Fluorescence hyperspectral imaging for live monitoring of multiple spheroids in microfluidic chips

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Analysis of cellular response to drugs with a microfluidic single-cell platform based on hyperspectral imaging

BACKGROUND

Cellular response to pharmacological action of drugs is significant for drug development. Traditional detection method for cellular response to drugs normally rely on cell proliferation assay and metabolomics examination. In principle, these analytical methods often required cell labeling, invasion analysis, and hours of co-culture with drugs, which are relatively complex and time-consuming. Moreover, these methods can only indicate the drug effectiveness on cell colony rather than single cells. Thus, to meet the requirements of personal precision medicine, the development of drug response analysis on the high resolution of single cell is demanded.

RESULTS

To provide precise result for drug response on single-cell level, a microfluidic platform coupled with the label-free hyperspectral imaging was developed. With the help of horizontal single-cell trapping sieves, hundreds of single cells were trapped independently in microfluidic channels for the purposes of real-time drug delivery and single-cell hyperspectral image recording. To significantly identify the cellular hyperspectral change after drug stimulation, the differenced single-cell spectrum was proposed. Compared with the deep learning classification method based on hyperspectral images, an optimal performance can be achieved by the classification strategy based on differenced spectra. And the cellular response to different reagents, for example, K+, Epidermal Growth Factor (EGF), and Gefitinib at different concentrations can be accurately characterized by the differenced single-cell spectra analysis.

SIGNIFICANCE AND NOVELTY

The high-throughput, rapid analysis of cellular response to drugs at the single-cell level can be accurately performed by our platform. After systematically analyzing the materials and the structures of the single-cell microfluidic chip, the optimal single-cell trapping method was proposed to contribute to the further application of hyperspectral imaging on microfluidic single-cell analysis. And the hyperspectral characterization of single-cell with cancer drug stimulation proved the application potential of our method in personal cancer medication.

Microscopy is better in color: development of a streamlined spectral light path for real-time multiplex fluorescence microscopy

Spectroscopic image data has provided molecular discrimination for numerous fields including: remote sensing, food safety and biomedical imaging. Despite the various technologies for acquiring spectral data, there remains a trade-off when acquiring data. Typically, spectral imaging either requires long acquisition times to collect an image stack with high spectral specificity or acquisition times are shortened at the expense of fewer spectral bands or reduced spatial sampling. Hence, new spectral imaging microscope platforms are needed to help mitigate these limitations. Fluorescence excitation-scanning spectral imaging is one such new technology, which allows more of the emitted signal to be detected than comparable emission-scanning spectral imaging systems. Here, we have developed a new optical geometry that provides spectral illumination for use in excitation-scanning spectral imaging microscope systems. This was accomplished using a wavelength-specific LED array to acquire spectral image data. Feasibility of the LED-based spectral illuminator was evaluated through simulation and benchtop testing and assessment of imaging performance when integrated with a widefield fluorescence microscope. Ray tracing simulations (TracePro) were used to determine optimal optical component selection and geometry. Spectral imaging feasibility was evaluated using a series of 6-label fluorescent slides. The LED-based system response was compared to a previously tested thin-film tunable filter (TFTF)-based system. Spectral unmixing successfully discriminated all fluorescent components in spectral image data acquired from both the LED and TFTF systems. Therefore, the LED-based spectral illuminator provided spectral image data sets with comparable information content so as to allow identification of each fluorescent component. These results provide proof-of-principle demonstration of the ability to combine output from many discrete wavelength LED sources using a double-mirror (Cassegrain style) optical configuration that can be further modified to allow for high speed, video-rate spectral image acquisition. Real-time spectral fluorescence microscopy would allow monitoring of rapid cell signaling processes (i.e., Ca²⁺ and other second messenger signaling) and has potential to be translated to clinical imaging platforms.

Radiotherapy on-chip: microfluidics for translational radiation oncology

The clinical importance of radiotherapy in the treatment of cancer patients justifies the development and use of research tools at the fundamental, pre-clinical, and ultimately clinical levels, to investigate their toxicities and synergies with systemic agents on relevant biological samples. Although microfluidics has prompted a paradigm shift in drug discovery in the past two decades, it appears to have yet to translate to radiotherapy research. However, the materials, dimensions, design versatility and multiplexing capabilities of microfluidic devices make them well-suited to a variety of studies involving radiation physics, radiobiology and radiotherapy. This review will present the state-of-the-art applications of microfluidics in these fields and specifically highlight the perspectives offered by radiotherapy on-a-chip in the field of translational radiobiology and precision medicine. This body of knowledge can serve both the microfluidics and radiotherapy communities by identifying potential collaboration avenues to improve patient care.

