Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips

Quantitative phase imaging based on holography: trends and new perspectives

In 1948, Dennis Gabor proposed the concept of holography, providing a pioneering solution to a quantitative description of the optical wavefront. After 75 years of development, holographic imaging has become a powerful tool for optical wavefront measurement and quantitative phase imaging. The emergence of this technology has given fresh energy to physics, biology, and materials science. Digital holography (DH) possesses the quantitative advantages of wide-field, non-contact, precise, and dynamic measurement capability for complex-waves. DH has unique capabilities for the propagation of optical fields by measuring light scattering with phase information. It offers quantitative visualization of the refractive index and thickness distribution of weak absorption samples, which plays a vital role in the pathophysiology of various diseases and the characterization of various materials. It provides a possibility to bridge the gap between the imaging and scattering disciplines. The propagation of wavefront is described by the complex amplitude. The complex-value in the complex-domain is reconstructed from the intensity-value measurement by camera in the real-domain. Here, we regard the process of holographic recording and reconstruction as a transformation between complex-domain and real-domain, and discuss the mathematics and physical principles of reconstruction. We review the DH in underlying principles, technical approaches, and the breadth of applications. We conclude with emerging challenges and opportunities based on combining holographic imaging with other methodologies that expand the scope and utility of holographic imaging even further. The multidisciplinary nature brings technology and application experts together in label-free cell biology, analytical chemistry, clinical sciences, wavefront sensing, and semiconductor production.

Recent advances in nano/microfluidics-based cell isolation techniques for cancer diagnosis and treatments

Miniaturization has improved significantly in the recent decade, which has enabled the development of numerous microfluidic systems. Microfluidic technologies have shown great potential for separating desired cells from heterogeneous samples, as they offer benefits such as low sample consumption, easy operation, and high separation accuracy. Microfluidic cell separation approaches can be classified into physical (label-free) and biological (labeled) methods based on their working principles. Each method has remarkable and feasible benefits for the purposes of cancer detection and therapy, as well as the challenges that we have discussed in this article. In this review, we present the recent advances in microfluidic cell sorting techniques that incorporate both physical and biological aspects, with an emphasis on the methods by which the cells are separated. We first introduce and discuss the biological cell sorting techniques, followed by the physical cell sorting techniques. Additionally, we explore the role of microfluidics in drug screening, drug delivery, and lab-on-chip (LOC) therapy. In addition, we discuss the challenges and future prospects of integrated microfluidics for cell sorting.

Label-free cell classification in holographic flow cytometry through an unbiased learning strategy

Nowadays, label-free imaging flow cytometry at the single-cell level is considered the stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology, life sciences and healthcare. In this framework, digital holography in microscopy promises to be a powerful imaging modality thanks to its multi-refocusing and label-free quantitative phase imaging capabilities, along with the encoding of the highest information content within the imaged samples. Moreover, the recent achievements of new data analysis tools for cell classification based on deep/machine learning, combined with holographic imaging, are urging these systems toward the effective implementation of point of care devices. However, the generalization capabilities of learning-based models may be limited from biases caused by data obtained from other holographic imaging settings and/or different processing approaches. In this paper, we propose a combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder, used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features. We demonstrate the proposed approach in the challenging classification task related to the identification of drug-resistant endometrial cancer cells.

Microfluidics Coupled Mass Spectrometry for Single Cell Multi-Omics

Population-level analysis masks significant heterogeneity between individual cells, making it difficult to accurately reflect the true intricacies of life activities. Microfluidics is a technique that can manipulate individual cells effectively and is commonly coupled with a variety of analytical methods for single-cell analysis. Single-cell omics provides abundant molecular information at the single-cell level, fundamentally revealing differences in cell types and biological states among cell individuals, leading to a deeper understanding of cellular phenotypes and life activities. Herein, this work summarizes the microfluidic chips designed for single-cell isolation, manipulation, trapping, screening, and sorting, including droplet microfluidic chips, microwell arrays, hydrodynamic microfluidic chips, and microchips with microvalves. This work further reviews the studies on single-cell proteomics, metabolomics, lipidomics, and multi-omics based on microfluidics and mass spectrometry. Finally, the challenges and future application of single-cell multi-omics are discussed.

Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry

To efficiently tackle certain tumor types, finding new biomarkers for rapid and complete phenotyping of cancer cells is highly demanded. This is especially the case for the most common pediatric solid tumor of the sympathetic nervous system, namely, neuroblastoma (NB). Liquid biopsy is in principle a very promising tool for this purpose, but usually enrichment and isolation of circulating tumor cells in such patients remain difficult due to the unavailability of universal NB cell-specific surface markers. Here, we show that rapid screening and phenotyping of NB cells through stain-free biomarkers supported by artificial intelligence is a viable route for liquid biopsy. We demonstrate the concept through a flow cytometry based on label-free holographic quantitative phase-contrast microscopy empowered by machine learning. In detail, we exploit a hierarchical decision scheme where at first level NB cells are classified from monocytes with 97.9% accuracy. Then we demonstrate that different phenotypes are discriminated within NB class. Indeed, for each cell classified as NB its belonging to one of four NB sub-populations (i.e., CHP212, SKNBE2, SHSY5Y, and SKNSH) is evaluated thus achieving accuracy in the range 73.6%-89.1%. The achieved results solve the realistic problem related to the identification circulating tumor cell, i.e., the possibility to recognize and detect tumor cells morphologically similar to blood cells, which is the core issue in liquid biopsy based on stain-free microscopy. The presented approach operates at lab-on-chip scale and emulates real-world scenarios, thus representing a future route for liquid biopsy by exploiting intelligent biomedical imaging.

Single-cell omics: a new perspective for early detection of pancreatic cancer?

Pancreatic cancer is one of the most lethal cancers, mostly due to late diagnosis and limited treatment options. Early detection of pancreatic cancer in high-risk populations bears the potential to greatly improve outcomes, but current screening approaches remain of limited value despite recent technological advances. This review explores the possible advantages of liquid biopsies for this application, particularly focusing on circulating tumour cells (CTCs) and their subsequent single-cell omics analysis. Originating from both primary and metastatic tumour sites, CTCs provide important information for diagnosis, prognosis and tailoring of treatment strategies. Notably, CTCs have even been detected in the blood of subjects with pancreatic precursor lesions, suggesting their suitability as a non-invasive tool for the early detection of malignant transformation in the pancreas. As intact cells, CTCs offer comprehensive genomic, transcriptomic, epigenetic and proteomic information that can be explored using rapidly developing techniques for analysing individual cells at the molecular level. Studying CTCs during serial sampling and at single-cell resolution will help to dissect tumour heterogeneity for individual patients and among different patients, providing new insights into cancer evolution during disease progression and in response to treatment. Using CTCs for non-invasive tracking of cancer features, including stemness, metastatic potential and expression of immune targets, provides important and readily accessible molecular insights. Finally, the emerging technology of ex vivo culturing of CTCs could create new opportunities to study the functionality of individual cancers at any stage and develop personalised and more effective treatment approaches for this lethal disease.

AI-aided holographic flow cytometry for label-free identification of ovarian cancer cells in the presence of unbalanced datasets

Liquid biopsy is a valuable emerging alternative to tissue biopsy with great potential in the noninvasive early diagnostics of cancer. Liquid biopsy based on single cell analysis can be a powerful approach to identify circulating tumor cells (CTCs) in the bloodstream and could provide new opportunities to be implemented in routine screening programs. Since CTCs are very rare, the accurate classification based on high-throughput and highly informative microscopy methods should minimize the false negative rates. Here, we show that holographic flow cytometry is a valuable instrument to obtain quantitative phase-contrast maps as input data for artificial intelligence (AI)-based classifiers. We tackle the problem of discriminating between A2780 ovarian cancer cells and THP1 monocyte cells based on the phase-contrast images obtained in flow cytometry mode. We compare conventional machine learning analysis and deep learning architectures in the non-ideal case of having a dataset with unbalanced populations for the AI training step. The results show the capacity of AI-aided holographic flow cytometry to discriminate between the two cell lines and highlight the important role played by the phase-contrast signature of the cells to guarantee accurate classification.

Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry

Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells' refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method.

Digital holographic approaches to the detection and characterization of microplastics in water environments

Microplastic (MP) pollution is seriously threatening the environmental health of the world, which has accelerated the development of new identification and characterization methods. Digital holography (DH) is one of the emerging tools to detect MPs in a high-throughput flow. Here, we review advances in MP screening by DH. We examine the problem from both the hardware and software viewpoints. Automatic analysis based on smart DH processing is reported by highlighting the role played by artificial intelligence for classification and regression tasks. In this framework, the continuous development and availability in recent years of field-portable holographic flow cytometers for water monitoring also is discussed.

TissueGrinder, a novel technology for rapid generation of patient-derived single cell suspensions from solid tumors by mechanical tissue dissociation

Introduction

Recent advances hold promise of making personalized medicine a step closer to implementation in clinical settings. However, traditional sample preparation methods are not robust and reproducible. In this study, the TissueGrinder, a novel mechanical semi-automated benchtop device, which can isolate cells from tissue in a very fast and enzyme-free way is tested for cell isolation from surgically resected tumor tissues.

Methods

Thirty-three surgically resected tumor tissues from various but mainly pancreatic, liver or colorectal origins were processed by both novel TissueGrinder and explant method. An optimized processing program for tumors from pancreatic, liver or colorectal cancer was developed. The viability and morphological characteristics of the isolated cells were evaluated microscopically. Expression of pancreatic cancer markers was evaluated in cells isolated from pancreatic tumors. Finally, the effect of mechanical stress on the cells was evaluated by assessing apoptosis markers via western blotting.

Results

TissueGinder was more efficient in isolating cells from tumor tissue with a success rate of 75% when compared to explant method 45% in terms of cell outgrowth six weeks after processing. Cells isolated with TissueGinder had a higher abundance and were more heterogeneous in composition as compared to explant method. Mechanical processing of the cells with TissueGrinder does not lead to apoptosis but causes slight stress to the cells.

Discussion

Our results show that TissueGrinder can process solid tumor tissues more rapidly and efficiently and with higher success rate compared to the conventionally used explant method. The results of the study suggest that the TissueGrinder might be a suitable method for obtaining cells, which is important for its application in individualized therapy. Due to the great variance in different tumor entities and the associated individual tissue characteristics, a further development of the dissociation protocol for other types of tumors and normal tissue will be targeted.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Quantitative Phase Imaging (QPI) has gained popularity in bioimaging because it can avoid the need for cell staining, which in some cases is difficult or impossible. However, as a result, QPI does not provide labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for QPI techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed through the tomographic phase microscopy in flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy (FM) data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting specific three-dimensional intracellular structures directly from the phase-contrast data in a typical flow cytometry configuration.

Efficient bioparticle extraction using a miniaturized inertial microfluidic centrifuge

