scholarly journals Paired Analyses of AML at Diagnosis and Relapse By Single-Cell RNA Sequencing Identifies Two Distinct Relapse Patterns

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 183-183
Author(s):  
Kai Wu ◽  
Qianyi Ma ◽  
Darren King ◽  
Jun Li ◽  
Sami Malek
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Single Cell ◽  
Bone Marrow Cells ◽  
Single Cells ◽  
Leukemia Cells ◽  
Driver Genes ◽  
Cell Gene Expression ◽  
Normal Human ◽  
Relapse Patterns

Introduction: Despite achievement of complete remission (CR) following chemotherapy, Acute Myelogenous Leukemia (AML) relapses in the majority of adult patients. While relapsed AML is almost always clonally related to the disease at diagnosis, the actual molecular and cellular contributors to chemotherapy resistance and to AML relapse remain incompletely understood. Some molecular determinants of relapse have been identified in genomic, epigenetic and proteomic aberrations, while cellular relapse reservoirs have been identified in leukemia stem cells as well as in more mature leukemic cell compartments. Here, we set out to determine the cellular composition, gene mutation status and gene expression of paired AML specimens procured at diagnosis and at relapse aiming at a better understanding of the AML relapse process. Methods: We employed the drop-seq 3' single cell RNA sequencing (scRNA-seq) method (Macosko 2015) with minor modifications to analyze the mRNA expression in single cells derived from 12 paired AML specimens procured at diagnosis and at relapse from prior CR. We obtained scRNA-seq data on 1000-2000 single cells per sample detecting approximately 2000-3000 unique molecular identifiers (UMIs) and 800-1500 genes per cell. Using WES or panel-based sequencing we determined mutations in known driver genes. Subsequently, we optimized novel methods for detection and mapping of mutated driver genes to individual cells using mutation specific PCR conditions and novel bioinformatics approaches. We annotated scRNA-seq expression profiles of the diagnosis and relapsed AML specimens individually using publicly available data for cell type-specific RNA markers derived from sorted normal cell populations and further compared the scRNA-seq data to scRNA-seq data of 5 pooled normal human bone marrows generated for this study. Results: Through analyses of scRNA-seq data of paired diagnosis and relapse AML specimens via principle components analyses (PCA) or t-distributed stochastic neighbor embedding (t-SNE) we detected varying degrees of separation of cell clusters in all cases analyzed indicative of substantial changes in single cell gene expression between AML diagnosis and relapse. A few of these observed cluster shifts were paralleled by gain or loss of mutated genes (e.g. FLT3-ITD) at relapse while most others lacked obvious clonal genomic markers. Through subsequent comparison of the expression similarities of single AML cells to sorted normal human bone marrow cells we detected two distinct AML relapse patterns: i) a pattern of relapse suggesting simple leukemia regrowth as evidenced by similar proportions of leukemia cells mapping onto discrete normal bone marrow cells (e.g. monocyte-like or GMPs or CMPs), and, ii) a pattern of relapse whereby the gene expression of relapsed cells (but not diagnosis cells) had similarity to normal hematopoietic cells that are conventionally placed more apical in the classical hematopoiesis differentiation cascade (HSCs, MPPs, CMPs; a phenotypic shift to immaturity). In addition, no leukemia sample mapped to just one classically defined bone marrow cell type but instead to multiple cell types, suggesting that most AML leukemia cells harbor aberrant hybrid cell gene expression patterns. Finally, we detected quantitative shifts in T cells and NK cells in some samples at relapse, which will be analyzed in greater detail. Conclusions: The comparative analysis of scRNA-seq data of paired AML specimens procured at diagnosis and relapse, identifies frequent and previously unrecognized changes in gene expression in leukemia cells at relapse. Through a comparison of gene mutation and gene expression at single cell resolution we identify two distinct AML relapse patterns in adult AML. Disclosures No relevant conflicts of interest to declare.

scholarly journals Single-Cell RNA-Seq Reveals the Differentiation Hierarchy of Normal Human Bone Marrow and a Distinct Transcriptome Signature of Monosomy 7 Cells

