Faculty Opinions recommendation of High-sensitivity measurements of multiple kinase activities in live single cells.

2014 ◽  
Author(s):  
Larry Kane
Keyword(s):  
Single Cells ◽  
High Sensitivity

scholarly journals HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

2021 ◽  
Author(s):  
Qing Xie ◽  
Chengong Han ◽  
Victor Jin ◽  
Shili Lin
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Real Data ◽  
Cell Types ◽  
Sequencing Depth ◽  
2D Data ◽  
Structural Zeros

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

scholarly journals scHiCSRS: A Self-Representation Smoothing Method with Gaussian Mixture Model for Imputing single-cell Hi-C Data

2021 ◽  
Author(s):  
Shili Lin ◽  
Qing Xie
Keyword(s):  
Single Cell ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Single Cells ◽  
High Sensitivity ◽  
Gaussian Mixture ◽  
Smoothing Method ◽  
Intermediate Step ◽  
Downstream Analysis ◽  
Structural Zeros

Motivation: Single-cell Hi-C techniques make it possible to study cell-to-cell variability in genomic features. However, excess zeros are commonly seen in single-cell Hi-C (scHi-C) data, making scHi-C matrices extremely sparse and bringing extra difficulties in downstream analysis. The observed zeros are a combination of two events: structural zeros for which the loci never inter- act due to underlying biological mechanisms, and dropouts or sampling zeros where the two loci interact but are not captured due to insufficient sequencing depth. Although quality improvement approaches have been proposed as an intermediate step for analyzing scHi-C data, little has been done to address these two types of zeros. We believe that differentiating between structural zeros and dropouts would benefit downstream analysis such as clustering. Results: We propose scHiCSRS, a self-representation smoothing method that improves the data quality, and a Gaussian mixture model that identifies structural zeros among observed zeros. scHiCSRS not only takes spatial dependencies of a scHi-C 2D data structure into account but also borrows information from similar single cells. Through an extensive set of simulation studies, we demonstrate the ability of scHiCSRS for identifying structural zeros with high sensitivity and for accurate imputation of dropout values in sampling zeros. Downstream analysis for three real datasets show that data improved from scHiCSRS yield more accurate clustering of cells than simply using observed data or improved data from several comparison methods.

scholarly journals MQuad enables clonal substructure discovery using single cell mitochondrial variants

2021 ◽  
Author(s):  
Aaron Wing Cheung Kwok ◽  
Chen Qiao ◽  
Rongting Huang ◽  
Mai-Har Sham ◽  
Joshua W. K. Ho ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Copy Number Variations ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Single Cell Sequencing ◽  
Mtdna Variants ◽  
Python Package

AbstractMitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available via https://github.com/single-cell-genetics/MQuad.

scholarly journals High-Plex Multiomic Analysis in FFPE Tissue at Single-Cellular and Subcellular Resolution by Spatial Molecular Imaging

2021 ◽  
Author(s):  
Shanshan He ◽  
Ruchir Bhatt ◽  
Brian Birditt ◽  
Carl Brown ◽  
Emily Brown ◽  
...  
Keyword(s):  
Data Analysis ◽  
Hamming Distance ◽  
Detection Efficiency ◽  
Single Cells ◽  
Protein Detection ◽  
High Sensitivity ◽  
Cell Segmentation ◽  
Cell Detection ◽  
Subcellular Resolution ◽  
Per Gene

Spatial Molecular Imager (SMI) is an automated microscope imaging system with microfluidic reagent cycling, for high-plex, spatial in-situ detection of multiomic targets (RNA and protein) on FFPE and other intact samples with subcellular resolution. The key attributes of the CosMx™ SMI platform (NanoString®, Seattle, WA) include: 1) high-plex and high-sensitivity imaging chemistry that works for both RNA and protein detection, 2) three-dimensional subcellular-resolution image analysis with a target localization accuracy of ~50 nm in the XY plane, 3) large Hamming-distance encoding scheme with low error rate (0.0092 false calls per cell per gene) and low background (~ 0.04 counts per cell per gene), 4) high-throughput (up to 1 million cells per sample, four samples per run), 5) antibody-based cell segmentation methods, and 6) compatibility with formalin-fixed, paraffin-embedded (FFPE) samples. In this study, 980 RNAs and 80 proteins were measured at subcellular resolution in FFPE cultured cell pellets, as well as FFPE tissues from biobanked samples of non-small cell lung cancer (NSCLC) and breast cancer. Cross-platform analysis using 16 cancer cell lines validated high-correlation (R2 ~0.77) and high sensitivity (~1.44 FPKM/TPM; roughly 1 to 2 copies of RNA per cell) when compared to RNA-seq. Real-world archived NSCLC FFPE tumor sections revealed greater than 94% cell detection efficiency for RNA, despite the low RNA quality QV200 20% to the medium quality 65%. The accuracy of protein expression measurements was independent of the level of multiplexing, as demonstrated by the linear behavior of nested multiplexing panels (R2 > 0.9). At 980-plex RNA detection, data analysis allowed identification of over 18 distinct cell types, at least 10 unique tumor microenvironment neighborhoods, and over 100 pairwise ligand-receptor interactions. Data from 8 NSCLC samples comprising over 800,000 single cells and ~260 million transcripts are released into the public domain (www.nanostring.com) to allow for extended data analysis by the entire spatial biology research community.

