scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

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.

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.


2019 ◽  
Author(s):  
Arnav Moudgil ◽  
Michael N. Wilkinson ◽  
Xuhua Chen ◽  
June He ◽  
Alex J. Cammack ◽  
...  

AbstractIn situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 298-298
Author(s):  
Cornelis A.M. van Bergen ◽  
Marvyn T. Koning ◽  
Edwin Quinten ◽  
Agnieszka Mykowiecka ◽  
Julieta Sepulveda ◽  
...  

Objectives: Follicular lymphoma (FL) typically originates from premalignant mature B cells that carry the founder t(14;18) BCL2 translocation. Mutations in epigenetic modifiers and acquisition of N-glycosylation sites in CDR regions of the B-cell receptor (BCR) are recurrent secondary events in FL pathogenesis. Despite these oncogenic drivers, FL can remain indolent and clinically stable for years. The molecular events driving subclonal evolution into symptomatic progression and eventual transformation to aggressive lymphoma are insufficiently understood. FL cells are frozen in their B-cell development at the germinal center stage and undergo continuous somatic hypermutation mediated by expression of activation-induced deaminase (AID). We aim to identify crucial drivers of subclonal FL evolution by high-throughput mapping at single-cell resolution. Methods: Viable FL cells were isolated and cryopreserved from 23 histologically or immunocytologically confirmed FL samples from 13 patients with informed consent. Full-length VDJ/VJ transcripts were isolated by unbiased template-switching ARTISAN PCR and massive parallel NGS sequencing on the PacBio platform. The clonal primordial FL BCR (pBCR) was reconstructed from unmutated IGV/IGJ sequences with the CDR3 of the least mutated BCR. Since the IgTree program was unable to process the obtained numbers of BCR sequences, we developed the WILLOW algorithm for analysis of BCR evolution based on the principle of maximum parsimony and on distance from the pBCR. Intraclonal BCR variability was quantified by Shannon's diversity index. 5' single cell transcriptomics and VDJ/VJ sequencing was performed on 2 pools of highly purified FL cells from 5 lymph node biopsies on the 10x Genomics platform. Data were deconvoluted based on expressed variants by the Single Cell Sample Matcher (SCSM) algorithm. Clustering based on gene expression profiles was performed by shared nearest neighbour (SNN) modularity optimization within the R Seurat package. Genes whose expression differed significantly (adjusted p<0.05) between clusters were assigned to gene ontology terms. Results: ARTISAN PCR/PacBio NGS yielded a median of 743 full-length VDJ and VJ sequences (range 62-12782) per BCR chain with expected high intraclonal diversity (median 200 subclones, range 15-3301). WILLOW revealed dominant FL subclones with a subclonal hierarchy wherein multiple routes converged to offspring nodes with identical additional mutations rather than tree-like branching (Figure). In serial samples of 4 patients, lymph node biopsies had only marginally higher subclonal diversity than blood or bone marrow samples (p=0,055; Wilcoxon's matched-pairs signed rank test). Overall BCR mutational burden increased over time in sequential biopsies. Two cases of histological FL transformation were dominated by a single subclone (65% and 80% of all VDJ/VJ sequences, respectively) that was rare in the preceding FL BCR network (0.2% and 1.8%). Pooled transcriptomics data from 6050-6500 cells were assigned to individual samples by SCSM and revealed up to seven transcriptional clusters per FL. In 9 of 10 FL, genes assigned to immune function strongly contributed to separation into one or more clusters. Single cell VDJ/VJ sequencing yielded combined heavy and light chain BCR sequences for a median of 502 FL cells per biopsy (range 22 - 1919) that permitted mapping of subclonal evolution by WILLOW based on complete BCR information. Transcriptome clusters were not distributed evenly throughout the WILLOW FL BCR networks but rather statistically associated with distinct major FL subclones. Vice versa, major FL subclones within the same biopsy were distinguished by particular gene expression profiles. Conclusions: WILLOW facilitates mapping of subclonal FL evolution based on high-throughput BCR sequencing. FL evolution proceeds in networks rather than tree-like branching, whereby acquisition of certain combinations of several BCR mutations can occur in parallel in different trajectories. Transcriptomic profiling of single FL cells identifies distinct clusters within a single biopsy. Mapping of these clusters to the FL cell position in the subclonal FL evolutionary network identifies putative mechanisms that are associated with subclonal progression. These mechanisms involve physiological B-cell signalling pathways. Figure Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1800-1800
Author(s):  
Masahiro Marshall Nakagawa ◽  
Ryosaku Inagaki ◽  
Yasuhito Nannya ◽  
Lanying Zhao ◽  
Yutaka Kuroda ◽  
...  

