scholarly journals Molecular Signature of Megakaryocyte-Erythroid Progenitors Reveals Role of Cell Cycle in Fate Specification

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3828-3828
Author(s):  
Yi-Chien Lu ◽  
Diane S. Krause ◽  
Juliana Xavier-Ferrucio ◽  
Lin Wang ◽  
Nathan Salomonis ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Cell Fate ◽  
Molecular Signature ◽  
Erythroid Progenitors ◽  
Single Cell Sequencing ◽  
Lineage Differentiation ◽  
Single Cell Rna Sequencing ◽  
Colony Number ◽  
Fate Decision

Abstract Megakaryocytic-Erythroid Progenitors (MEP) produce megakaryocytes (Mk) and erythroid (E) cells. The detailed molecular mechanisms underlying the MEP fate decision have not been determined. One of the challenges in studying the fate decisions in MEP has been the lack of high purity populations of the specific cell type. We established an improved method for enriching primary adult human MEP, in which CFU-Mk/E (single cells that give rise to colonies containing exclusively Mk and E) are enriched to ~50% with the remaining cells being CFU-Mk and BFU-E.. We applied single cell RNA sequencing to identify the molecular signature of this enriched MEP population, and compared this to that of CMP, and enriched populations of Mk or E committed progenitors (MKP or ERP), which produce >90% Mk or E colonies in CFU assays). Single cell sequencing results indicate that MEP have a unique gene expression signature consistent with a transition state from CMP to MKP and ERP. MEP have random co-expression of a fraction of 60 genes that are otherwise expressed exclusively in CMP, MKP or ERP. Amongst the most differentially expressed groups of genes between MEP, MKP, and ERP are those related to cell cycle. Bioinformatic analysis suggested that MYC and E2F may accelerate MEP cell cycling as cells commit toward the E or Mk lineage.To determine whether the change in cell cycle is the consequence of cell fate determinant or itself can also regulate the cell fate decision, we used chemical and molecular approaches to modify cell cycling of MEP. Our data show that ATRA and mTOR can each reduce the MEP proliferation rate, and bias MEP toward Mk lineage differentiation (see Figure, > 1.65-fold increased in Mk colony number). We tested whether effect is mediated by downstream MYC pathways, and found that suppression of MYC or MAX (heterodimeric partner of MYC) similarly slowed proliferation and induced an Mk bias in primary human MEP (1.5 and 1.8 fold increased in Mk colony number). If slowing the cell cycle promotes Mk fate commitment, then acceleration of the cell cycle may promote erythroid fate commitment. Indeed, MEP cycling was enhanced by lentiviral-mediated overexpression of Cyclins-CDKs or shRNA mediated p53, and these MEP were significantly (p < 0.05) biased toward erythroid lineage differentiation (> 1.7-fold increased in E colony number). These results support that the speed or frequency of the cell cycle regulates cell fate decisions. In summary, we apply single cell sequencing on pure human CMP, MEP, MKP and ERP and identify the unique MEP gene signature. Thus, by enriching primary human subpopulations, functionally confirming their fate commitment potential, performing single cell RNA sequencing, analyzing the data for gene expression patterns, and testing by both genetic and pharmacological approaches, we have confirmed that the fate commitment of primary human bipotent MEP can be toggled by cell cycle speed. Now that we have proven that cell cycle activity mechanistically controls MEP fate decisions, specific genetic and epigenetic mechanisms by which Mk vs erythroid specification is determined are being explored. The data obtained from healthy cells can now be applied to the mechanisms underlying benign and malignant disease states of Mk and E production. Figure. Figure. Disclosures No relevant conflicts of interest to declare.

