Smart Hydrogel Microfluidics for Single-Cell Multiplexed Secretomic Analysis with High Sensitivity

Small ◽  
2018 ◽  
Vol 14 (49) ◽  
pp. 1802918 ◽  
Author(s):  
Myat Noe Hsu ◽  
Shih-Chung Wei ◽  
Song Guo ◽  
Dinh-Tuan Phan ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
High Sensitivity ◽  
Smart Hydrogel

scholarly journals Differentiating and quantifying exosome secretion from a single cell using quasi-bound states in the continuum

Nanophotonics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 1081-1086 ◽  
Author(s):  
Abdoulaye Ndao ◽  
Liyi Hsu ◽  
Wei Cai ◽  
Jeongho Ha ◽  
Junhee Park ◽  
...  
Keyword(s):  
Single Cell ◽  
Optical Sensors ◽  
Figure Of Merit ◽  
Bound States ◽  
Single Cell Analysis ◽  
Optical Cavity ◽  
High Sensitivity ◽  
Cell Secretion ◽  
The Continuum ◽  
Refractive Index Detection

AbstractOne of the key challenges in biology is to understand how individual cells process information and respond to perturbations. However, most of the existing single-cell analysis methods can only provide a glimpse of cell properties at specific time points and are unable to provide cell secretion and protein analysis at single-cell resolution. To address the limits of existing methods and to accelerate discoveries from single-cell studies, we propose and experimentally demonstrate a new sensor based on bound states in the continuum to quantify exosome secretion from a single cell. Our optical sensors demonstrate high-sensitivity refractive index detection. Because of the strong overlap between the medium supporting the mode and the analytes, such an optical cavity has a figure of merit of 677 and sensitivity of 440 nm/RIU. Such results facilitate technological progress for highly conducive optical sensors for different biomedical applications.

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 CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing

2020 ◽  
Author(s):  
Liang Fang ◽  
Guipeng Li ◽  
Qionghua Zhu ◽  
Huanhuan Cui ◽  
Yunfei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Concanavalin A ◽  
Cancer Cell Line ◽  
High Sensitivity ◽  
Cost Effective ◽  
Subtle Difference ◽  
Single Cell Sequencing ◽  
Minimal Sample ◽  
Ifn Γ ◽  
Multiple Drugs

AbstractSample multiplexing facilitates single cell sequencing by reducing costs, revealing subtle difference between similar samples, and identifying artifacts such as cell doublets. However, universal and cost-effective strategies are rather limited. Here, we reported a Concanavalin A-based Sample Barcoding strategy (CASB), which could be followed by both single-cell mRNA and ATAC (assay for transposase accessible chromatin) sequencing techniques. The method involves minimal sample processing, thereby preserving intact transcriptomic or epigenomic patterns. We demonstrated its high labeling efficiency, high accuracy in assigning cells/nuclei to samples regardless of cell type and genetic background, as well as high sensitivity in detecting doublets by two applications: 1) CASB followed by scRNA-seq to track the transcriptomic dynamics of a cancer cell line perturbed by multiple drugs, which revealed compound-specific heterogeneous response; 2) CASB together with both snATAC-seq and scRNA-seq to illustrate the IFN-γ-mediated dynamic changes on epigenome and transcriptome profile, which identified the transcription factor underlying heterogeneous IFN-γ response.

scholarly journals Normalisr: normalization and association testing for single-cell CRISPR screen and co-expression

2021 ◽  
Author(s):  
Lingfei Wang
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Large Scale ◽  
High Sensitivity ◽  
Statistical Hypothesis ◽  
P Value ◽  
Statistical Hypothesis Testing ◽  
Experimental Conditions ◽  
Library Size ◽  
Association Testing

AbstractSingle-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Here we present Normalisr, a linear-model-based normalization and statistical hypothesis testing framework that unifies single-cell differential expression, co-expression, and CRISPR scRNA-seq screen analyses. By systematically detecting and removing nonlinear confounding from library size, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased P-value estimation. We use Normalisr to reconstruct robust gene regulatory networks from trans-effects of gRNAs in large-scale CRISPRi scRNA-seq screens and gene-level co-expression networks from conventional scRNA-seq.

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.

