Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1028404 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26391818.fastq.gz | Fastqsanger.gz | |||
| SRR26391819.fastq.gz | Fastqsanger.gz | |||
| SRR26391820.fastq.gz | Fastqsanger.gz | |||
| SRR26391821.fastq.gz | Fastqsanger.gz | |||
| SRR26391822.fastq.gz | Fastqsanger.gz |
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Project description:We present a target-unbiased approach for antibody discovery that relies on generating mAbs against native target cell surfaces via phage display. This method combines a previously reported method for improved whole-cell phage display selections with next-generation sequencing analysis to efficiently identify mAbs with the desired target cell reactivity. This approach enabled the identification of three multiple myeloma cell surface antigens, and cognate monoclonal antibody probes.
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Project description:High-density phage epitope microarray from 31 samples were used for unsupervised analysis (GSM36153...GSM36183). 129 samples from prostate cancer patients and controls were screened on small focused epitope chips, which contained 180 phage elements. These data were used to train GA/KNN program (GSM36184...GSM36312). 128 samples from localized prostate cancer patients and controls were screened on small focused epitope chips. These independent data were used to validate the epitomic profile (GSM36313...GSM36375, GSM40203...GSM40213, GSM40216, GSM40218, GSM40219, GSM40222, GSM40225, GSM40227, GSM40229, GSM40233, GSM40237, GSM40246...GSM40294). Three subgroups of samples were used as test sets to validate the specificity of epitomic profile (GSM36376...GSM36410, GSM40214, GSM40215, GSM40217, GSM40220, GSM40221, GSM40224, GSM40226, GSM40228, GSM40231, GSM40234...GSM40236, GSM40238...GSM40244). Project----Identification of humoral signature for prostate cancer diagnosis We constructed a prostate cancer cDNA phage display library. cDNAs were reverse-synthesized from mDNA pool isolated from prostate cancer tissues. Enzyme-digested cDNA fragments were then inserted into phage vector to make a whole prostate cancer phage expressed cDNA library. In order to select cancer specific phage epitope from this library, we performed several cycles of affinity enrichment. We used the bounded IgG pool isolated from prostate cancer patient sera to select the tumor specific phage epitope clones. Once we had the enriched phage epitope library, we cultured the phage library on LB-agar dish for individual phage colonies. About 2300 phage colonies from agar dish were picked up using toothstick and cultured in 96-well microtiter plates. Each clone was labeled as microtiter plate #, column #, row#, i.e. clone ID. These 2300 clones were then spotted on slides in single spot (no any duplicate), i.e. each spot (labeled by clone ID) represents a single phage clone. The phage epitope microarrays were then screened using cancer or control sera. We employed two color system. Cy5-anti human IgG was to detect human IgG. For green color, we used Cy3-labeled anti-phage capsid protein as internal reference to normalize the ammount difference of phage particles spotted on each spot. Thus the ratio of Cy5/Cy3 would count for the immune response in cancer or control sera. Once we identified humoral signature in prostate cancer patients, we could sequence the phage clone to characterize the nature of the genes or proteins.
