scholarly journals Gene Expression Profiling of CD93-Selected CP-CML Stem Cells Confirms Their Quiescent Character and Biomarker Potential

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4231-4231
Author(s):  
Gillian A. Horne ◽  
Chinmay Rajiv Munje ◽  
Ross Kinstrie ◽  
Eduardo Gómez-Castañeda ◽  
Helen Wheadon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Single Cells ◽  
Gene Clustering ◽  
Single Cell Level ◽  
Cell Level ◽  
Advisory Committees ◽  
Two Populations

Abstract The introduction of BCR-ABL tyrosine kinase inhibitors has revolutionized the treatment of chronic myeloid leukemia (CML). A major clinical aim remains the identification and elimination of low-level disease persistence, termed "minimal residual disease". Disease persistence suggests, that despite targeted therapeutic approaches, BCR-ABL-independent mechanisms exist which sustain the survival of a small population of cells, termed leukemic stem cells (LSC). We previously identified CD93 expression as a promising biomarker of LSC in chronic phase (CP)-CML. Our group has described the long term self-renewal potential of Lin-CD34+93+ CP-CML cells compared to their Lin-CD34+93- counterparts through LTCIC assays (n=3, p<0.0001) and NSG engraftment models (3.5-30-fold increased in engraftment with Lin-CD34+93+ cells, p<0.03). We hypothesized that CD93+-selected cells would represent a more immature functional phenotype compared to CD93- selected cells. The aim of this study was to characterize differences in the gene expression profile between CD93+ and CD93- CML LSC populations and determine heterogeneity of each population at a single cell level. To interrogate this, we initially identified CP-CML subpopulations with the greatest functional capability compared to normal. Normal and CP-CML samples were FACS-sorted into HSC/LSC, CMP, GMP, and MEP sub-populations. Results suggest a significant change in functional status between normal and CP-CML subpopulations within the HSC/LSC compartment (lin-CD34+CD38-CD45RA-CD90+), where CML LSC demonstrated significantly increased proliferation (14 fold expansion; P<0.001) compared to normal HSC (no expansion) after 5 days in vitro culture in physiological growth factors. In addition, equivalent numbers of CML LSC produce ~4-fold more colonies in colony forming cell (CFC) assays than normal HSC (329±56 versus 86±17 per 2,000 cells, respectively (p<0.05)). Furthermore, fluorescence in situ hybridization demonstrated that >90% of lin-CD34+CD38-CD45RA-CD90+ CML LSC from all patient samples were BCR-ABL positive. Subsequent experiments were confined to the LSC population. We hypothesized that lin-CD34+CD38-CD90+CD93- CML cells would have a more mature gene expression profile compared to lin-CD34+CD38-CD90+CD93+ cells. CP-CML cells were sorted into (1) lin-CD34+, (2) lin-CD34+CD38-CD90+CD93- and (3) lin-CD34+CD38-CD90+CD93+ populations. RNA was harvested at baseline from bulk populations (1) to (3) and cDNA was generated from single cells using the Fluidigm C1 autoprep system. Using Fluidigm technology, quantitative PCR of 90 lineage-specific and cell survival genes was performed within all populations of cells (1) to (3) in 'bulk' samples (n=3), and at single cell level (n=123 CD93+, n=120 CD93-single cells; n=3 samples in total). Bulk sample analysis demonstrated a significant increase in expression of lineage commitment genes within the lin-CD34+CD38-CD90+CD93- population, as shown by increased expression of GATA1 (p=0.0007), and CBX8 (p=0.0002). The lin-CD34+CD38-CD90+CD93+ population displayed a less lineage-restricted profile with increased expression of CDK6 (p=0.05), HOXA6 (ns), CDKN1C (ns) and CKIT (p=0.0014), compared to the lin-CD34+CD38-CD90+CD93- population. Furthermore, the two populations could be segregated by differential gene expression through gene clustering. At a single cell level, differences were noted in the frequency of expression between lin-CD34+CD38-CD90+CD93- and lin-CD34+CD38-CD90+CD93+ populations, particularly in GATA1, TPOR, and VWF. Although a statistically significant change was demonstrated in gene expression between the lin-CD34+CD38-CD90+CD93- and lin-CD34+CD38-CD90+CD93+ populations in a number of genes, we were not able to segregate the populations by differential expression using gene clustering. This highlights the heterogeneous nature of the cell populations and the inability to distinctly characterize between the two populations at a single cell level. Our results validate CD93 as a potential biomarker to separate the primitive CP-CML LSC population and highlight key lineage and cell survival pathways that are altered in CML LSC. The results demonstrate the heterogeneity seen within gene expression at the single cell level, which may allow for further insight into the CML LSC compartment with further analyses. Disclosures Wheadon: GlaxoSmithKline: Research Funding. Copland:Shire: Honoraria; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; ARIAD: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria.

scholarly journals Single Cell DNA Sequencing Identifies Combinatorial Mutation Patterns and Clonal Architecture in Myeloid Malignancies