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 574-574
Author(s):  
Xin Zhao ◽  
Shouguo Gao ◽  
Xingmin Feng ◽  
Delong Liu ◽  
Sachiko Kajigaya ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Single Cell ◽  
Signaling Pathway ◽  
Copy Number ◽  
Research Funding ◽  
Single Cells ◽  
Myeloid Cells ◽  
Healthy Controls ◽  
Monosomy 7

Abstract Monosomy 7 is a frequent cytogenetic abnormality in hematopoietic malignancies and a general indicator of poor prognosis. Due to lack of distinct cell surface markers between monosomy 7 cells and normal cells, it is not feasible to physically separate aneuploid from diploid cells. We performed single-cell RNA-seq (scRNA-seq), which allows the entire transcriptome of large numbers of single cells to be assayed in an unbiased way, to investigate hematopoietic differentiation of normal and aneuploid human hematopoietic cells. Bone marrow samples were collected from four patients (P1-P4) with myelodysplastic syndrome, and four healthy volunteers. Conventional cytogenetics showed -7/7q- in bone marrow cells from P1, P3 and P4, and dup(1)(q21q32) in cells from P2; retrospectively, P2 was found positive for monosomy 7 as well as trisomy 8 by fluorescence in situ hybridization. Fresh CD34+CD38- and CD34+CD38+cells were sorted by flow cytometry and then subjected to Fluidigm C1 Single-Cell Auto Prep System for scRNA-seq. After excluding cells with low transcriptome coverage, 326 cells from P1 and P2 (analysis is in progress for P3 and P4), and 391 cells from healthy subjects were analyzed by comparison of transcriptomes from 17,071 genes. Nonlinear dimension reduction and visualization were achieved using t-distributed Stochastic Neighbor Embedding (tSNE). Cells from healthy controls clustered into seven subgroups based on their gene expression pattern, and each group could be associated with a previously reported hematopoietic cell type by known marker genes (Laurenti E, Nat Immunol, 2013). These cell types included hematopoietic stem cell (HSC), multilymphoid progenitor (MLP), granulocyte-monocyte progenitor (GMP), Pro-B cell (ProB), earliest thymic progenitor (ETP), and megakaryocytic-erythroid progenitor (MEP) (Fig 1a). Individual cells from healthy controls were ordered by Monocle software based on their expression profile similarity to uncover a differentiation hierarchy. A two-branch trajectory of development from HSC was revealed, with one branch progressing towards erythroid cell and the other to lymphoid/myeloid cells (Fig 1b). This pattern differs from the classic hematopoietic model, but is consistent with reports claiming existence of early-lymphoid-biased progenitors that retain myeloid but not erythroid potential (Doulatov S, Nat Immunol, 2010), and of dominance of multipotent and unipotent progenitors over scarce oligopotent progenitors in the adult marrow hematopoietic hierarchy (Notta F, Science, 2016). We compared single cells from patients and healthy controls for regional and chromosomal copy number differences in gene expression. We identified subclonal populations of cells from patients that showed decreased expression of chromosome 7 genes (60% in P1, and 55% in P2; Fig 1c and 1d), and increased expression of chromosome 8 (77% in P2) and chromosome 1 long arm genes (P2), at FDR=0.05 estimated with cells from health donors. Gene Ontology enrichment analysis using topGO indicated that cells with low global expression of chromosome 7 genes had dysregulated expression of immune related genes, including B cell receptor signaling pathway, T cell activation and differentiation, antigen receptor-mediated signaling pathway, as well as signal transduction and Fc-γ Receptor signaling pathway. ScRNA-seq analysis reveals a simple pattern of normal human hematopoietic development and the molecular signature of aneuploid cells from patients with developing "clonal evolution". This powerful method should improve characterization of functional changes in human cells with chromosome abnormalities. Figure 1 a. Single-cell gene expression patterns assigned single cells from healthy controls to seven clusters. 38N: CD34+CD38- population; 38P: CD34+CD38+ population. Different shapes represent cells from different subjects. b. Pseudo-time ordering of cells using Monocle reveals a two-branch stepwise development from stem cells to erythroid or lymphoid/myeloid cells. c. Heatmap of the copy-number variation (CNV) signal normalized against healthy controls shows CNV changes by chromosome (columns) for patients' individual cells (rows). d. Genome-wide gene expression binned per chromosome in single cells from P1, P2 and healthy controls. Chromosomal mapping reads values were median centered. Figure 1. a. Single-cell gene expression patterns assigned single cells from healthy controls to seven clusters. 38N: CD34+CD38- population; 38P: CD34+CD38+ population. Different shapes represent cells from different subjects. b. Pseudo-time ordering of cells using Monocle reveals a two-branch stepwise development from stem cells to erythroid or lymphoid/myeloid cells. c. Heatmap of the copy-number variation (CNV) signal normalized against healthy controls shows CNV changes by chromosome (columns) for patients' individual cells (rows). d. Genome-wide gene expression binned per chromosome in single cells from P1, P2 and healthy controls. Chromosomal mapping reads values were median centered. Disclosures Desierto: GSK/Novartis: Research Funding. Townsley:GSK/Novartis: Research Funding. Young:Novartis: Research Funding.

