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Single cell

Tìm thấy 12+ kết quả cho từ khóa "Single cell"

Rapid single cell evaluation of human disease and disorder targets using REVEAL: SingleCell

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Rapid single cell evaluation of human. Background: Single-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying extensive single-cell datasets..

Chuyên đ 1 : 0 1 A F 0 1 A 0 1 E E C 1 E A 4 1 E A 2 1 E A E 1 E D A PH NG PHÁP X LÝ CH T TH I R N V I QUÁ TRÌNH SINGLE CELL PROTEIN AND ETHANOL

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Chuyên đề 1 : PHƯƠNG PHÁP XỬ LÝ CHẤT THẢI RẮN VỚI QUÁ TRÌNH SINGLE CELL PROTEIN AND ETHANOL 1.1 . Sự giải nghĩa của khái niệm : Sự thủy phân bao gồm sự sử dụng chất thải như nguyên liệu để sản xuất Single-cell protein và rượu ethanol. Nói chính xác, 2 khái niệm được liên quan mật thiết với nhau, thứ nhất là sự sản xuất thức ăn dinh dưỡng cho sự tiêu thụ bởi vật nuôi hoặc bởi con người. Thứ hai là sản phẩm ethanol đó có thể đáp ứng như là nhiên liệu trong sản xuất năng lượng.

Nghiên cứu thu hoạch và sử dụng SCD (Single cell detritus) từ rong câu (Gracilaria tenuistipitata) làm thức ăn cho động vật ăn lọc

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Nghiên cứu được thực hiện nhằm xác định phương pháp thích hợp để thu hoạch tế bào đơn (single cell detritus, SCD) từ rong câu chỉ (Gracilaria tenuistipitata) và đánh giá ảnh hưởng của việc sử dụng SCD làm thức ăn đến sinh trưởng và tỷ lệ sống của Artemia franciscana-một đối tượng ăn lọc. Artemia được nuôi với 7 nghiệm thức thức ăn, trong đó, nghiệm thức đối chứng là thức ăn tôm sú số 0, 6 nghiệm thức còn lại gồm SCD-N, SCD-L và SCD-Y với các mức thay thế 100% và 50%.

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

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Single-cell analyses inform mechanisms of myeloid-targeted therapies in Colon Cancer. Comparative analysis of single- cell RNA sequencing methods. Power analysis of single-cell RNA-sequencing experiments. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Benchmarking single cell RNA- sequencing analysis pipelines using mixture control experiments. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-Seq systems..

Blinatumomab-induced T cell activation at single cell transcriptome resolution

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Recently, single-cell RNA-seq (scRNA-seq) has been widely used in the analysis of T cell subpopulations [35 – 37]. The dose-dependent specific lysis induced by blinatumomab in RS4;11 and SUP-B15 cells are shown in Additional file 1, Fig. By applying unsupervised clustering in the principal component space of this dataset, we identi- fied five cell clusters (Fig. Cluster C1 was composed of T cells exhibiting a highly specific expression of the T cell markers CD3D and CD3E..

Exploring the changing landscape of cellto-cell variation after CTCF knockdown via single cell RNA-seq

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Single-cell proteomic analysis of S. Accounting for technical noise in single-cell RNA-seq experiments

An efficient single-cell transcriptomics workflow for microbial eukaryotes benchmarked on Giardia intestinalis cells

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Morphological identification and single- cell genomics of marine Diplonemids. Evaluation of single-cell genomics to address evolutionary questions using three SAGs of the choanoflagellate Monosiga brevicollis. Single-cell transcriptomics for microbial eukaryotes. Single-cell transcriptomics of small microbial eukaryotes: limitations and potential. Full- length RNA-seq from single cells using Smart-seq2. Smart-seq2 for sensitive full-length transcriptome profiling in single cells..

MethylStar: A fast and robust pre-processing pipeline for bulk or single-cell whole-genome bisulfite sequencing data

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To show that MethylStar can also be applied to single-cell WGBS data, we analyzed DNA methylation of 200 single cells from Human early embryo tissue (paired-end, 845 GB. MethylStar’s processing times were compared to Methylpy which also supports single-cell data. Hence, MethylStar presents an efficient analysis solution for deep single-cell WGBS experiments..

