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Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, Li N, Szpankowski L, Fowler B, Chen P, Ramalingam N, Sun G, Thu M, Norris M, Lebofsky R, Toppani D, Kemp DW, Wong M, Clerkson B, Jones BN, Wu S, Knutsson L, Alvarado B, Wang J, Weaver LS, May AP, Jones RC, Unger MA, Kriegstein AR, West JAA. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol 2014; 32:1053-8. [PMID: 25086649 PMCID: PMC4191988 DOI: 10.1038/nbt.2967] [Citation(s) in RCA: 597] [Impact Index Per Article: 59.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2014] [Accepted: 06/25/2014] [Indexed: 01/17/2023]
Abstract
Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships, but require efficient methods for cell capture and mRNA sequencing1–4. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths5, the limitations of shallow sequencing have not been directly investigated. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In developing cortex we identify diverse cell types including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.
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Affiliation(s)
- Alex A Pollen
- 1] Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California, USA. [2] Department of Neurology, University of California, San Francisco, San Francisco, California, USA. [3]
| | - Tomasz J Nowakowski
- 1] Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California, USA. [2] Department of Neurology, University of California, San Francisco, San Francisco, California, USA. [3]
| | - Joe Shuga
- 1] Fluidigm Corporation, South San Francisco, California, USA. [2]
| | - Xiaohui Wang
- 1] Fluidigm Corporation, South San Francisco, California, USA. [2]
| | - Anne A Leyrat
- Fluidigm Corporation, South San Francisco, California, USA
| | - Jan H Lui
- 1] Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California, USA. [2] Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Nianzhen Li
- Fluidigm Corporation, South San Francisco, California, USA
| | | | - Brian Fowler
- Fluidigm Corporation, South San Francisco, California, USA
| | - Peilin Chen
- Fluidigm Corporation, South San Francisco, California, USA
| | | | - Gang Sun
- Fluidigm Corporation, South San Francisco, California, USA
| | - Myo Thu
- Fluidigm Corporation, South San Francisco, California, USA
| | - Michael Norris
- Fluidigm Corporation, South San Francisco, California, USA
| | | | | | - Darnell W Kemp
- Fluidigm Corporation, South San Francisco, California, USA
| | - Michael Wong
- Fluidigm Corporation, South San Francisco, California, USA
| | - Barry Clerkson
- Fluidigm Corporation, South San Francisco, California, USA
| | | | - Shiquan Wu
- Fluidigm Corporation, South San Francisco, California, USA
| | | | | | - Jing Wang
- Fluidigm Corporation, South San Francisco, California, USA
| | | | - Andrew P May
- Fluidigm Corporation, South San Francisco, California, USA
| | - Robert C Jones
- Fluidigm Corporation, South San Francisco, California, USA
| | - Marc A Unger
- Fluidigm Corporation, South San Francisco, California, USA
| | - Arnold R Kriegstein
- 1] Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California, USA. [2] Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Jay A A West
- Fluidigm Corporation, South San Francisco, California, USA
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Prabu JR, Thamotharan S, Khanduja JS, Alipio EZ, Kim CY, Waldo GS, Terwilliger TC, Segelke B, Lekin T, Toppani D, Hung LW, Yu M, Bursey E, Muniyappa K, Chandra NR, Vijayan M. Structure of Mycobacterium tuberculosis RuvA, a protein involved in recombination. Acta Crystallogr Sect F Struct Biol Cryst Commun 2006; 62:731-4. [PMID: 16880543 PMCID: PMC2242936 DOI: 10.1107/s1744309106024791] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2006] [Accepted: 06/27/2006] [Indexed: 11/10/2022]
Abstract
The process of recombinational repair is crucial for maintaining genomic integrity and generating biological diversity. In association with RuvB and RuvC, RuvA plays a central role in processing and resolving Holliday junctions, which are a critical intermediate in homologous recombination. Here, the cloning, purification and structure determination of the RuvA protein from Mycobacterium tuberculosis (MtRuvA) are reported. Analysis of the structure and comparison with other known RuvA proteins reveal an octameric state with conserved subunit-subunit interaction surfaces, indicating the requirement of octamer formation for biological activity. A detailed analysis of plasticity in the RuvA molecules has led to insights into the invariant and variable regions, thus providing a framework for understanding regional flexibility in various aspects of RuvA function.
