scholarly journals MODL-09. FEASIBILITY OF ACUTE SLICE CULTURE-SINGLE CELL SEQUENCING DRUG SCREENING AS A TOOL TO SELECT THERAPY FOR CHILDREN WITH RELAPSED BRAIN TUMORS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii412-iii413
Author(s):  
Bradley Gampel ◽  
Luca Szalontay ◽  
Wenting Zhao ◽  
James Garvin ◽  
Chankrit Sethi ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Single Cell ◽  
Drug Screening ◽  
Patient Specific ◽  
Small Scale ◽  
Specific Drug ◽  
Rna Seq ◽  
Drug Responses ◽  
Single Cell Sequencing ◽  
Multiple Drugs

Abstract Children with relapsed brain tumors are less responsive to treatment. These children often receive therapies without having any robust predictive method of potential benefit. Acute slice culturing(ASC) is a methodology permitting freshly operated tumor to undergo a culturing process preserving the tumor’s micro-environment. With the current study, we investigated the feasibility of obtaining therapeutically meaningful data in a timely manner (3–5 days), performing direct drug testing and single cell sequencing using ASC. Previously, we have combined ex vivo slices of intact, patient-derived Glioblastoma tissue with single-cell RNA-seq for small-scale drug screening and assessment of patient and cell type-specific drug responses. We generated slices from preclinical mouse glioma models and surgical specimens from adult Glioblastoma patients, as well as from children with relapsed Ependymomas, Medulloblastomas, and Gliomas. We demonstrated that these acute slices preserved both the tumor heterogeneity and tumor microenvironment observed in single-cell RNA-seq of cells directly isolated from tumor tissue. Testing drug responses, we then treated tissue slices from the Glioblastoma mouse models and different patients with multiple drugs and combinations. This technique allowed us to identify drug-induced transcriptional responses in specific subpopulations of tumor cells, patient-specific drug sensitivities, and drug effects conserved in both mouse and human tumors. Preliminary data suggests that we can apply this procedure within 5–7 days and provide real-time drug screening/single cell sequencing ASC results to Recurrent/ Progressive pediatric Low-Grade Gliomas, High Grade Gliomas, Ependymomas and Medulloblastomas.

scholarly journals COMP-12. SINGLE-CELL TRANSCRIPTOME PROFILING OF GBM TISSUE ACUTE SLICE CULTURES FOR PERSONALIZED DRUG SCREENING

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi63-vi63
Author(s):  
Wenting Zhao ◽  
Eleonora Spinazzi ◽  
Athanassios Dovas ◽  
Pavan Upadhyayula ◽  
Tamara Marie ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Tumor Cells ◽  
Drug Screening ◽  
Model Systems ◽  
Patient Specific ◽  
Specific Drug ◽  
Cell Type ◽  
Tissue Slices ◽  
Drug Responses

Abstract Glioblastoma (GBM) is the most common and malignant type of primary brain tumor, and more effective treatment options are needed. Both inter- and intratumoral heterogeneity present major challenges to the application of targeted therapies in GBM. Therefore, precision medicine approaches to GBM would benefit significantly from the ability to predict drugs or drug combinations that target specific subpopulations of tumor cells. Model systems, such as adherent cell lines, neurospheres, patient-derived xenografts (PDXs), and patient-derived organoids, have been reported as platforms for drug screening and accessing drug responses. However, these models do not recapitulate the full heterogeneity of GBM tissue, lack key components of the tumor microenvironment or take weeks to months to establish, which limits the predictive power of drug response assays or delays clinical decision-making. To address these limitations, we are combining ex vivo slices of intact, patient-derived GBM tissue with single-cell RNA-seq (scRNA-seq) for small-scale drug screening and assessment of patient- and cell type-specific drug responses. We generated slices from both preclinical mouse glioma models and surgical specimens from GBM patients and showed that acute slices preserved both the tumor heterogeneity and tumor microenvironment observed in scRNA-seq of cells directly isolated from tumor tissue. To test drug responses, we treated tissue slices from GBM mouse models and five different patients with six drugs for 18hr. By performing scRNA-Seq and analyzing transcriptional profiles of treated and untreated control slices, we identified drug-induced transcriptional responses in specific subpopulations of tumor cells, patient-specific drug sensitivities, and drug effects conserved in both mouse and human tumors. The GBM tissue slices were generated immediately following surgical resection, and experiments were completed within 24 hours. With these features, our method is attractive for rapidly accessing cell type- and patient-specific drug responses and has potential for preclinical drug screening and guiding personalized treatment for GBM.

