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> Genes & Molecular Sequences > Gene or Genome > V-fos Genes

V-fos Genes

V-fos genes are members of the fos gene family, which encode nuclear proteins that function as transcriptional regulators.
The v-fos gene was first identified in the Finkel-Biskis-Jinkins murine osteosarcoma virus and is the viral counterpart of the cellular proto-oncogene c-fos.
V-fos genes play a key role in cellular proliferation, differentiation, and transformation, and have been implicated in the development of various cancers.
Researcehrs studying V-fos genes can leverage PubCompare.ai to quickly identify the most accurate and reproducible experimental protocols from the scientific literature, preprints, and patent data, enhancing the productivity and quality of their investigations.

Most cited protocols related to «V-fos Genes»

1
Gut-Brain Axis Modulation with Prebiotics
Rats were administered a daily oral administration (gavage) of either water, FOS (3 g/kg) or GOS (4 g/kg), for 5 weeks (n = 8/group). This dosing regimen was based on previous studies (Anthony et al., 2006 (link)). Copies of Bifidobacteria spp. genes in DNA extracted from faecal pellets were determined with standard QPCR at the end of the study, as previously described (Ketabi et al., 2011 (link)). Twenty-four hours after the last gavage, the animals were sacrificed, their brains removed and trunk blood collected in EDTA-coated tubes. Blood was centrifuged (5000 rpm, 15 min) to obtain plasma which was then stored at −80 °C. The frontal cortex and hippocampus were dissected out from half of the harvested brains. Brain hemispheres and isolated regions were snap-frozen in isopentane on dry-ice and stored with plasmas at −80 °C prior to use. Additional faecal pellets were collected from each animal (n = 8/group), weighed, homogenised in PBS (1:1, w/v), and then centrifuged at 14,000 rpm for 10 min at 4 °C. Supernatants were removed and stored at −80 °C prior to HPLC analysis.
Savignac H.M., Corona G., Mills H., Chen L., Spencer J.P., Tzortzis G, & Burnet P.W. (2013). Prebiotic feeding elevates central brain derived neurotrophic factor, N-methyl-d-aspartate receptor subunits and d-serine. Neurochemistry International, 63(8), 756-764.
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Publication 2013
Administration, Oral Animals Bifidobacterium BLOOD Brain Cerebral Hemispheres Dry Ice Edetic Acid Feces Freezing Genes High-Performance Liquid Chromatographies isopentane Lobe, Frontal Pellets, Drug Plasma Rattus Seahorses Treatment Protocols Tube Feeding
2
Recombinant TCR Production and Purification
Soluble pMHCI manufacture, tetramerization and biophysical studies were performed as previously described 31 (link). The mutations in HLA-A68 and the biophysical validation of their effects are published elsewhere 13 (link), 31 (link). In order to obtain the sequence of an HLA-A68-restricted TCR, cDNA from c23 was used as a template in 42 separate PCR using a primer set to cover all TCRAV and TCRAJ genes. Only one reaction generated a product. Sequencing confirmed a TCR α chain made from the TCRAV 14 gene with a TCRAJ 20 joining region (IMGT nomenclature). The TCRB sequence was generated by a single PCR with a combined primer set 34 (link). The reaction yielded a single product. Sequence analysis showed a TCRBV 7-9 gene with a TCRBJ 1-1 joining region. The TCR α chain was cloned into pGMT7 expression vector as a fusion construct with the c-jun leucine zipper region 35 (link). A TCR β chain pGMT7 expression vector was constructed to express the TCR chain as a fusion with the v-fos leucine zipper 35 (link). Expression vectors were transformed into Escherichia coli Rosetta DE3 (pLysS) and protein was produced as inclusion bodies by inducing protein expression with 0.5 mM isopropyl-β-d-thiogalactopyranoside.
