Expression Profiling of Single Mammalian Cells – Small is Beautiful

Increasingly mRNA expression patterns established using a variety of molecular technologies such as cDNA microarrays, SAGE and cDNA display are being used to identify potential regulatory genes and as a means of providing valuable insights into the biological status of the starting sample. Until recently, the application of these techniques has been limited to mRNA isolated from millions or, at very best, several thousand cells thereby restricting the study of small samples and complex tissues. To overcome this limitation a variety of amplification approaches have been developed which are capable of broadly evaluating mRNA expression patterns in single cells. This review will describe approaches that have been employed to examine global gene expression patterns either in small numbers of cells or, wherever possible, in actual isolated single cells. The first half of the review will summarize the technical aspects of methods developed for single-cell analysis and the latter half of the review will describe the areas of biological research that have benefited from single-cell expression analysis.

Download Full-text

Expression Profiling of Single Mammalian Cells – Small is Beautiful

Yeast ◽

10.1155/2000/302362 ◽

2000 ◽

Vol 1 (3) ◽

pp. 211-217

Author(s):

Gerard Brady

Keyword(s):

Mrna Expression ◽

Single Cell ◽

Mammalian Cells ◽

Single Cell Analysis ◽

Single Cells ◽

Expression Patterns ◽

Global Gene Expression ◽

Small Samples ◽

Biological Research ◽

Cell Expression

Download Full-text

The single-cell transcriptional landscape of the axolotl

10.21203/rs.3.rs-960203/v1 ◽

2021 ◽

Author(s):

Fang Ye ◽

Guodong Zhang ◽

Weigao E ◽

Haide Chen ◽

Chengxuan Yu ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Limb Development ◽

Single Cell Analysis ◽

Single Cells ◽

Expression Patterns ◽

Ambystoma Mexicanum ◽

System Level ◽

Molecular Characteristics ◽

Developmental Studies

Abstract The Mexican axolotl (Ambystoma mexicanum) is a promising tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, during which their regeneration capacity and lifespan gradually decline. However, a system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls is still lacking. Here, we developed a single-cell RNA-seq method based on combinatorial hybridization to generate a tissue-based transcriptomic atlas of the adult axolotl. We performed gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell atlas. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls revealed the heterogeneity of structural cells in different tissues and established their regulatory network. Furthermore, we described dynamic gene expression patterns during limb development in neotenic axolotls. These data serve as a resource to explore the molecular identity of the axolotl as well as its metamorphosis.

Download Full-text

Combined mRNA and protein single cell analysis in a dynamic cellular system using SPARC

10.1101/749473 ◽

2019 ◽

Cited By ~ 1

Author(s):

Johan Reimegård ◽

Marcus Danielsson ◽

Marcel Tarbier ◽

Jens Schuster ◽

Sathishkumar Baskaran ◽

...

Keyword(s):

Protein Expression ◽

Mrna Expression ◽

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Embryonic Stem ◽

Single Cell Protein ◽

Cell Protein ◽

Depth Analysis ◽

Mrna And Protein Expression

ABSTRACTCombined measurements of mRNA and protein expression in single cells enables in-depth analysis of cellular states. We present single-cell protein and RNA co-profiling (SPARC), an approach to simultaneously measure global mRNA and large sets of intracellular protein in individual cells. Using SPARC, we show that mRNA expression fails to accurately reflect protein abundance at the time of measurement in human embryonic stem cells, although the direction of changes of mRNA and protein expression are in agreement during cellular differentiation. Moreover, protein levels of transcription factors better predict their downstream effects than do the corresponding transcripts. We further show that changes of the balance between protein and mRNA expression levels can be applied to infer expression kinetic trajectories, revealing future states of individual cells. Finally, we highlight that mRNA expression may be more varied among cells than levels of the corresponding proteins. Overall, our results demonstrate that mRNA and protein measurements in single cells provide different and complementary information regarding cell states. Accordingly, SPARC can offer valuable insights in gene expression programs of single cells.

Download Full-text

Neural Data Visualization for Scalable and Generalizable Single Cell Analysis

10.1101/289223 ◽

2018 ◽

Cited By ~ 2

Author(s):

Hyunghoon Cho ◽

Bonnie Berger ◽

Jian Peng

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Data Sets ◽

Cell Analysis ◽

Data Set ◽

Unseen Data ◽

Sequencing Experiment ◽

Cell Expression

SummarySingle-cell RNA sequencing is becoming effective and accessible as emerging technologies push its scale to millions of cells and beyond. Visualizing the landscape of single cell expression has been a fundamental tool in single cell analysis. However, standard methods for visualization, such as t-stochastic neighbor embedding (t-SNE), not only lack scalability to data sets with millions of cells, but also are unable to generalize to new cells, an important ability for transferring knowledge across fast-accumulating data sets. We introduce net-SNE, which trains a neural network to learn a high quality visualization of single cells that newly generalizes to unseen data. While matching the visualization quality of t-SNE on 14 benchmark data sets of varying sizes, from hundreds to 1.3 million cells, net-SNE also effectively positions previously unseen cells, even when an entire subtype is missing from the initial data set or when the new cells are from a different sequencing experiment. Furthermore, given a “reference” visualization, net-SNE can vastly reduce the computational burden of visualizing millions of single cells from multiple days to just a few minutes of runtime. Our work provides a general framework for newly bootstrapping single cell analysis from existing data sets.

