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.

scholarly journals Electrochemical Sensors for Detection of Markers on Tumor Cells

2021 ◽  
Vol 22 (15) ◽  
pp. 8184
Author(s):  
Han Zhou ◽  
Xin Du ◽  
Zhenguo Zhang
Keyword(s):  
Tumor Cell ◽  
Tumor Cells ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Protein Detection ◽  
High Sensitivity ◽  
Immunological Methods ◽  
Cell Detection ◽  
Tumor Cell Detection ◽  
Incidence And Mortality

In recent years, the increasing incidence and mortality of cancer have inspired the development of accurate and rapid early diagnosis methods in order to successfully cure cancer; however, conventional methods used for detecting tumor cells, including histopathological and immunological methods, often involve complex operation processes, high analytical costs, and high false positive rates, in addition to requiring experienced personnel. With the rapid emergence of sensing techniques, electrochemical cytosensors have attracted wide attention in the field of tumor cell detection because of their advantages, such as their high sensitivity, simple equipment, and low cost. These cytosensors are not only able to differentiate tumor cells from normal cells, but can also allow targeted protein detection of tumor cells. In this review, the research achievements of various electrochemical cytosensors for tumor cell detection reported in the past five years are reviewed, including the structures, detection ranges, and detection limits of the cytosensors. Certain trends and prospects related to the electrochemical cytosensors are also discussed.

scholarly journals Integrated Field’s metal microelectrodes based microfluidic impedance cytometry for cell-in-droplet quantification

2018 ◽  
Author(s):  
Jatin Panwar ◽  
Rahul Roy
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Microfluidic Channel ◽  
High Sensitivity ◽  
Cell Detection ◽  
Label Free ◽  
Single Cell Detection ◽  
Electrode Fabrication ◽  
Impedance Cytometry

AbstractMicrofluidic impedance cytometry (MIC) provides a non-optical and label-free method for single cell detection and classification in microfluidics. However, the cleanroom intensive infrastructure required for MIC electrode fabrication limits its wide implementation in microfluidic analysis. To bypass the conventional metal (platinum) electrode fabrication protocol, we fabricated coplanar ‘in-contact’ Field’s metal (icFM) microelectrodes in multilayer elastomer devices with a single photolithography step. Our icFM microelectrodes displayed excellent and comparable performance to the platinum electrodes for detection of single erythrocytes with a lock-in amplifier based MIC setup. We further characterized it for water-in-oil droplets generated in a T-junction microfluidic channel and found high sensitivity and long-term operational stability of these electrodes. Finally, to facilitate droplet based single cell analysis, we demonstrate detection and quantification of single cells entrapped in aqueous droplets.

scholarly journals Nanopore Technology for the Application of Protein Detection

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1942
Author(s):  
Xiaoqing Zeng ◽  
Yang Xiang ◽  
Qianshan Liu ◽  
Liang Wang ◽  
Qianyun Ma ◽  
...  
Keyword(s):  
Single Molecule ◽  
Protein Detection ◽  
High Sensitivity ◽  
Single Molecule Detection ◽  
Sequence Detection ◽  
Fast Detection ◽  
Material Basis ◽  
Sensing Technology ◽  
Detection Technology ◽  
Structure Recognition

Protein is an important component of all the cells and tissues of the human body and is the material basis of life. Its content, sequence, and spatial structure have a great impact on proteomics and human biology. It can reflect the important information of normal or pathophysiological processes and promote the development of new diagnoses and treatment methods. However, the current techniques of proteomics for protein analysis are limited by chemical modifications, large sample sizes, or cumbersome operations. Solving this problem requires overcoming huge challenges. Nanopore single molecule detection technology overcomes this shortcoming. As a new sensing technology, it has the advantages of no labeling, high sensitivity, fast detection speed, real-time monitoring, and simple operation. It is widely used in gene sequencing, detection of peptides and proteins, markers and microorganisms, and other biomolecules and metal ions. Therefore, based on the advantages of novel nanopore single-molecule detection technology, its application to protein sequence detection and structure recognition has also been proposed and developed. In this paper, the application of nanopore single-molecule detection technology in protein detection in recent years is reviewed, and its development prospect is investigated.

