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

Blood ◽  
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.

scholarly journals High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip

2021 ◽  
Author(s):  
Jongmin Woo ◽  
Sarah M. Williams ◽  
Victor Aguilera-Vazquez ◽  
Ryan L. Sontag ◽  
Ronald J. Moore ◽  
...  
Keyword(s):  
Single Cell ◽  
High Efficiency ◽  
Single Cells ◽  
Protein Recovery ◽  
Processing Capacity ◽  
Protein Abundance ◽  
Proteome Coverage ◽  
Low Protein ◽  
Functional Differences ◽  
Cell Proteome

AbstractGlobal quantification of protein abundances in single cells would provide more direct information on cellular function phenotypes and complement transcriptomics measurements. However, single-cell proteomics (scProteomics) is still immature and confronts technical challenges, including limited proteome coverage, poor reproducibility, as well as low throughput. Here we describe a nested nanoPOTS (N2) chip to dramatically improve protein recovery, operation robustness, and processing throughput for isobaric-labeling-based scProteomics workflow. The N2 chip allows reducing cell digestion volume to <30 nL and increasing processing capacity to > 240 single cells in one microchip. In the analysis of ∼100 individual cells from three different cell lines, we demonstrate the N2 chip-based scProteomics platform can robustly quantify ∼1500 proteins and reveal functional differences. Our analysis also reveals low protein abundance variations (median CVs < 16.3%), highlighting the utility of such measurements, and also suggesting the single-cell proteome is highly stable for the cells cultured under identical conditions.

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 SISUA: Semi-Supervised Generative Autoencoder for Single Cell Data

2019 ◽  
Author(s):  
Trung Ngo Trong ◽  
Roger Kramer ◽  
Juha Mehtonen ◽  
Gerardo González ◽  
Ville Hautamäki ◽  
...  
Keyword(s):  
Single Cell ◽  
Network Architecture ◽  
Surface Protein ◽  
Protein Quantification ◽  
Additional Information ◽  
Protein Levels ◽  
Variational Autoencoder ◽  
Cell Gene Expression ◽  
Cell Phenotypes ◽  
Cell Data

ABSTRACTSingle-cell transcriptomics offers a tool to study the diversity of cell phenotypes through snapshots of the abundance of mRNA in individual cells. Often there is additional information available besides the single cell gene expression counts, such as bulk transcriptome data from the same tissue, or quantification of surface protein levels from the same cells. In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model. The generative model is based on the deep variational autoencoder (VAE) neural network architecture.

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 Characterization of CRISPR/Cas9 RANKL knockout mesenchymal stem cell clones based on single-cell printing technology and Emulsion Coupling assay as a low-cellularity workflow for single-cell cloning

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0238330
Author(s):  
Tobias Gross ◽  
Csaba Jeney ◽  
Darius Halm ◽  
Günter Finkenzeller ◽  
G. Björn Stark ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Protein Detection ◽  
High Yield ◽  
Fluorescent Properties ◽  
Cell Printing ◽  
Protein Levels ◽  
Cell Clones

The homogeneity of the genetically modified single-cells is a necessity for many applications such as cell line development, gene therapy, and tissue engineering and in particular for regenerative medical applications. The lack of tools to effectively isolate and characterize CRISPR/Cas9 engineered cells is considered as a significant bottleneck in these applications. Especially the incompatibility of protein detection technologies to confirm protein expression changes without a preconditional large-scale clonal expansion creates a gridlock in many applications. To ameliorate the characterization of engineered cells, we propose an improved workflow, including single-cell printing/isolation technology based on fluorescent properties with high yield, a genomic edit screen (Surveyor assay), mRNA RT-PCR assessing altered gene expression, and a versatile protein detection tool called emulsion-coupling to deliver a high-content, unified single-cell workflow. The workflow was exemplified by engineering and functionally validating RANKL knockout immortalized mesenchymal stem cells showing bone formation capacity of these cells. The resulting workflow is economical, without the requirement of large-scale clonal expansions of the cells with overall cloning efficiency above 30% of CRISPR/Cas9 edited cells. Nevertheless, as the single-cell clones are comprehensively characterized at an early, highly parallel phase of the development of cells including DNA, RNA, and protein levels, the workflow delivers a higher number of successfully edited cells for further characterization, lowering the chance of late failures in the development process.

scholarly journals Comprehensive characterization of single cell full-length isoforms in human and mouse with long-read sequencing

2020 ◽  
Author(s):  
Luyi Tian ◽  
Jafar S. Jabbari ◽  
Rachel Thijssen ◽  
Quentin Gouil ◽  
Shanika L. Amarasinghe ◽  
...  
Keyword(s):  
Data Integration ◽  
Single Cell ◽  
Ribosome Biogenesis ◽  
Single Cells ◽  
Transcript Level ◽  
Full Length ◽  
Alternative Transcript ◽  
Long Read ◽  
Comprehensive Characterization

AbstractAlternative splicing shapes the phenotype of cells in development and disease. Long-read RNA-sequencing recovers full-length transcripts but has limited throughput at the single-cell level. Here we developed single-cell full-length transcript sequencing by sampling (FLT-seq), together with the computational pipeline FLAMES to overcome these issues and perform isoform discovery and quantification, splicing analysis and mutation detection in single cells. With FLT-seq and FLAMES, we performed the first comprehensive characterization of the full-length isoform landscape in single cells of different types and species and identified thousands of unannotated isoforms. We found conserved functional modules that were enriched for alternative transcript usage in different cell populations, including ribosome biogenesis and mRNA splicing. Analysis at the transcript-level allowed data integration with scATAC-seq on individual promoters, improved correlation with protein expression data and linked mutations known to confer drug resistance to transcriptome heterogeneity. Our methods reveal previously unseen isoform complexity and provide a better framework for multi-omics data integration.

