Low-background neutron reflectometry from solid/liquid interfaces

Liquid cells are an increasingly common sample environment for neutron reflectometry experiments and are critical for measuring the properties of materials at solid/liquid interfaces. Background scattering determines the maximum useful scattering vector, and hence the spatial resolution, of the neutron reflectometry measurement. The primary sources of background are the liquid in the cell reservoir and the materials forming the liquid cell itself. Thus, characterization and mitigation of these background sources are necessary for improvements in the signal-to-background ratio and resolution of neutron reflectometry measurements employing liquid cells. Single-crystal silicon is a common material used for liquid cells due to its low incoherent scattering cross section for neutrons, and the path lengths of the neutron beam through silicon can be several centimetres in modern cell designs. Here, a liquid cell is constructed with a sub-50 µm thick liquid reservoir encased in single-crystal silicon. It is shown that, at high scattering vectors, inelastic scattering from silicon represents a significant portion of the scattering background and is, moreover, structured, confounding efforts to correct for it by established background subtraction techniques. A significant improvement in the measurement quality is achieved using energy-analyzed detection. Energy-analyzed detection reduces the scattering background from silicon by nearly an order of magnitude, and from fluids such as air and liquids by smaller but significant factors. Combining thin liquid reservoirs with energy-analyzed detection and the high flux of the CANDOR polychromatic reflectometer at the NIST Center for Neutron Research, a background-subtracted neutron reflectivity smaller than 10−8 from a liquid cell sample is reported.

Few-Photon Spectral Confocal Microscopy for Cell Imaging Using Superconducting Transition Edge Sensor

A superconducting transition edge sensor (TES) is an energy-dispersive single-photon detector that distinguishes the wavelength of each incident photon from visible to near-infrared (NIR) without using spectral dispersive elements. Here, we introduce an application of the TES technique for confocal laser scanning microscopy (CLSM) as proof of our concept of ultra-sensitive and wide-band wavelength range color imaging for biological samples. As a reference sample for wide-band observation, a fixed fluorescence-labeled cell sample stained with three different color dyes was observed using our TES-based CLSM method. The three different dyes were simultaneously excited by irradiating 405 and 488 nm lasers, which were coupled using an optical fiber combiner. Even when irradiated at low powers of 80 and 120 nW with the 405 and 488 nm lasers respectively, emission signals were spectrally detected by the TES and categorized into four wavelength bands: up to 500 nm (blue), from 500 to 600 nm (green), from 600 to 800 nm (red), and from 800 to 1,200 nm (NIR). Using a single scan, an RGB color image and an NIR image of the fluorescent cell sample were successfully captured with tens of photon signals in a 40 ms exposure time for each pixel. This result demonstrates that TES is a useful wide-band spectral photon detector in the field of life sciences.

Pulsing Test for Defect of Resistive Word Line in DRAM Main Cell using WGFMU (Waveform Generator Fast Measurement Unit)

In this paper, we compare and describe the difference between the oscilloscope pulsing test and the WGFMU (Waveform Generator Fast Measurement Unit) in analyzing the defect of high resistance in DRAM main cell sample. The nanoprobe system has many constraints in the pulsing analysis utilizing the oscilloscope and pulse generator. There are certain cases where the system cannot support analysis when the saturation current is extremely minimal, such as the DRAM cell. In this paper, we address this constraint and propose a new way to conduct pulsing tests using the WGFMU's arbitrary linear waveform generator in the nanoprobe system.

Single cell high-throughput qRT-PCR protocol

Single cell high-throughput qRT-PCR protocol combines high sensitivity technique of single cell qPCR with high-throughput qPCR technology that can generate data from 96 samples and 96 genes in a single experiment. It can be adapted for various sample types- cell culture, tissue samples and extracted RNA (10 pg) and measured on traditional qPCR and high-throughput qPCR platforms. The workflow is comprised of four steps – cell lysis, reverse transcription, pre-amplification and qPCR. Key features of this protocol are; processing low input samples directly to reverse transcription without RNA extraction which minimizes sample loss, pre-amplification enables amplification of cDNA from single cells to detectable levels for qPCR and measuring up to 400 genes from a single cell sample/10 pg of RNA (starting material). Robust, reproducible and versatile this protocol can be adapted to several upstream and downstream techniques.

A flat embedding method for transmission electron microscopy reveals an unknown mechanism of tetracycline

Transmission electron microscopy of cell sample sections is a popular technique in microbiology. Currently, ultrathin sectioning is done on resin-embedded cell pellets, which consumes milli- to deciliters of culture and results in sections of randomly orientated cells. This is problematic for rod-shaped bacteria and often precludes large-scale quantification of morphological phenotypes due to the lack of sufficient numbers of longitudinally cut cells. Here we report a flat embedding method that enables observation of thousands of longitudinally cut cells per single section and only requires microliter culture volumes. We successfully applied this technique to Bacillus subtilis, Escherichia coli, Mycobacterium bovis, and Acholeplasma laidlawii. To assess the potential of the technique to quantify morphological phenotypes, we monitored antibiotic-induced changes in B. subtilis cells. Surprisingly, we found that the ribosome inhibitor tetracycline causes membrane deformations. Further investigations showed that tetracycline disturbs membrane organization and localization of the peripheral membrane proteins MinD, MinC, and MreB. These observations are not the result of ribosome inhibition but constitute a secondary antibacterial activity of tetracycline that so far has defied discovery.

