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Myeloid Cells

Myeloid Cells are a diverse group of immune cells derived from the myeloid lineage of hematopoietic stem cells.
These cells play a crucial role in innate immunity, inflammation, and hematopoiesis.
Myeloid Cells include granulocytes (neutrophils, eosinophils, and basophils), monocytes, macrophages, and dendritic cells.
They are responsible for phagocytosis, cytokine production, and antigen presentation, making them essential in the body's defense against pathogens and the regulation of immune responses.
Understanding the biology and function of Myeloid Cells is crucial for the study of various diseases, including autoimmune disorders, cancer, and infectious diseases.
Reseachers can leverge the power of PubCompare.ai to optimzie their Myeloid Cells reserach, locateing the most accurate and reproducible protocols from literature, pre-prints, and patents.

Most cited protocols related to «Myeloid Cells»

1
Comprehensive Preprocessing of Sequencing Datasets
Full details of each dataset9 (link),10 (link),17 (link),20 ,21 (link),37 (link),42 ,50 (link),55 ,64 (link)–69 (link), including data type, sample type, source, and normalization approach, are available in Supplementary Table 1. Briefly, next generation sequencing datasets were downloaded and analyzed using the authors’ normalization settings unless otherwise specified; these consisted of transcripts per million (TPM), reads per kilobase of transcript per million (RPKM), or fragments per kilobase of transcript per million (FPKM) space. For analyses in log2 space, we added 1 to expression values prior to log2 adjustment. Affymetrix microarray datasets were summarized and normalized as described in ‘Gene expression profiling – Microarrays’ (Supplementary Note 1), using RMA in cases where bulk tissues and ground truth cell subsets were profiled on the same Affymetrix platform, and otherwise using MAS5 normalization. NanoString nCounter data were downloaded from the supplement of Chen et al.20 and analyzed with batch correction in non-log linear space, but without any additional preprocessing.
Two publicly available PBMC datasets from healthy donors profiled by Chromium v2 (5’ and 3’ kits) were downloaded (Supplementary Table 1) and preprocessed as described in ‘Gene expression profiling – Single-cell RNA-seq’ (Supplementary Note 1), with the following minor modifications. During quality control, we excluded cells with >5000 expressed genes for 5’ PBMCs, >4000 expressed genes for 3’ PBMCs, and <200 expressed genes for both datasets. Seurat “FindClusters” was applied on the first 20 principal components, with the resolution parameter set to 0.6. Cell labels were assigned as described above. In addition, myeloid cells were defined by high CD68 expression, megakaryocytes by high PPBP expression, and dendritic cells by high FCER1A expression.
For the 3’ FL signature matrix in Supplementary Figs. 11d, and14a-b, publicly available 10x Chromium v2 scRNA-seq data (3’ kit)70 were downloaded (Supplementary Table 1) and preprocessed as described for the 10x PBMC signature matrices above, but with the following differences. Seurat “FindClusters” was applied on the first 10 principal components, with the resolution parameter set to 0.6. Cell labels were assigned based on the following canonical marker genes (MS4A1 = B cells; CD3E, CD8A and CD8B = CD8 T cells; CD3E and CD4 = CD4 T cells).
Newman A.M., Steen C.B., Liu C.L., Gentles A.J., Chaudhuri A.A., Scherer F., Khodadoust M.S., Esfahani M.S., Luca B.A., Steiner D., Diehn M, & Alizadeh A.A. (2019). Determining cell-type abundance and expression from bulk tissues with digital cytometry. Nature biotechnology, 37(7), 773-782.
Publication 2019
B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Cells Chromium Dendritic Cells Dietary Fiber Dietary Supplements Donors Figs Genes Megakaryocytes Microarray Analysis Myeloid Cells platelet basic protein, human RNA-Seq Single-Cell RNA-Seq Tissues
2
Natural Killer Cell Lysis Assay Protocol
Browne et al. [1 (link)] analysed the correlation matrix for eight measures of immune system function of 72 females with breast cancer, recorded during investigation of the physiological consequences of a psychological intervention [3 (link),4 (link)]. Four 51Cr-release measures of natural killer cell lysis were obtained using effector (NK cell) to target cell (K562 human myeloid cell) ratios of 100:1, 50:1, 25:1 and 12.5:1. Following Browne et al. [1 (link)] we designate these measures by their effector to target (E:T) ratios, NK100, NK50, NK25 and NK12 respectively. Similarly, natural killer cell lysis measured in the presence of recombinant interferon gamma (rIFNγ) using E:T ratios of 50:1, 25:1, 12.5:1 and 6.25:1, are designated IFN50, IFN25, IFN12, and IFN6 respectively. Lower E:T ratios are used in the presence of rIFNγ because rIFNγ increases NK cells' ability to rupture target cells.