Following cell harvest by centrifugation, inclusion bodies were isolated by sonication and purified with three successive detergent washes using 0.5% Triton X-100. Inclusion bodies were given a final wash in resuspension buffer to remove any detergent before being resolubilized in guanidine solution (6 M guanidine, 50 mM Tris pH 8.1, 100 mM NaCl, 10 mM EDTA, 10 mM dithiothreitol). Insoluble material was pelleted by centrifugation and the supernatant stored at –80°C. TCR-zipper chains were refolded at a 5:1 ratio of α:β chain. Each solubilized inclusion body chain was diluted to 5 mg/mL in guanidine solution. To ensure complete denaturation, dithiothreitol was added to a concentration of 10 mM and chains were incubated at 37°C for 30 min. Refolding of soluble TCR was initiated by injecting the dissolved α and β chain inclusion bodies simultaneously into a vigorously stirring refolding buffer (5 M urea, 0.4 M l-arginine, 100 mM Tris pH 8.1, 6.5 mM cysteamine-HCl, 3.7 mM cystamine di-hydrochloride) chilled to 4°C, to a final concentration of 60 mg/L.
The solution was left for 3 h, then dialyzed for 24 h against ten volumes of demineralized water, followed by ten volumes of 10 mM Tris pH 8.1. All dialysis steps were carried out at 4°C. Dialyzed TCR was isolated from impurities by filtering and loading onto a POROS 50 HQ anion exchange column (Applied Biosystems). The column was washed with 10 mM Tris pH 8.1 and bound protein was eluted with a NaCl gradient (0–1 M) in the same buffer. Correctly refolded protein was confirmed by reduced and non-reduced sodium dodecyl sulfate (SDS)-PAGE. Fractions containing correctly refolded TCR were pooled, concentrated and further purified on a Superdex 200 gel filtration column (Amersham Biosciences, Uppsala, Sweden) in HEPES-buffered saline (HEPES pH 7.4, 150 mM NaCl, 3 mM EDTA). The final purified c23 TCR was analyzed by SDS-PAGE in reducing and non-reducing conditions. Peak fractions were pooled and concentrated prior to BIAcore™ surface plasmon resonance studies.
Gostick E., Cole D.K., Hutchinson S.L., Wooldridge L., Tafuro S., Laugel B., Lissina A., Oxenius A., Boulter J.M., Price D.A, & Sewell A.K. (2007). Functional and biophysical characterization of an HLA-A*6801-restricted HIV-specific T cell receptor. European Journal of Immunology, 37(2), 479-486.
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Publication 2007
3
Evaluating Gene Modulation in EcPV2-Induced Cancer
In this study, we evaluated gene modulation as a result of EcPV2 infection and cancer development. To this purpose, receptor activator of nuclear factor-kappa B ligand (RANKL), nuclear factor kappa-light-chain-enhancer of activated B cells (NFKB)p50, NFKBp65, interleukin (IL)-6, IL17, IL23p19, β-catenin (BCATN1), FOS like 1 (FOSL1), and lymphoid enhancer binding factor 1 (LEF1) were selected for gene expression evaluation. All the primers were designed through Primer3web tool v. 4.1.0 (https://primer3.ut.ee). In addition, 3 other genes, namely, IL8, IL12p35, and IL12p40, were tested using previously evaluated primer pairs [4 (link),30 (link)]. B2M was chosen as reference gene to normalize relative gene expression evaluation. The primer set is reported in Table 2. As previously described [31 (link),32 (link)] a Real-Time PCR amplification using SsoFast EvaGreen Supermix (BioRad, Hercules, CA, USA) was performed in a CFX96 Real-Time System with 5 μL of 1:5 diluted cDNA. Each sample was tested in triplicate and fluorescence data were collected at the end of the second step of each cycle. Relative expression was calculated through the ΔΔCq method.
Mecocci S., Porcellato I., Armando F., Mechelli L., Brachelente C., Pepe M., Gialletti R., Passeri B., Modesto P., Ghelardi A., Cappelli K, & Razzuoli E. (2021). Equine Genital Squamous Cell Carcinoma Associated with EcPV2 Infection: RANKL Pathway Correlated to Inflammation and Wnt Signaling Activation. Biology, 10(3), 244.
