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1
Mosier, Charles T., and Farzad Mahmoodi. "Work sequencing in a manufacturing cell with limited labour constraints." International Journal of Production Research 40, no. 12 (2002): 2883–99. http://dx.doi.org/10.1080/00207540210136577.
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Zhao, Xin, Shouguo Gao, Sachiko Kajigaya, et al. "Single-Cell RNA Sequencing of Healthy Human Marrow Hematopoietic Cells." Blood 134, Supplement_1 (2019): 4997. http://dx.doi.org/10.1182/blood-2019-123249.
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Hematopoiesis, especially the early events of blood cell formation, has been mainly studied in bulk populations of cells and using progenitor colony formation assays; the familiar hierarchy of cell lineage differentiation and maturation, and associated regulatory factors have been inferred from these methods. However, these techniques often require extensive manipulation of cells, the exposure of cells to unphysiological conditions, aggregation of heterogeneous populations, and prior assumptions concerning cell function and gene expression. New single cell methodology avoids many of these potential experimental deficiencies. Here we have applied single-cell RNA-sequencing(scRNA-seq)to fresh human bone marrow CD34+cells: we profiled 391 single hematopoietic stem/progenitor cells (HSPCs) from four healthy donors by deep sequencing of individual cell transcriptomes. An average of 4560 protein-coding genes were detected per cell. Cells clustered into six distinct groups, which could be assigned to known HSPC subpopulations (Fig 1A), based on expression of lineage-specific genes. Lin-CD34+CD38+cells emerged as locally clustered cell populations (Clusters 2-6, including MEP, GMP, ETP and ProB), while Lin-CD34+CD38-cells formed a single cluster (HSC/MLP). Reconstruction of differentiation trajectories by transcription in single cells revealed four committed lineages derived from stem cell compartment. The earliest fate split separates MEPs from MLPs, which partition further into lymphoid, and granulocyte-monocyte progenitors (Fig 1B). The overall pattern differs from the classical hematopoietic model describing a single binary split between myeloid and lymphoid differentiation immediately downstream of multipotent cells. However, our data align well to recently published scRNA-seq data showing sequential commitment of stem cells to the lymphoid, erythroid/megakaryocytic, and finally myeloid lineages (Setty M, Nat Biotechnol2019; Pellin D, Nat Commun2019). We further examined trends in gene expression in each of the branches and found dynamic expression changes underlying cell fate during early lineage differentiation (Fig 1C). As confirmation, PCA plot of published single-cell assay for transposase-accessible chromatin (scATAC-seq) shows similar differentiation pattern. After projecting scATAC-seq data to our transcriptomic clusters' specific genes, MEP-dependent and myeloid/lymphoid-dependent genes were located on opposing sides of the PC1 with same direction (Fig 1D), indicating transcriptome and epigenome work on differentiation in concerted effort. scRNA-seq provides opportunities for discovery and characterization at the molecular levels of early HSC differentiation and developmental intermediates, retrospectively, without the need to isolate purified populations. However, information inferred from scRNA-seq may be obscured due to missing reads and limited cell numbers. More cells would provide greater detail and higher resolution mapping.Given the low frequency of megakaryocyte progenitors within the CD34+cells as well as the neglected Lin-CD34-BM compartment, we could not fully resolve the separation and maturation of all lineages. Nonetheless, we found good coverage of cell types and a similar HSPC Atlas as other published studies (Velten L, Nat Cell Biol2017; Pellin D, Nat Commun2019)despite our limited numbers of starting cells. Our data accurately reflect the pattern of normal hematopoiesis, which may help to revise and refine characterization of hematopoiesis and provide a general reference framework to investigate the complexities of blood cell production at single-cell resolution - especially when cell numbers are limited, as from patient samples and in marrow failure syndromes. Fig. 1scRNA-seq of human hematopoietic stem and progenitor cells. (A) Unsupervised hierarchical clustering of gene expression data for all cells. C1, HSC/MLP; C2, MEP; C3, GMP; C4, ProB; C5-C6, ETP. (B)Visualization of the HSPC continuum. Each ball represents one cell.(C) Large-scale shifts in gene expression during development of hematopoietic cells.Bars on top indicate locations of individual cells, colored by stages of development, along this developmental trajectory. (D) Projections of five transcriptomic gene modules onto PCA of scATAC-seq data (Buenrostro JD,Cell 2018). Each dot represents a transcriptional factor. Figure 1 Disclosures No relevant conflicts of interest to declare.
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Sahoo, Malaya K., Susanna K. Tan, Sharon F. Chen, et al. "Limited Variation in BK Virus T-Cell Epitopes Revealed by Next-Generation Sequencing." Journal of Clinical Microbiology 53, no. 10 (2015): 3226–33. http://dx.doi.org/10.1128/jcm.01385-15.
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BK virus (BKV) infection causing end-organ disease remains a formidable challenge to the hematopoietic cell transplant (HCT) and kidney transplant fields. As BKV-specific treatments are limited, immunologic-based therapies may be a promising and novel therapeutic option for transplant recipients with persistent BKV infection. Here, we describe a whole-genome, deep-sequencing methodology and bioinformatics pipeline that identify BKV variants across the genome and at BKV-specific HLA-A2-, HLA-B0702-, and HLA-B08-restricted CD8 T-cell epitopes. BKV whole genomes were amplified using long-range PCR with four inverse primer sets, and fragmentation libraries were sequenced on the Ion Torrent Personal Genome Machine (PGM). An error model and variant-calling algorithm were developed to accurately identify rare variants. A total of 65 samples from 18 pediatric HCT and kidney recipients with quantifiable BKV DNAemia underwent whole-genome sequencing. Limited genetic variation was observed. The median number of amino acid variants identified per sample was 8 (range, 2 to 37; interquartile range, 10), with the majority of variants (77%) detected at a frequency of <5%. When normalized for length, there was no statistical difference in the median number of variants across all genes. Similarly, the predominant virus population within samples harbored T-cell epitopes similar to the reference BKV strain that was matched for the BKV genotype. Despite the conservation of epitopes, low-level variants in T-cell epitopes were detected in 77.7% (14/18) of patients. Understanding epitope variation across the whole genome provides insight into the virus-immune interface and may help guide the development of protocols for novel immunologic-based therapies.
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Ma, Shi-Xun, and Su Bin Lim. "Single-Cell RNA Sequencing in Parkinson’s Disease." Biomedicines 9, no. 4 (2021): 368. http://dx.doi.org/10.3390/biomedicines9040368.
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Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) technologies have enhanced the understanding of the molecular pathogenesis of neurodegenerative disorders, including Parkinson’s disease (PD). Nonetheless, their application in PD has been limited due mainly to the technical challenges resulting from the scarcity of postmortem brain tissue and low quality associated with RNA degradation. Despite such challenges, recent advances in animals and human in vitro models that recapitulate features of PD along with sequencing assays have fueled studies aiming to obtain an unbiased and global view of cellular composition and phenotype of PD at the single-cell resolution. Here, we reviewed recent sc/snRNA-seq efforts that have successfully characterized diverse cell-type populations and identified cell type-specific disease associations in PD. We also examined how these studies have employed computational and analytical tools to analyze and interpret the rich information derived from sc/snRNA-seq. Finally, we highlighted important limitations and emerging technologies for addressing key technical challenges currently limiting the integration of new findings into clinical practice.
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Noé, Andrés, Tamsin N. Cargill, Carolyn M. Nielsen, Andrew J. C. Russell, and Eleanor Barnes. "The Application of Single-Cell RNA Sequencing in Vaccinology." Journal of Immunology Research 2020 (August 6, 2020): 1–19. http://dx.doi.org/10.1155/2020/8624963.
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Single-cell RNA sequencing allows highly detailed profiling of cellular immune responses from limited-volume samples, advancing prospects of a new era of systems immunology. The power of single-cell RNA sequencing offers various opportunities to decipher the immune response to infectious diseases and vaccines. Here, we describe the potential uses of single-cell RNA sequencing methods in prophylactic vaccine development, concentrating on infectious diseases including COVID-19. Using examples from several diseases, we review how single-cell RNA sequencing has been used to evaluate the immunological response to different vaccine platforms and regimens. By highlighting published and unpublished single-cell RNA sequencing studies relevant to vaccinology, we discuss some general considerations how the field could be enriched with the widespread adoption of this technology.
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Paglino, Chiara, and Camillo Porta. "Sequencing or not sequencing multikinase inhibitors in kidney cancer: this is the dilemma." Oncology Reviews 4, no. 1 (2011): 1. http://dx.doi.org/10.4081/oncol.2010.1.
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With the recent development of targeted therapies (Sorafenib, Sunitinib, Temsirolimus, Bevacizumab plus Interferon-a, Everolimus and now also Pazopanib) patients with advanced renal cell carcinoma (RCC) now have a wide range of treatment options, all of which have shown both relevant clinical activity and manageable safety profile. This abundance of active treatments, coupled with relatively limited information, we have gathered from registrative phase III trials have raised the question of how to use these agents optimally...
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Vu, Trung Nghia, Ha-Nam Nguyen, Stefano Calza, Krishna R. Kalari, Liewei Wang, and Yudi Pawitan. "Cell-level somatic mutation detection from single-cell RNA sequencing." Bioinformatics 35, no. 22 (2019): 4679–87. http://dx.doi.org/10.1093/bioinformatics/btz288.
