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 ◽  
...  

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.

2020 ◽  
Vol 3 (4) ◽  
pp. 72
Author(s):  
Anupama Prakash ◽  
Antónia Monteiro

Butterflies are well known for their beautiful wings and have been great systems to understand the ecology, evolution, genetics, and development of patterning and coloration. These color patterns are mosaics on the wing created by the tiling of individual units called scales, which develop from single cells. Traditionally, bulk RNA sequencing (RNA-seq) has been used extensively to identify the loci involved in wing color development and pattern formation. RNA-seq provides an averaged gene expression landscape of the entire wing tissue or of small dissected wing regions under consideration. However, to understand the gene expression patterns of the units of color, which are the scales, and to identify different scale cell types within a wing that produce different colors and scale structures, it is necessary to study single cells. This has recently been facilitated by the advent of single-cell sequencing. Here, we provide a detailed protocol for the dissociation of cells from Bicyclus anynana pupal wings to obtain a viable single-cell suspension for downstream single-cell sequencing. We outline our experimental design and the use of fluorescence-activated cell sorting (FACS) to obtain putative scale-building and socket cells based on size. Finally, we discuss some of the current challenges of this technique in studying single-cell scale development and suggest future avenues to address these challenges.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3828-3828
Author(s):  
Yi-Chien Lu ◽  
Diane S. Krause ◽  
Juliana Xavier-Ferrucio ◽  
Lin Wang ◽  
Nathan Salomonis ◽  
...  

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.


2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.


Author(s):  
Kenneth H. Hu ◽  
John P. Eichorst ◽  
Chris S. McGinnis ◽  
David M. Patterson ◽  
Eric D. Chow ◽  
...  

ABSTRACTSpatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.


2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu

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.


2017 ◽  
Author(s):  
Alice Moussy ◽  
Jérémie Cosette ◽  
Romuald Parmentier ◽  
Cindy da Silva ◽  
Guillaume Corre ◽  
...  

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.


2017 ◽  
Author(s):  
Anissa Guillemin ◽  
Angelique Richard ◽  
Sandrine Gonin-Giraud ◽  
Olivier Gandrillon

AbstractRecent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. How cell cycle and cell size influences gene expression variability at single-cell level is not yet clearly understood. To deconvolute such influences, most of the single-cell studies used dedicated methods that could include some bias. Here, we provide a universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.


2021 ◽  
Author(s):  
Chaohao Gu ◽  
Zhandong Liu

Abstract Spatial gene-expression is a crucial determinant of cell fate and behavior. Recent imaging and sequencing-technology advancements have enabled scientists to develop new tools that use spatial information to measure gene-expression at close to single-cell levels. Yet, while Fluorescence In-situ Hybridization (FISH) can quantify transcript numbers at single-cell resolution, it is limited to a small number of genes. Similarly, slide-seq was designed to measure spatial-expression profiles at the single-cell level but has a relatively low gene-capture rate. And although single-cell RNA-seq enables deep cellular gene-expression profiling, it loses spatial information during sample-collection. These major limitations have stymied these methods’ broader application in the field. To overcome spatio-omics technology’s limitations and better understand spatial patterns at single-cell resolution, we designed a computation algorithm that uses glmSMA to predict cell locations by integrating scRNA-seq data with a spatial-omics reference atlas. We treated cell-mapping as a convex optimization problem by minimizing the differences between cellular-expression profiles and location-expression profiles with an L1 regularization and graph Laplacian based L2 regularization to ensure a sparse and smooth mapping. We validated the mapping results by reconstructing spatial- expression patterns of well-known marker genes in complex tissues, like the mouse cerebellum and hippocampus. We used the biological literature to verify that the reconstructed patterns can recapitulate cell-type and anatomy structures. Our work thus far shows that, together, we can use glmSMA to accurately assign single cells to their original reference-atlas locations.


2021 ◽  
Author(s):  
Fang Ye ◽  
Guodong Zhang ◽  
Weigao E ◽  
Haide Chen ◽  
Chengxuan Yu ◽  
...  

Abstract The Mexican axolotl (Ambystoma mexicanum) is a promising tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, during which their regeneration capacity and lifespan gradually decline. However, a system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls is still lacking. Here, we developed a single-cell RNA-seq method based on combinatorial hybridization to generate a tissue-based transcriptomic atlas of the adult axolotl. We performed gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell atlas. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls revealed the heterogeneity of structural cells in different tissues and established their regulatory network. Furthermore, we described dynamic gene expression patterns during limb development in neotenic axolotls. These data serve as a resource to explore the molecular identity of the axolotl as well as its metamorphosis.


2019 ◽  
Author(s):  
Yiliang Zhang ◽  
Kexuan Liang ◽  
Molei Liu ◽  
Yue Li ◽  
Hao Ge ◽  
...  

AbstractSingle-cell RNA sequencing technologies are widely used in recent years as a powerful tool allowing the observation of gene expression at the resolution of single cells. Two of the major challenges in scRNA-seq data analysis are dropout events and batch effects. The inflation of zero(dropout rate) varies substantially across single cells. Evidence has shown that technical noise, including batch effects, explains a notable proportion of this cell-to-cell variation. To capture biological variation, it is necessary to quantify and remove technical variation. Here, we introduce SCRIBE (Single-Cell Recovery Imputation with Batch Effects), a principled framework that imputes dropout events and corrects batch effects simultaneously. We demonstrate, through real examples, that SCRIBE outperforms existing scRNA-seq data analysis tools in recovering cell-specific gene expression patterns, removing batch effects and retaining biological variation across cells. Our software is freely available online at https://github.com/YiliangTracyZhang/SCRIBE.


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