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Publication 2021
beta-Catenin DNA, Complementary Fluorescence Gene Expression Genes IL17A protein, human Infection Interleukin-6 Interleukin-12 Subunit p35 Interleukin-23 Subunit p19 Lymphoid Enhancer-Binding Factor 1 Malignant Neoplasms NF-kappa B Oligonucleotide Primers Real-Time Polymerase Chain Reaction TRANCE Protein
4
Transcriptional Profiling of Socioeconomic Status Effects
Genome-wide transcriptional profiling was performed on isolated peripheral blood mononuclear cells (PBMCs) from all 78 participants in one batch. Assays were conducted as previously described (32 ), with PBMCs isolated by density gradient centrifugation and total RNA extracted (RNeasy; Qiagen), tested for suitable mass (Nanodrop ND1000) and integrity (Bioanalyzer; Agilent), and converted to fluorescent cRNA for hybridization to Illumina Human HT-12 v4 BeadArrays following the manufacturer’s standard protocol in the University of California, Los Angeles, Neuroscience Genomics Core Laboratory. Gene expression values were quantile-normalized, log2-transformed, and subject to general linear model analyses relating the expression of each assayed gene to SES (0 = high SES, 1 = low SES; results are interpretable in terms of the effects of low SES) while also controlling for age, sex, BMI, and presence of pre-defined co-existing medical conditions at the time of preparative regimen. Primary analyses tested an a priori-defined contrast score representing the 53-gene CTRA profile of up-regulated expression of 19 pro-inflammatory genes (IL1A, IL1B, IL6, IL8, TNF, PTGS1, PTGS2, FOS, FOSB, FOSL1, FOSL2, JUN, JUNB, JUND, NFKB1, NFKB2, REL, RELA, and RELB) and down-regulated expression of 31 genes involved in type I interferon (IFN) responses (GBP1, IFI16, IFI27, IFI27L1-2, IFI30, IFI35, IFI44, IFI44L, IFI6, IFIH1, IFIT1-3, IFIT5, IFIT1L, IFITM1-3, IFITM4P, IFITM5, IFNB1, IRF2, IRF7-8, MX1-2, OAS1-3, and OASL) and three genes involved in antibody synthesis (IGJ, IGLL1, and IGLL3) (17 , 20 (link), 21 , 33 (link)) to evaluate whether low SES might be associated with increased expression of this CTRA profile. Contrast coefficient-weighted association statistics were averaged to summarize the magnitude of association over the entire CTRA gene set, and standard errors were derived from 200 cycles of bootstrap resampled residual vectors (to account for potential correlation among residuals across genes) (34 ).
To identify transcription control pathways that may mediate observed transcriptional differences, initial “low-level” genome-wide analyses identified all transcripts showing a model-adjusted point estimate of ≥20% difference in expression between low- vs. high-SES HCT recipients. Those putatively associated genes were subject to Transcription Element Listening System (TELiS) promoter-based bioinformatic analysis (35 (link)) to assess activity of NF-κB, AP-1, IRF, and CREB and GR family transcription factors previously linked to CTRA transcriptional dynamics (TRANSFAC V$CREL_01, V$AP1_Q4, V$ISRE_01, V$CREB_Q4, V$GR_Q6) (35 (link)), with results averaged over nine parametric variations of MatInspector scan stringency and promoter length (35 (link)). To ensure that results were not confounded by individual differences in the prevalence of specific leukocyte subtypes within the PBMC pool (36 (link)), analyses also controlled for the prevalence of transcripts marking T lymphocyte subsets (CD3D, CD3E, CD4, CD8A), B lymphocytes (CD19), natural killer cells (CD16/FCGR3A, CD56/NCAM1), and monocytes (CD14) (18 (link)). Transcript origin analysis (TOA) was applied to the low-level association data to identify the specific PBMC subtypes mediating the observed differences in gene expression, as previously described (32 ). Low-level transcript-phenotype associations were estimated solely as inputs into high-level TELiS and TOA gene set expression analyses and are not tested for statistical reliability at the level of individual genes.
Knight J.M., Rizzo J.D., Logan B.R., Wang T., Arevalo J.M., Ma J, & Cole S.W. (2015). Low socioeconomic status, adverse gene expression profiles, and clinical outcomes in hematopoietic stem cell transplant recipients. Clinical cancer research : an official journal of the American Association for Cancer Research, 22(1), 69-78.