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.

Trends in hyperspectral imaging: from environmental and health sensing to structure-property and nano-bio interaction studies

Hyperspectral imaging (HSI) is a technique that allows for the simultaneous acquisition of both spatial and spectral information. While HSI has been known for years in the field of remote sensing, for instance in geology, cultural heritage, or food industries, it recently emerged in the fields of nano- and micromaterials as well as bioimaging and -sensing. Herein, the attractiveness of HSI arises from the suitability for generating knowledge about environment-specific optical properties, such as photoluminescence of optical probes in a biological sample or at a single-crystal/particle level, to be leveraged into better understanding of structure-property relationships and nano-bio interactions, respectively. Moreover, given its excellent spectral resolution, HSI is highly suitable for optical multiplexing in multiple dimensions, as sought after for, e.g., high throughput biological imaging by simultaneous tracking of multiple targets. Overall, HSI is an emerging technique that has the potential to transform analytical approaches from biomedicine to advanced materials research. This Trends Article provides insight into the potential of HSI, highlighting selected examples from well-established fields including environmental monitoring and food quality control to set the stage for the discussion of emerging opportunities at the micro- and nanoscale. Herein, special focus is set on photoluminescent micro- and nanoprobes for health and spectral conversion applications.

Cell Death Analysis in Cancer Spheroids from a Microfluidic Device

Microfluidic technology facilitates the generation of 3D spheroids from cancer cells, a more suitable model for preclinical therapeutic studies. This system opens the possibility to test many drugs combination at a low cost. Here we describe the use of microfluidic devices for cytotoxicity evaluation on cancer spheroids for the discovery of drugs that could be used in combination with radiotherapy. Device fabrication, preparation, and seeding are also covered. Cell death arising following treatment is detected and characterized according to spheroid size, colony formation assays, and flow cytometry analysis of apoptotic marker annexin V.

Technological Advances in Tumor-On-Chip Technology: From Bench to Bedside

Simple Summary

Various 3D in vitro tumor models are rapidly advancing cancer research. Unlike animal models, they can be produced quickly and are amenable to high-throughput studies. Growing tumor spheroids in microfluidic tumor-on-chip platforms has particularly elevated the capabilities of such models. Tumor-on-chip devices can mimic multiple aspects of the dynamic in vivo tumor microenvironment in a precisely controlled manner. Moreover, new technologies for the on- and off-chip analysis of these tumor mimics are continuously emerging. There is thus an urgent need to review the latest developments in this rapidly progressing field. Here, we present an overview of the technological advances in tumor-on-chip technology by reviewing state-of-the-art tools for on-chip analysis. In particular, we evaluate the potential for tumor-on-chip technology to guide personalized cancer therapies. We strive to appeal to cancer researchers and biomedical engineers alike, informing on current progress, while provoking thought on the outstanding developments needed to achieve clinical-stage research.

Abstract

Tumor-on-chip technology has cemented its importance as an in vitro tumor model for cancer research. Its ability to recapitulate different elements of the in vivo tumor microenvironment makes it promising for translational medicine, with potential application in enabling personalized anti-cancer therapies. Here, we provide an overview of the current technological advances for tumor-on-chip generation. To further elevate the functionalities of the technology, these approaches need to be coupled with effective analysis tools. This aspect of tumor-on-chip technology is often neglected in the current literature. We address this shortcoming by reviewing state-of-the-art on-chip analysis tools for microfluidic tumor models. Lastly, we focus on the current progress in tumor-on-chip devices using patient-derived samples and evaluate their potential for clinical research and personalized medicine applications.

Advanced Devices for Tumor Diagnosis and Therapy

At present, tumor diagnosis is performed using common procedures, which are slow, costly, and still presenting difficulties in diagnosing tumors at their early stage. Tumor therapeutic methods also mainly rely on large-scale equipment or non-intelligent treatment approaches. Thus, an early and accurate tumor diagnosis and personalized treatment may represent the best treatment option for a successful result, and the efforts in finding them are still in progress and mainly focusing on non-destructive, integrated, and multiple technologies. These objectives can be achieved with the development of advanced devices and smart technology that represent the topic of the current investigations. Therefore, this review summarizes the progress in tumor diagnosis and therapy and briefly explains the advantages and disadvantages of the described microdevices, finally proposing advanced micro smart devices as the future development trend for tumor diagnosis and therapy.