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Affiliation(s)
- J. Rajan Prabu
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - S. Thamotharan
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | | | | | - Chang-Yub Kim
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, USA
| | - Geoffrey S. Waldo
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, USA
| | | | - Brent Segelke
- Biology and Biotechnology Program, Lawrence Livermore National Laboratory, Livermore, USA
| | - Tim Lekin
- Biology and Biotechnology Program, Lawrence Livermore National Laboratory, Livermore, USA
| | - Dominique Toppani
- Biology and Biotechnology Program, Lawrence Livermore National Laboratory, Livermore, USA
| | - Li-Wei Hung
- Physics Division, Los Alamos National Laboratory, Los Alamos, USA
| | - Minmin Yu
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, USA
| | - Evan Bursey
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, USA
| | - K. Muniyappa
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Nagasuma R. Chandra
- Bioinformatics Centre and Super Computer Education and Research Centre, Indian Institute of Science, Bangalore, India
- Correspondence e-mail: ,
| | - M. Vijayan
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- Correspondence e-mail: ,
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Abstract
At Lawrence Livermore National Laboratory, the development of the TB structural genomics consortium crystallization facility has paralleled several local proteomics research efforts that have grown out of gene expression microarray and comparative genomics studies. Collective experience gathered from TB consortium labs and other centers involved in the NIH-NIGMS protein structure initiative allows us to explore the possibilities and challenges of pursuing structural genomics on an academic laboratory scale. We discuss our procedures and protocols for genomic targeting approaches, primer design, cloning, small scale expression screening, scale-up and purification, through to automated crystallization screening and data collection. The procedures are carried out by a small group using a combination of traditional approaches, innovative molecular biochemistry approaches, software automation, and a modest investment in robotic equipment.
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Affiliation(s)
- Brent W Segelke
- Macromolecular Crystallography and Structural Genomics Group, Biology and Biotechnology Research Program, P.O. Box 808, Lawrence Livermore National Laboratory, Livermore, CA 94551, USA
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Rupp B, Segelke BW, Krupka HI, Lekin T, Schäfer J, Zemla A, Toppani D, Snell G, Earnest T. The TB structural genomics consortium crystallization facility: towards automation from protein to electron density. Acta Crystallogr D Biol Crystallogr 2002; 58:1514-8. [PMID: 12351851 DOI: 10.1107/s0907444902014282] [Citation(s) in RCA: 40] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2002] [Accepted: 08/07/2002] [Indexed: 11/10/2022]
Abstract
The crystallization facility of the TB (Tuberculosis) structural genomics consortium, one of nine NIH sponsored p50 structural genomic centres, provides TB consortium members with automated crystallization, data collection and basic molecular replacement (MR) structure solution up to bias minimized electron density maps. Crystallization setup of up to ten proteins per day follows the CRYSTOOL combinatorial screen protocol using a modular and affordable robotic design with an open architecture. Components include screen preparation, plate setup, automated image acquisition and analysis, and optimisation design. A new 96 well crystallization plate has been designed for optimal robotic handling while maintaining ease of manual crystal harvesting. Robotic crystal mounting, screening, and data collection are conducted in-house and at the Advanced Light Source (ALS) in Berkeley. A simple automated protocol based on MR and homology based structure prediction automatically solves modestly difficult problems. Multiple search models are evaluated in parallel MR and the best multi-segment rigid body refined MR solution is subjected to simulated annealing torsion angle molecular dynamics using CNS, bringing even marginal MR solutions within the convergence radius of the subsequent highly effective bias removal and map reconstruction protocol, Shake&wARP, used to generate electron density for initial rebuilding. Real space correlation plots allow rapid assessment of local structure quality. Modular design of robotics and automated scripts using publicly available programs for structure solution allow for efficient high throughput crystallography - at a reasonable cost.
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Affiliation(s)
- Bernhard Rupp
- Macromolecular Crystallography and TB Structural Genomics Consortium, Lawrence Livermore National Laboratory, Livermore, CA 94551, USA.
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