Publication 2015
5
Personality Traits and Immune Gene Expression

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Publication 2014

Most recents protocols related to «V-fos Genes»

1
Analyzing AP-1 Dynamics in B Cell Subtypes
Enrichment analysis of common highly expressed AP-1 related naïve and memory B cell subtypes was performed using the Metascape resource, which identified enriched biological process (BP) terms and constructed the functional network. Other overexpression enrichment analysis and gene set enrichment analysis based on BP terms, KEGG pathways and Hallmark were using ClusterProfiler R package32 (link) (V.4.4.4), where BP and Hallmark gene sets were download from MsigDB33 (link) and KEGG pathways were acquired by KEGGREST R package (V.1.36.3). The genes used to measure AP-1 complex activity included FOS, FOSB, FOSL1, FOSL2, JUN, JUNB, and JUND. Gene set module scores in single cells were calculated using the AddModuleScore function in the Seurat package. Differential abundance of B cell lineage subtypes between NewlyDx and normal samples testing based on k-nearest neighbor graphs was implemented by miloR R package34 (link) (V.1.4.0). The kernel density enrichment of sample types and antibody isotypes distribution was assessed along the differentiation trajectory. Taking sample types as an example, we estimated the probability density distribution of AML and normal isotypes along pseudotime using Gaussian kernel functions. Subsequently, the ratio of each pseudotime point was calculated. For the analysis of ligand-receptor interactions between B cells and AML cells, we specifically examined genes that were expressed in more than 10% of cells within each cell subtype, based on experimentally confirmed interactions. Manually collected confirmed ligand-receptor interactions are presented in online supplemental table 5. To compare ligand-receptor gene expression levels in bulk datasets, we calculated the average expression value for each gene within each sample and then determined the overall average of all ligand-receptor genes. Smoothed Cox proportional hazard analysis were conducted using the phenoTest R package (V.1.44.0) and Kaplan-Meier Curves were visualized by survminer R package (V.0.4.9).
Statistical analyses and data visualization were implemented by R programming language (V.4.2.1). Two-sided Wilcoxon rank-sum and signed rank test and Kruskal-Wallis rank sum test were adopted to test the difference from two or more than two comparable groups, respectively. Correlation analyses were used the Spearman rank correlation test.
Guo S., Mohan G.S., Wang B., Li T., Daver N., Zhao Y., Reville P.K., Hao D, & Abbas H.A. (2024). Paired single-B-cell transcriptomics and receptor sequencing reveal activation states and clonal signatures that characterize B cells in acute myeloid leukemia. Journal for Immunotherapy of Cancer, 12(2), e008318.
Publication 2024
2
Clustering Analysis of Mouse Hypothalamus ScRNA-seq Data
The data for the mouse hypothalamus atlas study2 (link) were downloaded from the link provided in the Data availability section. We used the data from all 11 naive animals (485,657 cells) and processed them with BANKSY’s multisample mode, as described next. As done in the original study, the gene Fos was removed from the dataset because it contained not a number (‘NaN’) entries. This dataset consisted of gene–cell matrices ( C ) that were normalized by the imaged volume of each cell; therefore, it did not need to be count-normalized further. We ran BANKSY and nonspatial clustering using default parameters (λ ∈ {0, 0.2}), with a clustering resolution of 0.5. The multisample mode involved concatenating the 11 neighbor-augmented matrices along the columns to form a larger multisample matrix. The resulting cluster labels were harmonized using our cluster consensus algorithm, as described in Methods, ‘Cluster consensus across runs’. As in the original study, we removed all cells marked ‘ambiguous’ from the subsequent analysis. To compute the genes that were differentially expressed between the two oligodendrocyte subclusters, we used the scran package (v.1.18.7) as before, taking the union of the top ten DEGs from both the t-test and the Wilcoxon rank-sum test, with expression z-scaled across the oligodendrocytes.
