scholarly journals Single cell quantification of ribosome occupancy in early mouse development

2021 ◽  
Author(s):  
Tori Tonn ◽  
Hakan Ozadam ◽  
Crystal Han ◽  
Alia Segura ◽  
Duc Tran ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Germinal Vesicle ◽  
Ribosome Profiling ◽  
Protein Abundance ◽  
Translation Efficiency ◽  
Proteomics Data ◽  
Mouse Development ◽  
High Coverage ◽  
Early Mouse

Technological limitations precluded transcriptome-wide analyses of translation at single cell resolution. To solve this challenge, we developed a novel microfluidic isotachophoresis approach, named RIBOsome profiling via IsoTachoPhoresis (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key regulatory mechanism of genes involved in centrosome organization and N6-methyladenosine modification of RNAs. Our high coverage measurements enabled the first analysis of allele-specific ribosome engagement in early development and led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes. Finally, by integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle stage oocytes is the predominant determinant of protein abundance in the zygote. Taken together, these findings resolve the long-standing paradox of low correlation between RNA expression and protein abundance in early embryonic development. The novel Ribo-ITP approach will enable numerous applications by providing high coverage and high resolution ribosome occupancy measurements from ultra-low input samples including single cells.

scholarly journals Massively parallel gene expression variation measurement of a synonymous codon library

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexander Schmitz ◽  
Fuzhong Zhang
Keyword(s):  
Gene Expression ◽  
Codon Usage ◽  
Single Cells ◽  
Massively Parallel ◽  
Protein Abundance ◽  
Translation Efficiency ◽  
Gene Expression Variation ◽  
Expression Variation ◽  
Change In Mean ◽  
Adaptation Index

Abstract Background Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood. Results Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance. Conclusions Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.

scholarly journals Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development

2014 ◽  
Vol 31 (7) ◽  
pp. 1060-1066 ◽  
Author(s):  
Haifen Chen ◽  
Jing Guo ◽  
Shital K. Mishra ◽  
Paul Robson ◽  
Mahesan Niranjan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Transcriptional Analysis ◽  
Mouse Development ◽  
Cell Fate Decisions ◽  
Regulatory Circuits ◽  
Early Mouse

scholarly journals Single-cell RNA cap and tail sequencing (scRCAT-seq) reveals subtype-specific isoforms differing in transcript demarcation

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Youjin Hu ◽  
Jiawei Zhong ◽  
Yuhua Xiao ◽  
Zheng Xing ◽  
Katherine Sheu ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Single Gene ◽  
Cell Types ◽  
Machine Learning Algorithms ◽  
Translation Efficiency ◽  
Transcription Start Sites ◽  
Long Read ◽  
Mrna Gene ◽  
Gene Isoforms

Abstract The differences in transcription start sites (TSS) and transcription end sites (TES) among gene isoforms can affect the stability, localization, and translation efficiency of mRNA. Gene isoforms allow a single gene diverse functions across different cell types, and isoform dynamics allow different functions over time. However, methods to efficiently identify and quantify RNA isoforms genome-wide in single cells are still lacking. Here, we introduce single cell RNA Cap And Tail sequencing (scRCAT-seq), a method to demarcate the boundaries of isoforms based on short-read sequencing, with higher efficiency and lower cost than existing long-read sequencing methods. In conjunction with machine learning algorithms, scRCAT-seq demarcates RNA transcripts with unprecedented accuracy. We identified hundreds of previously uncharacterized transcripts and thousands of alternative transcripts for known genes, revealed cell-type specific isoforms for various cell types across different species, and generated a cell atlas of isoform dynamics during the development of retinal cones.

scholarly journals SCELLECTOR: ranking amplification bias in single cells using shallow sequencing

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Vivekananda Sarangi ◽  
Alexandre Jourdon ◽  
Taejeong Bae ◽  
Arijit Panda ◽  
Flora Vaccarino ◽  
...  
Keyword(s):  
Single Cell ◽  
Multiple Displacement Amplification ◽  
Single Cells ◽  
Sequencing Data ◽  
High Coverage ◽  
Amplification Bias ◽  
Single Cell Profiling ◽  
High Concordance ◽  
Human Neuronal Cells

Abstract Background The study of mosaic mutation is important since it has been linked to cancer and various disorders. Single cell sequencing has become a powerful tool to study the genome of individual cells for the detection of mosaic mutations. The amount of DNA in a single cell needs to be amplified before sequencing and multiple displacement amplification (MDA) is widely used owing to its low error rate and long fragment length of amplified DNA. However, the phi29 polymerase used in MDA is sensitive to template fragmentation and presence of sites with DNA damage that can lead to biases such as allelic imbalance, uneven coverage and over representation of C to T mutations. It is therefore important to select cells with uniform amplification to decrease false positives and increase sensitivity for mosaic mutation detection. Results We propose a method, Scellector (single cell selector), which uses haplotype information to detect amplification quality in shallow coverage sequencing data. We tested Scellector on single human neuronal cells, obtained in vitro and amplified by MDA. Qualities were estimated from shallow sequencing with coverage as low as 0.3× per cell and then confirmed using 30× deep coverage sequencing. The high concordance between shallow and high coverage data validated the method. Conclusion Scellector can potentially be used to rank amplifications obtained from single cell platforms relying on a MDA-like amplification step, such as Chromium Single Cell profiling solution.