Detection of Clonal Hodgkin and Reed-Sternberg Cells With Identical Somatically Mutated and Rearranged VH Genes in Different Biopsies in Relapsed Hodgkin’s Disease

Blood ◽  
1998 ◽  
Vol 92 (8) ◽  
pp. 2899-2907 ◽  
Author(s):  
Martina Vockerodt ◽  
Marta Soares ◽  
Holger Kanzler ◽  
Ralf Küppers ◽  
Dieter Kube ◽  
...  
Keyword(s):  
Single Cell ◽  
Hodgkin's Disease ◽  
Mononuclear Cells ◽  
Residual Disease ◽  
Hodgkin’S Disease ◽  
Clinical Importance ◽  
Variable Region ◽  
High Sensitivity ◽  
Gene Rearrangements ◽  
American Society

Abstract Hodgkin’s disease (HD) represents a malignant lymphoma in which the putative malignant Hodgkin and Reed-Sternberg (H-RS) cells are rare and surrounded by abundant reactive cells. Single-cell analyses showed that H-RS cells regularly bear clonal Ig gene rearrangements. However, there is little information on the clinical evolution of HD in a given patient. In this study, we used the single-cell polymerase chain reaction (PCR) to identify H-RS cells with clonal Ig gene rearrangements in biopsy specimens of patients with relapsed HD. The obtained clonal variable region heavy-chain (VH) gene rearrangements were used to construct tumor-clone-specific oligonucleotides spanning the complementarity determining region (CDR) III and somatically mutated areas in the rearranged VHgene. A number of biopsies were obtained during a period of 3 years from two HD patients. H-RS cells with identical VHrearrangements were detected in two separate infiltrated lymph nodes from one patient with nodular sclerosis HD. In a second patient with mixed cellularity HD subtype, clonal VH rearrangements with identical sequences were detected in infiltrated spleen and two lymph node biopsies. Despite the high sensitivity of the PCR method used (one clonal cell in 105 mononuclear cells), residual H-RS cells were not found in peripheral blood, leukapheresis material, purified CD34+ stem cells or bone marrow. The results show that different specimens from relapsed patients suffering from classical HD carry the same clonotypic IgH rearrangements with identical somatic mutations, demonstrating the persistence and the dissemination of a clonal tumor cell population. Thus, PCR assays with CDRIII-specific probes derived from clonal H-RS cells are of clinical importance in monitoring the dissemination of HD and tumor progression and could be useful for analysis of minimal residual disease after autologous stem cell transplantation. © 1998 by The American Society of Hematology.

High-throughput single-cell analysis of low copy number β-galactosidase by a laboratory-built high-sensitivity flow cytometer

2013 ◽  
Vol 48 ◽  
pp. 49-55 ◽  
Author(s):  
Lingling Yang ◽  
Tianxun Huang ◽  
Shaobin Zhu ◽  
Yingxing Zhou ◽  
Yunbin Jiang ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Copy Number ◽  
Single Cell Analysis ◽  
High Sensitivity ◽  
Flow Cytometer ◽  
Cell Analysis ◽  
Low Copy Number

Detection and Quantification of Bacterial Autofluorescence at the Single-Cell Level by a Laboratory-Built High-Sensitivity Flow Cytometer

2012 ◽  
Vol 84 (3) ◽  
pp. 1526-1532 ◽  
Author(s):  
Lingling Yang ◽  
Yingxing Zhou ◽  
Shaobin Zhu ◽  
Tianxun Huang ◽  
Lina Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
High Sensitivity ◽  
Flow Cytometer ◽  
Single Cell Level ◽  
Cell Level ◽  
Detection And Quantification

scholarly journals Visualizing and isolating iron-reducing microorganisms at single cell level

2020 ◽  
Author(s):  
Cuifen Gan ◽  
Rongrong Wu ◽  
Yeshen Luo ◽  
Jianhua Song ◽  
Dizhou Luo ◽  
...  
Keyword(s):  
Single Cell ◽  
Iron Reduction ◽  
High Sensitivity ◽  
Ex Situ ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Sorter ◽  
Fluorescent Intensity

AbstractIron-reducing microorganisms (FeRM) play key roles in many natural and engineering processes. Visualizing and isolating FeRM from multispecies samples are essential to understand the in-situ location and geochemical role of FeRM. Here, we visualized FeRM by a “turn-on” Fe2+-specific fluorescent chemodosimeter (FSFC) with high sensitivity, selectivity and stability. This FSFC could selectively identify and locate active FeRM from either pure culture, co-culture of different bacteria or sediment-containing samples. Fluorescent intensity of the FSFC could be used as an indicator of Fe2+ concentration in bacterial cultures. By integrating FSFC with a single cell sorter, we obtained three FSFC-labeled cells from an enriched consortia and all of them were subsequently evidenced to be capable of iron-reduction and two unlabeled cells were evidenced to have no iron-reducing capability, further confirming the feasibility of the FSFC.ImportanceVisualization and isolation of FeRM from samples containing multispecies are commonly needed by researchers from different disciplines, such as environmental microbiology, environmental sciences and geochemistry. However, no available method has been reported. In this study, we provid a solution to visualize FeRM and evaluate their activity even at single cell level. Integrating with single cell sorter, FeRM can also be isolated from samples containing multispecies. This method can be used as a powerful tool to uncover the in-situ or ex-situ role of FeRM and their interactions with ambient microbes or chemicals.

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