Conventional bioparticle extraction requires labor-intensive operation, and expensive and bulky centrifuges. Herein, we report a miniaturized centrifuge by cascading four paralleled inertial spiral channels with a two-stage serpentine channel, allowing for the efficient washing and acquisition of concentrated bioparticles from background fluids. First, the effects of channel size and flow rate on particle focusing dynamics and solution exchange performances are explored to enable the optimization and wide application of our device. Then, the integrated device is fabricated and tested experimentally. The results indicate that 10-20 μm particles can be washed from the original samples with increased concentrations and with recovery efficiencies of >93%. Finally, to verify its versatility, we use our miniaturized centrifuge to successfully change the culture medium for cultured MCF-7 breast cancer cells, extract A549 lung cancer cells from a calcein-AM staining solution, purify white blood cells (WBCs) from lysed whole blood, and extract target cells from an unbonded magnetic microbead background. Compared with conventional centrifuges, our device has the advantages of simple fabrication, easy operation, and small footprint. More importantly, it offers outstanding capability for extracting bioparticles from various background fluids, and avoids bioparticle damage that may be caused by high-speed centrifugation. Therefore, we envision that our miniaturized centrifuge could be a promising alternative to traditional centrifuges in many applications.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

A Versatile Electrochemical Biosensor for the Detection of Circulating MicroRNA toward Non-Small Cell Lung Cancer Diagnosis

Circulating microRNAs (miRNAs) can be used as noninvasive biomarkers and are also found circulating in body fluids such as blood. Dysregulated miRNA expression is associated with many diseases, including non-small cell lung cancer (NSCLC), and the miRNA assay is helpful in cancer diagnosis, prognosis, and monitoring. In this work, a versatile electrochemical biosensing system is developed for miRNA detection by DNAzyme-cleavage cycling amplification and hybridization chain reaction (HCR) amplification. With cleavage by Mn²⁺ targeted DNAzyme, DNA-walker can move along the predesigned DNA tracks and contribute to the transduction and enhancement of signals. For the electrochemical process, the formation of multiple G-quadruplex-incorporated long double-stranded DNA (dsDNA/G-quadruplex) structures is triggered through HCR amplification. The introduction of G-quadruplex allows sensitive measurement of miRNA down to 5.68 fM with good specificity. Furthermore, by profiling miRNA in the NSCLC cohort, this designed strategy shows high efficiency (area under the curve (AUC) of 0.879 using receiver operating characteristic (ROC) analysis) with the sensitivity of 80.0% for NSCLC early diagnosis (stage I). For the discrimination of NSCLC and benign disease, the assay displays an AUC of 0.907, superior to six clinically-acceptable protein tumor markers. Therefore, this platform holds promise in clinical application toward NSCLC diagnosis and prognosis.

Surface-Functionalized Terahertz Metamaterial Biosensor Used for the Detection of Exosomes in Patients

Owing to their stability in bodily fluids, exosomes have attracted increased attention as colorectal cancer (CRC) biomarkers for early diagnosis. To validate the potential of the plasma exosomes as a novel biomarker for the monitoring of CRC, we demonstrated a terahertz (THz) metamaterials (MMs) biosensor for the detection of exosomes in this work. The biosensor with two resonant frequencies is designed using full wave electromagnetic simulation software based on the finite integration time domain (FITD) method and fabricated by a surface micromachining process. The biosensor surface is first modified using Au nanoparticles (AuNPs), and then, anti-KRAS and anti-CD147, which are specific to the exosomes, are modified on the AuNPs assembled with HS-poly(ethylene glycol)-COOH (HS-PEG-COOH). Exosomes used in the experiment are extracted via the instructions in the exosomes isolation and purification kit and identified by using transmission electron microscopy (TEM), Western blot (WB), and nanoparticle tracking analysis (NTA). The biosensor covered with plasma-derived exosomes of CRC patients has a different resonance frequency shift compared to that with healthy-control-derived exosomes. This study proposes an emerging and quick method for diagnosing the CRC.

