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Physiology, Cell

Physiology, Cell: The study of the functions and activities of living cells, including their structure, metabolism, growth, and differentiation.
Encompasses a broad range of cellular processes essential for maintaining homeostasis and driving biological systems forward.
Explores the intracate mechanisms underlying cellular respiration, signal transduction, gene expression, and cell division.
Provides a fundamental undestanding of how cells respond to environmental stimuli and communicate with one another.
This knowlege is critical for advancing fields like immunology, neuroscience, and regenerative medicine.

Most cited protocols related to «Physiology, Cell»

1
Integrative Analysis of Chromatin States
Cited 367 times
ChIP-seq analysis was performed in biological replicate as described4 using antibodies validated by Western blots and peptide competitions. ChIP DNA and input controls were sequenced using the Illumina Genome Analyzer. Expression profiles were acquired using Affymetrix GeneChip arrays. Chromatin states were learned jointly by applying an HMM8 to 10 data tracks for each of the 9 cell types. We focused on a 15 state model that provides sufficient resolution to resolve biologically-meaningful patterns yet is reproducible across cell types when independently processed. We used this model to produce 9 genome-wide chromatin state annotations, which were validated by additional ChIP experiments and reporter assays. Multi-cell type clustering was conducted on locations assigned to strong promoter state 1 (or strong enhancer state 4) in at least one cell type using the k-means algorithm. Enhancer-target gene linkages were predicted by correlating normalized signal intensities of H3K27ac, H3K4me1 and H3K4me2 with gene expression across cell types as a function of distance to the TSS. Upstream regulators were predicted using a set of known TF motifs assembled from multiple sources. Motif instances were identified by sequence match and evolutionary conservation. P-values for GWAS studies were based on randomizing the location of SNPs, and the FDR based on randomizing the assignment of SNPs across studies. Datasets are available from the ENCODE website (http://genome.ucsc.edu/ENCODE), the supporting website for this paper (http://compbio.mit.edu/ENCODE_chromatin_states), and the Gene Expression Omnibus (GSE26386).
Ernst J., Kheradpour P., Mikkelsen T.S., Shoresh N., Ward L.D., Epstein C.B., Zhang X., Wang L., Issner R., Coyne M., Ku M., Durham T., Kellis M, & Bernstein B.E. (2011). Systematic analysis of chromatin state dynamics in nine human cell types. Nature, 473(7345), 43-49.
Publication 2011
Antibodies Biological Assay Biological Evolution Biopharmaceuticals Cells Chromatin Chromatin Immunoprecipitation Sequencing DNA Chips DNA Replication Gene Chips Gene Expression Genome Genome-Wide Association Study Linkage, Genetic Peptides Physiology, Cell Single Nucleotide Polymorphism Western Blot
2
Comprehensive Analysis of T2D Genetic Associations
Cited 194 times
We obtained summary statistics (association P-values and Z-scores for direction of effect or allelic effects and standard errors) for lead T2D SNPs in GWAS meta-analyses of metabolic traits in European descent populations. Summary statistics were aligned to the T2D risk allele from the combined meta-analysis. We obtained summary statistics for lead SNPs in all newly discovered and established loci for glycemic traits in non-diabetic individuals from the MAGIC Investigators5 (link),34 . For fasting glucose and fasting insulin, the meta-analysis comprised up to 133,010 individuals, genotyped with GWAS arrays and imputed on up to ~2.5 million SNPs, or genotyped with Metabochip. We also considered surrogate