Investigating transcriptional states at single-cell-resolution

Tischler, Julia; Surani, M. Azim

doi:10.1016/j.copbio.2012.09.013

Cited by 30 publications

(28 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Population-based approaches do not recognize rare cell types nor do they reveal spatial correlations of genes that define cell identities with active signaling pathways. In contrast, single cell analysis technologies provide a powerful method to study global cell heterogeneity and to describe mechanisms on a local level (Tischler and Surani, 2013). Our aim was to use the mouse otocyst as an example of a simple but highly organized system of cells, and to apply single cell quantitative gene expression analysis in order to gain insight into regional cell identities, dynamic processes, and areas of active signaling.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of the Mouse Otocyst and Early Neuroblast Lineage at Single-Cell Resolution

Durruthy-Durruthy

Gottlieb

Hartman

et al. 2014

Cell

139

160

View full text Add to dashboard Cite

Summary The otocyst harbors progenitors for most cell types of the mature inner ear. Developmental lineage analysis and gene expression studies suggest that distinct progenitor populations are compartmentalized to discrete axial domains in the early otocyst. Here, we conducted highly parallel quantitative RT-PCR reactions on 382 individual cells from the developing otocyst and neuroblast lineages to assay 96 genes representing established otic markers, signaling pathway associated transcripts, and novel otic-specific genes. By applying multivariate cluster, principal component and network analyses to the data matrix, we were able to readily distinguish the delaminating neuroblasts, and to describe progressive states of gene expression in this population at single cell resolution. It further established a three-dimensional model of the otocyst where each individual cell can be precisely mapped into spatial expression domains. Our bioinformatic modeling revealed spatial dynamics of different signaling pathways active during early neuroblast development and prosensory domain specification.

show abstract

Section: Introductionmentioning

confidence: 99%

Reconstruction of the Mouse Otocyst and Early Neuroblast Lineage at Single-Cell Resolution

Durruthy-Durruthy

Gottlieb

Hartman

et al. 2014

Cell

139

160

View full text Add to dashboard Cite

show abstract

“…However, those methods provide averages of bulk transcrpitome measurements (grind-and-bind RNA analysis) (5), a process that excludes analysis of intrinsic heterogeneity and spatial distribution of gene expression in biological systems (6). Two major technologies - single-cell mRNA-Seq analysis and single-cell real-time qPCR - have been recently developed to measure gene expression in single cells (7). These technologies isolate single cells, using laser capture microdissection or a fluorescence-activated cell sorter (7).…”

Section: Introductionmentioning

confidence: 99%

Single-molecule fluorescence in situ hybridization: Quantitative imaging of single RNA molecules

Kwon¹

2013

BMB Reports

View full text Add to dashboard Cite

In situ detection of RNAs is becoming increasingly important for analysis of gene expression within and between intact cells in tissues. International genomics efforts are now cataloging patterns of RNA transcription that play roles in cell function, differentiation, and disease formation, and they are demonstrating the importance of coding and noncoding RNA transcripts in these processes. However, these techniques typically provide ensemble averages of transcription across many cells. In situ hybridization-based analysis methods complement these studies by providing information about how expression levels change between cells within normal and diseased tissues, and they provide information about the localization of transcripts within cells, which is important in understanding mechanisms of gene regulation. Multi-color, single-molecule fluorescence in situ hybridization (smFISH) is particularly useful since it enables analysis of several different transcripts simultaneously. Combining smFISH with immunofluorescent protein detection provides additional information about the association between transcription level, cellular localization, and protein expression in individual cells. [BMB Reports 2013; 46(2): 65-72]

show abstract

“…Such studies will likely redefine the boundaries separating cell types or key cellular states in statistical terms 18 . Here we have used a simple mixture model, to capture the uncertainty in expression magnitude observed in a given cell, propagating this uncertainty into subsequent analyses.…”

mentioning

confidence: 99%

Bayesian approach to single-cell differential expression analysis

2014

View full text Add to dashboard Cite

Single-cell data provides means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression magnitude distortions typical of single-cell RNA sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.

show abstract

Investigating transcriptional states at single-cell-resolution

Cited by 30 publications

References 71 publications

Reconstruction of the Mouse Otocyst and Early Neuroblast Lineage at Single-Cell Resolution

Reconstruction of the Mouse Otocyst and Early Neuroblast Lineage at Single-Cell Resolution

Single-molecule fluorescence in situ hybridization: Quantitative imaging of single RNA molecules

Bayesian approach to single-cell differential expression analysis

Contact Info

Product

Resources

About