Xenografts of Pediatric Acute Lymphoblastic Leukemia Retain the Gene Expression Pattern From the Diagnostic Material They Originated From Over Serial Passages.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1629-1629
Author(s):  
Manon Queudeville ◽  
Elena Vendramini ◽  
Marco Giordan ◽  
Sarah M. Eckhoff ◽  
Giuseppe Basso ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Bone Marrow Cells ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Leukemia Cells ◽  
Diagnostic Samples ◽  
Xenotransplantation Model

Abstract Abstract 1629 Poster Board I-655 Primary childhood acute lymphoblastic leukemia (ALL) samples are very difficult to culture in vitro and the currently available cell lines only poorly reflect the heterogeneous nature of the primary disease. Many groups therefore use mouse xenotransplantation models not only for in vivo testing but also as a means to amplify the number of leukemia cells to be used for various analysis. It remains unclear as to what extent the xenografted samples recapitulate their respective primary leukemia. It has been suggested for example that transplantation may result in the selection of a specific clone present only to a small amount in the primary diagnostic sample. We used a NOD/SCID xenotransplantation model and injected leukemia cells isolated from fresh primary diagnostic material of 4 pediatric ALL patients [2 pre-B-ALL, 1 pro-B-ALL (MLL/AF4}, 1 cortical T-ALL] intravenously into the lateral tail vein of unconditioned mice. As soon as the mice presented clinical signs of leukemia, leukemia cells were isolated from bone marrow and spleen. Isolated leukemia cells were retransplanted into secondary and tertiary recipients. RNA was isolated from diagnostic material and serial xenograft passages and gene expression profiles were obtained using a human whole genome array (Affymetrix U133 2.0). Simultaneously, immunophenotypic analysis via multicolor surface and cytoplasmatic staining by flow cytometry was performed for the diagnostic samples and respective serial xenograft passages. In an unsupervised clustering analysis the diagnostic sample of each patient clustered together with the 3 derived xenograft samples, although the 3 xenograft samples clustered stronger to each other than to their respective diagnostic sample. Comparison of the 4 diagnostic samples vs. all xenograft samples resulted in a gene list of 270 genes upregulated at diagnosis and 8 genes upregulated in the xenograft passages (Wilcoxon, p< .05). The high number of genes upregulated at diagnosis is most likely due to contamination of primary patient samples with normal peripheral blood and/or bone marrow cells as 15% of genes are attributed to myeloid cells, 7% to erythroid cells, 7% to lymphoid cells, 32% to bone marrow in general as well as to innate immunity, chemokines, immunoglobulins. The remaining genes can not be attributed to a specific hematopoetic cell lineage and are not known to be related to leukemia or cancer in general. Accordingly, there are no statistically significant differences between the primary, secondary and tertiary xenograft passages. The immunophenotype analysis are also in accordance with these findings, as the diagnostic blast population retains its immunophenotypic appearance during serial transplantation, whereas the contaminating CD45-positive non- leukemic cells disappear after the first xenograft passage. The few genes upregulated in xenograft samples compared to diagnosis are mainly involved in cell cycle regulation, protein translation and apoptosis resistance. Some of the identified genes have already been described in connection with cancer subtypes, their upregulation therefore being indicative of a high proliferative state in general and could argue towards a more aggressive potential of the engrafted leukemia cells but alternatively could also simply be due to the fact that the xenograft samples are pure leukemic blasts and are not contaminated with up to 15% of non-cycling healthy bone marrow cells as in the diagnostic samples. We conclude that the gene expression profiles generated from xenografted leukemias are very similar to those of their respective primary leukemia and moreover remain stable over serial retransplantation passages as we observed no statistically significant differences between the primary, secondary and tertiary xenografts. The differentially expressed genes between diagnosis and primary xenotransplant are most likely to be due to contaminating healthy cells in the diagnostic samples. Hence, the NOD/SCID-xenotransplantation model recapitulates the primary human leukemia in the mouse and is therefore an appropriate tool for in vivo and ex vivo studies of pediatric acute leukemia. Disclosures No relevant conflicts of interest to declare.