We used Seurat v.4.1.1 to perform the clustering analysis using the guilt-by-association genes in the scRNA-seq data provided by Moffitt et al.2 (link). The data matrix was filtered so that only cells with less than 20% mitochondrial RNA (pct.mito) and cells with more than 1,000 detected genes were kept (nFeature_RNA). Next, we subset the data to keep only cells labeled MODs by the authors of the original study2 (link) and divided the counts in each cell by the total counts in that cell, multiplied by 10,000 (Seurat default), log-transformed using log1p, and z-scaled the data (centered to 0 and the standard deviation scaled to 1). Next we identified the 25 most highly correlated genes to the DEGs found above. We removed genes that did not have a count of at least 1 in at least 1% of all MODs. The original data were subset to the resulting set of guilt-by-association genes and to the MODs, count-normalized to 10,000, log1p-normalized and z-scaled, as before. Next, we used Seurat’s FindNeighbours with default parameters and the top five principal components to build a neighborhood graph in the expression space, followed by the FindClusters function with a resolution of 0.05. The uniform manifold approximation and projections (UMAPs) were generated with the top five principal components.
Singhal V., Chou N., Lee J., Yue Y., Liu J., Chock W.K., Lin L., Chang Y.C., Teo E.M., Aow J., Lee H.K., Chen K.H, & Prabhakar S. (2024). BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nature Genetics, 56(3), 431-441.
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Publication 2024
3
Cytotoxicity Evaluation of Copper Oxide Nanoparticles
Human colon and gastric adenocarcinoma cells (LoVo and MKN-45 cell lines, respectively) and human dermal fibroblasts (HDF cell line) were obtained from the Pasteur Institute in Tehran, Iran. LoVo cells express carcinoembryonic antigen and MYB, c-MYC, H-RAS, and FOS oncoproteins and are capable of forming tumors when transplanted into nude mice [14 (link)]. MKN-45 cells are capable of expressing wild-type genes P53, P16, and P15, with mutations reported in the oncogenes c-MET and E-cadherin [15 (link)]. Dulbecco's modified Eagle's medium (Gibco) was used for the culture of MKN-45 and HDF cells, whereas Roswell Park Memorial Institute-1640 (Capricorn) was employed for the growth of LoVo cells. The culture media for all cell types consisted of 10 % fetal bovine serum (Gibco) and 1 % penicillin-streptomycin. For normoxic incubation, cells were maintained at 37 °C with 5 % CO2 and 21 % O2 in air (Memmert incubator). For hypoxic condition, LoVo cells were placed in a triple incubator with a gas mixture consisting of 93 % N2, 5 % CO2, and 2 % O2 (Binder incubator).
The cytotoxicity of CuONPs was evaluated by determining the half-maximal inhibitory concentration (IC50) using an alamarBlue assay [16 (link)]. LoVo and MKN-45 cells were seeded at 14,000 cells per well in 96-well plates, while HDF cells were seeded at 10,000 cells per well. Followed by over-night incubation, cells were exposed to CuONPs at concentrations of 50, 100, and 200 μg/ml, all prepared using a complete medium immediately before use. To note, untreated cells served as control, and viability assay was performed for each cell type at least three times. After 24 h of treatment, alamarBlue solution (0.1 mg/ml, Sigma-Aldrich) was added to each well (10 % v/v) and cells were incubated at 37 °C in the dark for 3 h. The absorbance (A) was then measured at 600 nm (BioTek spectrophotometer) and the percentage of cell viability was calculated using the following equation: (100-(AT-AU)/(AB-AU)) × 100, where AT, AU, and AB were the absorbances of treated cells, untreated cells, and blank control, respectively.
Talebian S., Shahnavaz B., Shakiba M, & Rassouli F.B. (2024). Illuminating new possibilities: Effects of copper oxide nanoparticles on gastrointestinal adenocarcinoma cells in hypoxic condition. Heliyon, 10(10), e31414.
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Publication 2024
4
Single-cell transcriptomics of intestinal organoids
Not available on PMC !
Single cell RNA-seq libraries of organoid derived material, and KIT + enriched cells from fetal and adult intestines were sequenced on an Illumina NextSeq platform, at a median sequencing depth of 49,861 reads per cell. Reads were mapped to a human genome (hg38) integrated with the Clover transcript using STAR (version 2.7.8a), reads with multiple mapping positions were excluded. Reads were associated with genes if they were mapped to an exon. Reads were demultiplexed and collapsed into UMI tables using umi_tools (version 1.1.1) allowing up to one hamming distance of the cell barcode. Cells with less than 500 UMI, or with more than 40% mitochondrial genes were excluded from analysis.
