Circulating melanoma cells isolated from clinical blood samples and characterized by full-length mRNA sequencing at single-cell level.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10539-10539 ◽  
Author(s):  
Yu-Chieh Wang ◽  
Daniel Ramskold ◽  
Shujun Luo ◽  
Robin Li ◽  
Qiaolin Deng ◽  
...  

10539 Background: Melanoma is the most aggressive type of skin cancer. Late-stage melanoma is highly metastatic and currently lacks effective treatment. This discouraging clinical observation highlights the need for a better understanding of the molecular mechanisms underlying melanoma initiation and progression and for developing new therapeutic approaches based on novel targets. Although genome-wide transcriptome analyses have been frequently used to study molecular alterations in clinical samples, it has been technically challenging to obtain the transcriptomic profiles at single-cell level. Methods: Using antibody-mediated magnetic activated cell separation (MACS), we isolated and individualized putative circulating melanoma cells (CMCs) from the blood samples of the melanoma patients at advance stages. The transcriptomic analysis based on a novel and robust mRNA-Seq protocol (Smart-Seq) was established and applied to the putative CMCs for single-cell profiling. Results: We have discovered distinct gene expression patterns, including new putative markers for CMCs. Meanwhile, the gene expression profiles derived of the CMC candidates isolated from the patient’s blood samples are closely-related to the expression profiles of other cells originated from human melanocytes, including normal melanocytes in primary culture and melanoma cell lines. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which provides advantage for better analyzing transcript isoforms and SNPs. Conclusions: Our results suggest that the techniques developed in this research for cell isolation and transcriptomic analyses can potentially be used for addressing many biological and clinical questions requiring genomewide transcriptome profiling in rare cells.

2017 ◽  
Author(s):  
Shilo Rosenwasser ◽  
Miguel J. Frada ◽  
David Pilzer ◽  
Ron Rotkopf ◽  
Assaf Vardi

AbstractMarine viruses are major evolutionary and biogeochemical drivers of microbial life in the ocean. Host response to viral infection typically includes virus-induced rewiring of metabolic network to supply essential building blocks for viral assembly, as opposed to activation of anti-viral host defense. Nevertheless, there is a major bottleneck to accurately discern between viral hijacking strategies and host defense responses when averaging bulk population response. Here we use Emiliania huxleyi, a bloom-forming alga and its specific virus (EhV), as one of the most ecologically important host-virus model system in the ocean. Using automatic microfluidic setup to capture individual algal cells, we quantified host and virus gene expression on a single-cell resolution during the course of infection. We revealed high heterogeneity in viral gene expression among individual cells. Simultaneous measurements of expression profiles of host and virus genes at a single-cell level allowed mapping of infected cells into newly defined infection states and uncover a yet unrecognized early phase in host response that occurs prior to viral expression. Intriguingly, resistant cells emerged during viral infection, showed unique expression profiles of metabolic genes which can provide the basis for discerning between viral resistant and sensitive cells within heterogeneous populations in the marine environment. We propose that resolving host-virus arms race at a single-cell level will provide important mechanistic insights into viral life cycles and will uncover host defense strategies.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1152-1152
Author(s):  
Keisuke Sumide ◽  
Yoshikazu Matsuoka ◽  
Ryusuke Nakatsuka ◽  
Hiroshi Kawamura ◽  
Tatsuya Fujioka ◽  
...  