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Abstract Motivation Both single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data. Results Mutation detection methods developed for either bulk-cell sequencing data or scDNA-seq data do not work well for the scRNA-seq data, as they produce substantial numbers of false positives. We develop a novel and robust statistical method—called SCmut—to identify specific cells that harbor mutations discovered in bulk-cell data. Statistically SCmut controls the false positives using the 2D local false discovery rate method. We apply SCmut to several scRNA-seq datasets. In scRNA-seq breast cancer datasets SCmut identifies a number of highly confident cell-level mutations that are recurrent in many cells and consistent in different samples. In a scRNA-seq glioblastoma dataset, we discover a recurrent cell-level mutation in the PDGFRA gene that is highly correlated with a well-known in-frame deletion in the gene. To conclude, this study contributes a novel method to discover cell-level mutation information from scRNA-seq that can facilitate investigation of cell-to-cell heterogeneity. Availability and implementation The source codes and bioinformatics pipeline of SCmut are available at https://github.com/nghiavtr/SCmut. Supplementary information Supplementary data are available at Bioinformatics online.
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Diaz-Mejia, J. Javier, Elaine C. Meng, Alexander R. Pico, et al. "Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data." F1000Research 8 (March 15, 2019): 296. http://dx.doi.org/10.12688/f1000research.18490.1.
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Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and the fact that some dedicated methods are available only as web servers with limited cell type gene expression signatures. Methods: In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Results: Our results show that, in general, all four methods show a high performance in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.94, sd = 0.036), whereas precision-recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). Conclusions: CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.
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Lim, Su Bin, Chwee Teck Lim, and Wan-Teck Lim. "Single-Cell Analysis of Circulating Tumor Cells: Why Heterogeneity Matters." Cancers 11, no. 10 (2019): 1595. http://dx.doi.org/10.3390/cancers11101595.
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Unlike bulk-cell analysis, single-cell approaches have the advantage of assessing cellular heterogeneity that governs key aspects of tumor biology. Yet, their applications to circulating tumor cells (CTCs) are relatively limited, due mainly to the technical challenges resulting from extreme rarity of CTCs. Nevertheless, recent advances in microfluidics and immunoaffinity enrichment technologies along with sequencing platforms have fueled studies aiming to enrich, isolate, and sequence whole genomes of CTCs with high fidelity across various malignancies. Here, we review recent single-cell CTC (scCTC) sequencing efforts, and the integrated workflows, that have successfully characterized patient-derived CTCs. We examine how these studies uncover DNA alterations occurring at multiple molecular levels ranging from point mutations to chromosomal rearrangements from a single CTC, and discuss their cellular heterogeneity and clinical consequences. Finally, we highlight emerging strategies to address key challenges currently limiting the translation of these findings to clinical practice.
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Contino, Gianmarco, Matthew D. Eldridge, Maria Secrier, et al. "Whole-genome sequencing of nine esophageal adenocarcinoma cell lines." F1000Research 5 (June 10, 2016): 1336. http://dx.doi.org/10.12688/f1000research.7033.1.
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Esophageal adenocarcinoma (EAC) is highly mutated and molecularly heterogeneous. The number of cell lines available for study is limited and their genome has been only partially characterized. The availability of an accurate annotation of their mutational landscape is crucial for accurate experimental design and correct interpretation of genotype-phenotype findings. We performed high coverage, paired end whole genome sequencing on eight EAC cell lines—ESO26, ESO51, FLO-1, JH-EsoAd1, OACM5.1 C, OACP4 C, OE33, SK-GT-4—all verified against original patient material, and one esophageal high grade dysplasia cell line, CP-D. We have made available the aligned sequence data and report single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number alterations, identified by comparison with the human reference genome and known single nucleotide polymorphisms (SNPs). We compare these putative mutations to mutations found in primary tissue EAC samples, to inform the use of these cell lines as a model of EAC.
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Diaz-Mejia, J. Javier, Elaine C. Meng, Alexander R. Pico, et al. "Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data." F1000Research 8 (August 27, 2019): 296. http://dx.doi.org/10.12688/f1000research.18490.2.
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Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.
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Diaz-Mejia, J. Javier, Elaine C. Meng, Alexander R. Pico, et al. "Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data." F1000Research 8 (October 14, 2019): 296. http://dx.doi.org/10.12688/f1000research.18490.3.
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Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.
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Myers, Matthew A., Simone Zaccaria, and Benjamin J. Raphael. "Identifying tumor clones in sparse single-cell mutation data." Bioinformatics 36, Supplement_1 (2020): i186—i193. http://dx.doi.org/10.1093/bioinformatics/btaa449.
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Abstract Motivation Recent single-cell DNA sequencing technologies enable whole-genome sequencing of hundreds to thousands of individual cells. However, these technologies have ultra-low sequencing coverage (&lt;0.5× per cell) which has limited their use to the analysis of large copy-number aberrations (CNAs) in individual cells. While CNAs are useful markers in cancer studies, single-nucleotide mutations are equally important, both in cancer studies and in other applications. However, ultra-low coverage sequencing yields single-nucleotide mutation data that are too sparse for current single-cell analysis methods. Results We introduce SBMClone, a method to infer clusters of cells, or clones, that share groups of somatic single-nucleotide mutations. SBMClone uses a stochastic block model to overcome sparsity in ultra-low coverage single-cell sequencing data, and we show that SBMClone accurately infers the true clonal composition on simulated datasets with coverage at low as 0.2×. We applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained using two different sequencing technologies. On the first patient, sequenced using the 10X Genomics CNV solution with sequencing coverage ≈0.03×, SBMClone recovers the major clonal composition when incorporating a small amount of additional information. On the second patient, where pre- and post-treatment tumor samples were sequenced using DOP-PCR with sequencing coverage ≈0.5×, SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset. Availability and implementation SBMClone is available on the GitHub repository https://github.com/raphael-group/SBMClone. Supplementary information Supplementary data are available at Bioinformatics online.
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Weber, Leah L., Palash Sashittal, and Mohammed El-Kebir. "doubletD: detecting doublets in single-cell DNA sequencing data." Bioinformatics 37, Supplement_1 (2021): i214—i221. http://dx.doi.org/10.1093/bioinformatics/btab266.
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Abstract Motivation While single-cell DNA sequencing (scDNA-seq) has enabled the study of intratumor heterogeneity at an unprecedented resolution, current technologies are error-prone and often result in doublets where two or more cells are mistaken for a single cell. Not only do doublets confound downstream analyses, but the increase in doublet rate is also a major bottleneck preventing higher throughput with current single-cell technologies. Although doublet detection and removal are standard practice in scRNA-seq data analysis, options for scDNA-seq data are limited. Current methods attempt to detect doublets while also performing complex downstream analyses tasks, leading to decreased efficiency and/or performance. Results We present doubletD, the first standalone method for detecting doublets in scDNA-seq data. Underlying our method is a simple maximum likelihood approach with a closed-form solution. We demonstrate the performance of doubletD on simulated data as well as real datasets, outperforming current methods for downstream analysis of scDNA-seq data that jointly infer doublets as well as standalone approaches for doublet detection in scRNA-seq data. Incorporating doubletD in scDNA-seq analysis pipelines will reduce complexity and lead to more accurate results. Availability and implementation https://github.com/elkebir-group/doubletD. Supplementary information Supplementary data are available at Bioinformatics online.
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Tong, Suxiang, Jairam R. Lingappa, Qi Chen, et al. "Direct Sequencing of SARS-Coronavirus S and N Genes from Clinical Specimens Shows Limited Variation." Journal of Infectious Diseases 190, no. 6 (2004): 1127–31. http://dx.doi.org/10.1086/422849.
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Abstract Severe acute respiratory syndrome-associated coronavirus (SARS-CoV) emerged, in November 2002, as a novel agent causing severe respiratory illness. To study sequence variation in the SARS-CoV genome, we determined the nucleic acid sequence of the S and N genes directly from clinical specimens from 10 patients—1 specimen with no matched SARS-CoV isolate, from 2 patients; multiple specimens from 3 patients; and matched clinical-specimen/ cell-culture-isolate pairs from 6 patients. We identified 3 nucleotide substitutions that were most likely due to natural variation and 2 substitutions that arose after cell-culture passage of the virus. These data demonstrate the overall stability of the S and N genes of SARS-CoV over 3 months during which a minimum of 4 generations for transmission events occurred. These findings are a part of the expanding investigation of the evolution of how this virus adapts to a new host.
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Duan, Bin, Chenyu Zhu, Guohui Chuai, et al. "Learning for single-cell assignment." Science Advances 6, no. 44 (2020): eabd0855. http://dx.doi.org/10.1126/sciadv.abd0855.
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Efficient single-cell assignment without prior marker gene annotations is essential for single-cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single-cell assignment. They failed to achieve a well-generalized performance in different tasks because of the inherent heterogeneity of different single-cell sequencing datasets and different single-cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets. To this end, we present scLearn, a learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single-cell assignment tasks, achieving a well-generalized assignment performance on different single-cell types. We evaluated scLearn on a comprehensive set of publicly available benchmark datasets. We proved that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single-cell type identification and categorizing ability.
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Fu, Rui, Austin E. Gillen, Ryan M. Sheridan, et al. "clustifyr: an R package for automated single-cell RNA sequencing cluster classification." F1000Research 9 (April 1, 2020): 223. http://dx.doi.org/10.12688/f1000research.22969.1.
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Assignment of cell types from single-cell RNA sequencing (scRNA-seq) data remains a time-consuming and error-prone process. Current packages for identity assignment use limited types of reference data and often have rigid data structure requirements. We developed the clustifyr R package to leverage several external data types, including gene expression profiles to assign likely cell types using data from scRNA-seq, bulk RNA-seq, microarray expression data, or signature gene lists. We benchmark various parameters of a correlation-based approach and implement gene list enrichment methods. clustifyr is a lightweight and effective cell-type assignment tool developed for compatibility with various scRNA-seq analysis workflows. clustifyr is publicly available at https://github.com/rnabioco/clustifyr
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Fu, Rui, Austin E. Gillen, Ryan M. Sheridan, et al. "clustifyr: an R package for automated single-cell RNA sequencing cluster classification." F1000Research 9 (July 16, 2020): 223. http://dx.doi.org/10.12688/f1000research.22969.2.