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 913-913 ◽  
Author(s):  
Linde A Miles ◽  
Robert L Bowman ◽  
Tiffany R Merlinsky ◽  
Aik Ooi ◽  
Pedro Mendez ◽  
...  
Keyword(s):  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Clonal Diversity ◽  
Single Cell Level ◽  
Cell Level ◽  
Advisory Committees ◽  
Parallel Acquisition ◽  
Ras Pathway ◽  
Clonal Architecture

Genomic studies of myeloid malignancies (MM), including acute myeloid leukemia (AML), myeloproliferative neoplasms (MPN) and myelodysplasia (MDS), identified mutations with different allele frequencies. Recent studies of clonal hematopoiesis (CH) discovered a subset of MM disease alleles, while other alleles are only observed in overt MM. These observations suggest an important pathogenetic role for the chronology of mutational acquisition. Although bulk sequencing informs prognostication, it cannot distinguish which mutations occur in the same clone and cannot offer definitive evidence of mutational order. Delineation of clonal architecture at the single cell level is key to understanding how the sequential/parallel acquisition of somatic mutations contributes to myeloid transformation. In order to elucidate the clonal structure of MM, we designed a custom single cell 109 amplicon panel of the most frequently mutated amplicons in 50 MM genes using the Mission Bio Tapestri v2 platform. Viable cells were sorted from 90 samples from 78 patients with CH, AML, and MPN/post-MPN AML followed by single cell amplification/sequencing. Mutation calls were filtered based on read depth, quality, and alleles genotyped per cell. We reconstructed a random distribution of clones by permuting genotype calls across cells and generated empirical p values for each clone. To identify dominant clones, we used a Poisson test to determine clones were significantly enriched compared to the mean clone size. Clones with significant p-values (p &lt;0.05) were used to generate plots of clonal architecture of each sample (Figure 1A). Despite significant clonal complexity, the majority of MM patients (80%;72/90) present with one (51/90; 56.7%) or two (21/90; 23.3%) dominant clones. These data show there are specific genotypic combinations which lead to clonal dominance with increased fitness relative to other clones and/or suppression of minor clones by dominant clone(s). We next investigated whether specific molecularly defined AML subtypes had increased clonal complexity. FLT3-ITD mutant AML samples had a significantly greater number of clones (p &lt; 0.002) compared to AML samples with multiple epigenetic modifier mutations. Similar findings were not observed when comparing AML samples with epigenetic mutations to RAS pathway mutant samples. We next investigated whether specific mutations were likely to co-occur/be mutually exclusive at a single cell level. We observed evidence of oligoclonality in CH, including parallel acquisition of DNMT3A mutations and clones with multiple mutations in the absence of progression to MM. By contrast, in MM the dominant clone(s) almost always harbored multiple epigenetic modifier mutations, suggesting cooperative epigenetic remodeling in myeloid transformation. Mutations in signaling effectors (FLT3-ITD/TKD; RAS/RAS) were mutually exclusive. We observed distinct FLT3-mutant clones in FLT3-mutant AML patients and parallel acquisition of different RAS pathway mutations. We used this data to develop clonal architecture trees in each patient, giving us a definitive picture of mutational acquisition and transformation at a single cell level. We calculated a Shannon diversity score and observed an increase in clonal complexity with disease evolution; CH samples had the lowest clonal diversity and FLT3-ITD AML patients the highest clonal diversity (Figure 1B). We extended our findings by combining cell surface marker assessment and single cell mutational analysis. Patient samples were stained with an antibody cocktail of 6 oligo-conjugated antibodies with barcode tags prior to single cell sequencing, which allowed simultaneous acquisition of single cell immunophenotypic and genotypic data. This allows us to identify distinct populations of stem/progenitor cells with distinct clonal/mutational repertoires (Figure 1C). Additional data will be presented with this novel approach, which allows us to combine an assessment of stem/progenitor cell frequency with genetic data. This includes studies of CD34+ and CD34- AML, which show striking differences in mutational representation in different stem/progenitor compartments. In summary, our studies of clonal architecture at a single cell level provide us novel insights into the pathogenesis of myeloid transformation and give us new insights into how clonal complexity contributes to disease progression. Disclosures Ooi: Mission Bio: Employment, Equity Ownership. Mendez:Mission Bio: Employment, Equity Ownership. Carroll:Janssen Pharmaceuticals: Consultancy; Incyte: Research Funding; Astellas Pharmaceuticals: Research Funding. Papaemmanuil:Celgene: Research Funding. Viny:Mission Bio: Other: Sponsored travel; Hematology News: Membership on an entity's Board of Directors or advisory committees. Levine:Roche: Consultancy, Research Funding; Amgen: Honoraria; Imago Biosciences: Membership on an entity's Board of Directors or advisory committees; Isoplexis: Membership on an entity's Board of Directors or advisory committees; Qiagen: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy; Prelude Therapeutics: Research Funding; Loxo: Membership on an entity's Board of Directors or advisory committees; Lilly: Honoraria; Gilead: Consultancy; Celgene: Consultancy, Research Funding.

