High-throughput quantification of cell-surface proteins for accelerated cancer therapeutic discovery
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1114101 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR29110509.fastq.gz | Fastqsanger.gz | |||
| SRR29110510.fastq.gz | Fastqsanger.gz | |||
| SRR29110511.fastq.gz | Fastqsanger.gz | |||
| SRR29110512.fastq.gz | Fastqsanger.gz | |||
| SRR29110513.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 21 |
Similar Datasets
Project description:Cell-surface proteins on tumor cells serve as both key targets for immunotherapeutic strategies and markers of blast phenotype and differentiation status. Compounds that modulate surface expression of key immunotherapeutic targets offer an attractive strategy for more robust and precise targeting by immunotherapeutics when delivered as a co-therapy. Similarly, identification of compounds that induce differentiation of tumor blasts over indeterminate self-renewal is an area of great therapeutic promise. Both conventional and newer methods for characterizing cell surface protein expression on tumor cells, including flow cytometry and CITE-Seq, are invaluable for characterizing phenotypes but do not readily scale for high-throughput drug screening. Here we present Surface-plex, a new technology which enables a multiplexed, high-throughput sequencing readout of cell-surface proteins on tumor cells following treatment with thousands of compounds. We applied this technology to acute myeloid leukemia and identified PFI90 as an inducer of the therapeutically targetable antigen CD47 and several compounds as inducers of blast differentiation. Surface-plex thus offers a scalable platform enabling the rapid identification of compounds with therapeutic potential, applicable across cancer types, to accelerate targeted cancer therapeutic development.
2025-05-21 | GSE268052 | GEO
Project description:Due to the large size, complex splicing and wide dynamic range of eukaryotic transcriptomes, RNA sequencing samples the majority of expressed genes infrequently, resulting in sparse sequencing coverage that can hinder robust isoform assembly and quantification. Targeted RNA sequencing addresses this challenge by using oligonucleotide probes to capture selected genes or regions of interest for focused sequencing. This enhanced sequencing coverage confers sensitive gene discovery, robust transcript assembly and accurate gene quantification. Here we describe a detailed protocol for all stages of targeted RNA sequencing, from initial probe design considerations, capture of targeted genes, to final assembly and quantification of captured transcripts. Initial probe design and final analysis can take less than a day, while the central experimental capture stage requires ~7 days. Targetted RNA sequencing of long noncoding RNAs
2014-03-01 | E-GEOD-52503 | biostudies-arrayexpress
Project description:Due to the large size, complex splicing and wide dynamic range of eukaryotic transcriptomes, RNA sequencing samples the majority of expressed genes infrequently, resulting in sparse sequencing coverage that can hinder robust isoform assembly and quantification. Targeted RNA sequencing addresses this challenge by using oligonucleotide probes to capture selected genes or regions of interest for focused sequencing. This enhanced sequencing coverage confers sensitive gene discovery, robust transcript assembly and accurate gene quantification. Here we describe a detailed protocol for all stages of targeted RNA sequencing, from initial probe design considerations, capture of targeted genes, to final assembly and quantification of captured transcripts. Initial probe design and final analysis can take less than a day, while the central experimental capture stage requires ~7 days.
2014-03-01 | GSE52503 | GEO
Project description:How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. Here, we present OPEX, an optimal experimental design method to identify informative omics experiments for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's cross-behavior potential, when exposed to novel biocide and antibiotic combinations, led to accelerated knowledge discovery with predictive models that are more accurate while needing 44% fewer data to train. Selecting experiments favoring broader exploration followed by fine-tuning emerged as the optimal strategy. This led to the discovery of 29 cross-protection and 4 cross-vulnerability conditions, with further validation revealing the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to automate omics data collection for training accurate predictive models, evidence-driven decision making and accelerated knowledge discovery in life sciences.