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Assignment of cell types from single-cell RNA sequencing (scRNA-seq) data remains a time-consuming and error-prone process. Current packages for identity assignment use limited types of reference data and often have rigid data structure requirements. We developed the clustifyr R package to leverage several external data types, including gene expression profiles to assign likely cell types using data from scRNA-seq, bulk RNA-seq, microarray expression data, or signature gene lists. We benchmark various parameters of a correlation-based approach and implement gene list enrichment methods. clustifyr is a lightweight and effective cell-type assignment tool developed for compatibility with various scRNA-seq analysis workflows. clustifyr is publicly available at https://github.com/rnabioco/clustifyr
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Ocwieja, K. E., T. K. Hughes, C. C. M. Baker, et al. "#33: Single-cell RNA sequencing analysis of Zika virus infection in human stem cell-derived neuroprogenitor cells and cerebral organoids." Journal of the Pediatric Infectious Diseases Society 10, Supplement_2 (2021): S11—S12. http://dx.doi.org/10.1093/jpids/piab031.025.
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Abstract Background The molecular mechanisms underpinning the neurologic and congenital pathologies caused by Zika virus (ZIKV) infection remain poorly understood. It is also unclear why congenital ZIKV disease was not reported prior to the recent epidemics in French Polynesia and the Americas, despite evidence that Zika virus has actively circulated in parts of Africa and Asia since 1947 and 1966, respectively. Methods Due to advances in the stem cell-based technologies, we can now model ZIKV infections of the central nervous system in human stem cell-derived neural progenitor cells and cerebral organoids, which recapitulate complex 3-dimensional neural architecture. We apply Seq-Well — a simple, portable platform for massively parallel single-cell RNA sequencing — to characterize these neural models infected with ZIKV. We detect and quantify host mRNA transcripts and viral RNA with single-cell resolution, thereby defining transcriptional features of both uninfected and infected cells. Results Although flavivirus RNAs lack a poly(A) tail, we present evidence that viral RNAs are specifically primed for reverse transcription at internal runs of adenosines, and that sequencing reads cover the entire non-polyadenylated viral genome. In neural progenitor cells, single cell sequencing reveals that while uninfected bystander cells strongly upregulate interferon pathway genes, these pathways are largely suppressed in cells infected with ZIKV within the same culture dish. Single cell sequencing identifies multiple cell types in our cerebral organoids including neural progenitor cells, intermediate progenitor cells, and neurons of varied maturity. Using this model, we find that neurons, not typically considered targets of ZIKV in the developing brain, contain high copy numbers of ZIKV genomes. It remains uncertain whether neurons are directly infected, or if infected neural progenitor cells differentiate into neurons, carrying virus with them. Notably, the neuronal bystander cell population shows limited interferon gene pathway upregulation compared to neural progenitors. Conclusions Overall, our work provides insight into the pathogenesis of ZIKV associated microcephaly, identifies potential new tropisms of ZIKV in the human brain, and suggests that both virus replication and host response mechanisms underlie the neuropathology of ZIKV infection.
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Li, Haikuo, and Benjamin D. Humphreys. "Single Cell Technologies: Beyond Microfluidics." Kidney360 2, no. 7 (2021): 1196–204. http://dx.doi.org/10.34067/kid.0001822021.
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AbstractSingle-cell RNA-sequencing (scRNA-seq) has been widely adopted in recent years due to standardized protocols and automation, reliability, and standardized bioinformatic pipelines. The most widely adopted platform is the 10× Genomics solution. Although powerful, this system is limited by its high cost, moderate throughput, and the inability to customize due to fixed kit components. This study will cover new approaches that do not rely on microfluidics and thus have low entry costs, are highly customizable, and are within the reach of any laboratory possessing molecular biology expertise.
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Borcherding, Nicholas, Sydney Crotts, Luana Ortolan, Nicholas Bormann, and Ali Jabbari. "Single-Cell mRNA Sequencing of Murine and Human Alopecia Areata Identifies Immune Cell Profiles Predictive of Human Disease State." American Journal of Clinical Pathology 154, Supplement_1 (2020): S5. http://dx.doi.org/10.1093/ajcp/aqaa137.008.
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Abstract Alopecia areata (AA) is one of the most common autoimmune conditions, presenting initially with loss of hair without overt skin changes. The unremarkable appearance of the skin surface contrasts with the complex immune activity occurring at the hair follicle. AA pathogenesis is due to the loss of immune privilege of the hair follicle leading to autoimmune attack. Although the literature has focused on CD8+ T cells, vital roles for CD4+ T cells and antigen-presenting cells have been suggested. Here, we use single-cell mRNA sequencing to reveal distinct expression profiles of immune cells in AA. We found clonal expansions of both CD4+ and CD8+ T cells, with shared clonotypes across varied transcriptional states. Demonstrating distinct gene and clonotypic variations, AA murine data were used to generate highly predictive models of human AA disease. In order to corroborate the results, single-cell sequencing of T cells in human AA recapitulated the clonotypic findings and the gene expression of the predictive models. Taken together, this work demonstrates the unique transcriptomic environment of AA skin, not just limited to CD8+ T cells. This work also represents the first single-cell sequencing for the autoimmune condition AA.
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O'Byrne, Kenneth John, Joanna Kapeleris, Arutha Kulasinghe, et al. "Culture of circulating tumour cells derived from non-small cell lung cancer." Journal of Clinical Oncology 38, no. 15_suppl (2020): e21692-e21692. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e21692.
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e21692 Background: Tumour tissue-based information is limited. Liquid biopsy can provide valuable real-time information through circulating tumour cells (CTCs). Profiling and expanding CTCs may provide avenues to study transient metastatic disease. Methods: 70 NSCLC patients were recruited. CTCs were enriched using the spiral microfluidic chip and a RosetteSep™ using bloods from NSCLC patients. CTC cultures were carried out using the Clevers media under hypoxic conditions. CTCs were characterized using immunofluorescence and mutation-specific antibodies for samples with known mutation profiles. Exome sequencing was used to characterized CTC cultures. Results: CTCs ( > 2 cells) were detected in 38/70 (54.3%) of patients ranging from 0-385 CTCs per 7.5ml blood. In 4/5 patients where primary tumours harboured an EGFR exon 19 deletion, this EGFR mutation was also captured in CTCs. ALK translocation was confirmed on CTCs from a patient harbouring an ALK-rearrangement in the primary tumour. Short term CTC cultures were successfully generated in 9/70 NSCLC patients. Whole exome sequencing confirmed the presence of somatic mutations in the CTC cultures with mutational signatures consistent with NSCLC. Conclusions: This study demonstrates a workflow for ex vivo culture of CTCs from NSCLC blood samples.
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Wu, Peng, Yan Gao, Weilong Guo, and Ping Zhu. "Using local alignment to enhance single-cell bisulfite sequencing data efficiency." Bioinformatics 35, no. 18 (2019): 3273–78. http://dx.doi.org/10.1093/bioinformatics/btz125.
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Abstract Motivation Single-cell bisulfite sequencing (BS-seq) techniques have been developed for DNA methylation heterogeneity detection and studies with limited materials. However, the data deficiency such as low read mapping ratio is still a critical issue. Results We comprehensively characterize single-cell BS-seq data and reveal chimerical molecules to be the major source of alignment failures. These chimerical molecules are produced by recombination of genomic proximal sequences with microhomology regions (MR) after bisulfite conversion. In addition, we find DNA methylation within MR is highly variable, suggesting the necessity of removing these regions to accurately estimate DNA methylation levels. We further develop scBS-map to perform quality control and local alignment of bisulfite sequencing data, chimerical molecule determination and MR removal. Using scBS-map, we show remarkable increases in uniquely mapped reads, genomic coverage and number of CpG sites, and recover more functional elements with precise DNA methylation estimation. Availability and implementation The scBS-map software is freely available at https://github.com/wupengomics/scBS-map. Supplementary information Supplementary data are available at Bioinformatics online.
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Sperling, Adam, Naim Rashid, Niccolo Bolli, et al. "Differential and Limited Expression of Mutant Alleles in Multiple Myeloma." Blood 124, no. 21 (2014): 2007. http://dx.doi.org/10.1182/blood.v124.21.2007.2007.
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Abstract Multiple Myeloma (MM) is a heterogeneous disease but the hallmark genetic changes involve large numbers of genomic rearrangements. Recent studies have focused on attempts to identify individual driver mutations that might provide both prognostic information and unique therapeutic targets. Whole genome and exome sequencing of increasingly large numbers of patient samples have identified a number of commonly mutated genes in MM patients. However, none of these mutations are found in more than one quarter of patients and most are found in less than 10% of samples sequenced. We recently reported a large cohort of MM exome sequences involving 84 samples from 67 patients (Nat Commun. 2014;5:2997). We defined a diverse set of gene mutations with significant heterogeneity across our cohort with a median of 52 (range 21-488) mutations identified per sample. Although computational approaches can be used to prioritize mutations that are expected to alter protein structure and function, it is more challenging to determine which mutations are likely to be clinically meaningful. As a first step towards that understanding, here we report the frequency of expression of mutant alleles in Multiple Myeloma. In this study we report RNA-seq (100 million paired end reads on Illumina HiSeq) data on 14 samples from 10 MM patients for which we have previously performed exome sequencing and correlate allele-specific expression to the DNA mutant allele frequency. We find that a minority, average 27% (range 11-48%), of previously identified DNA mutations are expressed at detectable levels in MM patients. We also compared the allele frequency found in the RNA-seq to that from our exome sequencing to identify genes that demonstrate differential allelic expression and show that this is a common phenomenon in MM patients. We identified 42 such mutations in our analysis supported by at least 10 RNA-seq reads that showed a significant difference as determined by Bayesian hypothesis testing. For instance, the CCND1 mutant allele is expressed at a higher level than would be predicted based on exome-seq frequencies. Another gene showing a similar pattern of increased expression of the mutant allele in one patient was PARP4 (87% in RNA-seq vs 49% in exome-seq). Conversely, the mutant allele frequency of EIF1AX was lower than would be expected suggesting that the mutant allele may be suppressed in our patient (15% in RNA-seq vs 67% in exome-seq). Moreover, among a subset of genes previously identified as recurrently mutated within our patient samples we see that 8/11 (73%) express the mutant allele, providing further evidence that these genes may in fact be important in disease pathogenesis. Therefore, while a large number of mutations have been described in MM, only a small fraction of the mutant alleles have detectable expression and are likely to be biologically relevant. Unbalanced allelic expression of mutant alleles appears to be a relatively common occurrence in MM patients and may help explain why patients with the same identified mutation do not always behave in a similar fashion. This analysis for the first time highlights the important issue that DNA-based reporting of mutations may have significant limitations. It will be important in the future to study expression of mutant alleles in order to understand the biology, generate prognostic models and develop targeted therapies. Disclosures No relevant conflicts of interest to declare.
