Literature DB >> 33376219

HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes.

Jamie L Marshall1, Benjamin R Doughty1, Vidya Subramanian1, Philine Guckelberger1,2, Qingbo Wang1,3,4, Linlin M Chen1, Samuel G Rodriques1,5,6, Kaite Zhang1, Charles P Fulco1, Joseph Nasser1, Elizabeth J Grinkevich1, Teia Noel1, Sarah Mangiameli1, Drew T Bergman1, Anna Greka1,7, Eric S Lander8,9,10, Fei Chen8, Jesse M Engreitz8,11,12.   

Abstract

Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present Hybridization of Probes to RNA for sequencing (HyPR-seq), a method to sensitively quantify the expression of hundreds of chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects nonpolyadenylated and low-abundance transcripts, and can profile more than 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell-type frequencies in tissue using low-abundance marker genes. By directing sequencing power to genes of interest and sensitively quantifying individual transcripts, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome single-cell RNA-sequencing, making HyPR-seq a powerful method for targeted RNA profiling in single cells.

Entities:  

Keywords:  enhancers; gene regulation; genomics; single cell

Mesh:

Substances:

Year:  2020        PMID: 33376219      PMCID: PMC7776864          DOI: 10.1073/pnas.2010738117

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   12.779


  45 in total

1.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells.

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Review 2.  Podocytes: the Weakest Link in Diabetic Kidney Disease?

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Authors:  Eleen Y Shum; Elisabeth M Walczak; Christina Chang; H Christina Fan
Journal:  Adv Exp Med Biol       Date:  2019       Impact factor: 2.622

4.  Multiplexed Engineering and Analysis of Combinatorial Enhancer Activity in Single Cells.

Authors:  Shiqi Xie; Jialei Duan; Boxun Li; Pei Zhou; Gary C Hon
Journal:  Mol Cell       Date:  2017-04-13       Impact factor: 17.970

5.  Genetic and genomic studies of the BTBR ob/ob mouse model of type 2 diabetes.

Authors:  Susanne M Clee; Samuel T Nadler; Alan D Attie
Journal:  Am J Ther       Date:  2005 Nov-Dec       Impact factor: 2.688

6.  Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells.

Authors:  Eleni P Mimitou; Anthony Cheng; Antonino Montalbano; Stephanie Hao; Marlon Stoeckius; Mateusz Legut; Timothy Roush; Alberto Herrera; Efthymia Papalexi; Zhengqing Ouyang; Rahul Satija; Neville E Sanjana; Sergei B Koralov; Peter Smibert
Journal:  Nat Methods       Date:  2019-04-22       Impact factor: 28.547

7.  HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes.

Authors:  Jamie L Marshall; Benjamin R Doughty; Vidya Subramanian; Philine Guckelberger; Qingbo Wang; Linlin M Chen; Samuel G Rodriques; Kaite Zhang; Charles P Fulco; Joseph Nasser; Elizabeth J Grinkevich; Teia Noel; Sarah Mangiameli; Drew T Bergman; Anna Greka; Eric S Lander; Fei Chen; Jesse M Engreitz
Journal:  Proc Natl Acad Sci U S A       Date:  2020-12-21       Impact factor: 12.779

8.  Stochastic mRNA synthesis in mammalian cells.

Authors:  Arjun Raj; Charles S Peskin; Daniel Tranchina; Diana Y Vargas; Sanjay Tyagi
Journal:  PLoS Biol       Date:  2006-10       Impact factor: 8.029

9.  Single-chromosome transcriptional profiling reveals chromosomal gene expression regulation.

Authors:  Marshall J Levesque; Arjun Raj
Journal:  Nat Methods       Date:  2013-02-17       Impact factor: 28.547

10.  Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing.

Authors:  Joseph M Replogle; Thomas M Norman; Albert Xu; Jeffrey A Hussmann; Jin Chen; J Zachery Cogan; Elliott J Meer; Jessica M Terry; Daniel P Riordan; Niranjan Srinivas; Ian T Fiddes; Joseph G Arthur; Luigi J Alvarado; Katherine A Pfeiffer; Tarjei S Mikkelsen; Jonathan S Weissman; Britt Adamson
Journal:  Nat Biotechnol       Date:  2020-03-30       Impact factor: 54.908

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Authors:  Yuriy Baglaenko; Dana Macfarlane; Alexander Marson; Peter A Nigrovic; Soumya Raychaudhuri
Journal:  Nat Rev Rheumatol       Date:  2021-06-29       Impact factor: 20.543

2.  Light-Seq: light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing.

Authors:  Jocelyn Y Kishi; Ninning Liu; Emma R West; Kuanwei Sheng; Jack J Jordanides; Matthew Serrata; Constance L Cepko; Sinem K Saka; Peng Yin
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Review 3.  In vivo Pooled Screening: A Scalable Tool to Study the Complexity of Aging and Age-Related Disease.

Authors:  Martin Borch Jensen; Adam Marblestone
Journal:  Front Aging       Date:  2021-08-31

4.  HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes.

Authors:  Jamie L Marshall; Benjamin R Doughty; Vidya Subramanian; Philine Guckelberger; Qingbo Wang; Linlin M Chen; Samuel G Rodriques; Kaite Zhang; Charles P Fulco; Joseph Nasser; Elizabeth J Grinkevich; Teia Noel; Sarah Mangiameli; Drew T Bergman; Anna Greka; Eric S Lander; Fei Chen; Jesse M Engreitz
Journal:  Proc Natl Acad Sci U S A       Date:  2020-12-21       Impact factor: 12.779

5.  Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering.

Authors:  Andres F Vallejo; James Davies; Amit Grover; Ching-Hsuan Tsai; Robert Jepras; Marta E Polak; Jonathan West
Journal:  iScience       Date:  2021-02-05

6.  Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues.

Authors:  Erteng Jia; Huajuan Shi; Ying Wang; Ying Zhou; Zhiyu Liu; Min Pan; Yunfei Bai; Xiangwei Zhao; Qinyu Ge
Journal:  BMC Genomics       Date:  2021-11-10       Impact factor: 3.969

7.  scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.

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Review 8.  Current Methodological Challenges of Single-Cell and Single-Nucleus RNA-Sequencing in Glomerular Diseases.

Authors:  Dries Deleersnijder; Jasper Callemeyn; Ingrid Arijs; Maarten Naesens; Amaryllis H Van Craenenbroeck; Diether Lambrechts; Ben Sprangers
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  8 in total

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