Highly sensitive detection of cancer cells using femtosecond dual-wavelength near-IR two-photon imaging

A machine learning based approach for quantitative evaluation of cell migration in Transwell assays based on deformation characteristics

Many pathological and physiological processes, including embryonic development, immune response and cancer metastasis, involve studies on cell migration, and especially detection methods, for which it is difficult to satisfy the requirements for rapid and quantitative evaluation and analysis. In view of the shortcomings in simultaneously quantifying the number of migrated cells and non-migrated cells using Transwell assays, we propose a novelty approach for the evaluation of cell migration by distinguishing whether the cells have migrated based on the regularity of the cell morphology changes. Traditionally, the status of living cells and dead cells are detected and analyzed by machine learning using some common morphological characteristics, e.g., area and perimeter of the cells. However, the accuracy of detecting whether cells have migrated or not using these common characteristics is not high, and the characteristics are not appropriate for our studies. Therefore, from the point of view of mechanism analysis for the migration behavior, we examined the regularity of different morphology changes of migrated cells and non-migrated cells, and thus discovered the distinguishable morphological characteristics. Then, two deformation characteristics, deformation index and taper index are proposed. Then, a machine learning based algorithm that can identify migrated cells according to the proposed deformation characteristics was devised. In addition, images of migrated cells and non-migrated cells were obtained from the Transwell assays. This algorithm was trained, and was able to successfully identify migrated cells with an accuracy of 84% using the proposed morphological characteristics. This method greatly improves the identification accuracy when compared with the identification of traditional characteristics of which the accuracy was about 54.7%. This machine learning based method might be employed as a potential tool for cell counting and evaluation of cell migration with the aim of reducing time and improving automation compared with the traditional method. This method is effective, rapid, and incorporate advances in artificial intelligence which could be used for adapting the current evaluation methods.

Photoisomerization of Linear and Stacked Isomers of a Charged Styryl Dye: A Tandem Ion Mobility Study

The photoisomerization behavior of styryl 9M, a common dye used in material sciences, is investigated using tandem ion mobility spectrometry (IMS) coupled with laser spectroscopy. Styryl 9M has two alkene linkages, potentially allowing for four geometric isomers. IMS measurements demonstrate that at least three geometric isomers are generated using electrospray ionization with the most abundant forms assigned to a combination of EE (major) and ZE (minor) geometric isomers, which are difficult to distinguish using IMS as they have similar collision cross sections. Two additional but minor isomers are generated by collisional excitation of the electrosprayed styryl 9M ions and are assigned to the EZ and ZZ geometric isomers, with the latter predicted to have a π-stacked configuration. The isomer assignments are supported through calculations of equilibrium structures, collision cross sections, and statistical isomerization rates. Photoexcitation of selected isomers using an IMS-photo-IMS strategy shows that each geometric isomer photoisomerizes following absorption of near-infrared and visible light, with the EE isomer possessing a S₁ ← S₀ electronic transition with a band maximum near 680 nm and shorter wavelength S₂ ← S₀ electronic transition with a band maximum near 430 nm. The study demonstrates the utility of the IMS-photo-IMS strategy for providing fundamental gas-phase photochemical information on molecular systems with multiple isomerizable bonds.

Exploring the LDS 821 dye as a potential NIR probe for the two photon imaging of amyloid fibrils

We report a commercially available benzothiazolium based dye LDS 821 (Styryl 9M) as a near infrared fluorescent probe for the detection of lysozyme amyloid fibrils. Change in the photophysical properties of the dye with respect to the change in viscosity of the environment is investigated. Increment in fluorescence lifetime and quantum yield with increment in viscosity proves the dye as a molecular rotor. The dye, upon binding with lysozyme fibrils, exhibits a red shift in the absorption spectrum with increased quantum yield. Strong fluorescence emission near the biological window as compared with Thioflavin T makes the LDS 821 dye a potential probe for imaging amyloid fibrils in vivo. Molecular docking studies were carried out to understand the mode of interaction between the dye and amyloid fibrils. Nonlinear optical properties of the dye upon incorporation with amyloid fibrils were explored, and they show a sizeable enhancement in two photon absorption with an increase in the concentration of amyloid fibrils. The findings suggest that the nonlinear optical absorption of the LDS 821 dye can be used as an alternative marker for amyloid fibrils.

A Rapid Detection Method for Morphological Characteristics of Biological Cells Based on Phase Imaging

This paper presents a rapid label-free method for the identification of morphological characteristics of biological cells. Based on quantitative phase microscopy as well as the connotation of phase value, the gradient operator of phase and the associated analytic processing are employed to determine the edge of different parts of the samples. A heterogeneous biological cell model is established by simulation to show the mechanism of this method and a polystyrene bead is selected as a sample to confirm its validity by optical experiment. The result agrees well with the actual situation and this approach is proved to have good antinoise ability. Furthermore, a neutrophil is investigated by this method. Based on the optical experiment and the related analysis, the basic structure characteristics of the cell are obtained. It is indicated that the method presented in this paper could be applied to rapid identification and classification of living cells.

Optimizing ultrafast illumination for multiphoton-excited fluorescence imaging

We study the optimal conditions for high throughput two-photon excited fluorescence (2PEF) and three-photon excited fluorescence (3PEF) imaging using femtosecond lasers. We derive relations that allow maximization of the rate of imaging depending on the average power, pulse repetition rate, and noise characteristics of the laser, as well as on the size and structure of the sample. We perform our analysis using ~100 MHz, ~1 MHz and 1 kHz pulse rates and using both a tightly-focused illumination beam with diffraction-limited image resolution, as well loosely focused illumination with a relatively low image resolution, where the latter utilizes separate illumination and fluorescence detection beam paths. Our theoretical estimates agree with the experiments, which makes our approach especially useful for optimizing high throughput imaging of large samples with a field-of-view up to 10x10 cm(2).

Optimization of light delivery by a nanowire-based single cell optical endoscope

Here we present a new design and FDTD simulations of light delivery by a nanowire-based intracellular endoscope. Nanowires can be used for minimally invasive and very local light delivery inside cells. One of the main challenges is coupling of light into the nanowire. We propose a new plasmonic coupler interface between cleaved optical fiber and a nanowire, and optimize light coupling efficiency and contrast.