scholarly journals Cell fixation and preservation for droplet-based single-cell transcriptomics

2017 ◽  
Author(s):  
Jonathan Alles ◽  
Nikos Karaiskos ◽  
Samantha D. Praktiknjo ◽  
Stefanie Grosswendt ◽  
Philipp Wahle ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Transcriptional Profiling ◽  
Cost Effective ◽  
R Package ◽  
Fixation Method ◽  
Recent Developments ◽  
Cell Gene Expression ◽  
Rare Cells ◽  
Cell Fixation

ABSTRACTBackgroundRecent developments in droplet-based microfluidics allow the transcriptional profiling of thousands of individual cells, in a quantitative, highly parallel and cost-effective way. A critical, often limiting step is the preparation of cells in an unperturbed state, not compromised by stress or ageing. Another challenge are rare cells that need to be collected over several days, or samples prepared at different times or locations.ResultsHere, we used chemical fixation to overcome these problems. Methanol fixation allowed us to stabilize and preserve dissociated cells for weeks. By using mixtures of fixed human and mouse cells, we showed that individual transcriptomes could be confidently assigned to one of the two species. Single-cell gene expression from live and fixed samples correlated well with bulk mRNA-seq data. We then applied methanol fixation to transcriptionally profile primary single cells from dissociated complex tissues. Low RNA content cells from Drosophila embryos, as well as mouse hindbrain and cerebellum cells sorted by FACS, were successfully analysed after fixation, storage and single-cell droplet RNA-seq. We were able to identify diverse cell populations, including neuronal subtypes. As an additional resource, we provide ‘dropbead’, an R package for exploratory data analysis, visualization and filtering of Drop-seq data.ConclusionsWe expect that the availability of a simple cell fixation method will open up many new opportunities in diverse biological contexts to analyse transcriptional dynamics at single cell resolution.

scholarly journals Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

2017 ◽  
Author(s):  
Junyue Cao ◽  
Jonathan S. Packer ◽  
Vijay Ramani ◽  
Darren A. Cusanovich ◽  
Chau Huynh ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Transcriptional Profiling ◽  
Cost Effective ◽  
Cell Types ◽  
Cell Capture ◽  
Rna Seq ◽  
Major Step ◽  
C Elegans

AbstractConventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals Memory sequencing reveals heritable single cell gene expression programs associated with distinct cellular behaviors

2018 ◽  
Author(s):  
Sydney M. Shaffer ◽  
Benjamin L. Emert ◽  
Raul Reyes-Hueros ◽  
Christopher Coté ◽  
Guillaume Harmange ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cancer Therapeutics ◽  
Population Based ◽  
Fluctuation Analysis ◽  
Genetic Heritability ◽  
The Face ◽  
Cell Gene Expression ◽  
Cell Subpopulations ◽  
Rare Cells

AbstractNon-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. Yet it remains unclear whether these fluctuations can persist for much longer than the time of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we report a method combining Luria and Delbrück’s fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several cell divisions. MemorySeq revealed multiple gene modules that are expressed together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct behaviors, such as the ability to proliferate in the face of anti-cancer therapeutics, in different cancer cell lines. The identification of non-genetic, multigenerational fluctuations has the potential to reveal new forms of biological memory at the level of single cells and suggests that non-genetic heritability of cellular state may be a quantitative property.

scholarly journals Single cell network analysis with a mixture of Nested Effects Models

2018 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
New Technologies ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Data Sets ◽  
Cell Network ◽  
A Cell ◽  
Supplementary Material ◽  
Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

scholarly journals BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies

2018 ◽  
Author(s):  
Zhe Sun ◽  
Li Chen ◽  
Hongyi Xin ◽  
Qianhui Huang ◽  
Anthony R Cillo ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
R Package ◽  
Clustering Methods ◽  
Model Framework ◽  
Bayesian Hierarchical ◽  
Bayesian Mixture ◽  
Population Scale ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractThe recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.

scholarly journals Paired Analyses of AML at Diagnosis and Relapse By Single-Cell RNA Sequencing Identifies Two Distinct Relapse Patterns

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 183-183
Author(s):  
Kai Wu ◽  
Qianyi Ma ◽  
Darren King ◽  
Jun Li ◽  
Sami Malek
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Single Cell ◽  
Bone Marrow Cells ◽  
Single Cells ◽  
Leukemia Cells ◽  
Driver Genes ◽  
Cell Gene Expression ◽  
Normal Human ◽  
Relapse Patterns

