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

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.

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LINE-1 Retrotransposon expression in cancerous, epithelial and neuronal cells revealed by 5’-single cell RNA-Seq

10.1101/2021.01.19.427347 ◽

2021 ◽

Author(s):

Wilson McKerrow ◽

Shane A. Evans ◽

Azucena Rocha ◽

John Sedivy ◽

Nicola Neretti ◽

...

Keyword(s):

Single Cell ◽

Cancer Cells ◽

Early Development ◽

Immune Cells ◽

Copy Number ◽

Single Cells ◽

Single Cell Level ◽

Rna Seq ◽

Cell Level ◽

Mouse Hippocampus

AbstractLINE-1 retrotransposons are known to be expressed in early development, in tumors and in the germline. Less is known about LINE-1 expression at the single cell level, especially outside the context of cancer. Because LINE-1 elements are present at a high copy number, many transcripts that are not driven by the LINE-1 promoter nevertheless terminate at the LINE-1 3’ UTR. Thus, 3’ targeted single cell RNA-seq datasets are not appropriate for studying LINE-1. However, 5’ targeted single cell datasets provide an opportunity to analyze LINE-1 expression at the single cell level. Most LINE-1 copies are 5’ truncated, and a transcript that contains the LINE-1 5’ UTR as its 5’ end is likely to have been transcribed from its promoter. We developed a method, L1-sc (LINE-1 expression for single cells), to quantify LINE-1 expression in 5’ targeted 10x genomics single cell RNA-seq datasets. Our method confirms that LINE-1 expression is high in cancer cells, but low or absent from immune cells. We also find that LINE-1 expression is elevated in epithelial compared to immune cells outside of the context of cancer and that it is also elevated in neurons compared to glia in the mouse hippocampus.

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Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development

Nature Biotechnology ◽

10.1038/nbt.3854 ◽

2017 ◽

Vol 35 (6) ◽

pp. 551-560 ◽

Cited By ~ 118

Author(s):

Abbas H Rizvi ◽

Pablo G Camara ◽

Elena K Kandror ◽

Thomas J Roberts ◽

Ira Schieren ◽

...

Keyword(s):

Single Cell ◽

Cellular Differentiation ◽

Rna Seq

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High Throughput Immunophenotyping and Expression Profiling at Single Cell Level Reveal BCR-ABL1 Dependent Surface Markers of Chronic Myeloid Leukemia Stem Cells

Blood ◽

10.1182/blood-2019-126924 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 2920-2920

Author(s):

Marianna Romzova ◽

Dagmar Smitalova ◽

Peter Taus ◽

Jiri Mayer ◽

Martin Culen

Keyword(s):

