Literature DB >> 35368355

Single Cell Technologies: Beyond Microfluidics.

Haikuo Li1,2, Benjamin D Humphreys1,2.   

Abstract

Single-cell RNA-sequencing (scRNA-seq) has been widely adopted in recent years due to standardized protocols and automation, reliability, and standardized bioinformatic pipelines. The most widely adopted platform is the 10× Genomics solution. Although powerful, this system is limited by its high cost, moderate throughput, and the inability to customize due to fixed kit components. This study will cover new approaches that do not rely on microfluidics and thus have low entry costs, are highly customizable, and are within the reach of any laboratory possessing molecular biology expertise.
Copyright © 2021 by the American Society of Nephrology.

Entities:  

Keywords:  basic science; genetics; genomics; single-cell analysis; small cytoplasmic RNA

Mesh:

Year:  2021        PMID: 35368355      PMCID: PMC8786099          DOI: 10.34067/KID.0001822021

Source DB:  PubMed          Journal:  Kidney360        ISSN: 2641-7650


  65 in total

1.  Sequencing thousands of single-cell genomes with combinatorial indexing.

Authors:  Sarah A Vitak; Kristof A Torkenczy; Jimi L Rosenkrantz; Andrew J Fields; Lena Christiansen; Melissa H Wong; Lucia Carbone; Frank J Steemers; Andrew Adey
Journal:  Nat Methods       Date:  2017-01-30       Impact factor: 28.547

2.  Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding.

Authors:  Alexander B Rosenberg; Charles M Roco; Richard A Muscat; Anna Kuchina; Paul Sample; Zizhen Yao; Lucas T Graybuck; David J Peeler; Sumit Mukherjee; Wei Chen; Suzie H Pun; Drew L Sellers; Bosiljka Tasic; Georg Seelig
Journal:  Science       Date:  2018-03-15       Impact factor: 47.728

3.  Single-Nucleus RNA-Sequencing Profiling of Mouse Lung. Reduced Dissociation Bias and Improved Rare Cell-Type Detection Compared with Single-Cell RNA Sequencing.

Authors:  Jeffrey R Koenitzer; Haojia Wu; Jeffrey J Atkinson; Steven L Brody; Benjamin D Humphreys
Journal:  Am J Respir Cell Mol Biol       Date:  2020-12       Impact factor: 6.914

4.  Joint profiling of chromatin accessibility and gene expression in thousands of single cells.

Authors:  Junyue Cao; Darren A Cusanovich; Vijay Ramani; Delasa Aghamirzaie; Hannah A Pliner; Andrew J Hill; Riza M Daza; Jose L McFaline-Figueroa; Jonathan S Packer; Lena Christiansen; Frank J Steemers; Andrew C Adey; Cole Trapnell; Jay Shendure
Journal:  Science       Date:  2018-08-30       Impact factor: 47.728

5.  Construction of a human cell landscape at single-cell level.

Authors:  Xiaoping Han; Ziming Zhou; Lijiang Fei; Huiyu Sun; Renying Wang; Yao Chen; Haide Chen; Jingjing Wang; Huanna Tang; Wenhao Ge; Yincong Zhou; Fang Ye; Mengmeng Jiang; Junqing Wu; Yanyu Xiao; Xiaoning Jia; Tingyue Zhang; Xiaojie Ma; Qi Zhang; Xueli Bai; Shujing Lai; Chengxuan Yu; Lijun Zhu; Rui Lin; Yuchi Gao; Min Wang; Yiqing Wu; Jianming Zhang; Renya Zhan; Saiyong Zhu; Hailan Hu; Changchun Wang; Ming Chen; He Huang; Tingbo Liang; Jianghua Chen; Weilin Wang; Dan Zhang; Guoji Guo
Journal:  Nature       Date:  2020-03-25       Impact factor: 49.962

Review 6.  Integrative single-cell analysis.

Authors:  Tim Stuart; Rahul Satija
Journal:  Nat Rev Genet       Date:  2019-05       Impact factor: 53.242

7.  An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome.

Authors:  Chenxu Zhu; Miao Yu; Hui Huang; Ivan Juric; Armen Abnousi; Rong Hu; Jacinta Lucero; M Margarita Behrens; Ming Hu; Bing Ren
Journal:  Nat Struct Mol Biol       Date:  2019-11-06       Impact factor: 15.369

8.  Systematic comparison of single-cell and single-nucleus RNA-sequencing methods.

Authors:  Xian Adiconis; Sean K Simmons; Jiarui Ding; Monika S Kowalczyk; Cynthia C Hession; Nemanja D Marjanovic; Travis K Hughes; Marc H Wadsworth; Tyler Burks; Lan T Nguyen; John Y H Kwon; Boaz Barak; William Ge; Amanda J Kedaigle; Shaina Carroll; Shuqiang Li; Nir Hacohen; Orit Rozenblatt-Rosen; Alex K Shalek; Alexandra-Chloé Villani; Aviv Regev; Joshua Z Levin
Journal:  Nat Biotechnol       Date:  2020-04-06       Impact factor: 54.908

9.  A benchmark of batch-effect correction methods for single-cell RNA sequencing data.

Authors:  Hoa Thi Nhu Tran; Kok Siong Ang; Marion Chevrier; Xiaomeng Zhang; Nicole Yee Shin Lee; Michelle Goh; Jinmiao Chen
Journal:  Genome Biol       Date:  2020-01-16       Impact factor: 13.583

10.  Sci-fate characterizes the dynamics of gene expression in single cells.

Authors:  Junyue Cao; Wei Zhou; Frank Steemers; Cole Trapnell; Jay Shendure
Journal:  Nat Biotechnol       Date:  2020-04-13       Impact factor: 54.908

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  1 in total

1.  scATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data.

Authors:  Kai Hu; Haibo Liu; Nathan D Lawson; Lihua Julie Zhu
Journal:  Front Cell Dev Biol       Date:  2022-09-27
  1 in total

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