Use of hyperspectral imaging for label-free decoding and detection of biomarkers

Rapid, Highly Sensitive, and Label-Free Pathogen Assay System Using a Solid-Phase Self-Interference Recombinase Polymerase Amplification Chip and Hyperspectral Interferometry

Recombinase polymerase amplification (RPA) is a useful pathogen identification method. Several label-free detection methods for RPA amplicons have been developed in recent years. However, these methods still lack sensitivity, specificity, efficiency, or simplicity. In this study, we propose a rapid, highly sensitive, and label-free pathogen assay system based on a solid-phase self-interference RPA chip (SiSA-chip) and hyperspectral interferometry. The SiSA-chips amplify and capture RPA amplicons on the chips, rather than irrelevant amplicons such as primer dimers, and the SiSA-chips are then analysed by hyperspectral interferometry. Optical length increases of SiSA-chips are used to demonstrate RPA detection results, with a limit of detection of 1.90 nm. This assay system can detect as few as six copies of the target 18S rRNA gene of Plasmodium falciparum within 20 min, with a good linear relationship between the detection results and the concentration of target genes (R² = 0.9903). Single nucleotide polymorphism (SNP) genotyping of the dhfr gene of Plasmodium falciparum is also possible using the SiSA-chip, with as little as 1% of mutant gene distinguished from wild-type loci (m/wt). This system offers a high-efficiency (20 min), high-sensitivity (6 copies/reaction), high-specificity (1% m/wt), and low-cost (∼1/50 of fluorescence assays for RPA) diagnosis method for pathogen DNA identification. Therefore, this system is promising for fast identification of pathogens to help diagnose infectious diseases, including SNP genotyping.

Visualization of quantitative lipid distribution in mouse liver through near-infrared hyperspectral imaging

Lipid distribution in the liver provides crucial information for diagnosing the severity of fatty liver and fatty liver-associated liver cancer. Therefore, a noninvasive, label-free, and quantitative modality is eagerly anticipated. We report near-infrared hyperspectral imaging for the quantitative visualization of lipid content in mouse liver based on partial least square regression (PLSR) and support vector regression (SVR). Analysis results indicate that SVR with standard normal variate pretreatment outperforms PLSR by achieving better root mean square error (15.3 mg/g) and higher determination coefficient (0.97). The quantitative mapping of lipid content in the mouse liver is realized using SVR.

Development of a fast calibration method for image mapping spectrometry

An image mapping spectrometer (IMS) is a snapshot hyperspectral imager that simultaneously captures both the spatial (x, y) and spectral (λ) information of incoming light. The IMS maps a three-dimensional (3D) datacube (x, y, λ) to a two-dimensional (2D) detector array (x, y) for parallel measurement. To reconstruct the original 3D datacube, one must construct a lookup table that connects voxels in the datacube and pixels in the raw image. Previous calibration methods suffer from either low speed or poor image quality. We herein present a slit-scan calibration method that can significantly reduce the calibration time while maintaining high accuracy. Moreover, we quantitatively analyzed the major artifact in the IMS, the striped image, and developed three numerical methods to correct for it.

Snapshot hyperspectral light field imaging using image mapping spectrometry

In this Letter, we present a snapshot hyperspectral light field imaging system using a single camera. By integrating an unfocused light field camera with a snapshot hyperspectral imager, the image mapping spectrometer, we captured a five-dimensional (5D) ($x,y,u,v,\lambda $x,y,u,v,λ) ($x,y,$x,y, spatial coordinates; $u,v,$u,v, emittance angles; $\lambda ,$λ, wavelength) datacube in a single camera exposure. The corresponding volumetric image ($x,y,z$x,y,z) at each wavelength is then computed through a scale-depth space transform. We demonstrated the snapshot advantage of our system by imaging the spectral-volumetric scenes in real time.

