, thus failing to recapitulate tissue-specific architecture and biomechanical cues present in vivo even in the case of 3D models. On the contrary, microfluidics might offer a more rapid and cost-effective alternative. Although considered incompatible with high-throughput systems for years, technological advancements have demonstrated how this gap is rapidly reducing. In this Review, we want to further outline the role of microfluidics in high-throughput drug screening applications by looking at the multiple strategies for cell seeding, compartmentalization, continuous flow, stimuli administration (e.g., drug gradients or shear stresses), and single-cell analyses.

Inhibition of B-cell lymphoma 2 family proteins alters optical redox ratio, mitochondrial polarization, and cell energetics independent of cell state

SIGNIFICANCE

The optical redox ratio (ORR) [autofluorescence intensity of the reduced form of nicotinamide adenine dinucleotide (phosphate) (NAD(P)H)/flavin adenine dinucleotide (FAD)] provides a label-free method to quantify cellular metabolism. However, it is unclear whether changes in the ORR with B-cell lymphoma 2 (Bcl-2) family protein inhibition are due to metabolic stress alone or compromised cell viability.

AIM

Determine whether ABT-263 (navitoclax, Bcl-2 family inhibitor) changes the ORR due to changes in mitochondrial function that are independent of changes in cell viability.

APPROACH

SW48 colon cancer cells were used to investigate changes in ORR, mitochondrial membrane potential, oxygen consumption rates, and cell state (cell growth, viability, proliferation, apoptosis, autophagy, and senescence) with ABT-263, TAK-228 [sapanisertib, mammalian target of rapamycin complex 1/2 (mTORC 1/2) inhibitor], and their combination at 24 h.

RESULTS

Changes in the ORR with Bcl-2 inhibition are driven by increases in both NAD(P)H and FAD autofluorescence, corresponding with increased basal metabolic rate and increased mitochondrial polarization. ABT-263 treatment does not change cell viability or induce autophagy but does induce a senescent phenotype. The metabolic changes seen with ABT-263 treatment are mitigated by combination with mTORC1/2 inhibition.

CONCLUSIONS

The ORR is sensitive to increases in mitochondrial polarization, energetic state, and cell senescence, which can change independently from cell viability.

Impact of baseline culture conditions of cancer organoids when determining therapeutic response and tumor heterogeneity

Representative models are needed to screen new therapies for patients with cancer. Cancer organoids are a leap forward as a culture model that faithfully represents the disease. Mouse-derived cancer organoids (MDCOs) are becoming increasingly popular, however there has yet to be a standardized method to assess therapeutic response and identify subpopulation heterogeneity. There are multiple factors unique to organoid culture that could affect how therapeutic response and MDCO heterogeneity are assessed. Here we describe an analysis of nearly 3500 individual MDCOs where individual organoid morphologic tracking was performed. Change in MDCO diameter was assessed in the presence of control media or targeted therapies. Individual organoid tracking was identified to be more sensitive to treatment response than well-level assessment. The impact of different generations of mice of the same genotype, different regions of the colon, and organoid specific characteristics including baseline size, passage number, plating density, and location within the matrix were examined. Only the starting size of the MDCO altered the subsequent growth. These results were corroborated using ~ 1700 patient-derived cancer organoids (PDCOs) isolated from 19 patients. Here we establish organoid culture parameters for individual organoid morphologic tracking to determine therapeutic response and growth/response heterogeneity for translational studies.

Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity

Molecular, morphological, and physiological heterogeneity is the inherent property of cells which governs differences in their response to external influence. Tumor cell metabolic heterogeneity is of a special interest due to its clinical relevance to tumor progression and therapeutic outcomes. Rapid, sensitive, and noninvasive assessment of metabolic heterogeneity of cells is a great demand for biomedical sciences. Fluorescence lifetime imaging (FLIM), which is an all-optical technique, is an emerging tool for sensing and quantifying cellular metabolism by measuring fluorescence decay parameters of endogenous fluorophores, such as NAD(P)H. To achieve accurate discrimination between metabolically diverse cellular subpopulations, appropriate approaches to FLIM data collection and analysis are needed. In this paper, the unique capability of FLIM to attain the overarching goal of discriminating metabolic heterogeneity is demonstrated. This has been achieved using an approach to data analysis based on the nonparametric analysis, which revealed a much better sensitivity to the presence of metabolically distinct subpopulations compared to more traditional approaches of FLIM measurements and analysis. The approach was further validated for imaging cultured cancer cells treated with chemotherapy. These results pave the way for accurate detection and quantification of cellular metabolic heterogeneity using FLIM, which will be valuable for assessing therapeutic vulnerabilities and predicting clinical outcomes.