scholarly journals Inferring copy number variation from gene expression data: methods, comparisons, and applications to oncology

2021 ◽  
Author(s):  
Joseph Boen ◽  
Joel P. Wagner ◽  
Noemi Di Nanni
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Copy Number ◽  
Single Cells ◽  
Rapid Development ◽  
Cell Cancer ◽  
Copy Number Variations ◽  
Expression Data ◽  
Cell Gene Expression

Copy number variations (CNVs) are genomic events where the number of copies of a particular gene varies from cell to cell. Cancer cells are associated with somatic CNV changes resulting in gene amplifications and gene deletions. However, short of single-cell whole-genome sequencing, it is difficult to detect and quantify CNV events in single cells. In contrast, the rapid development of single-cell RNA sequencing (scRNA-seq) technologies has enabled easy acquisition of single-cell gene expression data. In this work, we employ three methods to infer CNV events from scRNA-seq data and provide a statistical comparison of the methods' results. In addition, we combine the analysis of scRNA-seq and inferred CNV data to visualize and determine subpopulations and heterogeneity in tumor cell populations.

scholarly journals Full-length, single-cell RNA-sequencing of human bone marrow subpopulations reveals hidden complexity

2021 ◽  
Author(s):  
Marcel O Schmidt ◽  
Anne Deslattes Mays ◽  
Megan E Barefoot ◽  
Anna T Riegel ◽  
Anton Wellstein
Keyword(s):  
Bone Marrow ◽  
Single Cell ◽  
Rna Sequencing ◽  
Bone Marrow Cells ◽  
Single Cells ◽  
Full Length ◽  
Human Bone ◽  
Human Bone Marrow ◽  
Total Bone Marrow ◽  
Novel Isoforms

Bone marrow progenitor cell differentiation has frequently been used as a model for studying cellular plasticity and cell-fate decisions. Recent analysis at the level of single-cells has expanded knowledge of the transcriptional landscape of human hematopoietic cell lineages. Using single-molecule real-time (SMRT) full-length RNA sequencing, we have previously shown that human bone marrow lineage-negative (Lin-neg) cell populations contain a surprisingly diverse set of mRNA isoforms. Here, we report from single cell, full-length RNA sequencing that this diversity is also reflected at the single-cell level. From fresh human bone marrow unselected and lineage-negative progenitor cells were isolated by droplet-based single-cell selection (10xGenomics). The single cell-derived mRNAs were analyzed by full-length SMRT and short-read sequencing. In both samples we detected an average of 8000 different genes using short-read sequencing. Differential expression analysis arranged the single-cells of the total bone marrow into only four clusters whereas the Lin-neg population was much more diverse with nine clusters. mRNA isoform analysis of the single-cell populations using full-length sequencing revealed that Lin-neg cells contain on average 24% more novel splice variants than the total bone marrow cells. Interestingly, among the most frequent genes expressing novel isoforms were members of the spliceosome, e.g. HNRNPs, DEAD box helicases and SRSFs. Mapping the isoforms from all genes to the cell type clusters revealed that total bone marrow cells express novel isoforms only in a small subset of clusters. On the other hand, lineage-negative progenitor cells expressing novel isoforms were present in nearly all subpopulations. In conclusion, on a single-cell level lineage-negative cells express a higher diversity of genes and more alternatively spliced novel isoforms suggesting that cells in this subpopulation are poised for different fates.

