Evaluation of flow cytometry as a method for quantification of circulating blood cell populations in salmonid fish

1993 ◽  
Vol 42 (1) ◽  
pp. 131-141 ◽  
Author(s):  
J. A. W. Morgan ◽  
T. G. Pottinger ◽  
P. Rippon
2019 ◽  
Vol 95 (7) ◽  
pp. 737-745 ◽  
Author(s):  
Sinmanus Vimonpatranon ◽  
Kesinee Chotivanich ◽  
Kasama Sukapirom ◽  
Sakaorat Lertjuthaporn ◽  
Ladawan Khowawisetsut ◽  
...  

Blood ◽  
2016 ◽  
Vol 128 (11) ◽  
pp. 1465-1474 ◽  
Author(s):  
Leo D. Wang ◽  
Scott B. Ficarro ◽  
John N. Hutchinson ◽  
Roland Csepanyi-Komi ◽  
Phi T. Nguyen ◽  
...  

Key Points Combining flow cytometry and high-performance mass spectrometry enables phosphoproteomic analysis of rare blood cell populations. ARHGAP25 dephosphorylation augments activity and promotes blood stem and progenitor cell mobilization by enhancing CXCL12 and Rac signaling.


Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 4727-4727
Author(s):  
Guat Bee Tan ◽  
Christina Sum ◽  
Ponnudurai Kuperan

Abstract Abstract 4727 The examination of blood films by microscopy remains one of the major labour intensive procedures in the laboratory and the challenge is to reduce the number of blood films examined without missing important diagnostic information. Automated blood cell counters offer a leucocyte count, red cell and platelet count and five-part (some 6-part) leucocyte differential. Haematology instrument differentials provide only limited information on cell morphology using abnormal cell flags and are often unable to reliably classify abnormal and immature cells. The examination of blood films is not only time consuming, it also requires highly trained staff. The impact of a wrong diagnosis necessitates that experienced staff are present in the laboratory 24 hours a day. Furthermore, manual cell classification is subjective, with significant inter and intra observer variation (Koepke et al. 1985) and is also subject to significant statistical variance (Rumke 1985). There have recently been several reports of using monoclonal antibody cocktails for an extended leucocyte differential by flow cytometry (Faucher et al. 2007, Roussel et al. 2010). The aim of this study was to compare a flow cytometric method for the white blood cell differential with the automated count from the Beckman Coulter LH750 haematology analyser and the reference manual microscopic 2 × 200 cell count (CLSI H20-A2). Cell morphology was also assessed microscopically for the presence of cells such as reactive or abnormal lymphocytes or blasts. The flow cytometric method, described by Faucher et al. 2007, uses 6 antibodies (CD45, CD36, CD2, CD294, CD19 and CD16) premixed in a single tube. The protocol allows detection of all white blood cells, mature neutrophils, total lymphocytes, total monocytes, eosinophils, basophils, immature granulocytes, B lymphocytes, non-cytotoxic T-lymphocytes, cytotoxic T/NK lymphocytes, CD16 positive and CD16 negative monocytes, and blasts cells with lineage orientation. A 5-colour flow cytometer, the Beckman Coulter FC500, was used for analysis. The gating strategy described by Faucher et al. (2007) was used. EDTA blood was analysed on 27 normal samples and 148 abnormal samples which demonstrated abnormal cell flags on the LH750. These samples included the presence of blast cells, immature granulocytes and abnormal lymphocytes. Results for most cell populations measured by the flow cytometric differential compared well with both the LH750 automated differential and the manual reference method. Comparative results using Pearson correlation show that the automated LH750 differential produced r values of greater than 0.94 for neutrophils, lymphocytes and eosinophils. The manual reference method produced r values of greater than 0.89 for neutrophils, lymphocytes and eosinophils. Results for flow cytometric monocytes compared to the LH750 and manual differential gave an r value of 0.84 and 0.87 respectively. Results for basophils were significantly better when the flow cytometric method was compared to the LH750 rather than the manual method, r = 0.68 for flow cytometry versus LH750 and r = 0.43 for flow cytometry versus manual method. The value of the manual differential is diminished because of the low number of cells counted; the precision is not good for smaller cell populations (Hübl et al. 1995). Very good correlation of blast cells, r = 0.98 and immature granulocytes, r = 0.92 was seen between the manual and flow cytometric method. The flow cytometric differential is superior to the microscopic method since it is objective and due to the higher number of cells counted, it can detect subpopulations of cells that are present in smaller number with greater statistical and interpretive confidence. More importantly, it recognises and quantitates morphologically abnormal cells such as reactive lymphocytes, inflammatory monocytes and the lineage of blast cells. However, the examination of blood cell morphology by microscopy still has an important role in the diagnosis of diseases. Disclosures: No relevant conflicts of interest to declare.