Identification of Rare Cell Populations in Autofluorescence Lifetime Image Data

Drug‐resistant cells and anti‐inflammatory immune cells within tumor masses contribute to tumor aggression, invasion, and worse patient outcomes. These cells can be a small proportion (<10%) of the total cell population of the tumor. Due to their small quantity, the identification of rare cells is challenging with traditional assays. Single cell analysis of autofluorescence images provides a live‐cell assay to quantify cellular heterogeneity. Fluorescence intensities and lifetimes of the metabolic coenzymes reduced nicotinamide adenine dinucleotide and oxidized flavin adenine dinucleotide allow quantification of cellular metabolism and provide features for classification of cells with different metabolic phenotypes. In this study, Gaussian distribution modeling and machine learning classification algorithms are used for the identification of rare cells within simulated autofluorescence lifetime image data of a large tumor comprised of tumor cells and T cells. A Random Forest machine learning algorithm achieved an overall accuracy of 95% for the identification of cell type from the simulated optical metabolic imaging data of a heterogeneous tumor of 20,000 cells consisting of 70% drug responsive breast cancer cells, 5% drug resistant breast cancer cells, 20% quiescent T cells and 5% activated T cells. High resolution imaging methods combined with single‐cell quantitative analyses allows identification and quantification of rare populations of cells within heterogeneous cultures

In vivo fluorescence lifetime imaging of macrophage intracellular metabolism during wound responses in zebrafish

The function of macrophages in vitro is linked to their metabolic rewiring. However, macrophage metabolism remains poorly characterized in situ. Here, we used two-photon intensity and lifetime imaging of autofluorescent metabolic coenzymes, nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD), to assess the metabolism of macrophages in the wound microenvironment. Inhibiting glycolysis reduced NAD(P)H mean lifetime and made the intracellular redox state of macrophages more oxidized, as indicated by reduced optical redox ratio. We found that TNFα+ macrophages had lower NAD(P)H mean lifetime and were more oxidized compared to TNFα- macrophages. Both infection and thermal injury induced a macrophage population with a more oxidized redox state in wounded tissues. Kinetic analysis detected temporal changes in the optical redox ratio during tissue repair, revealing a shift toward a more reduced redox state over time. Metformin reduced TNFα+ wound macrophages, made intracellular redox state more reduced and improved tissue repair. By contrast, depletion of STAT6 increased TNFα+ wound macrophages, made redox state more oxidized and impaired regeneration. Our findings suggest that autofluorescence of NAD(P)H and FAD is sensitive to dynamic changes in intracellular metabolism in tissues and can be used to probe the temporal and spatial regulation of macrophage metabolism during tissue damage and repair.

Tumor organoids: synergistic applications, current challenges, and future prospects in cancer therapy

Patient-derived cancer cells (PDCs) and patient-derived xenografts (PDXs) are often used as tumor models, but have many shortcomings. PDCs not only lack diversity in terms of cell type, spatial organization, and microenvironment but also have adverse effects in stem cell cultures, whereas PDX are expensive with a low transplantation success rate and require a long culture time. In recent years, advances in three-dimensional (3D) organoid culture technology have led to the development of novel physiological systems that model the tissues of origin more precisely than traditional culture methods. Patient-derived cancer organoids bridge the conventional gaps in PDC and PDX models and closely reflect the pathophysiological features of natural tumorigenesis and metastasis, and have led to new patient-specific drug screening techniques, development of individualized treatment regimens, and discovery of prognostic biomarkers and mechanisms of resistance. Synergistic combinations of cancer organoids with other technologies, for example, organ-on-a-chip, 3D bio-printing, and CRISPR-Cas9-mediated homology-independent organoid transgenesis, and with treatments, such as immunotherapy, have been useful in overcoming their limitations and led to the development of more suitable model systems that recapitulate the complex stroma of cancer, inter-organ and intra-organ communications, and potentially multiorgan metastasis. In this review, we discuss various methods for the creation of organ-specific cancer organoids and summarize organ-specific advances and applications, synergistic technologies, and treatments as well as current limitations and future prospects for cancer organoids. Further advances will bring this novel 3D organoid culture technique closer to clinical practice in the future.

High-resolution positron emission microscopy of patient-derived tumor organoids

Tumor organoids offer new opportunities for translational cancer research, but unlike animal models, their broader use is hindered by the lack of clinically relevant imaging endpoints. Here, we present a positron-emission microscopy method for imaging clinical radiotracers in patient-derived tumor organoids with spatial resolution 100-fold better than clinical positron emission tomography (PET). Using this method, we quantify 18F-fluorodeoxyglucose influx to show that patient-derived tumor organoids recapitulate the glycolytic activity of the tumor of origin, and thus, could be used to predict therapeutic response in vitro. Similarly, we measure sodium-iodine symporter activity using 99mTc- pertechnetate and find that the iodine uptake pathway is functionally conserved in organoids derived from thyroid carcinomas. In conclusion, organoids can be imaged using clinical radiotracers, which opens new possibilities for identifying promising drug candidates and radiotracers, personalizing treatment regimens, and incorporating clinical imaging biomarkers in organoid-based co-clinical trials.

Metabolic Reprogramming: A Friend or Foe to Cancer Therapy?

