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TUBB3 protein, human

TUBB3 is a member of the beta-tubulin protein family and plays a critical role in the formation and function of microtubules, which are essential structural components of the cytoskeleton.
This protein is involved in various cellular processes, including cell division, intracellular transport, and neuronal development.
Mutations in the TUBB3 gene have been linked to several neurological disorders, such as congenital fibrosis of the extraocular muscles and other developmental brain abnormalities.
Researchers studying the TUBB3 protien can optimize their work using PubCompare.ai, an AI-driven platform that enhances reproducibility and accuracy by providing access to protocols from literature, pre-prints, and patents, and using AI-driven comparisons to identify the best protocols and products.
This streamines the research process and helps researchers obtain accurate results for their TUBB3 protien studies.

Most cited protocols related to «TUBB3 protein, human»

1
Versatile CRISPR/Cas9 Vector Construction
Cited 22 times
Vector expressing both gRNA and mCherry (pCAGmCherry-gRNA) was generated as previously described30 (link). To construct gRNA expression vectors, each 20 bp target sequence was sub-cloned into pCAGmCherry-gRNA or gRNA_Cloning Vector (Addgene 41824). The CRISPR/Cas9 target sequences (20 bp target and 3 bp PAM sequence (underlined)) used in this study include: scramble, GCTTAGTTACGCGTGGACGAAGG; mutant GFP, CAGG GTAATCTCGAGAGCTTAGG; MH1, GCCGCTTTACTTAGGTCCCCGGG; and MH2, GGAGATCCACTCTCGAGCCCGGG; for PITCh donor: mouse Tubb3, AGCTGCGAGCAACTTCACTTGGG; human TUBB3, AGCTGCGAGCAGCTT CACTTGGG; human KCNQ1, AGTACGTGGGCCTCTGGGGGCGG; the downstream of CAG promoter in Ai14 mouse, TAGGAACTTCTTAGGGCCCGCGG; rat Mertk for HITI, GAGGACCACTGCAACGGGGCTGG; rat Mertk for HDR, TCAGGTGCTTAGGCATTTCGTGG. The Scramble-gRNA target sequence we designed is an artificial sequence that does not exist in human, mouse and rat genomes. We used the off-target finder software Cas-OFFinder (http://www.rgenome.net/cas-offinder/) to confirm that there were no genomic target sites within 2-bp mismatches. We have confirmed that the Scramble-gRNA can cut its target site in the donor vector (Extended Data Fig. 1b). pMDLg/pRRE, pRSV-Rev and pMD2.G (Addgene 12251, 12253 and 12259) were used for packaging lentiviruses. pEGIP*35 and tGFP (Addgene 26776 and 26864) were used for examining HDR and HITI efficiencies. To construct IRESmCherry-0c, IRESmCherry-1c, IRESmCherry-2c, IRESmCherry-MH, IRESmCherry-HDR-0c and IRESmCherry-HDR-2c, IRES and mCherry sequences were amplified with Cas9 target sequence by PCR from pEGIP*35 and pCAGmCherry-gRNA, respectively and co-integrated into pCR-bluntII vector (Invitrogen) with zero, one or two CAS9/gRNA target sequences. Cas9 expression plasmid (hCas9) was purchased from Addgene (41815). To generate different NLS-dCas9 constructs, pMSCV-LTR-dCas9-VP64-BFP (Addgene 46912) was used to amplify dCas9, which was subsequently subcloned into pCAG-containing plasmid with different NLS and 3 × Flag tag. To construct pCAG-Cas9 (no NLS), pCAG-1NLS-Cas9-1NLS and pCAG-1BPNLS-Cas9-1BPNLS, D10A and H840A mutations of dCas9 plasmids were exchanged to wild-type sequence by In-Fusion HD Cloning kit (Clontech). Then, pCAG-Cas9-2AGFP (no NLS), pCAG-1NLS-Cas9-1NLS-2AGFP and pCAG-1BPNLS-Cas9-1BPNLS-2AGFP were constructed by adding 2AGFP downstream of Cas9. To construct pCAG-floxSTOP-1BPNLS-Cas9-1BPNLS, 1BPNLS-Cas9-1BPNLS was amplified by PCR and exchanged with GFP of pCAG-floxSTOP-EGFP-N1 vector31 (link). To construct HITI donor plasmids for mouse and human Tubb3 gene (Tubb3-1c, Tubb3-2c, Tubb3-2cd, hTUBB3-1c and hTUBB3-2c) and PITCh donor (Tubb3-MH), GFP was subcloned into pCAG-floxSTOP plasmid with one or two CAS9/gRNA target sequences. To construct HDR donor for mouse Tubb3 gene (Tubb3-HR), GFP, 5′ and 3′ homology arms were amplified from pCAG-GFP-N1 or mouse genome, then subcloned into