The correlations reported in Browne et al.'s [1 (link)] Table 1 indicate that the four NK measures correlate highly with one another (average r = 0.852), and that the four rIFNγ enhanced NK measures also correlate highly with one another (averaging 0.960). However, the low correlations between the sets of NK and rIFNγ measurements (averaging only .111) indicate that the two sets of measurements reflect relatively distinct aspects of natural killer cell functioning. Browne et al. [1 (link)] viewed this as justifying the use of an exploratory two-factor model (Figure 1) which, unfortunately, was significantly inconsistent with the data (χ2 = 103.59, degrees of freedom (df) = 13, and probability p < 10-15). The small but significant residual differences between the data correlations and the correlations implied by the two-factor model were dismissed by Browne et al.[1 (link)] as "negligible from a practical point of view". SEMNET discussion of this model prompted Hayduk to investigate whether some unrecognized measurement feature was producing the significant, even if seemingly slight, ill fit.
Andersen, Farrar, Golden-Kreutz, Kutz, MacCallum, Courtney & Glaser [3 (link)] provide a description of the reasonably standard procedures used to obtain the Browne et al. [1 (link)] data. Peripheral blood leukocytes (PBLs) were obtained from 60 mL of venous blood, counted so that a known number of PBLs could be suspended in medium and incubated with either additional medium or additional medium plus rIFNγ. K562 target cells (a human myeloid cell line sensitive to NK cell activity) were labelled with 51Cr and aliquoted with the effector cells (either the NK, or the rIFNγ activated NK cells) in the ratios reported above. The cell mixture was centrifuged to ensure cell surface contact, and incubated to provide an opportunity for the NK cells to bind and rupture the target cells, thereby releasing the radioactive target cell cytoplasm. Gamma radioactivity of the supernatant collected from a second centrifuging indicated the effectiveness of the NK or rIFNγ-activated-NK cells at lysing the target cells, with larger measurements corresponding to more effective NK cell activity.
Hayduk L.A., Pazderka-Robinson H., Cummings G.G., Levers M.J, & Beres M.A. (2005). Structural equation model testing and the quality of natural killer cell activity measurements. BMC Medical Research Methodology, 5, 1.
Publication 2005
BLOOD Breast Carcinoma Cell Lines Cells Cytoplasm Females Gamma Rays Homo sapiens Interferon Type II K562 Cells Leukocytes Myeloid Cells Natural Killer Cells Radioactivity System, Immune Veins
3
Microarray Analysis of Pediatric Brain Tumors
Microarray processing was performed for pediatric brain tumor and NT samples as described previously (27 (link)). The gene expression study sample dataset included 15 PA, 46 EPN, 20 GBM, 22 MED and 13 NT brain samples. Tumors were obtained from pediatric patients at initial presentation and with no prior treatment. NT brain was collected from autopsy or epilepsy surgery. Sample collection was conducted in compliance with Institutional Review Board regulations (COMIRB 95-500 and 09-0906). Briefly, RNA was extracted from snap frozen surgical samples, processed and applied to HG-U133 Plus 2 GeneChip microarrays (Affymetrix). Scanned microarray data were background corrected and normalized using the guanine cytosine Robust Multiarray Average (gcRMA) algorithm resulting in log 2 gene expression values (28 ). Gene expression data corresponding to myeloid and T-cell markers were then extracted for further analysis. The probeset with the highest expression was selected in cases where multiple probesets for the same gene existed. Expression levels of PD-1 were below the threshold for accurate detection (log2 normalized expression <3) in all samples, and were thus excluded from this analysis. CD45R0 is a splice form of CD45, and could therefore not be distinguished in this analysis. Gene expression levels associated with the more prevalent myeloid cells were accordingly higher, allowing all myeloid markers to be assessed by this gene expression analysis. PCA and unsupervised clustering of gene expression data was performed as described above. The microarray data discussed in this publication have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database and are publicly accessible through GEO Series accession number GSE50161 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50161).
Griesinger A.M., Birks D.K., Donson A.M., Amani V., Hoffman L.M., Waziri A., Wang M., Handler M.H, & Foreman N.K. (2013). Characterization of Distinct Immunophenotypes Across Pediatric Brain Tumor Types. Journal of immunology (Baltimore, Md. : 1950), 191(9), 10.4049/jimmunol.1301966.