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Wen, Lei, Changguo Shan, Da Liu, Cheng Zhou, and Linbo Cai. "BSCI-09. Multiomic single cell analysis reveals emerging principles of tumor immune microenvironment inherent to NSCLC brain metastases." Neuro-Oncology Advances 3, Supplement_3 (2021): iii3. http://dx.doi.org/10.1093/noajnl/vdab071.008.
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Abstract Brain is one of the most common sites for distant metastasis of lung cancer. Treatment naïve lung cancer patients diagnosed with brain metastasis are left with very limited options. Checkpoint inhibition is a powerful immunotherapy strategy but delivers benefit only to a small population of patients. Here we harnessed the power and resolution of single cell RNA sequencing and single cell TCR/BCR sequencing to investigate the tumor immune microenvironment (TIME) of NSCLC brain metastases. We enrolled treatment naïve lung cancer patients with brain metastasis. The enrolled subjects covered different histology types and driver gene mutation status. We revealed the emerging principles of innate and adaptive immune components inherent to NSCLC brain metastases. We also uncovered several significant intercellular communication patterns that potentiates cancer cell seeding and fosters cancer cell proliferation. Those results served as a starting point to design optimal immunotherapy strategies for advanced lung cancer patients with limited options.
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Darmanis, Spyros, Steven A. Sloan, Ye Zhang, et al. "A survey of human brain transcriptome diversity at the single cell level." Proceedings of the National Academy of Sciences 112, no. 23 (2015): 7285–90. http://dx.doi.org/10.1073/pnas.1507125112.
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The human brain is a tissue of vast complexity in terms of the cell types it comprises. Conventional approaches to classifying cell types in the human brain at single cell resolution have been limited to exploring relatively few markers and therefore have provided a limited molecular characterization of any given cell type. We used single cell RNA sequencing on 466 cells to capture the cellular complexity of the adult and fetal human brain at a whole transcriptome level. Healthy adult temporal lobe tissue was obtained during surgical procedures where otherwise normal tissue was removed to gain access to deeper hippocampal pathology in patients with medical refractory seizures. We were able to classify individual cells into all of the major neuronal, glial, and vascular cell types in the brain. We were able to divide neurons into individual communities and show that these communities preserve the categorization of interneuron subtypes that is typically observed with the use of classic interneuron markers. We then used single cell RNA sequencing on fetal human cortical neurons to identify genes that are differentially expressed between fetal and adult neurons and those genes that display an expression gradient that reflects the transition between replicating and quiescent fetal neuronal populations. Finally, we observed the expression of major histocompatibility complex type I genes in a subset of adult neurons, but not fetal neurons. The work presented here demonstrates the applicability of single cell RNA sequencing on the study of the adult human brain and constitutes a first step toward a comprehensive cellular atlas of the human brain.
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Zekri, Jamal, Abdelrazak Meliti, Mohammed Abhas Baghdadi, Turki Sobahy, and Saba Imtiaz. "Prognostic and predictive biomarkers for clear cell renal cell carcinoma utilizing next generation sequencing." Journal of Clinical Oncology 37, no. 15_suppl (2019): e16070-e16070. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e16070.
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e16070 Background: The management of the clear cell renal cell carcinoma (cc-RCC) has evolved over the past decade. Clinical patients’ and tumor characteristics have a limited role in predicting the outcome of these patients. The search for reliable prognostic and predictive biomarkers should be pursued in particular during the current era of next generation sequencing (NGS) technology. Methods: Formalin-Fixed Paraffin-Embedded (FFPE) tissue specimens of cc-RCC were sequenced using NGS and a customized gene panel testing for 72 tumor-related genes. High potential variants were defined by mutation effect (stop-loss, stop-gain, frame-shift substitutions or non-synonymous SNV) and class (pathogenic or likely pathogenic). Cases with identical variants were identified. Results: In total, all 47 cases had 69,052 variants, of which 20,453 were classified as high potential variants. Identical alterations in 15 genes were present in all samples. These genes are: MUC3A, MUC12, MUC7, SRRT, MUC2, MUC5AC, MUC5B, MUC22, MUC6, CR1, MUC4, MUC16, MUC19, MUC17 and MERTK. The numbers of identical and non-identical variants in these 15 genes were counted for each sample. Median number of variants was 377 and was selected as a cut off to define cases with high ( > 377) and low (≤377) variants number (HVN and LVN respectively). For the whole cohort, HVN was associated with shorter overall survival compared to LVN (Median 50 months vs. not reached; Log Rank P = 0.041). In the 11 patients who received anti-angiogenic tyrosine kinase inhibitors (TKIs), HVN was associated with a trend of shorter progression free survival (Median 5 vs. 10 months; Log Rank P = not significant). Conclusions: Alterations in SRRT, CR1, MERTK and MUCIN family genes are very common in RCC. HVN ( > 377) is associated with worse prognosis and may predict decreased benefit from anti-angiogenic TKIs.
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Chow, Jacky, Nicholas C. Hoffend, Scott I. Abrams, Thomas Schwaab, Anurag K. Singh, and Jason B. Muhitch. "Radiation induces dynamic changes to the T cell repertoire in renal cell carcinoma patients." Proceedings of the National Academy of Sciences 117, no. 38 (2020): 23721–29. http://dx.doi.org/10.1073/pnas.2001933117.
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Clinical studies combining radiation and immunotherapy have shown promising response rates, strengthening efforts to sensitize tumors to immune-mediated attack. Thus, there is an ongoing surge in trials using preconditioning regimens with immunotherapy. Yet, due to the scarcity of resected tumors treated in situ with radiotherapy, there has been little investigation of radiation’s sole contributions to local and systemic antitumor immunity in patients. Without this access, translational studies have been limited to evaluating circulating immune subsets and systemic remodeling of peripheral T cell receptor repertoires. This constraint has left gaps in how radiation impacts intratumoral responses and whether tumor-resident T cell clones are amplified following treatment. Therefore, to interrogate the immune impact of radiation on the tumor microenvironment and test the hypothesis that radiation initiates local and systemic expansion of tumor-resident clones, we analyzed renal cell carcinomas from patients treated with stereotactic body radiation therapy. Transcriptomic comparisons were evaluated by bulk RNA sequencing. T cell receptor sequencing monitored repertoires during treatment. Pathway analysis showed radiation-specific enrichment of immune-related processes, and T cell receptor sequencing revealed increased clonality in radiation-treated tumors. The frequency of identified, tumor-enriched clonotypes was tracked across serial blood samples. We observed increased abundance of tumor-enriched clonotypes at 2 wk postradiation compared with pretreatment levels; however, this expansion was not sustained, and levels contracted toward baseline by 4 wk posttreatment. Taken together, these results indicate robust intratumoral immune remodeling and a window of tumor-resident T cell expansion following radiation that may be leveraged for the rational design of combinatorial strategies.
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Fabre, Margarete A., Thomas McKerrell, Maximillian Zwiebel, et al. "Concordance for clonal hematopoiesis is limited in elderly twins." Blood 135, no. 4 (2020): 269–73. http://dx.doi.org/10.1182/blood.2019001807.
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Abstract Although acquisition of leukemia-associated somatic mutations by 1 or more hematopoietic stem cells is inevitable with advancing age, its consequences are highly variable, ranging from clinically silent clonal hematopoiesis (CH) to leukemic progression. To investigate the influence of heritable factors on CH, we performed deep targeted sequencing of blood DNA from 52 monozygotic (MZ) and 27 dizygotic (DZ) twin pairs (aged 70-99 years). Using this highly sensitive approach, we identified CH (variant allele frequency ≥0.5%) in 62% of individuals. We did not observe higher concordance for CH within MZ twin pairs as compared with that within DZ twin pairs, or to that expected by chance. However, we did identify 2 MZ pairs in which both twins harbored identical rare somatic mutations, suggesting a shared cell of origin. Finally, in 3 MZ twin pairs harboring mutations in the same driver genes, serial blood samples taken 4 to 5 years apart showed substantial twin-to-twin variability in clonal trajectories. Our findings propose that the inherited genome does not exert a dominant influence on the behavior of adult CH and provide evidence that CH mutations may be acquired in utero.
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Nahku, Ranno, Karl Peebo, Kaspar Valgepea, Jeffrey E. Barrick, Kaarel Adamberg, and Raivo Vilu. "Stock culture heterogeneity rather than new mutational variation complicates short-term cell physiology studies of Escherichia coli K-12 MG1655 in continuous culture." Microbiology 157, no. 9 (2011): 2604–10. http://dx.doi.org/10.1099/mic.0.050658-0.