Carboplatin sensitivity in epithelial ovarian cancer cell lines: The impact of model systems

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy in North America, underscoring the need for the development of new therapeutic strategies for the management of this disease. Although many drugs are pre-clinically tested every year, only a few are selected to be evaluated in clinical trials, and only a small number of these are successfully incorporated into standard care. Inaccuracies with the initial in vitro drug testing may be responsible for some of these failures. Drug testing is often performed using 2D monolayer cultures or 3D spheroid models. Here, we investigate the impact that these different in vitro models have on the carboplatin response of four EOC cell lines, and in particular how different 3D models (polydimethylsiloxane-based microfluidic chips and ultra low attachment plates) influence drug sensitivity within the same cell line. Our results show that carboplatin responses were observed in both the 3D spheroid models tested using apoptosis/cell death markers by flow cytometry. Contrary to previously reported observations, these were not associated with a significant decrease in spheroid size. For the majority of the EOC cell lines (3 out of 4) a similar carboplatin response was observed when comparing both spheroid methods. Interestingly, two cell lines classified as resistant to carboplatin in 2D cultures became sensitive in the 3D models, and one sensitive cell line in 2D culture showed resistance in 3D spheroids. Our results highlight the challenges of choosing the appropriate pre-clinical models for drug testing.

Wide-field optical spectroscopy system integrating reflectance and spatial frequency domain imaging to measure attenuation-corrected intrinsic tissue fluorescence in radical prostatectomy specimens

The development of a multimodal optical imaging system is presented that integrates endogenous fluorescence and diffuse reflectance spectroscopy with single-wavelength spatial frequency domain imaging (SFDI) and surface profilometry. The system images specimens at visible wavelengths with a spatial resolution of 70 µm, a field of view of 25 cm² and a depth of field of ∼1.5 cm. The results of phantom experiments are presented demonstrating the system retrieves absorption and reduced scattering coefficient maps using SFDI with <6% reconstruction errors. A phase-shifting profilometry technique is implemented and the resulting 3-D surface used to compute a geometric correction ensuring optical properties reconstruction errors are maintained to <6% in curved media with height variations <20 mm. Combining SFDI-computed optical properties with data from diffuse reflectance spectra is shown to correct fluorescence using a model based on light transport in tissue theory. The system is used to image a human prostate, demonstrating its ability to distinguish prostatic tissue (anterior stroma, hyperplasia, peripheral zone) from extra-prostatic tissue (urethra, ejaculatory ducts, peri-prostatic tissue). These techniques could be integrated in robotic-assisted surgical systems to enhance information provided to surgeons and improve procedural accuracy by minimizing the risk of damage to extra-prostatic tissue during radical prostatectomy procedures and eventually detect residual cancer.

Long-term fluorescence hyperspectral imaging of on-chip treated co-culture tumour spheroids to follow clonal evolution

Multicellular tumour spheroids are an ideal in vitro tumour model to study clonal heterogeneity and drug resistance in cancer research because different cell types can be mixed at will. However, measuring the individual response of each cell population over time is challenging: current methods are either destructive, such as flow cytometry, or cannot image throughout a spheroid, such as confocal microscopy. Our group previously developed a wide-field fluorescence hyperspectral imaging system to study spheroids formed and cultured in microfluidic chips. In the present study, two subclones of a single parental ovarian cancer cell line transfected to express different fluorophores were produced and co-culture spheroids were formed on-chip using ratios forming highly asymmetric subpopulations. We performed a 3D proliferation assay on each cell population forming the spheroids that matched the 2D growth behaviour. Response assays to PARP inhibitors and platinum-based drugs were also performed to follow the clonal evolution of mixed populations. Our experiments show that hyperspectral imaging can detect spheroid response before observing a decrease in spheroid diameter. Hyperspectral imaging and microfluidic-based spheroid assays provide a versatile solution to study clonal heterogeneity, able to measure response in subpopulations presenting as little as 10% of the initial spheroid.