Drug resistance is a major cause of cancer treatment failure, effectively driven by processes that promote escape from therapy-induced cell death. The mechanisms driving evasion of apoptosis have been widely studied across multiple cancer types, and have facilitated new and exciting therapeutic discoveries with the potential to improve cancer patient care. However, an increasing understanding of the crosstalk between cancer hallmarks has highlighted the complexity of the mechanisms of drug resistance, co-opting pathways outside of the canonical "cell death" machinery to facilitate cell survival in the face of cytotoxic stress. Rewiring of cellular metabolism is vital to drive and support increased proliferative demands in cancer cells, and recent discoveries in the field of cancer metabolism have uncovered a novel role for these programs in facilitating drug resistance. As a key organelle in both metabolic and apoptotic homeostasis, the mitochondria are at the forefront of these mechanisms of resistance, coordinating crosstalk in the event of cellular stress, and promoting cellular survival. Importantly, the appreciation of this role metabolism plays in the cytotoxic response to therapy, and the ability to profile metabolic adaptions in response to treatment, has encouraged new avenues of investigation into the potential of exploiting metabolic addictions to improve therapeutic efficacy and overcome drug resistance in cancer. Here, we review the role cancer metabolism can play in mediating drug resistance, and the exciting opportunities presented by imposed metabolic vulnerabilities.

Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions

Fluorescence lifetime imaging (FLIm) is an optical spectroscopic imaging technique capable of real-time assessments of tissue properties in clinical settings. Label-free FLIm is sensitive to changes in tissue structure and biochemistry resulting from pathological conditions, thus providing optical contrast to identify and monitor the progression of disease. Technical and methodological advances over the last two decades have enabled the development of FLIm instrumentation for real-time, in situ, mesoscopic imaging compatible with standard clinical workflows. Herein, we review the fundamental working principles of mesoscopic FLIm, discuss the technical characteristics of current clinical FLIm instrumentation, highlight the most commonly used analytical methods to interpret fluorescence lifetime data and discuss the recent applications of FLIm in surgical oncology and cardiovascular diagnostics. Finally, we conclude with an outlook on the future directions of clinical FLIm.

Label-Free Deep Profiling of the Tumor Microenvironment

Label-free nonlinear microscopy enables nonperturbative visualization of structural and metabolic contrast within living cells in their native tissue microenvironment. Here a computational pipeline was developed to provide a quantitative view of the microenvironmental architecture within cancerous tissue from label-free nonlinear microscopy images. To enable single-cell and single-extracellular vesicle (EV) analysis, individual cells, including tumor cells and various types of stromal cells, and EVs were segmented by a multiclass pixelwise segmentation neural network and subsequently analyzed for their metabolic status and molecular structure in the context of the local cellular neighborhood. By comparing cancer tissue with normal tissue, extensive tissue reorganization and formation of a patterned cell-EV neighborhood was observed in the tumor microenvironment. The proposed analytic pipeline is expected to be useful in a wide range of biomedical tasks that benefit from single-cell, single-EV, and cell-to-EV analysis. SIGNIFICANCE: The proposed computational framework allows label-free microscopic analysis that quantifies the complexity and heterogeneity of the tumor microenvironment and opens possibilities for better characterization and utilization of the evolving cancer landscape.

Autofluorescence Imaging of Treatment Response in Neuroendocrine Tumor Organoids

Gastroenteropancreatic neuroendocrine tumors (GEP-NET) account for roughly 60% of all neuroendocrine tumors. Low/intermediate grade human GEP-NETs have relatively low proliferation rates that animal models and cell lines fail to recapitulate. Short-term patient-derived cancer organoids (PDCOs) are a 3D model system that holds great promise for recapitulating well-differentiated human GEP-NETs. However, traditional measurements of drug response (i.e., growth, proliferation) are not effective in GEP-NET PDCOs due to the small volume of tissue and low proliferation rates that are characteristic of the disease. Here, we test a label-free, non-destructive optical metabolic imaging (OMI) method to measure drug response in live GEP-NET PDCOs. OMI captures the fluorescence lifetime and intensity of endogenous metabolic cofactors NAD(P)H and FAD. OMI has previously provided accurate predictions of drug response on a single cell level in other cancer types, but this is the first study to apply OMI to GEP-NETs. OMI tested the response to novel drug combination on GEP-NET PDCOs, specifically ABT263 (navitoclax), a Bcl-2 family inhibitor, and everolimus, a standard GEP-NET treatment that inhibits mTOR. Treatment response to ABT263, everolimus, and the combination were tested in GEP-NET PDCO lines derived from seven patients, using two-photon OMI. OMI measured a response to the combination treatment in 5 PDCO lines, at 72 h post-treatment. In one of the non-responsive PDCO lines, heterogeneous response was identified with two distinct subpopulations of cell metabolism. Overall, this work shows that OMI provides single-cell metabolic measurements of drug response in PDCOs to guide drug development for GEP-NET patients.

