Gene counts were obtained by aligning reads to the hg38 genome (GRCh38.p5 (NCBI:GCA_000001405.20) using CellRanger software (2.0.0 version) (10× Genomics). To account for unspliced nuclear transcripts, reads mapping to pre-mRNA were counted. After quantification of pre-mRNA using the CellRanger count pipeline on each of the 48 individual libraries, the CellRanger aggr pipeline was employed to aggregate all libraries to equalize the read depth between libraries before data merging (with the default parameter) to generate a gene count matrix. Then, a UMI cutoff of200 was used to select single cells for further analysis. From our pilot sample analysis, we realized that the default cell detection method employed by 10× Genomics (assuming UMI values - a reflection of the RNA content - varies by roughly an order of magnitude among cells) failed to detect a large fraction of the microglia population. Therefore, to determine a more appropriate UMI cutoff value, we plotted a histogram showing cell density as a function of UMI values. Based on this analysis we determined 200 UMIs as the lower cutoff for cell filtering. This resulted in an initial dataset that was then further examined to exclude low quality libraries (see Quality control for cell inclusion ).
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Cytoplasmic Inclusion
Cytoplasmic Inclusion
Cytoplasmiic Inclusions are discrete, abnormal structures found within the cytoplasm of cells.
These inclusions may contain proteins, lipids, carbohydrates, or other cellular components, and can be associated with various disease states or cellular dysfunctions.
Understanding the composition and function of cytoplasmic inclusions is an important area of research, as they can provide insight into underlying pathological processes and potential therapeutic targets.
These inclusions may contain proteins, lipids, carbohydrates, or other cellular components, and can be associated with various disease states or cellular dysfunctions.
Understanding the composition and function of cytoplasmic inclusions is an important area of research, as they can provide insight into underlying pathological processes and potential therapeutic targets.
Most cited protocols related to «Cytoplasmic Inclusion»
Cells
Cytoplasmic Inclusion
Genes
Genome
Microglia
mRNA Precursor
Physiology, Cell
Reflex
Single-Cell Analysis
ApoE protein, human
Brain
Cells
Chromosome Mapping
Cytoplasmic Inclusion
Gene Expression
Gene Products, Protein
Genes
Genome-Wide Association Study
Homo sapiens
Mus
Pancreas
Prefrontal Cortex
Proteins
Single-Cell RNA-Seq
Tissue Microarray Analysis
Tissues
Tissue Specificity
alpha-Synuclein
Amygdaloid Body
Amyloid beta-Peptides
Amyotrophic Lateral Sclerosis
Antibodies, Anti-Idiotypic
Brain
Brain Stem
Cerebellum
Cerebral Hemispheres
Clone Cells
Cortex, Cerebral
Cytoplasmic Inclusion
Dorsolateral Prefrontal Cortex
Entorhinal Area
Eosin
Frontotemporal Lobar Degeneration
Gyrus, Anterior Cingulate
Gyrus, Dentate
Hematoxylin
Inclusion Bodies
Infarction
Lewy Bodies
Lewy Body Disease
Lobe, Frontal
Microscopy
Monoclonal Antibodies
Neurodegenerative Disorders
Neurofibrillary Tangle
Neuroglia
Neurons
nickel sulfate
paraform
Parietal Cortex, Inferior
Parietal Lobe
Proteins
protein TDP-43, human
Seahorses
Senile Plaques
Serum Amyloid A Protein
Stains
Striate Cortex
Subiculum
Substantia Nigra
Temporal Lobe
Tissue Preservation
Tissues
Vision
Chromosomes
Cytoplasmic Inclusion
Genes
Genome
Neoplasms
Signal Transduction
Alzheimer's Disease
Amyotrophic Lateral Sclerosis
Anesthesia, Conduction
azo rubin S
Basal Forebrain
Brain
Cytoplasmic Inclusion
Frontotemporal Lobar Degeneration
Immunoglobulins
Insula of Reil
Neurites
Neurons
Nuclear Inclusion
Olivary Nucleus
Paraffin
Peptide Fragments
polypeptide C
protein TDP-43, human
Substantia Nigra
Tectum Mesencephali
Ventral Striatum
Most recents protocols related to «Cytoplasmic Inclusion»
Tissues were fixed in 4% buffered formaldehyde, stored > 24 h prior to further processing, embedded in paraffin, and cut into 2 µm thick sections. Hematoxylin and eosin (H&E), Picrosirius red (PSR) and wheat germ agglutinin (WGA)/DAPI fluorescence staining were performed as previously described (14 (link)). Microscopy of H&E and PSR stains was performed using a BX43 light microscope (Olympus, Tokyo, Japan) and an Observer.Z1 fluorescence microscope (Zeiss, Wetzler, Germany) for WGA/DAPI stains. Liver fat content was assessed as area percentage of hepatocytes with macro- or microvesicular cytoplasmatic fat inclusions. Hepatic fibrosis was evaluated according to the ISHAK score (21 (link)), and liver sections were scored for steatosis, lobular inflammation, and hepatocyte ballooning according to the NAFLD activity score (NAS) (22 (link)). For immunohistological quantification of skeletal muscle fiber area, sections were deparaffinized, stained with WGA, and sections with WGA staining of the cellular membrane were selected for stereological quantification, which was performed using the AxioVision software (Zeiss, Wetzler, Germany). Kidney sections showing cortex and medulla were evaluated for histopathologic abnormalities with focus on the presence of tubular ischemia and necrosis, glomerular ischemia, microthrombi, arteriosclerosis and glomerulosclerosis. Sections were analyzed by an experienced pathologist blinded for the group of mice investigated.
