Single-cell transcriptomics

Summary

Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the RNA concentration (conventionally only messenger RNA (mRNA)) of hundreds to thousands of genes. Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics — all previously masked in bulk RNA sequencing. Background The development of high-throughput RNA sequencing (RNA-seq) and microarrays has made gene expression analysis a routine. RNA analysis was previously limited to tracing individual transcripts by Northern blots or quantitative PCR. Higher throughput and speed allow researchers to frequently characterize the expression profiles of populations of thousands of cells. The data from bulk assays has led to identifying genes differentially expressed in distinct cell populations, and biomarker discovery. These studies are limite

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https://en.wikipedia.org/wiki/Single-cell_transcriptomics

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In this thesis, I present a self-organizing neural tube organoid that is strikingly similar in morphology, cell-type composition, and patterning to the mouse embryonic neural tube. When exposed to a sequence of epiblast culture conditions and neural differentiation conditions in 3D Matrigel, single mouse embryonic stem cells develop into spontaneously elongating neuroepithelial tissues organized with key hallmarks of the neural tube. Single cell transcriptomics revealed the presence of regionalized cell types spanning the region from the midbrain to the spinal cord along the AP axis of the embryo. In long-term culture experiments for organoid maturation, I observed the emergence of neural crest cells and mature neurons. Furthermore, I developed a microfluidics-based system to promote a similar DV patterning to a neural tube. Also, maturation of neural tube organoid exhibited the emergence and migration of multipotent neural crest cells. Collectively, the accessibility and scalability of these organoids make them a unique model of the developing neural tube, allowing the study of key mechanisms involved in striking morphogenesis by self-organization, cell regionalization, neural crest development, and morphogen-triggered patterning in vivo.

EPFL2021

Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity

Gioele La Manno, Xiaoyan Sun

The murine epidermis with its hair follicles represents an invaluable model system for tissue regeneration and stem cell research. Here we used single-cell RNA-sequencing to reveal how cellular heterogeneity of murine telogen epidermis is tuned at the transcriptional level. Unbiased clustering of 1,422 single-cell transcriptomes revealed 25 distinct populations of interfollicular and follicular epidermal cells. Our data allowed the reconstruction of gene expression programs during epidermal differentiation and along the proximal-distal axis of the hair follicle at unprecedented resolution. Moreover, transcriptional heterogeneity of the epidermis can essentially be explained along these two axes, and we show that heterogeneity in stem cell compartments generally reflects this model: stem cell populations are segregated by spatial signatures but share a common basal-epidermal gene module. This study provides an unbiased and systematic view of transcriptional organization of adult epidermis and highlights how cellular heterogeneity can be orchestrated in vivo to assure tissue homeostasis.

2016

The emergence and promise of single-cell temporal-omics approaches

Gioele La Manno

Single-cell transcriptomics enables the measurement of gene expression in complex biological systems at the resolution of individual cells. Multivariate analysis of single-cell data helps describe the variation in expression accompanying cellular processes during embryonic development, disease progression and in response to stimuli. Likewise, new methods have extended the possibilities of single-cell analysis by measuring the transcriptome while simultaneously capturing information on lineage or past molecular events. These emerging approaches have the common strategy of querying a static snapshot for information related to different temporal stages. Single-cell temporal-omics methods open new possibilities such as extrapolating the future or correlating past events to present gene expression. We highlight advancements in the single-cell field, describe novel toolkits for investigation, and consider the potential impact of temporal-omics approaches for the study of disease progression.

CURRENT BIOLOGY LTD2020