Abstract Recent advances in single-cell sequencing (sc-Seq) technologies have enabled high-throughput transcriptome analysis in thousands of cells to understand the heterogeneity among cancer populations in terms of genome-wide gene expression. However, its application to the analysis of clonal evolution of cancer populations is largely limited by the lack of an efficient sc-Seq platform that allows for accurate detection of gene mutations at the same time with transcriptome analysis. The major challenge here is a frequent allele dropout of just two copies per single cell, which results in an inaccurate genotype assignment for many cells, preventing identification of relevant genotype-phenotype correlations. To overcome this, we developed a novel sc-Seq platform (scMutSeq) that allows for precise determination of both genotype and genome-wide gene expression simultaneously with negligible allele dropouts, on the basis of the Fluidigm C1 Single-Cell mRNA Seq HT system and applied it to the analysis of clonal evolution and intratumor heterogeneity of myelodysplastic syndromes (MDS) characterized by frequent clonal evolution to acute amyloid leukemia (AML). We first evaluated the performance of our plat form using an AML-derived cell line with heterozygous SF3B1K700E mutation, HNT-34, for which efficiency of the detection of both wild-type and mutant allele, together with global gene expression, was evaluated. Among 400 cells subjected to scMutSeq analysis, a total of 125 passed QC, in which cell viability was assessed in terms of expression of mitochondrial genes. Global gene expression and heterozygous SF3B1mutation were successfully detected in all the QC-confirmed cells with none of the cells showing the wild-type allele or homozygous SF3B1mutation, where evaluable transcript reads (unique molecular identifier >=1) were obtained for a median of 2,753 genes, designated as nGene. The performance was also tested for flow-sorted hematopoietic stem/progenitor cells (HSPCs) (Lin−CD34+) from an MDS patient positive for the SF3B1K700E mutation. Gene expression was successfully analyzed all the QC-confirmed cells (n=81) with a median nGene of 1,953. No substantial allele dropouts were suspected, because none of the cells genotyped had homozygous SF3B1mutation. We then applied scMutSeq to the analysis of TP53-mutated AML/MDS with complex karyotype, including del(5q) and del(7q), for which longitudinal samples were obtained for the assessment of clonal evolution. scMutSeq successfully analyzed the mutation status of TP53and global gene expression profiles at a single-cell level, where copy number abnormalities were also evaluated on the basis of gene expression. We identified two discrete clones in the HSPC fraction, carrying both del(5q) and del(7q) and del(5q) alone, respectively, even though the analysis of bulk DNA had failed to detect the latter clone, indicating that a minor clone having a distinct genotype came under detection with scMutSeq. Moreover, the HSPCs with both del(5q) and del(7q) showed aberrant expression of erythroid and megakaryocytic genes, increased expression of inflammatory signals and decreased expression of cell cycle-related genes, exhibiting a clear genotype phenotype correlation. Subsequent analysis of samples at later time points further disclosed evolution of clones having discrete del(5q) deletions and expression, revealing a complexity of clonal evolution in MDS. Next, to investigate the early process of MDS development, we analyzed clonal hematopoiesis found in a minor fraction (1.2-12%) of bone marrow samples from three elder individuals having hip replacement surgery, in which DNMT3A(n=1) (R882H) and TET2(n=2) (D905fs and Q1540fs) mutations had been detected by ddPCR or targeted deep sequencing, respectively. scMutSeq analysis of the HSPCs from these individuals revealed that mutant HSPCs showed distinct gene expression profiles, depending on the type of CHIP mutations. To summarize, our single-cell sequencing platform enables to detect both genetic and transcriptional heterogeneities, providing a powerful clue to understand clonal evolution and intratumor heterogeneity of MDS. Disclosures Nakagawa: Sumitomo Dainippon Pharma Co., Ltd.: Research Funding. Inagaki:Sumitomo Dainippon Pharma Co., Ltd.: Employment. Yoda:Chordia Therapeutics Inc.: Research Funding.