2008 ◽  
Vol 89 (4) ◽  
pp. 741-747 ◽  
Author(s):  
D. Blanchard ◽  
V. Bruneau ◽  
F. Germond-Arnoult ◽  
D. Bernard ◽  
A. Gourbil ◽  
...  

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 4193-4193
Author(s):  
Jean-Emmanuel Sarry ◽  
Gwenn-ael Danet-Desnoyers ◽  
Martin Carroll ◽  
Stephen G. Emerson ◽  
Fevzi Daldal ◽  
...  

Abstract Mitochondria play a special role in iron metabolism as the site of heme synthesis for hemoglobin. Mitochondria also function in cellular respiration, apoptosis, amino acid synthesis, Fe-S cluster formation and repair, and redox homeostasis; different blood cell lineages depend on some or all of these diverse mitochondrial functions. Mitochondrial abnormalities in hematopoietic stem cells might manifest themselves in proteomes of all the hematopoietic lineages. Therefore, we have begun characterization of mitochondria from different peripheral blood cell populations: platelets, lymphocytes, neutrophils and reticulocytes with the objective of comparing their function and proteomes in normals and in certain disease states. The procedures utilized as starting material a blood draw of approximately 80 ml from normal volunteers. The peripheral blood samples were separated by centrifugation and Hypaque density gradient into platelet, mononuclear cell, neutrophil and red cell populations. The red cells were further sorted by density gradient and magnetic cell sorting with specific CD71 microbeads to obtain enrichment of reticulocytes (reticulocytes retain their mitochondria and lose these upon maturation into mature red cells). The various cell fractions were evaluated by cell counting, flow cytometry and staining for morphology and identification. In accordance with differences in size and surface characteristics of these cell types, different procedures for cell rupture were utilized: shearing with a home-made device using ball bearings (mononuclear cells, neutrophils), nitrogen cavitation (platelets) and hypotonic shock (reticulocytes). Mitochondria were prepared by differential centrifugation and Percoll density gradient separation. The mitochondria were evaluated by fluorescence microscopy, flow cytometry, marker enzyme activity (succinate dehydrogenase) and Western blotting with compartment-specific antibodies. Mitochondrial protein profiles were obtained using 2-dimensional gel electrophoresis coupled to mass spectrometry. From 80 ml blood, 50 million lymphoctes were obtained equivalent to 150 microgram mitochondrial protein and 10 fold enrichment of succinate dehydrogenase activity. In parallel, K562 cell mitochondria were studied. The imaging analysis revealed significant differences in the protein patterns due to hematopoietic cell lineage. This work seeks to establish a proteomic database of shared and distinct erythroid, myeloid and lymphoid mitochondrial proteins that will form the basis of future studies of blood diseases in which perturbations of mitochondrial proteins are expected to occur. We are especially interested in examining the mitochondrial proteome and correlating with mitochondrial function in myelodysplasia and sideroblastic anemia.


Cytometry ◽  
1996 ◽  
Vol 25 (3) ◽  
pp. 287-294 ◽  
Author(s):  
Kovit Pattanapanyasat ◽  
Kosol Yongvanitchit ◽  
D. Gray Heppner ◽  
Pongsri Tongtawe ◽  
Dennis E. Kyle ◽  
...  

2021 ◽  
Vol 6 (3) ◽  
pp. 121
Author(s):  
Alison Luce-Fedrow ◽  
Suchismita Chattopadhyay ◽  
Teik-Chye Chan ◽  
Gregory Pearson ◽  
John B. Patton ◽  
...  

The antigenic diversity of Orientia tsutsugamushi as well as the interstrain difference(s) associated with virulence in mice impose the necessity to dissect the host immune response. In this study we compared the host response in lethal and non-lethal murine models of O. tsutsugamushi infection using the two strains, Karp (New Guinea) and Woods (Australia). The models included the lethal model: Karp intraperitoneal (IP) challenge; and the nonlethal models: Karp intradermal (ID), Woods IP, and Woods ID challenges. We monitored bacterial trafficking to the liver, lung, spleen, kidney, heart, and blood, and seroconversion during the 21-day challenge. Bacterial trafficking to all organs was observed in both the lethal and nonlethal models of infection, with significant increases in average bacterial loads observed in the livers and hearts of the lethal model. Multicolor flow cytometry was utilized to analyze the CD4+ and CD8+ T cell populations and their intracellular production of the cytokines IFNγ, TNF, and IL2 (single, double, and triple combinations) associated with both the lethal and nonlethal murine models of infection. The lethal model was defined by a cytokine signature of double- (IFNγ-IL2) and triple-producing (IL2-TNF-IFNγ) CD4+ T-cell populations; no multifunctional signature was identified in the CD8+ T-cell populations associated with the lethal model. In the nonlethal model, the cytokine signature was predominated by CD4+ and CD8+ T-cell populations associated with single (IL2) and/or double (IL2-TNF) populations of producers. The cytokine signatures associated with our lethal model will become depletion targets in future experiments; those signatures associated with our nonlethal model are hypothesized to be related to the protective nature of the nonlethal challenges.