Liquid Biopsy and Dielectrophoretic Analysis—Complementary Methods in Skin Cancer Monitoring

The incidence and prevalence of skin cancers is currently increasing worldwide, with early detection, adequate treatment, and prevention of recurrences being topics of great interest for researchers nowadays. Although tumor biopsy remains the gold standard of diagnosis, this technique cannot be performed in a significant proportion of cases, so that the use of alternative methods with high sensitivity and specificity is becoming increasingly desirable. In this context, liquid biopsy appears to be a feasible solution for the study of cellular and molecular markers relevant to different types of skin cancers. Circulating tumor cells are just one of the components of interest obtained from performing liquid biopsy, and their study by complementary methods, such as dielectrophoresis, could bring additional benefits in terms of characterizing skin tumors and subsequently applying personalized therapy. One purpose of this review is to demonstrate the utility of liquid biopsy primarily in monitoring the most common types of skin tumors: basal cell carcinoma, squamous cell carcinoma, and malign melanoma. In addition, the originality of the article is based on the detailed presentation of the dielectrophoretic analysis method of the most important elements obtained from liquid biopsy, with direct impact on the clinical and therapeutic approach of skin tumors.

Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning

Tomographic flow cytometry by digital holography is an emerging imaging modality capable of collecting multiple views of moving and rotating cells with the aim of recovering their refractive index distribution in 3D. Although this modality allows us to access high-resolution imaging with high-throughput, the huge amount of time-lapse holographic images to be processed (hundreds of digital holograms per cell) constitutes the actual bottleneck. This prevents the system from being suitable for lab-on-a-chip platforms in real-world applications, where fast analysis of measured data is mandatory. Here we demonstrate a significant speeding-up reconstruction of phase-contrast tomograms by introducing in the processing pipeline a multi-scale fully-convolutional context aggregation network. Although it was originally developed in the context of semantic image analysis, we demonstrate for the first time that it can be successfully adapted to a holographic lab-on-chip platform for achieving 3D tomograms through a faster computational process. We trained the network with input-output image pairs to reproduce the end-to-end holographic reconstruction process, i.e. recovering quantitative phase maps (QPMs) of single cells from their digital holograms. Then, the sequence of QPMs of the same rotating cell is used to perform the tomographic reconstruction. The proposed approach significantly reduces the computational time for retrieving tomograms, thus making them available in a few seconds instead of tens of minutes, while essentially preserving the high-content information of tomographic data. Moreover, we have accomplished a compact deep convolutional neural network parameterization that can fit into on-chip SRAM and a small memory footprint, thus demonstrating its possible exploitation to provide onboard computations for lab-on-chip devices with low processing hardware resources.

Cellular Features Revealed by Transverse Laser Modes in Frequency Domain

Biological lasers which utilize Fabry-Pérot (FP) cavities have attracted tremendous interest due to their potential in amplifying subtle biological changes. Transverse laser modes generated from cells serve as distinct fingerprints of individual cells; however, most lasing signals lack the ability to provide key information about the cell due to high complexity of transverse modes. The missing key, therefore, hinders it from practical applications in biomedicine. This study reveals the key mechanism governing the frequency distributions of transverse modes in cellular lasers. Spatial information of cells including curvature can be interpreted through spectral information of transverse modes by means of hyperspectral imaging. Theoretical studies are conducted to explore the correlation between the cross-sectional morphology of a cell and lasing frequencies of transverse modes. Experimentally, the spectral characteristics of transverse modes are investigated in live and fixed cells with different morphological features. By extracting laser modes in frequency domain, the proposed concept is applied for studying cell adhesion process and cell classification from rat cortices. This study expands a new analytical dimension of cell lasers, opening an avenue for subcellular analysis in biophotonic applications.

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning

About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.

Roadmap on digital holography [Invited]

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

Material-assisted mass spectrometric analysis of low molecular weight compounds for biomedical applications

Low molecular weight compounds play an important role in encoding the current physiological state of an individual. Laser desorption/ionization mass spectrometry (LDI MS) offers high sensitivity with low cost for molecular detection, but it is not able to cover small molecules due to the drawbacks of the conventional matrix. Advanced materials are better alternatives, showing little background interference and high LDI efficiency. Herein, we first classify the current materials with a summary of compositions and structures. Matrix preparation protocols are then reviewed, to enhance the selectivity and reproducibility of MS data better. Finally, we highlight the biomedical applications of material-assisted LDI MS, at the tissue, bio-fluid, and cellular levels. We foresee that the advanced materials will bring far-reaching implications in LDI MS towards real-case applications, especially in clinical settings.

