Targeted TCR Amplification from Single-Cell cDNA Libraries

AbstractModern high-throughput single-cell technologies facilitate the efficient processing of hundreds of individual cells to comprehensively study their morphological and genomic heterogeneity. Fluidigm’s C1 Auto Prep system isolates fluorescence-stained cells into specially designed capture sites, generates high-resolution image data and prepares the associated cDNA libraries for mRNA sequencing. Current statistical methods focus on the analysis of the gene expression profiles and ignore the important information carried by the images. Here we propose a new direction for single-cell data analysis and develop CONFESS, a customized cell detection and fluorescence signal estimation model for images coming from the Fluidigm C1 system. Applied to a set of HeLa cells expressing fluorescence cell cycle reporters, the method predicted the progression state of hundreds of samples and enabled us to study the spatio-temporal dynamics of the HeLa cell cycle. The output can be easily integrated with the associated single-cell RNA-seq expression profiles for deeper understanding of a given biological system. CONFESS R package is available at Bioconductor (http://bioconductor.org/packages/release/bioc/html/CONFESS.html).

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cDNA library preparation from total RNA extracts of Single-cell marine protists (e.g. Acantharia, Strombidium basimorphum, and Prymnesium parvum) for transcriptome sequencing v2

10.17504/protocols.io.b3ifqkbn ◽

2022 ◽

Author(s):

Joost S S. Mansour ◽

Konstantinos Anestis ◽

Fabrice Not ◽

Uwe John

Keyword(s):

Single Cell ◽

Cdna Library ◽

Mammalian Cells ◽

Single Cells ◽

Rna Extraction ◽

Cdna Libraries ◽

Prymnesium Parvum ◽

Large Variability ◽

Total Rna ◽

Marine Protists

Many marine protists are not culturable and therefore challenging to study, nonetheless, they are essential in all marine ecosystems. The development of single-cell techniques is allowing for more marine protists to be studied. Such genomic approaches aim to help to disentangle heterotrophic processes such as phagotrophy from osmotrophy and phototrophic-induced anabolic activities. This information will then support cellular and metabolic modeling by better elucidating the physiological mechanisms and quantifying their importance in different scenarios. However, single-cell protocols and low input RNA kits for transcriptomics are usually made for and tested with mammalian cells, as such the feasibility and efficiency of single-cell transcriptomics on highly diverse mixotrophic protists is not always known. Often single-cell transcriptomics of microbial eukaryotes shows low transcript recovery rates and large variability. We report on transcriptomic methods that we have successfully performed on single cells of Acantharia, Strombidium basimorphum, and Prymnesium parvum. This protocol follows up after total RNA extraction (from the protocol at dx.doi.org/10.17504/protocols.io.bp6xmrfn) to prepare cDNA libraries for Illumina sequencing. The described protocol uses the SMART-Seq4 kit (Takara #634891) for cDNA synthesis and amplification, but this can also be successfully performed with the NEBNext kit (NEB #E6421). The NEBNext kit protocol is very similar to the protocol described here and generally the manufacture's protocol can be followed but see the notes at step 4 and step 18 of this protocol, and do the final elution after cDNA purification in 10 mM Tris (pH 8.0). The subsequent cDNA library is prepared following the .

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Single nucleus RNASeq profiling of mouse lung: reduced dissociation bias and improved detection of rare cell types compared with single cell RNASeq

10.1101/2020.03.06.981407 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jeffrey R Koenitzer ◽

Haojia Wu ◽

Jeffrey J Atkinson ◽

Steven L Brody ◽

Benjamin D Humphreys

Keyword(s):

Single Cell ◽

Stress Responses ◽

Cell Types ◽

Neuroendocrine Cells ◽

Cdna Libraries ◽

Mouse Lung ◽

Cell Detection ◽

Mrna Levels ◽

Gene Detection ◽

Single Nucleus

AbstractRATIONALESingle cell RNA-sequencing (scRNASeq) has led to multiple recent advances in our understanding of lung biology and pathophysiology, but utility is limited by the need for fresh samples, loss of cell types due to death or inadequate dissociation, and the induction of transcriptional stress responses during tissue digestion. Single nucleus RNASeq (snRNASeq) has addressed these deficiencies in some other tissues, but no protocol exists for lung. We sought to develop such a protocol and compare its results with scRNA-seq.METHODSSingle nucleus suspensions were prepared rapidly (45 min) from two mouse lungs in lysis buffer on ice while a single cell suspension from an additional mouse lung was generated using a combination of enzymatic and mechanical dissociation (1.5 h). Cells and nuclei were processed using the 10x Genomics platform, and following sequencing of cDNA libraries single cell data was analyzed by Seurat.RESULTS16,656 single nucleus and 11,934 single cell transcriptomes were generated. Despite reduced mRNA levels in nuclei vs. cells, gene detection rates were equivalent in snRNASeq and scRNASeq (∼1,750 genes and 3,000 UMI per cell) when mapping intronic and exonic reads. snRNASeq identified a much greater proportion of epithelial cells than scRNASeq (46% vs 2% of total), including basal and neuroendocrine cells, while reducing immune cells from 54% to 15%. snRNASeq transcripts are enriched for transcription factors and signaling proteins, with reduced detection of housekeeping genes, mitochondrial genes, and artifactual stress response genes. Both techniques improved mesenchymal cell detection over previous studies, and analysis of fibroblast diversity showed two transcriptionally distinct populations of Col13a1+ cells, termed Bmper+ and Brinp1+ fibroblasts. To define homeostatic signaling relationships among cell types, receptor-ligand mapping of was performed for alveolar compartment cells using snRNASeq data, revealing complex interplay among epithelial, mesenchymal, and capillary endothelial cells.CONCLUSIONSingle nucleus RNASeq can be readily applied to snap frozen, archival murine lung samples, improves dissociation bias, eliminates artifactual gene expression and provides similar gene detection compared to scRNASeq.