pCAG-floxSTOP plasmid. pCAG-ERT2-Cre-ERT2 was purchased from Addgene (13777). PX551 and PX552 were purchased from Addgene (60957 and 60958). To construct AAV-Cas9, nEF (hybrid EF1 α/HTLV) promoter (Invivogen) was exchanged with Mecp2 promoter of PX551. To construct donor/gRNA AAVs for HITI, donor DNA sandwiched by Cas9/gRNA target sequence, gRNA expression cassette and GFPKASH (or mCherryKASH) expression cassettes were subcloned between ITRs of PX552, and generated pAAV-mTubb3, pAAV-Ai14-HITI, pAAV-Ai14-luc, pAAV-Ai14-scramble and pAAV-rMertk-HITI. For pAAV-rMertk-HITI, exon 2 of rat Mertk gene including the surrounding intron is sandwiched by Cas9/gRNA target sequence, which is expected to integrate within intron 1 of Mertk by HITI. For HDR AAV (pAAV-Ai14-HDR and pAAV-rMertk-HDR), the homology arms were amplified by PCR from mouse and rat genome DNA, and subcloned into AAV backbone plasmid. The plasmids described in this manuscript will be available to academic researchers through Addgene.
Suzuki K., Tsunekawa Y., Hernandez-Benitez R., Wu J., Zhu J., Kim E.J., Hatanaka F., Yamamoto M., Araoka T., Li Z., Kurita M., Hishida T., Li M., Aizawa E., Guo S., Chen S., Goebl A., Soligalla R.D., Qu J., Jiang T., Fu X., Jafari M., Esteban C.R., Berggren W.T., Lajara J., Nuñez-Delicado E., Guillen P., Campistol J.M., Matsuzaki F., Liu G.H., Magistretti P., Zhang K., Callaway E.M., Zhang K, & Belmonte J.C. (2016). In vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration. Nature, 540(7631), 144-149.
Publication 2016
Arm, Upper c-Mer Tyrosine Kinase Cloning Vectors Clustered Regularly Interspaced Short Palindromic Repeats Elongation Factor 1alpha Exons Genes Genome Homo sapiens Hybrids Internal Ribosome Entry Sites Introns Lentivirus MECP2 protein, human Mice, Laboratory mitogen-activated protein kinase 3, human Mutation NO-BP Plasmids T-Cell Leukemia Viruses, Human Tissue Donors TUBB3 protein, human Vertebral Column
2
Quantifying Differential Gene Expression in Dorsal Root Ganglia
Cited 12 times
Total RNA from the homogenized DRGs was isolated from the aqueous phase following the manufacturer’s instructions using Trizol reagent (Invitrogen, Carlsbad, CA). Control L4 and L5 DRGs were processed together as a single sample from one control animal, whereas L4 and L5 DRGs of a single SNL animal were processed separately (n=3). Although L6 DRGs were ligated as part of the standardized SNL technique, they were not used for analysis. After DNase treatment, cDNA was synthesized from amounts of RNA that were standardized for each experiment (ranging from 265ng to 625ng for different experiments) using random hexamer primers (Superscript III first strand synthesis kit, Invitrogen). cDNA (2µl of 50µl cDNA reaction mix) was taken in each reaction for real time PCR carried out using IQ Syber Green supermix (Biorad Laboratories, Hercules, CA) on a Biorad CFX96 Real Time PCR Machine and specific primers to quantify the cDNA levels of various housekeeping genes; Tubb5, GAPDH, MAPK6, actin, Tubb3, HPRT1 and 18S rRNA (Table 1). The efficiency of the primers was 92–100% with r2>0.990. For each sample, two inter-run determinations were carried out and two replicates in each run were averaged. Expression of the two additional target genes STIM1 and galanin were also determined from the same set of samples. For the comparative CT method, day 3 control samples were used as the reference point for calculation of fold differences in expression of all the HKGs, galanin, and STIM1 in the various time and injury groups. Additional statistical analysis to determine the overall influence of injury and time was performed on the ΔCT values of the HKGs using 2-way ANOVA (factors were day and injury, and all candidate HKGs were combined into a single group for a given day and injury category), followed by Bonferroni post hoc comparisons. Galanin expression was analyzed by 2-way ANOVA, followed by post hoc comparisons of each group with the control from that day, using Bonferroni’s correction.