2005-10-04 | E-GEOD-2010 | biostudies-arrayexpress
Project description:High-density phage epitope microarray from 31 samples were used for unsupervised analysis (GSM36153...GSM36183). 129 samples from prostate cancer patients and controls were screened on small focused epitope chips, which contained 180 phage elements. These data were used to train GA/KNN program (GSM36184...GSM36312). 128 samples from localized prostate cancer patients and controls were screened on small focused epitope chips. These independent data were used to validate the epitomic profile (GSM36313...GSM36375, GSM40203...GSM40213, GSM40216, GSM40218, GSM40219, GSM40222, GSM40225, GSM40227, GSM40229, GSM40233, GSM40237, GSM40246...GSM40294). Three subgroups of samples were used as test sets to validate the specificity of epitomic profile (GSM36376...GSM36410, GSM40214, GSM40215, GSM40217, GSM40220, GSM40221, GSM40224, GSM40226, GSM40228, GSM40231, GSM40234...GSM40236, GSM40238...GSM40244). Project----Identification of humoral signature for prostate cancer diagnosis We constructed a prostate cancer cDNA phage display library. cDNAs were reverse-synthesized from mDNA pool isolated from prostate cancer tissues. Enzyme-digested cDNA fragments were then inserted into phage vector to make a whole prostate cancer phage expressed cDNA library. In order to select cancer specific phage epitope from this library, we performed several cycles of affinity enrichment. We used the bounded IgG pool isolated from prostate cancer patient sera to select the tumor specific phage epitope clones. Once we had the enriched phage epitope library, we cultured the phage library on LB-agar dish for individual phage colonies. About 2300 phage colonies from agar dish were picked up using toothstick and cultured in 96-well microtiter plates. Each clone was labeled as microtiter plate #, column #, row#, i.e. clone ID. These 2300 clones were then spotted on slides in single spot (no any duplicate), i.e. each spot (labeled by clone ID) represents a single phage clone. The phage epitope microarrays were then screened using cancer or control sera. We employed two color system. Cy5-anti human IgG was to detect human IgG. For green color, we used Cy3-labeled anti-phage capsid protein as internal reference to normalize the ammount difference of phage particles spotted on each spot. Thus the ratio of Cy5/Cy3 would count for the immune response in cancer or control sera. Once we identified humoral signature in prostate cancer patients, we could sequence the phage clone to characterize the nature of the genes or proteins. Keywords: autoantibody signatures
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Project description:Whole-genome sequencing is an important way to understand the genetic information, gene function, biological characteristics, and living mechanisms of organisms. There is no difficulty to have mega-level genomes sequenced at present. However, we encountered a hard-to-sequence genome of Pseudomonas aeruginosa phage PaP1. The shotgun sequencing method failed to dissect this genome. After insisting for 10 years and going over 3 generations of sequencing techniques, we successfully dissected the PaP1 genome with 91,715 bp in length. Single-molecule sequencing revealed that this genome contains lots of modified bases, including 51 N6-methyladenines (m6A) and 152 N4-methylcytosines (m4C). At the same time, further investigations revealed a novel immune mechanism of bacteria, by which the host bacteria can recognize and repel the modified bases containing inserts in large scale, and this led to the failure of the shotgun method in PaP1 genome sequencing. Strategy of resolving this problem is use of non-library dependent sequencing techniques or use of the nfi- mutant of E. coli DH5M-NM-1 as the host bacteria to construct the shotgun library. In conclusion, we unlock the mystery of phage PaP1 genome hard to be sequenced, and discover a new mechanism of bacterial immunity in present study. Methylation profiling of Pseudomonas aeruginosa phage PaP1 using kinetic data generated by single-molecule, real-time (SMRT) sequencing on the PacBio RS.
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Project description:Single cell multi-omic readouts of both the cellular transcriptome and proteome have significantly enhanced our ability to comprehensively characterize cellular states. Most approaches in this area rely on oligonucleotide barcode-conjugated antibodies that target cell surface epitopes of interest, enabling their concomitant detection with the transcriptome. However, a similar high-throughput measurement of other cellular modalities such as the epigenome in concert with protein levels have not been described. Moreover, detection of epitopes is limited to antigens for which a specific antibody is available. Here, we introduce PHAGE-ATAC, an approach that enables the scalable and simultaneous detection of protein levels and chromatin accessibility data in single cells using the assay of transposase-accessible chromatin with sequencing (ATAC-seq). Quantitative detection of proteins by PHAGE-ATAC is accomplished through the use of engineerable nanobody-displaying phages that are genetically barcoded within the nanobody-encoding phagemids. We demonstrate the utility of PHAGE-ATAC for multimodal single cell genomic analysis in both cell lines and primary human cells. Analogous to phage display approaches, we further establish a synthetic high-complexity library of nanobody-displaying phages and demonstrate its utility to select novel antigen-specific nanobodies for PHAGE-ATAC.
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