Defining the cell surface proteomic landscape of multiple myeloma reveals immunotherapeutic strategies and biomarkers of drug resistance

ABSTRACTThe myeloma cell surface proteome (“surfaceome”) not only determines tumor interaction with the microenvironment but serves as an emerging arena for therapeutic development. Here, we use glycoprotein capture proteomics to first define surface markers most-enriched on myeloma when compared to B-cell malignancy models, revealing unexpected biological signatures unique to malignant plasma cells. We next integrate our proteomic dataset with existing transcriptome databases, nominating CCR10 and TXNDC11 as possible monotherapeutic targets and CD48 as a promising co-target for increasing avidity of BCMA-directed cellular therapies. We further identify potential biomarkers of resistance to both proteasome inhibitors and lenalidomide including changes in CD53, EVI2B, CD10, and CD33. Comparison of short-term treatment with chronic resistance delineates large differences in surface proteome profile under each type of drug exposure. Finally, we develop a miniaturized version of the surface proteomics protocol and present the first surface proteomic profile of a primary myeloma patient plasma cell sample. Our dataset provides a unique resource to advance the biological, therapeutic, and diagnostic understanding of myeloma.

PSIII-7 Evaluating sow colostrum somatic cell count as an indicator for colostrum composition or litter characteristics

Sow colostrum is essential during early piglet life to provide passive immunity via immunoglobulins and leukocytes.The somatic cell population in colostrum and milk consists of leukocytes and epithelial cells. Somatic cell count (SCC) of milk is commonly used as an indicator of cow milk quality and health status, but not commonly measured in lactating gilts or sows. An experiment was conducted to evaluate the relationship between colostrum SCC and colostrum composition or litter performance. A total of 194 frozen (-20℃) colostrum samples from Large White × Landrace females were evaluated for SCC. Cells were pelleted and washed twice by centrifugation (10 min. at 400 x g; 4℃) in Phosphate Buffered Saline (PBS; pH 7.2, LifeTechnologies). The resulting cell sample was labelled with a nuclear dye (LDS751) and cells counted via micro-capillary bench-top flow cytometer (Guava EasyCyte Plus, Millipore). Final SCC (cells/mL) was calculated by dividing sample cell count (cells/sample) by original sample volume (mL/sample). Data was analyzed for relationship between SCC and response variables using Pearson correlation. A pairwise comparison was used to evaluate SCC by parity category (gilts vs. sows). Results were considered significant at P< 0.05. Final somatic cell counts ranged from 5.8×104 to 2.9×106 cells/mL. Sows had decreased (P=0.033) SCC compared to gilts (3.0 ×105 vs. 3.7 ×105). There was no evidence for a relationship (P >0.05) between SCC and piglet weight at 24h or weaning, 24h litter gain, 24h or pre-wean mortality, sow backfat or body weight. Colostrum total solids, protein, lactose, and immunoglobulin G concentration showed no evidence for relationship (P >0.05) to SCC. Colostrum fat showed a weak positive correlation (P=0.018, R=0.18) with SCC. In conclusion, colostrum SCC is lower in sows than gilts, but does not appear to correlate to colostrum composition or litter performance.

Multi-set Pre-processing of Multicolor Flow Cytometry Data

Flow Cytometry is an analytical technology to simultaneously measure multiple markers per single cell. Ten thousands to millions of single cells can be measured per sample and each sample may contain a different number of cells. All samples may be bundled together, leading to a ‘multi-set’ structure. Many multivariate methods have been developed for Flow Cytometry data but none of them considers this structure in their quantitative handling of the data. The standard pre-processing used by existing multivariate methods provides models mainly influenced by the samples with more cells, while such a model should provide a balanced view of the biomedical information within all measurements. We propose an alternative ‘multi-set’ preprocessing that corrects for the difference in number of cells measured, balancing the relative importance of each multi-cell sample in the data while using all data collected from these expensive analyses. Moreover, one case example shows how multi-set pre-processing may benefit removal of undesired measurement-to-measurement variability and another where class-based multi-set pre-processing enhances the studied response upon comparison to the control reference samples. Our results show that adjusting data analysis algorithms to consider this multi-set structure may greatly benefit immunological insight and classification performance of Flow Cytometry data.

Prefrontal neural dynamics for behavioral decisions and attentional control

ABSTRACTComplex neural dynamics in the prefrontal cortex contribute to context-dependent decisions and attentional competition. To analyze these dynamics, we apply demixed principal component analysis to activity of a primate prefrontal cell sample recorded in a cued target detection task. The results track dynamics of cue and object coding, feeding into movements along a target present-absent decision axis in a low-dimensional subspace of population activity. For a single stimulus, object and cue coding are seen mainly in the contralateral hemisphere. Later, a developing decision code in both hemispheres may reflect interhemispheric communication. With a target in one hemifield and a competing distractor in the other, each hemisphere initially encodes the contralateral object, but finally, decision coding is dominated by the task-relevant target. Tracking complex neural events in a low-dimensional activity subspace illuminates information flow towards task-appropriate behavior, unravelling mechanisms of prefrontal computation.