A Comprehensive Analysis of Single-Cell Chromatin Accessibility and Gene Expression Identifies Intra-Tumor Heterogeneity and Molecular Treatment Responses in Relapsed/Refractory Multiple Myeloma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 575-575
Author(s):  
Alexandra M Poos ◽  
Jan-Philipp Mallm ◽  
Stephan M Tirier ◽  
Nicola Casiraghi ◽  
Hana Susak ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Tumor Heterogeneity ◽  
Mek Inhibitor ◽  
Chromatin Accessibility ◽  
University Research ◽  
Sample Processing ◽  
Advisory Committees

Introduction: Multiple myeloma (MM) is a heterogeneous malignancy of clonal plasma cells that accumulate in the bone marrow (BM). Despite new treatment approaches, in most patients resistant subclones are selected by therapy, resulting in the development of refractory disease. While the subclonal architecture in newly diagnosed patients has been investigated in great detail, intra-tumor heterogeneity in relapsed/refractory (RR) MM is poorly characterized. Recent technological and computational advances provide the opportunity to systematically analyze tumor samples at single-cell (sc) level with high accuracy and througput. Here, we present a pilot study for an integrative analysis of sc Assay for Transposase-Accessible Chromatin with high-throughput sequencing (scATAC-seq) and scRNA-seq with the aim to comprehensively study the regulatory landscape, gene expression, and evolution of individual subclones in RRMM patients. Methods: We have included 20 RRMM patients with longitudinally collected paired BM samples. scATAC- and scRNA-seq data were generated using the 10X Genomics platform. Pre-processing of the sc-seq data was performed with the CellRanger software (reference genome GRCh38). For downstream analyses the R-packages Seurat and Signac (Satija Lab) as well as Cicero (Trapnell Lab) were used. For all patients bulk whole genome sequencing (WGS) data was available, which we used for confirmatory studies of intra-tumor heterogeneity. Results: A comprehensive study at the sc level requires extensive quality controls (QC). All scATAC-seq files passed the QC, including the detected number of cells, number of fragments in peaks or the ratio of mononucleosomal to nucleosome-free fragments. Yet, unsupervised clustering of the differentially accessible regions resulted in two main clusters, strongly associated with sample processing time. Delay of sample processing by 1-2 days, e.g. due to shipment from participating centers, resulted in global change of chromatin accessibility with more than 10,000 regions showing differences compared to directly processed samples. The corresponding scRNA-seq files also consistently failed QC, including detectable genes per cell and the percentage of mitochondrial RNA. We excluded these samples from the study. Analysing scATAC-seq data, we observed distinct clusters before and after treatment of RRMM, indicating clonal adaptation or selection in all samples. Treatment with carfilzomib resulted in highly increased co-accessibility and &gt;100 genes were differentially accessible upon treatment. These genes are related to the activation of immune cells (including T-, and B-cells), cell-cell adhesion, apoptosis and signaling pathways (e.g. NFκB) and include several chaperone proteins (e.g. HSPH1) which were upregulated in the scRNA-seq data upon proteasome inhibition. The power of our comprehensive approach for detection of individual subclones and their evolution is exemplarily illustrated in a patient who was treated with a MEK inhibitor and achieved complete remission. This patient showed two main clusters in the scATAC-seq data before treatment, suggesting presence of two subclones. Using copy number profiles based on WGS and scRNA-seq data and performing a trajectory analysis based on scATAC-seq data, we could confirm two different subclones. At relapse, a seemingly independent dominant clone emerged. Upon comprehensive integration of the datasets, one of the initial subclones could be identified as the precursor of this dominant clone. We observed increased accessibility for 108 regions (e.g. JUND, HSPA5, EGR1, FOSB, ETS1, FOXP2) upon MEK inhibition. The most significant differentially accessible region in this clone and its precursor included the gene coding for krüppel-like factor 2 (KLF2). scRNA-seq data showed overexpression of KLF2 in the MEK-inhibitor resistant clone, confirming KLF2 scATAC-seq data. KLF2 has been reported to play an essential role together with KDM3A and IRF1 for MM cell survival and adhesion to stromal cells in the BM. Conclusions: Our data strongly suggest to use only immediately processed samples for single cell technologies. Integrating scATAC- and scRNA-seq together with bulk WGS data showed that detection of individual clones and longitudinal changes in the activity of cis-regulatory regions and gene expression is feasible and informative in RRMM. Disclosures Goldschmidt: John-Hopkins University: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Research Funding; Molecular Partners: Research Funding; Dietmar-Hopp-Stiftung: Research Funding; Janssen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Generation of genetic tools for gauging multiple gene expression at single cell level

2021 ◽  
Author(s):  
Marta Mellini ◽  
Massimiliano Lucidi ◽  
Francesco Imperi ◽  
Paolo Visca ◽  
Livia Leoni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Image Processing ◽  
Single Cell ◽  
Fluorescent Protein ◽  
Single Cells ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Multiple Gene ◽  
Genetic Tools