scholarly journals Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Wenting Zhao ◽  
Athanassios Dovas ◽  
Eleonora Francesca Spinazzi ◽  
Hanna Mendes Levitin ◽  
Matei Alexandru Banu ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Tissue ◽  
Patient Specific ◽  
Slice Culture ◽  
Specific Drug ◽  
Cell Type ◽  
Drug Responses ◽  
Molecular Features ◽  
Cell Type Specific ◽  
Multiple Drugs

Abstract Background Preclinical studies require models that recapitulate the cellular diversity of human tumors and provide insight into the drug sensitivities of specific cellular populations. The ideal platform would enable rapid screening of cell type-specific drug sensitivities directly in patient tumor tissue and reveal strategies to overcome intratumoral heterogeneity. Methods We combine multiplexed drug perturbation in acute slice culture from freshly resected tumors with single-cell RNA sequencing (scRNA-seq) to profile transcriptome-wide drug responses in individual patients. We applied this approach to drug perturbations on slices derived from six glioblastoma (GBM) resections to identify conserved drug responses and to one additional GBM resection to identify patient-specific responses. Results We used scRNA-seq to demonstrate that acute slice cultures recapitulate the cellular and molecular features of the originating tumor tissue and the feasibility of drug screening from an individual tumor. Detailed investigation of etoposide, a topoisomerase poison, and the histone deacetylase (HDAC) inhibitor panobinostat in acute slice cultures revealed cell type-specific responses across multiple patients. Etoposide has a conserved impact on proliferating tumor cells, while panobinostat treatment affects both tumor and non-tumor populations, including unexpected effects on the immune microenvironment. Conclusions Acute slice cultures recapitulate the major cellular and molecular features of GBM at the single-cell level. In combination with scRNA-seq, this approach enables cell type-specific analysis of sensitivity to multiple drugs in individual tumors. We anticipate that this approach will facilitate pre-clinical studies that identify effective therapies for solid tumors.

DDRE-42. TOWARDS PRECISION MEDICINE: AUTOMATED DRUG SCREENING PLATFORM UTILIZING TUMOR-ORGANOIDS TO IDENTIFY PATIENT-SPECIFIC DRUG-RESPONSES IN MENINGIOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi83-vi83
Author(s):  
Gerhard Jungwirth ◽  
Adrian Paul ◽  
Cao Junguo ◽  
Andreas Unterberg ◽  
Amir Abdollahi ◽  
...  
Keyword(s):  
Drug Screening ◽  
Cellular Heterogeneity ◽  
Patient Specific ◽  
Great Promise ◽  
Specific Drug ◽  
Tissue Samples ◽  
Drug Responses ◽  
Screening Platform ◽  
Effective Drugs ◽  
Tumor Organoids

