Single cell CUT&Tag-pro v1

2021 ◽  
Author(s):  
Bingjie Zhang
Keyword(s):  
Histone Modification ◽  
Single Cell ◽  
Single Cells ◽  
Surface Proteins ◽  
Transcriptomic Profiling

scCUT&Tag-pro is a multimodal assay for profiling histone modification coupled with the abundance of surface proteins in single cells. It was developed based on CUT&Tag (Kaya-Okur et al., 2019) and scASAP-seq (Eleni Mimitou et al., 2021). Our approach is compatible with the widely used 10x Genomics Chromium system, and complements recently introduced technologies for simultaneous CUT&Tag and transcriptomic profiling that leverage custom combinatorial indexing workflows. Preprint: https://www.biorxiv.org/content/10.1101/2021.09.13.460120v1.abstract

scholarly journals BREM-SC: A Bayesian Random Effects Mixture Model for Joint Clustering Single Cell Multi-omics Data

2020 ◽  
Author(s):  
Xinjun Wang ◽  
Zhe Sun ◽  
Yanfu Zhang ◽  
Zhongli Xu ◽  
Heng Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Mixture Model ◽  
Random Effects ◽  
Single Cells ◽  
Real Data ◽  
Transcriptome Profiling ◽  
Surface Expression ◽  
Surface Proteins ◽  
Cell Surface Expression ◽  
Cell Transcriptome

ABSTRACTDroplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10X Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Despite the rapid advances in technologies, novel statistical methods and computational tools for analyzing multi-modal CITE-Seq data are lacking. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data. Through simulation studies and analysis of public and in-house real data sets, we successfully demonstrated the validity and advantages of this method in fully utilizing both types of data to accurately identify cell clusters. In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the clustering uncertainty for each single cell. This new method will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries, particularly in the area of immunology.

scholarly journals BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data

2020 ◽  
Vol 48 (11) ◽  
pp. 5814-5824 ◽  
Author(s):  
Xinjun Wang ◽  
Zhe Sun ◽  
Yanfu Zhang ◽  
Zhongli Xu ◽  
Hongyi Xin ◽  
...  
Keyword(s):  
Single Cell ◽  
Mixture Model ◽  
Random Effects ◽  
Single Cells ◽  
Real Data ◽  
Transcriptome Profiling ◽  
Surface Expression ◽  
Surface Proteins ◽  
Cell Surface Expression ◽  
Cell Transcriptome

Abstract Droplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10× Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Despite the rapid advances in technologies, novel statistical methods and computational tools for analyzing multi-modal CITE-Seq data are lacking. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data. Through simulation studies and analysis of public and in-house real data sets, we successfully demonstrated the validity and advantages of this method in fully utilizing both types of data to accurately identify cell clusters. In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the clustering uncertainty for each single cell. This new method will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries, particularly in the area of immunology.

scholarly journals Digital Assays Part II: Digital Protein and Cell Assays

2017 ◽  
Vol 22 (4) ◽  
pp. 387-405 ◽  
Author(s):  
Amar S. Basu
Keyword(s):  
Enzymatic Activity ◽  
Single Cell ◽  
Single Cells ◽  
Digital Pcr ◽  
Surface Proteins ◽  
Broad Perspective ◽  
Antigenic Proteins ◽  
Conventional Elisa ◽  
Cell Assays ◽  
Biological Entities

A digital assay is one in which the sample is partitioned into many containers such that each partition contains a discrete number of biological entities (0, 1, 2, 3, . . .). A powerful technique in the biologist’s toolkit, digital assays bring a new level of precision in quantifying nucleic acids, measuring proteins and their enzymatic activity, and probing single-cell genotype and phenotype. Where part I of this review focused on the fundamentals of partitioning and digital PCR, part II turns its attention to digital protein and cell assays. Digital enzyme assays measure the kinetics of single proteins with enzymatic activity. Digital enzyme-linked immunoassays (ELISAs) quantify antigenic proteins with 2 to 3 log lower detection limit than conventional ELISA, making them well suited for low-abundance biomarkers. Digital cell assays probe single-cell genotype and phenotype, including gene expression, intracellular and surface proteins, metabolic activity, cytotoxicity, and transcriptomes (scRNA-seq). These methods exploit partitioning to 1) isolate single cells or proteins, 2) detect their activity via enzymatic amplification, and 3) tag them individually by coencapsulating them with molecular barcodes. When scaled, digital assays reveal stochastic differences between proteins or cells within a population, a key to understanding biological heterogeneity. This review is intended to give a broad perspective to scientists interested in adopting digital assays into their workflows.