Analysis of ΔΔCT values used for the target gene STIM1, normalized with MAPK6/GAPDH, was performed using 2-way ANOVA. The influence of reference gene choice on apparent STIM1 expression in day 7 samples was analyzed by Mann-Whitney U test. Graphs were plotted using 2ΔCT values for HKGs data as well as for galanin and 2−ΔΔCT values for STIM1 gene expression.
Bangaru M.L., Park F., Hudmon A., McCallum J.B, & Hogan Q.H. (2011). Quantification of Gene Expression after Painful Nerve Injury: Validation of Optimal Reference Genes. Journal of Molecular Neuroscience, 46(3), 497-504.
Publication 2011
Actins Anabolism Animals Deoxyribonuclease I DNA, Complementary Galanin GAPDH protein, human Gene Expression Genes Genes, Housekeeping Genetic Selection Injuries neuro-oncological ventral antigen 2, human Oligonucleotide Primers Real-Time Polymerase Chain Reaction RNA, Ribosomal, 18S STIM1 protein, human trizol TUBB3 protein, human
3
Stable Clustering of Somatosensory Neurons
Cited 7 times
All analysis reported in this paper made use of the Seurat package developed by the Satija lab [13 (link), 20 (link)]. In essence, we used the methods that they recommend in their tutorial for analyzing a dataset of 2,700 peripheral blood mononuclear cells for identification and display of clustering. Briefly, data were log normalized and center scaled; variable markers were identified and used for linear dimensional reduction (principle component analysis, PCA). Informative principle components were identified by plotting their standard deviations using the PCElbowPlot function of the Seurat package as described in the Satija lab tutorial. These principle components were used for clustering of STAMPS using a smart local moving algorithm [21 ]; multidimensional data were displayed using a tSNE 2-dimensional representation. A first round of clustering was performed using all the STAMPs that contained at least 1,000 transcripts; libraries containing less than 200 or more than 8,500 different genes and STAMPs containing more than 0.3% mitochondrial transcripts were excluded from analysis leaving 6998 single cell libraries. Clusters of STAMPs from somatosensory neurons were identified by their expression of known marker genes including Scn9a, Tubb3 and Snap25 as well as more specific transcripts like Trpv1, Trpm8 and Piezo2 and lack of expression of markers for other cells including Plp1, Mbp and Epcam. Somatosensory neurons were re-clustered using more stringent criteria for inclusion: 500–7,500 genes, 0.2% mitochondrial transcripts; genes expressed in less than 6 neurons were also excluded leaving a dataset of 3580 neurons and more than 15,000 genes.