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Nutrient-limited continuous cultures in chemostats have been used to study microbial cell physiology for over 60 years. Genome instability and genetic heterogeneity are possible uncontrolled factors in continuous cultivation experiments. We investigated these issues by using high-throughput (HT) DNA sequencing to characterize samples from different phases of a glucose-limited accelerostat (A-stat) experiment with Escherichia coli K-12 MG1655 and a duration regularly used in cell physiology studies (20 generations of continuous cultivation). Seven consensus mutations from the reference sequence and five subpopulations characterized by different mutations were detected in the HT-sequenced samples. This genetic heterogeneity was confirmed to result from the stock culture by Sanger sequencing. All the subpopulations in which allele frequencies increased (betA, cspG/cspH, glyA) during the experiment were also present at the end of replicate A-stats, indicating that no new subpopulations emerged during our experiments. The fact that ~31 % of the cells in our initial cultures obtained directly from a culture stock centre were mutants raises concerns that even if cultivations are started from single colonies, there is a significant chance of picking a mutant clone with an altered phenotype. Our results show that current HT DNA sequencing technology allows accurate subpopulation analysis and demonstrates that a glucose-limited E. coli K-12 MG1655 A-stat experiment with a duration of tens of generations is suitable for studying cell physiology and collecting quantitative data for metabolic modelling without interference from new mutations.
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Rodriguez-Fraticelli, Alejo E., Caleb S. Weinreb, Allon Moshe Klein, Shou-Wen Wang, and Fernando D. Camargo. "Combined Single Cell Lineage and Transcriptome Sequencing Unveils Cell-Autonomous Regulators of Hematopoietic Stem Cell Fate." Blood 134, Supplement_1 (2019): 446. http://dx.doi.org/10.1182/blood-2019-123447.
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Blood regeneration upon transplantation relies on the activity of long-term repopulating hematopoietic stem cells (LT-HSCs). One of the major controversies in hematopoiesis relates to the apparently different properties that HSCs have in transplantation versus unperturbed settings. In unperturbed steady state hematopoiesis, the most potent HSCs appear to be mostly dormant, and only producing platelet-lineage cells. In turn, upon transplant, even a single transplanted HSC can actively divide and regenerate hundreds of millions of blood progenitors of all lineages. It would thus appear that HSCs have different fundamental properties in each study system. However, most transplantation studies have only tracked the lineage output of the transplanted HSC clones, and rarely the regeneration of the HSC compartment itself. In addition, clonal assays have not been performed at sufficient resolution to fully capture the diversity and clonal complexity of the regenerated HSC compartment. Here, we have used expressible barcodes, which can be sequenced in conventional single cell RNAseq assays, to simultaneously record the functional outcomes and transcriptional states of thousands of HSCs. Our analysis revealed multiple clonal HSC behaviors following transplantation that drastically differ in their differentiation activity, lineage-bias and self-renewal. Surprisingly, we witnessed a large fraction of clones that efficiently repopulate the HSC compartment but show limited contribution to differentiated progeny. Furthermore, these inactive clones have increased competitive multilineage serial repopulating capacity, implying that shortly after transplant a subset of clones reestablishes the native-like LT-HSC behaviors. Our results also argue that this clonal distribution of labor is controlled by cell autonomous, heritable properties (i.e. the epigenetic cell state). Then, using only our clonal readouts to segregate single HSC transcriptomes, we unveiled the transcriptional signatures that associated with unique HSC outcomes (platelet bias, clonal expansion, dormancy, etc.) and unraveled, for the first time, a gene signature for functional long-term serially repopulating clones. We interrogated the drivers of this cell state using an in vivo inducible CRISPR screening and identified 5 novel regulators that are required to regenerate the HSC compartment in a cell autonomous fashion. In conclusion, we demonstrate that functional LT-HSCs share more similar properties in native and transplantation hematopoiesis than previously expected. Consequently, we unveil a definition of the essential, common functional properties of HSCs and the molecular programs that control them. Figure 1 Disclosures No relevant conflicts of interest to declare.
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Fu, Yusi, Chunmei Li, Sijia Lu, et al. "Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification." Proceedings of the National Academy of Sciences 112, no. 38 (2015): 11923–28. http://dx.doi.org/10.1073/pnas.1513988112.
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Whole-genome amplification (WGA) for next-generation sequencing has seen wide applications in biology and medicine when characterization of the genome of a single cell is required. High uniformity and fidelity of WGA is needed to accurately determine genomic variations, such as copy number variations (CNVs) and single-nucleotide variations (SNVs). Prevailing WGA methods have been limited by fluctuation of the amplification yield along the genome, as well as false-positive and -negative errors for SNV identification. Here, we report emulsion WGA (eWGA) to overcome these problems. We divide single-cell genomic DNA into a large number (105) of picoliter aqueous droplets in oil. Containing only a few DNA fragments, each droplet is led to reach saturation of DNA amplification before demulsification such that the differences in amplification gain among the fragments are minimized. We demonstrate the proof-of-principle of eWGA with multiple displacement amplification (MDA), a popular WGA method. This easy-to-operate approach enables simultaneous detection of CNVs and SNVs in an individual human cell, exhibiting significantly improved amplification evenness and accuracy.
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Aleshin, Alexey, Robert Durruthy-Durruthy, Bruno C. Medeiros, Dennis J. Eastburn, and Peter L. Greenberg. "Single-Cell Mutational Profiling Describes the Molecular Heterogeneity of Clonal Evolution in MDS during Therapy and Relapse." Blood 132, Supplement 1 (2018): 5503. http://dx.doi.org/10.1182/blood-2018-99-120368.
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Abstract Background: Myelodysplastic syndromes (MDS) are a collection of clonal diseases of dysfunctional hematopoietic stem cells, characterized by ineffective hematopoiesis, cytopenias, and dysplasia. Increased understanding of the mutational landscape of MDS has led to initial improvements in prognostic models based on clinical and cytogenetic variables. However, bulk sequencing techniques are limited in their ability to delineate clonal complexity and identify rare drug resistant subclones. To better understand clonal heterogeneity and clonal evolution of MDS we applied a high-throughput single cell sequencing technique to both diagnostic and longitudinal MDS samples. Methods: Samples were examined for 5 patients with MDS at diagnosis and, when available, progression. Mutational bulk sequencing was performed by NGS panel sequencing and exon sequencing was available in select cases. Single cell processing was performed using the Tapestri (Mission Bio) platform. Briefly, individual cells were isolated using a microfluidic approach, followed by barcoding and genomic DNA amplification for individual cancer cells confined to droplets. Barcodes are then used to reassemble the genetic profiles of cells from next generation sequencing data. We applied this approach to individual MDS samples, genotyping the most clinically relevant loci across upwards of 10,000 individual cells. Results: Single-cell sequencing was able to be performed successfully on all samples tested and recapitulated bulk sequencing data. We observed high concordance between bulk variant allele frequencies (VAFs) and sample level VAFs derived from single cell sequencing data (r2 = 0.98). Additionally, single cell analysis allowed for resolution of subclonal architecture and tumor phylogenetic evolution beyond what was predicted from bulk sequencing alone. Single-cell SNVs were able to resolve host and donor cell populations after bone marrow transplant and accurately predict chimerism and disease relapse. Furthermore, we were able to resolve the co-occurance of molecular alterations within subclones and establish zygosity of individual mutations at a single cell level. Rare subclones associated with disease relapse, were able to be identified in initial diagnostic samples that were frequently under the limit of detection of bulk NGS. Conclusions: Our results suggest more molecular complexity in MDS tumor samples than implied from bulk sequencing methods alone and indicates utility of single-cell sequencing for identification of resistant clones and longitudinal therapy monitoring. Disclosures Aleshin: Mission Bio, Inc.: Consultancy; Natera, Inc.: Employment. Durruthy-Durruthy:Mission Bio, Inc.: Employment, Equity Ownership. Medeiros:Genentech: Employment; Celgene: Consultancy, Research Funding. Eastburn:Mission Bio, Inc.: Employment, Equity Ownership.
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Gao, Fang-Fang, Li Chen, Shi-Ping Bo, et al. "ChromInst: A single cell sequencing technique to accomplish pre-implantation comprehensive chromosomal screening overnight." PLOS ONE 16, no. 5 (2021): e0251971. http://dx.doi.org/10.1371/journal.pone.0251971.
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Next Generation Sequencing (NGS) is a powerful tool getting into the field of clinical examination. Its preliminary application in pre-implantation comprehensive chromosomal screening (PCCS) of assisted reproduction (test-tube baby) has shown encouraging outcomes that improves the success rate of in vitro fertilization. However, the conventional NGS library construction is time consuming. In addition with the whole genome amplification (WGA) procedure in prior, makes the single cell NGS assay hardly be accomplished within an adequately short turnover time in supporting fresh embryo implantation. In this work, we established a concise single cell sequencing protocol, ChromInst, in which the single cell WGA and NGS library construction were integrated into a two-step PCR procedure of ~ 2.5hours reaction time. We then validated the feasibility of ChromInst for overnight PCCS assay by examining 14 voluntary donated embryo biopsy samples in a single sequencing run of Miseq with merely 13M reads production. The good compatibility of ChromInst with the restriction of Illumina sequencing technique along with the good library yield uniformity resulted superior data usage efficiency and reads distribution evenness that ensures precisely distinguish of 6 normal embryos from 8 abnormal one with variable chromosomal aneuploidy. The superior succinctness and effectiveness of this protocol permits its utilization in other time limited single cell NGS applications.
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Ganan-Gomez, Irene, Hui Yang, Feiyang Ma, et al. "Single-Cell RNA Sequencing Reveals Distinct Hematopoietic Stem Cell Hierarchies in MDS." Blood 134, Supplement_1 (2019): 771. http://dx.doi.org/10.1182/blood-2019-128798.
