bibliography.bib

@article{Lake2016,
abstract = {Identifying the genes expressed at the level of a single cell nucleus can better help us understand the human brain. Blue et al. developed a single-nuclei sequencing technique, which they applied to cells in classically defined Brodmann areas from a postmortem brain. Clustering of gene expression showed concordance with the area of origin and defining 16 neuronal subtypes. Both excitatory and inhibitory neuronal subtypes show regional variations that define distinct cortical areas and exhibit how gene expression clusters may distinguish between distinct cortical areas. This method opens the door to widespread sampling of the genes expressed in a diseased brain and other tissues of interest.Science, this issue p. 1586The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.},
author = {Lake, Blue B and Ai, Rizi and Kaeser, Gwendolyn E and Salathia, Neeraj S and Yung, Yun C and Liu, Rui and Wildberg, Andre and Gao, Derek and Fung, Ho-Lim and Chen, Song and Vijayaraghavan, Raakhee and Wong, Julian and Chen, Allison and Sheng, Xiaoyan and Kaper, Fiona and Shen, Richard and Ronaghi, Mostafa and Fan, Jian-Bing and Wang, Wei and Chun, Jerold and Zhang, Kun},
doi = {10.1126/science.aaf1204},
issn = {0036-8075},
journal = {Science},
number = {6293},
pages = {1586--1590},
publisher = {American Association for the Advancement of Science},
title = {{Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain}},
url = {http://science.sciencemag.org/content/352/6293/1586},
volume = {352},
year = {2016}
}
@article{Lacar2016,
author = {Lacar, Benjamin and Linker, Sara B and Jaeger, Baptiste N and Krishnaswami, Suguna Rani and Barron, Jerika J and Kelder, Martijn J E and Parylak, Sarah L and Paquola, Apu{\~{a}} C M and Venepally, Pratap and Novotny, Mark and O'Connor, Carolyn and Fitzpatrick, Conor and Erwin, Jennifer A and Hsu, Jonathan Y and Husband, David and McConnell, Michael J and Lasken, Roger and Gage, Fred H},
journal = {Nature Communications},
month = {apr},
pages = {11022},
publisher = {The Author(s)},
title = {{Nuclear RNA-seq of single neurons reveals molecular signatures of activation}},
url = {http://dx.doi.org/10.1038/ncomms11022 http://10.0.4.14/ncomms11022 https://www.nature.com/articles/ncomms11022{\#}supplementary-information},
volume = {7},
year = {2016}
}
@article{Macosko2015,
abstract = {Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. Video Abstract},
annote = {doi: 10.1016/j.cell.2015.05.002},
archivePrefix = {arXiv},
arxivId = {15334406},
author = {Macosko, Evan Z. and Basu, Anindita and Satija, Rahul and Nemesh, James and Shekhar, Karthik and Goldman, Melissa and Tirosh, Itay and Bialas, Allison R. and Kamitaki, Nolan and Martersteck, Emily M. and Trombetta, John J. and Weitz, David A. and Sanes, Joshua R. and Shalek, Alex K. and Regev, Aviv and McCarroll, Steven A.},
doi = {10.1016/j.cell.2015.05.002},
eprint = {15334406},
isbn = {1097-4172 (Electronic)$\backslash$r0092-8674 (Linking)},
issn = {10974172},
journal = {Cell},
month = {dec},
number = {5},
pages = {1202--1214},
pmid = {26000488},
publisher = {Elsevier},
title = {{Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets}},
url = {http://dx.doi.org/10.1016/j.cell.2015.05.002},
volume = {161},
year = {2015}
}
@article{Ziegenhain2017,
abstract = {Summary Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.},
author = {Ziegenhain, Christoph and Vieth, Beate and Parekh, Swati and Reinius, Bj{\"{o}}rn and Guillaumet-Adkins, Amy and Smets, Martha and Leonhardt, Heinrich and Heyn, Holger and Hellmann, Ines and Enard, Wolfgang},
doi = {https://doi.org/10.1016/j.molcel.2017.01.023},
issn = {1097-2765},
journal = {Molecular Cell},
keywords = {cost-effectiveness,method comparison,power analysis,simulation,single-cell RNA-seq,transcriptomics},
number = {4},
pages = {631 -- 643.e4},
title = {{Comparative Analysis of Single-Cell RNA Sequencing Methods}},
url = {http://www.sciencedirect.com/science/article/pii/S1097276517300497},
volume = {65},
year = {2017}
}
@article{Shekhar2016,
