Corrigendum to: A novel two-layer-integrated microfluidic device for high-throughput yeast proteomic dynamics analysis at the single-cell level

2021 ◽  
Author(s):  
Kaiyue Chen ◽  
Nan Rong ◽  
Shujing Wang ◽  
Chunxiong Luo
Keyword(s):  
Single Cell ◽  
Microfluidic Device ◽  
High Throughput ◽  
Dynamics Analysis ◽  
Single Cell Level ◽  
Cell Level

scholarly journals Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ali Rohani ◽  
Jennifer A. Kashatus ◽  
Dane T. Sessions ◽  
Salma Sharmin ◽  
David F. Kashatus
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Data Science ◽  
Structural Information ◽  
Morphological Changes ◽  
Mitochondrial Morphology ◽  
Single Cell Level ◽  
Mitochondrial Network ◽  
Cell Level ◽  
Wide Range

Abstract Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

scholarly journals High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Filippos Porichis ◽  
Meghan G. Hart ◽  
Morgane Griesbeck ◽  
Holly L. Everett ◽  
Muska Hassan ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Single Cell Level ◽  
Cell Level ◽  
High Throughput Detection ◽  
Specific Mrna

Marker-free detection of progenitor cell differentiation by analysis of Brownian motion in micro-wells

2015 ◽  
Vol 7 (2) ◽  
pp. 178-183 ◽  
Author(s):  
Farzad Sekhavati ◽  
Max Endele ◽  
Susanne Rappl ◽  
Anna-Kristina Marel ◽  
Timm Schroeder ◽  
...  
Keyword(s):  
Brownian Motion ◽  
Cell Differentiation ◽  
Single Cell ◽  
High Throughput ◽  
Progenitor Cell ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level ◽  
Marker Free

The analysis of Brownian motion is a sensitive and robust tool for a label-free high-throughput investigation of cell differentiation at the single-cell level.

scholarly journals Single-Cell Signature Explorer for comprehensive visualization of single cell signatures across scRNA-seq datasets

2019 ◽  
Vol 47 (21) ◽  
pp. e133-e133 ◽  
Author(s):  
Frédéric Pont ◽  
Marie Tosolini ◽  
Jean J Fournié
Keyword(s):  
Lung Cancer ◽  
Single Cell ◽  
High Throughput ◽  
Metabolic Pathways ◽  
Single Cell Level ◽  
Cell Level ◽  
Human Pbmc ◽  
Adult Testis ◽  
Cell Lineages ◽  
Gene Feature

Abstract The momentum of scRNA-seq datasets prompts for simple and powerful tools exploring their meaningful signatures. Here we present Single-Cell_Signature_Explorer (https://sites.google.com/site/fredsoftwares/products/single-cell-signature-explorer), the first method for qualitative and high-throughput scoring of any gene set-based signature at the single cell level and its visualization using t-SNE or UMAP. By scanning datasets for single or combined signatures, it rapidly maps any multi-gene feature, exemplified here with signatures of cell lineages, biological hallmarks and metabolic pathways in large scRNAseq datasets of human PBMC, melanoma, lung cancer and adult testis.

Abstract 3410: Hodgkin and Reed-Sternberg cells genome-wide copy number alteration analysis at single cell level by high-throughput automated platforms

2019 ◽  
Author(s):  
Andrea Raspadori ◽  
Paola Tononi ◽  
Chiara Mangano ◽  
Marianna Garonzi ◽  
Claudio Forcato ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Copy Number ◽  
Copy Number Alteration ◽  
Single Cell Level ◽  
Cell Level ◽  
Genome Wide

Abstract 3410: Hodgkin and Reed-Sternberg cells genome-wide copy number alteration analysis at single cell level by high-throughput automated platforms

2019 ◽  
Author(s):  
Andrea Raspadori ◽  
Paola Tononi ◽  
Chiara Mangano ◽  
Marianna Garonzi ◽  
Claudio Forcato ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Copy Number ◽  
Copy Number Alteration ◽  
Single Cell Level ◽  
Cell Level ◽  
Genome Wide
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