An interferometric imaging biosensor using weighted spectrum analysis to confirm DNA monolayer films with attogram sensitivity

Interferometric imaging biosensors are powerful and convenient tools for confirming the existence of DNA monolayer films on silicon microarray platforms. However, their accuracy and sensitivity need further improvement because DNA molecules contribute to an inconspicuous interferometric signal both in thickness and size. Such weaknesses result in poor performance of these biosensors for low DNA content analyses and point mutation tests. In this paper, an interferometric imaging biosensor with weighted spectrum analysis is presented to confirm DNA monolayer films. The interferometric signal of DNA molecules can be extracted and then quantitative detection results for DNA microarrays can be reconstructed. With the proposed strategy, the relative error of thickness detection was reduced from 88.94% to merely 4.15%. The mass sensitivity per unit area of the proposed biosensor reached 20 attograms (ag). Therefore, the sample consumption per unit area of the target DNA content was only 62.5 zeptomoles (zm), with the volume of 0.25 picolitres (pL). Compared with the fluorescence resonance energy transfer (FRET), the measurement veracity of the interferometric imaging biosensor with weighted spectrum analysis is free to the changes in spotting concentration and DNA length. The detection range was more than 1µm. Moreover, single nucleotide mismatch could be pointed out combined with specific DNA ligation. A mutation experiment for lung cancer detection proved the high selectivity and accurate analysis capability of the presented biosensor.

Label-Free Method Using a Weighted-Phase Algorithm To Quantitate Nanoscale Interactions between Molecules on DNA Microarrays

White light interference is used as a label-free method to detect nanoscale changes on surfaces. However, the signal-to-noise ratio of the white light interference method is very low, thus resulting in inaccurate results. In this paper, we report a corrected label-free method based on hyperspectral interferometry to overcome the shortcoming of the white light interference method. A platform based on hyperspectral interferometry was established, and a DNA hybridization microarray was constructed to quantitate thickness variation of molecules on a solid surface. We used fluorescence resonance energy transfer (FRET) to validate the results of our method. Compared to conventional fluorescence-labeled method like FRET, our method has advantages because it does not require a fluorescent label and has a detection limit of 1.78 nm, a high accuracy, and wide detection range (5-64 bp).

A smart device for label-free and real-time detection of gene point mutations based on the high dark phase contrast of vapor condensation

A smart device for label-free and real-time detection of gene point mutation-related diseases was developed based on the high dark phase contrast of vapor condensation. The main components of the device included a Peltier cooler and a mini PC board for image processing. Heat from the hot side of the Peltier cooler causes the fluid in a copper chamber to evaporate, and the vapor condenses on the surface of a microarray chip placed on the cold side of the cooler. The high dark phase contrast of vapor condensation relative to the analytes on the microarray chip was explored. Combined with rolling circle amplification, the device visualizes less-to-more hydrophilic transitions caused by gene trapping and DNA amplification. A lung cancer gene point mutation was analysed, proving the high selectivity and multiplex analysis capability of this low-cost device.

Nanostructural morphology master-regulated the cell capture efficiency of multivalent aptamers

The nanostructural features of stretched multivalent aptamers significantly improve the cell enrichment efficiency to about 16 fold higher than normal multivalent aptamers.

Push-broom hyperspectral image calibration and enhancement by 2D deconvolution with a variant response function estimate

In this paper, we propose a novel method for spectral and spatial calibration and resolution enhancement of hyperspectral images by a two-step procedure. The spectral and spatial variability of the hyperspectral imaging system response function is characterized by a global parametric model, which is derived from a pair of calibration images corresponding to an exactly defined calibration target and a set of gas-discharge lamps. A 2D Richardson-Lucy deconvolution-based algorithm is used to remove the distortions and enhance the resolution of subsequently acquired hyperspectral images. The results of the characterization and deconvolution process obtained by the proposed method are thoroughly evaluated by an independent set of exactly defined calibration and spectral targets, and compared to the existing state-of-the-art characterization method. The proposed method significantly improves the spectral and spatial coregistration and provides more than five-fold resolution enhancement in the spatial and two-fold resolution enhancement in the spectral domain.