The use of advanced spectral imaging to reveal nanoparticle identity in biological samples.

Nanoparticles (NPs) have been used in drug delivery therapies, medical diagnostic strategies, and as current Covid-19 vaccine carriers. Many microscope-based imaging systems have been introduced to facilitate detection and visualization of NPs. Unfortunately, none can differentiate the core and the shell of NPs. Spectral imaging has been used to distinguish a drug molecule and its metabolite. We have recently integrated this technology to a resolution of 9 nm by using artificial intelligence-driven analyses. Such a resolution allowed us to collect many robust datapoints for each pixel of an image. Our analyses could recognize 45 spectral points within a pixel to detect unlabeled Ag-NPs and Au-NPs in single live cells and tissues (liver, heart, spleen and kidneys). The improved resolution and software provided a more specific fingerprinting for each single molecule, allowing simultaneous analyses of 990 complex interactions from the 45 points for each molecule within a pixel of an image. This in turn allowed us to detect surface-functionalization of Ag-NPs to distinguish the core from the shell of Ag-NPs for the first time. Our studies were validated using various laborious and time-consuming conventional techniques. We propose that spectral imaging has tremendous potential to study NP localization and identification in biological samples at a high temporal and spatial resolution, based primarily on spectral identity information.

Label-free spectral imaging to study drug distribution and metabolism in single living cells.

During drug development, evaluation of drug and its metabolite is an essential process to understand drug activity, stability, toxicity and distribution. Liquid chromatography (LC) coupled with mass spectrometry (MS) has become the standard analytical tool for screening and identifying drug metabolites. Unlike LC/MS approach requiring liquifying the biological samples, we showed that spectral imaging (or spectral microscopy) could provide high-resolution images of doxorubicin (dox) and its metabolite doxorubicinol (dox'ol) in single living cells. Using this new method, we performed measurements without destroying the biological samples. We calculated the rate constant of dox translocating from extracellular moiety into the cell and the metabolism rate of dox to dox'ol in living cells. The translocation rate of dox into a single cell for spectral microscopy and LC/MS approaches was similar (~ 1.5 pM min-1 cell-1). When compared to spectral microscopy, the metabolism rate of dox was underestimated for about every 500 cells using LC/MS. The microscopy approach further showed that dox and dox'ol translocated to the nucleus at different rates of 0.8 and 0.3 pM min-1, respectively. LC/MS is not a practical approach to determine drug translocation from cytosol to nucleus. Using various methods, we confirmed that when combined with a high-resolution imaging, spectral characteristics of a molecule could be used as a powerful approach to analyze drug metabolism. We propose that spectral microscopy is a new method to study drug localization, translocation, transformation and identification with a resolution at a single cell level, while LC/MS is more appropriate for drug screening at an organ or tissue level.

Novel automated three-dimensional genome scanning based on the nuclear architecture of telomeres.

Telomeres, the end of chromosomes, are organized in a nonoverlapping fashion and form microterritories in nuclei of normal cells. Previous studies have shown that normal and tumor cell nuclei differ in their 3D telomeric organization. The differences include a change in the spatial organization of the telomeres, in telomere numbers and sizes and in the presence of telomeric aggregates. Previous attempts to identify the above parameters of 3D telomere organization were semi-automated. Here we describe the automation of 3D scanning for telomere signatures in interphase nuclei based on three-dimensional fluorescent in situ hybridization (3D-FISH) and, for the first time, define its sensitivity in tumor cell detection. The data were acquired with a high-throughput scanning/acquisition system that allows to measure cells and acquire 3D images of nuclei at high resolution with 40 × or 60 × oil and at a speed of 10,000-15,000 cells h(-1) , depending on the cell density on the slides. The automated scanning, TeloScan, is suitable for large series of samples and sample sizes. We define the sensitivity of this automation for tumor cell detection. The data output includes 3D telomere positions, numbers of telomeric aggregates, telomere numbers, and telomere signal intensities. We were able to detect one aberrant cell in 1,000 normal cells. In conclusions, we are able to detect tumor cells based on 3D architectural profiles of the genome. This new tool could, in the future, assist in patient diagnosis, in the detection of minimal residual disease, in the analysis of treatment response and in treatment decisions.

Three-dimensional nuclear telomere architecture is associated with differential time to progression and overall survival in glioblastoma patients.

The absence of biological markers allowing for the assessment of the evolution and prognosis of glioblastoma (GBM) is a major impediment to the clinical management of GBM patients. The observed variability in patients' treatment responses and in outcomes implies biological heterogeneity and the existence of unidentified patient categories. Here, we define for the first time three GBM patient categories with distinct and clinically predictive three-dimensional nuclear-telomeric architecture defined by telomere number, size, and frequency of telomeric aggregates. GBM patient samples were examined by three-dimensional fluorescent in situ hybridization of telomeres using two independent three-dimensional telomere-measurement tools (TeloView program [P(1)] and SpotScan system [P(2)]). These measurements identified three patients categories (categories 1-3), displaying significant differences in telomere numbers/nucleus (P(1) = .0275; P(2)