Massively parallel single-nucleus RNA-seq with DroNc-seq

Habib, Naomi; Avraham‐Davidi, Inbal; Basu, Anindita; Burks, Tyler; Shekhar, Karthik; Hofree, Matan; Choudhury, Sourav; Aguet, François; Gelfand, Ellen; Ardlie, Kristin; Weitz, David A.; Rozenblatt-Rosen, Orit; Zhang, Feng; Regev, Aviv

doi:10.1038/nmeth.4407

Cited by 936 publications

(1,110 citation statements)

References 31 publications

(94 reference statements)

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“…Thus, sNucDrop-Seq analysis captures transcriptomic distinctions between closely related subtypes in each cortical layer, which is in high concordance with subtypes previously identified in human (Lake et al, 2016) and mouse (Habib et al, 2017; Tasic et al, 2016; Zeisel et al, 2015) cortices (Figure S3C–G). …”

Section: Resultssupporting

confidence: 83%

“…This unbiased sampling strategy captured enough cells to resolve heterogeneity among non-neuronal cell types present in relatively low abundance in the adult cortex (Figure 1E and S2B), including two oligodendrocyte subtypes (Oligo1: Mog + / Enpp6 − ; Oligo2: Mog − / Enpp6 + ) recently identified through single-cell deep sequencing of full-length mRNAs (Tasic et al, 2016) (Figure S3C). Finally, the cell types and their signatures from sNucDrop-Seq were comparable to those obtained with DroNc-Seq (a recently published approach similar to sNucDrop-Seq) of mouse prefrontal cortex (Habib et al, 2017) (Figure 1G). …”

Section: Resultssupporting

confidence: 77%

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Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq

et al. 2017

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SUMMARY Massively parallel single-cell RNA sequencing can precisely resolve cellular diversity in a high-throughput manner at low cost, but unbiased isolation of intact single cells from complex tissues, such as adult mammalian brains, is challenging. Here, we integrate sucrose-gradient assisted purification of nuclei with droplet microfluidics to develop a highly scalable single-nucleus RNA-Seq approach (sNucDrop-Seq), which is free of enzymatic dissociation and nuclei sorting. By profiling ~18,000 nuclei isolated from cortical tissues of adult mice, we demonstrate that sNucDrop-Seq not only accurately reveals neuronal and non-neuronal subtype composition with high sensitivity, but also enables in-depth analysis of transient transcriptional states driven by neuronal activity, at single-cell resolution, in vivo.

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Section: Resultssupporting

confidence: 83%

Section: Resultssupporting

confidence: 77%

Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq

et al. 2017

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“…Overall, these results illustrate the value of lowly sequenced large data sets. Nevertheless, for even sparser data sets, such as those obtained from the sequencing of nuclei (Habib et al 2017), the performance of bigSCale still needs to be evaluated. Furthermore, the heuristic bigSCale uses for DE analysis leaves space for future improvements.…”

Section: Discussionmentioning

confidence: 99%

bigSCale: an analytical framework for big-scale single-cell data

et al. 2018

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Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (Reln)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.[Supplemental material is available for this article.]Single-cell RNA sequencing (scRNA-seq) is at the forefront of techniques to chart molecular properties of individual cells. Recent microfluidic-based methods are scalable to tens of thousands of cells, enabling an unbiased sampling and in-depth characterization without prior knowledge Macosko et al. 2015;Zheng et al. 2017). Consequently, studies are less confined by the number of cells and aim to produce comprehensive cellular atlases of entire tissues, organs, and organisms (Regev et al. 2017). Increasing cell numbers, however, generate extremely large data sets, which extend processing time and challenge computing resources. Current scRNA-seq analysis tools are not designed to analyze data sets larger than thousands of cells and often lack sensitivity and specificity to identify marker genes for cell populations or experimental conditions.To address the challenges of large scRNA-seq data sets, we developed bigSCale, an analytical framework for the sensitive detection of population markers and differentially expressed genes, being scalable to analyze mil...

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“…Gene expression studies of neurons and glial cells have contributed to our understanding of the variety of gene expression profiles that advance distinct functions in different cell types [133][134][135][136][137][138][139][140]. Microarray analyses of isolated astrocytes have identified particular transcription profiles in AD and related animal models [141,142].…”

Section: Astrocytopathymentioning

confidence: 99%

Astrogliopathy in Tauopathies

Ferrer

2018

Neuroglia

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Astrocytes are involved in many diseases of the central nervous system, not only as reactive cells to neuronal damage but also as primary actors in the pathological process. Astrogliopathy is a term used to designate the involvement of astrocytes as key elements in the pathogenesis and pathology of diseases and injuries of the central nervous system. Astrocytopathy is utilized to name non-reactive astrogliosis covering hypertrophy, atrophy and astroglial degeneration with loss of function in astrocytes and pathological remodeling, as well as senescent changes. Astrogliopathy and astrocytopathy are hallmarks of tauopathies-neurodegenerative diseases with abnormal hyper-phosphorylated tau aggregates in neurons and glial cells. The involvement of astrocytes covers different disease-specific types such as tufted astrocytes, astrocytic plaques, thorn-shaped astrocytes, granular/fuzzy astrocytes, ramified astrocytes and astrocytes with globular inclusions, as well as others which are unnamed but not uncommon in familial frontotemporal degeneration linked to mutations in the tau gene. Knowledge of molecular differences among tau-containing astrocytes is only beginning, and their distinct functional implications remain rather poorly understood. However, tau-containing astrocytes in certain conditions have deleterious effects on neuronal function and nervous system integrity. Moreover, recent studies have shown that tau-containing astrocytes obtained from human brain tauopathies have a capacity for abnormal tau seeding and spreading in wild type mice. Inclusive conceptions include a complex scenario involving neurons, glial cells and local environmental factors that potentiate each other and promote disease progression in tauopathies.

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Massively parallel single-nucleus RNA-seq with DroNc-seq

Cited by 936 publications

References 31 publications

Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq

Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq

bigSCale: an analytical framework for big-scale single-cell data

Astrogliopathy in Tauopathies

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