Sequencing Diversity One Cell at a Time

Oldham, Michael C.; Kreitzer, Anatol C.

doi:10.1016/j.cell.2018.07.024

Cited by 4 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, the use of proteases and/or storage in less-than-optimal buffers (physiological or otherwise) can introduce both visible (morphological) and invisible (molecular) changes, which may not reflect the native state of the cell within the tissue. Third, technical bottlenecks including the size and shape of cells within a tissue at different ages, their RNA content, and library preparations across hundreds and thousands of single cells needs to be standardized for each tissue ( Oldham and Kreitzer, 2018 ) and here in the ocular lens for each developmental stage/age. Fourth, single-cell transcriptomics has a critical limitation in detecting molecular indicators of cellular interactions, which may only materialize when the cells are in contact with each other, in a fashion that is constrained by the tissue morphology and physiology.…”

Section: Discussionmentioning

confidence: 99%

Spatial Analysis of Single Fiber Cells of the Developing Ocular Lens Reveals Regulated Heterogeneity of Gene Expression

et al. 2018

View full text Add to dashboard Cite

SummaryThe developing eye lens presents an exceptional paradigm for spatial transcriptomics. It is composed of highly organized long, slender transparent fiber cells, which differentiate from the edges of the anterior epithelium of the lens (equator), attended by high expression of crystallins, which generates transparency. Every fiber cell, therefore, is an optical unit whose refractive properties derive from its gene activity. Here, we probe this tangible relationship between the gene activity and the phenotype by studying the expression of all known 17 crystallins and 77 other non-crystallin genes in single fiber cells isolated from three states/regions of differentiation, allowing us to follow molecular progression at the single-cell level. The data demonstrate highly variable gene activity in cortical fibers, interposed between the nascent and the terminally differentiated fiber cell transcription. These data suggest that the so-called stochastic, highly heterogeneous gene activity is a regulated intermediate in the realization of a functional phenotype.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatial Analysis of Single Fiber Cells of the Developing Ocular Lens Reveals Regulated Heterogeneity of Gene Expression

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Advances in technology platforms are ushering in an unparalleled expansion of big data where both the size and complexity of datasets are increasing at an accelerated rate (Lowe et al 2017 ). This can be seen most readily by the recent confluence of datasets produced by single-cell next-generation sequencing approaches (Liu and Trapnell 2016 ; Shapiro et al 2013 ; Levitin et al 2018 ; Oldham and Kreitzer 2018 ). Limitations to high-throughput data generation are continuing to fall across multiple axes, whether it be through the rapid increase in the number of tissues, genes, cells, or regulatory data types that can be profiled (Koch 2018 ; Medioni and Besse 2018 ; Lacar et al 2016 ).…”

Section: Big Data Continues To Get Biggermentioning

confidence: 99%

The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond

Mar

2019

Biophys Rev

View full text Add to dashboard Cite

The application of statistics has been instrumental in clarifying our understanding of the genome. While insights have been derived for almost all levels of genome function, most importantly, statistics has had the greatest impact on improving our knowledge of transcriptional regulation. But the drive to extract the most meaningful inferences from big data can often force us to overlook the fundamental role that statistics plays, and specifically, the basic assumptions that we make about big data. Normality is a statistical property that is often swept up into an assumption that we may or may not be consciously aware of making. This review highlights the inherent value of non-normal distributions to big data analysis by discussing use cases of non-normality that focus on gene expression data. Collectively, these examples help to motivate the premise of why at this stage, now more than ever, non-normality is important for learning about gene regulation, transcriptomics, and more.

show abstract

“…Second, the use of proteases and/or storage in less-thanoptimal buffers (physiological or otherwise) can introduce both visible (morphological) and invisible (molecular) changes, which may not reflect the native state of the cell within the tissue. Third, technical bottlenecks including the size and shape of cells within a tissue at different ages, their RNA content, and library preparations across hundreds and thousands of single cells needs to be standardized for each tissue (Oldham and Kreitzer, 2018) and here in the ocular lens for each developmental stage/ age. Fourth, single-cell transcriptomics has a critical limitation in detecting molecular indicators of cellular interactions, which may only materialize when the cells are in contact with each other, in a fashion that is constrained by the tissue morphology and physiology.…”

Section: Limitations Of the Studymentioning

confidence: 99%

Spatial Analysis of Single Fiber Cells of the Developing Ocular Lens Reveals Regulated Heterogeneity of Gene Expression

et al. 2018

View full text Add to dashboard Cite

The developing eye lens presents an exceptional paradigm for spatial transcriptomics. It is composed of highly organized long, slender transparent fiber cells, which differentiate from the edges of the anterior epithelium of the lens (equator), attended by high expression of crystallins, which generates transparency. Every fiber cell, therefore, is an optical unit whose refractive properties derive from its gene activity. Here, we probe this tangible relationship between the gene activity and the phenotype by studying the expression of all known 17 crystallins and 77 other non-crystallin genes in single fiber cells isolated from three states/regions of differentiation, allowing us to follow molecular progression at the single-cell level. The data demonstrate highly variable gene activity in cortical fibers, interposed between the nascent and the terminally differentiated fiber cell transcription. These data suggest that the so-called stochastic, highly heterogeneous gene activity is a regulated intermediate in the realization of a functional phenotype.

show abstract

Sequencing Diversity One Cell at a Time

Cited by 4 publications

References 11 publications

Spatial Analysis of Single Fiber Cells of the Developing Ocular Lens Reveals Regulated Heterogeneity of Gene Expression

Spatial Analysis of Single Fiber Cells of the Developing Ocular Lens Reveals Regulated Heterogeneity of Gene Expression

The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond

Spatial Analysis of Single Fiber Cells of the Developing Ocular Lens Reveals Regulated Heterogeneity of Gene Expression

Contact Info

Product

Resources

About