abstract = {Summary Patterns of gene expression can be used to characterize and classify neuronal types. It is challenging, however, to generate taxonomies that fulfill the essential criteria of being comprehensive, harmonizing with conventional classification schemes, and lacking superfluous subdivisions of genuine types. To address these challenges, we used massively parallel single-cell RNA profiling and optimized computational methods on a heterogeneous class of neurons, mouse retinal bipolar cells (BCs). From a population of ∼25,000 BCs, we derived a molecular classification that identified 15 types, including all types observed previously and two novel types, one of which has a non-canonical morphology and position. We validated the classification scheme and identified dozens of novel markers using methods that match molecular expression to cell morphology. This work provides a systematic methodology for achieving comprehensive molecular classification of neurons, identifies novel neuronal types, and uncovers transcriptional differences that distinguish types within a class.},
author = {Shekhar, Karthik and Lapan, Sylvain W and Whitney, Irene E and Tran, Nicholas M and Macosko, Evan Z and Kowalczyk, Monika and Adiconis, Xian and Levin, Joshua Z and Nemesh, James and Goldman, Melissa and McCarroll, Steven A and Cepko, Constance L and Regev, Aviv and Sanes, Joshua R},
doi = {https://doi.org/10.1016/j.cell.2016.07.054},
issn = {0092-8674},
journal = {Cell},
number = {5},
pages = {1308 -- 1323.e30},
title = {{Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics}},
url = {http://www.sciencedirect.com/science/article/pii/S0092867416310078},
volume = {166},
year = {2016}
}
@article{Habib2016,
abstract = {Gene expression can vary greatly within a single cell. Using techniques that they developed for sequencing single nuclei and labeling proliferating cells in vivo, Habib et al. performed RNA sequencing of 1402 single nuclei from the adult mouse hippocampus. Combining this approach with a clustering algorithm for single-cell and -nucleus RNA sequencing data delineated specific cell types during cell differentiation and development. By providing polyadenylated RNA from nuclei alone, as opposed to cytoplasmic RNA, these methods open the application of single-cell transcriptomics to tissues in which individual cells are difficult to isolate.Science, this issue p. 925Single-cell RNA sequencing (RNA-Seq) provides rich information about cell types and states. However, it is difficult to capture rare dynamic processes, such as adult neurogenesis, because isolation of rare neurons from adult tissue is challenging and markers for each phase are limited. Here, we develop Div-Seq, which combines scalable single-nucleus RNA-Seq (sNuc-Seq) with pulse labeling of proliferating cells by 5-ethynyl-2'-deoxyuridine (EdU) to profile individual dividing cells. sNuc-Seq and Div-Seq can sensitively identify closely related hippocampal cell types and track transcriptional dynamics of newborn neurons within the adult hippocampal neurogenic niche, respectively. We also apply Div-Seq to identify and profile rare newborn neurons in the adult spinal cord, a noncanonical neurogenic region. sNuc-Seq and Div-Seq open the way for unbiased analysis of diverse complex tissues.},
author = {Habib, Naomi and Li, Yinqing and Heidenreich, Matthias and Swiech, Lukasz and Avraham-Davidi, Inbal and Trombetta, John J and Hession, Cynthia and Zhang, Feng and Regev, Aviv},
doi = {10.1126/science.aad7038},
issn = {0036-8075},
journal = {Science},
number = {6302},
pages = {925--928},
publisher = {American Association for the Advancement of Science},
title = {{Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons}},
url = {http://science.sciencemag.org/content/353/6302/925},
volume = {353},
year = {2016}
}
@article{Habib2017,
author = {Habib, Naomi and Avraham-Davidi, Inbal and Basu, Anindita and Burks, Tyler and Shekhar, Karthik and Hofree, Matan and Choudhury, Sourav R and Aguet, Fran{\c{c}}ois and Gelfand, Ellen and Ardlie, Kristin and Weitz, David A and Rozenblatt-Rosen, Orit and Zhang, Feng and Regev, Aviv},
journal = {Nature Methods},
month = {aug},
pages = {955},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
title = {{Massively parallel single-nucleus RNA-seq with DroNc-seq}},
url = {http://dx.doi.org/10.1038/nmeth.4407 http://10.0.4.14/nmeth.4407 https://www.nature.com/articles/nmeth.4407{\#}supplementary-information},
volume = {14},
year = {2017}
}