Stem Cells ◽

Single Cell ◽

Expression Profiling ◽

Myeloid Leukemia ◽

Single Cells ◽

Transcript Level ◽

Surface Expression ◽

Surface Markers ◽

Cell Level ◽

Tki Treatment

BACKGROUND: Bcr-abl1 oncogene targeted treatment with tyrosine kinase inhibitors (TKI) showed an impressive efficacy against proliferating chronic myeloid leukemia (CML) cells. However, rapid relapses in more than half of CML patients after discontinuation of the treatment suggest a presence of quiescent leukemic stem cells inherently resistant to BCR-ABL1 inhibition. Understanding the heterogeneity of CML stem cell compartment is crucial for preventing the treatment failure. Specificity of already established leukemic stem cell (LSC) markers has been tested mainly in bulk CD34+CD38- populations at diagnosis. Phenotypes and molecular signatures of therapy resistant BCR ABL1 positive stem cells is however yet to be established. AIMS: Identification of BCR-ABL1 dependent LSC markers at single cell level by direct comparison their surface and transcript expression with the levels and the presence of BCR-ABL1 transcript at diagnosis and after administration of TKI treatment. METHODS: Total number of 375 cells were obtained from bone marrow and peripheral blood of 4 chronic phase CML patients. Cells were collected prior any treatment and three months after TKI treatment initiation. Normal bone marrow cells and BCR-ABL1 positive K562 cell line were used as controls. Indexed immuno-phenotyping and sorting of CD34+CD38- single cells was performed using a panel of 11 specific surface markers. Collected single cells were lysed and cDNA was enriched for 11 targets using 22 cycle pre-amplification. Expression profiling was carried on SmartChip real-time PCR system (Takara Bio) detecting following genes: BCR-ABL1, CD26, CD25, IL1-Rap, CD56, CD90, CD93, CD69, KI67, and control genes GUS and HPRT. Unsupervised clustering was performed using principal component analysis (PCA). Correlations were measured by Spearman rank method. RESULTS: At diagnosis, majority of BCR-ABL1+ C34+CD38- stem cells co-express IL1-Rap, CD26, and CD69 on their surface (88%, 82%, 78% overlap). Only 56% of BCR-ABL1+ cells positive for aforementioned markers co-express CD25, 28% CD93 and 16% CD56. The expression of these markers could also be detected in 4-11% of BCR-ABL1- cell, although this could be technical inaccuracy caused by the single cell profiling. CD90 marker did not show any correlation with BCR-ABL1 expression. At transcript level the expression of IL-1Rap, CD26, CD25 and CD56 was observed in 62%, 52% 45% and 16% BCR-ABL1+ cells, and up to 7% of BCR-ABL1- cells. CD69 expression was observed in 90% of BCR-ABL+ cells at transcript level, but also in 71% BCR-ABL- cells. BCR-ABL1 independent expression was observed for cKIT. (60% vs. 76 % in positive vs negative). Finally proliferation marker KI67 was expressed only in 6% of the BCR-ABL1+ cells. PCA analysis divided cells into several distinct clusters with BCR-ABL1 as the main contributor, and cKIT, CD69 and CD26, IL-1RAP as other significant factors. Interestingly BCR-ABL1+ cells collected during TKI treatment showed persistent surface expression of IL-1Rap and CD26, while CD56, CD69 and CD93 were only on part of the BCR-ABL1+ cells. CD25 was significantly deregulated during TKI treatment. CONCLUSION: At diagnosis up to 80% of LSC co-express 3 specific surface markers - IL-1RAP, CD26 and CD69. Variable portion of LSC co-express additional markers such are CD25, CD56 and CD93. During TKI treatment the surface expression of majority of markers is decreased, where the best correlated LSC marker is IL-1Rap, followed by CD26 and CD69. CD56 marker seems to persist in the same proportion of cells while CD25 disappears. cKIT is highly expressed in normal BM and HSC from CML patients, but also in some LSC. CD34+CD38- cells show non-proliferating phenotype. Disclosures Mayer: AOP Orphan Pharmaceuticals AG: Research Funding.

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Microfluidic Platforms Designed for Morphological and Photosynthetic Investigations of Chlamydomonas reinhardtii on a Single-Cell Level

Cells ◽

10.3390/cells11020285 ◽

2022 ◽

Vol 11 (2) ◽

pp. 285

Author(s):

Eszter Széles ◽

Krisztina Nagy ◽

Ágnes Ábrahám ◽

Sándor Kovács ◽

Anna Podmaniczki ◽

...

Keyword(s):

Chlamydomonas Reinhardtii ◽

Single Cell ◽

Single Cells ◽

Fluorescence Induction ◽

Photochemical Quenching ◽

Support Surface ◽

Single Cell Level ◽

Microfluidic Platforms ◽

Cell Level ◽

Non Photochemical Quenching

Chlamydomonas reinhardtii is a model organism of increasing biotechnological importance, yet, the evaluation of its life cycle processes and photosynthesis on a single-cell level is largely unresolved. To facilitate the study of the relationship between morphology and photochemistry, we established microfluidics in combination with chlorophyll a fluorescence induction measurements. We developed two types of microfluidic platforms for single-cell investigations: (i) The traps of the “Tulip” device are suitable for capturing and immobilizing single cells, enabling the assessment of their photosynthesis for several hours without binding to a solid support surface. Using this “Tulip” platform, we performed high-quality non-photochemical quenching measurements and confirmed our earlier results on bulk cultures that non-photochemical quenching is higher in ascorbate-deficient mutants (Crvtc2-1) than in the wild-type. (ii) The traps of the “Pot” device were designed for capturing single cells and allowing the growth of the daughter cells within the traps. Using our most performant “Pot” device, we could demonstrate that the FV/FM parameter, an indicator of photosynthetic efficiency, varies considerably during the cell cycle. Our microfluidic devices, therefore, represent versatile platforms for the simultaneous morphological and photosynthetic investigations of C. reinhardtii on a single-cell level.

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Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

10.1101/553040 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ning Wang ◽

Andrew E. Teschendorff

Keyword(s):

Transcription Factors ◽

Single Cell ◽

Cell Fate ◽

Regulatory Networks ◽

Large Scale ◽

Single Cells ◽

Differential Expression Analysis ◽

Dropout Rate ◽

Rna Seq ◽

Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

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Characterizing Intercellular Communication of Pan-Cancer Reveals SPP1+ Tumor-Associated Macrophage Expanded in Hypoxia and Promoting Cancer Malignancy Through Single-Cell RNA-Seq Data

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.749210 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jinfen Wei ◽

Zixi Chen ◽

Meiling Hu ◽

Ziqing He ◽

Dawei Jiang ◽

...