Key microbial processes in many bacterial species are heterogeneously expressed in single cells of bacterial populations. However, the paucity of adequate molecular tools for live, real-time monitoring of multiple gene expression at the single cell level has limited the understanding of phenotypic heterogeneity. In order to investigate phenotypic heterogeneity in the ubiquitous opportunistic pathogen Pseudomonas aeruginosa, a genetic tool that allows gauging multiple gene expression at the single cell level has been generated. This tool, named pRGC, consists in a promoter-probe vector for transcriptional fusions that carries three reporter genes coding for the fluorescent proteins mCherry, green fluorescent protein (GFP) and cyan fluorescent protein (CFP). The pRGC vector has been characterized and validated via single cell gene expression analysis of both constitutive and iron-regulated promoters, showing clear discrimination of the three fluorescence signals in single cells of a P. aeruginosa population, without the need of image-processing for spectral crosstalk correction. In addition, two pRGC variants have been generated for either i) integration of the reporter gene cassette into a single neutral site of P. aeruginosa chromosome, that is suitable for long-term experiments in the absence of antibiotic selection, or ii) replication in bacterial genera other than Pseudomonas. The easy-to-use genetic tools generated in this study will allow rapid and cost-effective investigation of multiple gene expression in populations of environmental and pathogenic bacteria, hopefully advancing the understanding of microbial phenotypic heterogeneity. IMPORTANCE Within a bacterial population single cells can differently express some genes, even though they are genetically identical and experience the same chemical and physical stimuli. This phenomenon, known as phenotypic heterogeneity, is mainly driven by gene expression noise and results in the emergence of bacterial sub-populations with distinct phenotypes. The analysis of gene expression at the single cell level has shown that phenotypic heterogeneity is associated with key bacterial processes, including competence, sporulation and persistence. In this study, new genetic tools have been generated that allow easy cloning of up to three promoters upstream of distinct fluorescent genes, making it possible to gauge multiple gene expression at the single cell level by fluorescent microscopy, without the need of advanced image-processing procedures. A proof of concept has been provided by investigating iron-uptake and iron-storage gene expression in response to iron availability in P. aeruginosa.

scholarly journals Comprehensive Genetic Interrogation of Circulating Multiple Myeloma Cells at Single Cell Resolution

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 800-800
Author(s):  
Jens G Lohr ◽  
Sora Kim ◽  
Joshua Gould ◽  
Birgit Knoechel ◽  
Yotam Drier ◽  
...  
Keyword(s):  
Single Cell ◽  
Board Of Directors ◽  
Rna Sequencing ◽  
Research Funding ◽  
Single Cells ◽  
Advisory Committees ◽  
Single Cell Rna Sequencing ◽  
Genomic Characterization ◽  
Whole Transcriptome

Abstract Continuous genomic evolution has been a major limitation to curative treatment of multiple myeloma (MM). Frequent monitoring of the genetic heterogeneity in MM from blood, rather than serial bone marrow (BM) biopsies, would therefore be desirable. We hypothesized that genomic characterization of circulating MM cells (CMMCs) recapitulates the genetics of MM in BM biopsies, enables MM classification, and is feasible in the majority of MM patients with active disease. Methods: To test these hypotheses, we developed a method to enrich, purify and isolate single CMMCs with a sensitivity of at least 1:10(5). We then performed DNA- and RNA-sequencing of single CMMCs and compared them to single BM-derived MM cells. We determined CMMC numbers in 24 randomly selected MM patient samples and compared them to numbers of circulating MM cells obtained by flow cytometry. We performed single-cell whole genome amplification of single cells from 10 MM patients, and targeted sequencing of the 35 most recurrently mutated loci in MM. A total of 568 single primary cells representing CMMCs, BM MM cells, CD19+ B lymphocytes, CD45+CD138- WBC from these patients were subjected to DNA-sequencing. By processing 80 single cells from four MM cell lines with known mutations we determined the mean sensitivity of mutation detection in single cells to be 93 ± 9%. In addition to DNA-sequencing we also isolated 57 single MM cells from the BM and peripheral blood of two MM patients and performed whole transcriptome single cell RNA-sequencing. Results: In 24 randomly selected MM patient samples we detected >12 CMMCs per 1ml of blood in all 24 patients. In comparison, by flow cytometry, we detected ≥10 CMMCs per 10(5) white blood cells in 10/24 cases (42%), ≥1 CMMC but ≤ 10 CMMCs in 13/24 cases (54%), and < 1 CTCs in 1/24 patients (4%). Mutational analysis of 35 recurrently mutated loci in 335 high quality single MM cells from the blood and BM of 10 patients, including one MGUS patient, revealed the presence of a total of 12 mutations (in KRAS, NRAS, BRAF, IRF4 and TP53). All targeted mutations that were detected by clinical-grade genotyping of bulk BM were also detected in single cell analysis of CMMCs. While in most patients, the fraction of mutated single cells was similar between blood and BM, in three patients, the proportion of MM cells harboring TP53 R273C, BRAF G469A and NRAS G13D mutations was significantly higher in the blood than in the BM, suggesting a different clonal composition. We developed an analytical model to predict whether a genetic locus underwent loss of heterozygosity, using the distribution of known allelic fractions of previously described mutations in MM cell lines as a benchmark. In two patients who simultaneously harbored two mutations, we predicted a BRAF G469E and a KRAS G12C mutation to be heterozygous, whereas the loci harboring a TP53 R273C and a TP53 R280T mutation were predicted to be associated with LOH with high statistical confidence. Whole transcriptome single cell RNA-sequencing of 57 MM cells from the BM and peripheral blood of two patients showed >3,700 transcripts per cell. Single-cell RNA-sequencing allowed for a clear distinction between normal plasma cells and MM cells, either based on analysis of CD45, CD27, and CD56 alone, or by unsupervised hierarchical clustering of detected transcripts in single cells. In addition, single cell CMMC expression analysis could be used to infer the existence of key MM chromosomal translocations. For example, CCND1 and CCND3 were highly upregulated in single MM cells from the blood and BM of two patients, whose MM was found by FISH analysis to harbor a t(11;14) and a t(6;14) translocation, respectively. Conclusion: We demonstrate that extensive genomic characterization of MM is feasible from very small numbers of CMMCs with single cell resolution. Interrogation of single CMMCs faithfully reproduces the pattern of somatic mutations present in MM in the BM, identifies actionable oncogenes, and reveals if somatic mutated loci underwent loss of heterozygosity. Single CMMCs also reveal mutations that are not detectable in the BM either by single cell sequencing or clinical grade bulk sequencing. Single cell RNA-sequencing of CMMCs provides robust transcriptomic profiling, allowing for class-differentiation and inference of translocations in MM patients. Disclosures Raje: Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Research Funding; Eli Lilly: Research Funding.

