Faculty Opinions recommendation of Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain.

2018 ◽  
Author(s):  
Bruce Appel
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Vertebrate Brain ◽  
Single Cell Profiling

scholarly journals Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain by scGESTALT

2017 ◽  
Author(s):  
Bushra Raj ◽  
Daniel E. Wagner ◽  
Aaron McKenna ◽  
Shristi Pandey ◽  
Allon M. Klein ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Brain Regions ◽  
Marker Genes ◽  
Cell Type ◽  
Zebrafish Brain ◽  
Vertebrate Brain ◽  
Single Cell Rna Sequencing ◽  
Single Cell Profiling ◽  
Lineage Trees

ABSTRACTHundreds of cell types are generated during development, but their lineage relationships are largely elusive. Here we report a technology, scGESTALT, which combines cell type identification by single-cell RNA sequencing with lineage recording by cumulative barcode editing. We sequenced ~60,000 transcriptomes from the juvenile zebrafish brain and identified more than 100 cell types and marker genes. We engineered an inducible system that combines early and late barcode editing and isolated thousands of single-cell transcriptomes and their associated barcodes. The large diversity of edited barcodes and cell types enabled the generation of lineage trees with hundreds of branches. Inspection of lineage trajectories identified restrictions at the level of cell types and brain regions and helped uncover gene expression cascades during differentiation. These results establish scGESTALT as a new and widely applicable tool to simultaneously characterize the molecular identities and lineage histories of thousands of cells during development and disease.

scholarly journals Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain

2018 ◽  
Vol 36 (5) ◽  
pp. 442-450 ◽  
Author(s):  
Bushra Raj ◽  
Daniel E Wagner ◽  
Aaron McKenna ◽  
Shristi Pandey ◽  
Allon M Klein ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Vertebrate Brain ◽  
Single Cell Profiling

scholarly journals Emergence of neuronal diversity during vertebrate brain development

2019 ◽  
Author(s):  
Bushra Raj ◽  
Jeffrey A. Farrell ◽  
Aaron McKenna ◽  
Jessica L. Leslie ◽  
Alexander F. Schier
Keyword(s):  
Brain Development ◽  
Single Cell ◽  
Time Course ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Developmental Time ◽  
Neural Progenitors ◽  
Neuronal Diversity ◽  
Gene Markers ◽  
Vertebrate Brain

ABSTRACTNeurogenesis in the vertebrate brain comprises many steps ranging from the proliferation of progenitors to the differentiation and maturation of neurons. Although these processes are highly regulated, the landscape of transcriptional changes and progenitor identities underlying brain development are poorly characterized. Here, we describe the first developmental single-cell RNA-seq catalog of more than 200,000 zebrafish brain cells encompassing 12 stages from 12 hours post-fertilization to 15 days post-fertilization. We characterize known and novel gene markers for more than 800 clusters across these timepoints. Our results capture the temporal dynamics of multiple neurogenic waves from embryo to larva that expand neuronal diversity from ∼20 cell types at 12 hpf to ∼100 cell types at 15 dpf. We find that most embryonic neural progenitor states are transient and transcriptionally distinct from long-lasting neural progenitors of post-embryonic stages. Furthermore, we reconstruct cell specification trajectories for the retina and hypothalamus, and identify gene expression cascades and novel markers. Our analysis reveal that late-stage retinal neural progenitors transcriptionally overlap cell states observed in the embryo, while hypothalamic neural progenitors become progressively distinct with developmental time. These data provide the first comprehensive single-cell transcriptomic time course for vertebrate brain development and suggest distinct neurogenic regulatory paradigms between different stages and tissues.

scholarly journals Urinary Single-Cell Profiling Captures the Cellular Diversity of the Kidney

2021 ◽  
Vol 32 (3) ◽  
pp. 614-627
Author(s):  
Amin Abedini ◽  
Yuan O. Zhu ◽  
Shatakshee Chatterjee ◽  
Gabor Halasz ◽  
Kishor Devalaraja-Narashimha ◽  
...  
Keyword(s):  
Single Cell ◽  
Collecting Duct ◽  
Microscopic Analysis ◽  
Human Kidney ◽  
Cell Types ◽  
Urine Sediment ◽  
Type Composition ◽  
Single Cell Profiling ◽  
Bladder Cells ◽  
Almost All