Arteriosclerosis
Cell-Derived Microparticles
Congenital Abnormality
Cortex, Cerebral
Cytoplasmic Inclusion
DAPI
Eosin
Fibrosis, Liver
Fluorescence
Formaldehyde
Hematoxylin
Hepatocyte
Inflammation
Ischemia
Kidney
Kidney Glomerulus
Light Microscopy
Liver
Medulla Oblongata
Mice, House
Microscopy
Microscopy, Fluorescence
Necrosis
Non-alcoholic Fatty Liver Disease
Paraffin Embedding
Pathologists
Plasma Membrane
Skeletal Myocytes
Steatohepatitis
Tissues
Wheat Germ Agglutinins
To assess our models we used a 10-fold cross validation strategy which randomized training and test set inclusion across the cell line and compound combinations. Thus, for any given viability curve resulting from treatment of a cell line with a compound, all of the results from the assay were considered as one unit for cross validation purposes. All steps of feature selection were also conducted under this cross validation framework as well. For every fold of our data, we recalculated the correlation coefficient between cell viability and the features available to the model (kinase inhibition state, gene expression, etc) using only the data in the training set. The number of features was varied as specified in the results section. We used the entire data set to build the final model used to make the predictions in S1 Table and the results displayed in Fig 6 .
We used random forest, XGBoost, TabNet and linear regression for all of our modeling efforts. All of our models are implemented using the tidymodels framework in R. We used the ranger random forest engine [43 ], the default XGBoost engine [44 ] and the default ordinary least squares linear regression engine. For all of our initial testing of these models we used the default single set of hyperparameter settings to narrow our search for an acceptable model. This search indicated that the random forest model performed the best, so we attempted to further tune three additional parameters, the number of trees, the number of selected predictors and the minimal node size across the following ranges:
We used random forest, XGBoost, TabNet and linear regression for all of our modeling efforts. All of our models are implemented using the tidymodels framework in R. We used the ranger random forest engine [43 ], the default XGBoost engine [44 ] and the default ordinary least squares linear regression engine. For all of our initial testing of these models we used the default single set of hyperparameter settings to narrow our search for an acceptable model. This search indicated that the random forest model performed the best, so we attempted to further tune three additional parameters, the number of trees, the number of selected predictors and the minimal node size across the following ranges:
Biological Assay
Cell Lines
Cell Survival
Cytoplasmic Inclusion
Gene Expression
Phosphotransferases
Psychological Inhibition
Trees
Automated patch clamp recording was performed using the Nanion SyncroPatch 768PE platform (Nanion Technologies, Munich, Germany) (17 (link)). Single-hole low resistance (2–3.5 MΩ) recording chips were used for this study. Pulse generation and data collection were performed using PatchControl384 v1.6.6 and DataControl384 v1.6.0 software (Nanion Technologies). Whole-cell currents were acquired at 10 kHz, series resistance was compensated 80%, and leak currents were subtracted using P/4 subtraction. Whole-cell currents were recorded at room temperature using voltage protocols illustrated in Fig. S1 . The external solution contained (in mM): 140 NaCl, 4 KCl, 2 CaCl2, 1 MgCl2, 10 HEPES, 5 glucose, with the final pH adjusted to 7.4 with NaOH, and osmolality adjusted to 300 mOsm/kg with sucrose. The composition of the internal solution was (in mM): 110 CsF, 10 CsCl, 10 NaCl, 20 EGTA, 10 HEPES, with the final pH adjusted to 7.2 with CsOH, and osmolality adjusted to 300 mOsm/kg with sucrose. High resistance seals were obtained by addition of 10 μl seal enhancer solution comprised of (in mM): 125 NaCl, 3.75 KCl, 10.25 CaCl2, 3.25 MgCl2, 10 HEPES, final pH adjusted to 7.4 with NaOH, followed immediately by addition of 30 μl of external solution to each well. Prior to recording, cells were washed twice with external solution, and the final concentrations of CaCl2 and MgCl2 were 3 mM and 1.3 mM, respectively. Stringent criteria were used to select cells for inclusion in the final analysis (seal resistance ≥ 200 MΩ, access resistance ≤ 20 MΩ, capacitance ≥ 2 pF, and sodium reversal potential between 45 and 85 mV. All biophysical data were collected from cells whose currents were larger than −200 pA. Voltage control was assessed from conductance-voltage (GV) curves and cells were included in the final analysis if two adjacent points on the GV curve showed no more than a 7-fold increase. Unless otherwise noted, all chemicals were obtained from SigmaAldrich (St. Louis, MO, USA).