estimates of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) derived by homeostasis model assessment in up to 38,238 individuals (from GWAS meta-analysis only since these traits were not investigated in the enlarged MAGIC Metabochip study). We obtained summary statistics for lead SNPs in the newly discovered T2D loci (also including GRB14 and HMG20A) for BMI in up to 119,600 individuals from the GIANT Consortium15 (link). To eliminate potential bias in BMI allelic effect estimates at T2D susceptibility loci54 (link), we restricted our attention to meta-analysis of population-based studies not ascertained for disease status for ~2.8 million directly genotyped and/or imputed SNPs. We obtained summary statistics for the same SNPs for plasma lipid concentrations from the Global Lipids Genetics Consortium16 (link). This meta-analysis comprised ~2.6 million directly genotyped and/or imputed SNPs assessed for association to plasma concentrations of: total cholesterol (up to 100,184 individuals); LDL (up to 95,454 individuals); HDL (up to 99,900 individuals); and triglycerides (up to 96,598 individuals).
We also examined T2D association summary statistics at lead SNPs for 37 established T1D susceptibility loci. For each of these SNPs, we reported the allelic OR (aligned to the T2D risk-allele) and P-values in: (i) our Stage 1 T2D meta-analysis; and (ii) a GWAS meta-analysis of 7,514 T1D cases and 9,045 population controls from European descent populations from the Type 1 Diabetes Genetics Consortium35 (link).
Morris A.P., Voight B.F., Teslovich T.M., Ferreira T., Segrè A.V., Steinthorsdottir V., Strawbridge R.J., Khan H., Grallert H., Mahajan A., Prokopenko I., Kang H.M., Dina C., Esko T., Fraser R.M., Kanoni S., Kumar A., Lagou V., Langenberg C., Luan J., Lindgren C.M., Müller-Nurasyid M., Pechlivanis S., Rayner N.W., Scott L.J., Wiltshire S., Yengo L., Kinnunen L., Rossin E.J., Raychaudhuri S., Johnson A.D., Dimas A.S., Loos R.J., Vedantam S., Chen H., Florez J.C., Fox C., Liu C.T., Rybin D., Couper D.J., Kao W.H., Li M., Cornelis M.C., Kraft P., Sun Q., van Dam R.M., Stringham H.M., Chines P.S., Fischer K., Fontanillas P., Holmen O.L., Hunt S.E., Jackson A.U., Kong A., Lawrence R., Meyer J., Perry J.R., Platou C.G., Potter S., Rehnberg E., Robertson N., Sivapalaratnam S., Stančáková A., Stirrups K., Thorleifsson G., Tikkanen E., Wood A.R., Almgren P., Atalay M., Benediktsson R., Bonnycastle L.L., Burtt N., Carey J., Charpentier G., Crenshaw A.T., Doney A.S., Dorkhan M., Edkins S., Emilsson V., Eury E., Forsen T., Gertow K., Gigante B., Grant G.B., Groves C.J., Guiducci C., Herder C., Hreidarsson A.B., Hui J., James A., Jonsson A., Rathmann W., Klopp N., Kravic J., Krjutškov K., Langford C., Leander K., Lindholm E., Lobbens S., Männistö S., Mirza G., Mühleisen T.W., Musk B., Parkin M., Rallidis L., Saramies J., Sennblad B., Shah S., Sigurðsson G., Silveira A., Steinbach G., Thorand B., Trakalo J., Veglia F., Wennauer R., Winckler W., Zabaneh D., Campbell H., van Duijn C., Uitterlinden89- A.G., Hofman A., Sijbrands E., Abecasis G.R., Owen K.R., Zeggini E., Trip M.D., Forouhi N.G., Syvänen A.C., Eriksson J.G., Peltonen L., Nöthen M.M., Balkau B., Palmer C.N., Lyssenko V., Tuomi T., Isomaa B., Hunter D.J., Qi L., Shuldiner A.R., Roden M., Barroso I., Wilsgaard T., Beilby J., Hovingh K., Price J.F., Wilson J.F., Rauramaa R., Lakka T.A., Lind L., Dedoussis G., Njølstad I., Pedersen N.L., Khaw K.T., Wareham N.J., Keinanen-Kiukaanniemi S.M., Saaristo T.E., Korpi-Hyövälti E., Saltevo J., Laakso M., Kuusisto J., Metspalu A., Collins F.S., Mohlke K.L., Bergman R.N., Tuomilehto J., Boehm B.O., Gieger C., Hveem K., Cauchi S., Froguel P., Baldassarre D., Tremoli E., Humphries S.E., Saleheen D., Danesh J., Ingelsson E., Ripatti S., Salomaa V., Erbel R., Jöckel K.H., Moebus S., Peters A., Illig T., de Faire U., Hamsten A., Morris A.D., Donnelly P.J., Frayling T.M., Hattersley A.T., Boerwinkle E., Melander O., Kathiresan S., Nilsson P.M., Deloukas P., Thorsteinsdottir U., Groop L.C., Stefansson K., Hu F., Pankow J.S., Dupuis J., Meigs J.B., Altshuler D., Boehnke M, & McCarthy M.I. (2012). Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nature genetics, 44(9), 981-990.