2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 45.2-45
Author(s):  
I. Heggli ◽  
R. Schüpbach ◽  
N. Herger ◽  
T. A. Schweizer ◽  
A. Juengel ◽  
...  

Background:Modic type 1 changes (MC1) are vertebral bone marrow (BM) edema that associate with non-specific low back pain (LBP). Two etiologies have been described. In the infectious etiology the anaerobic aerotolerant Cutibacterium acnes (C. acnes) invades damaged intervertebral discs (IVDs) resulting in disc infection and endplate damage, which leads to the evocation of an immune response. In the autoinflammatory etiology disc and endplate damage lead to the exposure of immune privileged disc cells and matrix to leukocytes, thereby evoking an immune response in the BM. Different etiologies require different treatment strategies. However, it is unknown if etiology-specific pathological mechanisms exist.Objectives:The aim of this study was to identify etiology-specific dysregulated pathways of MC1 and to perform in-depth analysis of immune cell populations of the autoinflammatory etiology.Methods:BM aspirates and biopsies were obtained from LBP patients with MC1 undergoing spinal fusion. Aspirates/biopsies were taken prior screw insertion through the pedicle screw trajectory. From each patient, a MC1 and an intra-patient control aspiration/biopsy from the adjacent vertebral level was taken. If C. acnes in IVDs adjacent to MC1 were detected by anaerobic bacterial culture, patients were assigned to the infectious, otherwise to the autoinflammatory etiology.Total RNA was isolated from aspirates and sequenced (Novaseq) (infectious n=3 + 3, autoinflammatory n=5 + 5). Genes were considered as differentially expressed (DEG) if p-value < 0.01 and log2fc > ± 0.5. Gene ontology (GO) enrichment was performed in R (GOseq), gene set enrichment analysis (GSEA) with GSEA software.Changes in cell populations of the autoinflammatory etiology were analyzed with single cell RNA sequencing (scRNAseq): Control and MC1 biopsies (n=1 + 1) were digested, CD45+CD66b- mononuclear cells isolated with fluorescence activated cell sorting (FACS), and 10000 cells were sequenced (10x Genomics). Seurat R toolkit was used for quality-control, clustering, and differential expression analysis.Transcriptomic changes (n=5 + 5) of CD45+CD66b+ neutrophils isolated with flow cytometry from aspirates were analyzed as for total bulk RNAseq. Neutrophil activation (n=3 + 3) was measured as CD66b+ expression with flow cytometry. CD66bhigh and CD66blow fractions in MC1 and control neutrophils were compared with paired t-test.Results:Comparing MC1 to control in total bulk RNAseq, 204 DEG in the autoinflammatory and 444 DEG in the infectious etiology were identified with only 67 shared genes (Fig. 1a). GO enrichment revealed “T-cell activation” (p = 2.50E-03) in the autoinflammatory and “complement activation, classical pathway” (p=1.1E-25) in the infectious etiology as top enriched upregulated biological processes (BP) (Fig 1b). ScRNAseq of autoinflammatory MC1 showed an overrepresentation of T-cells (p= 1.00E-34, OR=1.54) and myelocytes (neutrophil progenitor cells) (p=4.00E-05, OR=2.27) indicating an increased demand of these cells (Fig. 1c). Bulk RNAseq analysis of neutrophils from the autoinflammatory etiology revealed an activated, pro-inflammatory phenotype (Fig 1d), which was confirmed with more CD66bhigh neutrophils in MC1 (+11.13 ± 2.71%, p=0.02) (Fig. 1e).Figure 1.(a) Venn diagram of DEG from total bulk RNAseq (b) Top enriched upregulated BP of autoinflammatory (left) and infectious (right) etiology (c) Cell clustering of autoinflammatory MC1 BM (d) Enrichment of “inflammatory response” gene set in autoinflammatory MC1 neutrophils (e) Representative histogram of CD66b+ expression in MC1 and control neutrophils.Conclusion:Autoinflammatory and infectious etiologies of MC1 have different pathological mechanisms. T-cell and neutrophil activation seem to be important in the autoinflammatory etiology. This has clinical implication as it could be explored for diagnostic approaches to distinguish the two MC1 etiologies and supports developing targeted treatments for both etiologies.Disclosure of Interests:None declared


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