Publication 2013
Autopsy Brain Brain Neoplasms Cytosine Epilepsy Ethics Committees, Research Freezing Gene Chips Gene Expression Gene Expression Profiling Genes Guanine Microarray Analysis Myeloid Cells Neoplasms Operative Surgical Procedures Patients Specimen Collection T-Lymphocyte
4
Prioritizing Alzheimer's Disease Genetic Variants
To identify potential functional risk variants and genes at each
associated locus, we first annotated a list of prioritized variants from the 24
associated loci (excluding APOE) (n = 1,873).
This variant list combined variants in LD with the sentinel variants
(r2 ≥ 0.5) using INFERNO160 (link) LD expansion
(n = 1,339) and variants with suggestive significance
(P < 10−5) and LD
(r2 ≥ 0.5) with the sentinel variants for
the 24 associated loci (excluding APOE) (n =
1,421 variants). We then identified variants with regulatory potential in this
set of variants using four programs that incorporate various annotations to
identify likely regulatory variants: RegulomeDB56 (link), HaploReg v.4.1 (refs. 57 (link),161 (link)), GWAS4D59 (link), and the Ensembl Regulatory Build58 (link). We used the ChromHMM (core 15-state
model) as ‘source epigenomes’ for the HaploReg analyses. We used
immune (Monocytes-CD14+, GM12878 lymphoblastoid, HSMM myoblast) and
brain (NH-A astroctyes) for the Ensembl Regulatory Build analyses. We then used
the list of 1,873 prioritized variants to search for genes functionally linked
via eQTLs in LOAD relevant tissues including various brain and blood tissue
types, including all immune-related cell types, most specifically myeloid cells
(macrophages and monocytes) and B-lymphoid cells, which are cell types
implicated in LOAD and neurodegeneration by a number of recent studies14 (link),45 (link),162 (link),163 (link). While their specificity may be lower
for identifying Alzheimer’s disease risk eQTLs, we included whole blood
cell studies in our Alzheimer’s disease–relevant tissue class due
to their high correlation of eQTLs with Alzheimer’s
disease–relevant tissues (70% with brain164 (link); 51–70% for monocytes and
lymphoblastoid cell lines165 (link))
and their large sample sizes that allow for increased discovery power. See the
Supplementary Notefor details on the eQTL databases and studies searched, and Supplementary Table 13 for sample
sizes of each database/study.
Formal co-localization testing of our summary Stage 1 results was
conducted using (1) COLOC166 (link)via INFERNO and (2) Summary Mendelian Randomization (SMR)-Heidi
analysis167 (link). The
approximate Bayes factor (ABF), which was used to assess significance in the
INFERNO COLOC analysis, is a summary measure that provides an alternative to the
P value for the identification of associations as
significant. SMR-Heidi analysis, which employs a heterogeneity test (HEIDI test)
to distinguish pleiotropy or causality (a single genetic variant affecting both
gene expression and the trait) from linkage (two distinct genetic variants in
LD, one affecting gene expression and one affecting trait), was also employed
for co-localization analysis. Genes located less than 1 Mb from the GWAS
sentinel variants that pass a 5% Benjamini–Hochberg FDR-corrected SMR
P-value significance threshold and a HEIDI
P-value > 0.05 threshold were considered
significant. The Westra eQTL168 (link) summary data and Consortium for the Architecture of Gene
Expression (CAGE) eQTL summary data were used for analysis. These datasets,
conducted in whole blood, are large eQTL studies (Westra: discovery phase
n = 5,311, replication phase n = 2,775;
CAGE: n = 2,765), and while there is some overlap in samples
between the two datasets, CAGE provides finer coverage. The ADGC reference panel
dataset referenced above for GCTA COJO analysis was used for LD
calculations.