All analysis was performed in R. We used the MetaCell package 17 (link) to analyze all scRNA-seq data collected in this study. Default parameters were used unless otherwise stated. We derived a metacell cover of DAPI -/AVIL-Clover + and DAPI -epithelial cells from human ileal organoids. Mitochondrial genes and the highly variable immunoglobulin genes (IGH, IGK and IGL prefixes) were removed from the UMI tables. Gene features for metacell covers were selected using the parameter Tvm=0.1, total umi > 10, and more than 3 UMI in at least 3 cells.
We filtered the list of gene features used for metacell analysis from genes associated with cell cycle, immediate stress response and gene modules inducing strong patient-specific biases.
To this end, we first identified all genes with a correlation coefficient of at least 0.13 for one of the anchor genes TOP2A, NKI67, PCNA, MCM4, UBE2C, STMN1, FOS, EGR1, IER3, FOSB, HSPA1B, HSPA1A, HSP90AA1, and DNAJB1. We then hierarchically clustered the correlation matrix between these genes (filtering genes with low coverage and computing correlation using a down-sampled UMI matrix) and selected the gene clusters that contained the above anchor genes. We thus retained 94 genes as features. We used metacell to build a kNN graph, perform boot-strapped co-clustering (500 iterations; resampling 70% of the cells in each iteration), and derive a cover of the co-clustering kNN graph (K=30). Outlier cells featuring gene expression higher than 4-fold than the geometric mean in the metacells in at least one gene were discarded. Detailed annotation of the different tuft and epithelial cell subsets was performed using hierarchical clustering of the metacell confusion matrix.
ClusterProfiler 18 (version 3.14.0) and ChIPpeakAnno (version 3.20.0) were applied to perform gene functional annotation of differentially expressed genes.
scRNA-seq of passage 1 KIT -and KIT + derived organoids was performed using the Chromium Next GEM Single Cell 3' v 3.1 platform, and sequenced on an Illumina NovaSeq6000 platform.
Reads were mapped to the human genome (hg38) and demultiplexed using cellranger (version 7.1.0). Recovered cellplex barcodes were used to assign single cells to experimental batches. Single cells with less than 64 UMI of a specific cellplex barcodes were discarded from down-stream analysis. Single cells with less than 8-fold UMI count ratio between highest and second highest cellplex barcodes were marked as doublets and discarded from downstream analysis. Single cells with less than 1,000 genomic UMIs or more than 20% mitochondrial content failed to pass QC and were discarded from further analysis, resulting in 10,311 QC-positive cells.
Clustering of passage 1 KIT -and KIT + derived organoids was performed as stated above.
Gene features for the metacell covers were selected using the parameter Tvm=0.1, total umi > 15, and more than 3 UMI in at least 3 cells, resulting in 228 features.
We reanalyzed scRNA-seq data from human primary intestinal tissue 19 . We selected 15,184 single cells from healthy adult small intestine, with >1,000 and <20,000 total UMI for further analysis. Cells were analyzed with the metacell package as previously described to derive a 2D representation of the data for Extended Data Figures 2i and5a. Otherwise, we used predefined annotations to epithelial cell types. In Fig. 2h and 3m we sampled 500 cells from each cell types out of the total 77,364 healthy adult single cells in that database.
Huang L., Bernink J.H., Giladi A., Krueger D., Son G.v., Geurts M.H., Busslinger G., Lin L., Zandvliet M.M., Peters P.J., López‐Iglesias C., Buskens C.J., Bemelman W.A., Begthel H., & Clevers H. (2024). Tuft cells act as regenerative stem cells in the human intestine.