Organoid technology for personalized pancreatic cancer therapy

BACKGROUND

Pancreatic ductal adenocarcinoma has the lowest survival rate among all major cancers and is the third leading cause of cancer-related mortality. The stagnant survival statistics and dismal response rates to current therapeutics highlight the need for more efficient preclinical models. Patient-derived organoids (PDOs) offer new possibilities as powerful preclinical models able to account for interpatient variability. Organoid development can be divided into four different key phases: establishment, propagation, drug screening and response prediction. Establishment entails tailored tissue extraction and growth protocols, propagation requires consistent multiplication and passaging, while drug screening and response prediction will benefit from shorter and more precise assays, and clear decision-making tools.

CONCLUSIONS

This review attempts to outline the most important challenges that remain in exploiting organoid platforms for drug discovery and clinical applications. Some of these challenges may be overcome by novel methods that are under investigation, such as 3D bioprinting systems, microfluidic systems, optical metabolic imaging and liquid handling robotics. We also propose an optimized organoid workflow inspired by all technical solutions we have presented.

Breast cancer immunotherapy: current biomarkers and the potential of in vitro assays

Breakthroughs in metastatic breast cancer care require new model systems that can identify the unique features and vulnerabilities of each cancer. Primary tumor cultures are proposed to efficiently screen multiple treatment options in a patient-specific strategy to maximize therapeutic benefit, minimize toxicity, and enable mechanistic insights that inspire future biomarkers for patient selection. To realize the potential of patient-specific cultures, new tools are needed to capture cell-by-cell variability in behavior and dynamic response to treatments in living 3D specimens. Potential bioengineering tools that can achieve this include optical microscopy to image single-cell dynamics and microphysiological in vitro systems to evaluate cell-cell interactions and immunotherapies.

Patient-derived cancer organoid tracking with wide-field one-photon redox imaging to assess treatment response

SIGNIFICANCE

Accessible tools are needed for rapid, non-destructive imaging of patient-derived cancer organoid (PCO) treatment response to accelerate drug discovery and streamline treatment planning for individual patients.

AIM

To segment and track individual PCOs with wide-field one-photon redox imaging to extract morphological and metabolic variables of treatment response.

APPROACH

Redox imaging of the endogenous fluorophores, nicotinamide dinucleotide (NADH), nicotinamide dinucleotide phosphate (NADPH), and flavin adenine dinucleotide (FAD), was used to monitor the metabolic state and morphology of PCOs. Redox imaging was performed on a wide-field one-photon epifluorescence microscope to evaluate drug response in two colorectal PCO lines. An automated image analysis framework was developed to track PCOs across multiple time points over 48 h. Variables quantified for each PCO captured metabolic and morphological response to drug treatment, including the optical redox ratio (ORR) and organoid area.

RESULTS

The ORR (NAD(P)H/(FAD + NAD(P)H)) was independent of PCO morphology pretreatment. Drugs that induced cell death decreased the ORR and growth rate compared to control. Multivariate analysis of redox and morphology variables identified distinct PCO subpopulations. Single-organoid tracking improved sensitivity to drug treatment compared to pooled organoid analysis.

CONCLUSIONS

Wide-field one-photon redox imaging can monitor metabolic and morphological changes on a single organoid-level, providing an accessible, non-destructive tool to screen drugs in patient-matched samples.

Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression

Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells.

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Insulin signaling is heterogeneous between cells in the same population

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The temporal response of signaling components within a cell is highly reproducible

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Upstream responses (Akt) can only partially predict downstream response (GLUT4)

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Protein expression variance is a driver of intercellular signaling heterogeneity

In Vivo Optical Metabolic Imaging of Long-Chain Fatty Acid Uptake in Orthotopic Models of Triple-Negative Breast Cancer

Simple Summary

A dysregulated metabolism is a hallmark of cancer. Once understood, tumor metabolic reprogramming can lead to targetable vulnerabilities, spurring the development of novel treatment strategies. Beyond the common observation that tumors rely heavily on glucose, building evidence indicates that a subset of tumors use lipids to maintain their proliferative or metastatic phenotype. This study developed an intra-vital microscopy method to quantify lipid uptake in breast cancer murine models using a fluorescently labeled palmitate molecule, Bodipy FL c16. This work highlights optical imaging’s ability to both measure metabolic endpoints non-destructively and repeatedly, as well as inform small animal metabolic phenotyping beyond in vivo optical imaging of breast cancer alone.

