Single‐Cell Analysis of Cytokine mRNA and Protein Expression by Flow Cytometry

2020 ◽  
Vol 92 (1) ◽  
Author(s):  
Rubina Pal ◽  
Jayne Schaubhut ◽  
Darcey Clark ◽  
Lynette Brown ◽  
Jennifer J. Stewart
Keyword(s):  
Flow Cytometry ◽  
Protein Expression ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Cytokine Mrna ◽  
Mrna And Protein Expression

scholarly journals The Expression and Prognostic Value of SUMO1-Activating Enzyme Subunit 1 and Its Potential Mechanism in Triple-Negative Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Qingshui Wang ◽  
Wenting Zhong ◽  
Lin Deng ◽  
Qili Lin ◽  
Youyu Lin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Cycle ◽  
Protein Expression ◽  
Single Cell ◽  
Prognostic Model ◽  
Triple Negative ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Mrna And Protein Expression

Background: Triple-negative breast cancer (TNBC) is the most invasive and metastatic subtype of breast cancer. SUMO1-activating enzyme subunit 1 (SAE1), an E1-activating enzyme, is indispensable for protein SUMOylation. SAE1 has been found to be a relevant biomarker for progression and prognosis in several tumor types. However, the role of SAE1 in TNBC remains to be elucidated.Methods: In the research, the mRNA expression of SAE1 was analyzed via the cancer genome atlas (TCGA) and gene expression omnibus (GEO) database. Cistrome DB Toolkit was used to predict which transcription factors (TFs) are most likely to increase SAE1 expression in TNBC. The correlation between the expression of SAE1 and the methylation of SAE1 or quantity of tumor-infiltrating immune cells was further invested. Single-cell analysis, using CancerSEA, was performed to query which functional states are associated with SAE1 in different cancers in breast cancer at the single-cell level. Next, weighted gene coexpression network (WGCNA) was applied to reveal the highly correlated genes and coexpression networks of SAE1 in TNBC patients, and a prognostic model containing SAE1 and correlated genes was constructed. Finally, we also examined SAE1 protein expression of 207 TNBC tissues using immunohistochemical (IHC) staining.Results: The mRNA and protein expression of SAE1 were increased in TNBC tissues compared with adjacent normal tissues, and the protein expression of SAE1 was significantly associated with overall survival (OS) and disease-free survival (DFS). Correlation analyses revealed that SAE1 expression was positively correlated with forkhead box M1 (FOXM1) TFs and negatively correlated with SAE1 methylation site (cg14042711) level. WGCNA indicated that the genes coexpressed with SAE1 belonged to the green module containing 1,176 genes. Through pathway enrichment analysis of the module, 1,176 genes were found enriched in cell cycle and DNA repair. Single-cell analysis indicated that SAE1 and its coexpression genes were associated with cell cycle, DNA damage, DNA repair, and cell proliferation. Using the LASSO COX regression, a prognostic model including SAE1 and polo-like kinase 1 (PLK1) was built to accurately predict the likelihood of DFS in TNBC patients.Conclusion: In conclusion, we comprehensively analyzed the mRNA and protein expression, prognosis, and interaction genes of SAE1 in TNBC and constructed a prognostic model including SAE1 and PLK1. These results might be important for better understanding of the role of SAE1 in TNBC. In addition, DNA methyltransferase and TFs inhibitor treatments targeting SAE1 might improve the survival of TNBC patients.

Electroporative Flow Cytometry for Single-Cell Analysis

2010 ◽  
pp. 125-142
Author(s):  
Chang Lu ◽  
Jun Wang ◽  
Ning Bao ◽  
Hsiang-Yu Wang
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis

Single Cell Analysis of Microalgae and Associated Bacteria Flora by Using Flow Cytometry

2021 ◽  
Vol 26 (6) ◽  
pp. 898-909
Author(s):  
Fabrizio Di Caprio ◽  
Simone Posani ◽  
Pietro Altimari ◽  
Alessandro Concas ◽  
Francesca Pagnanelli
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Associated Bacteria

Single-Cell Analysis of Morphological and Metabolic Heterogeneity in Euglena gracilis by Fluorescence-Imaging Flow Cytometry

2018 ◽  
Vol 90 (19) ◽  
pp. 11280-11289 ◽  
Author(s):  
Hector E. Muñoz ◽  
Ming Li ◽  
Carson T. Riche ◽  
Nao Nitta ◽  
Eric Diebold ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Fluorescence Imaging ◽  
Euglena Gracilis ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Imaging Flow Cytometry ◽  
Metabolic Heterogeneity

scholarly journals Droplet flow cytometry for single-cell analysis

RSC Advances ◽  
2021 ◽  
Vol 11 (34) ◽  
pp. 20944-20960
Author(s):  
Ming Li ◽  
Hangrui Liu ◽  
Siyuan Zhuang ◽  
Keisuke Goda
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Droplet Flow ◽  
Recent Advances

This work reviews recent advances in the integration of emulsion microdroplets and flow cytometry technologies, so-called droplet flow cytometry (DFC), for high-throughput single-cell analysis.