scholarly journals Human Ribosomal Proteins RPeL27, RPeL43, and RPeL41 Are Upregulated in Nasopharyngeal Carcinoma Cell Lines

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Edmund Ui-Hang Sim ◽  
Stella Li-Li Chan ◽  
Kher-Lee Ng ◽  
Choon-Weng Lee ◽  
Kumaran Narayanan
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Ribosomal Protein ◽  
Cell Lines ◽  
Ribosomal Proteins ◽  
Ribosome Biogenesis ◽  
Expression Patterns ◽  
Transcript Level ◽  
Ribosomal Protein Genes ◽  
Significant Differential Expression ◽  
Protein Levels

Apart from their canonical role in ribosome biogenesis, there is increasing evidence of ribosomal protein genes’ involvement in various cancers. A previous study by us revealed significant differential expression of three ribosomal protein genes (RPeL27, RPeL41, and RPeL43) between cell lines derived from tumor and normal nasopharyngeal epithelium. However, the results therein were based on a semiquantitative assay, thus preliminary in nature. Herein, we provide findings of a deeper analysis of these three genes in the context to nasopharyngeal carcinoma (NPC) tumorigenesis. Their expression patterns were analyzed in a more quantitative manner at transcript level. Their protein expression levels were also investigated. We showed results that are contrary to previous report. Rather than downregulation, these genes were significantly overexpressed in NPC cell lines compared to normal control at both transcript and protein levels. Nevertheless, their association with NPC has been established. Immunoprecipitation pulldown assays indicate the plausible interaction of either RPeL27 or RPeL43 with POTEE/TUBA1A and ACTB/ACTBL2 complexes. In addition, RPeL43 is shown to bind with MRAS and EIF2S1 proteins in a NPC cell line (HK1). Our findings support RPeL27, RPeL41, and RPeL43 as potential markers of NPC and provide insights into the interaction targets of RPeL27 and RPeL43 proteins.

scholarly journals Large-scale simultaneous measurement of epitopes and transcriptomes in single cells

2017 ◽  
Author(s):  
Marlon Stoeckius ◽  
Christoph Hafemeister ◽  
William Stephenson ◽  
Brian Houck-Loomis ◽  
Pratip K. Chattopadhyay ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Cellular Proteins ◽  
Surface Markers ◽  
Cell Surface Markers ◽  
Complex Cell ◽  
Single Cell Sequencing ◽  
Protein Levels ◽  
Phenotypic Information

Recent high-throughput single-cell sequencing approaches have been transformative for understanding complex cell populations, but are unable to provide additional phenotypic information, such as protein levels of cell-surface markers. Using oligonucleotide-labeled antibodies, we integrate measurements of cellular proteins and transcriptomes into an efficient, sequencing-based readout of single cells. This method is compatible with existing single-cell sequencing approaches and will readily scale as the throughput of these methods increase.

scholarly journals Single-cell Visualization and Quantification of Trace Metals in Chlamydomonas Lysosome-Related Organelles

2021 ◽  
Author(s):  
Stefan Schmollinger ◽  
Si Chen ◽  
Daniela Strenkert ◽  
Colleen Hui ◽  
Martina Ralle ◽  
...  
Keyword(s):  
Trace Metal ◽  
Chlamydomonas Reinhardtii ◽  
Single Cell ◽  
Imaging Techniques ◽  
Single Cells ◽  
Experimental System ◽  
Absolute Quantification ◽  
Global Scale ◽  
Oxygenic Photosynthesis ◽  
High Calcium

AbstractThe acidocalcisome is an acidic organelle in the cytosol of eukaryotes, defined by its low pH and high calcium and polyphosphate content. It is visualized as an electron-dense object by transmission electron microscopy (TEM) or described with mass-spectrometry (MS)-based imaging techniques or multimodal X-ray fluorescence microscopy (XFM) based on its unique elemental composition. Compared to MS-based imaging techniques, XFM offers the advantage of absolute quantification of trace metal content, since sectioning of the cell is not required and metabolic states can be preserved rapidly by either vitrification or chemical fixation. We employed XFM in Chlamydomonas reinhardtii, to determine single-cell and organelle trace metal quotas within algal cells in situations of trace metal over-accumulation (Fe, Cu). We found up to 70% of the cellular Cu and 80% of Fe sequestered in acidocalcisomes in these conditions, and identified two distinct populations of acidocalcisomes, defined by their unique trace elemental makeup. We utilized the vtc1 mutant, defective in polyphosphate synthesis and failing to accumulate Ca to show that Fe sequestration is not dependent on either. Finally, quantitation of the Fe and Cu contents of individual cells and compartments via XFM, over a range of cellular metal quotas created by nutritional and genetic perturbations, indicated excellent correlation with bulk data from corresponding cell cultures, establishing a framework to distinguish the nutritional status of single cells.Significance statementTransition metals are of crucial importance for primary productivity; their scarcity limits crop yield in agriculture and carbon sequestration at global scale. Copper (Cu), iron (Fe) and manganese (Mn) are among the most important trace elements that enable the redox chemistry in oxygenic photosynthesis. The single-celled, eukaryotic green alga Chlamydomonas reinhardtii is a choice experimental system for studying trace metal homeostasis in the context of phototrophy, offering all the advantages of a classical microbial system with a well-characterized photosystem and trace metal metabolism machinery of relevance to plants. This project identifies and differentiates different trace metal storage sites in Chlamydomonas and uncovers the dynamics of trace metal storage and mobilization in situations of fluctuating resources.

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