In-Situ Dual Beam (FIBSEM) Techniques for Probe Pad Deposition and Dielectric Integrity Inspection in 0.2 μm Technology DRAM Single Cells

1999 ◽  
Author(s):  
Gunnar Zimmermann ◽  
Richard Chapman
Keyword(s):  
Electron Beam ◽  
Single Cell ◽  
Single Cells ◽  
Ion Beam ◽  
Gate Oxide ◽  
Ion Milling ◽  
Dual Beam ◽  
Sem Imaging ◽  
Innovative Techniques

Abstract Dual beam FIBSEM systems invite the use of innovative techniques to localize IC fails both electrically and physically. For electrical localization, we present a quick and reliable in-situ FIBSEM technique to deposit probe pads with very low parasitic leakage (Ipara < 4E-11A at 3V). The probe pads were Pt, deposited with ion beam assistance, on top of highly insulating SiOx, deposited with electron beam assistance. The buried plate (n-Band), p-well, wordline and bitline of a failing and a good 0.2 μm technology DRAM single cell were contacted. Both cells shared the same wordline for direct comparison of cell characteristics. Through this technique we electrically isolated the fail to a single cell by detecting leakage between the polysilicon wordline gate and the cell diffusion. For physical localization, we present a completely in-situ FIBSEM technique that combines ion milling, XeF2 staining and SEM imaging. With this technique, the electrically isolated fail was found to be a hole in the gate oxide at the bad cell.

scholarly journals TUP1, CPH1 and EFG1 Make Independent Contributions to Filamentation in Candida albicans

Genetics ◽  
2000 ◽  
Vol 155 (1) ◽  
pp. 57-67 ◽  
Author(s):  
Burkhard R Braun ◽  
Alexander D Johnson
Keyword(s):  
Candida Albicans ◽  
Environmental Conditions ◽  
Target Genes ◽  
Single Cells ◽  
Filamentous Growth ◽  
Surface Proteins ◽  
Pathway Expression ◽  
Separate Pathway ◽  
The Common ◽  
Growth Pathway

Abstract The common fungal pathogen, Candida albicans, can grow either as single cells or as filaments (hyphae), depending on environmental conditions. Several transcriptional regulators have been identified as having key roles in controlling filamentous growth, including the products of the TUP1, CPH1, and EFG1 genes. We show, through a set of single, double, and triple mutants, that these genes act in an additive fashion to control filamentous growth, suggesting that each gene represents a separate pathway of control. We also show that environmentally induced filamentous growth can occur even in the absence of all three of these genes, providing evidence for a fourth regulatory pathway. Expression of a collection of structural genes associated with filamentous growth, including HYR1, ECE1, HWP1, ALS1, and CHS2, was monitored in strains lacking each combination of TUP1, EFG1, and CPH1. Different patterns of expression were observed among these target genes, supporting the hypothesis that these three regulatory proteins engage in a network of individual connections to downstream genes and arguing against a model whereby the target genes are regulated through a central filamentous growth pathway. The results suggest the existence of several distinct types of filamentous forms of C. albicans, each dependent on a particular set of environmental conditions and each expressing a unique set of surface proteins.

scholarly journals Visually precise, low-damage, single-cell spatial manipulation with single-pixel resolution

2021 ◽  
Vol 12 (11) ◽  
pp. 4111-4118
Author(s):  
Qi Zhang ◽  
Yunlong Shao ◽  
Boye Li ◽  
Yuanyuan Wu ◽  
Jingying Dong ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Pixel Resolution ◽  
Single Pixel

We achieved the low-damage spatial puncture of single cells at specific visual points with an accuracy of <65 nm.

scholarly journals Single cell transcriptome atlas of mouse mammary epithelial cells across development

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Bhupinder Pal ◽  
Yunshun Chen ◽  
Michael J. G. Milevskiy ◽  
François Vaillant ◽  
Lexie Prokopuk ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Developmental Stages ◽  
Mammary Epithelial Cells ◽  
Expression Profiles ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

scholarly journals SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells

2021 ◽  
Vol 7 (8) ◽  
pp. eabe3610
Author(s):  
Conor J. Kearney ◽  
Stephin J. Vervoort ◽  
Kelly M. Ramsbottom ◽  
Izabela Todorovski ◽  
Emily J. Lelliott ◽  
...  
Keyword(s):  
Single Cell ◽  
Posttranslational Modification ◽  
Cellular Differentiation ◽  
Single Cells ◽  
Simultaneous Detection ◽  
Surface Protein ◽  
Rna Seq ◽  
Cell Level ◽  
Simultaneous Integration ◽  
Unique Surface

Multimodal single-cell RNA sequencing enables the precise mapping of transcriptional and phenotypic features of cellular differentiation states but does not allow for simultaneous integration of critical posttranslational modification data. Here, we describe SUrface-protein Glycan And RNA-seq (SUGAR-seq), a method that enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Integrated SUGAR-seq and glycoproteome analysis identified tumor-infiltrating T cells with unique surface glycan properties that report their epigenetic and functional state.

Sign in / Sign up

Export Citation Format

Share Document