scholarly journals Combined aptamer and transcriptome sequencing of single cells

2017 ◽  
Author(s):  
Cyrille L. Delley ◽  
Leqian Liu ◽  
Maen F. Sarhan ◽  
Adam R. Abate
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Expression Patterns ◽  
Cell Types ◽  
Single Cell Protein ◽  
Protein Characterization ◽  
Cell Protein ◽  
Surface Binding ◽  
Distinct Cell

AbstractThe transcriptome and proteome encode distinct information that is important for characterizing heterogeneous biological systems. We demonstrate a method to simultaneously characterize the transcriptomes and proteomes of single cells at high throughput using aptamer probes and droplet-based single cell sequencing. With our method, we differentiate distinct cell types based on aptamer surface binding and gene expression patterns. Aptamers provide advantages over antibodies for single cell protein characterization, including rapid, in vitro, and high-purity generation via SELEX, and the ability to amplify and detect them with PCR and sequencing.

scholarly journals Comprehensive integration of single cell data

2018 ◽  
Author(s):  
Tim Stuart ◽  
Andrew Butler ◽  
Paul Hoffman ◽  
Christoph Hafemeister ◽  
Efthymia Papalexi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Substantial Improvement ◽  
Single Cell Protein ◽  
Cell Protein ◽  
Spatial Positioning ◽  
Cell Data

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to “anchor” diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets.Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

scholarly journals ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes

2020 ◽  
Author(s):  
Kenneth H. Hu ◽  
John P. Eichorst ◽  
Chris S. McGinnis ◽  
David M. Patterson ◽  
Eric D. Chow ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Real Time ◽  
Dimensional Space ◽  
Single Cells ◽  
Expression Patterns ◽  
Live Cells ◽  
Glass Substrates ◽  
Complete Mapping

ABSTRACTSpatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

scholarly journals Direct correlation between motile behavior and protein abundance in single cells

2016 ◽  
Author(s):  
Yann S Dufour ◽  
Sébastien Gillet ◽  
Nicholas W Frankel ◽  
Douglas B Weibel ◽  
Thierry Emonet
Keyword(s):  
Protein Expression ◽  
Single Cell ◽  
Phenotypic Diversity ◽  
Single Cells ◽  
Selective Advantage ◽  
Single Cell Protein ◽  
Cell Protein ◽  
Chemotaxis Proteins

AbstractUnderstanding how stochastic molecular fluctuations affect cell behavior requires the quantification of both behavior and protein numbers in the same cells. Here, we combine automated microscopy with in situ hydrogel polymerization to measure single-cell protein expression after tracking swimming behavior. We characterized the distribution of non-genetic phenotypic diversity in Escherichia coli motility, which affects single-cell exploration. By expressing fluorescently tagged chemotaxis proteins (CheR and CheB) at different levels, we quantitatively mapped motile phenotype (tumble bias) to protein numbers using thousands of single-cell measurements. Our results disagreed with established models until we incorporated the role of CheB in receptor deamidation and the slow fluctuations in receptor methylation. Beyond refining models, our central finding is that changes in numbers of CheR and CheB affect the population mean tumble bias and its variance independently. Therefore, it is possible to adjust the degree of phenotypic diversity of a population by adjusting the global level of expression of CheR and CheB while keeping their ratio constant, which, as shown in previous studies, confers functional robustness to the system. Since genetic control of protein expression is heritable, our results suggest that non-genetic diversity in motile behavior is selectable, supporting earlier hypotheses that such diversity confers a selective advantage.

scholarly journals High-throughput single-cell transcriptomics reveals the female germline differentiation trajectory in Arabidopsis thaliana

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhimin Hou ◽  
Yanhui Liu ◽  
Man Zhang ◽  
Lihua Zhao ◽  
Xingyue Jin ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cells ◽  
Expression Patterns ◽  
Cell Types ◽  
Developmental Time ◽  
Developmental Trajectory ◽  
Germline Differentiation ◽  
Female Germline