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Single-cell transcriptome analysis of Physcomitrella leaf cells during reprogramming using microcapillary manipulation

10.1101/463448 ◽

2018 ◽

Author(s):

Minoru Kubo ◽

Tomoaki Nishiyama ◽

Yosuke Tamada ◽

Ryosuke Sano ◽

Masaki Ishikawa ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Transcriptome Analysis ◽

Physcomitrella Patens ◽

Expression Profiles ◽

Digital Gene Expression ◽

Positional Information ◽

Cdna Libraries ◽

Amplification Bias ◽

Excised Tissues

AbstractBackgroundNext-generation sequencing technologies have made it possible to carry out transcriptome analysis at the single-cell level. Single-cell RNA-sequencing (scRNA-seq) data provide insights into cellular dynamics, including intercellular heterogeneity as well as inter- and intra-cellular fluctuations in gene expression that cannot be studied using populations of cells. The utilization of scRNA-seq is, however, restricted to specific types of cells that can be isolated from their original tissues, and it can be difficult to obtain precise positional information for these cells in situ.ResultsHere, we established single cell-digital gene expression (1cell-DGE), a method of scRNA-seq that uses micromanipulation to extract the contents of individual living cells in intact tissue while recording their positional information. Furthermore, we employed a unique molecular identifier to reduce amplification bias in the cDNA libraries. With 1cell-DGE, we could detect differentially expressed genes (DEGs) during the reprogramming of leaf cells into stem cells in excised tissues of the moss Physcomitrella patens, identifying 6,382 DEGs between cells at 0 h and 24 h after excision. We found substantial variations in both the transcript levels of previously reported reprogramming factors and the overall expression profiles between cells, which appeared to be related to their different reprogramming abilities or the estimated states of the cells according to the pseudotime based on the transcript profiles.ConclusionsWe developed 1cell-DGE with microcapillary manipulation, a technique that can be used to analyze the gene expression of individual cells without detaching them from their tightly associated tissues, enabling us to retain positional information and investigate cell–cell interactions.

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Single Cell Transcriptional Profiling Reveals Activation of TNF-Alpha Signaling in Hematopoietic Stem and Progenitor Cells from Shwachman-Diamond Syndrome Patients

Blood ◽

10.1182/blood.v128.22.335.335 ◽

2016 ◽

Vol 128 (22) ◽

pp. 335-335

Author(s):

Cailin Joyce ◽

Assieh Saadatpour ◽

Lan Jiang ◽

Melisa Ruiz-Gutierrez ◽

Ozge Vargel Bolukbasi ◽

...

Keyword(s):

Bone Marrow ◽

Single Cell ◽

Bone Marrow Failure ◽

Continuous Process ◽

Cdna Libraries ◽

Tnf Alpha ◽

Normal Donor ◽

Cell Transplant ◽

Hematopoietic Stem ◽

Shwachman Diamond Syndrome

Abstract Shwachman-Diamond Syndrome (SDS) is an inherited bone marrow failure caused by mutations in SBDS, which encodes a conserved ribosome assembly factor. Despite simple genetic underpinnings, SDS is surprisingly complex. Patients suffer varying degrees of neutropenia, thrombocytopenia and anemia, and are predisposed to myelodysplasia and acute myeloid leukemia. The only curative treatment is stem cell transplant, but patients are unusually susceptible to toxic side effects. Mapping molecular pathways in affected lineages is a critical step towards developing safer, targeted therapies. The affected cell type(s) and genetic networks in SDS have remained elusive, but bone marrow hypocellularity and the involvement of multiple lineages points to a defect in the CD34+ hematopoietic stem and progenitor cell (HSPC) population. To identify the molecular basis of SDS, we set out to transcriptionally profile HSPC from SDS patients. However, these cells are rare and heterogeneous, even in normal donors. To overcome this challenge, we developed a pipeline to define the transcriptional architecture of early hematopoiesis at single cell resolution.Key features include rapid conversion of fresh cells to stable cDNA libraries which preserves their natural biology and unbiased sampling to capture all aspects of HSPC heterogeneity. To date, we have sequenced full-length cDNA libraries from 92 normal donor and 176 SDS patient cells. To deconvolute developmental heterogeneity among HSPC, we clustered cells based on the expression of empirically-determined lineage signature genes. Normal and SDS cells were similarly distributed along the developmental continuum, though there was an increased proportion of SDS cells in the stem and multipotent progenitor stage (HSC/MPP). This analysis produced the first single cell roadmap of human hematopoiesis, which illustrates that hematopoiesis is a continuous process rather than a series of discrete steps. To identify genes that contribute to impaired hematopoiesis in SDS, we used the MAST statistical framework for single cell expression analysis. The top upregulated pathway in SDS was TNF-alpha signaling via NF-kB. Interestingly, when we mapped this pathway back to our single cell roadmap we found that it was activated to varying degrees in cells at the HSC/MPP stage, but not in more committed progenitors. This finding is intriguing given that TNF-alpha has suppressive effects on HSC growth and long-term engraftment in mice. Moreover, HSC from patients with Fanconi Anemia, a related bone marrow failure, are hypersensitive to TNF-alpha-mediated suppression. Our study establishes the first link between an inflammatory pathway, TNF-alpha, and bone marrow failure in SDS. We are currently investigating the mechanistic basis for this link using patient-derived induced pluripotent stem cells. In the future, we will examine whether TNF-alpha inhibition is a viable therapy to counteract bone marrow failure in SDS. Disclosures No relevant conflicts of interest to declare.