2020 ◽  
Author(s):  
Lirong Yang ◽  
Yang Yang ◽  
Mingyao Meng ◽  
Wenju Wang ◽  
Shan He ◽  
...  

Abstract Background Cervical cancer is the fourth most common cancer in women worldwide. The metastasis and invasion of this type of cancer are closely related to the tumor microenvironment. Immune cells and stromal cells dominate the tumor microenvironment in cervical cancer. Therefore, we should further understand the association between tumor progress and immune cells or stromal cells.Methods we downloaded the gene expression profiles and clinical data of 307 patients with cervical cancers based on the TCGA database. Subsequently the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm was used to calculate the scores of stromal cells and immune cells to find differential genes, and analyzed the correlation between their scores and patient survival. Moreover, we also used R language packs and network tools to analyze GO term, gene enrichment pathway, and protein-protein relationship to find genes related to inflammation and immune regulation.Results The gene expression profiles and corresponding clinical data of 307 patients were obtained from TCGA datasets. The results showed that there was a statistically significant difference between the high immunescore group and the low immunescore group. And the low immunescore group had shorter lifetimes than the high scores group (P = 0.035).Moreover, PPI network analysis CCR5 and CXCL9, -10, -11 / CXCR3 axis might be new target for cervical cancer treatment. Finally, Kaplan-Meier survival curves found out nine representative genes significantly related to survival including BTNL8 , CCR7 , CD1E , CD6 , CD27 , CD79A , GRAP2 , SP1B , LY9 .Conclusions These genes can be used as markers for the prognosis and diagnosis of cervical cancer and also might be used as treatment targets.


2020 ◽  
Author(s):  
Alexander Yermanos ◽  
Andreas Agrafiotis ◽  
Josephine Yates ◽  
Chrysa Papadopoulou ◽  
Damiano Robbiani ◽  
...  

AbstractHigh-throughput single-cell sequencing (scSeq) technologies are revolutionizing the ability to molecularly profile B and T lymphocytes by offering the opportunity to simultaneously obtain information on adaptive immune receptor repertoires (VDJ repertoires) and transcriptomes. An integrated quantification of immune repertoire parameters such as germline gene usage, clonal expansion, somatic hypermutation and transcriptional states opens up new possibilities for the high-resolution analysis of lymphocytes and the inference of antigen-specificity. While multiple tools now exist to investigate gene expression profiles from scSeq of transcriptomes, there is a lack of software dedicated to single-cell immune repertoires. Here, we present Platypus, an open-source software platform providing a user-friendly interface to investigate B cell receptor (BCR) and T cell receptor (TCR) repertoires from single-cell sequencing experiments. Platypus provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression, and gene ontology. To showcase the capabilities of Platypus, we use it to analyze and visualize single-cell immune repertoires and transcriptomes from B and T cells from convalescent COVID-19 patients, revealing unique insight into the repertoire features and transcriptional profiles of clonally expanded lymphocytes. Platypus will expedite progress by increasing accessibility to the broader immunology community by facilitating the analysis of single-cell immune repertoire and transcriptome sequencing.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1231-1231
Author(s):  
Chih Long Liu ◽  
Bo Dai ◽  
Aaron M. Newman ◽  
Ravi Majeti ◽  
Ash A Alizadeh

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.


2019 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  

AbstractSingle-cell RNA-sequencing (scRNA-seq) has become an essential tool for characterizing multi-celled eukaryotic systems but current methods are not compatible with bacteria. Here, we introduce microSPLiT, a low cost and high-throughput scRNA-seq method that works for gram-negative and gram-positive bacteria and can resolve transcriptional states that remain hidden at a population level. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled from different growth stages, creating a detailed atlas of changes in metabolism and lifestyle. We not only retrieve detailed gene expression profiles associated with known but rare states such as competence and PBSX prophage induction, but also identify novel and unexpected gene expression states including heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. microSPLiT empowers high-throughput analysis of gene expression in complex bacterial communities.