Kunkle B.W., Grenier-Boley B., Sims R., Bis J.C., Damotte V., Naj A.C., Boland A., Vronskaya M., van der Lee S.J., Amlie-Wolf A., Bellenguez C., Frizatti A., Chouraki V., Martin E.R., Sleegers K., Badarinarayan N., Jakobsdottir J., Hamilton-Nelson K.L., Moreno-Grau S., Olaso R., Raybould R., Chen Y., Kuzma A.B., Hiltunen M., Morgan T., Ahmad S., Vardarajan B.N., Epelbaum J., Hoffmann P., Boada M., Beecham G.W., Garnier J.G., Harold D., Fitzpatrick A.L., Valladares O., Moutet M.L., Gerrish A., Smith A.V., Qu L., Bacq D., Denning N., Jian X., Zhao Y., Del Zompo M., Fox N.C., Choi S.H., Mateo I., Hughes J.T., Adams H.H., Malamon J., Sanchez-Garcia F., Patel Y., Brody J.A., Dombroski B.A., Deniz Naranjo M.C., Daniilidou M., Eiriksdottir G., Mukherjee S., Wallon D., Uphill J., Aspelund T., Cantwell L.B., Garzia F., Galimberti D., Hofer E., Butkiewicz M., Fin B., Scarpini E., Sarnowski C., Bush W.S., Meslage S., Kornhuber J., White C.C., Song Y., Barber R.C., Engelborghs S., Sordon S., Voijnovic D., Adams P.M., Vandenberghe R., Mayhaus M., Adrienne Cupples L., Albert M.S., De Deyn P.P., Gu W., Himali J.J., Beekly D., Squassina A., Hartmann A.M., Orellana A., Blacker D., Rodriguez-Rodriguez E., Lovestone S., Garcia M.E., Doody R.S., Munoz-Fernadez C., Sussams R., Lin H., Fairchild T.J., Benito Y.A., Holmes C., Karamujić-Čomić H., Frosch M.P., Thonberg H., Maier W., Roshchupkin G., Ghetti B., Giedraitis V., Kawalia A., Li S., Huebinger R.M., Kilander L., Moebus S., Hernández I., Ilyas Kamboh M., Brundin R., Turton J., Yang Q., Katz M.J., Concari L., Lord J., Beiser A.S., Dirk Keene C., Helisalmi S., Kloszewska I., Kukull W.A., Maria Koivisto A., Lynch A., Tarraga L., Larson E.B., Haapasalo A., Lawlor B., Mosley T.H., Lipton R.B., Solfrizzi V., Gill M., Longstreth WT J.r., Montine T.J., Frisardi V., Diez-Fairen M., Rivadeneira F., Petersen R.C., Deramecourt V., Alvarez I., Salani F., Ciaramella A., Boerwinkle E., Reiman E.M., Fievet N., Rotter J.I., Reisch J.S., Hanon O., Cupidi C., Uitterlinden A.G., Royall D.R., Dufouil C., Giovanni Maletta R., de Rojas I., Sano M., Brice A., Cecchetti R., St George-Hyslop P., Ritchie K., Tsolaki M., Tsuang D.W., Dubois B., Craig D., Wu C.K., Soininen H., Avramidou D., Albin R.L., Fratiglioni L., Germanou A., Apostolova L.G., Keller L., Koutroumani M., Arnold S.E., Panza F., Gkatzima O., Asthana S., Hannequin D., Whitehead P., Atwood C.S., Caffarra P., Hampel H., Quintela I., Carracedo Á., Lannfelt L., Rubinsztein D.C., Barnes L.L., Pasquier F., Frölich L., Barral S., McGuinness B., Beach T.G., Johnston J.A., Becker J.T., Passmore P., Bigio E.H., Schott J.M., Bird T.D., Warren J.D., Boeve B.F., Lupton M.K., Bowen J.D., Proitsi P., Boxer A., Powell J.F., Burke J.R., Kauwe J.S., Burns J.M., Mancuso M., Buxbaum J.D., Bonuccelli U., Cairns N.J., McQuillin A., Cao C., Livingston G., Carlson C.S., Bass N.J., Carlsson C.M., Hardy J., Carney R.M., Bras J., Carrasquillo M.M., Guerreiro R., Allen M., Chui H.C., Fisher E., Masullo C., Crocco E.A., DeCarli C., Bisceglio G., Dick M., Ma L., Duara R., Graff-Radford N.R., Evans D.A., Hodges A., Faber K.M., Scherer M., Fallon K.B., Riemenschneider M., Fardo D.W., Heun R., Farlow M.R., Kölsch H., Ferris S., Leber M., Foroud T.M., Heuser I., Galasko D.R., Giegling I., Gearing M., Hüll M., Geschwind D.H., Gilbert J.R., Morris J., Green R.C., Mayo K., Growdon J.H., Feulner T., Hamilton R.L., Harrell L.E., Drichel D., Honig L.S., Cushion T.D., Huentelman M.J., Hollingworth P., Hulette C.M., Hyman B.T., Marshall R., Jarvik G.P., Meggy A., Abner E., Menzies G.E., Jin L.W., Leonenko G., Real L.M., Jun G.R., Baldwin C.T., Grozeva D., Karydas A., Russo G., Kaye J.A., Kim R., Jessen F., Kowall N.W., Vellas B., Kramer J.H., Vardy E., LaFerla F.M., Jöckel K.H., Lah J.J., Dichgans M., Leverenz J.B., Mann D., Levey A.I., Pickering-Brown S., Lieberman A.P., Klopp N., Lunetta K.L., Wichmann H.E., Lyketsos C.G., Morgan K., Marson