Download Full-text

Single-cell nucleic acid profiling in droplets (SNAPD) enables high-throughput analysis of heterogeneous cell populations

10.1101/2020.08.21.261834 ◽

2020 ◽

Author(s):

Leland B. Hyman ◽

Clare R. Christopher ◽

Philip A. Romero

Keyword(s):

Nucleic Acid ◽

Single Cell ◽

Mammalian Cells ◽

Cancer Biology ◽

Single Cell Analysis ◽

Single Cells ◽

Cell Types ◽

Cell Populations ◽

Specific Cell ◽

High Throughput Analysis

AbstractExperimental methods that capture the individual properties of single cells are revealing the key role of cell-to-cell variability in countless biological processes. These single-cell methods are becoming increasingly important across the life sciences in fields such as immunology, regenerative medicine, and cancer biology. Existing single-cell analysis methods are often limited by their low analysis throughput, their inability to profile high-dimensional phenotypes, and complicated experimental workflows with slow turnaround times. In this work, we present Single-cell Nucleic Acid Profiling in Droplets (SNAPD) to analyze the transcriptional states of hundreds of thousands of single mammalian cells. Individual cells are encapsulated in aqueous droplets on a microfluidic chip and the content of each cell is profiled by amplifying a targeted panel of transcriptional markers. Molecular logic circuits then integrate this multi-dimensional information to categorize cells based on their transcriptional profile and produce a detectable fluorescence output. SNAPD analyzes over 100,000 cells per hour and can be used to quantify distinct cell types within populations, detect rare cells at frequencies down to 0.1%, and enrich specific cell types using microfluidic sorting. SNAPD provides a simple, rapid, low cost, and scalable approach to study complex phenotypes in heterogeneous cell populations.

Download Full-text

ScLRTC: Imputation for Single-Cell RNA-Seq Data Via Low-Rank Tensor Completion

10.21203/rs.3.rs-757792/v1 ◽

2021 ◽

Author(s):

Zhong Li ◽

Xiutao Pan ◽

Shengwei Qin ◽

Minzhe Yu ◽

Hang Hu

Keyword(s):

Gene Expression ◽

Single Cell ◽

State Of The Art ◽

Single Cells ◽

Pearson Correlation ◽

Expression Patterns ◽

Low Rank ◽

Tensor Completion ◽

Rank Tensor ◽

Cell Expression

Abstract Background: With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results: In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories.Conclusions: a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC.

Download Full-text

ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion

BMC Genomics ◽

10.1186/s12864-021-08101-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Xiutao Pan ◽

Zhong Li ◽

Shengwei Qin ◽

Minzhe Yu ◽

Hang Hu

Keyword(s):

Gene Expression ◽

Single Cell ◽

State Of The Art ◽

Single Cells ◽

Pearson Correlation ◽

Expression Patterns ◽

Low Rank ◽

Tensor Completion ◽

Rank Tensor ◽

Cell Expression

Abstract Background With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories. Conclusions a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC.

Download Full-text

Automated sample preparation for high-throughput single-cell proteomics

10.1101/399774 ◽

2018 ◽

Cited By ~ 9

Author(s):

Harrison Specht ◽

Guillaume Harmange ◽

David H. Perlman ◽

Edward Emmott ◽

Zachary Niziolek ◽

...

Keyword(s):

Sample Preparation ◽

Single Cell ◽

Mammalian Cells ◽

Es Cells ◽

Single Cells ◽

Low Noise ◽

Small Samples ◽

Cycle Phase ◽

Hek 293 ◽

Mouse Es Cells

A major limitation to applying quantitative LC-MS/MS proteomics to small samples, such as single cells, are the losses incured during sample cleanup. To relieve this limitation, we developed a Minimal ProteOmic sample Preparation (mPOP) method for culture-grown mammalian cells. mPOP obviates cleanup and thus eliminates cleanup-related losses while expediting sample preparation and simplifying its automation. Bulk SILAC samples processed by mPOP or by conventional urea-based methods indicated that mPOP results in complete cell lysis and accurate relative quantification. We integrated mPOP lysis with the Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) sample preparation, and benchmarked the quantification of such samples on a Q-exactive instrument. The results demonstrate low noise and high technical reproducibility. Then, we FACS sorted single U-937, HEK-293, and mouse ES cells into 96-well plates and analyzed them by automated mPOP and SCoPE-MS. The quantified proteins enabled separating the single cells by cell-type and cell-division-cycle phase.

Download Full-text

Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

Nature Communications ◽

10.1038/s41467-021-23238-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jeremy A. Lombardo ◽

Marzieh Aliaghaei ◽

Quy H. Nguyen ◽

Kai Kessenbrock ◽

Jered B. Haun

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Cell Analysis ◽

Processing Technologies ◽

Microfluidic Platform ◽

Tissue Processing ◽

Single Cell Rna Sequencing ◽

Culture Models ◽

Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

Download Full-text

Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications ◽

10.1038/s41467-021-21583-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Rongxin Fang ◽

Sebastian Preissl ◽

Yang Li ◽

Xiaomeng Hou ◽

Jacinta Lucero ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Expression Patterns ◽

Regulatory Elements ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Open Chromatin ◽

Cell Type ◽

Process Data ◽

Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

Download Full-text