Single-cell analysis of the murine chemokines MIP-1α, MIP-1β, RANTES and ATAC/lymphotactin by flow cytometry

2003 ◽  
Vol 274 (1-2) ◽  
pp. 83-91 ◽  
Author(s):  
Brigitte G Dorner ◽  
Sabine Steinbach ◽  
Martin B Hüser ◽  
Richard A Kroczek ◽  
Alexander Scheffold
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis

Single Cell Analysis of IG Genes in CLL: Cases with Multiple IGH Rearrangements Are Constituted of Several Independent Clones Even When Indistinguishable By Flow Cytometry

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4139-4139
Author(s):  
Kamila Brazdilova ◽  
Karla Plevova ◽  
Hana Skuhrova Francova ◽  
Helena Kockova ◽  
Magdalena Chmelikova ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
B Cell ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Gene Rearrangements ◽  
Surface Markers ◽  
Immunoglobulin Gene ◽  
Cell Analysis ◽  
Cell Clones ◽  
Immunoglobulin Gene Rearrangements

Abstract Current evidence suggests that in 2-5% of chronic lymphocytic leukemia (CLL) cases, multiple B cell clones - each expressing unique productive immunoglobulin gene rearrangements (P-IGH)- can be detected. However, only in 35% of them, coexistence of at least two leukemic clones was revealed by flow-cytometry (Plevova et al, Haematologica 2014). In the remaining 65% of cases, presence of multiple clones was presumed based on the number of immunoglobulin gene rearrangements detected, but underlying biological cause, also possibly including lack of allelic exclusion, was not satisfactorily proven at the level of single cells. Therefore, we developed a technique for simultaneous analysis of IGH, IGK and IGL rearrangements at the single cell level and performed further investigation of cases with multiple P-IGH. In years 2006-2015, patients with multiple clonal P-IGHs were searched among 1670 CLL patients examined for IGHV mutation status. Identified cases were submitted to flow-cytometric measurement of surface markers CD5, CD19, CD20, CD23, CD43, CD45, sIgK, sIgL and FMC7 on sorted cells where possible. Based on this examination, patients with clonal P-IGHs exceeding the number of CLL populations distinguished by flow-cytometry were tested using single cell analysis. Patients with corresponding number of P-IGHs and CLL populations (i.e. confirmed as biclonal CLL according to the flow-cytometry and PCR-based IGH detection) served as a control of the technique accuracy. Single cell analysis technique was developed at our department to detect transcribed IGH, IGK and IGL simultaneously in individual cells. B cells from peripheral blood of CLL patients were separated by gradient centrifugation with depletion of non-B cells. Single CD19+ cells were then sorted using FACS Aria III into 96-well plates containing lysis buffer, followed by 2 rounds of multiplex nested RT-PCR, capillary electrophoresis and Sanger sequencing. For each patient, 1-3 plates were analyzed. To consider an IG rearrangement clonal, it had to be detected at least in 3 wells. We detected multiple clonal P-IGHs in 76/1670 (4,6%) CLL patients analyzed. Expression of surface markers was assessed by flow cytometry in 37/76 patients: In 24/37(65%), the number of P-IGHs exceeded number of distinguished populations. Single cell analysis was performed in 16/24 cases, 15/16 patients displayed only one homogeneous CLL population by flow cytometry and 1/16 patient displayed two distinguished populations but three P-IGHs. Two patients with corresponding number of P-IGHs and CLL populations were used as a control. The median of wells tested per patient was 92 and P-IGH detection efficacy 83%. In 12/16 tested patients, as well as in two controls, no cell with more than one P-IGH was detected, confirming the expansion of multiple B-cell clones in all of them. Also, based on the structure of detected clonal IGH rearrangements in each case, a possibility of VH replacement was excluded. In 2 cases, we observed intraclonal diversification within one of the present clones (expressing either IGHV1-69, or IGHV3-72), a rare phenomenon described in CLL. In the remaining 4/16 cases we failed to detect one of the expected P-IGHs likely due to its underrepresentation in a sample, which is supported by results obtained from quantitative PCR with allele-specific primers in a bulk sample. Light chains were successfully detected in 10/12 analyzed cases and two controls; in 4 cases, single cell analysis revealed transcribed clonal IG rearrangements previously undetected in bulk samples. Importantly, each identified clonal IGK/IGL was repeatedly detected exclusively with only one distinct clonal P-IGH, thus constituting independent B cell receptors in independent leukemic clones. We confirm and substantially extend the notion that oligoclonality is the major cause of multiple P-IGH detection in CLL. Obtained information on P-IGH and P-IGK/L pairing will help in further investigation of IG receptors in oligoclonal CLL, as the biological background of oligoclonality in CLL still remains to be elucidated. Supported by grants IGA NT13493-4/2012, MUNI/A/1180/2014 and AZV 15-30015A. Disclosures No relevant conflicts of interest to declare.

scholarly journals Large-scale label-free single-cell analysis of paramylon in Euglena gracilis by high-throughput broadband Raman flow cytometry

2020 ◽  
Vol 11 (4) ◽  
pp. 1752 ◽  
Author(s):  
Kotaro Hiramatsu ◽  
Koji Yamada ◽  
Matthew Lindley ◽  
Kengo Suzuki ◽  
Keisuke Goda
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Euglena Gracilis ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
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