Publication 2012
Alleles Attention Cholesterol Diabetes Mellitus, Insulin-Dependent Europeans Genome-Wide Association Study Gigantism Glucose GRB14 protein, human Homeostasis Insulin Insulin Resistance Lipids Physiology, Cell Plasma Single Nucleotide Polymorphism Susceptibility, Disease Triglycerides
3
Transcriptional and Post-Transcriptional Regulation
Cited 114 times
Transcriptional and post-transcriptional gene regulation plays important roles in many biological processes and cellular functions. We have added two key players in gene regulation: TFs and miRNAs. TarBase (19 ) and miRTarbase (20 (link)) have been used to obtain experimentally validated miRNA–gene target information, while ENCODE (21 (link)), JASPAR (22 (link)) and CHEA (23 (link)) have been used to obtain TF–gene target information. We also included the TF–miRNA–gene coregulatory networks built by RegNetwork (24 (link)).
Zhou G., Soufan O., Ewald J., Hancock R.E., Basu N, & Xia J. (2019). NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Research, 47(W1), W234-W241.
Publication 2019
Biological Processes Gene Expression Regulation Gene Regulatory Networks Genes MicroRNAs Physiology, Cell Transcription, Genetic
4
High-Throughput Cancer Cell Line Sensitivity Profiling
Cited 112 times
An Informer Set of 481 small molecules was measured against 860 publicly available human CCLs encompassing 25 lineages. Small-molecule and cell-line information, including CCL contexts and growth conditions, is provided in Supplementary Data Sets 1–2 and the CTRP website (www.broadinstitute.org/ctrp) and downloadable from the NCI-CTD2 Data Portal (ctd2.nci.nih.gov/). To verify that CCLs tested in sensitivity profiling uniquely matched those used to generate microarray expression measurements, genomic DNA was extracted from 803/860 CCLs tested (93%) and used to genotype 96 SNPs using the Fluidigm 96.96 system; SNP calls were matched to a database of 1045 CCLs7 (link),47 (link). 771 CCLs positively matched to a CCLE reference genotype; 32 (4%) did not match any reference CCL (Supplementary Data Set 1). As further information becomes available (e.g., matching of unconfirmed samples), we will provide updated information and analyses reflecting any changes at the NCI-CTD2 Data Portal and in the CTRP.
Small molecules were selected individually to interrogate important targets and/or cellular processes in cancer with high reported selectivity, and collectively to target diverse nodes in cancer cell circuitry, from sources including FDA-approved drugs, clinical candidates, previous screening and sensitivity profiling experiments, scientific literature and patents, bioactives, and collaborator contributions (Supplementary Data Set 2)8 (link),12 (link). CCLs were plated at a density of 500 cells/well in white opaque tissue-culture-treated Aurora 1536-well MaKO plates (Brooks Automation) in the provider-recommended growth media using a highly automated platform. Compounds were added by acoustic transfer using a Labcyte Echo 555 (Labcyte Inc.) 24 hours after plating. The effects of small molecules were measured over a 16-point concentration range (two-fold dilution) in duplicate. DMSO was used at a constant concentration of 0.33%, including vehicle-only control wells. As a surrogate for viability, cellular ATP levels were assessed 72 hours after compound transfer by addition of CellTiterGlo (Promega) followed by luminescence measurement using a ViewLux Microplate Imager (PerkinElmer). Duplicates were averaged and luminescence values normalized to vehicle (DMSO) treatment and background (media-only) wells.