Fluorescence microscopy of molecular organization and dynamics in cultured cells

1992 ◽  
Vol 50 (1) ◽  
pp. 550-551
Author(s):  
Yu-li Wang
Keyword(s):  
Image Processing ◽  
Cultured Cells ◽  
Single Cells ◽  
High Sensitivity ◽  
Light Level ◽  
Molecular Organization ◽  
Live Cells ◽  
Weak Signals ◽  
Simple Processing ◽  
Sensitivity Specificity

Over the past ten years various technical advances have allowed the direct study of molecular activities in cultured cells under a fluorescence microscope. Fluorescent probes are well known for their high sensitivity, specificity and amenability to various spectroscopic analyses. When used in conjunction with low-light-level detectors and image processing computers, high resolution images of weak signals from single cells can be successfully acquired. In addition, the availability of digitized images has greatly facilitated the extraction of photometric and morphometric information.We use Zeiss inverted microscopes equipped with epi-illuminators and Dage-MTI ISIT video cameras or Photometrics cooled CCD cameras. Custom incubator systems built on the microscope stages allow the maintenance of live cells for up to several days. The signals are processed with image processing systems (Imaging Technologies) interfaced with graphics workstations (Silicon Graphics, Model 3130 or 4D/20) or personal computers (386/33). All images are acquired by frame averaging/signal integration, followed by subtraction of the dark noise, and storage as computer files. A variation of this simple processing strategy has allowed the detection of extremely weak signals that are essentially invisible on unprocessed ISIT images. Computer programs are then used to display sequences or images as motion pictures, to measure the linear dimension and angular orientation, and to integrate intensities over defined areas.

Nitric Oxide Detection and Visualization in Biological Systems. Applications of the FNOCT Method

2000 ◽  
Vol 381 (7) ◽  
pp. 575-582 ◽  
Author(s):  
Petra Meineke ◽  
Ursula Rauen ◽  
Herbert de Groot ◽  
Hans-Gert Korth ◽  
Reiner Sustmann
Keyword(s):  
Nitric Oxide ◽  
Endothelial Cells ◽  
Single Cells ◽  
High Sensitivity ◽  
Electron System ◽  
No Production ◽  
No Formation ◽  
Extracellular Release ◽  
No Release ◽  
Spermine Nonoate

Abstract Fluorescent Nitric Oxide Cheletropic Traps (FNOCTs) were applied to specifically trap nitric oxide (NO) with high sensitivity. The fluorescent oquinoid ?electron system of the FNOCTs (? = 460 nm, ? = 600 nm) reacts rapidly with NO to a fluorescent phenanthrene system (? = 380 nm, ? = 460 nm). The cyclic nitroxides thus formed react further to nonradical products which exhibit identical fluorescence properties. Using the acid form of the trap (FNOCT-4), NO release by spermine NONOate and by lipopolysaccharide (LPS) activated alveolar macrophages were studied. A maximum extracellular release of NO of 37.5 nmol h[-1] (10[6] cells)[-1] from the macrophages was determined at 11 h after activation. Furthermore, intracellular NO release by LPSactivated macrophages and by microvascular omentum endothelial cells stimulated by the Ca[2+] ionophore A-23187, respectively, was monitored on the single cell level by means of fluorescence microscopy. After loading the cells with the membranepermeating acetoxymethylester derivative FNOCT-5,which is hydrolyzed to a nonpermeating dicarboxylate by intracellular hydrolases, NO formation by the endothelial cells started immediately upon stimulation, whereas start of NO production by the macrophages was delayed with a variation between 4 and 8 h for individual cells. These results demonstrate that the FNOCTs can be used to monitor NO release from single cells, as well as from NOdonating compounds, with high sensitivity and with temporal and spatial resolution.

scholarly journals Flow Virometry Quantification of Host Proteins on the Surface of HIV-1 Pseudovirus Particles