Publication 2024
5
Comprehensive Multimodal Cell Atlas Protocol
Most datasets shown in this article are from publicly available datasets (Data availability section). For these datasets and the VeraFISH dataset, no statistical method was used to predetermine sample size and no data were excluded from the analyses. Full sample sizes are as follows: Slide-seq and Slide-seq v.2 datasets: 25,551 and 39,496 cells, respectively; MERFISH mouse hypothalamus data: all 11 naive animals (485,657 cells); scRNA-seq study of MODs in the mouse hypothalamus, all cells labeled MODs by the authors of the original study (6,611 cells); VeraFISH mouse hippocampus dataset: all 10,994 cells; corresponding scRNA-seq analysis: all cells in neuronal clusters (as labeled by the authors, 2,386 cells) and all cells labeled ‘Oligo’ (231 cells); MERSCOPE colorectal cancer data: all 677,451 cells; DLPFC Visium dataset: all 12 samples; STARmap dataset: all 1,207 cells annotated by the authors; CODEX data: all 33,958 cells from the ileum, all 25,403 cells from the right colon region and all 27,784 cells from the transverse colon region from donor B0012 for tissue domain annotation. For community annotation: 38,371 cells from donor B006 in the ascending colon region; simulated data: we created three samples for each gene set condition (400, 600, 800 and 1,020 genes), including 4,996 cells each.
The only data exclusions were as follows. In the mouse hypothalamus MERFISH data, we removed cells marked ‘ambiguous’ (as per the original study) and the gene Fos, which contained ‘NaN’ entries. In the DLPFC Visium data, we removed spots marked ‘ambiguous’ by the authors. In the CosMx data, there were two major connected regions of field of view, along with some field of views with scattered cells. We used the larger of these connected regions for analysis.
The DLPFC data include four samples (two pairs of ‘spatial replicates’9 (link)) from each of three patients, resulting in 12 datasets. Following the usual practice in the field, we reported median statistics and box plots over these 12 samples. For the simulated data, we generated three replicates for each gene count condition (400, 600, 800, all 1,020) and reported the median values of ARIs over these, replicated for all tested methods. In all other datasets, the entire dataset was clustered and analyzed as a single dataset, after appropriate quality control.
Randomization was used in the simulated dataset: the genes to subset were picked randomly to generate 400, 600 and 800 gene sets of the full 1,020 gene set. No other randomization was used in this study (deterministic subsetting methods like HVGs were used for gene selection, and quality control metrics like NODG were used for cell subsetting).
No blinding was applicable in this study because no sample group allocation was performed.
Singhal V., Chou N., Lee J., Yue Y., Liu J., Chock W.K., Lin L., Chang Y.C., Teo E.M., Aow J., Lee H.K., Chen K.H, & Prabhakar S. (2024). BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nature Genetics, 56(3), 431-441.
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Publication 2024

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More about "V-fos Genes"

V-fos genes are members of the fos gene family, which encode nuclear proteins that function as transcriptional regulators.
The v-fos gene was first identified in the Finkel-Biskis-Jinkins murine osteosarcoma virus and is the viral counterpart of the cellular proto-oncogene c-fos.
V-fos genes play a crucial role in cellular proliferation, differentiation, and transformation, and have been implicated in the development of various cancers.
Researchers studying V-fos genes can utilize a range of advanced tools and techniques to enhance their investigations.
Microplate readers, such as the ABI 7500 real-time PCR instrument or the IQ5 real-time PCR system, can be leveraged for high-throughput gene expression analysis.
The RT2 First Strand Kit and PrimeScript RT Master Mix can be used for cDNA synthesis, while the TB Green Premix Ex Taq and SsoFast EvaGreen Supermix can facilitate sensitive and accurate real-time PCR analyses.
For more in-depth studies, researchers may employ immunoprecipitation techniques using specialized kits to investigate protein-protein interactions and posttranslational modifications.
The TRIzol reagent can be utilized for efficient RNA extraction, a critical step in many molecular biology workflows.
By combining these advanced tools and techniques with the insights gained from the scientific literature, preprints, and patent data, researchers can unleash the full potential of their V-fos gene studies.
PubCompare.ai, an AI-driven research platform, can help identify the most accurate and reproducible experimental protocols, enhancing the productivity and quality of their investigations.
With these resources at their fingertips, researchers can explore the complexities of V-fos genes and their role in cellular processes, ultimately contributing to the advancement of our understanding and the development of novel therapeutic strategies.