scholarly journals Cite-Seq Reveals Distinct Patterns and Potential Mechanisms of Relapse in Pediatric AML

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3458-3458
Author(s):  
Tsz-Kwong Man ◽  
Mohammad Javad Najaf Panah ◽  
Jessica L. Elswood ◽  
Pavel Sumazin ◽  
Michele S. Redell

Abstract Introduction - Acute myeloid leukemia (AML) is an aggressive disease with a relapse rate of approximately 40% in children. Progress in improving cure rates has been slow, in part because AML is very heterogeneous. Molecular studies consistently show that most cases are comprised of distinct subclones that diminish or expand over the course of therapy. Single-cell profiling methods now allow parsing of the leukemic population into subsets based on gene and/or protein expression patterns. We hypothesized that comparing the features of the subsets that are dominant at relapse with those that are dominant at diagnosis would reveal mechanisms of treatment failure. Methods - We profiled diagnosis-relapse pairs from 6 pediatric AML patients by Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq). All patients were treated at Texas Children's Cancer Center and consented to banking of tissue for research. CITE-Seq was performed by Immunai (New York, NY) using a customized panel of 65 oligonucleotide-tagged antibodies, the 10x Genomics Chromium system for single-cell RNA library generation, and the Novaseq 6000 for sequencing. After data cleanup and normalization, clustering by scRNA-seq was done using the Seurat package. Cell-type identification of clusters was facilitated by published healthy bone marrow scRNA-seq datasets (van Galen et al, Cell 2019). Differentially expressed genes (DEGs) and proteins (DEPs) between diagnosis and relapse were determined using Wilcoxin ranked sum tests. Results - We generated single-cell transcriptomes for a total of 28,486 cells from 12 samples, with a mean of 2373 cells and 1416 genes per sample. Samples were integrated with batch effect correction, producing 30 distinct clusters (cell types) in total (Figure 1A). Cell types with expression profiles consistent with lymphocytes and erythroid precursors were identified in multiple patients, whereas AML cell types tended to be specific to individual patients (Figure 1B). For patients TCH1, TCH2 and TCH3, the most abundant cell types at diagnosis were rare at relapse, and cell types that were rare at diagnosis became dominant at relapse. For these 3 cases, we identified DEGs between the dominant diagnosis cell types and dominant relapse cell types. We found 18 genes that were upregulated at relapse in at least 2 of the cases. Several genes related to actin polymerization were enriched (ARPC1B, ACTB, PFN1), possibly reflecting an enhanced capacity for adhesion and migration. Also of note, macrophage migration inhibitory factor (MIF) and its receptor CD74 were upregulated at relapse, suggesting a role in chemoresistance. For patients TCH4, TCH5 and TCH6, the same cell types that were abundant at diagnosis were also abundant at relapse, and few genes were significantly altered between diagnosis and relapse in multiple cases. Only SRGN, which encodes the proteoglycan serglycin, and GAPDH were altered in 2 of these 3 cases, and both were downregulated at relapse. We performed similar comparisons to identify proteins that were differentially expressed between diagnosis and relapse pairs. The number of DEPs between the dominant diagnosis and relapse cell types ranged from 0 (TCH1 and TCH6) to 5 (TCH2). The only protein altered in more than one case was CD7, which was enriched at relapse in TCH2, TCH3 and TCH4. Conclusions - From CITE-Seq profiling of 6 pediatric AML cases we identified two distinct patterns of relapse. For 3 cases, relapse occurred by expansion of a subset that was small but present at diagnosis. Enrichment of genes associated with adhesion and survival signaling suggests that these cells survived because they were well-equipped to take advantage of interactions with the microenvironment. For 3 other cases, the population that was dominant at diagnosis persisted and expanded at relapse with few substantial changes in gene or protein expression profiles. This pattern suggests that these AML cells were a priori equipped to survive chemotherapy, even though bulk disease levels were transiently reduced below the limit of detection. Most profiled proteins did not change substantially between diagnosis and relapse. An exception is CD7, which was enriched at relapse in 50% of our cases and represents a potential therapeutic target. Analysis of more cases will refine these relapse patterns, reveal potential mechanisms of chemoresistance and inform the development of novel therapies. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1729-1729
Author(s):  
Sabrina Bossio ◽  
Laura De Stefano ◽  
Mariavaleria Pellicanò ◽  
Angela Palummo ◽  
Francesca Storino ◽  
...  