Rees M.G., Seashore-Ludlow B., Cheah J.H., Adams D.J., Price E.V., Gill S., Javaid S., Coletti M.E., Jones V.L., Bodycombe N.E., Soule C.K., Alexander B., Li A., Montgomery P., Kotz J.D., Hon C.S., Munoz B., Liefeld T., Dančík V., Haber D.A., Clish C.B., Bittker J.A., Palmer M., Wagner B.K., Clemons P.A., Shamji A.F, & Schreiber S.L. (2015). Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nature chemical biology, 12(2), 109-116.
Publication 2015
Acoustics Cataract, Embryonic Nuclear Cell Lines Cells Culture Media ECHO protocol Genetic Selection Genome Genotype Growth Disorders Homo sapiens Hypersensitivity Luminescence Luminescent Measurements Malignant Neoplasms Microarray Analysis Pharmaceutical Preparations Physiology, Cell Promega Sulfoxide, Dimethyl Technique, Dilution Tissues
5
Factors Limiting Bacterial Division Rate
Cited 88 times
To show the power and usefulness of BioNumbers we address a specific thought experiment: What limits the maximal rate at which a bacterium can divide? That is, why does E. coli under ideal conditions of LB medium and 37°C divide every ∼20 min (BNID 100260) and not every ∼2 min? Clearly the ability to divide at faster rates would provide an overwhelming selective advantage, at least in laboratory conditions. There are many cellular processes that could potentially limit E. coli to a ∼20 min doubling time. But for most such processes, it seems possible for the bacterium to overcome the limitation by increasing the amount of the limiting factor, for instance by increasing the number of nutrient transporters, the number of DNA replication circles, or the number of RNA polymerase complexes. But ribosomes are an interesting partial exception to this rule. Ribosomes translate all the proteins in the cell including those that are assembled into new ribosomes. Doubling ribosome content would necessitate translating twice the number of ribosomal proteins. Here then is a potentially limiting rate: the time that it takes a ribosome to translate enough amino acids to copy itself (4 ). We demonstrate the use of the BioNumbers database with a brief analysis of these considerations. An E. coli ribosome contains in total ∼7500 amino acids (7459, Search term: ‘ribosome’, BNID101175) and the translation rate is as high as ∼21 aa/sec (Search term: ‘translation ribosome’, BNID100059). Translating a single copy of all of the ribosomal proteins thus minimally requires ∼7500/21 ≈ 400 sec ≈ 7 min. In order to make a new cell of the same size, each ribosome must make a copy of itself. Taking into account essential translational cofactors like the elongation factors EF-Tu and EF-G would increase the required time to ∼9 min. It therefore seems impossible to obtain a cellular doubling time faster than ∼9 min. Perhaps when further requirements for ribosome duplication are taken into account, it will be evident why E. coli double in ∼20 min. We thus see that with simple calculations and with several useful biological numbers on hand, we can generate an intriguing hypothesis for what sets a lower bound on the proliferation rate of E. coli.
Milo R., Jorgensen P., Moran U., Weber G, & Springer M. (2009). BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Research, 38(Database issue), D750-D753.
Publication 2009
Amino Acids Bacteria Biopharmaceuticals Cells Culture Media, Conditioned DNA-Directed RNA Polymerase DNA Replication EEF1A1 protein, human Escherichia coli Membrane Transport Proteins Nutrients Physiology, Cell Protein Biosynthesis Proteins ribosomal A-protein Ribosomal Proteins Ribosomes