Distinct Pathogenesis of Clonal Hematopoiesis Revealed By Single Cell RNA Sequencing Integrated with Highly Sensitive Genotyping Method

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 34-34
Author(s):  
Masahiro Marshall Nakagawa ◽  
Ryosaku Inagaki ◽  
Yutaka Kuroda ◽  
Yasuhito Nannya ◽  
Lanying Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Simultaneous Detection ◽  
Advisory Committees ◽  
Hematopoietic Stem ◽  
Single Cell Sequencing ◽  
Clonal Hematopoiesis

Background Recent evidence suggests that age-related clonal hematopoiesis (CH) might represent the earliest precursor of myeloid neoplasms. Although the exact mechanism of clonal selection that shapes CH is still to be elucidated, both cell intrinsic and non-cell intrinsic effects of mutations, including the interplay between mutated cells and the bone marrow environment, are thought to play important roles, which are best studied using single-cell sequencing analysis of both mutations and gene expression. Methods We performed single-cell sequencing of hematopoietic stem and progenitors (HSPCs) from BM of the 16 patients with CH along with 16 control patients without CH identified by screening otherwise healthy individuals who received hip joint replacement, using a novel platform that enables simultaneous detection of gene mutations and expression based on the Fluidigm C1-HT system. Sequence data were analyzed with Seurat (Stuart et al Cell 2019) with integration of genotyping information. Cells were clustered and each cluster was assigned by marker-gene expressions for major cell-types in HSPCs, including hematopoietic stem cell (HSC)-like and erythroid progenitors. Cells were grouped by their genotypes and pathway analysis were performed. Results In total, we identified 35 subjects who had CH-related mutations, including those affecting DNMT3A, TET2, ASXL1, SF3B1, PPM1D, IDH1, GNB1 and TP53, of which 11 had more than one CH-related mutation. Most of these mutations showed a low variant allele frequency (VAF) ≤ 0.05. However, clones having double mutations of DNMT3A/TET2 or those having biallelic TET2 mutations tended to show a higher VAF as high as 0.4, suggesting an enhanced clonal advantage for clones having multiple mutations. Using our novel single-cell platform, we analyzed 3,767 cells from control patients without CH and 1,474 mutated cells and 7,234 wild-type (WT) cells from patients with CH. By targeting both genomic DNA and RNA, we successfully obtained a sufficient number of single-cell reads for genes whose expression was too low to evaluate by only targeting RNA, such as TET2 and DNMT3A. Although some clones having a high-VAF mutation caused a skewed clustering to be detected as a CH clone, many clones with low-VAF mutations did not make distinct clusters, indicating the importance of genotyping at a single cell level to identify and characterize mutated cells. Simultaneous detection of genotype and expression allowed us to see the effect of CH-mutations on cell phenotype and differentiation. For example, cells having compound TET2/DNMT3A mutations were significantly enriched in the erythroid cluster, while another clone with double TET2 mutations were more enriched in the HSC-like cluster, compared to cells from individuals without CH (WTcont). These are in line with the previous findings of TET2/DNMT3A double knockout mice or TET2 knockout mice, respectively. In another case with an IDH1 mutation, IDH1-mutated (MUTIDH1) cells less contributed to the HSC-like fraction, showing an enhancement of cell proliferation-signature, compared to WT (WTIDH1) cells in the same patient. Strikingly, compared to WTcont cells, WTIDH1 cells were significantly enriched in the HSC-like fraction and showed an enhanced expression of cytokine-related pathway genes, which was in line with a finding seen in mouse cells treated with 2-hydroxy-glutalate, an mutant IDH-related oncometabolite. Similarly, when compared to WTcont cells, WT cells from patients with DNMT3A- (WTDNMT3A) or TET2- (WTTET2) mutated CH significantly showed an enhanced cell proliferation. HSC-like WTTET2 cells also showed aberrant IFN-response signatures compared to corresponding WTcont cells, which was confirmed in competitive transplantation of Tet2 heterozygous knockout (hKO) and WT cells in a mouse model; HSPCs of WT competitors transplanted with Tet2-hKO cells showed a significant enhancement of IFN-response signatures compared to those transplanted with WT cells. Intriguingly, monocytes of Tet2-hKO donors showed aberrant expression of S100a8/a9, which might contribute to the non-cell intrinsic effect of Tet2-hKO cells. Conclusions In CH, not only mutated cells but also surrounding WT cells show an aberrant gene expression phenotype, suggesting the presence of non-cell autonomous phenotype or an altered bone marrow environment that favors the positive selection of CH-clones. Disclosures Nakagawa: Sumitomo Dainippon Pharma Co., Ltd.: Research Funding. Inagaki:Sumitomo Dainippon Pharma Co., Ltd.: Current Employment. Ogawa:Eisai Co., Ltd.: Research Funding; KAN Research Institute, Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Asahi Genomics Co., Ltd.: Current equity holder in private company; Otsuka Pharmaceutical Co., Ltd.: Research Funding; Sumitomo Dainippon Pharma Co., Ltd.: Research Funding; Chordia Therapeutics, Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding.