AbstractFemale germline cells in flowering plants differentiate from somatic cells to produce specialized reproductive organs, called ovules, embedded deep inside the flowers. We investigated the molecular basis of this distinctive developmental program by performing single-cell RNA sequencing (scRNA-seq) of 16,872 single cells of Arabidopsis thaliana ovule primordia at three developmental time points during female germline differentiation. This allowed us to identify the characteristic expression patterns of the main cell types, including the female germline and its surrounding nucellus. We then reconstructed the continuous trajectory of female germline differentiation and observed dynamic waves of gene expression along the developmental trajectory. A focused analysis revealed transcriptional cascades and identified key transcriptional factors that showed distinct expression patterns along the germline differentiation trajectory. Our study provides a valuable reference dataset of the transcriptional process during female germline differentiation at single-cell resolution, shedding light on the mechanisms underlying germline cell fate determination.

scholarly journals A self-exciting point process to study multi-cellular spatial signaling patterns

2020 ◽  
Author(s):  
Archit Verma ◽  
Siddhartha G. Jena ◽  
Danielle R. Isakov ◽  
Kazuhiro Aoki ◽  
Jared E. Toettcher ◽  
...  
Keyword(s):  
Point Process ◽  
Point Processes ◽  
Single Cells ◽  
Cell Types ◽  
Single Cell Protein ◽  
Cellular Systems ◽  
Cell Protein ◽  
Drug Induced ◽  
Spatiotemporal Model ◽  
A Cell

Multi-cellular organisms rely on spatial signaling among cells to drive their organization, development, and response to stimuli. Several models have been proposed to capture the behavior of spatial signaling in multi-cellular systems, but existing approaches fail to capture both the autonomous behavior of single cells and the interactions of a cell with its neighbors simultaneously. We propose a spatiotemporal model of dynamic cell signaling based on Hawkes processes—self-exciting point processes—that model the signaling processes within a cell and spatial couplings between cells. With this cellular point process (CPP) model, we capture both the single-cell protein bursting rate and the magnitude and duration of signaling between cells relative to spatial locations. Furthermore, our model captures tissues composed of heterogeneous cell types with different bursting rates and signaling behaviors across multiple signaling proteins. We apply our model to epithelial cell systems that exhibit a range of autonomous and spatial signaling behaviors basally and under pharmacological exposure. Our model identifies known drug-induced signaling deficits, characterizes differences in signaling across a wound front, and generalizes to multi-channel observations.

scholarly journals NOMA: a high-throughput microchip for robust sequential measurements of secretions from the same single-cells

2021 ◽  
Author(s):  
Meimei Liu ◽  
Yahui Ji ◽  
Fengjiao Zhu ◽  
Xue Bai ◽  
Linmei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Protein Secretion ◽  
Single Cells ◽  
Extended Period ◽  
Extracellular Vesicle ◽  
Single Cell Protein ◽  
Cell Protein ◽  
Cell Secretion ◽  
Suspension Cells ◽  
Sequential Measurements

AbstractDespite advances in single-cell secretion analysis technologies, lacking simple methods to reliably keep the live single-cells traceable for longitudinal detection poses a significant obstacle. Here we developed the high-density NOMA (narrow-opening microwell array) microchip that realized the retention of ≥97% of trapped single cells during repetitive detection procedures, regardless of adherent or suspension cells. We demonstrated its use to decode the correlation of protein abundance between secreted extracellular vesicles (EVs) and its donor cells at the same single-cell level, in which we found that these two were poorly correlated with each other. We further applied it in monitoring single-cell protein secretions sequentially from the same single cells. Notably, we observed the digital protein secretion patterns dominate the protein secretion. We also applied the microchip for longitudinally tracking of the single-cell integrative secretions over days, which revealed the presence of “super secretors” within the cell population that could be more persistent to secrete protein or extracellular vesicle for an extended period. The NOMA platform reported here is simple, robust, and easy to operate for realizing sequential measurements from the same single cells, representing a novel and informative tool to inspire new observations in biomedical research.