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 Characterization of CRISPR/Cas9 RANKL knockout mesenchymal stem cell clones based on single-cell printing technology and emulsion coupling assay as a low-cellularity workflow for single-cell cloning

2020 ◽  
Author(s):  
Tobias Groß ◽  
Csaba Jeney ◽  
Darius Halm ◽  
Günter Finkenzeller ◽  
G. Björn Stark ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Large Scale ◽  
Single Cells ◽  
Protein Detection ◽  
Cell Line Development ◽  
Cell Printing ◽  
Cell Clones ◽  
The University

AbstractThe homogeneity of the genetically modified single-cells is a necessity for many applications such as cell line development, gene therapy, and tissue engineering and in particular for regenerative medical applications. The lack of tools to effectively isolate and characterize CRISPR/Cas9 engineered cells is considered as a significant bottleneck in these applications. Especially the incompatibility of protein detection technologies to confirm protein expression changes without a preconditional large-scale clonal expansion, creates a gridlock in many applications. To ameliorate the characterization of engineered cells, we propose an improved workflow, including single-cell printing/isolation technology based on fluorescent properties with high yield, a genomic edit screen (surveyor assay), mRNA rtPCR assessing altered gene expression and a versatile protein detection tool called emulsion-coupling to deliver a high-content, unified single-cell workflow. The workflow was exemplified by engineering and functionally validating RANKL knockout immortalized mesenchymal stem cells showing altered bone formation capacity of these cells. The resulting workflow is economical, without the requirement of large-scale clonal expansions of the cells with overall cloning efficiency above 30% of CRISPR/Cas9 edited cells. Nevertheless, as the single-cell clones are comprehensively characterized at an early, highly parallel phase of the development of cells including DNA, RNA, and protein levels, the workflow delivers a higher number of successfully edited cells for further characterization, lowering the chance of late failures in the development process.Author summaryI completed my undergraduate degree in biochemistry at the University of Ulm and finished my master's degree in pharmaceutical biotechnology at the University of Ulm and University of applied science of Biberach with a focus on biotechnology, toxicology and molecular biology. For my master thesis, I went to the University of Freiburg to the department of microsystems engineering, where I developed a novel workflow for cell line development. I stayed at the institute for my doctorate, but changed my scientific focus to the development of the emulsion coupling technology, which is a powerful tool for the quantitative and highly parallel measurement of protein and protein interactions. I am generally interested in being involved in the development of innovative molecular biological methods that can be used to gain new insights about biological issues. I am particularly curious to unravel the complex and often poorly understood protein interaction pathways that are the cornerstone of understanding cellular functionality and are a fundamental necessity to describe life mechanistically.

scholarly journals Limitations in the assessment of radon dose: the role of activity measurements in the human body

2019 ◽  
Vol 24 ◽  
pp. 92
Author(s):  
J. Kalef-Ezra ◽  
S. Valakis
Keyword(s):  
Effective Dose ◽  
Human Body ◽  
Detection Efficiency ◽  
Medical Physics ◽  
High Sensitivity ◽  
Temporal Variations ◽  
Cold Season ◽  
Whole Body ◽  
Group I ◽  
Activity Measurements

Radon-222 is classified in the Group I of the human carcinogens. The in situ decay of inhaled 222Rn and its short-lived decay products (T1/2 <30 min) is the main source of radiation burden to the general population of natural origin. The corresponding effective dose is routinely calculated as the product of the 222Rn concentration in air, a predetermined dosimetric constant and a factor that depends on the space type (e.g. residential or public building, cave, mine, etc). However, in practice, there are large spatial and temporal variations in the activity ratio of each progeny to 222Rn in air, the characteristics of the progeny carrying particles and the metabolism of each progeny depending on air quality, as well as differences in the anatomic and physiological characteristics between individuals, that vary substantially even with time. Therefore, the currently employed dosimetric approach may introduce large uncertainties. In the hypothetical case of acute deposition and full retention in the human body of equal activities of all 222Rn progeny, about 93% of the effective dose is due to the decaying 214Po. The 214Po activity can be assessed by measurement of its γ-emitting precursor, 214Bi, which is in full equilibrium with 214Po in the human body. The 214Bi activity can be measured using a high-sensitivity whole-body counter with high counting uniformity, such as the one in use at the Ioannina University Medical Physics Department. Its detection efficiency and its dependence on body shape and size were assessed by Monte Carlo simulations. Measurements carried out in healthy adult volunteers residing at a short distance from the counter, indicated a mean total body 214Bi activity (TBBi) of ~100 Bq during the cold season of the year and lower during the hot one. Higher mean TBBi levels were found in male than in female adults. Therefore, TBBi measurements may allow for accurate radon-related risk assessment on individual base.

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 Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells

2002 ◽  
Vol 24 (2-3) ◽  
pp. 101-111 ◽  
Author(s):  
Carolina Wählby ◽  
Joakim Lindblad ◽  
Mikael Vondrus ◽  
Ewert Bengtsson ◽  
Lennart Björkesten
Keyword(s):  
Statistical Analysis ◽  
Single Cells ◽  
Quality Measurement ◽  
Feedback System ◽  
Cell Segmentation ◽  
New Combination ◽  
Processing Step ◽  
Segmentation Quality ◽  
Fully Automatic

Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO‐cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

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