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Myelodysplastic Syndromes (MDS) are a group of heterogeneous stem cell disorders that result in inefficient hematopoiesis. Although the genetic and cytogenetic landscapes of MDS have been well characterized (Papaemmanuil 2013, Sperling 2017), little is known about the differentiation abnormalities that underlie the MDS phenotype. Gaining insights on how different hematopoietic stem and progenitor cell (HSPC) types contribute to MDS is essential for the design of new targeted therapies to supplement the currently limited effective therapeutic options. To understand the contribution of different cell types to the pathogenesis of MDS, we analyzed the expression profile of the Lin-CD34+ HSPC compartment at the single-cell level. Single-cell RNA-sequencing (scRNA-seq) analysis of HSPCs isolated from 2 MDS patients and 2 age-matched healthy donor samples revealed distinct cell clusters driven by the sample type and the differentiation potential of the cells. To annotate the specific subsets of HSPCs in each cluster, we scored them on the basis of previously reported population-specific gene signatures (Laurenti 2013, Psaila 2016, Van Galen 2019). Whereas CD34+ cells from the 2 healthy donor bone marrow (BM) samples largely overlapped with each other and displayed 2 distinct erythroid/megakaryocytic (Er/Mk; cluster 3) and lympho/myeloid (clusters 2, 5) differentiation trajectories in line with the current view of hematopoiesis, CD34+ cells from the 2 MDS BM samples clustered separately and showed predominantly myeloid differentiation routes (Fig a). Importantly, differential expression analysis of the HSPCs from the 2 MDS samples (Fig b) showed that cells residing atop of the HSPC hierarchy retained the transcriptional profile of immature HSCs in one of the samples (clusters 2, 4), while they were characterized by the expression of genes involved in the differentiation of myelo/lympho multipotent progenitor cells (clusters 0, 1) in the other. However, pseudotime analysis of the HSPCs' transcriptional dynamics showed that, despite the distinct differentiation state of the early hematopoietic cells in each group, the differentiation trajectories of those cells converged at the late myeloid progenitor state (clusters 3, 5, 6). These results suggest that, although the earlier HSC architecture is heterogeneous across MDS patients, the more differentiated myeloid progenitor compartment is similarly compromised and is responsible for the clinical phenotypes of MDS. To confirm differential cell-type contributions to the MDS hierarchy, we immunophenotyped BM samples from 123 untreated patients using multicolor flow cytometry. We applied principal component analysis and logistic regression to group samples based on their cellular compositions. Our mathematical classifier stratified patients in 2 groups, which had markedly different cellular repertoires consistent with our scRNA-seq results (Fig c). Patients with different MDS stem cell hierarchies did not present with significantly different clinical characteristics at diagnosis. These data confirm that different abnormal hematopoietic trajectories converge in the myeloid bias typically observed in MDS hematopoiesis. Next, we exome-sequenced mononuclear cells and T-cells from 45 untreated MDS patients and identified high-confidence somatic mutations in known oncogenes and/or leukemia driver genes. The median number of mutations (n=3) was not significantly different between MDS groups 1 and 2. We identified 4 genes that were differentially mutated in the 2 MDS architectures (Fig d), which suggested that certain mutations may predispose for a specific HSPC phenotype. However, mutation specificity could not fully account for the origin of the 2 differentiation architectures, which were independent on the genetic background in most patients. In conclusion, we demonstrated that MDS are sustained by distinct and recurrent abnormal HSPC differentiation hierarchies. Diverse cellular compositions suggest that different cell-type specific signaling pathways maintain the disease in each group of patients. Our work shows that the characterization of the cellular diversity in the hematopoietic compartment can be used as a biomarker to stratify MDS patients, and warrants further studies to predict the intrinsic vulnerabilities of the cells involved in the pathogenesis and maintenance of MDS in a patient-specific manner. Figure Disclosures Garcia-Manero: Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Colla:IONIS: Other: Intellectual property and research material IONIS); Amgen: Research Funding; Abbvie: Research Funding.
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McDermott, David F., Jae-Lyun Lee, Frede Donskov, et al. "Association of gene expression with clinical outcomes in patients with renal cell carcinoma treated with pembrolizumab in KEYNOTE-427." Journal of Clinical Oncology 38, no. 15_suppl (2020): 5024. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.5024.
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5024 Background: We assessed the association of baseline RNA-sequencing–based gene expression signatures and DNA alterations with response or resistance to pembrolizumab in patients with advanced renal cell carcinoma in cohorts A (clear cell; n = 110) and B (non-clear cell; n = 165) of the phase 2 KEYNOTE-427 study (NCT02853344). Methods: Using RNA-sequencing, we analyzed the association of gene expression signatures (18-gene T-cell–inflamed gene expression profile [GEP]; 10 non–T-cell–inflamed GEP canonical signatures [angiogenesis, gMDSC, glycolysis, hypoxia, mMDSC, MYC, proliferation, RAS, stromal/EMT/TGFβ, WNT]) quantifying tumor microenvironment elements (TME) with objective response rate (ORR) and progression-free survival (PFS). Canonical signatures were derived from 2 databases (TCGA, Moffit) using an algorithm that included genes based on their correlation to reference signatures in the literature. Signature definitions were finalized before linking to the clinical data, and significance was prespecified at 0.10 given the potential for limited power. Canonical signatures were analyzed through regression testing of response for association with consensus signatures after adjusting for T-cell–inflamed GEP and International Metastatic RCC Database Consortium scores in the model. P values were adjusted for multiplicity. Using whole exome sequencing, we also summarized the association of renal cell carcinoma driver gene mutations with ORR. Clinical data cutoff: Jan 30, 2019. Results: Patient characteristics for this analysis were comparable to the overall population. In cohort A, T-cell–inflamed GEP (n = 78) was statistically significantly associated with a better ORR ( P = 0.021; AUROC = 0.65) but not PFS ( P = 0.116). No other TME canonical signatures showed a correlation with ORR or PFS. ORR was estimated for mutations (Table). Conclusions: RNA-sequencing–based, T-cell–inflamed GEP was associated with ORR in patients with clear cell renal cell carcinoma receiving first-line pembrolizumab. Precision was limited by sample size for estimating ORR by specific gene mutation status. Evaluation of tissue-based biomarkers in larger studies are planned. Biomarker analyses from patients in cohort B will also be presented. Clinical trial information: NCT02853344 . [Table: see text]
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Yang, Shunli, Pei Zhihua, Jianing Yu, et al. "Early cancer detection using low-coverage whole-genome sequencing of cell-free DNA fragments." Journal of Clinical Oncology 39, no. 15_suppl (2021): e22510-e22510. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e22510.
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e22510 Background: Recent advances in circulating cell-free DNA (cfDNA) of plasma have shown that tumor diagnosis based on tumor-specific genetic and epigenetic changes (e.g., somatic mutations, copy number variations, and DNA methylation) is a promising non-invasive method. However, the number of tumor-specific genomic variants identified by whole-genome sequencing (WGS) in early cancer patients is very limited. Moreover, the mutations generated by clonal hematopoiesis in cfDNA can further confound the detection of cancer-specific mutations. It has been shown that ctDNA and cfDNA fragments have differences in length distribution. Compared with a limited number of genomic mutations, cfDNA fragment size index (FSI) is more abundant and easier to be detected. Methods: We designed a novel method for fragment detection of plasma cfDNA based on low-coverage WGS. The fragment length differences between healthy individuals and tumor patients were systematically analyzed. The training dataset includes 50 healthy individuals and 354 patients from eight different cancers. After the data preprocessing, we calculated the weight of fragmental bins and built a model for distinguishing healthy individuals from cancer patients. An independent dataset involving 22 healthy controls and 340 cancer patients was used to validate the model. The performance of our method was measured by the area under the curve (AUC) using the one-versus-all approach. Results: In our analysis, a total of 504 markers were selected from the dataset for model construction. Our model performed well for all cancer types on both training (AUC = 0.804) and validation (AUC = 0.837) datasets. Conclusions: The good performance of our model in large-scale plasma samples demonstrates the potential clinical application of cfDNA fragment analysis in early cancer detection based on low-coverage WGS.
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Tytgat, Olivier, Yannick Gansemans, Jana Weymaere, Kaat Rubben, Dieter Deforce, and Filip Van Nieuwerburgh. "Nanopore Sequencing of a Forensic STR Multiplex Reveals Loci Suitable for Single-Contributor STR Profiling." Genes 11, no. 4 (2020): 381. http://dx.doi.org/10.3390/genes11040381.
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Nanopore sequencing for forensic short tandem repeats (STR) genotyping comes with the advantages associated with massively parallel sequencing (MPS) without the need for a high up-front device cost, but genotyping is inaccurate, partially due to the occurrence of homopolymers in STR loci. The goal of this study was to apply the latest progress in nanopore sequencing by Oxford Nanopore Technologies in the field of STR genotyping. The experiments were performed using the state of the art R9.4 flow cell and the most recent R10 flow cell, which was specifically designed to improve consensus accuracy of homopolymers. Two single-contributor samples and one mixture sample were genotyped using Illumina sequencing, Nanopore R9.4 sequencing, and Nanopore R10 sequencing. The accuracy of genotyping was comparable for both types of flow cells, although the R10 flow cell provided improved data quality for loci characterized by the presence of homopolymers. We identify locus-dependent characteristics hindering accurate STR genotyping, providing insights for the design of a panel of STR loci suited for nanopore sequencing. Repeat number, the number of different reference alleles for the locus, repeat pattern complexity, flanking region complexity, and the presence of homopolymers are identified as unfavorable locus characteristics. For single-contributor samples and for a limited set of the commonly used STR loci, nanopore sequencing could be applied. However, the technology is not mature enough yet for implementation in routine forensic workflows.
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Joseph, Richard W., Ryan J. Sullivan, Ahmad A. Tarhini, and Richard M. Sherry. "Combination and Sequencing of Therapies for the Treatment of Metastatic Melanoma." Oncology & Hematology Review (US) 09, no. 01 (2013): 36. http://dx.doi.org/10.17925/ohr.2013.09.1.36.