Abstract

Targeting a tumor’s metabolic dependencies is a clinically actionable therapeutic approach; however, identifying subtypes of tumors likely to respond remains difficult. The use of lipids as a nutrient source is of particular importance, especially in breast cancer. Imaging techniques offer the opportunity to quantify nutrient use in preclinical tumor models to guide development of new drugs that restrict uptake or utilization of these nutrients. We describe a fast and dynamic approach to image fatty acid uptake in vivo and demonstrate its relevance to study both tumor metabolic reprogramming directly, as well as the effectiveness of drugs targeting lipid metabolism. Specifically, we developed a quantitative optical approach to spatially and longitudinally map the kinetics of long-chain fatty acid uptake in in vivo murine models of breast cancer using a fluorescently labeled palmitate molecule, Bodipy FL c16. We chose intra-vital microscopy of mammary tumor windows to validate our approach in two orthotopic breast cancer models: a MYC-overexpressing, transgenic, triple-negative breast cancer (TNBC) model and a murine model of the 4T1 family. Following injection, Bodipy FL c16 fluorescence increased and reached its maximum after approximately 30 min, with the signal remaining stable during the 30–80 min post-injection period. We used the fluorescence at 60 min (Bodipy₆₀), the mid-point in the plateau region, as a summary parameter to quantify Bodipy FL c16 fluorescence in subsequent experiments. Using our imaging platform, we observed a two- to four-fold decrease in fatty acid uptake in response to the downregulation of the MYC oncogene, consistent with findings from in vitro metabolic assays. In contrast, our imaging studies report an increase in fatty acid uptake with tumor aggressiveness (6NR, 4T07, and 4T1), and uptake was significantly decreased after treatment with a fatty acid transport inhibitor, perphenazine, in both normal mammary pads and in the most aggressive 4T1 tumor model. Our approach fills an important gap between in vitro assays providing rich metabolic information at static time points and imaging approaches visualizing metabolism in whole organs at a reduced resolution.

A FACS-Free Purification Method to Study Estrogen Signaling, Organoid Formation, and Metabolic Reprogramming in Mammary Epithelial Cells

Few in vitro models are used to study mammary epithelial cells (MECs), and most of these do not express the estrogen receptor α (ERα). Primary MECs can be used to overcome this issue, but methods to purify these cells generally require flow cytometry and fluorescence-activated cell sorting (FACS), which require specialized instruments and expertise. Herein, we present in detail a FACS-free protocol for purification and primary culture of mouse MECs. These MECs remain differentiated for up to six days with >85% luminal epithelial cells in two-dimensional culture. When seeded in Matrigel, they form organoids that recapitulate the mammary gland's morphology in vivo by developing lumens, contractile cells, and lobular structures. MECs express a functional ERα signaling pathway in both two- and three-dimensional cell culture, as shown at the mRNA and protein levels and by the phenotypic characterization. Extracellular metabolic flux analysis showed that estrogens induce a metabolic switch favoring aerobic glycolysis over mitochondrial respiration in MECs grown in two-dimensions, a phenomenon known as the Warburg effect. We also performed mass spectrometry (MS)-based metabolomics in organoids. Estrogens altered the levels of metabolites from various pathways, including aerobic glycolysis, citric acid cycle, urea cycle, and amino acid metabolism, demonstrating that ERα reprograms cell metabolism in mammary organoids. Overall, we have optimized mouse MEC isolation and purification for two- and three-dimensional cultures. This model represents a valuable tool to study how estrogens modulate mammary gland biology, and particularly how these hormones reprogram metabolism during lactation and breast carcinogenesis.

Non-Destructive Tumor Aggregate Morphology and Viability Quantification at Cellular Resolution, During Development and in Response to Drug

Three-dimensional (3D) tissue-engineered in vitro models, particularly multicellular spheroids and organoids, have become important tools to explore disease progression and guide the development of novel therapeutic strategies. These avascular constructs are particularly powerful in oncological research due to their ability to mimic several key aspects of in vivo tumors, such as 3D structure and pathophysiologic gradients. Advancement of spheroid models requires characterization of critical features (i.e., size, shape, cellular density, and viability) during model development, and in response to treatment. However, evaluation of these characteristics longitudinally, quantitatively and non-invasively remains a challenge. Herein, Optical Coherence Tomography (OCT) is used as a label-free tool to assess 3D morphologies and cellular densities of tumor spheroids generated via the liquid overlay technique. We utilize this quantitative tool to assess Matrigel's influence on spheroid morphologic development, finding that the absence of Matrigel produces flattened, disk-like aggregates rather than 3D spheroids with physiologically-relevant features. Furthermore, this technology is adapted to quantify cell number within tumor spheroids, and to discern between live and dead cells, to non-destructively provide valuable information on tissue/construct viability, as well as a proof-of-concept for longitudinal drug efficacy studies. Together, these findings demonstrate OCT as a promising noninvasive, quantitative, label-free, longitudinal and cell-based method that can assess development and drug response in 3D cellular aggregates at a mesoscopic scale.

Autofluorescence Imaging of 3D Tumor-Macrophage Microscale Cultures Resolves Spatial and Temporal Dynamics of Macrophage Metabolism