Abstract Tumor-organoids (TO) are mini-tumors generated from tumor tissue preserving its genotype and phenotype by maintaining the cellular heterogeneity and important components of the tumor microenvironment. We recently developed a protocol to reliably establish TOs from meningioma (MGM) in large quantities. The use of TOs in combination with lab automation holds great promise for drug discovery and screening of comprehensive drug libraries. This might help to tailor patient-specific therapy in the future. The aim of our study was to establish an automated drug screening platform utilizing TOs. For this purpose, we established TOs by controlled reaggregation of freshly prepared single cell suspension of MGM tissue samples in the high-throughput format of 384-well plates. The drug screening was performed fully automated by utilizing the robotic liquid handler Hamilton Microlab STAR and a drug library containing 166 FDA-approved oncology agents. Viability was assessed with CellTiterGlo3D. In total, we performed the drug screening with 166 drugs on TOs from 11 patients suffering from MGM (n=8 WHO°I, n=2 WHO°II, n=1 WHO°III). The top five most effective drugs resulted in a decrease of TO viability ranging from 84.6–63.3%. K-means clustering analysis resulted in groupings of drugs with similar modes of action. One cluster consisted of epigenetic drugs while another cluster consisted of several proteasome inhibitors. However, when looking at a patient-individual level, in 11 patients 44 of 166 drugs, were among the top 10 most effective drugs, providing strong evidence for heterogeneous drug-responses in MGM patients. Taken together, we successfully developed an automated drug screening platform pipeline utilizing TOs from MGM to identify patient-specific drug-responses. The observed intra-individual differences of drug responses mandate for a personalized testing of comprehensive drug libraries in TOs to tailor more effective therapies in MGM patients.

scholarly journals Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

2020 ◽  
Author(s):  
Viacheslav Mylka ◽  
Jeroen Aerts ◽  
Irina Matetovici ◽  
Suresh Poovathingal ◽  
Niels Vandamme ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Comparative Analysis ◽  
Single Cell ◽  
Cell Lines ◽  
Clinical Studies ◽  
Clinical Samples ◽  
Rna Seq ◽  
Batch Effects ◽  
Single Cell Sequencing ◽  
Single Nucleus

ABSTRACTMultiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called ‘hashing’. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

scholarly journals K-mer counting with low memory consumption enables fast clustering of single-cell sequencing data without read alignment

2019 ◽  
Author(s):  
Christina Huan Shi ◽  
Kevin Y. Yip
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Rna Seq ◽  
Sequencing Data ◽  
Memory Consumption ◽  
Analysis Pipeline ◽  
Cell Clusters ◽  
Single Cell Sequencing ◽  
Sequencing Errors ◽  
Full Analysis

AbstractK-mer counting has many applications in sequencing data processing and analysis. However, sequencing errors can produce many false k-mers that substantially increase the memory requirement during counting. We propose a fast k-mer counting method, CQF-deNoise, which has a novel component for dynamically identifying and removing false k-mers while preserving counting accuracy. Compared with four state-of-the-art k-mer counting methods, CQF-deNoise consumed 49-76% less memory than the second best method, but still ran competitively fast. The k-mer counts from CQF-deNoise produced cell clusters from single-cell RNA-seq data highly consistent with CellRanger but required only 5% of the running time at the same memory consumption, suggesting that CQF-deNoise can be used for a preview of cell clusters for an early detection of potential data problems, before running a much more time-consuming full analysis pipeline.

scholarly journals CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing

2020 ◽  
Author(s):  
Liang Fang ◽  
Guipeng Li ◽  
Qionghua Zhu ◽  
Huanhuan Cui ◽  
Yunfei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Concanavalin A ◽  
Cancer Cell Line ◽  
High Sensitivity ◽  
Cost Effective ◽  
Subtle Difference ◽  
Single Cell Sequencing ◽  
Minimal Sample ◽  
Ifn Γ ◽  
Multiple Drugs

AbstractSample multiplexing facilitates single cell sequencing by reducing costs, revealing subtle difference between similar samples, and identifying artifacts such as cell doublets. However, universal and cost-effective strategies are rather limited. Here, we reported a Concanavalin A-based Sample Barcoding strategy (CASB), which could be followed by both single-cell mRNA and ATAC (assay for transposase accessible chromatin) sequencing techniques. The method involves minimal sample processing, thereby preserving intact transcriptomic or epigenomic patterns. We demonstrated its high labeling efficiency, high accuracy in assigning cells/nuclei to samples regardless of cell type and genetic background, as well as high sensitivity in detecting doublets by two applications: 1) CASB followed by scRNA-seq to track the transcriptomic dynamics of a cancer cell line perturbed by multiple drugs, which revealed compound-specific heterogeneous response; 2) CASB together with both snATAC-seq and scRNA-seq to illustrate the IFN-γ-mediated dynamic changes on epigenome and transcriptome profile, which identified the transcription factor underlying heterogeneous IFN-γ response.