Introduction: Despite achievement of complete remission (CR) following chemotherapy, Acute Myelogenous Leukemia (AML) relapses in the majority of adult patients. While relapsed AML is almost always clonally related to the disease at diagnosis, the actual molecular and cellular contributors to chemotherapy resistance and to AML relapse remain incompletely understood. Some molecular determinants of relapse have been identified in genomic, epigenetic and proteomic aberrations, while cellular relapse reservoirs have been identified in leukemia stem cells as well as in more mature leukemic cell compartments. Here, we set out to determine the cellular composition, gene mutation status and gene expression of paired AML specimens procured at diagnosis and at relapse aiming at a better understanding of the AML relapse process. Methods: We employed the drop-seq 3' single cell RNA sequencing (scRNA-seq) method (Macosko 2015) with minor modifications to analyze the mRNA expression in single cells derived from 12 paired AML specimens procured at diagnosis and at relapse from prior CR. We obtained scRNA-seq data on 1000-2000 single cells per sample detecting approximately 2000-3000 unique molecular identifiers (UMIs) and 800-1500 genes per cell. Using WES or panel-based sequencing we determined mutations in known driver genes. Subsequently, we optimized novel methods for detection and mapping of mutated driver genes to individual cells using mutation specific PCR conditions and novel bioinformatics approaches. We annotated scRNA-seq expression profiles of the diagnosis and relapsed AML specimens individually using publicly available data for cell type-specific RNA markers derived from sorted normal cell populations and further compared the scRNA-seq data to scRNA-seq data of 5 pooled normal human bone marrows generated for this study. Results: Through analyses of scRNA-seq data of paired diagnosis and relapse AML specimens via principle components analyses (PCA) or t-distributed stochastic neighbor embedding (t-SNE) we detected varying degrees of separation of cell clusters in all cases analyzed indicative of substantial changes in single cell gene expression between AML diagnosis and relapse. A few of these observed cluster shifts were paralleled by gain or loss of mutated genes (e.g. FLT3-ITD) at relapse while most others lacked obvious clonal genomic markers. Through subsequent comparison of the expression similarities of single AML cells to sorted normal human bone marrow cells we detected two distinct AML relapse patterns: i) a pattern of relapse suggesting simple leukemia regrowth as evidenced by similar proportions of leukemia cells mapping onto discrete normal bone marrow cells (e.g. monocyte-like or GMPs or CMPs), and, ii) a pattern of relapse whereby the gene expression of relapsed cells (but not diagnosis cells) had similarity to normal hematopoietic cells that are conventionally placed more apical in the classical hematopoiesis differentiation cascade (HSCs, MPPs, CMPs; a phenotypic shift to immaturity). In addition, no leukemia sample mapped to just one classically defined bone marrow cell type but instead to multiple cell types, suggesting that most AML leukemia cells harbor aberrant hybrid cell gene expression patterns. Finally, we detected quantitative shifts in T cells and NK cells in some samples at relapse, which will be analyzed in greater detail. Conclusions: The comparative analysis of scRNA-seq data of paired AML specimens procured at diagnosis and relapse, identifies frequent and previously unrecognized changes in gene expression in leukemia cells at relapse. Through a comparison of gene mutation and gene expression at single cell resolution we identify two distinct AML relapse patterns in adult AML. Disclosures No relevant conflicts of interest to declare.

scholarly journals Preparation of Tissues and Heterogeneous Cellular Samples for Single-Cell Analysis

2021 ◽  
Author(s):  
E. Celeste Welch ◽  
Anubhav Tripathi
Keyword(s):  
Sample Preparation ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Processing Times ◽  
Tissue Dissociation ◽  
Emerging Trends ◽  
Recent Developments ◽  
Preparation Techniques

While sample preparation techniques for the chemical and biochemical analysis of tissues are fairly well advanced, the preparation of complex, heterogenous samples for single-cell analysis can be difficult and challenging. Nevertheless, there is growing interest in preparing complex cellular samples, particularly tissues, for analysis via single-cell resolution techniques such as single-cell sequencing or flow cytometry. Recent microfluidic tissue dissociation approaches have helped to expedite the preparation of single cells from tissues through the use of optimized, controlled mechanical forces. Cell sorting and selective cellular recovery from heterogenous samples have also gained traction in biosensors, microfluidic systems, and other diagnostic devices. Together, these recent developments in tissue disaggregation and targeted cellular retrieval have contributed to the development of increasingly streamlined sample preparation workflows for single-cell analysis technologies, which minimize equipment requirements, enable lower processing times and costs, and pave the way for high-throughput, automated technologies. In this chapter, we survey recent developments and emerging trends in this field.