Single Cell Transcriptome Profiling of Highly Purified Human Megakaryocyte-Erythroid Progenitors (MEP) Reveals New Insights into the MEP Fate Decision

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2903-2903
Author(s):  
Chad D Sanada ◽  
Elizabeth Min ◽  
Siying Zou ◽  
Huiyan Jin ◽  
Ping-Xia Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Deep Sequencing ◽  
Single Cells ◽  
Expression Patterns ◽  
Erythroid Progenitors ◽  
Single Cell Sequencing ◽  
New Findings ◽  
Fate Decision

Abstract Megakaryocyte-Erythroid Progenitors (MEPs) are bipotent cells capable of generating megakaryocytic (Mk) or erythroid (E) progeny. However, neither the cell fate-determining componentry nor the initial molecular consequences of lineage specification have been defined. To elucidate this, it is critical to rigorously purify MEP from primary cell sources. Unfortunately, existing purification strategies to do this fail to yield pure, bipotent cells. To improve upon existing approaches for the enrichment of primary human MEPs from G-CSF mobilized peripheral blood (MPB) and BM, we used the cell surface markers CD36 and CD110 in order to further enrich MEP from CD34+CD38+Lin-Flt3-CD45RA- cells. We then quantitated the Mk and E potential of those cells using single cell colony assays. Using this approach demonstrated that CD36/CD110 selection led to an increase of biphenotypic MEP (assessed as CFU-Mk/E) from ~15% to ~40% of colonies that grew. However, it was unclear from colony assay data alone whether or not the heterogeneity of the underlying population was accurately reflected. To address this, we subjected the FACS-sorted MEP-enriched population to single cell mRNA deep sequencing using the Fluidigm C1 platform. For comparison to MEP, we also performed single cell deep sequencing of CD34+CD38+CD41+Flt3- and CD34+CD38+Flt3-CD36+ cells, which are highly enriched for megakaryocyte progenitors (MkP) and erythroid progenitors (ErP), respectively. A total of 150 single cells were captured and sequenced with an average of 3 million reads per cell (1x100bp sequencing). The mRNA deep sequencing data was analyzed by a combination of gene and cell bi-clustering approach to identify both transcripts and cells that exhibited shared or differential patterning. Initial expression patterns and cell groups were identified using stringent expression filtering for transcripts that exhibited >10 FPKM in at least one cell, and iteratively defined and refined based on known E, Mk, and other hematopoietic genes, and then extended for all strongly expressed transcripts. For the MkP and ErP groups, the resulting clusters of cells expressed genes indicative of commitment to E or Mk differentiation. In contrast, within the MEP-enriched population, while a few cells clustered with MkP and ErP, the vast majority of cells fell into distinct subsets of uncommitted cells, supporting the idea that the MEP-enriched population was unique and distinct from MkP or ErP. Analysis of the gene expression patterns from the MEP, ErP and MkP revealed two remarkable trends. First, the transcription factors GATA1 and GATA2 showed distinct expression patterns in the different clusters of cells; there was a subset of MEP that had high GATA2 expression with little to no GATA1 expression (GATA2 cluster), and an opposite cluster containing high GATA1 expression and low or absent GATA2 expression (GATA1 cluster). The genes most positively correlated with GATA2 expression were also low or absent in the GATA 1 cluster. Closer analysis revealed that the GATA 1 cluster cells were predominantly erythroid and megakaryocyte committed, while the GATA2 cluster appeared uncommitted. A third cluster was present, containing intermediate expression of both GATA1 and GATA2. This cluster is as yet undefined, but appears to contain both MkP and MEP, suggesting a possible link between these two cell types. The second pattern we noted was that the genes in the GATA1 cluster correlated very strongly with cell cycle activity and cell cycle progression while the GATA2 cluster geneset had very low cell cycle activity. This suggested that the commitment of the MEP to E or Mk fates could not be unlinked from their cell cycling status. Such a finding could only be ascertained using single cell sequencing. Using single cell sequencing also provided us with a gene expression signature for primary human MkP, something which was not possible before because there is no reliable way to sort pure human MkP. Regarding GATA1 and GATA2 clusters, real time RT-PCR analysis of primary human ErP, MkP, and MEP point to a scenario where the ratio of GATA2/GATA1 is critical to determining the E vs. Mk fate decision. These findings will be further addressed in future studies aiming to understand the link between cell cycle and the MEP fate decision. Our new findings will help clarify genetic events critical for the E/Mk fate decision. Disclosures No relevant conflicts of interest to declare.

scholarly journals Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment

2017 ◽  
Author(s):  
Alice Moussy ◽  
Jérémie Cosette ◽  
Romuald Parmentier ◽  
Cindy da Silva ◽  
Guillaume Corre ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Fate ◽  
Morphological Changes ◽  
Gene Expression Pattern ◽  
Time Lapse ◽  
Dynamic Nature ◽  
Transcriptional Changes ◽  
Fate Decision

AbstractIndividual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.