Adjuvant CD49d Blockade Eradicates Chemoresistant ALL

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 869-869 ◽  
Author(s):  
Yao-Te Hsieh ◽  
Eugene Park ◽  
Enzi Jiang ◽  
Katrin Dauber ◽  
Doreen Chudziak ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Flow Cytometry ◽  
Bone Marrow Cells ◽  
Normal Karyotype ◽  
Leukemia Cells ◽  
Drug Resistant ◽  
Murine Leukemia

Abstract Abstract 869 Despite the recent advances in chemotherapy for acute lymphoblastic leukemia (ALL), drug resistance resulting in relapse and long-term side effects of current treatments warrant new treatment modalities. Integrin α4β1 (VLA4/ITGA4/CD49d) mediates adhesion of hematopoietic cells onto bone marrow cells and has been implicated in cell adhesion-mediated drug resistance of leukemia cells. Gene expression analyses indicate that VLA4 is upregulated in B-lineage Acute Lymphocytic Leukemia (ALL). Therefore, we hypothesize that VLA4 might be a potential target for treatment of drug resistant ALL To test our hypothesis, we determined the effect of VLA4 inhibition on engraftment of primary pre-B ALLs using a humanized CD49d antibody, Tysabri, as a single agent in our NOD/SCID xenograft model of primary pre-B ALL. Tysabri is known to mobilize normal hematopoietic progenitor cells into the circulation. It blocks binding of VLA-4 to its counter receptors VCAM-1 and osteopontin and we have shown previously in a small pilot study that adjuvant administration with chemotherapy sensitizes one drug resistant primary ALL in vivo to drug treatment. In this study, we injected primary ALL cells from eight different donors into NOD/SCID mice. The samples encompass various cytogenetic aberrations (BCR-ABL, E2A-PBX, MLL-AF, normal karyotype). Cells were luciferase-transduced for in vivo cell tracking and pretreated in vitro with either Tysabri (n=3 per leukemia, n=24 total) or human Ig as a control (n=3 per leukemia, n=24 total). Recipients of Tysabri treated leukemias showed significantly prolonged median survival time (BCR-ABL: MST=112days, E2A-PBX: MST=83days, MLL-AF4: MST=51days; Normal karyotype: MST=48days) compared to control groups (BCR-ABL: MST=84days, E2A-PBX: MST=54days, MLL-AF4: MST=35days; Normal: MST=39days) (p<0.05). Therefore, engraftment of leukemia was significantly delayed in the Tysabri-treated groups as determined by bioluminescent imaging (p<0.05) and survival analysis (p<0.05). Next, we injected two luciferase-labeled pre-B ALLs (US7R, RS4;11) into NOD/SCID mice, which were then treated intraperitoneally with saline (US7R: n=4; RS4;11: n=3), Tysabri (US7R: n=4; RS4;11: n=3), VDL (Vincristine, Dexamethasone and L-Asparaginase) (US7R: n=9; RS4;11: n=5), or VDL+Tysabri (US7R: n=9; RS4;11: n=5), for 4 weeks. Tysabri-treated groups showed prolonged survival time (US7R: MST=52days; RS4;11: MST=83) compared with saline-treated groups (US7R: MST=38days; RS4;11: MST=60 days) (p=0.007). VDL-only treated animals died rapidly (US7R: MST=74days; RS4;11: MST=109 days), however, the animals treated with the combination VDL+ Tysabri, survived disease-free until the end of follow-up (US7R: MST=151days; RS4;11: MST=141 days) (p<0.0001). The sacrificed animals showed absence of human CD45 in spleen, liver, bone marrow and lung by immunohistochemistry and flow cytometry indicating eradication of recalcitrant leukemia cells. We have also shown in vivo using an immunocompetent mouse model that VLA4 ablation does not result in dose-limiting toxicity to normal hematopoietic cells after VDL or 5-FU treatment. To understand further the role of VLA4 deletion in ALL, we established a model of murine leukemia using bone marrow cells from VLA4 floxed mice, retrovirally transformed with BCR-ABL1 p210 and cmyc. Subsequent to leukemic outgrowth, cells were transduced with either Empty GFP control, or Cre-GFP vector to delete VLA4. Knockout of VLA4 in transduced cells was detected by PCR on genomic DNA and by flow cytometry (Empty GFP control: 97% CD49+; Cre-GFP vector: 0.8% CD49+). Upon in vitro culturing of the cells 4-fold more VLA4 deleted cells were found in the supernatant compared to the control cells (p<0.05) determined by Trypan blue exclusion counts of dead cells, indicating that CD49d in murine leukemia is required for cell adhesion. Further functional studies addressing engraftment and gene expression upon induced VLA4 deletion are ongoing. Taken together, our data demonstrate that CD49d-blockade with adjuvant chemotherapy can eradicate chemotherapy-resistant leukemia. Further studies are warranted to understand and evaluate preclinically adjuvant inhibition of integrins to overcome relapse of leukemia. Disclosures: No relevant conflicts of interest to declare.