D.C., Brown K., Martiniuk F., Medway C., Mash D.C., Nöthen M.M., Masliah E., Hooper N.M., McCormick W.C., Daniele A., McCurry S.M., Bayer A., McDavid A.N., Gallacher J., McKee A.C., van den Bussche H., Mesulam M., Brayne C., Miller B.L., Riedel-Heller S., Miller C.A., Miller J.W., Al-Chalabi A., Morris J.C., Shaw C.E., Myers A.J., Wiltfang J., O’Bryant S., Olichney J.M., Alvarez V., Parisi J.E., Singleton A.B., Paulson H.L., Collinge J., Perry W.R., Mead S., Peskind E., Cribbs D.H., Rossor M., Pierce A., Ryan N.S., Poon W.W., Nacmias B., Potter H., Sorbi S., Quinn J.F., Sacchinelli E., Raj A., Spalletta G., Raskind M., Caltagirone C., Bossù P., Donata Orfei M., Reisberg B., Clarke R., Reitz C., Smith A.D., Ringman J.M., Warden D., Roberson E.D., Wilcock G., Rogaeva E., Cecilia Bruni A., Rosen H.J., Gallo M., Rosenberg R.N., Ben-Shlomo Y., Sager M.A., Mecocci P., Saykin A.J., Pastor P., Cuccaro M.L., Vance J.M., Schneider J.A., Schneider L.S., Slifer S., Seeley W.W., Smith A.G., Sonnen J.A., Spina S., Stern R.A., Swerdlow R.H., Tang M., Tanzi R.E., Trojanowski J.Q., Troncoso J.C., Van Deerlin V.M., Van Eldik L.J., Vinters H.V., Paul Vonsattel J., Weintraub S., Welsh-Bohmer K.A., Wilhelmsen K.C., Williamson J., Wingo T.S., Woltjer R.L., Wright C.B., Yu C.E., Yu L., Saba Y., Pilotto A., Bullido M.J., Peters O., Crane P.K., Bennett D., Bosco P., Coto E., Boccardi V., De Jager P.L., Lleo A., Warner N., Lopez O.L., Ingelsson M., Deloukas P., Cruchaga C., Graff C., Gwilliam R., Fornage M., Goate A.M., Sanchez-Juan P., Kehoe P.G., Amin N., Ertekin-Taner N., Berr C., Debette S., Love S., Launer L.J., Younkin S.G., Dartigues J.F., Corcoran C., Arfan Ikram M., Dickson D.W., Nicolas G., Campion D., Tschanz J., Schmidt H., Hakonarson H., Clarimon J., Munger R., Schmidt R., Farrer L.A., Van Broeckhoven C., O’Donovan M.C., Destefano A.L., Jones L., Haines J.L., Deleuze J.F., Owen M.J., Gudnason V., Mayeux R., Escott-Price V., Psaty B.M., Ramirez A., Wang L.S., Ruiz A., van Duijn C.M., Holmans P.A., Seshadri S., Williams J., Amouyel P., Schellenberg G.D., Lambert J.C, & Pericak-Vance M.A. (2019). Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nature genetics, 51(3), 414-430.
Publication 2019
ABCC5 protein, human Alzheimer's Disease ApoE protein, human B-Lymphocytes BLOOD Brain Cells DNA Replication Epigenome Gene Expression Genes Genetic Diversity Genetic Heterogeneity Genetic Loci Macrophage Monocytes Myeloid Cells Myoblasts Nerve Degeneration Tissues
5
Ultrastuctural Analysis of Dark Microglia
Ultrathin sections from the different brain regions and experimental conditions were examined and photographed at various magnifications ranging between 440× and 9,300× using an ORCA‐HR digital camera (10 MP; Hamamatsu). Profiles of neurons, synaptic elements, microglia, astrocytes, oligodendrocytes, and myelinated axons were identified according to well‐established criteria (Peters et al., 1991). In addition to their immunoreactivity for IBA1 or GFP (in the CX3CR1‐GFP mice), microglial cells were distinguished from oligodendrocytes by their paler cytoplasm, prevalent association with the extracellular space, distinctive long stretches of endoplasmic reticulum, frequent vacuoles and cellular inclusions, irregular contours with obtuse angles, and small elongated nucleus delineated by a narrow nuclear cistern (Milior et al., in press; Tremblay et al., 2010a). To assess colocalization of the dark microglia with various markers, ultrastructural observations were conducted at the tissue–resin border, where the penetration of antibodies and staining intensity is maximal (Tremblay et al., 2010b). This analysis was strictly conducted in tissue areas where intense immunostaining was observed. For cases where no colocalization was detected, the presence of immunostaining in the same field of view ruled out the possibility that these cells were not stained due to a limited penetration of the antibodies.