Most recents protocols related to «Physiology, Cell»

1
Paneth Cell Defect and Genetic Factors
Not available on PMC !

Example 2

We correlated the degree of Paneth cell defect (percentage of Paneth cells with normal morphology) with numbers of ATG16L1 T300A or NOD2 risk alleles. As shown in FIG. 8A and FIG. 8B, neither the numbers of risk alleles of ATG16L1 T300A nor NOD2 correlated with Paneth cell phenotype. Likewise, neither the sum total of ATG16L1 T300A nor NOD2 risk alleles correlated with Paneth cell phenotype (FIG. 8C). This suggests that in this population of pediatric CD patients, environmental factor(s) may play a more significant role in modulating Paneth cell function.

US11867701B2. Methods for prognosing crohn's disease comprising human defensin 5 (HD5) (2024-01-09). None [US], None [US], None [US]. Inventors: Thaddeus Stappenbeck [US], Ta-Chiang Liu [US], Kelli VanDussen [US].
Patent 2024
Alleles Genotype Paneth Cells Patients Phenotype Physiology, Cell
2
Anti-GD2 CAR T-cell Therapy for CT26 Tumors
Not available on PMC !

Example 9

CT26 cell line was engineered to express GD2 as described above (designated CT26 clone #7 or CT25#7 for short). Either 2×105 of wild type (wt) or GD2 positive CD26 cells were inoculated into the flanks of C57BL/6 mice (syngeneic with CT26). 10 days after tumour challenge, mock-transduced and anti-GD2 CAR transduced syngeneic splenocytes were prepared. Mice were divided into the following 4 cohorts: mice with GD2 expressing CT26 tumours receiving anti-GD2 CAR spleoncytes; GD2 expressing CT26 tumours receiving mock-transduced splenocytes; GD2 negative (wt) CT26 tumours with anti-GD2 CAR splenocytes; and GD2 expressing CT26 tumours receiving no splenocytes. Tumour was measured using a digital caliper in 3 dimension and volume estimated therewith. FIG. 11 shows the growth curves of the tumours. Only GD2 positive tumours in mice receiving anti-GD2 CAR T-cells had little or no growth.

US11879016B2. Chimeric antigen receptor (2024-01-23). AUTOLUS LIMITED [GB]. Inventors: Martin Pulé [GB], John Anderson [GB], Simon Thomas [GB].
Patent 2024
Cells Clone Cells DPP4 protein, human Mice, Inbred C57BL Mus Neoplasms Physiology, Cell Thomsen-Friedenreich antibodies
3
TAC T-Cells Destroy Myeloma Tumors
Not available on PMC !

Example 15

FIG. 27 illustrates that TACs proliferate when encountering antigen on cells, but not when the antigen is presented on artificial beads; but CARs proliferate irrespective if antigens are presented on beads or cells.

FIG. 28A-FIG. 28B illustrate TAC engineered T cells expand in vivo and provide long term protection, indicating cell persistence in a model of myeloma. FIG. 28A-FIG. 28B illustrate BCMA-TAC T cells reject multiple myeloma tumors in a KMS-11 xenograft model engineered with NanoLuc (KMS 11-NanoLuc) (BCMApos). Following tumor engraftment mice were treated with BCMA TAC-T cells (carrying Firefly Luciferase). TAC-T cells expand significantly following administration. This correlates with tumor regression. Treated mice were resistant to tumor rechallenge indicating long term persistence of TAC-T cells.

The data illustrates that TAC-T cells destroy tumor cells likely via a mechanism that mimics the natural process of T cell activation. The TAC technology illustrates 1) strong efficacy in liquid, 2) in vivo proliferation, 3) T cell persistence, protecting mice from re-challenge, and 4) cell expansion following T cell administration.