Macrophages within the tumor microenvironment (TME) exhibit a spectrum of protumor and antitumor functions, yet it is unclear how the TME regulates this macrophage heterogeneity. Standard methods to measure macrophage heterogeneity require destructive processing, limiting spatiotemporal studies of function within the live, intact 3D TME. Here, we demonstrate two-photon autofluorescence imaging of NAD(P)H and FAD to nondestructively resolve spatiotemporal metabolic heterogeneity of individual macrophages within 3D microscale TME models. Fluorescence lifetimes and intensities of NAD(P)H and FAD were acquired at 24, 48, and 72 hours poststimulation for mouse macrophages (RAW264.7) stimulated with IFNγ or IL4 plus IL13 in 2D culture, confirming that autofluorescence measurements capture known metabolic phenotypes. To quantify metabolic dynamics of macrophages within the TME, mouse macrophages or human monocytes (RAW264.7 or THP-1) were cultured alone or with breast cancer cells (mouse polyoma-middle T virus or primary human IDC) in 3D microfluidic platforms. Human monocytes and mouse macrophages in tumor cocultures exhibited significantly different FAD mean lifetimes and greater migration than monocultures at 24, 48, and 72 hours postseeding. In cocultures with primary human cancer cells, actively migrating monocyte-derived macrophages had greater redox ratios [NAD(P)H/FAD intensity] compared with passively migrating monocytes at 24 and 48 hours postseeding, reflecting metabolic heterogeneity in this subpopulation of monocytes. Genetic analyses further confirmed this metabolic heterogeneity. These results establish label-free autofluorescence imaging to quantify dynamic metabolism, polarization, and migration of macrophages at single-cell resolution within 3D microscale models. This combined culture and imaging system provides unique insights into spatiotemporal tumor-immune cross-talk within the 3D TME. SIGNIFICANCE: Label-free metabolic imaging and microscale culture technologies enable monitoring of single-cell macrophage metabolism, migration, and function in the 3D tumor microenvironment.

Pancreatic Ductal Adenocarcinoma (PDAC) Organoids: The Shining Light at the End of the Tunnel for Drug Response Prediction and Personalized Medicine

Pancreatic ductal adenocarcinoma (PDAC) represents 90% of pancreatic malignancies. In contrast to many other tumor entities, the prognosis of PDAC has not significantly improved during the past thirty years. Patients are often diagnosed too late, leading to an overall five-year survival rate below 10%. More dramatically, PDAC cases are on the rise and it is expected to become the second leading cause of death by cancer in western countries by 2030. Currently, the use of gemcitabine/nab-paclitaxel or FOLFIRINOX remains the standard chemotherapy treatment but still with limited efficiency. There is an urgent need for the development of early diagnostic and therapeutic tools. To this point, in the past 5 years, organoid technology has emerged as a revolution in the field of PDAC personalized medicine. Here, we are reviewing and discussing the current technical and scientific knowledge on PDAC organoids, their future perspectives, and how they can represent a game change in the fight against PDAC by improving both diagnosis and treatment options.

A Scaffold-Free 3-D Co-Culture Mimics the Major Features of the Reverse Warburg Effect In Vitro

Most tumors consume large amounts of glucose. Concepts to explain the mechanisms that mediate the achievement of this metabolic need have proposed a switch of the tumor mass to aerobic glycolysis. Depending on whether primarily tumor or stroma cells undergo such a commutation, the terms ‘Warburg effect’ or ‘reverse Warburg effect’ were coined to describe the underlying biological phenomena. However, current in vitro systems relying on 2-D culture, single cell-type spheroids, or basal-membrane extract (BME/Matrigel)-containing 3-D structures do not thoroughly reflect these processes. Here, we aimed to establish a BME/Matrigel-free 3-D microarray cancer model to recapitulate the metabolic interplay between cancer and stromal cells that allows mechanistic analyses and drug testing. Human HT-29 colon cancer and CCD-1137Sk fibroblast cells were used in mono- and co-cultures as 2-D monolayers, spheroids, and in a cell-chip format. Metabolic patterns were studied with immunofluorescence and confocal microscopy. In chip-based co-cultures, HT-29 cells showed facilitated 3-D growth and increased levels of hexokinase-2, TP53-induced glycolysis and apoptosis regulator (TIGAR), lactate dehydrogenase, and: translocase of outer mitochondrial membrane 20 (TOMM20), when compared with HT-29 mono-cultures. Fibroblasts co-cultured with HT-29 cells expressed higher levels of mono-carboxylate transporter 4, hexokinase-2, microtubule-associated proteins 1A/1B light chain 3, and ubiquitin-binding protein p62 than in fibroblast mono-cultures, in both 2-D cultures and chips. Tetramethylrhodamin-methylester (TMRM) live-cell imaging of chip co-cultures revealed a higher mitochondrial potential in cancer cells than in fibroblasts. The findings demonstrate a crosstalk between cancer cells and fibroblasts that affects cellular growth and metabolism. Chip-based 3-D co-cultures of cancer cells and fibroblasts mimicked features of the reverse Warburg effect.

