Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.
Utilizing its integrated camera as a spectrometer, we demonstrate the use of a smartphone as the detection instrument for a label-free photonic crystal biosensor. A custom-designed cradle holds the smartphone in fixed alignment with optical components, allowing for accurate and repeatable measurements of shifts in the resonant wavelength of the sensor. Externally provided broadband light incident upon an entrance pinhole is subsequently collimated and linearly polarized before passing through the biosensor, which resonantly reflects only a narrow band of wavelengths. A diffraction grating spreads the remaining wavelengths over the camera's pixels to display a high resolution transmission spectrum. The photonic crystal biosensor is fabricated on a plastic substrate and attached to a standard glass microscope slide that can easily be removed and replaced within the optical path. A custom software app was developed to convert the camera images into the photonic crystal transmission spectrum in the visible wavelength range, including curve-fitting analysis that computes the photonic crystal resonant wavelength with 0.009 nm accuracy. We demonstrate the functionality of the system through detection of an immobilized protein monolayer, and selective detection of concentration-dependent antibody binding to a functionalized photonic crystal. We envision the capability for an inexpensive, handheld biosensor instrument with web connectivity to enable point-of-care sensing in environments that have not been practical previously.
Breakthrough advances in chemistry and biology over the last two decades have vastly expanded the repertoire of nucleic acid structure and function with potential application in multiple areas of science and technology, including sensing and analytical applications. DNA oligonucleotides represent popular tools for the development of sensing platforms due to their low cost, rich structural polymorphism, and their ability to bind to cognate ligands with sensitivity and specificity rivaling those for protein enzymes and antibodies. In this review, we give an overview of the "label-free" approach that has been a particular focus of our group and others for the construction of luminescent DNA-based sensing platforms. The label-free strategy aims to overcome some of the drawbacks associated with the use of covalently-labeled oligonucleotides prevalent in electrochemical and optical platforms. Label-free DNA-based probes harness the selective interaction between luminescent dyes and functional oligonucleotides that exhibit a "structure-switching" response upon binding to analytes. Based on the numerous examples of label-free luminescent DNA-based probes reported recently, we envisage that this field would continue to thrive and mature in the years to come. This review article aims to introduce the principles of luminescent oligonucleotide-based probes and highlights the use of the label-free strategy for the construction of simple and inexpensive sensing platforms.
We present the very first worldwide ever-reported electrochemical biosensor based on a memristive effect and DNA aptamers. This novel device is developed to propose a completely new approach in cancer diagnostics. In this study, an affinity-based technique is presented for the detection of the prostate specific antigen (PSA) using DNA aptamers. The hysteretic properties of memristive silicon nanowires functionalized with these DNA aptamers provide a label-free and ultrasensitive biodetection technique. The ultrasensitive detection is hereby demonstrated for PSA with a limit of detection down to 23 aM, best ever published value for electrochemical biosensors in PSA detection. The effect of polyelectrolytes on our memristive devices is also reported to further show how positive or negative charges affect the memristive hysteresis. With such an approach, combining memristive nanowires and aptamers, memristive aptamer-based biosensors can be proposed to detect a wide range of cancer markers with unprecedent ultrasensitivities to also address the issue of an early detection of cancer.
Label-free nanosensors can detect disease markers to provide point-of-care diagnosis that is low-cost, rapid, specific and sensitive(1-13). However, detecting these biomarkers in physiological fluid samples is difficult because of problems such as biofouling and non-specific binding, and the resulting need to use purified buffers greatly reduces the clinical relevance of these sensors. Here, we overcome this limitation by using distinct components within the sensor to perform purification and detection. A microfluidic purification chip simultaneously captures multiple biomarkers from blood samples and releases them, after washing, into purified buffer for sensing by a silicon nanoribbon detector. This two-stage approach isolates the detector from the complex environment of whole blood, and reduces its minimum required sensitivity by effectively pre-concentrating the biomarkers. We show specific and quantitative detection of two model cancer antigens from a 10 mu l sample of whole blood in less than 20 min. This study marks the first use of label-free nanosensors with physiological solutions, positioning this technology for rapid translation to clinical settings.
Highly sensitive, label-free biodetection methods have applications in both the fundamental research and healthcare diagnostics arenas. Therefore, the development of new transduction methods and the improvement of the existing methods will significantly impact these areas. A brief overview of the different types of biosensors and the critical parameters governing their performance will be given. Additionally, a more in-depth discussion of optical devices, surface functionalization methods to increase device specificity, and fluidic techniques to improve sample delivery will be reviewed. The development of label-free biochemical sensors, including sensor modalities, measurand recognition techniques, and microfluidic delivery systems are discussed to provide a roadmap for future sensor design.
Current drug discovery is dominated by label-dependent molecular approaches, which screen drugs in the context of a predefined and target-based hypothesis in vitro. Given that target-based discovery has not transformed the industry, phenotypic screen that identifies drugs based on a specific phenotype of cells, tissues, or animals has gained renewed interest. However, owing to the intrinsic complexity in drug target interactions, there is often a significant gap between the phenotype screened and the ultimate molecular mechanism of action sought. This paper presents a label-free strategy for early drug discovery. This strategy combines label-free cell phenotypic profiling with computational approaches, and holds promise to bridge the gap by offering a kinetic and holistic representation of the functional consequences of drugs in disease relevant cells that is amenable to mechanistic deconvolution.
Nowadays, proteomic studies no longer focus only on identifying as many proteins as possible in a given sample, but aiming for an accurate quantification of them. Especially in clinical proteomics, the investigation of variable protein expression profiles can yield useful information on pathological pathways or biomarkers and drug targets related to a particular disease. Over the time, many quantitative proteomic approaches have been established allowing researchers in the field of proteomics to refer to a comprehensive toolbox of different methodologies. In this review we will give an overview of different methods of quantitative proteomics with focus on label-free proteomics and its use in clinical proteomics.
Lipid rafts are submicron proteolipid domains thought to be responsible for membrane trafficking and signaling. Their small size and transient nature put an understanding of their dynamics beyond the reach of existing techniques, leading to much contention as to their exact role. Here, we exploit the differences in light scattering from lipid bilayer phases to achieve dynamic imaging of nanoscopic lipid domains without any labels. Using phase-separated droplet interface bilayers we resolve the diffusion of domains as small as 50 nm in radius and observe nanodomain formation, destruction, and dynamic coalescence with a domain lifetime of 220 ± 60 ms. Domain dynamics on this timescale suggests an important role in modulating membrane protein function.
We present label-free functional photoacoustic imaging of the ocular microvasculature in living animals. The anterior segment of an adult mouse was imaged with a laser exposure level well within the American National Standards Institute safety standards. Individual red blood cells traveling along the iris capillaries were clearly resolved, and the hemoglobin oxygen saturation in the iris microvasculature was imaged spectrally. We believe that photoacoustic imaging has the potential to advance the diagnosis and treatment of ocular diseases in humans. (C) 2009 Optical Society of America