Machine learning–enabled perception-based detection of protein biomarkers was achieved in gynecologic cancer patient biofluids.

No abstract

A model has been developed to account for and prevent the anomalies encountered in topographic images of transition metal dichalcogenide monolayers using dynamic atomic force microscopy (dAFM). The height of WS2 monolayers measured using dAFM appeared to be increased or decreased, resulting from the interactions between the tip and the surface. The hydrophilic SiO2 substrate appeared higher than the weakly hydrophilic WS2 when the tip amplitude was low or at a high set point (high force). Large amplitudes and low set points corrected the step height inversion, but did not recover the true step height. Removing water from the sample resulted in an order of magnitude reduced variation in step height, but the WS2 appeared inverted except at low amplitudes and high set points. Our model explains the varying step heights in dAFM of TMDs as a result of varying tip-sample interactions between the sample and substrate, in the presence or absence of capillaries. To eliminate contrast inversion, high amplitudes can be used to reduce the effect of capillary forces. However, when capillaries are not present, low amplitudes and high set points produce images with proper contrast due to tool operation in the repulsive regime on both materials.

Amyloid-beta (Aβ) deposition occurs in the early stages of Alzheimer’s disease (AD), but the early detection of Aβ is a persistent challenge. Herein, we engineered a near-infrared optical nanosensor capable of detecting Aβ intracellularly in live cells and intracranially in vivo. The sensor is composed of single-walled carbon nanotubes functionalized with Aβ wherein Aβ-Aβ interactions drive the response. We found that the Aβ nanosensors selectively responded to Aβ via solvatochromic modulation of the near-infrared emission of the nanotube. The sensor tracked Aβ accumulation in live cells and, upon intracranial administration in a genetic model of AD, signaled distinct responses in aged mice. This technology enables the interrogation of molecular mechanisms underlying Aβ neurotoxicity in the development of AD in living systems.

Nanomaterials are the subject of a range of biomedical, commercial, and environmental investigations involving measurements in living cells and tissues. Accurate quantification of nanomaterials, at the tissue, cell, and organelle levels, is often difficult, however, in part due to their inhomogeneity. Here, we propose a method that uses the distinct optical properties of a heterogeneous nanomaterial preparation in order to improve quantification at the single-cell and organelle level. We developed “hyperspectral counting”, which employs diffraction-limited imaging via hyperspectral microscopy of a diverse set of fluorescent nanomaterials to estimate particle number counts in live cells and subcellular structures. A mathematical model was developed, and Monte Carlo simulations were employed, to improve the accuracy of these estimates, enabling quantification with single-cell and single-endosome resolution. We applied this nanometrology technique with single-walled carbon nanotubes and identified an upper limit of the rate of uptake into cellsapproximately 3,000 nanotubes endocytosed within 30 min. In contrast, conventional region-of-interest counting results in a 230% undercount. The method identified significant heterogeneity and a broad non-Gaussian distribution of carbon nanotube uptake within cells. For example, while a particular cell contained an average of 1 nanotube per endosome, the heterogeneous distribution resulted in over 7 nanotubes localizing within some endosomes, substantially changing the accounting of subcellular nanoparticle concentration distributions. This work presents a method to quantify the cellular and subcellular concentrations of a heterogeneous carbon nanotube reference material, with implications for the nanotoxicology, drug/gene delivery, and nanosensor fields.