How and Why Are Cancers Acidic? Carbonic Anhydrase IX and the Homeostatic Control of Tumour Extracellular pH

The acidic tumour microenvironment is now recognized as a tumour phenotype that drives cancer somatic evolution and disease progression, causing cancer cells to become more invasive and to metastasise. This property of solid tumours reflects a complex interplay between cellular carbon metabolism and acid removal that is mediated by cell membrane carbonic anhydrases and various transport proteins, interstitial fluid buffering, and abnormal tumour-associated vessels. In the past two decades, a convergence of advances in the experimental and mathematical modelling of human cancers, as well as non-invasive pH-imaging techniques, has yielded new insights into the physiological mechanisms that govern tumour extracellular pH (pH_e). In this review, we examine the mechanisms by which solid tumours maintain a low pH_e, with a focus on carbonic anhydrase IX (CAIX), a cancer-associated cell surface enzyme. We also review the accumulating evidence that suggest a role for CAIX as a biological pH-stat by which solid tumours stabilize their pH_e. Finally, we highlight the prospects for the clinical translation of CAIX-targeted therapies in oncology.

Metabolic Heterogeneity in Patient Tumor-Derived Organoids by Primary Site and Drug Treatment

New tools are needed to match cancer patients with effective treatments. Patient-derived organoids offer a high-throughput platform to personalize treatments and discover novel therapies. Currently, methods to evaluate drug response in organoids are limited because they overlook cellular heterogeneity. In this study, non-invasive optical metabolic imaging (OMI) of cellular heterogeneity was characterized in breast cancer (BC) and pancreatic cancer (PC) patient-derived organoids. Baseline heterogeneity was analyzed for each patient, demonstrating that single-cell techniques, such as OMI, are required to capture the complete picture of heterogeneity present in a sample. Treatment-induced changes in heterogeneity were also analyzed, further demonstrating that these measurements greatly complement current techniques that only gauge average cellular response. Finally, OMI of cellular heterogeneity in organoids was evaluated as a predictor of clinical treatment response for the first time. Organoids were treated with the same drugs as the patient's prescribed regimen, and OMI measurements of heterogeneity were compared to patient outcome. OMI distinguished subpopulations of cells with divergent and dynamic responses to treatment in living organoids without the use of labels or dyes. OMI of organoids agreed with long-term therapeutic response in patients. With these capabilities, OMI could serve as a sensitive high-throughput tool to identify optimal therapies for individual patients, and to develop new effective therapies that address cellular heterogeneity in cancer.

Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications

SIGNIFICANCE

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to distinguish the unique molecular environment of fluorophores. FLIM measures the time a fluorophore remains in an excited state before emitting a photon, and detects molecular variations of fluorophores that are not apparent with spectral techniques alone. FLIM is sensitive to multiple biomedical processes including disease progression and drug efficacy.

AIM

We provide an overview of FLIM principles, instrumentation, and analysis while highlighting the latest developments and biological applications.

APPROACH

This review covers FLIM principles and theory, including advantages over intensity-based fluorescence measurements. Fundamentals of FLIM instrumentation in time- and frequency-domains are summarized, along with recent developments. Image segmentation and analysis strategies that quantify spatial and molecular features of cellular heterogeneity are reviewed. Finally, representative applications are provided including high-resolution FLIM of cell- and organelle-level molecular changes, use of exogenous and endogenous fluorophores, and imaging protein-protein interactions with Förster resonance energy transfer (FRET). Advantages and limitations of FLIM are also discussed.

CONCLUSIONS

FLIM is advantageous for probing molecular environments of fluorophores to inform on fluorophore behavior that cannot be elucidated with intensity measurements alone. Development of FLIM technologies, analysis, and applications will further advance biological research and clinical assessments.

Human Colon Organoids and Other Laboratory Strategies to Enhance Patient Treatment Selection

OPINION STATEMENT

Though many advancements in personalized medicine have been made, better methods are still needed to predict treatment benefit for patients with colorectal cancer. Patient-derived cancer organoids (PDCOs) are a major advance towards true personalization of treatment strategies. A growing body of literature is demonstrating the feasibility of PDCOs as an accurate and high-throughput preclinical tool for patient treatment selection. Many studies demonstrate that these cultures are readily generated and represent the tumors they were derived from phenotypically and based on their mutation profile. This includes maintenance of the driver muatations giving the cancer cells a selective growth advantage, and also heterogeneity, including molecular and metabolic heterogeneity. Additionally, PDCOs are now being utilized to develop patient biospecimen repositories, perform high to moderate-throughput drug screening, and to potentially predict treatment response for individual patients that are undergoing anti-cancer treatments. In order to develop PDCOs as a true clinical tool, further studies are required to determine the reproducibility and accuracy of these models to predict patient response.

A Perspective on Expanding Our Understanding of Cancer Treatments by Integrating Approaches from the Biological and Physical Sciences

Multicellular systems such as cancer suffer from immense complexity. It is imperative to capture the heterogeneity of these systems across scales to achieve a deeper understanding of the underlying biology and develop effective treatment strategies. In this perspective article, we will discuss how recent technologies and approaches from the biological and physical sciences have transformed traditional ways of measuring, interpreting, and treating cancer. During the SLAS 2019 Annual Meeting, SBI² hosted a Special Interest Group (SIG) on this topic. Academic and industry leaders engaged in discussions surrounding what biological model systems are appropriate to study cancer complexity, what assays are necessary to interrogate this complexity, and how physical sciences approaches may be useful to detangle this complexity. In particular, we examined the utility of mathematical models in predicting cancer progression and treatment response when tightly integrated with reproducible, quantitative, and dynamic biological measurements achieved using high-content imaging and analysis. The dialogue centered around the impetus for convergent biosciences, bringing new perspectives to cancer research to further understand this complex adaptive system and successfully intervene therapeutically.