scholarly journals Single Cell ATAC Lineage Deconvolution Reveals Overlapping Subclones in Epigenetically Distinct AML Samples

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2381-2381
Author(s):  
Deepika Dilip ◽  
Richard Koche ◽  
Kamal Menghrajani ◽  
Ari Melnick ◽  
Olivier Elemento ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Single Cells ◽  
Great Promise ◽  
Sequencing Analysis ◽  
Advisory Committees ◽  
High Degree ◽  
Cluster 2

Abstract Introduction: Acute Myeloid Leukemia (AML) is a biologically diverse disease. Expanded mutation panels and novel epigenetic assays are identifying an increasing number of putative AML subtypes beyond the traditional 'Good', 'Intermediate', and 'Poor' risk designations. Although these approaches show great promise, identifying the relevant underlying disease biology remains difficult. Single cell studies highlight this difficulty, showing dynamic interactions between multiple subclones, each with its own set of cooperating mutations interfering with normal hematopoiesis. We have previously shown that bulk ATAC data can be used to 'deconvolve' and identify hematopoietic state in AML samples. Here we extend this work, showing that this approach can be used to identify normal hematopoietic states with a high degree of accuracy. In addition, we show that AML samples that appear different in bulk actually contain overlapping lineage characteristics at the single cell level. Methods: Single cell ATAC-seq count files were downloaded from GSE74310, GSE96769 as well as corresponding bulk ATAC-seq count files from GSE74912, GSE96771. These data were generated by flow sorting normal specimens into well-known stages of hematopoiesis followed by either bulk or single cell ATAC-seq. A set of AML samples was processed by both single cell and bulk ATAC as well. Bulk ATAC data was normalized using DESeq2 followed by variance stabilizing transformation. Single cell data was processed and normalized using the Seurat pipeline with default parameters. A common peak atlas was created for each dataset, and peaks characteristic of each stage of hematopoiesis were selected using a modified Kruskal-Wallis statistic and optimized using a set of well-characterized in-vitro sample mixtures. Lineage deconvolution was performed using a non-negative least squares regression comparing each unknown sample to the set of normal hematopoietic states. Results: Dimensionality reduction of single cell ATAC-seq using uniform manifold approximation and projection (UMAP) largely recapitulates stages of hematopoiesis used to sort the samples (Figure 1a). Single cell lineage deconvolution is able to identify the purity of these populations more precisely (Figure 1b), with HSC, MPP, LMPP, CLP, GMP, MEP, and Monocytic stages showing relatively pure lineage characteristics. In contrast, the CMP stage appears to be composed of a heterogeneous population, as has been previously shown. Dimensionality reduction of bulk ATAC-seq data using Principle Component Analysis (PCA) illustrates distinct stages of hematopoiesis, and separates the AML samples into two groups (Figure 2c). To further analyze these groups, bulk lineage deconvolution was performed, showing that cluster 1 (purple) has a more differentiated appearance characterized by GMP and Monocyte lineages while cluster 2 also reflects earlier stages of hematopoiesis including HSC, MPP, and LMPP (Figure 2d). One sample from each cluster (highlighted in red in figure 2c,d) was evaluated using single cell ATAC-seq. Lineage deconvolution on the component cells illustrates substantial lineage characteristic overlap between subclones of these samples, with lineage based hierarchical clustering generating two clusters with mixed sample origin (Figure 2e). These clusters are separated into more and less differentiated lineage groups, with the cluster 2 sample cells more commonly having an HSC or MPP dominant lineage. However, some cluster 1 cells do have HSC or MPP lineage features as well, which is reflected by the poor association of cluster with sample (Fisher's exact p=0.8). Conclusions: Lineage deconvolution can be performed on single cell ATAC-seq data with a high degree of precision on normal samples and illustrates clonal lineage heterogeneity in malignant specimens not previously appreciated in bulk sequencing analysis. Analysis of greater numbers of samples and cells are needed to draw general conclusions, but the approach shows promise as a means of computationally identifying or sorting normal single cells and more precisely characterizing leukemias. Figure 1 Figure 1. Disclosures Melnick: Janssen Pharmaceuticals: Research Funding; Sanofi: Research Funding; Daiichi Sankyo: Research Funding; Epizyme: Consultancy; Constellation: Consultancy; KDAC Pharma: Membership on an entity's Board of Directors or advisory committees. Elemento: Johnson and Johnson: Research Funding; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; Eli Lilly: Research Funding; Janssen: Research Funding; One Three Biotech: Consultancy, Other: Current equity holder; Champions Oncology: Consultancy; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Owkin: Consultancy, Other: Current equity holder; AstraZeneca: Research Funding. Levine: Lilly: Honoraria; Gilead: Honoraria; Janssen: Consultancy; Morphosys: Consultancy; Astellas: Consultancy; Roche: Honoraria, Research Funding; Incyte: Consultancy; Amgen: Honoraria; Celgene: Research Funding; Isoplexis: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Prelude: Membership on an entity's Board of Directors or advisory committees; Auron: Membership on an entity's Board of Directors or advisory committees; Ajax: Membership on an entity's Board of Directors or advisory committees; Zentalis: Membership on an entity's Board of Directors or advisory committees; Mission Bio: Membership on an entity's Board of Directors or advisory committees; Imago: Membership on an entity's Board of Directors or advisory committees; QIAGEN: Membership on an entity's Board of Directors or advisory committees. Glass: GLG: Consultancy.