To analyze dark microglia's and “normal” microglia's density across control conditions, chronic stress, aging, fractalkine signaling deficiency, and AD pathology, one ultrathin section containing the hippocampus CA1 strata radiatum and lacunosum‐moleculare was sampled in each of three mice per group (3‐month C57Bl/6J control, 14‐month C57Bl/6J control, 3‐month CX3CR1 knockout, 3‐month stressed C57Bl/6J, 3‐month stressed CX3CR1 knockout, and 14‐month APP‐PS1 model), for a total neuropil surface of ∼400,000 μm2 sampled in each animal. The entire section area was sequentially imaged at lowest magnification under the transmission electron microscope (440×) to determine systematically the total number of grid squares enclosing tissue from each of stratum radiatum and lacunosum‐moleculare. These two neuropil layers were identified based on their position to the CA1 pyramidal cell layer, as well as their cellular and subcellular contents. The total surface area was calculated at high precision by multiplying the number of grid squares containing each of stratum radiatum or lacunosum‐moleculare by the area of a single grid square. A schematic representation of all the grid squares included in the analysis was drawn for each section/animal. The ultrathin sections were afterward rigorously screened for the presence of dark microglia, strictly identified based on a series of ultrastructural features that are described in detail in the Results section. Only dark microglia showing a complete or a partial profile where part of the nucleus could be seen were included in the analysis, considering that the chromatin pattern is a distinctive feature of the dark microglia. Each dark microglia was photographed at magnifications between 4,600× and 9,300×, and marked on the schematic representation, for a total of 95 cells included in the analysis. Considering the heterogeneity in dark microglia's distribution, with these cells generally appearing within clusters, and the impossibility to identify them with light microscopy (see Discussion section), and hence to select the areas to examine based on their presence, their density was expressed as maximal numbers per mm2 of tissue surface across three animals/experimental conditions. The density of normal microglia was assessed in the same manner to allow for comparison. We did not attempt to distinguish normal microglia from bone marrow‐derived macrophages and other types of myeloid cells in the brain. In addition, using the same samples, we determined the percentage of dark microglia that were: (1) located in stratum radiatum versus lacunosum‐moleculare, (2) directly apposing one or more blood vessel, and (3) encircling one or more synaptic element (axon terminal, dendritic spine, and excitatory synapse between axon terminal and dendritic spine) with their processes.
Bisht K., Sharma K.P., Lecours C., Gabriela Sánchez M., El Hajj H., Milior G., Olmos‐Alonso A., Gómez‐Nicola D., Luheshi G., Vallières L., Branchi I., Maggi L., Limatola C., Butovsky O, & Tremblay M. (2016). Dark microglia: A new phenotype predominantly associated with pathological states. Glia, 64(5), 826-839.
Publication 2016
Animal Diseases Animals Antibodies Astrocytes Axon Blood Vessel Brain CA1 Pyramidal Cell Area CA1 Stratum Radiatum Cell Nucleus Cells Chromatin Cytoplasm Dendritic Spines Endoplasmic Reticulum Extracellular Space Fingers Fractalkine Genetic Heterogeneity Inclusion Bodies Light Microscopy Macrophage Microglia Mus Myeloid Cells Neurons Neuropil Oligodendroglia Orcinus orca Resins, Plant Seahorses Synapses Tissues Training Programs Transmission Electron Microscopy Vacuole

Most recents protocols related to «Myeloid Cells»

1
Immune cell deconvolution in colorectal cancer
The immunedeconv package in R (https://www.aclbi.com/static/index.html#/immunoassay), which integrates CIBERSORT (17 (link)), is a deconvolution algorithm based on gene expression that is able to evaluate changes in the expression of one set of genes relative to all other genes in the sample. This package was used to analyze the levels of tumor-infiltrating immune cells. Among 478 COAD samples based on TCGA-COAD data, samples with the top 25% and the lowest 25% levels of GIPC2 expression were classified into the high- and low-expression groups, respectively. The abundance of 22 types of immune cells [naïve B cells, memory B cells, plasma B cells, CD8+ T cells, naïve CD4+ T cells, resting CD4+ memory T cells, activated CD4+ memory T cells, follicular helper T cells, regulatory T cells, γδ T cells, resting natural killer (NK) cells, activated NK cells, monocytes, M0 macrophages, M1 macrophages, M2 macrophages, resting myeloid dendritic cells, activated myeloid dendritic cells, activated mast cells, resting mast cells, eosinophils and neutrophils] were estimated using the CIBERSORT algorithm. Briefly, gene expression datasets from TCGA were uploaded to the Xiantao bioinformatics analysis tool, and after standard annotation, the immunedeconv R package was used to estimate the P-values for deconvolution via the CIBERSORT algorithm. This tool was then used to compare the expression of immune checkpoint-associated genes, including CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT and SIGLEC15, between patients with COAD in the high and low GIPC2 expression groups, respectively. The aforementioned analyses and R package were implemented using R foundation for statistical computing (2020) version 4.0.3 (18 ) and the software packages ggplot2 (https://cran.r-project.org/web/packages/ggplot2/index.html) and pheatmap (https://cran.r-project.org/web/packages/pheatmap/index.html) were used for generating images.