We examined the stability of the clustering reported here using a variety of conditions and by using different clustering methods including tSNE based clustering [10 (link)]. Random selection of STAMPs demonstrated that the number of clusters (and their markers) was not changed once 1,500 to 2,000 neurons were included in the analysis thus increasing sample size incrementally beyond 3,500 neurons would be unlikely to change our conclusions. Similarly, clusters were not changed when different criteria were used for selection of variable genes or when the number of principle components used for analysis was varied between 15 and 25. tSNE based clustering [10 (link)] also yielded very similar results. More stringent selection of cells by requiring 900–7,500 different genes to be present in a STAMP, selectively reduced the number of S100b expressing neurons and resulted in collapse of this group of three clusters into a single cluster. In contrast, eliminating genes expressed in limited numbers of cells had little effect on clustering. For example, C11 and C12 each consist of less than 100 STAMPs; nonetheless these clusters were still separated when all genes present in less than 120 cells were excluded from the analysis. Indeed, very similar clustering was still observed even when genes expressed in less than 500 cells were eliminated (e.g. including genes like Trpm8) with concomitant reduction of the number of genes used from more than 15,000 to less than 6,000. In this analysis, itch clusters C11 and C12 merged, C7 cells were incorporated in other clusters and the Ntrk2 rich cluster C5 merged with C4. Thus the clusters that we identified appear extremely stable and are not simply determined by expression of marker genes that are expressed in that class of cells or by the clustering parameters chosen and methods that were used.
Nguyen M.Q., Wu Y., Bonilla L.S., von Buchholtz L.J, & Ryba N.J. (2017). Diversity amongst trigeminal neurons revealed by high throughput single cell sequencing. PLoS ONE, 12(9), e0185543.
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Publication 2017
Cells Gene Expression Genes Mitochondrial Inheritance Neurons PBMC Peripheral Blood Mononuclear Cells Pruritus Shock SNAP25 protein, human TACSTD1 protein, human tropomyosin-related kinase-B, human TUBB3 protein, human
4
Unsupervised Clustering of Single-Cell Gene Expression
Cited 6 times
For filtering and unsupervised clustering, the gene expression matrix
from Cell Ranger was used for downstream analysis (using Python 2.7.8 and R
3.3.1). Of the initial 7,218 cells (3,784 male and 3,434 female), 89 cells with
less than 500 UMIs or >40% of mitochondrial reads were discarded. Genes
with at least 2 counts in 5 cells were used for downstream analysis. Gene
expression of the remaining 7,129 cells was normalized by the total number of
transcripts detected in each cell and multiplied by the median transcript count.
The normalized expression was log2-transformed after an addition of a
pseudocount. The top 1,000 genes with the most variance were identified based on
their mean expression in the population and dispersion (variance/mean
expression). Genes were binned into 50 different bins based on their mean
expression and dispersion scaled with respect to the median dispersion in each
bin. These genes were used to reduce the dimensions of the dataset using Barnes
Hut t-SNE with default parameters. Cells were clustered in t-SNE space using
DBSCAN. In each case, this was through an iterative process, testing different
parameters and visualizing ellipse thresholds to ensure that clusters were
optimally resolved into distinct units in t-SNE space. In the case of male and
female samples, both sexes were combined for the analysis. The calculation for
the top 1,000 genes, dimensionality reduction and clustering were performed on a
combined gene expression matrix, which led to a single representation of cells
in t-SNE space.
To differentiate neuronal and non-neuronal clusters, the entire gene vs
cell matrix was filtered and clustered as described above. This identified 20
initial clusters. To classify a cluster as a neuron or a non-neuron cluster, we
aggregated the median expression of the known neuron markers
Snap25, Syp, Tubb3 and
Elavl2 for each cluster. We aggregated the median
expression of these genes in each cluster and classified a cluster as high
(neuronal) or low (non-neuronal) expression using a simple Gaussian mixture
model. Every cell was then classified as a neuron or non-neuron based on their
cluster membership. A total of 185 cells were discarded as they were present in
clusters classified as both neuronal and non-neuronal. Subsequent clustering of
non-neuronal and neuronal populations was based on this classification. For
classification of GABAergic and glutamatergic clusters, clusters classified as
neurons were combined and re-clustered as described above, which yielded 30
clusters. Clusters were classified as GABAergic if the median expression of
Slc32a1 is greater than Slc17a6 in each
cluster and glutamatergic if the median expression of Slc17a6is greater than Slc32a1 (Fig.
3). A total of 803 cells were discarded in this step, as they were
unassigned to any of the 30 clusters or there was no difference in the median
expression of Slc17a6 and Slc32a1.