scholarly journals Dissecting Heterogeneity of Tumor Cells and Their Microenvironment in Refractory Multiple Myeloma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 571-571
Author(s):  
Stephan M Tirier ◽  
Jan-Philipp Mallm ◽  
Nicola Casiraghi ◽  
Hana Susak ◽  
Nicola Giesen ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Cell Types ◽  
Specific Gene ◽  
Refractory Disease ◽  
Advisory Committees ◽  
Cell Trajectory

Introduction: Despite significant improvements in therapy during the last decade, most multiple myeloma (MM) patients develop refractory disease over time. Treatment of refractory MM is a major challenge, likely due to the still poorly characterized inter- and intratumor heterogeneity at this stage of the disease, and the complex interplay of MM cells with the microenvironment (ME). In particular, there is an urgent need to unravel how these features of MM are linked to molecular mechanisms of drug resistance. Methods: We resolved the cellular composition, underlying transcriptional inter- and intra-patient heterogeneity and molecular treatment response of relapsed/refractory MM by single cell RNA sequencing (scRNA-seq). Using droplet-based microfluidics, ~230,000 single cell gene expression profiles from bone marrow (BM) aspirates of 21 patients sorted into CD138+and CD138- fractions were acquired, allowing for a comprehensive analysis of both MM cells as well as their ME. Patients had a median of 4 prior lines of therapy including both a proteasome inhibitor and an immunomodulator and were refractory to their immediate prior line of therapy at time of sampling. In addition, paired samples before either pomalidomide- or carfilzomib-based therapies were analyzed for 16/21 patients. Genomic aberrations in individual patients were mapped by interphase fluorescence in situ hybridization. Cells were clustered and CD138+ MM subtypes as well as immune cell-types of the ME were identified from their single cell transcriptomes and a copy number variation (CNV) analysis. As a reference for non-malignant cells and to construct a developmental B-cell trajectory the Human Cell Atlas BM scRNA-seq reference dataset was used. To characterize interactions of MM cells with their ME, the correlated expression of ligand-receptor pairs was exploited. Results: The analysis of inter- and intra-tumor heterogeneity of molecular MM subgroups revealed distinct transcriptome signatures with contributions that could be assigned to differences in heavy and light chain immunoglobulin expression as well as known genomic alterations, including t(11;14), t(4;14) and hyperdiploidy. MM cells from individual patients largely maintained a plasma cell specific gene expression profile but a partial loss of plasma cell identity was detected based on mapping to a developmental B-cell trajectory. It was characterized by the upregulation of subgroup transcriptome signatures associated with earlier stages of B-cell development in almost 50% of patients, such as a pre-B or mature B cell-like phenotype. Within individual samples, subclonal MM cell populations with specific gene expression programs were resolved based on the CNV analysis and included those characterized by expression of the immune-activator CD27 and the modulator of WNT signaling FRZB. The analysis of longitudinally collected samples revealed both changes in the cell subtype cluster structure as well as drug-specific adaptation of gene expression programs in distinct subpopulations persisting or emerging at relapse. These profile changes were characterized by e.g. downregulation of Myc target genes upon pomalidomide treatment or induction of heat shock proteins under carfilzomib. Within the ME of refractory MM patients, we observed that the fraction of B cells and CD4+ T cells was strongly reduced while CD14+ and CD16+ monocytes as well as dendritic cells expanded. Notably, the immune checkpoint protein PD-1H (aka VISTA) that inhibits T cell activation was highly expressed in cell types from the myeloid compartment in contrast to healthy donors. Further, a ligand-receptor analysis revealed that MM cells displayed the strongest interactions with monocytes, which were mediated by MIF, BAFF and other cytokines. Conclusions: Our study demonstrates the value of scRNA-seq analysis for identifying crucial transcriptome features that classify refractory MM subtypes and their evolution in response to treatment including regulation of drug resistance associated signaling pathways. Our data suggest that refractory MM cells shape the myeloid compartment in the BM to generate an immune suppressive ME. Understanding the evolution of MM cell heterogeneity and the bone marrow milieu in refractory disease will lead to novel treatment approaches and eventually improve patient outcome. Disclosures Müller-Tidow: MSD: Membership on an entity's Board of Directors or advisory committees. Goldschmidt:John-Hopkins University: Research Funding; Molecular Partners: Research Funding; Amgen: Consultancy, Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; Janssen: Consultancy, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding.