Kang M, & Su Z. (2023). Increased expression of GIPC2 in colon adenocarcinoma is associated with a favorable prognosis and high levels of immune cell infiltration. Oncology Reports, 49(4), 66.
Publication 2023
B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes CD274 protein, human Cell Cycle Checkpoints Cells Chronic Obstructive Airway Disease CTLA4 protein, human Dendrites Dendritic Cells Eosinophil Gene Expression Genes HAVCR2 protein, human Immunoassay Macrophage Mast Cell Memory B Cells Memory T Cells Monocytes Myeloid Cells Natural Killer Cells Neoplasms Neutrophil Patients PDCD1 protein, human Plasma Cells Regulatory T-Lymphocytes T-Lymphocyte T Follicular Helper Cells TIGIT protein, human
2
Genetic Variants and Brain Tumor Risk
SNPs that were significantly (P < 1×10-5) associated with the different types of malignant brain tumors were selected as IVs. Since the number of independent SNPs of malignant brain tumors was limited, we selected eligible SNPs by relaxing the GWAS P threshold to 1×10-5 when it was treated as exposure. Independent variables from each other were retained based on European ancestry reference data from the 1000 Genomes Project (linkage disequilibrium (LD), r2 < 0.001). As above, we also used the PhenoScanner tool to manually remove SNPs and their proxies (r2 > 0.80) that were significantly (P <5×10-8) associated with potential confounders of the VEGF-malignant relationship based on published studies: white cell (31 (link), 32 (link)). One SNP (rs147958197) for malignant brain tumors was associated with monocyte count, monocyte percentage of white cells or granulocyte percentage of myeloid white cells.
Zhang Q., Wu G., Zhang X., Zhang J., Jiang M., Zhang Y., Ding L, & Wang Y. (2023). Vascular endothelial growth factor and risk of malignant brain tumor: A genetic correlation and two-sample Mendelian randomization study. Frontiers in Oncology, 13, 991825.
Publication 2023
Brain Neoplasm, Malignant Europeans Genome Genome-Wide Association Study Granulocyte Leukocytes Malignant Primary Brain Neoplasms Monocytes Myeloid Cells Vascular Endothelial Growth Factors
3
Microglia Depletion and Repopulation Protocol
PLX5622 was provided by Plexxikon and formulated in AIN-76A rodent chow by Research Diets at a concentration of 1200 parts per million (56 (link), 57 (link)). Standard AIN-76A diet was provided as Veh control. Mice were provided ad libitum access to PLX5622 or Veh diet for 3 weeks to deplete microglia. After 3 weeks of depletion, mice were placed back on normal chow for either 4 or 12 weeks to allow for repopulation. The dose and time resulted in depletion of 95% of microglia and significant depletion of CD115/CSF1R-expressing myeloid cells in other peripheral organs consistent with previous studies (fig. S16) (58 (link)).
Ritzel R.M., Li Y., Jiao Y., Lei Z., Doran S.J., He J., Shahror R.A., Henry R.J., Khan R., Tan C., Liu S., Stoica B.A., Faden A.I., Szeto G., Loane D.J, & Wu J. (2023). Brain injury accelerates the onset of a reversible age-related microglial phenotype associated with inflammatory neurodegeneration. Science Advances, 9(10), eadd1101.
