A multi-scale approach to study biochemical and biophysical aspects of resveratrol on diesel exhaust particle-human primary lung cell interaction

Gaining Biological Insights through Supervised Data Visualization

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE's prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

Droplet Detection and Sorting System in Microfluidics: A Review

Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize a big material or biological population, so millions of droplets can be individually screened based on demand, while the sorting function could extract the droplets of interest to a separate pool from the main droplet library. In this paper, we reviewed droplet detection and active sorting methods that are currently still being widely used for high-through screening applications in microfluidic systems, including the latest updates regarding each technology. We analyze and summarize the merits and drawbacks of each presented technology and conclude, with our perspectives, on future direction of development.

Oxidative potential and in vitro toxicity of particles generated by pyrotechnic smokes in human small airway epithelial cells

Pyrotechnic smokes are widely used in civilian and military applications. The major issue arise from the release of particles after smoke combustion but the health risks related to their exposure are poorly documented whereas toxicity of airborne particles on the respiratory target are very well known. Therefore, this study aimed to explore the in vitro toxicity of the particle fraction of different pyrotechnic smokes. Particles from a red signalling smoke (RSS), an hexachloroethane-based obscuring smoke (HC-OS) and an anti-intrusion smoke (AIS) were collected from the cloud. RSS particles displayed the highest organic fraction (quinones and polycyclic aromatic hydrocarbons) of the three samples characterized. AIS particles contained K and cholesterol derivatives. HC-OS particles were mainly metallic with very high concentrations of Al, Fe and Ca. Intrinsic oxidative potential of smoke particles was measured with two assays. Depletions of DTT by RSS particles was greater than depletion obtained with AIS and HC-OS particles but depletion of acid ascorbic (AA) was only observed with HC-OS particles. In vitro toxicity was assessed by exposing human small airway epithelial cells (SAEC) to various concentrations of particles. After 24 h of exposure, cell viability was not affected but significant modifications of mRNA expression of antioxidant (SOD-1 and -2, catalase, HO-1, NQO-1) and inflammatory markers (IL-6, IL-8, TNF-α) were observed and were dependent on smoke type. Particles rich in metal, such as HC-OS, induced a greatest depletion of AA and a greatest inflammatory response, whereas particles rich in organic compounds, such as RSS, induced a greatest DTT depletion and a greatest antioxidant response. In conclusion, the three smoke particles have an intrinsic oxidative potential and triggered a cell adaptive response. Our study improved the knowledge of particle toxicity of pyrotechnic smokes and scientific approach developed here could be used to study other type of particles.

Granulocyte colony-stimulating factor promotes an aggressive phenotype of colon and breast cancer cells with biochemical changes investigated by single-cell Raman microspectroscopy and machine learning analysis

Granulocyte colony-stimulating factor (G-CSF) is produced at high levels in several cancers and is directly linked with metastasis in gastrointestinal (GI) cancers. In order to further understand the alteration of molecular compositions and biochemical features triggered by G-CSF treatment at molecular and cell levels, we sought to investigate the long term treatment of G-CSF on colon and breast cancer cells measured by label-free, non-invasive single-cell Raman microspectroscopy. Raman spectrum captures the molecule-specific spectral signatures ("fingerprints") of different biomolecules presented on cells. In this work, mouse breast cancer line 4T1 and mouse colon cancer line CT26 were treated with G-CSF for 7 weeks and subsequently analyzed by machine learning based Raman spectroscopy and gene/cytokine expression. The principal component analysis (PCA) identified the Raman bands that most significantly changed between the control and G-CSF treated cells. Notably, here we proposed the concept of aggressiveness score, which can be derived from the posterior probability of linear discriminant analysis (LDA), for quantitative spectral analysis of tumorigenic cells. The aggressiveness score was effectively applied to analyze and differentiate the overall cell biochemical changes of G-CSF-treated two model cancer cells. All these tumorigenic progressions suggested by Raman analysis were confirmed by pro-tumorigenic cytokine and gene analysis. A high correlation between gene expression data and Raman spectra highlights that the machine learning based non-invasive Raman spectroscopy offers emerging and powerful tools to better understand the regulation mechanism of cytokines in the tumor microenvironment that could lead to the discovery of new targets for cancer therapy.

Spectral Markers for T Cell Death and Apoptosis-A Pilot Study on Cell Therapy Drug Product Characterization Using Raman Spectroscopy

Application of Raman spectroscopy as a T cell characterization tool supporting cell therapy drug product development has been evaluated. Statistically significant correlations between a set of Raman signals and established flow cytometry markers associated with apoptosis of T cells detected during drug product cryopreservation are presented in this study. Our study results demonstrate the potential of Raman spectroscopy for label-free measurements of T cell characteristics relevant to cell therapy product design and process control.

Prognostic prospect of soluble programmed cell death ligand-1 in cancer management

Aggressive tissue biopsy is commonly unavoidable in the management of most suspected tumor cases to conclusively verify the presence of cancerous cells through histological assessment. The extracted tissue is also immunostained for detection of antigens (tissue tumor markers) of potential prognostic or therapeutic importance to assist in treatment decision. Although liquid biopsies can be a powerful tool for monitoring treatment response, they are still excluded from standard cancer diagnostics, and their utility is still being debated in the scientific community. With a myriad of soluble tissue tumor markers now being discovered, liquid biopsies could completely change the current paradigms of cancer management. Recently, soluble programmed cell death ligand-1 (sPD-L1), which is found in the peripheral blood, i.e. serum and plasma, has shown potential as a pre-therapeutic predictive marker as well as a prognostic biomarker to monitor treatment efficacy. Thus, this review focuses on the emergence of sPD-L1 and promising technologies for its detection in order to support liquid biopsies for future cancer management.

Imaging of PD-L1 in single cancer cells by SERS-based hyperspectral analysis

We developed a hyperspectral imaging tool based on surface-enhanced Raman spectroscopy (SERS) probes to determine the expression level and visualize the distribution of PD-L1 in individual cells. Electron-microscopic analysis of PD-L1 antibody - gold nanorod conjugates demonstrated binding the cell surface and internalization into endosomal vesicles. Stimulation of cells with IFN-γ or metformin was used to confirm the ability of SERS probes to report treatment-induced changes. The multivariate curve resolution-alternating least squares (MCR-ALS) analysis of spectra provided a greater signal-noise ratio than single peak mapping. However, single peak mapping allowed a systematic subtraction of background and the removal of non-specific binding and endocytic SERS signals. The mean or maximum peak height in the cell or the mean peak height in the area of specific PD-L1 positive pixels was used to estimate the PD-L1 expression levels in single cells. The PD-L1 levels were significantly up-regulated by IFN-γ and inhibited by metformin in human lung cancer cells from the A549 cell line. In conclusion, the method of analyzing hyperspectral SERS imaging data together with systematic and comprehensive removal of non-specific signals allows SERS imaging to be a quantitative tool in the detection of the cancer biomarker, PD-L1.