WASP: A versatile, web-accessible single cell RNA-Seq processing platform

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The result of this pre-processing is usually a gene expression matrix, in which each row represents a gene and each column corresponds to a cell (barcode).. Each entry contains the expression level of the gene in the given cell according to the unique UMIs detected.. Post-processing analyses of single-cell data differ funda- mentally from bulk RNA-seq data since the results for each individual cell have to be validated to remove low quality cells.

Cloud accelerated alignment and assembly of full-length single-cell RNA-seq data using Falco

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As with the evaluation of the Falco framework, the single-cell RNA-seq datasets used are a mouse embryonic stem cell (ESC) single cell dataset, con- taining 869 samples of 200 bp paired-end reads, stored in 1.02 Tb of gzipped FASTQ files [26].

The effect of methanol fixation on singlecell RNA sequencing data

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Methanol fixation does not cause cell-type specific drop-out in single cell RNA-seq data. Isolation of Full-Size mRNA From Ethanol-Fixed Cells After Cellular Immunefluroscence Staining and fluorescence-activated cell sorting (FACS)

Massively parallel gene expression variation measurement of a synonymous codon library

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Stochastic gene expression in a single cell. Noise in gene expression: origins, consequences, and control. Heterogeneity coordinates bacterial multi-gene expression in single cells. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell-to-cell variability in the propensity to transcribe explains correlated fluctuations in gene expression.. Promoter architecture dictates cell-to-cell variability in gene expression.

Millefy: Visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets

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In conclusion, Millefy will provide new opportunities to analyze scRNA- seq data from the point of view of cell-to-cell heterogene- ity in read coverage, and help researchers assess cellular heterogeneity and RNA biology using scRNA-seq data.. scRNA-seq: single-cell RNA sequencing. Single-cell rna-seq:

Hierarchicell: An R-package for estimating power for tests of differential expression with single-cell data

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in single-cell RNA-seq data.

Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

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Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. SPLIFF: a single-cell method to map protein-protein interactions in time and space.. In: Single Cell Protein Analysis. Single-cell RNA sequencing technologies and bioinformatics pipelines. Single-cell RNA-seq:. Current best practices in single-cell RNA-seq analysis: a tutorial. Defining cell types and states with single-cell genomics..

Identification of diverse cell populations in skeletal muscles and biomarkers for intramuscular fat of chicken by single-cell RNA sequencing

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In the current study, single-cell transcriptome sequen- cing by high-throughput scRNA-seq (10× Genomics Chromium) was conducted to clarify the diversity of the cell profiles of chicken breast muscles and identify marker genes for IMF. The breast muscles at Day 5 (D5) and Day 100 (D100) were used to represent the two developmental stages of the skeletal muscle—hyperplasia and hypertrophy. In total, single-cell transcriptomes of 8948 cells at D5 and 4504 at D100 were obtained (Table S1).

Comparative analysis of single-cell transcriptomics in human and zebrafish oocytes

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Results: We performed single-cell RNA-seq of the zebrafish oocyte and compared it with two studies that have performed single-cell RNA-seq of the human oocyte. We carried out a comparative analysis of genes expressed in the oocyte and genes highly expressed in the oocyte across the three studies. Overall, we found high consistency between the human studies and high concordance in expression for the orthologous genes in the two organisms.

Dual indexed library design enables compatibility of in-Drop single-cell RNAsequencing with exAMP chemistry sequencing platforms

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A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte

Cell-specific characterization of the placental methylome

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Single-cell RNA-seq reveals the diversity of trophoblast subtypes and patterns of differentiation in the human placenta. Prediction of genome-wide DNA methylation in repetitive elements. Cell specific patterns of methylation in the human placenta. Different epigenetic states de fi ne syncytiotrophoblast and cytotrophoblast nuclei in the trophoblast of the human placenta

Correction for both common and rare cell types in blood is important to identify genes that correlate with age

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Adding cell counts clearly improves the prediction of gene expression values. Single-cell RNA-seq data reveals the contribution of cell types to gene expression during aging. Every cell type has its own gene expression pattern, so the composition of blood cells influences the total gene expression observed in whole blood RNA-seq data.