A typical experiment recorded from cells expressing WT-NaV1.2 and either five NaV1.2 variants or four NaV1.2 variants plus non-transfected cells seeded into 64-well clusters of a 384-well patch clamp plate. Cells recorded at the same time were electroporated with either WT or variant plasmids in parallel on the same day. To ensure that sufficient numbers of cells were recorded to account for attrition from stringent quality control data filters, we recorded from two 384-well plates simultaneously. Because both plates were run simultaneously, and the plate layout was identical, we combined data from both plates and normalized the data for each variant to the average WT values recorded on the same day. Biophysical properties were listed as not-determined (ND) if less than 5 replicates were obtained for that property for any given variant.
A typical experiment recorded from cells expressing WT-NaV1.2 and either five NaV1.2 variants or four NaV1.2 variants plus non-transfected cells seeded into 64-well clusters of a 384-well patch clamp plate. Cells recorded at the same time were electroporated with either WT or variant plasmids in parallel on the same day. To ensure that sufficient numbers of cells were recorded to account for attrition from stringent quality control data filters, we recorded from two 384-well plates simultaneously. Because both plates were run simultaneously, and the plate layout was identical, we combined data from both plates and normalized the data for each variant to the average WT values recorded on the same day. Biophysical properties were listed as not-determined (ND) if less than 5 replicates were obtained for that property for any given variant.
Cells
cesium chloride
Cytoplasmic Inclusion
DNA Chips
Egtazic Acid
Glucose
HEPES
Magnesium Chloride
Phocidae
Plasmids
Pulse Rate
Sodium
Sodium Chloride
Sucrose
Tooth Attrition
The immunofluorescence procedure was similar to the previously reported immunohistochemistry protocol without the quenching step. Blocking was performed with 5% (vol/vol) NGS and 0.1% (vol/vol) Triton X-100 (Sigma-Aldrich) in PBS solution. Corresponding primary antibodies were incubated together overnight at 4°C in 2% (vol/vol) serum. Details about primary and secondary antibodies used for immunofluorescence can be found in Table 1 . Adequate Alexa 488 and/or 647-conjugated secondary antibodies were incubated simultaneously for 1 h at room temperature in 2% (vol/vol) serum. Nuclei were stained with Hoechst 33342 (1:2000, Thermo Fisher Scientific, ref: H3570) in 1× PBS for 10 min. Sections were coverslipped using the Dako Cytomation Fluorescent Mounting Medium (Dako).
Immunofluorescent images were taken with a LSM 980 with Airyscan 2 confocal microscopeand were analysed with ZEN 3.1 software (RRID: SCR_013672;http://www.zeiss.com/microscopy/en_us/products/microscope-software/zen.html#introduction ). The total number of p62-positive cytoplasmic inclusions was manually determined in a selected SNpc middle section/animal exhibiting high numbers of nigral neurons, including NM-containing neurons, from two different experimental groups: TYR (n = 8) and TYR+VMAT2 (n = 7), at 2 months post-AAV injections. Equivalent anatomical levels (based on Paxinos and Watson19 ) were analysed across animals. All quantifications were performed by an investigator blinded to the experimental groups.