Sustainable Carbon Materials toward Emerging Applications

It is desirable for a sustainable society that the production and utilization of renewable materials are net-zero in terms of carbon emissions. Carbon materials with emerging applications in CO₂ utilization, renewable energy storage and conversion, and biomedicine have attracted much attention both academically and industrially. However, the preparation process of some new carbon materials suffers from energy consumption and environmental pollution issues. Therefore, the development of low-cost, scalable, industrially and economically attractive, sustainable carbon material preparation methods are required. In this regard, the use of biomass and its derivatives as a precursor of carbon materials is a major feature of sustainability. Recent advances in the synthetic strategy of sustainable carbon materials and their emerging applications are summarized in this short review. Emphasis is made on the discussion of the original intentions and various sustainable strategies for producing sustainable carbon materials. This review provides basic insights and significant guidelines for the further design of sustainable carbon materials and their emerging applications in catalysis and the biomedical field.

Immunoengineered magnetic-quantum dot nanobead system for the isolation and detection of circulating tumor cells

Background

Highly efficient capture and detection of circulating tumor cells (CTCs) remain elusive mainly because of their extremely low concentration in patients’ peripheral blood.

Methods

We present an approach for the simultaneous capturing, isolation, and detection of CTCs using an immuno-fluorescent magnetic nanobead system (iFMNS) coated with a monoclonal anti-EpCAM antibody.

Results

The developed antibody nanobead system allows magnetic isolation and fluorescent-based quantification of CTCs. The expression of EpCAM on the surface of captured CTCs could be directly visualized without additional immune-fluorescent labeling. Our approach is shown to result in a 70–95% capture efficiency of CTCs, and 95% of the captured cells remain viable. Using our approach, the isolated cells could be directly used for culture, reverse transcription-polymerase chain reaction (RT-PCR), and immunocytochemistry (ICC) identification. We applied iFMNS for testing CTCs in peripheral blood samples from a lung cancer patient.

Conclusions

It is suggested that our iFMNS approach would be a promising tool for CTCs enrichment and detection in one step.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12951-021-00860-1.

Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non-invasive metabolic profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed to characterize the metabolic profiles in human blood. The elemental composition is optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion for thermally driven desorption. The surface structure of PdPtAu is further tuned with controlled mesopores, accommodating metabolites only, rather than large interfering compounds. Consequently, the optimized PdPtAu alloy yields direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds, consuming 500 nL of native plasma. A distinct metabolic phenotype is revealed for early GC by sparse learning, resulting in precise GC diagnosis with an area under the curve of 0.942. It is envisioned that the plasmonic alloy will open up a new era of minimally invasive blood analysis to improve the surveillance of cancer patients in the clinical setting.

Targeted Sub-Attomole Cancer Biomarker Detection Based on Phase Singularity 2D Nanomaterial-Enhanced Plasmonic Biosensor

