Design of a large-scale femtoliter droplet array for single-cell analysis of drug-tolerant and drug-resistant bacteria

A simple, low-cost technique for high throughput single-cell analysis, Microscale Oil-Covered Cell Array (MOCCA), is presented in this paper. Corresponding to recent research on single cell analysis, simple devices for isolated cell chambers are urgently needed and long sought-after. Instead of using microfabricated solid structures to capture cells, MOCCA isolates cells in discrete aqueous droplets that are separated by oil on the patterned hydrophilic areas on a relatively more hydrophobic flat substrate. In our pioneer study, we created an array of 700-picoliter droplets. The randomly seeded E. coli cell number in each discrete droplet approaches single-cell levels. The total time needed for MOCCA fabrication was no more than 10 minutes. Compared to traditional micro-fabrication techniques, MOCCA dramatically lowers the cost and enhances the efficiency for the fabrication procedure, while producing a microscale array as in those made using traditional methods.

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Large-scale arrays of picolitre chambers for single-cell analysis of large cell populations

Lab on a Chip ◽

10.1039/c0lc00139b ◽

2010 ◽

Vol 10 (21) ◽

pp. 2952 ◽

Cited By ~ 32

Author(s):

Won Chul Lee ◽

Sara Rigante ◽

Albert P. Pisano ◽

Frans A. Kuypers

Keyword(s):

Single Cell ◽

Large Scale ◽

Single Cell Analysis ◽

Large Cell ◽

Cell Populations ◽

Cell Analysis

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SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

10.1101/118901 ◽

2017 ◽

Cited By ~ 9

Author(s):

Bo Wang ◽

Daniele Ramazzotti ◽

Luca De Sano ◽

Junjie Zhu ◽

Emma Pierson ◽

...

Keyword(s):

Single Cell ◽

Large Scale ◽

Single Cell Analysis ◽

R Package ◽

Supplementary Information ◽

Cell Analysis ◽

Rna Seq ◽

A Cell ◽

Supplementary Material ◽

Public Datasets

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

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scSVAS: CNV clonal visualization online platform for large scale single-cell genomics

10.1101/2021.06.10.437122 ◽

2021 ◽

Author(s):

Lingxi Chen ◽

Yuhao Qing ◽

Ruikang Li ◽

Chaohui Li ◽

Hechen Li ◽

...

Keyword(s):

Copy Number Variation ◽

Single Cell ◽

Real Time ◽

Copy Number ◽

Large Scale ◽

Single Cell Analysis ◽

Interactive Visualization ◽

Cell Analysis ◽

Single Cell Genomics ◽

Number Variation

The recent advance of single-cell copy number variation analysis plays an essential role in addressing intra-tumor heterogeneity, identifying tumor subgroups, and restoring tumor evolving trajectories at single-cell scale. Pleasant visualization of copy number analysis results boosts productive scientific exploration, validation, and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, scSVAS, for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell analysis. Compared with other tools, scSVAS manifests the most comprehensive functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may make scientific discoveries, share interactive visualization, and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing, and publishing single-cell copy number variation profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

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Single-Cell Analysis Reveals the Immune Characteristics of Myeloid Cells and Memory T Cells in Recovered COVID-19 Patients With Different Severities

Frontiers in Immunology ◽

10.3389/fimmu.2021.781432 ◽

2022 ◽

Vol 12 ◽

Author(s):

Xu Li ◽

Manik Garg ◽

Tingting Jia ◽

Qijun Liao ◽

Lifang Yuan ◽

...

Keyword(s):

T Cells ◽

Dendritic Cells ◽

T Cell ◽

Single Cell ◽

Large Scale ◽

Single Cell Analysis ◽

Memory T Cells ◽

T Cell Differentiation ◽

Healthy Controls ◽

Cell Analysis

Despite many studies on the immune characteristics of Coronavirus disease 2019 (COVID-19) patients in the progression stage, a detailed understanding of pertinent immune cells in recovered patients is lacking. We performed single-cell RNA sequencing on samples from recovered COVID-19 patients and healthy controls. We created a comprehensive immune landscape with more than 260,000 peripheral blood mononuclear cells (PBMCs) from 41 samples by integrating our dataset with previously reported datasets, which included samples collected between 27 and 47 days after symptom onset. According to our large-scale single-cell analysis, recovered patients, who had severe symptoms (severe/critical recovered), still exhibited peripheral immune disorders 1–2 months after symptom onset. Specifically, in these severe/critical recovered patients, human leukocyte antigen (HLA) class II and antigen processing pathways were downregulated in both CD14 monocytes and dendritic cells compared to healthy controls, while the proportion of CD14 monocytes increased. These may lead to the downregulation of T-cell differentiation pathways in memory T cells. However, in the mild/moderate recovered patients, the proportion of plasmacytoid dendritic cells increased compared to healthy controls, accompanied by the upregulation of HLA-DRA and HLA-DRB1 in both CD14 monocytes and dendritic cells. In addition, T-cell differentiation regulation and memory T cell–related genes FOS, JUN, CD69, CXCR4, and CD83 were upregulated in the mild/moderate recovered patients. Further, the immunoglobulin heavy chain V3-21 (IGHV3-21) gene segment was preferred in B-cell immune repertoires in severe/critical recovered patients. Collectively, we provide a large-scale single-cell atlas of the peripheral immune response in recovered COVID-19 patients.

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