2020-07-09 | GSE144604 | GEO
Project description:Alternative splicing is widely acknowledged to be a crucial regulator of gene expression and is a key contributor to both normal developmental processes and disease states. While cost-effective and accurate for quantification, short-read RNA-seq lacks the ability to resolve full-length transcript isoforms despite increasingly sophisticated computational methods. Long-read sequencing platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) bypass the transcript reconstruction challenges of short-reads. Here we describe TALON, the ENCODE4 pipeline for analyzing PacBio cDNA and ONT direct-RNA transcriptomes. We apply TALON to three human ENCODE Tier 1 cell lines and show that while both technologies perform well at full-transcript discovery and quantification, each one displayed distinct artifacts. We further apply TALON to mouse cortical and hippocampal transcriptomes and find that a substantial proportion of neuronal genes have more reads associated with novel isoforms than with annotated ones. These data show that TALON is a technology-agnostic long-read transcriptome discovery and quantification pipeline capable of tracking both known and novel transcript models, as well as their expression levels, across datasets for both simple studies and in larger projects. These properties will enable TALON users to move beyond the limitations of short-read data to perform isoform discovery and quantification in a uniform manner on existing and future long-read platforms.
2020-03-18 | GSE147118 | GEO
Project description:Accelerated knowledge discovery from omics data by optimal experimental design
| PRJNA604190 | ENA
Project description:The discovery of highly enantioselective catalysts and elucidating their generality face great challenges due to the complex multidimensional chemical space of asymmetric catalysis and inefficient screening methods. Here, we develop a general strategy for ultra-high-throughput mapping the chemical space of asymmetric catalysis by escaping the time-consuming chiral chromatography separation. The ultrafast (~1000 reactions/day) and accurate (median error < ±1%) analysis of enantiomeric excess are achieved through the ion mobility-mass spectrometry combines with the diastereoisomerization strategy. A workflow for accelerated asymmetric reaction screening is established and verified by mapping the large-scale chemical space of more than 1600 reactions of α-asymmetric alkylation of aldehyde with organocatalysis and photocatalysis. Importantly, a class of high-enantioselectivity primary amine organocatalysts of 1,2-diphenylethane-1,2-diamine-based sulfonamides is discovered by the accelerated screening, and the mechanism for high-selectivity is demonstrated by computational chemistry. This study provides a practical and robust solution for large-scale screening and discovery of asymmetric reactions.
2023-10-18 | PXD045953 |
Project description:Alternative splicing is widely acknowledged to be a crucial regulator of gene expression and is a key contributor to both normal developmental processes and disease states. While cost-effective and accurate for quantification, short-read RNA-seq lacks the ability to resolve full-length transcript isoforms despite increasingly sophisticated computational methods. Long-read sequencing platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) bypass the transcript reconstruction challenges of short-reads. Here we describe TALON, the ENCODE4 pipeline for analyzing PacBio cDNA and ONT direct-RNA transcriptomes. We apply TALON to three human ENCODE Tier 1 cell lines and show that while both technologies perform well at full-transcript discovery and quantification, each technology has its distinct artifacts. We further apply TALON to mouse cortical and hippocampal transcriptomes and find that a substantial proportion of neuronal genes have more reads associated with novel isoforms than annotated ones. The TALON pipeline for technology-agnostic, long-read transcriptome discovery and quantification tracks both known and novel transcript models as well as expression levels across datasets for both simple studies and larger projects such as ENCODE that seek to decode transcriptional regulation in the human and mouse genomes to predict more accurate expression levels of genes and transcripts than possible with short-reads alone.
2019-06-15 | GSE132766 | GEO
Project description:Long-read RNA sequencing (RNA-seq) holds great potential for characterizing transcriptome variation and full-length transcript isoforms, but the relatively high error rate of current long-read sequencing platforms poses a major challenge. We present ESPRESSO, a computational tool for robust discovery and quantification of transcript isoforms from error-prone long reads. ESPRESSO jointly considers alignments of all long reads aligned to a gene and uses error profiles of individual reads to improve the identification of splice junctions and the discovery of their corresponding transcript isoforms. On both a synthetic spike-in RNA sample and human RNA samples, ESPRESSO outperforms multiple contemporary tools in not only transcript isoform discovery but also transcript isoform quantification. In total, we generated and analyzed ~1.1 billion nanopore RNA-seq reads covering 30 human tissue samples and three human cell lines. ESPRESSO and its companion dataset provide a useful resource for studying the RNA repertoire of eukaryotic transcriptomes.
2022-10-14 | GSE192955 | GEO
Project description:A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification
| PRJNA548942 | ENA