scholarly journals Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Brenda Marquina-Sanchez ◽  
Nikolaus Fortelny ◽  
Matthias Farlik ◽  
Andhira Vieira ◽  
Patrick Collombat ◽  
...  
Keyword(s):  
Single Cell ◽  
Pancreatic Islets ◽  
Drug Effects ◽  
Specific Drug ◽  
Rna Seq ◽  
Accurate Quantification ◽  
In Cells

scholarly journals Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient

2020 ◽  
Author(s):  
HARIPRIYA HARIKUMAR ◽  
Thomas P Quinn ◽  
Santu Rana ◽  
Sunil Gupta ◽  
Svetha Venkatesh
Keyword(s):  
Single Cell ◽  
Drug Response ◽  
Single Cells ◽  
Biological Data ◽  
Patient Specific ◽  
Expression Data ◽  
Drug Responses ◽  
Dual Channel ◽  
Cell Gene ◽  
Cell Networks

Abstract Background: The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient.Methods: We take 2 approaches to benchmarking a “dual-channel” random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses.Conclusions: Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.

scholarly journals HGG-26. SINGLE-CELL RNA-SEQ OF PEDIATRIC HIGH-GRADE GLIOMAS IDENTIFIES COMMON ONCOGENIC PROCESSES AMONG DISTINCT TUMOR HISTOLOGIES

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i22-i22
Author(s):  
John DeSisto ◽  
Andrew Donson ◽  
Rui Fu ◽  
Bridget Sanford ◽  
Kent Riemondy ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Treatment Modalities ◽  
Patient Specific ◽  
Cell Populations ◽  
High Grade ◽  
Methylation Data ◽  
Rna Seq

Abstract Background Pediatric high-grade glioma (PHGG) is a deadly childhood brain tumor that responds poorly to treatment. PHGG comprises two major subtypes: cortical tumors with wild-type H3K27 and diffuse midline gliomas (DMG) that occur in the midline and have characteristic H3K27M mutations. Cortical PHGG is heterogeneous with multiple molecular subtypes. In order to identify underlying commonalities in cortical PHGG that might lead to better treatment modalities, we performed molecular profiling, including single-cell RNA-Seq (scRNA-Seq), on PHGG samples from Children’s Hospital Colorado. Methods Nineteen cortical PHGG tumor samples, one DMG and one normal margin sample obtained at biopsy were disaggregated to isolate viable cells. Fifteen were glioblastomas (GBM), including five with epithelioid and/or giant cell features and five radiation-induced glioblastomas (RIG). There were also four non-GBM PHGG. We performed scRNA-Seq using 10X Genomics v.3 library preparation to enable capture of infiltrating immune cells. We also performed bulk RNA-Seq and DNA methylation profiling. Results After eliminating patient-specific and cell-cycle effects, RIG, epithelioid GBM, and other GBM each formed identifiable subgroups in bulk RNA-Seq and scRNA-Seq datasets. In the scRNA-Seq data, clusters with cells from multiple tumor samples included a PDGFRA-positive population expressing oligodendrocyte progenitor markers, astrocytic, mesenchymal and stemlike populations, macrophage/monocyte immune cells, and a smaller T-cell population. Analyses of DNA methylation data showed PDGFRA and CDK4 amplification and CDKN2A deletion are common alterations among PHGG. Inferred copy number variation analysis of the single-cell data confirmed that individual tumors include populations that both include and lack the molecular alterations identified in the methylation data. RNA velocity studies to define tumor cells of origin and further analyses of the immune cell populations are underway. Conclusions Single-cell analysis of PHGG confirms a large degree of tumor heterogeneity but also shows that PHGG have stemlike, mesenchymal and immune cell populations with common characteristics.

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