BackgroundMicroscopic analysis of urine sediment is probably the most commonly used diagnostic procedure in nephrology. The urinary cells, however, have not yet undergone careful unbiased characterization.MethodsSingle-cell transcriptomic analysis was performed on 17 urine samples obtained from five subjects at two different occasions, using both spot and 24-hour urine collection. A pooled urine sample from multiple healthy individuals served as a reference control. In total 23,082 cells were analyzed. Urinary cells were compared with human kidney and human bladder datasets to understand similarities and differences among the observed cell types.ResultsAlmost all kidney cell types can be identified in urine, such as podocyte, proximal tubule, loop of Henle, and collecting duct, in addition to macrophages, lymphocytes, and bladder cells. The urinary cell–type composition was subject specific and reasonably stable using different collection methods and over time. Urinary cells clustered with kidney and bladder cells, such as urinary podocytes with kidney podocytes, and principal cells of the kidney and urine, indicating their similarities in gene expression.ConclusionsA reference dataset for cells in human urine was generated. Single-cell transcriptomics enables detection and quantification of almost all types of cells in the kidney and urinary tract.

scholarly journals Sandwich cortical lamination and single-cell analysis decodes the developing spatial processing system

2019 ◽  
Author(s):  
Yong Liu ◽  
Tobias Bergmann ◽  
Julie Lee ◽  
Ulrich Pfisterer ◽  
Louis-Francois Handfield ◽  
...  
Keyword(s):  
Single Cell ◽  
Entorhinal Cortex ◽  
Pyramidal Neurons ◽  
Single Cell Analysis ◽  
Processing System ◽  
Cell Types ◽  
Inhibitory Neurons ◽  
Spatial Processing ◽  
Single Cell Profiling ◽  
Perception Of Time

SummaryThe entorhinal cortex consists of several important cell types including, the grid cells, speed cells, border cells and head-direction cells and is important for memory, spatial navigation and perception of time. Here, we trace in detail the development of the entorhinal cortex. Using single-cell profiling we provide unique transcriptional signatures for glia, excitatory and inhibitory neurons existing in the region, including RELN+ cells in layer (L) II and superficial pyramidal neurons. We identified a sandwich layered cortex, where LII emerges prior to LIII and superficial cells maintain a deep layer molecular identity after birth. Our findings contribute to the understanding of the formation of the brain’s cognitive memory and spatial processing system and provides insight into the transcriptional identity and spatial position of the entorhinal cells.

scholarly journals scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katharina T. Schmid ◽  
Barbara Höllbacher ◽  
Cristiana Cruceanu ◽  
Anika Böttcher ◽  
Heiko Lickert ◽  
...  
Keyword(s):  
Single Cell ◽  
Power Analysis ◽  
Optimal Parameter ◽  
Cell Types ◽  
R Package ◽  
Statistical Framework ◽  
Limited Budget ◽  
Single Cell Profiling ◽  
Differential Gene ◽  
Efficient Power

AbstractSingle cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.

scholarly journals CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics

2018 ◽  
Author(s):  
Chuner Guo ◽  
Wenjun Kong ◽  
Kenji Kamimoto ◽  
Guillermo C. Rivera-Gonzalez ◽  
Xue Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Cell Technologies ◽  
Cell Engraftment ◽  
Primary Mouse ◽  
Challenges And Opportunities ◽  
Single Cell Profiling

ABSTRACTSingle-cell technologies have seen rapid advancements in recent years, presenting new analytical challenges and opportunities. These high-throughput assays increasingly require special consideration in experimental design, sample multiplexing, batch effect removal, and data interpretation. Here, we describe a lentiviral barcode-based multiplexing approach, ‘CellTag Indexing’, where we transduce and label samples that can then be pooled together for downstream experimentation and analysis. By introducing predefined genetic barcodes that are transcribed and readily detected, we can reliably read out sample identity and transcriptional state via single-cell profiling. We validate and demonstrate the utility of CellTag Indexing by sequencing transcriptomes at single-cell resolution using a variety of cell types including mouse pre-B cells, primary mouse embryonic fibroblasts, and human HEK293T cells. A unique feature of CellTag Indexing is that the barcodes are heritable. This enables cell populations to be tagged, pooled and tracked over time within the same experimental replicate, then processed together to minimize unwanted biological and technical variation. We demonstrate this feature of CellTagging in long-term tracking of cell engraftment and differentiation, in vivo, in a mouse model of competitive transplant into the large intestine. Together, this presents CellTag Indexing as a broadly applicable genetic multiplexing tool that is complementary with existing single-cell technologies.

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