Keyword(s):

Single Cell ◽

Cancer Cells ◽

Cancer Progression ◽

Epithelial Mesenchymal Transition ◽

Single Cells ◽

Matrix Remodeling ◽

Rna Seq ◽

Mesenchymal Transition ◽

Tumor Associated Macrophage ◽

Cancer Types

Hypoxia is a characteristic of tumor microenvironment (TME) and is a major contributor to tumor progression. Yet, subtype identification of tumor-associated non-malignant cells at single-cell resolution and how they influence cancer progression under hypoxia TME remain largely unexplored. Here, we used RNA-seq data of 424,194 single cells from 108 patients to identify the subtypes of cancer cells, stromal cells, and immune cells; to evaluate their hypoxia score; and also to uncover potential interaction signals between these cells in vivo across six cancer types. We identified SPP1+ tumor-associated macrophage (TAM) subpopulation potentially enhanced epithelial–mesenchymal transition (EMT) by interaction with cancer cells through paracrine pattern. We prioritized SPP1 as a TAM-secreted factor to act on cancer cells and found a significant enhanced migration phenotype and invasion ability in A549 lung cancer cells induced by recombinant protein SPP1. Besides, prognostic analysis indicated that a higher expression of SPP1 was found to be related to worse clinical outcome in six cancer types. SPP1 expression was higher in hypoxia-high macrophages based on single-cell data, which was further validated by an in vitro experiment that SPP1 was upregulated in macrophages under hypoxia-cultured compared with normoxic conditions. Additionally, a differential analysis demonstrated that hypoxia potentially influences extracellular matrix remodeling, glycolysis, and interleukin-10 signal activation in various cancer types. Our work illuminates the clearer underlying mechanism in the intricate interaction between different cell subtypes within hypoxia TME and proposes the guidelines for the development of therapeutic targets specifically for patients with high proportion of SPP1+ TAMs in hypoxic lesions.

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

10.1101/173997 ◽

2017 ◽

Cited By ~ 1

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.

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RNA velocity in single cells

10.1101/206052 ◽

2017 ◽

Cited By ~ 15

Author(s):

Gioele La Manno ◽

Ruslan Soldatov ◽

Hannah Hochgerner ◽

Amit Zeisel ◽

Viktor Petukhov ◽

...

Keyword(s):

Single Cell ◽

Cellular Differentiation ◽

Single Cells ◽

Time Derivative ◽

Dynamic Processes ◽

Embryonic Brain ◽

Future State ◽

Single Cell Rna Sequencing ◽

Mouse Hippocampus ◽

Lineage Tree

AbstractRNA abundance is a powerful indicator of the state of individual cells, but does not directly reveal dynamic processes such as cellular differentiation. Here we show that RNA velocity—the time derivative of RNA abundance—can be estimated by distinguishing unspliced and spliced mRNAs in standard single-cell RNA sequencing protocols. We show that RNA velocity is a vector that predicts the future state of individual cells on a timescale of hours. We validate the accuracy of RNA velocity in the neural crest lineage, demonstrate its use on multiple technical platforms, reconstruct the branching lineage tree of the mouse hippocampus, and measure RNA kinetics in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

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Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data

Briefings in Functional Genomics ◽

10.1093/bfgp/ely001 ◽

2018 ◽

Vol 18 (6) ◽

pp. 434-434 ◽

Cited By ~ 3

Author(s):

Vilas Menon

Keyword(s):

Single Cell ◽

Single Cells ◽

Rna Seq

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scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz701 ◽

2019 ◽

Cited By ~ 2

Author(s):

Congting Ye ◽

Qian Zhou ◽

Xiaohui Wu ◽

Chen Yu ◽

Guoli Ji ◽

...

Keyword(s):

Single Cell ◽

Alternative Polyadenylation ◽

Cellular Heterogeneity ◽

Supplementary Information ◽

Rna Seq ◽

Computational Tool ◽

Cell Level ◽

Wilcoxon Rank Sum Test ◽

Transcriptional Regulatory ◽

Cell Groups

Abstract Motivation Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3′ enriched strategy in library construction, the most commonly used scRNA-seq protocol—10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data. Availability and implementation The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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