Abstract Background. We previously identified very primitive human cord blood (CB)-derived CD34-negative (CD34-) severe combined immunodeficiency (SCID)-repopulating cells (SRCs) using the intra-bone marrow injection (IBMI) method (Blood 2003:101;2924). A series of our studies suggests that the CD34- SRCs that we identified are a distinct class of primitive hematopoietic stem cells (HSCs), which show myeloid-biased long-term repopulating capacity, suggesting that they are at the apex of the human HSC hierarchy (Blood Cancer J 2015:5,e290). We recently developed a high-resolution purification method for primitive CD34- SRCs using 18 lineage (Lin)-specific antibodies, which can enrich CD34- SRC at the 1/1,000 level (Exp Hematol 2011:39;203). We then identified CD133 as a positive marker of human CB-derived CD34+/- SRCs. Moreover, limiting dilution analyses (LDAs) demonstrated that the frequencies of SRCs in the 18Lin- CD34+/- CD133+ fractions were 1/100 and 1/140, respectively (Leukemia 2014:28;1308). Since we aim to purify the CD34- SRCs (HSCs) at the single cell level, it was necessary to identify other specific positive markers for CD34- SRCs. We extensively analyzed the expression of candidate positive markers in the 18Lin- CD34- cell population by FACS. Finally, we identified glycosylphosphatidyl-inoshitol-anchored surface protein (GPI-80) as a useful marker of human CB-derived CD34+/- SRCs and succeeded in highly purifying primitive human CD34+/- SRCs to the level of 1/20 cells. Aim. We herein attempted to purify CD34+/- SRCs to the single cell level in order to precisely characterize the CD34- SRCs (HSCs) in the human HSC hierarchy. Materials and Methods. We first developed an ultra-high resolution purification method using two reliable markers for CD34+/- SRCs, including CD133 and GPI-80. Namely, we sorted 18Lin- CD34+ CD38- CD133+ GPI-80+ (34+ 38- 133+ 80+)and 18Lin- CD34- CD133+ GPI-80+ (34- 133+ 80+)cells by FACS. Thereafter, these two fractions of cells were transplanted by the IBMI technique into NOD/Shi-scid/IL-2Rγcnull (NOG) mice to investigate their long-term repopulating capacities. We ultimately performed single cell transplantations and analyzed their human hematopoietic cell reconstitution for up to 20 weeks. Finally, we analyzed their gene expression profiles, including the key genes for the self-renewal and maintenance of HSCs of single 34+ 38- 133+ 80+ and 34- 133+ 80+ cells using a BioMark System (Fluidigm). Results. Approximately 15% of the 34+ 38- 133+ and 34- 133+ cells expressed GPI-80. These highly purified cells showed very immature blast-like morphologies. These two fractions of cells were then transplanted into NOG mice by IBMI. We performed primary and secondary transplantations for up to 40 weeks. In the results, all of the mice received 200 34+ 38- 133+ 80+ (n=25)and 34- 133+ 80+ (n=23) cells were repopulated with human CD45+ cells (Mean % of human CD45+ cells, 47% vs. 35%), including CD34+, CD19+ and CD33+ cells. Most of the secondary transplanted mice were also repopulated with human CD45+ cells with multi-lineage reconstitution (Mean % of human CD45+ cells, 0.4% vs. 10%). An LDA is currently underway; however, the frequencies of CD34+/- SRCs in the 34+ 38- 133+ 80+ and 34- 133+ 80+ cells are estimated to be 1/5-1/10. Interestingly, significant numbers of the recipient mice that received single cells displayed multi-lineage human cell repopulation at 20 weeks after transplantation. In order to provide an independent line of evidence for characterizing our highly purified CD34+/- SRCs, we analyzed the gene expression profiles of these two types of SRCs at the single cell level. The principle component analysis clearly demonstrated that the gene expression profiles of individual CD34+ and CD34- SRCs were clearly different. Both SRCs expressed high levels of HSC maintenance genes, including RUNX1, TAL1, BMI1 and MYBC. Very interestingly, CD34+ SRCs expressed a high level of ETV6. In contrast, CD34- SRCs expressed higher levels of EZH2 and RING1. These results suggest that different mechanisms control HSC self-renewal and maintenance, as well as epigenetic regulation in these two SRCs. Conclusion. Wedeveloped an ultra-high resolution purification method using two markers for CD34+/- SRCs, including CD133 and GPI-80. The precise single cell-based analysis allows us to map CD34- SRCs (HSCs) at the apex of the human HSC hierarchy. Disclosures No relevant conflicts of interest to declare.


2021 ◽  
Author(s):  
Stella Belonwu ◽  
Yaqiao Li ◽  
Daniel Bunis ◽  
Arjun Arkal Rao ◽  
Caroline Warly Solsberg ◽  
...  

Abstract Alzheimer’s Disease (AD) is a complex neurodegenerative disease that gravely affects patients and imposes an immense burden on caregivers. Apolipoprotein E4 (APOE4) has been identified as the most common genetic risk factor for AD, yet the molecular mechanisms connecting APOE4 to AD are not well understood. Past transcriptomic analyses in AD have revealed APOE genotype-specific transcriptomic differences; however, these differences have not been explored at a single-cell level. Here, we leverage the first two single-nucleus RNA sequencing AD datasets from human brain samples, including nearly 55,000 cells from the prefrontal and entorhinal cortices. We observed more global transcriptomic changes in APOE4 positive AD cells and identified differences across APOE genotypes primarily in glial cell types. Our findings highlight the differential transcriptomic perturbations of APOE isoforms at a single-cell level in AD pathogenesis and have implications for precision medicine development in the diagnosis and treatment of AD.


2019 ◽  
Vol 15 (4) ◽  
pp. e1007708 ◽  
Author(s):  
Shilo Rosenwasser ◽  
Uri Sheyn ◽  
Miguel J. Frada ◽  
David Pilzer ◽  
Ron Rotkopf ◽  
...  

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.


RSC Advances ◽  
2015 ◽  
Vol 5 (7) ◽  
pp. 4886-4893 ◽  
Author(s):  
Hao Sun ◽  
Tim Olsen ◽  
Jing Zhu ◽  
Jianguo Tao ◽  
Brian Ponnaiya ◽  
...  

Gene expression analysis at the single-cell level is critical to understanding variations among cells in heterogeneous populations.


1989 ◽  
Vol 19 (9) ◽  
pp. 1619-1624 ◽  
Author(s):  
Dominique Emilie ◽  
Michel Peuchmaur ◽  
Marc Barad ◽  
HéLèNe Jouin ◽  
Marie-Christine Maillot ◽  
...  

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