Genome-Wide Characterization of Human Hematopoietic Progenitor Cell Heterogeneity by Expression Profiling of Single Cells: A Pilot Study

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1231-1231
Author(s):  
Chih Long Liu ◽  
Bo Dai ◽  
Aaron M. Newman ◽  
Ravi Majeti ◽  
Ash A Alizadeh
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Progenitor Cells ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Hematopoietic Stem ◽  
Single Donor ◽  
Normal Human

Abstract Abstract 1231 Background: Current methods for defining and isolating human hematopoietic stem and progenitor cells using surface markers enrich for unique functional properties of these populations. However, significant functional heterogeneity in these compartments remains with important implications for understanding normal and altered hematopoiesis. Using flow sorting to enrich >10,000 cells as progenitor subpopulations, we previously characterized the gene expression signature of normal human HSC (Majetiet al 2009 PNAS 106(9):3396–3401). We hypothesized that interrogation of the transcriptomes of single cells from this compartment could resolve remaining heterogeneity and help identify and better define features of progenitor cells and hematopoietic stem cells (HSCs). Methods: Using normal human bone marrow aspirates and a FACS Aria II instrument equipped with a specialized single-cell sorting apparatus, we sorted cells enriched for HSCs based on expression of Lin-CD34+CD38-CD90+CD45RA− into 1-cell, 10-cell, 100-cell, and 40000-cell (bulk) representations. We used at least 5 replicates per group and verified single cell deposition by direct visualization. We amplified cDNA from these corresponding inputs using an exponential whole transcriptome amplification (WTA) scheme (Miltenyi SuperAmp), and evaluated gene expression profiles by two microarray platforms (Agilent/GE Healthcare 60K, and Affymetrix U133 plus 2.0), and by RNA-Seq (Illumina). We used gene expression correlation between replicates within and between microarrays as means of assessing methodological reproducibility and estimating population heterogeneity. Results: Whole transcriptome amplification yielded cDNA ranging from 0.2–1 kb for 10 and 100 cells, with significantly lower size distribution of amplified cDNA observed for single cells. Gene expression profiles had significantly better replicate reproducibility and array coverage with the Agilent microarray platform when compared with the Affymetrix U133 Plus 2.0 platform (gene coverage of 84 % for 100 cells, 73 % for 10 cells and 50% for 1 cell for Agilent vs 24 % for 100 cells, 11 % for 10 cells and 5.7% for 1 cell for Affymetrix). RNA-Seq profiling of the same populations is ongoing with major technical optimizations focused on reducing amplification of non-human templates while maintaining library complexity and representation. Using biological replicates for each input size, we observed high inter-replicate correlation levels for expression profiles obtained for bulk sorted HSCs from 8 healthy donors (∼40000-cells, average r=0.97) and for 100-cell and 10-cell inputs from a single donor (r=0.96–0.99, respectively). While intra-array concordance of replicate measurements (n=14642) was high (r>0.91) within each of 5 single cells from a single donor, comparison of 5-single cells from the same donor identified significant heterogeneity, when compared to the 10-cell and 100-cell sub-clusters (Figure 1). Individual genes characteristically expressed by these heterogeneous single cell populations are currently being investigated by FACS and Fluidigm arrays. A larger experiment characterizing 192 single progenitor cells, employing Agilent microarrays and RNA-Seq is currently in progress. Conclusions: Single cell transcriptome profiling is feasible, with best performance on 60-mer microarrays. Single cell transcriptomes exhibit lower, but reasonable levels of reproducibility (r>0.7) and precision as compared with higher cell numbers. Gene expression profiles of single cells capture gene expression heterogeneity in HSCs. Disclosures: No relevant conflicts of interest to declare.