Multi tissue processing for single cell sequencing of human immune cells v1

2021 ◽  
Author(s):  
Daniel Rainbow ◽  
Sarah Howlett ◽  
Lorna Jarvis ◽  
Joanne Jones
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Human Tissues ◽  
Single Cell Sequencing ◽  
Tissue Processing ◽  
Simultaneous Processing ◽  
Single Cell Rna Sequencing ◽  
Human Immune

This protocol has been developed for the simultaneous processing of multiple human tissues to extract immune cells for single cell RNA sequencing using the 10X platform, and ideal for atlasing projects. Included in this protocol are the steps needed to go from tissue to loading the 10X Chromium for single cell RNA sequencing and includes the hashtag and CiteSeq labelling of cells as well as the details needed to stimulate cells with PMA+I.

scholarly journals Demuxalot: scaled up genetic demultiplexing for single-cell sequencing

2021 ◽  
Author(s):  
Alex Rogozhnikov ◽  
Pavan Ramkumar ◽  
Saul Kato ◽  
Sean Escola
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Single Cell Sequencing ◽  
Additional Information ◽  
Genotype Information ◽  
Single Cell Rna Sequencing ◽  
Genotype Inference

Demultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot: a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

scholarly journals The Molecular Signature of Megakaryocyte-Erythroid Progenitors Reveals a Role for the Cell Cycle in Fate Specification

Cell Reports ◽  
2018 ◽  
Vol 25 (11) ◽  
pp. 3229 ◽  
Author(s):  
Yi-Chien Lu ◽  
Chad Sanada ◽  
Juliana Xavier-Ferrucio ◽  
Lin Wang ◽  
Ping-Xia Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Molecular Signature ◽  
Erythroid Progenitors ◽  
Fate Specification

scholarly journals Combining single-cell RNA-sequencing with a molecular atlas unveils new markers for Caenorhabditis elegans neuron classes

2020 ◽  
Author(s):  
Ramiro Lorenzo ◽  
Michiho Onizuka ◽  
Matthieu Defrance ◽  
Patrick Laurent
Keyword(s):  
Caenorhabditis Elegans ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Single Neuron ◽  
Genetic Manipulations ◽  
C Elegans ◽  
Single Cell Rna Sequencing ◽  
Normal Differentiation

Abstract Single-cell RNA-sequencing (scRNA-seq) of the Caenorhabditis elegans nervous system offers the unique opportunity to obtain a partial expression profile for each neuron within a known connectome. Building on recent scRNA-seq data and on a molecular atlas describing the expression pattern of ∼800 genes at the single cell resolution, we designed an iterative clustering analysis aiming to match each cell-cluster to the ∼100 anatomically defined neuron classes of C. elegans. This heuristic approach successfully assigned 97 of the 118 neuron classes to a cluster. Sixty two clusters were assigned to a single neuron class and 15 clusters grouped neuron classes sharing close molecular signatures. Pseudotime analysis revealed a maturation process occurring in some neurons (e.g. PDA) during the L2 stage. Based on the molecular profiles of all identified neurons, we predicted cell fate regulators and experimentally validated unc-86 for the normal differentiation of RMG neurons. Furthermore, we observed that different classes of genes functionally diversify sensory neurons, interneurons and motorneurons. Finally, we designed 15 new neuron class-specific promoters validated in vivo. Amongst them, 10 represent the only specific promoter reported to this day, expanding the list of neurons amenable to genetic manipulations.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

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