Immunofluorescent images were taken with a LSM 980 with Airyscan 2 confocal microscopeand were analysed with ZEN 3.1 software (RRID: SCR_013672;
Animals
Antibodies
Cell Nucleus
Cytoplasmic Inclusion
Fluorescent Antibody Technique
HOE 33342
Immunohistochemistry
Microscopy
Neurons
Serum
Substantia Nigra
Triton X-100
The 65 kDa active Cry4Ba toxins were prepared as described previously [40 (link)]. In brief, 130 kDa Cry4Ba-R203Q and its mutants protoxins were overexpressed as cytoplasmic inclusions in E. coli JM109 upon induction with 0.1 mM IPTG for 4 h. After solubilization in carbonate buffer (50 mM Na2CO3/NaHCO3 (pH 9.2)) for 1 h, the protoxins were activated by digestion with TCPK-treated trypsin (1:20, w/w) for 16 h into 65 kDa active toxins. The 65 kDa trypsin-activated toxins were purified using a size exclusion fast protein liquid chromatography system on a Superdex-200 HR column from Amersham-Pharmacia Biotech (Piscataway, NJ, USA) as described previously [22 (link)] and then concentrated using a Vivaspin concentrator column (10 kDa MWCO). The purified proteins were determined for their concentrations based on Bradford assay and analyzed using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE).
Bicarbonate, Sodium
Biological Assay
Buffers
Carbonates
Cytoplasmic Inclusion
Digestion
Escherichia coli
Isopropyl Thiogalactoside
Molecular Sieve Chromatography
Proteins
SDS-PAGE
Toxins, Biological
Trypsin
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More about "Cytoplasmic Inclusion"
Cytoplasmic inclusions, also known as cellular inclusions or intracytoplasmic inclusions, are abnormal structures found within the cytoplasm of cells.
These structures can contain a variety of cellular components, including proteins, lipids, carbohydrates, and other biomolecules.
Understanding the composition and function of cytoplasmic inclusions is an important area of research, as they can provide insight into underlying pathological processes and potential therapeutic targets.
Cytoplasmic inclusions are associated with various disease states and cellular dysfunctions.
For example, the accumulation of misfolded proteins, such as those seen in neurodegenerative disorders, can lead to the formation of cytoplasmic inclusions.
These inclusions may disrupt normal cellular processes and contribute to the pathogenesis of the disease.
In addition to their role in disease, cytoplasmic inclusions can also be indicative of normal cellular processes.
For instance, the formation of lipid droplets, which are specialized organelles involved in lipid storage and metabolism, can be considered a type of cytoplasmic inclusion.
Researchers often utilize a variety of techniques to study cytoplasmic inclusions, including microscopy techniques like the Eclipse 50i fluorescent microscope, as well as molecular biology tools such as the RNA Cleaner & Concentrator-5 columns and the SsoAdvanced SYBR Green Master Mix.
Additionally, stains like Toluidine blue and Oil Red O can be used to visualize and characterize the composition of these inclusions.
The study of cytoplasmic inclusions has implications for various fields, such as cell biology, pathology, and drug development.
By understanding the underlying mechanisms and functions of these structures, researchers may be able to identify new therapeutic targets and develop more effective interventions for a range of diseases and cellular dysfunctions.
These structures can contain a variety of cellular components, including proteins, lipids, carbohydrates, and other biomolecules.
Understanding the composition and function of cytoplasmic inclusions is an important area of research, as they can provide insight into underlying pathological processes and potential therapeutic targets.
Cytoplasmic inclusions are associated with various disease states and cellular dysfunctions.
For example, the accumulation of misfolded proteins, such as those seen in neurodegenerative disorders, can lead to the formation of cytoplasmic inclusions.
These inclusions may disrupt normal cellular processes and contribute to the pathogenesis of the disease.
In addition to their role in disease, cytoplasmic inclusions can also be indicative of normal cellular processes.
For instance, the formation of lipid droplets, which are specialized organelles involved in lipid storage and metabolism, can be considered a type of cytoplasmic inclusion.
Researchers often utilize a variety of techniques to study cytoplasmic inclusions, including microscopy techniques like the Eclipse 50i fluorescent microscope, as well as molecular biology tools such as the RNA Cleaner & Concentrator-5 columns and the SsoAdvanced SYBR Green Master Mix.
Additionally, stains like Toluidine blue and Oil Red O can be used to visualize and characterize the composition of these inclusions.
The study of cytoplasmic inclusions has implications for various fields, such as cell biology, pathology, and drug development.
By understanding the underlying mechanisms and functions of these structures, researchers may be able to identify new therapeutic targets and develop more effective interventions for a range of diseases and cellular dysfunctions.