A zero-reflection-induced phase singularity is achieved through precisely controlling the resonance characteristics using two-dimensional nanomaterials. An atomically thin nano-layer having a high absorption coefficient is exploited to enhance the zero-reflection dip, which has led to the subsequent phase singularity and thus a giant lateral position shift. We have improved the detection limit of low molecular weight molecules by more than three orders of magnitude compared to current state-of-art nanomaterial-enhanced plasmonic sensors. Detection of small cancer biomarkers with low molecular weight and a low concentration range has always been challenging yet urgent in many clinical applications such as diagnosing early-stage cancer, monitoring treatment and detecting relapse. Here, a highly enhanced plasmonic biosensor that can overcome this challenge is developed using atomically thin two-dimensional phase change nanomaterial. By precisely engineering the configuration with atomically thin materials, the phase singularity has been successfully achieved with a significantly enhanced lateral position shift effect. Based on our knowledge, it is the first experimental demonstration of a lateral position signal change > 340 μm at a sensing interface from all optical techniques. With this enhanced plasmonic effect, the detection limit has been experimentally demonstrated to be 10^-15 mol L^-1 for TNF-α cancer marker, which has been found in various human diseases including inflammatory diseases and different kinds of cancer. The as-reported novel integration of atomically thin Ge₂Sb₂Te₅ with plasmonic substrate, which results in a phase singularity and thus a giant lateral position shift, enables the detection of cancer markers with low molecular weight at femtomolar level. These results will definitely hold promising potential in biomedical application and clinical diagnostics.

Universal G' ∼ L-3 Law for the Low-Frequency Shear Modulus of Confined Liquids

Liquids confined to sub-millimeter scales have remained poorly understood. One of the most striking effects is the large elasticity revealed using good wetting conditions, which grows upon further decreasing the confinement length, L. These systems display a low-frequency shear modulus in the order of 1-10³ Pa, contrary to our everyday experience of liquids as bodies with a zero low-frequency shear modulus. While early experimental evidence of this effect was met with skepticism and abandoned, further experimental results and, most recently, a new atomistic theoretical framework have confirmed that liquids indeed possess a finite low-frequency shear modulus G', which scales with the inverse cubic power of confinement length L. We show that this law is universal and valid for a wide range of materials (liquid water, glycerol, ionic liquids, non-entangled polymer liquids, isotropic liquids crystals). Open questions and potential applications in microfluidics mechanochemistry, energy, and other fields are highlighted.

Genetically Encoded Phase Contrast Agents for Digital Holographic Microscopy

Quantitative phase imaging and digital holographic microscopy have shown great promise for visualizing the motion, structure, and physiology of microorganisms and mammalian cells in three dimensions. However, these imaging techniques currently lack molecular contrast agents analogous to the fluorescent dyes and proteins that have revolutionized fluorescence microscopy. Here we introduce the first genetically encodable phase contrast agents based on gas vesicles. The relatively low index of refraction of the air-filled core of gas vesicles results in optical phase advancement relative to aqueous media, making them a "positive" phase contrast agent easily distinguished from organelles, dyes, or microminerals. We demonstrate this capability by identifying and tracking the motion of gas vesicles and gas vesicle-expressing bacteria using digital holographic microscopy, and by imaging the uptake of engineered gas vesicles by mammalian cells. These results give phase imaging a biomolecular contrast agent, expanding the capabilities of this powerful technology for three-dimensional biological imaging.

Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy

Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.

Zirconia Hybrid Nanoshells for Nutrient and Toxin Detection

Monitoring milk quality is of fundamental importance in food industry, because of the nutritional value and resulting position of milk in daily diet. The detection of small nutrients and toxins in milk is challenging, considering high sample complexity and low analyte abundance. In addition, the slow analysis and tedious sample preparation hinder the large-scale application of conventional detection techniques. Herein, zirconia hybrid nanoshells are constructed to enhance the performance of laser desorption/ionization mass spectrometry (LDI MS). Zirconia nanoshells with the optimized structures and compositions are used as matrices in LDI MS and achieve direct analysis of small molecules from 5 nL of native milk in ≈1 min, without any purification or separation. Accurate quantitation of small nutrient is achieved by introducing isotope into the zirconia nanoshell-assisted LDI MS as the internal standard, offering good consistency to biochemical analysis (BCA) with R²  = 0.94. Further, trace toxin is enriched and identified with limit-of-detection (LOD) down to 4 pm, outperforming the current analytical methods. This work sheds light on the personalized design of material-based tool for real-case bioanalysis and opens up new opportunities for the simple, fast, and cost-effective detection of various small molecules in a broad field.