Nucleic acid detection is widely used in life science and clinical medical diagnosis. Innovative methods and platform research to improve its key performances are of great significance to ensure population health, promote precision medical technology, and even ensure social stability and development. Most of the existing nucleic acid detection technologies utilized PCR as the amplification method, relying on professional and complex scientific instruments and thus is time-consuming and laborious. Fortunately, RPA offers a feasible alternative. It has the advantages of fast amplification speed, high sensitivity, simple primer design, no temperature cycle control and complex manual operations. However, the detection of amplified products is difficult and costly, and there is a lack of low-cost real-time detection methods with parallel multiple detection abilities. In this work, a label-free and real-time RPA amplicon detection method based on hyperspectral interferometry is presented. A solid-phase biochip helps to capture the RPA product in a real-time meaner and the interference spectrum signal is used to read the solid thickness increment brought by the amplicon. A Fourier domain thickness computation method contributes to calculating the thickness increase and excluding scattering noise. The detection sensitivity reaches 6 copies/reaction and the consuming time is less than 20 min. Moreover, the detection method can also be used for single point mutation readout with the specificity of merelya1%mutation-wild type ratio. Combined with a microfluidic platform, parallel, simultaneous and multiple tests can be realized with 3 microliters.
The rapid and accurate identification and classification of cell types is of great significance in scientific research and clinical diagnosis. We proposed the wide-field hyperspectral interferometry rapid label-free (WHIRL)imaging method. Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. In machine learning, support-vector machine (SVM) is one of most commonly used supervised learning models. We cultured a variety of cells and performed hyperspectral imaging by spot scanning with a spectrometer. Then use the SVM method for classification. The accuracy of distinguishing benign from malignant is >99%, and the accuracy of distinguishing different lung cancer subtypes is >90%,which indicates a promising prospect for cell identification and classification based on WHIRL imaging method.
As a biomarker for the diagnosis and treatment monitoring of various diseases, exosomes widely exist in body fluids such as blood, urine and saliva. However, its small particle size and low content are difficult to enrich. Therefore, a fast, high-purity enrichment method, and a fast, high-sensitivity, high-resolution, and low detection limit detection method are particularly important. We developed an automated fully integrated system for the enrichment and detection of exosomes. Using magnetic beads modified with anionic polymers to capture exosomes, adjust the pH of the exosome solution to acidic, and use the electrostatic adsorption between the positive charge on the surface of exosomes and the negative charge on the surface of the anionic polymer-modified substrate to achieve exosome capture. The captured extracellular vesicles are eluted from the surface of the magnetic beads by using a neutral or slightly alkaline eluent and using electrostatic repulsion to achieve the purpose of separation and enrichment of extracellular vesicles. The method is fast and efficient, can be automated with a small instrument, and can exclude favorable nucleic acid interferences. The eluted exosome protein is fixed on the substrate by chemical modification using quantitative interference exosome surface protein detection technology, and the connection between the exosome surface protein and the antibody is realized by immunoadsorption. Hyperspectral interferometry was used to quantitatively analyze the optical path increment on the substrate surface, to determine whether the exosome sample was bound to the antibody, and to detect the protein content of the exosome surface in parallel. This method can achieve sub-nanometer detection accuracy, and can detect exosomes whose size is smaller than the diffraction limit. Finally, the enrichment and detection of exosomes were automated.
Type 2 diabetes mellitus is one of the most common metabolic diseases in the world. However, frequent blood glucose testing causes continual harm to diabetics, which cannot meet the needs of early diagnosis and long-term tracking of diabetes. Thus non-invasive adjuvant diagnosis methods are urgently needed, enabling early screening of the population for diabetes, the evaluation of diabetes risk, and assessment of therapeutic effects. The human eye plays an important role in painless and non-invasive approaches, because it is considered an internal organ but can be easily be externally observed. We developed an AI model to predict the probability of diabetes from scleral images taken by a specially developed instrument, which could conveniently and quickly collect complete scleral images in four directions and perform artificial intelligence (AI) analysis in 3 min without any reagent consumption or the need for a laboratory. The novel optical instrument could adaptively eliminate reflections and collected shadow-free scleral images. 177 subjects were recruited to participate in this experiment, including 127 benign subjects and 50 malignant subjects. The blood sample and sclera images from each subject was obtained. The scleral image classification model achieved a mean AUC over 0.85, which indicates great potential for early screening of practical diabetes during periodic physical checkups or daily family health monitoring. With this AI scleral features imaging and analysis method, diabetic patients’ health conditions can be rapidly, noninvasively, and accurately analyzed, which offers a platform for noninvasive forecasting, early diagnosis, and long-term monitoring for diabetes and its complications.
Quantitative phase imaging (QPI) has quickly emerged as a powerful tool for label-free living cell morphology and metabolism monitoring. However, for current QPI techniques, interference signals from different layers overlay with each other and impede nanoscale optical sectioning. This phenomenon leads to unsatisfactory performances for optically thick or complex scattering biological samples. To address this challenge, we have developed an alternative quantitative phase microscopy with computational hyperspectral interferometry. Nanoscale optical sectioning could be achieved with Fourier domain spectral decomposition. Morphological fluctuations and refractive index distribution could be reconstructed simultaneously with 89.2 nm axial resolution and 1.91 nm optical path difference sensitivity. With this method, we established a label-free cell imaging system for long-term cellular dry mass measurement and in-situ dynamic single cell monitoring. Different intrinsic cell growth characteristics of dry mass between HeLa cells and Human Cervical Epithelial Cells (HCerEpiC) were studied. The dry mass of HeLa cells consistently increased before M phase, whereas that of HCerEpiC increased and then decreased. The maximum growth rate of HeLa cells was 11.7% higher than that of HCerEpiC. We also use the proposed method and system to explore the relationship between cellular dry mass distributions and drug effects for cancer cells. The results show that cells with higher nuclear dry mass and nuclear density standard deviations were more likely to survive the chemotherapy. The presented work shows potential values for cell growth dynamics research, cell health characterization, medication guidance and adjuvant drug development.
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