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Advances in molecular biology have lead to the development and approval of new treatments that improve survival for patients with metastatic melanoma. The limited duration of clinical response for targeted agents and the low overall response rate for immunotherapies illustrate that more work needs to be carried out. There are four Food and Drug Administration (FDA)-approved therapies for the treatment of metastatic melanoma. The optimal sequence of these therapies remains unclear with no prospective data to guide clinicians. It is also uncertain whether combining agents with different cell signaling properties or different immune mechanisms will provide therapeutic benefit to patients. Defining appropriate therapeutic sequences and or combinations may lead to improved treatment efficacy and is currently an area of active research. In this article we discuss recent advances in the treatment of metastatic melanoma and current treatment limitations. We summarize the limited clinical data on the sequencing and combination of therapies and discuss future therapeutic strategies.
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Salem, Karma, Jihye Park, Claudia Freymond, et al. "Whole Exome Sequencing and Targeted Sequencing Reveal the Heterogeneity of Genomic Evolution and Mutational Profile in Smoldering Multiple Myeloma." Blood 128, no. 22 (2016): 237. http://dx.doi.org/10.1182/blood.v128.22.237.237.
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Abstract Introduction: Recent data shows that multiple myeloma (MM) almost always arises from precursor states called Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), but not all patients with MGUS or SMM develop MM. Risk factors of progression for SMM patients are largely based on tumor load as represented by an M-protein ≥ 3 g/dL, a free light chain (FLC) ratio outside the range of 0.125 to 8, and ≥ 10% plasma cells in the BM. However, the genetic lesions that underlie progression, the molecular factors that cause rapid versus slow progression, and the factors that distinguish the relatively indolent MGUS from SMM are not well known. Further, the genomic landscape of SMM is not well characterized. One potential factor is MYC overexpression. Bergsagel et al. have found that MYC levels increase when comparing MGUS, SMM and overt MM. Other frequently altered pathways in MM are NF-kB, MAPK and DNA damage. In addition, limited studies of paired SMM and MM samples show that in many cases, the aggressive subclones can already be detected, in small cell fractions, before overt MM develops. However, the cause of progression to MM is unclear, in large part because sequential genomic studies of MGUS/SMM progression have yet to be undertaken. To address these questions, in this study we examine clinically-annotated samples from patients with SMM. Methods: We performed whole exome sequencing (WES) (mean target coverage 50X/100X) on 49 germline-tumor matched samples from patients with SMM (DNA from bone marrow CD138+ plasma cells matched with germline DNA from peripheral blood mononuclear cells). Libraries were constructed using Agilent SureSelect XT2 library prep kit, and hybridized to Agilent's whole exome V5+UTR capture probes and then sequenced on HiSeq 2500 (Illumina). We also performed targeted deep sequencing using a custom enrichment bait set on 25 samples of progressor (n=12) and non-progressor (n=13) SMM samples. Libraries were also constructed with Agilent SureSelect XT2 library prep kit and enriched by hybridizing to an in-house designed customized target bait, then sequenced on HiSeq 2500. Sequencing data were analyzed using previously established analytic pipelines including MuTect, RecapSeg, GISTIC, MutSig, and ABSOLUTE. Results: The number of Somatic Single Nucleotide Variants (SSNVs) seen in SMM ranged from 1 to 98 nonsilent mutations with an average of 1.14 mutations/Mb, which is slightly lower than MM (1.6 mutations/Mb) from previous studies (p-value=0.05). This large and varying range of mutational load among samples suggests that SMM is likely a heterogenous entity where some patients are closer to MGUS and others closer to MM. We identified likely drivers in SMM in about ~32% of the samples, including mutations in MM candidate driver genes such as NRAS, KRAS and PTPN11(overall 36 events were present in COSMIC). SMM also had somatic CNAs in about ~50% of SMM samples, such as hyperdiploidy, gain of chromosome 1q, deletion of 13p and 17p, which match the hallmark chromosome changes seen in MM. Comparing deep targeted sequencing of 100 genes (mean target coverage 361X) in samples from 12 SMM patients who progressed to myeloma vs. 13 SMM patients who did not, we found non-synonymous mutations exclusive to progressors, suggesting that with more samples we may find genetic alterations that predict progression in SMM. Conclusion: This study demonstrates that WES and targeted sequencing of SMM patients can detect MM candidate driver genes as well as hallmark CNAs seen in MM patients. Further, there may be potential different mutational features between progressors and non-progressors. Thus, this approach can be used to identify genetic drivers of clonal progression from MGUS/SMM to MM that may present opportunities for early therapeutic intervention and prevention of disease progression. Disclosures Roccaro: Takeda Pharmaceutical Company Limited: Honoraria. Ghobrial:Takeda: Honoraria; Noxxon: Honoraria; Amgen: Honoraria; Novartis: Honoraria; BMS: Honoraria, Research Funding; Celgene: Honoraria, Research Funding.
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Ediriwickrema, Asiri, Alexey Aleshin, Johannes G. Reiter, et al. "Single-cell mutational profiling enhances the clinical evaluation of AML MRD." Blood Advances 4, no. 5 (2020): 943–52. http://dx.doi.org/10.1182/bloodadvances.2019001181.
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Abstract Although most patients with acute myeloid leukemia (AML) achieve clinical remission with induction chemotherapy, relapse rates remain high. Next-generation sequencing enables minimal/measurable residual disease (MRD) detection; however, clinical significance is limited due to difficulty differentiating between pre-leukemic clonal hematopoiesis and frankly malignant clones. Here, we investigated AML MRD using targeted single-cell sequencing (SCS) at diagnosis, remission, and relapse (n = 10 relapsed, n = 4 nonrelapsed), with a total of 310 737 single cells sequenced. Sequence variants were identified in 80% and 75% of remission samples for patients with and without relapse, respectively. Pre-leukemic clonal hematopoiesis clones were detected in both cohorts, and clones with multiple cooccurring mutations were observed in 50% and 0% of samples. Similar clonal richness was observed at diagnosis in both cohorts; however, decreasing clonal diversity at remission was significantly associated with longer relapse-free survival. These results show the power of SCS in investigating AML MRD and clonal evolution.
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Ascensión, Alex M., Marcos J. Araúzo-Bravo, and Ander Izeta. "The need to reassess single-cell RNA sequencing datasets: more is not always better." F1000Research 10 (August 6, 2021): 767. http://dx.doi.org/10.12688/f1000research.54864.1.
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Background: The advent of single-cell RNA sequencing (scRNAseq) and additional single-cell omics technologies have provided scientists with unprecedented tools to explore biology at cellular resolution. However, reaching an appropriate number of good quality reads per cell and reasonable numbers of cells within each of the populations of interest are key to infer conclusions from otherwise limited analyses. For these reasons, scRNAseq studies are constantly increasing the number of cells analysed and the granularity of the resultant transcriptomics analyses. Methods: We aimed to identify previously described fibroblast subpopulations in healthy adult human skin by using the largest dataset published to date (528,253 sequenced cells) and an unsupervised population-matching algorithm. Results: Our reanalysis of this landmark resource demonstrates that a substantial proportion of cell transcriptomic signatures may be biased by cellular stress and response to hypoxic conditions. Conclusions: We postulate that the ”more is better” approach, currently prevalent in the scientific community, might undermine the extent of the analysis, possibly due to long computational processing times inherent to large datasets.
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Funkhouser, J. D., S. D. Tangada, M. Jones, S. J. O, and R. D. Peterson. "p146 type II alveolar epithelial cell antigen is identical to aminopeptidase N." American Journal of Physiology-Lung Cellular and Molecular Physiology 260, no. 4 (1991): L274—L279. http://dx.doi.org/10.1152/ajplung.1991.260.4.l274.
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A prominent membrane protein of rat type II alveolar cells, p146, was originally identified by one of many mouse monoclonal antibodies that were produced to rat lung cells in the course of a search for differentiation antigens that might prove useful in studying lung differentiation. We report here results from analysis of the primary structure of this molecule and, based on this knowledge, the elucidation of the function of the protein. Amino acid sequencing of the NH2-terminal portion of the p146 protein, plus partial sequencing of several peptides obtained by limited proteolysis, indicates it is identical to aminopeptidase N. Further, the immunoaffinity purified p146 protein has aminopeptidase N activity. The discussion includes references to other molecules such as CD 13 and CD 10 (CALLA) that were recognized as differentiation antigens and subsequently found to be peptidases. The possible biological implications of such a peptidase on the luminal surface of type II alveolar cells are also considered.
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AL-Dewik, Nader I., and M. Walid Qoronfleh. "Genomics and Precision Medicine: Molecular Diagnostics Innovations Shaping the Future of Healthcare in Qatar." Advances in Public Health 2019 (March 19, 2019): 1–11. http://dx.doi.org/10.1155/2019/3807032.
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Unprecedented developments in genomics research and ancillary technologies are creating the potential for astonishing changes in both the healthcare field and the life sciences sector. The innovative genomics applications include the following: (1) embracing next generation sequencing (NGS) in clinical diagnostics setting (applying both whole genome and exome sequencing), (2) single cell sequencing studies, (3) quantifying gene expression changes (including whole transcriptome sequencing), (4) pharmacogenomics, and (5) cell-free DNA blood-based testing. This minireview describes the impact of clinical genomics disruptive innovations on the healthcare system in order to provide better diagnosis and treatment. The observed evolution is not limited to the point-of-care services. Genomics technological breakthroughs are pushing the healthcare environment towards personalized healthcare with the real potential to attain better wellbeing. In this article, we will briefly discuss the Gulf region population-based genome initiatives that intend to improve personalized healthcare by offering better prevention, diagnosis, and therapy for the individual (precision medicine). Qatar’s endeavor in genomics medicine will be underscored including the private Applied Biomedicine Initiative (ABI).
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Aleshin, Alexey, Robert Durruthy-Durruthy, M. Ryan Corces, et al. "Single-Cell Mutational Profiling of Clonal Evolution in De Novo AML during Therapy and Relapse." Blood 132, Supplement 1 (2018): 1469. http://dx.doi.org/10.1182/blood-2018-99-118089.