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1296
Author(s):  
Jonathan Burnie ◽  
Vera A. Tang ◽  
Joshua A. Welsh ◽  
Arvin T. Persaud ◽  
Laxshaginee Thaya ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Single Cells ◽  
Surface Current ◽  
High Sensitivity ◽  
Viral Envelope ◽  
High Throughput Analysis ◽  
Associated Proteins ◽  
Hiv 1

The HIV-1 glycoprotein spike (gp120) is typically the first viral antigen that cells encounter before initiating immune responses, and is often the sole target in vaccine designs. Thus, characterizing the presence of cellular antigens on the surfaces of HIV particles may help identify new antiviral targets or impact targeting of gp120. Despite the importance of characterizing proteins on the virion surface, current techniques available for this purpose do not support high-throughput analysis of viruses, and typically only offer a semi-quantitative assessment of virus-associated proteins. Traditional bulk techniques often assess averages of viral preparations, which may mask subtle but important differences in viral subsets. On the other hand, microscopy techniques, which provide detail on individual virions, are difficult to use in a high-throughput manner and have low levels of sensitivity for antigen detection. Flow cytometry is a technique that traditionally has been used for rapid, high-sensitivity characterization of single cells, with limited use in detecting viruses, since the small size of viral particles hinders their detection. Herein, we report the detection and surface antigen characterization of HIV-1 pseudovirus particles by light scattering and fluorescence with flow cytometry, termed flow virometry for its specific application to viruses. We quantified three cellular proteins (integrin α4β7, CD14, and CD162/PSGL-1) in the viral envelope by directly staining virion-containing cell supernatants without the requirement of additional processing steps to distinguish virus particles or specific virus purification techniques. We also show that two antigens can be simultaneously detected on the surface of individual HIV virions, probing for the tetraspanin marker, CD81, in addition to α4β7, CD14, and CD162/PSGL-1. This study demonstrates new advances in calibrated flow virometry as a tool to provide sensitive, high-throughput characterization of the viral envelope in a more efficient, quantitative manner than previously reported techniques.

scholarly journals sNucDrop-Seq: Dissecting cell-type composition and neuronal activity state in mammalian brains by massively parallel single-nucleus RNA-Seq

2017 ◽  
Author(s):  
Peng Hu ◽  
Emily Fabyanic ◽  
Zhaolan Zhou ◽  
Hao Wu
Keyword(s):  
Single Cell ◽  
Neuronal Activity ◽  
Low Cost ◽  
Single Cells ◽  
High Sensitivity ◽  
Droplet Microfluidics ◽  
Massively Parallel ◽  
Rna Seq ◽  
Single Nucleus

Massively parallel single-cell RNA sequencing can precisely resolve cellular diversity in a high-throughput manner at low cost, but unbiased isolation of intact single cells from complex tissues, such as adult mammalian brains, is challenging. Here, we integrate sucrose-gradient assisted nuclear purification with droplet microfluidics to develop a highly scalable single-nucleus RNA-Seq approach (sNucDrop-Seq), which is free of enzymatic dissociation and nucleus sorting. By profiling ~11,000 nuclei isolated from adult mouse cerebral cortex, we demonstrate that sNucDrop-Seq not only accurately reveals neuronal and non-neuronal subtype composition with high sensitivity, but also enables analysis of long non-coding RNAs and transient states such as neuronal activity-dependent transcription at single-cell resolution in vivo.

scholarly journals Silicon Photomultipliers as a Low-Cost Fluorescence Detector for Capillary Electrophoresis

2020 ◽  
Author(s):  
Brae Petersen ◽  
Luke Gallion ◽  
Nancy Allbritton
Keyword(s):  
Capillary Electrophoresis ◽  
Open Source ◽  
Dynamic Range ◽  
Low Cost ◽  
Single Cells ◽  
Detection Limits ◽  
High Sensitivity ◽  
Small Sample ◽  
Fluorescence Detector ◽  
Silicon Photomultipliers

Capillary electrophoresis (CE) is a highly efficient separation method capable of handling small sample volumes (~pL) and low (~yoctomole) detection limits, and as such is ideal for applications that require high sensitivity such as single-cell analysis. Low-cost CE instrumentation is quickly expanding but low-cost, open-source fluorescence detectors with ultra-sensitive detection limits are lacking. Silicon photomultipliers (SiPM) are inexpensive, low-footprint detectors with the potential to fill the role as a detector when cost, size, and customization are important. In this work we demonstrate the use of a SiPM in CE with zeptomolar detection limits and a dynamic range spanning five orders of magnitude, comparable to photomultiplier detectors. We characterize the performance of the SiPM as a highly sensitive detector by measuring enzyme activity in single cells. This simple, small footprint, and low-cost (<$130) light detection circuit will be beneficial for open-source, portable, and budget friendly instrumentation requiring high sensitivity.<br>

Sign in / Sign up

Export Citation Format

Share Document