scholarly journals Single cell analysis of lincRNA expression during human blastocyst differentiation identifies TERT(+) multi-lineage precursor cells

2016 ◽  
Author(s):  
Jens Durruthy-Durruthy ◽  
Mark Wossidlo ◽  
Vittorio Sebastiano ◽  
Gennadi Glinsky

SummaryChromosome instability and aneuploidies occur very frequently in human embryos, impairing proper embryogenesis and leading to cell cycle arrest, loss of cell viability, and developmental failures in 50-80% of cleavage-stage embryos. This high frequency of cellular extinction events represents a significant experimental obstacle challenging analyses of individual cells isolated from human preimplantation embryos. Here, we carried out single cell expression profiling analyses of 241 individual cells recovered from 32 human embryos during the early and late stages of viable human blastocyst differentiation. Classification of embryonic cells was performed solely based on expression patterns of human pluripotency-associated transcripts (HPAT), which represent a family of transposable element-derived lincRNAs highly expressed in human embryonic stem cells (hESCs) and regulating nuclear reprogramming and pluripotency induction. We then validated our findings by analyzing 1,708 individual embryonic cells recovered from more than 100 human embryos and 259 mouse embryonic cells at different stages of preimplantation embryogenesis. Our experiments demonstrate that segregation of human blastocyst cells into distinct sub-populations based on single-cell expression profiling of just three HPATs (HPAT-21; -2; and -15) appears to inform key molecular and cellular events of naïve pluripotency induction and accurately captures a full spectrum of cellular diversity during human blastocyst differentiation. HPAT’s expression-guided spatiotemporal reconstruction of human embryonic development inferred from single-cell expression analysis of viable blastocyst differentiation enabled identification of TERT(+) sub-populations, which are significantly enriched for cells expressing key naïve pluripotency regulatory genes and genetic markers of all three major lineages created during human blastocyst differentiation. Results of our analyses suggest that during early stages of preimplantation embryogenesis putative immortal multi-lineage precursor cells (iMPCs) are created, which then differentiate into trophectoderm, primitive endoderm and pluripotent epiblast lineages. We propose that cellular extinction events in cleavage-stage embryos are triggered by premature activation of HPAT lincRNAs reflecting failed iMPC’s creation attempts.HighlightsSingle cell analysis of 1,949 human & 259 mouse embryonic cellsIdentification of 5 most abundant HPAT lincRNAs in viable human blastocystsExpression profiling of just 3 lincRNAs captures cellular diversity of human blastocystsIdentification & characterization of TERT(+) multi-lineage precursor cellsMTTH/HPAT lincRNAs regulatory axis of naïve pluripotency induction in vivo




2016 ◽  
Author(s):  
Jinzhou Yuan ◽  
Peter A. Sims

Recent developments have enabled rapid, inexpensive RNA sequencing of thousands of individual cells from a single specimen, raising the possibility of unbiased and comprehensive expression profiling from complex tissues. Microwell arrays are a particularly attractive microfluidic platform for single cell analysis due to their scalability, cell capture efficiency, and compatibility with imaging. We report an automated microwell array platform for single cell RNA-Seq with significantly improved performance over previous implementations. We demonstrate cell capture efficiencies of >50%, compatibility with commercially available barcoded mRNA capture beads, and parallel expression profiling from thousands of individual cells. We evaluate the level of cross-contamination in our platform by both tracking fluorescent cell lysate in sealed microwells and with a human-mouse mixed species RNA-Seq experiment. Finally, we apply our system to comprehensively assess heterogeneity in gene expression of patient-derived glioma neurospheres and uncover subpopulations similar to those observed in human glioma tissue.



2018 ◽  
Author(s):  
Hyunghoon Cho ◽  
Bonnie Berger ◽  
Jian Peng

SummarySingle-cell RNA sequencing is becoming effective and accessible as emerging technologies push its scale to millions of cells and beyond. Visualizing the landscape of single cell expression has been a fundamental tool in single cell analysis. However, standard methods for visualization, such as t-stochastic neighbor embedding (t-SNE), not only lack scalability to data sets with millions of cells, but also are unable to generalize to new cells, an important ability for transferring knowledge across fast-accumulating data sets. We introduce net-SNE, which trains a neural network to learn a high quality visualization of single cells that newly generalizes to unseen data. While matching the visualization quality of t-SNE on 14 benchmark data sets of varying sizes, from hundreds to 1.3 million cells, net-SNE also effectively positions previously unseen cells, even when an entire subtype is missing from the initial data set or when the new cells are from a different sequencing experiment. Furthermore, given a “reference” visualization, net-SNE can vastly reduce the computational burden of visualizing millions of single cells from multiple days to just a few minutes of runtime. Our work provides a general framework for newly bootstrapping single cell analysis from existing data sets.



PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140831 ◽  
Author(s):  
Ioannis Kokkinopoulos ◽  
Hidekazu Ishida ◽  
Rie Saba ◽  
Prashant Ruchaya ◽  
Claudia Cabrera ◽  
...  


2020 ◽  
Vol 30 (6) ◽  
pp. 814-825 ◽  
Author(s):  
Margaret R. Starostik ◽  
Olukayode A. Sosina ◽  
Rajiv C. McCoy


Author(s):  
Alexander Lind ◽  
Falastin Salami ◽  
Anne‐Marie Landtblom ◽  
Lars Palm ◽  
Åke Lernmark ◽  
...  


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