Publication 2023
Diet Gene, c-fms Microglia Mus Myeloid Cells PLX5622 Rodent
4
Immunohistochemical Analysis of CD74 and CD33 in Breast Cancer
28 tissue samples of BC were collected in Shunde Hospital of Guangzhou University of Chinese Medicine to analyze the correlation between CD74 and myeloid cells. To reduce error, Immunohistochemistry (IHC) for CD74 (1:200 dilution; EPR4064, Abcam) and CD33 (1:200 dilution; EPR23051-101, Abcam) were performed by an autostrainer system (Lumatas Titan, LumatasBiosystem Inc.) on 3-μm-thick,formalin-fixed and paraffin-embedded (FFPE) Sections. Counterstaining was done with hematoxylin. The average optical density (AOD) was calculated with Image Pro Plus6.0 to determine the protein expression level.
Ren C., Gao A., Fu C., Teng X., Wang J., Lu S., Gao J., Huang J., Liu D, & Xu J. (2023). The biomarkers related to immune infiltration to predict distant metastasis in breast cancer patients. Frontiers in Genetics, 14, 1105689.
Publication 2023
Chinese Formalin Hematoxylin Immunohistochemistry Myeloid Cells Paraffin Pharmaceutical Preparations Proteins Technique, Dilution Tissues Vision
5
Competitive Hematopoietic Stem Cell Assay
Donor BM cells from Foxp3Cre or Foxp3CreBatffl/fl mice (CD45.1− CD45.2+) were mixed with support BM from Boy/J mice (CD45.1+ CD45.2−) and injected intravenously into lethally irradiated (950 cGy) Boy/J mice. Percentage donor CD45.1− CD45.2+ cells in PB were determined by flow cytometry at 1.5 and 3 months. At 3 months BM from recipient Boy/J mice was analyzed for percent donor cell engraftment, recovery of myeloid cells, and percentage of donor-derived LT-HSC, ST-HSC, MPP, CMP, GMP and MEP by flow cytometry. Complete blood counts (CBCs) were performed on recipient mouse blood collected via cardiac puncture at time of euthanasia utilizing a Heska Element HT5. For these experiments, cages (4–5 mice/cage) were randomly selected for transplantation group based on cage location in the cage rack. Animal studies were blinded.
Tikka C., Beasley L., Xu C., Yang J., Cooper S., Lechner J., Gutch S., Kaplan M.H., Capitano M, & Yang K. (2023). BATF sustains homeostasis and functionality of bone marrow Treg cells to preserve homeostatic regulation of hematopoiesis and development of B cells. Frontiers in Immunology, 14, 1026368.
Publication 2023
Animals BLOOD Cells Complete Blood Count Euthanasia Flow Cytometry Heart Mus Myeloid Cells Punctures Tissue Donors Transplantation

Top products related to «Myeloid Cells»

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The LSRFortessa is a flow cytometer designed for multiparameter analysis of cells and other particles. It features a compact design and offers a range of configurations to meet various research needs. The LSRFortessa provides high-resolution data acquisition and analysis capabilities.
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The FACSCalibur is a flow cytometry system designed for multi-parameter analysis of cells and other particles. It features a blue (488 nm) and a red (635 nm) laser for excitation of fluorescent dyes. The instrument is capable of detecting forward scatter, side scatter, and up to four fluorescent parameters simultaneously.
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The FACSCanto II is a flow cytometer instrument designed for multi-parameter analysis of single cells. It features a solid-state diode laser and up to four fluorescence detectors for simultaneous measurement of multiple cellular parameters.
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
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Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.

More about "Myeloid Cells"

Myeloid cells are a diverse group of immune cells derived from the myeloid lineage of hematopoietic stem cells.
These cells play a crucial role in innate immunity, inflammation, and hematopoiesis.
Myeloid cells include granulocytes (neutrophils, eosinophils, and basophils), monocytes, macrophages, and dendritic cells.
They are responsible for phagocytosis, cytokine production, and antigen presentation, making them essential in the body's defense against pathogens and the regulation of immune responses.
Understanding the biology and function of myeloid cells is crucial for the study of various diseases, including autoimmune disorders, cancer, and infectious diseases.
Researchers can leverage the power of PubCompare.ai to optimize their myeloid cell research, locating the most accurate and reproducible protocols from literature, pre-prints, and patents.
PubCompare.ai is an AI-driven platform that enhances research efficiency and confidence by providing seamless comparisons and insights.
Researchers can use this tool to discover the most reliable and reproducible protocols for myeloid cell studies, including those involving the use of flow cytometry instruments like the LSRFortessa, FACSCalibur, FACSCanto II, LSRII flow cytometer, and FACSAria, as well as reagents like FBS, DNase I, and Penicillin/streptomycin.
By incorporating the latest advancements in myeloid cell biology and leveraging powerful tools like PubCompare.ai, researchers can unlock the full potential of their myeloid cell studies and contribute to the understanding and treatment of various health conditions.