Abstract Proteomic approaches are commonly secondary to genetic studies but are essential in the multi-disciplinary field of hematological research. As opposed to mRNA microarray data, proteomics provides a better understanding of which proteins are actually expressed, although the identification of specific proteins remains challenging (Unwin et al Blood Rev 2014; Boyd J Proteomics 2010). In neoplastic hematology such as CLL, protein studies have contributed to the elucidation of disease mechanisms, defined prognostic or therapeutic biomarkers (Boyd J Proteomics 2010). In this study we used proteomics and 2DE analysis to evaluate differential protein expression patterns after treatment with Len. Len can improve immune dysfunction in CLL by repairing F-actin polymerization and signaling at the immunological synapse (Ramsay et al 2008 J Clin Invest). Our previous data obtained from MALDI-TOF analysis identified Tβ4, a G-actin sequestering protein involved in the regeneration of injured tissues and cell migration, as a downregulated protein in CLL patients, also confirmed by an independent GEP analysis comparing B-cell from CLL cases (n=80) and normal controls (n=6), supported by Tβ4 mRNA down-regulation in CLL (3604±1244 vs 5715±1004, respectively; mean±SD; p=0.001). Here, we investigated whether purified B-CLL cells respond differently to the chemoattractant SDF1a and whether different protein expression patterns can be identified after exogenous Tβ4 and Len treatment using 2DE analysis. Highly purified B-CLL lymphocytes were isolated from untreated Binet stage A CLL patients prospectively enrolled from diagnosis (O-CLL1 protocol, clinicaltrial.gov identifier NCT00917540) and healthy controls. Tb4 was identified by MALDI TOF using 100 patient samples. Next, cells were pre-treated with Len (5uM) and then treated with Tb4 (100nM) for 30min. Cells were plated in transwells using 5.0 um pores with SDF1a as chemoattractant for migration assays. Protein was extracted from CLL cell pellets by RIPA buffer and quantified. Sample preparation and 2DE was performed as described by Scielzo et al (J Clin Inves, 2005). Protein samples (100 ug) were applied to 7-cm IPG strips, pH 3-11NL (Amersham Biosciences), respectively, by in-gel rehydration. Isoelectric focusing was performed with a Protean i12 IEF system (Biorad). Strips were equilibrated and loaded onto 9-16% gradient acrylamide SDS-PAGE gels for the second dimension separation. Silver nitrate staining (Sinha P et al Proteomic, 2001) was used to visualize proteins and images were digitally acquired (ChemiDoc MP, Biorad) and spots were analyzed using PDQuest basic 2D Gel Analysis Software (Biorad). CLL samples with the lowest Tβ4 expression (n=12) also had higher F-actin levels as evaluated by FC analysis than normal controls. B-CLL strongly responded to the migratory stimulus SDF-1a, which was further increased (by 20%) in presence of Len treatment, likely due to an alteration in actin remodeling and changes in the expression of unknown proteins. Purified CD19+CD5+ leukemic cells were lysed and proteins resolved on 2DE and visualized by silver nitrate staining. The protein profile analysis on silver-stained gels showed a number of protein spots ranging from 18 to 60kDa that were differentially expressed with respect to untreated cells. Our preliminary qualitative analysis suggests that there are groups of proteins with a lower expression in the presence of Tβ4 or Len, which are more strongly inhibited following exposure to their combination. Conversely, an opposite pattern of protein expression was observed whereby an additive effect on protein expression was observed by combined exposure to Tβ4 and Len. This approach allowed us to identify an altered protein expression pattern after treatment with Len and Tβ4, and may be useful to identify changes in expression profiles of CLL proteins, which may translate into functional differences in the malignant clone. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3020-3020
Author(s):  
Alicia Báez ◽  
Beatriz Martin-Antonio ◽  
Concepción Prats-Martín ◽  
Isabel Álvarez-Laderas ◽  
María Victoria Barbado ◽  
...  