scholarly journals scds: computational annotation of doublets in single-cell RNA sequencing data

2019 ◽  
Author(s):  
Abha S Bais ◽  
Dennis Kostka
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Binary Classification ◽  
Single Cells ◽  
Computational Cost ◽  
Original Data ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study’s conclusions, and therefore computational strategies for the identification of doublets are needed. Results With scds, we propose two new approaches for in silico doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and, employing a binomial model for the co-expression of pairs of genes, yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from original data. We apply our methods and existing computational doublet identification approaches to four datasets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, at comparably little computational cost. We observe appreciable differences between methods and across datasets and that no approach dominates all others. In summary, scds presents a scalable, competitive approach that allows for doublet annotation of datasets with thousands of cells in a matter of seconds. Availability and implementation scds is implemented as a Bioconductor R package (doi: 10.18129/B9.bioc.scds). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs

2014 ◽  
Vol 42 (15) ◽  
pp. 9880-9891 ◽  
Author(s):  
Arne H. Smits ◽  
Rik G.H. Lindeboom ◽  
Matteo Perino ◽  
Simon J. van Heeringen ◽  
Gert Jan C. Veenstra ◽  
...  
Keyword(s):  
Single Cell ◽  
Ribosomal Proteins ◽  
Single Cells ◽  
Transcript Level ◽  
Absolute Quantification ◽  
Protein Quantification ◽  
Protein Levels ◽  
Recent Developments ◽  
Basal Transcription Factors ◽  
Cell Proteome

Abstract While recent developments in genomic sequencing technology have enabled comprehensive transcriptome analyses of single cells, single cell proteomics has thus far been restricted to targeted studies. Here, we perform global absolute protein quantification of fertilized Xenopus laevis eggs using mass spectrometry-based proteomics, quantifying over 5800 proteins in the largest single cell proteome characterized to date. Absolute protein amounts in single eggs are highly consistent, thus indicating a tight regulation of global protein abundance. Protein copy numbers in single eggs range from tens of thousands to ten trillion copies per cell. Comparison between the single-cell proteome and transcriptome reveal poor expression correlation. Finally, we identify 439 proteins that significantly change in abundance during early embryogenesis. Downregulated proteins include ribosomal proteins and upregulated proteins include basal transcription factors, among others. Many of these proteins do not show regulation at the transcript level. Altogether, our data reveal that the transcriptome is a poor indicator of the proteome and that protein levels are tightly controlled in X. laevis eggs.

scholarly journals Saturating Single-Cell atlas Datasets

2017 ◽  
Author(s):  
Aparna Bhaduri ◽  
Tomasz J. Nowakowski ◽  
Alex A. Pollen ◽  
Arnold R. Kriegstein
Keyword(s):  
Population Structure ◽  
Single Cell ◽  
Mouse Brain ◽  
Large Scale ◽  
Single Cells ◽  
Cost Effective ◽  
Cell Types ◽  
Cell Number ◽  
Cell Type ◽  
The Relationship

AbstractHigh throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. Efficient generation of such an atlas will depend on sufficient sampling of the diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. To examine the relationship between cell number and transcriptional heterogeneity in the context of unbiased cell type classification, we explicitly explored the population structure of a publically available 1.3 million cell dataset from the E18.5 mouse brain. We propose a computational framework for inferring the saturation point of cluster discovery in a single cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a “complexity index”, which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells (20,000). Together, these findings suggest that most of the biologically interpretable insights from the 1.3 million cells can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high “cellular coverage”, the much anticipated cell atlasing studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage.Recent efforts seek to create a comprehensive cell atlas of the human body1,2 Current technology, however, makes it precipitously expensive to perform analysis of every cell. Therefore, designing effective sampling strategies be critical to generate a working atlas in an efficient, cost-effective, and streamlined manner. The advent of single cell and single nucleus mRNA sequencing (RNAseq) in droplet format3,4 now enables large scale sampling of cells from any tissue, and a recently released publicly available dataset of 1.3 million single cells from the E18.5 mouse brain generated with the 10X Chromium5 provides an opportunity to explore the relationship between population structure and the number of sampled cells necessary to reveal the underlying diversity of cell types. Here, we present a framework for how researchers can evaluate whether a dataset has reached saturation, and we estimate how many cells would be required to generate an atlas of the sample analyzed here. This framework can be applied to any organ or cell type specific atlas for any organism.

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