US11878035B2. T cell-antigen coupler with various construct optimizations (2024-01-23). Triumvira Immunologics USA, Inc. [US], McMaster University [CA]. Inventors: Jonathan Lorne Bramson [CA], Christopher W. Helsen [CA], Joanne Alicia Hammill [CA], Kenneth Anthony Mwawasi [CA].
Patent 2024
Antigens Cells Luciferases, Firefly Malignant Neoplasms Multiple Myeloma Mus nanoluc Neoplasms Physiology, Cell T-Lymphocyte TNFRSF17 protein, human Xenografting
4
Transcriptomic Analysis of Rhodosporidium toruloides
The RNAflow differential gene expression pipeline v1.1.0 [29 ] was used for transcriptome analysis. This includes read quality control, count normalization, reference-based mapping, gene quantification, differential expression, and visualization. Samples were quality-checked with FastQC, and raw reads were trimmed with fastp to remove low-value bases and adapter contamination [30 (link)]. After quality-trimming, the transcriptomics data had an average coverage of 1.6 M (0.8–3.8 M) reads per sample, with an expected read depth of 28X. For read depth estimation, the whole transcriptome size was calculated to be 8,484,192 bp, based on 5958 average number of transcribed genes (TPM > 0) per sample, and an average transcript length of 1424 bp. The remaining rRNA was detected and removed using SortMeRNA [31 (link)]. Then, reads were splice-aware aligned with HISAT2 [32 (link)] to the R. toruloides assembly as previously reported [26 (link)]. FeatureCounts was used for gene-level expression quantification [33 (link)] only considering uniquely mapped reads and using the annotation file from [26 (link)]. To identify and remove low-expressed genes, the TPM values (transcripts per million kilobases) were determined using RNAflow [29 ]. The TPM value was determined for each sample and each gene. Furthermore, the mean TPM value per condition was calculated over all biological replicates. A gene had to have a particular mean TPM value above a defined threshold to be considered in the subsequent analyses. We performed all calculations using a TPM 5 as the default threshold. Finally, differential gene expression analysis was performed using DESeq2 [34 (link)] to identify significantly (adjusted p value < 0.05) differentially expressed genes. We adjusted the p values attained by the Wald test in DESeq2 [34 (link)] for multiple testing using the Benjamini and Hochberg method, implemented as a default in DESeq2's results() function. As recommended, we used these adjusted p values to identify genes with significantly different expressions. Corresponding R packages were used to conduct principal component analysis and generate expression heatmaps and box plots. Details of the tool versions, R packages, and custom scripts used can be found at https://github.com/hoelzer-lab/rnaflow.
Visualization of gene expression density in the genome of R. toruloides CBS14 was performed using Circa (http://omgenomics.com/circa; accessed on May 2022). The respective KEGG orthology (KO) numbers were assigned to those annotated proteins encoded by differentially expressed genes. Subsequently, metabolic pathways and cellular processes were determined using KofamKOALA [35 (link)]. Gene ontology (GO) terms from differentially expressed genes that occurred at least 10 times were visualized using REVIGO [36 (link)] in semantic similarity-based scatterplots. Blast homology search (v 2.13.0+) was performed to identify genes and proteins belonging to central metabolic pathways annotated with a similar function in CBS14 [37 (link), 38 (link)].
Martín-Hernández G.C., Chmielarz M., Müller B., Brandt C., Viehweger A., Hölzer M, & Passoth V. (2023). Enhanced glycerol assimilation and lipid production in Rhodotorula toruloides CBS14 upon addition of hemicellulose primarily correlates with early transcription of energy-metabolism-related genes. Biotechnology for Biofuels and Bioproducts, 16, 42.
Publication 2023
Biopharmaceuticals Gene Expression Gene Expression Profiling Genes Genome Physiology, Cell Proteins Ribosomal RNA Transcriptome
5
Rat Model of A2M Overexpression
SPF Sprague–Dawley rats (6~8 weeks age, 180~200 g weight) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (SCXK 2012-0001, Beijing, China). Based on a previous report [25 (link)], we established an A2M-overexpression rat model via tail vein injection as previously described [26 (link)]. Briefly, on gestational day (GD) 8.5, rats (excluding non-pregnant rats) were injected with adenoviruses expressing A2M (OBiO Technology Co., Shanghai, China), and the sequencing results are shown in Additional file 1: Supplementary Result 1. We injected an adenoviral dose of approximately 1–2×109 pfu per animal, and the adenoviruses were dissolved in phosphate-buffered saline (PBS) to a total volume of 400 μl. The treated rats were sacrificed on GD19.5 (corresponding to the third trimester) for further study. The following parameters were assessed: blood pressure, blood flow, and proteinuria. The primary outcome of this study will be hypertension with blood pressure measurement. Secondary outcomes constitute blood flow, proteinuria, and histological analyses to measure the morphology and cell function of the spiral artery and placental vascular. For the rat samples, the pregnant rats were first euthanized to collect placentas and fetuses. According to Resource Equation Approach [27 (link)], a total of 20 rats were studied, the rats were randomly divided into two groups (n = 10 in each group): the control and A2M-overexpression groups (note: the rats used in this experiment came from at least three different modelling batches). Random numbers were generated using the standard = RAND() function in Microsoft Excel. Each rat was euthanized by cervical dislocation after the experiment. Experiments involving animals were performed in accordance with the ARRIVE guidelines. All experimental processes involving animal treatments were conducted in accordance with the procedures of the Ethics Committee for Animal Experimentation, Jinan University (approval number: 20210302-46).
Wang J., Zhang P., Liu M., Huang Z., Yang X., Ding Y., Liu J., Cheng X., Xu S., He M., Zhang F., Wang G., Li R, & Yang X. (2023). Alpha-2-macroglobulin is involved in the occurrence of early-onset pre-eclampsia via its negative impact on uterine spiral artery remodeling and placental angiogenesis. BMC Medicine, 21, 90.
Publication 2023
Adenoviruses Animals Animals, Laboratory Arteries Blood Circulation Blood Pressure Blood Vessel Determination, Blood Pressure Ethics Committees Fetus High Blood Pressures Joint Dislocations Neck Phosphates Physiology, Cell Placenta Pregnancy Rats, Sprague-Dawley Rivers Saline Solution Tail Veins