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Abstract Background: De novo acute myeloid leukemia (AML) is a molecularly heterogeneous disorder with clinically variable outcomes. Recent studies on the mutational landscape of AML have been informative in better stratifying risk of relapse. However, bulk sequencing techniques have been limited in their ability to delineate the true complexity of tumoral molecular heterogeneity and allow for efficient identification of drug resistant subclones. Here, we applied high-throughput single cell sequencing technique to identify patterns of clonal heterogeneity and evolution in longitudinal samples from patients with AML undergoing induction chemotherapy. Methods: Matched diagnosis, remission, and relapse samples were examined for 20 de novo AML cases including 15 relapsed and 5 non-relapsed controls. Mutational bulk sequencing was performed by NGS panel sequencing and exome sequencing was available in select cases. Single cell processing was performed using the Tapestri (Mission Bio) platform. Briefly, individual cells were isolated using a microfluidic approach, followed by barcoding and genomic DNA amplification for individual cancer cells confined to droplets. Barcodes were then used to reassemble the genetic profiles of cells from next generation sequencing data. We applied this approach to individual AML samples, genotyping the most clinically relevant loci across upwards of 10,000 individual cells. Results: Targeted single-cell sequencing was able to recapitulate bulk sequencing data from both peripheral blood and bone marrow aspirate samples. We observed high concordance between bulk VAFs and sample level VAFs derived from single cell sequencing data. Additionally, single cell analysis allowed for resolution of subclonal architecture and tumor phylogenetic evolution beyond what was predicted from bulk sequencing alone. Rare subclones associated with disease relapse, were identified in initial diagnostic samples that were frequently under the limit of detection of bulk NGS. Conclusions:Taken together, our results suggest a greater degree of heterogeneity in de novo AML samples than suggested with bulk sequencing methods alone and shows the utility of single-cell sequencing for longitudinal monitoring and identification of resistant clones prior to therapy initiation in select patients. We show here that this approach is a feasible and effective way to identify and track heterogeneous populations of cells in AML and may be valuable for MRD identification. Disclosures Aleshin: Mission Bio, Inc.: Consultancy; Natera, Inc.: Employment. Durruthy-Durruthy:Mission Bio, Inc.: Employment, Equity Ownership. Liedtke:Prothena: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech/Roche: Research Funding; Caelum: Membership on an entity's Board of Directors or advisory committees; Amgen/Onyx: Consultancy, Honoraria, Research Funding; BlueBirdBio: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; celgene: Research Funding. Medeiros:Celgene: Consultancy, Research Funding; Genentech: Employment. Eastburn:Mission Bio, Inc.: Employment, Equity Ownership.
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Obulareddy, Nisita, Shweta Panchal, and Maeli Melotto. "Guard Cell Purification and RNA Isolation Suitable for High-Throughput Transcriptional Analysis of Cell-Type Responses to Biotic Stresses." Molecular Plant-Microbe Interactions® 26, no. 8 (2013): 844–49. http://dx.doi.org/10.1094/mpmi-03-13-0081-ta.
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Stomata, micro-pores on the leaf surface, are formed by a pair of guard cells. In addition to controlling water loss and gas exchange between the plant and the environment, these cells act as immunity gates to prevent pathogen invasion of the plant apoplast. Here, we report a brief procedure to obtain highly pure guard cell preparations using conditions that preserve the guard cell transcriptome as much as possible for a robust high-throughput RNA sequence analysis. The advantages of this procedure included i) substantial shortening of the time required for obtaining high yield of >97% pure guard cell protoplasts (GCP), ii) extraction of enough high quality RNA for direct sequencing, and iii) limited RNA decay during sample manipulation. Gene expression analysis by reverse transcription quantitative polymerase chain reaction revealed that wound-related genes were not induced during release of guard cells from leaves. To validate our approach, we performed a high-throughput deep-sequencing of guard cell transcriptome (RNA-seq). A total of 18,994 nuclear-encoded transcripts were detected, which expanded the transcriptome by 70%. The optimized GCP isolation and RNA extraction protocols are simple, reproducible, and fast, allowing the discovery of genes and regulatory networks inherent to the guard cells under various stresses.
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Lozano, Maria D., Tania Labiano, Jose Ignacio Echeveste, et al. "Clinical validation of mutational analysis of EGFR and KRAS in fine needle aspiration and small core needle biopsies using a real-time PCR method." Journal of Clinical Oncology 31, no. 15_suppl (2013): e19027-e19027. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e19027.
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e19027 Background: About 75% of NSCLC is diagnosed at an advanced stage, thus analysis is often performed from small samples. There is a growing need for multiple biomarker testing when tissue is limited. We evaluated two real-time PCR based assays to assess EGFR and KRAS mutation status in limited tissue specimens and compared results to a lab validated method. Methods: 117 NSCLC patient samples (46 CNB, 71 FNA) were tested for EGFR and KRAS mutations using a single section. DNA was extracted directly from one stained smear in FNA samples (without pre-processing) and one 5-micron section in CNB using the cobas DNA Sample Preparation Kit. Sixty samples were also analyzed for both genes by direct sequencing using the same DNA. Eleven patients had matched CNB and FNA samples. Results: We tested 68 ADCs, 38 SqCCs, 8 NSCLC-NOS, 2 ADCs in situ non-mucinous, and 1 large cell carcinoma (Table). Median DNA concentrations for CNB and FNA were 14.17 ng/ul and 3.75 ng/ul. The failure rate for the cobas were 1.7% (EGFR), 6.8 % (KRAS) in FNA samples, and 0% for CNB. The failure rate for EGFR sequencing was 13.1% (FNA), 16.4% (CNB), and 0% for KRAS. The 11 patients with FNA and CNB showed concordant results between cobas and sequencing. One patient had two mutations; one (L782F) was detected only by sequencing. No discordant findings were seen other than invalid results comparing both systems. Conclusions: Assessment of mutations in limited tissue samples of CNB and FNA using a single section performed on the cobas EGFR and KRAS test is feasible and reliable. There was a lower invalid rate for EGFR testing on cobas test compared to Sanger sequencing. The cobas tests have rapid turnaround time to results, require limited input DNA, and are easier to use compared to sequencing. Still, it is mandatory that pathologists maintain careful quality control of the samples. [Table: see text]
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Cheng, Michael L., Mark T. A. Donoghue, François Audenet, et al. "Germ Cell Tumor Molecular Heterogeneity Revealed Through Analysis of Primary and Metastasis Pairs." JCO Precision Oncology, no. 4 (October 2020): 1307–20. http://dx.doi.org/10.1200/po.20.00166.
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PURPOSE Although primary germ cell tumors (GCTs) have been extensively characterized, molecular analysis of metastatic sites has been limited. We performed whole-exome sequencing and targeted next-generation sequencing on paired primary and metastatic GCT samples in a patient cohort enriched for cisplatin-resistant disease. PATIENTS AND METHODS Tissue sequencing was performed on 100 tumor specimens from 50 patients with metastatic GCT, and sequencing of plasma cell-free DNA was performed for a subset of patients. RESULTS The mutational landscape of primary and metastatic pairs from GCT patients was highly discordant (68% of all somatic mutations were discordant). Whereas genome duplication was common and highly concordant between primary and metastatic samples, only 25% of primary-metastasis pairs had ≥ 50% concordance at the level of DNA copy number alterations (CNAs). Evolutionary-based analyses revealed that most mutations arose after CNAs at the respective loci in both primary and metastatic samples, with oncogenic mutations enriched in the set of early-occurring mutations versus variants of unknown significance (VUSs). TP53 pathway alterations were identified in nine cisplatin-resistant patients and had the highest degree of concordance in primary and metastatic specimens, consistent with their association with this treatment-resistant phenotype. CONCLUSION Analysis of paired primary and metastatic GCT specimens revealed significant molecular heterogeneity for both CNAs and somatic mutations. Among loci demonstrating serial genetic evolution, most somatic mutations arose after CNAs, but oncogenic mutations were enriched in the set of early-occurring mutations as compared with VUSs. Alterations in TP53 were clonal when present and shared among primary-metastasis pairs.
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Chung, L. Ping, and Kenneth B. M. Reid. "Structural and functional studies on C4b-binding protein, a regulatory component of the human complement system." Bioscience Reports 5, no. 10-11 (1985): 855–65. http://dx.doi.org/10.1007/bf01119897.
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The binding and cofactor activities of C4b-binding protein were examined before and after limited proteolysis by pepsin, trypsin and chymotrypsin. The major fragments generated were characterized by amino acid sequencing, thus establishing the precise points of limited proteolysis. These studies allow a tentative assignment of the cofactor activity site to the residues 177–322 of the 549 amino acid long chain of C4b-binding protein but indicated that residues in the region 332–395 are important in the binding activity.
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Radford, Alan D., David Chapman, Linda Dixon, Julian Chantrey, Alistair C. Darby, and Neil Hall. "Application of next-generation sequencing technologies in virology." Journal of General Virology 93, no. 9 (2012): 1853–68. http://dx.doi.org/10.1099/vir.0.043182-0.
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The progress of science is punctuated by the advent of revolutionary technologies that provide new ways and scales to formulate scientific questions and advance knowledge. Following on from electron microscopy, cell culture and PCR, next-generation sequencing is one of these methodologies that is now changing the way that we understand viruses, particularly in the areas of genome sequencing, evolution, ecology, discovery and transcriptomics. Possibilities for these methodologies are only limited by our scientific imagination and, to some extent, by their cost, which has restricted their use to relatively small numbers of samples. Challenges remain, including the storage and analysis of the large amounts of data generated. As the chemistries employed mature, costs will decrease. In addition, improved methods for analysis will become available, opening yet further applications in virology including routine diagnostic work on individuals, and new understanding of the interaction between viral and host transcriptomes. An exciting era of viral exploration has begun, and will set us new challenges to understand the role of newly discovered viral diversity in both disease and health.