Abstract Abstract 3020 Introduction: Hematopoietic progenitors cells (HPCs) used in allogenic transplantation (allo-HSCT) may have different biological properties depending on their source of origin: mobilized peripheral blood (PB), bone marrow (BM) or umbilical cord (UC), which may be reflected in miRNAs or gene expression. The identification of different patterns of expression could have clinical implications. The aim of this study was to determine differences in miRNAs and gene expression patterns in the different sources of HPCs used in allo-HSCT. Materials and Method: CD34 + cells were isolated by immunomagnetic separation and sorting from 5 healthy donors per type of source: UC, BM and PB mobilized with G-CSF. A pool of samples from PB not mobilized was used as reference group. We analyzed the expression of 375 miRNAs using TaqMan MicroRNA Arrays Human v2.0 (Applied Biosystems), and gene expression using Whole Human Genome Oligo microarray kit 4×44K (Agilent). The expression levels of genes and miRNAs were obtained by the 2-ΔΔCTmethod. From expression data hierarchical clustering was performed using the Euclidean distance. To identify genes and miRNAs differentially expressed between the different sources of HPCs statistical Kruskal Wallis test was applied. All analysis were performed using the Multiexperiment Viewer 4.7.1. The function of the miRNAs and genes of interest was determined from the various databases available online (TAM database, Gene Ontology and TargetScan Human). Results: Forty-two miRNAs differentially expressed between the different sources were identified. As compared to BM or UC, in mobilized PB most miRNAs were overexpressed, including the miRNA family of miR515, which is characteristic of embryonic stem cells. On the other hand, 47 genes differentially expressed between the different sources were identified. Interestingly, a similar pattern of expression was observed between movilized PB and UC as compared to BM. Interestingly, 13 of these genes are targets of the miRNAs also identified in this study, which suggests that their expression might be regulated by these miRNAs. Conclusion: There are significant differences in miRNAs and gene expression levels between the different sources of HPCs Disclosures: No relevant conflicts of interest to declare.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 35-36
Author(s):  
Fieke W Hoff ◽  
Anneke D. van Dijk ◽  
Yihua Qiu ◽  
Eveline S. de Bont ◽  
Steven M. Kornblau ◽  
...  