Furthermore, 17 cells were unassigned to non-neuronal clusters, 80 cells were
unassigned to glutamatergic clusters and 71 cells were unassigned to GABAergic
clusters. Marker genes were identified using area under a receiver operating
characteristic curve (AUROC) analysis. All genes that are greater than twofold
expressed in the cluster compared with the rest of the population were analyzed
using AUROC. Genes that had greater than 85% classification score were defined
as markers specific to the cell type. Genes differentially expressed in a given
cluster were computed using edgeR. Expression of marker genes in each cluster
were represented as violin plots. Violin plots were rendered using the ggplot2
package in R. They represent the distribution of log-transformed normalized gene
expression (gene UMIs/total cell UMIs) in each cluster. The maximum expression
for each of violin plot was set to 5. Marker genes were used for provisional
cluster names that included one to three markers. A cluster name denoted by a
single marker indicates a gene that is selectively and robustly expressed by a
single cluster and is sufficient to define that cluster. The addition of a
second marker indicates a secondary identifier that is also strongly, although
not necessarily uniquely, expressed in the cluster. Gad1 was
added to the four LHAGlut clusters that robustly expressed it,
despite being classified as glutamatergic. Finally, we found that although
Pmch and Hcrt expression was sufficient to
identify LHAGlut clusters 1 and 6, respectively, they were unique in
also being expressed at low, but consistent, levels in every other
LHAGlut and LHAGABA cluster. This is likely explained
by ambient mRNA released from damaged neurons during dissociation.
Mickelsen L.E., Bolisetty M., Chimileski B.R., Fujita A., Beltrami E.J., Costanzo J.T., Naparstek J.R., Robson P, & Jackson A.C. (2019). Single-cell transcriptomic analysis of the lateral hypothalamic area reveals molecularly distinct populations of inhibitory and excitatory neurons. Nature neuroscience, 22(4), 642-656.
Publication 2019
Cells Females Gene Expression Genes Germ Cells glutamate decarboxylase 1 (brain, 67kDa), human HCRT protein, human Males Mitochondrial Inheritance Neurons Python RNA, Messenger TUBB3 protein, human
5
Luciferase Assay Protocol for Inducible Targets
Cited 6 times
Luciferase assays were done essentially as described, using an inducible luciferase target (25 (link)), except that the medium was changed to fresh MEMα medium with 1 µM 4-OH-tamoxifen (Sigma) 24 h after transfection, and cells were lysed 45–48 h after transfection. The DNA amounts per one well of a 12-well plate were as follows: UAS-luc, UAS-luc-miR-155as, UAS-luc-ND1-3′-UTR or UAS-luc-Tubb3-3′-UTR, 80 ng; CS2+cβgal, 50 ng; CS2+G4D-ER™-G4A, 100 ng; CS2+SIBR, US2-SIBR, UI2-SIBR or UI4-SIBR expression vectors, 400 ng. For the experiments in Figures 7 and 9, the expression vectors were 100, 200 or 400 ng, with US2-MT added to maintain a constant DNA amount per well (other plasmid amounts as above). Reporter activity was assayed by using the Dual-Light system (Applied Biosystems) and was normalized to β-galactosidase activity to control for transfection efficiency variation among different wells. All reporter assays shown are based on data averaged from at least three independent transfections.
Chung K.H., Hart C.C., Al-Bassam S., Avery A., Taylor J., Patel P.D., Vojtek A.B, & Turner D.L. (2006). Polycistronic RNA polymerase II expression vectors for RNA interference based on BIC/miR-155. Nucleic Acids Research, 34(7), e53.