Organoid of ovarian cancer: genomic analysis and drug screening

Ovarian cancer is the most common malignant tumors of the female reproductive system, and its standard treatments are cytoreductive surgery and platinum-based adjuvant chemotherapy. Great advances have been achieved in novel treatment strategies, including targeted therapy and immunotherapy. However, ovarian cancer has the highest mortality rate among gynecological tumors due to therapeutic resistance and the gap between preclinical data and actual clinical efficacy. Organoids are a 3D culture model that markedly affects gene analysis, drug screening, and drug sensitivity determination of tumors, especially when used in targeted therapy and immunotherapy. In addition, organoid can lead to advances in the preclinical research of ovarian cancer due to its convenient cultivation, good genetic stability, and high homology with primary tumors.

New luminescence lifetime macro-imager based on a Tpx3Cam optical camera

The properties of a novel ultra-fast optical imager, Tpx3Cam, were investigated for macroscopic wide-field phosphorescent lifetime imaging (PLIM) applications. The camera is based on a novel optical sensor and Timepix3 readout chip with a time resolution of 1.6 ns, recording of photon arrival time and time over threshold for each pixel, and readout rate of 80 megapixels per second. In this study, we coupled the camera to an image intensifier, a 760 nm emission filter and a 50 mm lens, and with a super-bright 627nm LED providing pulsed excitation of a 18 × 18 mm sample area. The resulting macro-imager with compact and rigid optical alignment of its main components was characterised using planar phosphorescent O2 sensors and a resolution plate mask. Several acquisition and image processing algorithms were evaluated to optimise the system resolution and performance for the wide-field PLIM, followed by imaging a variety of phosphorescent samples. The new PLIM system looks promising, particularly for phosphorescence lifetime-based imaging of O2 in various chemical and biological samples.

Quantitative Spatial Analysis of Metabolic Heterogeneity Across in vivo and in vitro Tumor Models

Metabolic preferences of tumor cells vary within a single tumor, contributing to tumor heterogeneity, drug resistance, and patient relapse. However, the relationship between tumor treatment response and metabolically distinct tumor cell populations is not well-understood. Here, a quantitative approach was developed to characterize spatial patterns of metabolic heterogeneity in tumor cell populations within in vivo xenografts and 3D in vitro cultures (i.e., organoids) of head and neck cancer. Label-free images of cell metabolism were acquired using two-photon fluorescence lifetime microscopy of the metabolic co-enzymes NAD(P)H and FAD. Previous studies have shown that NAD(P)H mean fluorescence lifetimes can identify metabolically distinct cells with varying drug response. Thus, density-based clustering of the NAD(P)H mean fluorescence lifetime was used to identify metabolic sub-populations of cells, then assessed in control, cetuximab-, cisplatin-, and combination-treated xenografts 13 days post-treatment and organoids 24 h post-treatment. Proximity analysis of these metabolically distinct cells was designed to quantify differences in spatial patterns between treatment groups and between xenografts and organoids. Multivariate spatial autocorrelation and principal components analyses of all autofluorescence intensity and lifetime variables were developed to further improve separation between cell sub-populations. Spatial principal components analysis and Z-score calculations of autofluorescence and spatial distribution variables also visualized differences between models. This analysis captures spatial distributions of tumor cell sub-populations influenced by treatment conditions and model-specific environments. Overall, this novel spatial analysis could provide new insights into tumor growth, treatment resistance, and more effective drug treatments across a range of microscopic imaging modalities (e.g., immunofluorescence, imaging mass spectrometry).

Novel and Alternative Targets Against Breast Cancer Stemness to Combat Chemoresistance

Breast cancer stem cells (BCSCs) play a vital role in tumor progression and metastasis. They are heterogeneous and inherently radio- and chemoresistant. They have the ability to self-renew and differentiate into non-BCSCs. These determinants of BCSCs including the plasticity between the mesenchymal and epithelial phenotypes often leads to minimal residual disease (MRD), tumor relapse, and therapy failure. By studying the resistance mechanisms in BCSCs, a combinatorial therapy can be formulated to co-target BCSCs and bulk tumor cells. This review addresses breast cancer stemness and molecular underpinnings of how the cancer stemness can lead to pharmacological resistance. This might occur through rewiring of signaling pathways and modulated expression of various targets that support survival and self-renewal, clonogenicity, and multi-lineage differentiation into heterogeneous bulk tumor cells following chemotherapy. We explore emerging novel and alternative molecular targets against BC stemness and chemoresistance involving survival, drug efflux, metabolism, proliferation, cell migration, invasion, and metastasis. Strategic targeting of such vulnerabilities in BCSCs may overcome the chemoresistance and increase the longevity of the metastatic breast cancer patients.