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Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization

10.1101/2020.10.05.326553 ◽

2020 ◽

Author(s):

Rhonda Bacher ◽

Li-Fang Chu ◽

Cara Argus ◽

Jennifer M. Bolin ◽

Parker Knight ◽

...

Keyword(s):

Single Cell ◽

Cdna Libraries ◽

Sequencing Data ◽

Specific Expression ◽

Detection Rates ◽

Biologically Relevant ◽

Expression Variability ◽

Biological Signals ◽

Experimental Protocols

AbstractConsiderable effort has been devoted to refining experimental protocols having reduced levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17-31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data.

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Single-cell cDNA libraries

PCR Applications ◽

10.1016/b978-012372185-3/50020-1 ◽

1999 ◽

pp. 307-328 ◽

Cited By ~ 1

Author(s):

Peter S. Nelson

Keyword(s):

Single Cell ◽

Cdna Libraries

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Time- and cost-efficient high-throughput transcriptomics enabled by Bulk RNA Barcoding and sequencing

10.1101/256594 ◽

2018 ◽

Cited By ~ 3

Author(s):

Daniel Alpern ◽

Vincent Gardeux ◽

Julie Russeil ◽

Bart Deplancke

Keyword(s):

Gene Expression ◽

Single Cell ◽

High Throughput ◽

Low Cost ◽

Cdna Libraries ◽

List Type ◽

Library Preparation ◽

Rna Seq ◽

Genome Wide ◽

Hands On

ABSTRACTGenome-wide gene expression analyses by RNA sequencing (RNA-seq) have quickly become a standard in molecular biology because of the widespread availability of high throughput sequencing technologies. While powerful, RNA-seq still has several limitations, including the time and cost of library preparation, which makes it difficult to profile many samples simultaneously. To deal with these constraints, the single-cell transcriptomics field has implemented the early multiplexing principle, making the library preparation of hundreds of samples (cells) markedly more affordable. However, the current standard methods for bulk transcriptomics (such as TruSeq Stranded mRNA) remain expensive, and relatively little effort has been invested to develop cheaper, but equally robust methods. Here, we present a novel approach, Bulk RNA Barcoding and sequencing (BRB-seq), that combines the multiplexing-driven cost-effectiveness of a single-cell RNA-seq workflow with the performance of a bulk RNA-seq procedure. BRB-seq produces 3’ enriched cDNA libraries that exhibit similar gene expression quantification to TruSeq and that maintain this quality, also in terms of number of detected differentially expressed genes, even with low quality RNA samples. We show that BRB-seq is about 25 times less expensive than TruSeq, enabling the generation of ready to sequence libraries for up to 192 samples in a day with only 2 hours of hands-on time. We conclude that BRB-seq constitutes a powerful alternative to TruSeq as a standard bulk RNA-seq approach. Moreover, we anticipate that this novel method will eventually replace RT-qPCR-based gene expression screens given its capacity to generate genome-wide transcriptomic data at a cost that is comparable to profiling 4 genes using RT-qPCR.‘SoftwareWe developed a suite of open source tools (BRB-seqTools) to aid with processing BRB-seq data and generating count matrices that are used for further analyses. This suite can perform demultiplexing, generate count/UMI matrices and trim BRB-seq constructs and is freely available at http://github.com/DeplanckeLab/BRB-seqToolsHighlightsRapid (~2h hands on time) and low-cost approach to perform transcriptomics on hundreds of RNA samplesStrand specificity preservedPerformance: number of detected genes is equal to Illumina TruSeq Stranded mRNA at same sequencing depthHigh capacity: low cost allows increasing the number of biological replicatesProduces reliable data even with low quality RNA samples (down to RIN value = 2)Complete user-friendly sequencing data pre-processing and analysis pipeline allowing result acquisition in a day

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