2019 ◽  
Vol 35 (22) ◽  
pp. 4688-4695 ◽  
Author(s):  
Rui Hou ◽  
Elena Denisenko ◽  
Alistair R R Forrest

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) measures gene expression at the resolution of individual cells. Massively multiplexed single-cell profiling has enabled large-scale transcriptional analyses of thousands of cells in complex tissues. In most cases, the true identity of individual cells is unknown and needs to be inferred from the transcriptomic data. Existing methods typically cluster (group) cells based on similarities of their gene expression profiles and assign the same identity to all cells within each cluster using the averaged expression levels. However, scRNA-seq experiments typically produce low-coverage sequencing data for each cell, which hinders the clustering process. Results We introduce scMatch, which directly annotates single cells by identifying their closest match in large reference datasets. We used this strategy to annotate various single-cell datasets and evaluated the impacts of sequencing depth, similarity metric and reference datasets. We found that scMatch can rapidly and robustly annotate single cells with comparable accuracy to another recent cell annotation tool (SingleR), but that it is quicker and can handle larger reference datasets. We demonstrate how scMatch can handle large customized reference gene expression profiles that combine data from multiple sources, thus empowering researchers to identify cell populations in any complex tissue with the desired precision. Availability and implementation scMatch (Python code) and the FANTOM5 reference dataset are freely available to the research community here https://github.com/forrest-lab/scMatch. Supplementary information Supplementary data are available at Bioinformatics online.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A4-A4
Author(s):  
Anushka Dikshit ◽  
Dan Zollinger ◽  
Karen Nguyen ◽  
Jill McKay-Fleisch ◽  
Kit Fuhrman ◽  
...  

BackgroundThe canonical WNT-β-catenin signaling pathway is vital for development and tissue homeostasis but becomes strongly tumorigenic when dysregulated. and alter the transcriptional signature of a cell to promote malignant transformation. However, thorough characterization of these transcriptomic signatures has been challenging because traditional methods lack either spatial information, multiplexing, or sensitivity/specificity. To overcome these challenges, we developed a novel workflow combining the single molecule and single cell visualization capabilities of the RNAscope in situ hybridization (ISH) assay with the highly multiplexed spatial profiling capabilities of the GeoMx™ Digital Spatial Profiler (DSP) RNA assays. Using these methods, we sought to spatially profile and compare gene expression signatures of tumor niches with high and low CTNNB1 expression.MethodsAfter screening 120 tumor cores from multiple tumors for CTNNB1 expression by the RNAscope assay, we identified melanoma as the tumor type with the highest CTNNB1 expression while prostate tumors had the lowest expression. Using the RNAscope Multiplex Fluorescence assay we selected regions of high CTNNB1 expression within 3 melanoma tumors as well as regions with low CTNNB1 expression within 3 prostate tumors. These selected regions of interest (ROIs) were then transcriptionally profiled using the GeoMx DSP RNA assay for a set of 78 genes relevant in immuno-oncology. Target genes that were differentially expressed were further visualized and spatially assessed using the RNAscope Multiplex Fluorescence assay to confirm GeoMx DSP data with single cell resolution.ResultsThe GeoMx DSP analysis comparing the melanoma and prostate tumors revealed that they had significantly different gene expression profiles and many of these genes showed concordance with CTNNB1 expression. Furthermore, immunoregulatory targets such as ICOSLG, CTLA4, PDCD1 and ARG1, also demonstrated significant correlation with CTNNB1 expression. On validating selected targets using the RNAscope assay, we could distinctly visualize that they were not only highly expressed in melanoma compared to the prostate tumor, but their expression levels changed proportionally to that of CTNNB1 within the same tumors suggesting that these differentially expressed genes may be regulated by the WNT-β-catenin pathway.ConclusionsIn summary, by combining the RNAscope ISH assay and the GeoMx DSP RNA assay into one joint workflow we transcriptionally profiled regions of high and low CTNNB1 expression within melanoma and prostate tumors and identified genes potentially regulated by the WNT- β-catenin pathway. This novel workflow can be fully automated and is well suited for interrogating the tumor and stroma and their interactions.GeoMx Assays are for RESEARCH ONLY, not for diagnostics.


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