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 Analysis at single-cell resolution identifies a stable mammalian tRNA-mRNA interface and increased translation efficiency in neurons

2021 ◽  
Author(s):  
William Gao ◽  
Carlos J Gallardo-Dodd ◽  
Claudia Kutter
Keyword(s):  
Amino Acid ◽  
Codon Usage ◽  
Single Cell ◽  
Adult Mouse ◽  
Trna Gene ◽  
Supply And Demand ◽  
Cell Types ◽  
Ribosome Profiling ◽  
Translation Efficiency ◽  
Gene Usage

The correlation between codon and anticodon pools influences the efficiency of translation, but whether differences exist in these pools across individual cells is unknown. We determined that codon usage and amino acid demand are highly stable across different cell types using single-cell RNA-sequencing atlases of adult mouse and fetal human. After demonstrating the robustness of ATAC-sequencing for analysis of tRNA gene usage, we quantified anticodon usage and amino acid supply in adult mouse and fetal human single-cell ATAC-seq atlases. We found that tRNA gene usage is overall coordinated across cell types, except in neurons which clustered separately from other cell types. Integration of these datasets revealed a strong and statistically significant correlation between amino acid supply and demand across almost all cell types. Neurons have an enhanced translation efficiency over other cell types, driven by an increased supply of Ala-AGC anticodons. This results in faster decoding of the Ala-GCC codon, as determined by cell-type specific ribosome profiling, and a reduction of Ala-AGC anticodon pools may be implicated in neurological pathologies. This study, the first such examination of codon usage, anticodon usage, and translation efficiency at single-cell resolution, identifies conserved features of translation elongation across mammalian cellular diversity and evolution.

scholarly journals Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

2021 ◽  
Author(s):  
Fredrik Salmen ◽  
Joachim De Jonghe ◽  
Tomasz S. Kaminski ◽  
Anna Alemany ◽  
Guillermo Parada ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Single Cell ◽  
Single Cells ◽  
Droplet Microfluidics ◽  
Mammalian Development ◽  
Mouse Development ◽  
Protein Coding ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

In recent years, single-cell transcriptome sequencing has revolutionized biology, allowing for the unbiased characterization of cellular subpopulations. However, most methods amplify the termini of polyadenylated transcripts capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts. Additionally, most workflows do not sequence the full transcript hindering the analysis of alternative splicing. We therefore developed VASA- seq to detect the total transcriptome in single cells. VASA-seq is compatible with both plate- based formats and droplet microfluidics. We applied VASA-seq to over 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. The dynamics of the total single-cell transcriptome result in the discovery of novel cell type markers many based on non-coding RNA, an in vivo cell cycle analysis and an improved RNA velocity characterization. Moreover, it provides the first comprehensive analysis of alternative splicing during mammalian development.

scholarly journals BIRD: identifying cell doublets via biallelic expression from single cells

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i251-i257
Author(s):  
Kerem Wainer-Katsir ◽  
Michal Linial
Keyword(s):  
Single Cell ◽  
Genetic Background ◽  
Human Peripheral Blood ◽  
Single Cells ◽  
Genomic Diversity ◽  
Experimental Methodology ◽  
Supplementary Information ◽  
Sequence Coverage ◽  
High Coverage ◽  
Biallelic Expression

ABSTRACT Summary Current technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods raises the risk of cell doublets contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell experimental data focus on doublets of different species, cell types or individuals. In this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power toward doublets’ identification. We present a pipeline called BIallelic Ratio for Doublets (BIRD) that relies on heterologous genetic variations, from single-cell RNA sequencing. For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-seq data from 163 primary fibroblast single cells. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets were verified based on a biallelic expression signal amongst X-chromosome of female fibroblasts. Data from 10X Genomics microfluidics of human peripheral blood cells achieved in average 83% (±3.7%) accuracy, and an area under the curve of 0.88 (±0.04) for a collection of ∼13 300 single cells. BIRD addresses instances of doublets, which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved for high-coverage data from Smart-seq. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage and depth. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

2021 ◽  
Author(s):  
Wenxuan Deng ◽  
Biqing Zhu ◽  
Seyoung Park ◽  
Tomokazu S. Sumida ◽  
Avraham Unterman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Earth Metal ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Proteomics Data ◽  
Data Annotation ◽  
Different Cell Types ◽  
Unique Molecular Identifier

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

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