Analyzing Blood Cells of High-Risk Myelodysplastic Syndrome Patients Using Interferometric Phase Microscopy and Fluorescent Flow Cytometry.

Myelodysplastic syndromes (MDSs) are a group of potentially deadly diseases that affect the morphology and function of neutrophils. Rapid diagnosis of MDS is crucial for the initiation of treatment that can vastly improve disease outcome. In this work, we present a new approach for detecting morphological differences between neutrophils isolated from blood samples of high-risk MDS patients and blood bank donors (BBDs). Using fluorescent flow cytometry, neutrophils were stained with 2',7'-dichlorofluorescin diacetate (DCF), which reacts with reactive oxygen species (ROS), and Hoechst, which binds to DNA. We observed that BBDs possessed two cell clusters (designated H and L), whereas MDS patients possessed a single cluster (L). Later, we used FACS to sort the H and the L cells and used interferometric phase microscopy (IPM) to image the cells without utilizing cell staining. IPM images showed that H cells are characterized by low optical path delay (OPD) in the nucleus relative to the cytoplasm, especially in cell vesicles containing ROS, whereas L cells are characterized by low OPD in the cytoplasm relative to the nucleus and no ROS-containing vesicles. Moreover, L cells present a higher average OPD and dry mass compared to H cells. When examining neutrophils from MDS patients and BBDs by IPM during flow, we identified ~20% of cells as H cells in BBDs in contrast to ~4% in MDS patients. These results indicate that IPM can be utilized for the diagnosis of complex hematological pathologies such as MDS.

Sub-second, super-resolved imaging of biological systems using parallel EO-STED.

We present a parallel stimulated emission depletion (STED) nanoscope with no mechanical moving parts and sub-millisecond pixel dwell times, relying on electro-optical (EO) phase modulators. The nanoscope offers 1225-fold parallelization over single-doughnut-scanning STED and achieves a spatial resolution of 35 nm. We imaged immunostained nuclear pore complexes of zebrafish within their natural biological environment, demonstrating spatial and temporal resolutions of 56 nm and 0.2 s, respectively. Furthermore, we show parallel EO-STED sub-second imaging of microtubules inside living cells. Finally, we reveal the nanodomain organization of a eukaryotic initiation factor within the processing bodies of fixed cells. The potential of parallel EO-STED to offer microsecond pixel dwell times over large fields of view promises millisecond STED imaging.

Simulation of single-protein nanopore sensing shows feasibility for whole-proteome identification.

Single-molecule techniques for protein sequencing are making headway towards single-cell proteomics and are projected to propel our understanding of cellular biology and disease. Yet, single cell proteomics presents a substantial unmet challenge due to the unavailability of protein amplification techniques, and the vast dynamic-range of protein expression in cells. Here, we describe and computationally investigate the feasibility of a novel approach for single-protein identification using tri-color fluorescence and plasmonic-nanopore devices. Comprehensive computer simulations of denatured protein translocation processes through the nanopores show that the tri-color fluorescence time-traces retain sufficient information to permit pattern-recognition algorithms to correctly identify the vast majority of proteins in the human proteome. Importantly, even when taking into account realistic experimental conditions, which restrict the spatial and temporal resolutions as well as the labeling efficiency, and add substantial noise, a deep-learning protein classifier achieves 97% whole-proteome accuracies. Applying our approach for protein datasets of clinical relevancy, such as the plasma proteome or cytokine panels, we obtain ~98% correct protein identification. This study suggests the feasibility of a method for accurate and high-throughput protein identification, which is highly versatile and applicable.

Optically-Monitored Nanopore Fabrication Using a Focused Laser Beam.

Solid-state nanopores (ssNPs) are extremely versatile single-molecule sensors and their potential have been established in numerous biomedical applications. However, the fabrication of ssNPs remains the main bottleneck to their widespread use. Herein, we introduce a rapid and localizable ssNPs fabrication method based on feedback-controlled optical etching. We show that a focused blue laser beam irreversibly etches silicon nitride (SiNx) membranes in solution. Furthermore, photoluminescence (PL) emitted from the SiNx is used to monitor the etching process in real-time, hence permitting rate adjustment. Transmission electron microscopy (TEM) images of the etched area reveal an inverted Gaussian thickness profile, corresponding to the intensity point spread function of the laser beam. Continued laser exposure leads to the opening of a nanopore, which can be controlled to reproducibly fabricate nanopores of different sizes. The optically-formed ssNPs exhibit electrical noise on par with TEM-drilled pores, and translocate DNA and proteins readily. Notably, due to the localized thinning, the laser-drilled ssNPs exhibit highly suppressed background PL and improved spatial resolution. Given the total control over the nanopore position, this easily implemented method is ideally suited for electro-optical sensing and opens up the possibility of fabricating large nanopore arrays in situ.

SOFI Simulation Tool: A Software Package for Simulating and Testing Super-Resolution Optical Fluctuation Imaging.

Super-resolution optical fluctuation imaging (SOFI) allows one to perform sub-diffraction fluorescence microscopy of living cells. By analyzing the acquired image sequence with an advanced correlation method, i.e. a high-order cross-cumulant analysis, super-resolution in all three spatial dimensions can be achieved. Here we introduce a software tool for a simple qualitative comparison of SOFI images under simulated conditions considering parameters of the microscope setup and essential properties of the biological sample. This tool incorporates SOFI and STORM algorithms, displays and describes the SOFI image processing steps in a tutorial-like fashion. Fast testing of various parameters simplifies the parameter optimization prior to experimental work. The performance of the simulation tool is demonstrated by comparing simulated results with experimentally acquired data.