scholarly journals Tracking single-cell gene regulation in dynamically controlled environments using an integrated microfluidic and computational setup

2016 ◽  
Author(s):  
Matthias Kaiser ◽  
Florian Jug ◽  
Olin Silander ◽  
Siddharth Deshpande ◽  
Thomas Pfohl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Environmental Conditions ◽  
Single Cells ◽  
Fine Tuning ◽  
Growth Media ◽  
Single Cell Level ◽  
Cell Level ◽  
Operon Regulation

AbstractBacteria adapt to changes in their environment by regulating gene expression, often at the level of transcription. However, since the molecular processes underlying gene regulation are subject to thermodynamic and other stochastic fluctuations, gene expression is inherently noisy, and identical cells in a homogeneous environment can display highly heterogeneous expression levels. To study how stochasticity affects gene regulation at the single-cell level, it is crucial to be able to directly follow gene expression dynamics in single cells under changing environmental conditions. Recently developed microfluidic devices, used in combination with quantitative fluorescence time-lapse microscopy, represent a highly promising experimental approach, allowing tracking of lineages of single cells over long time-scales while simultaneously measuring their growth and gene expression. However, current devices do not allow controlled dynamical changes to the environmental conditions which are needed to study gene regulation. In addition, automated analysis of the imaging data from such devices is still highly challenging and no standard software is currently available. To address these challenges, we here present an integrated experimental and computational setup featuring, on the one hand, a new dual-input microfluidic chip which allows mixing and switching between two growth media and, on the other hand, a novel image analysis software which jointly optimizes segmentation and tracking of the cells and allows interactive user-guided fine-tuning of its results. To demonstrate the power of our approach, we study the lac operon regulation in E. coli cells grown in an environment that switches between glucose and lactose, and quantify stochastic lag times and memory at the single cell level.

scholarly journals High-throughput imaging of mRNA at the single-cell level in human primary immune cells

2020 ◽  
Author(s):  
Manasi Gadkari ◽  
Jing Sun ◽  
Adrian Carcamo ◽  
Hugh Alessi ◽  
Zonghui Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Immune Cells ◽  
Single Cells ◽  
Transcript Abundance ◽  
Hybridization Chain Reaction ◽  
Single Cell Level ◽  
Cell Level ◽  
Experimental Conditions ◽  
General Applicability

AbstractMeasurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

scholarly journals Analysis of Phase-Specific Gene Expression at the Single-Cell Level in the White-Opaque Switching System ofCandida albicans

2001 ◽  
Vol 183 (12) ◽  
pp. 3761-3769 ◽  
Author(s):  
Anja Strauß ◽  
Sonja Michel ◽  
Joachim Morschhäuser
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Reporter Gene ◽  
Single Cells ◽  
Phenotypic Switching ◽  
Specific Gene ◽  
Single Cell Level ◽  
Switching System ◽  
Cell Type ◽  
Cell Level

ABSTRACT The opportunistic fungal pathogen Candida albicanscan switch spontaneously and reversibly between different cell forms, a capacity that may enhance adaptation to different host niches and evasion of host defense mechanisms. Phenotypic switching has been studied intensively for the white-opaque switching system of strain WO-1. To facilitate the molecular analysis of phenotypic switching, we have constructed homozygous ura3 mutants from strain WO-1 by targeted gene deletion. The two URA3 alleles were sequentially inactivated using theMPA R -flipping strategy, which is based on the selection of integrative transformants carrying a mycophenolic acid (MPA) resistance marker that is subsequently deleted again by site-specific, FLP-mediated recombination. To investigate a possible cell type-independent switching in the expression of individual phase-specific genes, two different reporter genes that allowed the analysis of gene expression at the single-cell level were integrated into the genome, using URA3 as a selection marker. Fluorescence microscopic analysis of cells in which aGFP reporter gene was placed under the control of phase-specific promoters demonstrated that the opaque-phase-specificSAP1 gene was detectably expressed only in opaque cells and that the white-phase-specific WH11 gene was detectably expressed only in white cells. WhenMPA R was used as a reporter gene, it conferred an MPA-resistant phenotype on opaque but not white cells in strains expressing it from the SAP1 promoter, which was monitored at the level of single cells by a significantly enlarged size of the corresponding colonies on MPA-containing indicator plates. Similarly, white but not opaque cells became MPA resistant whenMPA R was placed under the control of the WH11 promoter. The analysis of these reporter strains showed that cell type-independent phase variation in the expression of the SAP1 and WH11 genes did not occur at a detectable frequency. The expression of these phase-specific genes of C. albicans in vitro, therefore, is tightly linked to the cell type.

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