INTRODUCTION: Pediatric acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) are heterogeneous diseases mediated by changes in protein expression. As most chemotherapeutic agents target proteins, and because overall survival of pediatric AML is far inferior to both pre-B and T-ALL, we aimed to compare the proteomic landscape of pediatric T-ALL and AML, with the goal of determining common AML-T-ALL pathways that are potentially targetable with novel agents. METHODS: Reverse phase protein arrays (RPPA) analysis was used to measure protein expression in 858 acute leukemia samples (358 T-ALL and 500 AML, 723 pediatric (< 18 yrs.), 135 adults (≥18 yrs.)) and 61 normal CD34+ samples using 270 validated antibodies. Expression levels were normalized against CD34+ cells. Proteins were allocated into 30 functionally related subgroups (Protein Functional Group (PFG)). A progeny clustering algorithm was applied to each PFG to search for strong correlations between proteins and to identify an optimal number of Protein Clusters (PC). Block clustering identified PC that recurrently co-occurred together (Protein Constellation (CON)) and patients that expressed similar combination of CON were defined as Protein Signature (SIG). Proteins that were differentially expressed were identified using the Student's t-test or ANOVA, with a Bonferroni adjusted p-value (0.05/ 270 = 0.00019)). RESULTS: Of the 270 analyzed proteins, 131 proteins (49%) were differentially expressed between T-ALL and AML; 60 were higher in T-ALL, 71 in AML. Similar to our previous analysis in adult AML and ALL, cell cycle regulators (CDKN1A, CDKN1B) and 2 of the 5 histone marks (H3K36Me3 & H3K4Me3) were higher expressed in T-ALL compared to AML. Heat shock proteins (HSP90AA1_B1, HSPA1A_L, HSPB1 and HSPB1-pSer82) were higher in AML as well as translation proteins EIF2S1, EIF4E and EIF4EBP1 and ribosomal proteins RPS6-pSer235_236 and RPS6KB1, while expression of the translation inhibitory proteins EIF2S1-pSer51 and EIF2AK2-pThr451 was lower in AML compared to T-ALL. Next, cluster analysis in the context of 30 PFG resulted in 133 PC. The majority (n=102) of PC were expressed in both diseases, 30 PC (22.6%) were AML-specific, and only one PC was specific to T-ALL (characterized by high CDKN1A, CDKN1B and CCND1, but low WEE1, CCNB1 and RB1-pSer). Co-clustering of the 133 PC identified 14 CON that formed 17 SIG. Three CON (5, 9, 10) were associated with AML, 2 with T-ALL (2, 13) and 8 CON were observed in both diseases. In contrast, 15 of SIG were associated with either T-ALL or AML, and two SIG (9, 10) included a mixture of both diseases (P < 0.001, annotation bar Figure 1 "Disease")). SIG were associated with gender (P < 0.001), but not with CNS-status and ethnicity (Hispanic vs. non-Hispanic). No age-specific (kids vs. adults) signatures were observed. For each SIG and CON, proteins significantly higher or lower expressed compared to the normal CD34+ cells were identified. CONCLUSIONS: This study provides support for our previous hypothesis that pediatric T-ALL and AML can be characterized by recurrent protein expression patterns. While most PC and CON were found in both diseases, SIG (i.e. combinations of protein expression patterns) were specific to either T-ALL or AML. We found similar results when comparing B-ALL to AML in adults. Shared CON indicate that there are common protein expression patterns between pediatric T-ALL and AML. Proteins or pathways with similar utilization (e.g. CON3, 5) in both diseases may allow for information on clinical utility from one disease to be applicable to the other. Those with differential utilization are likely to be uninformative with respect to clinical utility in the other disease. Figure. "MetaGalaxy" analysis for pediatric AML and T-ALL. Each row represents one protein clusters (n = 133), each column represents one patient (n = 858). Blue indicates membership for that particular protein cluster. Annotation bar shows strong correlation with disease (yellow = T-ALL, blue = AML). No associations were seen for age (blue = adult, pink = pediatric) or Ethnicity (blue = Hispanic, yellow = non-Hispanic). Figure 1 Disclosures No relevant conflicts of interest to declare.


2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoteng Cui ◽  
Qixue Wang ◽  
Junhu Zhou ◽  
Yunfei Wang ◽  
Can Xu ◽  
...  

BackgroundThe main immune cells in GBM are tumor-associated macrophages (TAMs). Thus far, the studies investigating the activation status of TAM in GBM are mainly limited to bulk RNA analyses of individual tumor biopsies. The activation states and transcriptional signatures of TAMs in GBM remain poorly characterized.MethodsWe comprehensively analyzed single-cell RNA-sequencing data, covering a total of 16,201 cells, to clarify the relative proportions of the immune cells infiltrating GBMs. The origin and TAM states in GBM were characterized using the expression profiles of differential marker genes. The vital transcription factors were examined by SCENIC analysis. By comparing the variable gene expression patterns in different clusters and cell types, we identified components and characteristics of TAMs unique to each GBM subtype. Meanwhile, we interrogated the correlation between SPI1 expression and macrophage infiltration in the TCGA-GBM dataset.ResultsThe expression patterns of TMEM119 and MHC-II can be utilized to distinguish the origin and activation states of TAMs. In TCGA-Mixed tumors, almost all TAMs were bone marrow-derived macrophages. The TAMs in TCGA-proneural tumors were characterized by primed microglia. A different composition was observed in TCGA-classical tumors, which were infiltrated by repressed microglia. Our results further identified SPI1 as a crucial regulon and potential immunotherapeutic target important for TAM maturation and polarization in GBM.ConclusionsWe describe the immune landscape of human GBM at a single-cell level and define a novel categorization scheme for TAMs in GBM. The immunotherapy against SPI1 would reprogram the immune environment of GBM and enhance the treatment effect of conventional chemotherapy drugs.


Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2503-2503 ◽  
Author(s):  
Alfonso Quintás-Cardama ◽  
Yi Hua Qiu ◽  
Sean Post ◽  
Yiqun Zhang ◽  
Chad Creighton ◽  
...  

Abstract Abstract 2503 Background Having previously shown that protein expression signatures, based on the activation state of cell cycle, apoptosis and signal transduction regulating proteins, existed and were prognostic in acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), we extended this to evaluate protein expression patterns in CML. Methodology We have generated RPPA using protein derived from the leukemia-enriched fraction of 40 primary CML samples with the goal of defining comprehensive proteomic expression patterns in CML. Of the 40 patient samples included in this analysis, 25 were in chronic (CP), 5 in accelerated (AP), and 10 in blast phase (BP). Of the latter, 6 were lymphoid BP and 4 myeloid BP. All protein preps were made from fresh cells on the day of collection. Present as controls were 16 CD34+ BM and 9 normal PB lymphocyte samples. Samples are printed as 5 serial 1:2 dilutions in duplicate using an Aushon 2470 Arrayer. Each array has a total of 6912 dots printed. Slides were probed with 112 antibodies (ABs) against apoptosis, cell cycle, signaling (STP), regulating proteins, integrins, and phosphatases among other functional protein groups, including 85 vs. total protein, 22 vs. phospho-specific sites and 5 vs. caspase or PARP cleavage sites. Spot intensities were quantified using MicroVigene software. Data was analysed using R, with loading control and topographical background normalization being utilized. Results We first tested the differences of the protein expression between patients with CP, AP, and BP. To that end, we centered proteins on the median across all samples. An ANOVA analysis revealed 20 proteins (from a total of 112 proteins probed) were differentially expressed across the different phases of CML by using a minimum statistical significance cutoff of p<0.01. The expression of proteins such as HSP90, RB, AIF, PP2A, BCL2, XIAP, SMAD1, SSBP2α, PARP, GAB2, and TRIM24 was low in patients with CML-CP but progressively increased as patients progressed to BP, with samples obtained from patients with CML-AP exhibiting intermediate levels between CP and BP. Conversely, the expression of PKCΔ.p664, AKTpT308, actin, p70S6Kp, Rac1.2.3, PDK1p, MEK, and CDK4 decreased gradually as patients progressed from CP to BP, with samples obtained from patients with CML-AP exhibiting intermediate levels. Notably, downregulation of genes involved in the Ras-MEK-MAPK pathway and upregulation of those encoding cytoskeletal and adhesion proteins (actin, Rac) has been previously reported in gene expression profiles (Radich et al, PNAS 2006). A similar analysis was conducted to investigate differences in protein expression between the CD34+ (23 samples) and CD34- (37 samples) compartments, the former of them putatively containing the CML stem cell population. A t test was used for each protein and those whose expression were significantly (p<0.01) differentially expressed were selected. Forty-two proteins were identified as differentially expressed in CD34+ CML cells, including upregulation in the CD34+ compartment of those involved in the WNT/β-catenin (TCF4, survivin), adhesion proteins (integrin-β3, FAK, SRC), STAT pathway (STAT3, STAT3p705, BclXL), PARP, pPTEN, MYC, pPKCα, mTOR, and PP2A. Conversely, several proteins were downregulated in CD34+ cells: Mdm2, p38, MEK, AKTpT308, NFκB pathway (PKCΔ, NFκB.p65, SHIP1), and proapoptotic proteins (BID, BIM). When segregated according to BCR-ABL1 mutational status, no differences in protein expression were observed between samples carrying the F317L or the T315I mutations versus all other mutations. Similarly, no differences in protein expression were observed between patients carrying unmutated BCR-ABL1 and those carrying a mutant BCR-ABL1 enzyme. Conclusions We have identified by RPPA technology specific subsets of proteins whose expression is associated with CML progression. Likewise, specific proteins appear to be differentially expressed in the CML stem cell compartment. These proteins might represent therapeutic targets. Disclosures: No relevant conflicts of interest to declare.