scholarly journals Droplet sample preparation for single-cell proteomics applied to the cell cycle

2021 ◽  
Author(s):  
Andrew Leduc ◽  
R. Gray Huffman ◽  
Nikolai Slavov
Keyword(s):  
Cell Cycle ◽  
Sample Preparation ◽  
Single Cell ◽  
Cell Mass ◽  
Cell Types ◽  
Single Cell Protein ◽  
Cell Protein ◽  
Cycle Phase ◽  
Protein Markers ◽  
Cell Cycle Protein

Many biological functions, such as the cell division cycle, are intrinsically single-cell processes regulated in part by protein synthesis and degradation. Investigating such processes has motivated the development of single-cell mass spectrometry (MS) proteomics. To further advance single-cell MS proteomics, we developed a method for automated nano-ProteOmic sample Preparation (nPOP). nPOP uses piezo acoustic dispensing to isolate individual cells in 300 picoliter volumes and performs all subsequent preparation steps in small droplets on a hydrophobic slide. This allows massively parallel sample preparation, including lysing, digesting, and labeling individual cells in volumes below 20 nl. Single-cell protein analysis using nPOP classified cells by cell type and by cell cycle phase. Furthermore, the data allowed us to quantify the covariation between cell cycle protein markers and thousands of proteins. Based on this covariation, we identify cell cycle associated proteins and functions that are shared across cell types and those that differ between cell types.

SIFTER: Electrophoretic complex fractionation protocol

2021 ◽  
Author(s):  
Julea Vlassakis ◽  
Louise L Hansen ◽  
Amy E Herr
Keyword(s):  
Single Cell ◽  
Protein Interaction ◽  
Gel Electrophoresis ◽  
Protein Complexes ◽  
Single Cells ◽  
Polyacrylamide Gel Electrophoresis ◽  
Simultaneous Detection ◽  
Single Cell Protein ◽  
Cell Protein ◽  
A Cell

Abstract We introduce micro-arrayed, differential detergent fractionation for the simultaneous detection of protein complexes in 100s of individual cells with SIFTER (Single-cell protein Interaction Fractionation Through Electrophoresis and immunoassay Readout). Size-based fractionation of protein complexes is accomplished with five assay steps. First, a cell suspension generated by trypsinization is introduced onto a microwell array, and single cells are settled into the microwells by gravity. Cells are lysed in F-actin stabilization buffer that is delivered by a hydrogel lid. Second, the protein complexes are fractionated from the smaller monomers by polyacrylamide gel electrophoresis. Monomers are electrophoresed into the gel and are immobilized using a UV-induced covalent reaction to benzophenone. Third, a protein-complex depolymerization buffer is introduced by another hydrogel lid. Fourth, the recently depolymerized complexes are electrophoresed into a region of the gel separate from the immobilized monomers, where the complex fraction are in turn immobilized. Fifth, in-gel immunoprobing detects the immobilized populations of monomer and depolymerized complexes. These general steps are built on previously published protocols for bulk actin studies, single-cell western blotting, and bidirectional separations1-4.

ColorCells: a database of expression, classification and functions of lncRNAs in single cells

2020 ◽  
Author(s):  
Ling-Ling Zheng ◽  
Jing-Hua Xiong ◽  
Wu-Jian Zheng ◽  
Jun-Hao Wang ◽  
Zi-Liang Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Tissue Specificity ◽  
Single Cells ◽  
Noncoding Rnas ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Expression Variability ◽  
Expression Classification

Abstract Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs’ functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution.

scholarly journals Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Harrison Specht ◽  
Edward Emmott ◽  
Aleksandra A. Petelski ◽  
R. Gray Huffman ◽  
David H. Perlman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Single Cell Protein ◽  
Cellular Heterogeneity ◽  
Cell Protein ◽  
Alternatively Activated Macrophages ◽  
Monocytes And Macrophages ◽  
Molecular Phenotypes ◽  
Post Transcriptional Regulation

Abstract Background Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis. Results To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. Conclusions Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.

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