scholarly journals SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions

2017 ◽  
Author(s):  
Tao Peng ◽  
Qing Nie
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Single Cells ◽  
High Dimensional ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Gene Expression ◽  
Cell Data ◽  
Cell Gene

AbstractMeasurement of gene expression levels for multiple genes in single cells provides a powerful approach to study heterogeneity of cell populations and cellular plasticity. While the expression levels of multiple genes in each cell are available in such data, the potential connections among the cells (e.g. the cellular state transition relationship) are not directly evident from the measurement. Classifying the cellular states, identifying their transitions among those states, and extracting the pseudotime ordering of cells are challenging due to the noise in the data and the high-dimensionality in the number of genes in the data. In this paper we adapt the classical self-organizing-map (SOM) approach for single-cell gene expression data (SOMSC), such as those based on single cell qPCR and single cell RNA-seq. In SOMSC, a cellular state map (CSM) is derived and employed to identify cellular states inherited in the population of the measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers among the basins in CSM provide information on transitions among the cellular states. A cellular state transitions path (e.g. differentiation) and a temporal ordering of the measured single cells are consequently obtained. In addition, SOMSC could estimate the cellular state replication probability and transition probabilities. Applied to a set of synthetic data, one single-cell qPCR data set on mouse early embryonic development and two single-cell RNA-seq data sets, SOMSC shows effectiveness in capturing cellular states and their transitions presented in the high-dimensional single-cell data. This approach will have broader applications to analyzing cellular fate specification and cell lineages using single cell gene expression data

scholarly journals Challenges of analysing stochastic gene expression in bacteria using single-cell time-lapse experiments

2021 ◽  
Author(s):  
Georgeos Hardo ◽  
Somenath Bakshi
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Time Lapse ◽  
Careful Consideration ◽  
Stochastic Gene Expression ◽  
Bacterial Cells ◽  
Rigorous Framework ◽  
Cell Gene Expression ◽  
The Individual

Abstract Stochastic gene expression causes phenotypic heterogeneity in a population of genetically identical bacterial cells. Such non-genetic heterogeneity can have important consequences for the population fitness, and therefore cells implement regulation strategies to either suppress or exploit such heterogeneity to adapt to their circumstances. By employing time-lapse microscopy of single cells, the fluctuation dynamics of gene expression may be analysed, and their regulatory mechanisms thus deciphered. However, a careful consideration of the experimental design and data-analysis is needed to produce useful data for deriving meaningful insights from them. In the present paper, the individual steps and challenges involved in a time-lapse experiment are discussed, and a rigorous framework for designing, performing, and extracting single-cell gene expression dynamics data from such experiments is outlined.

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