Top products related to «Physiology, Cell»

1
Fetal bovine serum (fbs) by Thermo Fisher Scientific
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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.
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Penicillin is a type of antibiotic used in laboratory settings. It is a broad-spectrum antimicrobial agent effective against a variety of bacteria. Penicillin functions by disrupting the bacterial cell wall, leading to cell death.
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Streptomycin is a broad-spectrum antibiotic used in laboratory settings. It functions as a protein synthesis inhibitor, targeting the 30S subunit of bacterial ribosomes, which plays a crucial role in the translation of genetic information into proteins. Streptomycin is commonly used in microbiological research and applications that require selective inhibition of bacterial growth.
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L-glutamine is an amino acid that is commonly used as a dietary supplement and in cell culture media. It serves as a source of nitrogen and supports cellular growth and metabolism.

More about "Physiology, Cell"

Cellular biology, cell science, and cell physiology are all disciplines focused on understanding the fundamental units of life - the cell.
This broad field encompasses the study of cellular structure, function, metabolism, growth, and differentiation, providing critical insights for fields like immunology, neuroscience, and regenerative medicine.
At the core of cell biology is the exploration of essential cellular processes, such as cellular respiration, signal transduction, gene expression, and cell division.
These intracate mechanisms work in harmony to maintain homeostasis and drive biological systems forward.
Researchers may utilize tools like FBS (fetal bovine serum), Ingenuity Pathway Analysis (IPA), Lipofectamine 2000/3000, DMEM (Dulbecco's Modified Eagle Medium), and antibiotics like penicillin and streptomycin to study these cellular functions.
By unraveling the complex ways in which cells respond to environmental stimuli and communicate with one another, cell biologists can advance our fundamental understanding of living systems.
This knowledge is critical for fields like immunlogy, where researchers investigate how cells of the immune system function, and neuroscience, which examines the cellular basis of neural activity and behavior.
In the realm of regenerative medicine, cell biology insights inform strategies for tissue repair and organ regeneration.
Whether your research focus is on cellular metabolism, signaling pathways, or cellular differentiation, optimizing your protocols is key to driving your work forward.
Tools like PubCompare.ai can help you effortlessly locate the most effective products and techniques from the literature, pre-prints, and patents, allowing you to conduct your cell biology experiments with confidence and efficiency.