2020 ◽  
Vol 117 (23) ◽  
pp. 12856-12867 ◽  
Author(s):  
Gaurang Patel ◽  
Wen Fury ◽  
Hua Yang ◽  
Maria Gomez-Caraballo ◽  
Yu Bai ◽  
...  

The conventional outflow pathway is a complex tissue responsible for maintaining intraocular pressure (IOP) homeostasis. The coordinated effort of multiple cells with differing responsibilities ensures healthy outflow function and IOP maintenance. Dysfunction of one or more resident cell types results in ocular hypertension and risk for glaucoma, a leading cause of blindness. In this study, single-cell RNA sequencing was performed to generate a comprehensive cell atlas of human conventional outflow tissues. We obtained expression profiles of 17,757 genes from 8,758 cells from eight eyes of human donors representing the outflow cell transcriptome. Upon clustering analysis, 12 distinct cell types were identified, and region-specific expression of candidate genes was mapped in human tissues. Significantly, we identified two distinct expression patterns (myofibroblast- and fibroblast-like) from cells located in the trabecular meshwork (TM), the primary structural component of the conventional outflow pathway. We also located Schwann cell and macrophage signatures in the TM. The second primary component structure, Schlemm’s canal, displayed a unique combination of lymphatic/blood vascular gene expression. Other expression clusters corresponded to cells from neighboring tissues, predominantly in the ciliary muscle/scleral spur, which together correspond to the uveoscleral outflow pathway. Importantly, the utility of our atlas was demonstrated by mapping glaucoma-relevant genes to outflow cell clusters. Our study provides a comprehensive molecular and cellular classification of conventional and unconventional outflow pathway structures responsible for IOP homeostasis.


2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.


2021 ◽  
Vol 22 (4) ◽  
pp. 1901
Author(s):  
Brielle Jones ◽  
Chaoyang Li ◽  
Min Sung Park ◽  
Anne Lerch ◽  
Vimal Jacob ◽  
...  

Mesenchymal stromal cells derived from the fetal placenta, composed of an amnion membrane, chorion membrane, and umbilical cord, have emerged as promising sources for regenerative medicine. Here, we used next-generation sequencing technology to comprehensively compare amniotic stromal cells (ASCs) with chorionic stromal cells (CSCs) at the molecular and signaling levels. Principal component analysis showed a clear dichotomy of gene expression profiles between ASCs and CSCs. Unsupervised hierarchical clustering confirmed that the biological repeats of ASCs and CSCs were able to respectively group together. Supervised analysis identified differentially expressed genes, such as LMO3, HOXA11, and HOXA13, and differentially expressed isoforms, such as CXCL6 and HGF. Gene Ontology (GO) analysis showed that the GO terms of the extracellular matrix, angiogenesis, and cell adhesion were significantly enriched in CSCs. We further explored the factors associated with inflammation and angiogenesis using a multiplex assay. In comparison with ASCs, CSCs secreted higher levels of angiogenic factors, including angiogenin, VEGFA, HGF, and bFGF. The results of a tube formation assay proved that CSCs exhibited a strong angiogenic function. However, ASCs secreted two-fold more of an anti-inflammatory factor, TSG-6, than CSCs. In conclusion, our study demonstrated the differential gene expression patterns between ASCs and CSCs. CSCs have superior angiogenic potential, whereas ASCs exhibit increased anti-inflammatory properties.


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.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A


Sign in / Sign up

Export Citation Format

Share Document