Publication 2006
beta-Galactosidase Biological Assay Cells Cloning Vectors DNA, A-Form Light Luciferases Plasmids Tamoxifen Transfection TUBB3 protein, human

Most recents protocols related to «TUBB3 protein, human»

1
Pancreas Clearing and Imaging Protocol
The pancreas was microdissected from control-Ai14 and Isl1CKO-Ai14 mice (postnatal day P0). We used an advanced CUBIC protocol [65 (link)] for tissue clearing to enable efficient imaging by light-sheet microscopy. Briefly, the microdissected tissue was fixed in 4% PFA for 1 h, washed with PBS, and incubated in a clearing solution Cubic 1 for 5 days at 37 ℃. Before immunolabeling, samples were washed in PBT (0.5% Triton-X in PBS) 4 × for 30 min. In addition to tdTomato expression, cleared samples were immunolabeled using different combinations of antibodies (anti-INS, anti-GLP1, and anti-TUBB3). Samples were stored before imaging in Cubic 2 at room temperature. Zeiss Lightsheet Z.1 microscope with illumination objective Lightsheet Z.1 5x/0.1 and detection objective Dry objective Lightsheet Z.1 5x/0.16 was used for imaging at the Light Microscopy Core Facility of the Institute of Molecular Genetics of the Czech Academy of Sciences. IMARIS software v8.1.1 (Bitplane AG, CA, USA) was used for image processing.
Bohuslavova R., Fabriciova V., Lebrón-Mora L., Malfatti J., Smolik O., Valihrach L., Benesova S., Zucha D., Berkova Z., Saudek F., Evans S.M, & Pavlinkova G. (2023). ISL1 controls pancreatic alpha cell fate and beta cell maturation. Cell & Bioscience, 13, 53.
Full text: Click here
Publication 2023
Antibodies Cuboid Bone EHMT1 protein, human Light Microscopy Mus Pancreas tdTomato Tissues TUBB3 protein, human
2
Fluorescent Labeling of Brainstem Neurons
Mice were first deeply anesthetized with a combination of ketamine (100 mg/kg) and xylazine (10 mg/kg), then transcardially perfused with PBS, followed by ice-cold 4% paraformaldehyde (PFA). After removing the cerebral cortex, the brainstem was left in the skull and postfixed in the same fixative overnight at 4°C. Parasagittal or coronal sections, 40–50 μm thick, of the CN were cut with a vibratome (Leica 1000S).
Free floating sections were collected, permeabilized, blocked with 3% BSA, and incubated with primary anti-GFP antibody (rabbit, Abcam catalog #ab290, 1:500) overnight on a shaker at 4°C to preserve the fluorescent signal of CeFP of the hVOS probe in TRAP-labeled neurons. In some experiments, sections were double stained with anti-tubulin β-III antibody (TUBB3, mouse, BioLegend catalog #801202, 1:500), as a neuronal marker. Sections were then incubated with fluorescent-dye conjugated secondary antibodies for 2 h (Alexa 568 anti-rabbit, Abcam catalog #ab175692, 1:500; Alexa 488 anti-mouse, 1:500, ThermoFisher Scientific catalog #A21202) and counterstained with DAPI for 10 min at room temperature. Images were acquired with a confocal microscope (Nikon A1RS) and processed with ImageJ.
Ma Y., Shu W.C., Lin L., Cao X.J., Oertel D., Smith P.H, & Jackson M.B. (2023). Imaging Voltage Globally and in Isofrequency Lamina in Slices of Mouse Ventral Cochlear Nucleus. eNeuro, 10(3), ENEURO.0465-22.2023.
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Publication 2023
alexa 568 anti-H-2 antibodies Antibodies, Anti-Idiotypic Brain Stem Cold Temperature Cortex, Cerebral Cranium DAPI Fixatives Fluorescent Dyes Fluorescent Probes Ketamine Mice, House Microscopy, Confocal Neurons paraform Rabbits TUBB3 protein, human Tubulin Xylazine
3
Comprehensive Neuronal Marker Staining

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Publication 2023
alexa fluor 488 Alexa Fluor 555 Hybridomas Neural Cell Adhesion Molecules SOX10 Transcription Factor TCL1B protein, human TUBB3 protein, human
4
Scoping Neuronal Diversity in Mouse Hypothalamus
Cell Ranger h5 files were read into Seurat v4 98 (link) in R (version 4.1.0) and RStudio (version 1.4.1717) and merged by brain region (DMH, SCN) for clustering analysis. We filtered the initial datasets to remove low quality samples (i.e., cells with less than 100 genes detected or greater than 0.5% mitochondrial reads). We then log-normalized the data; selected 2,000 most variable genes (“feature selection”), and scaled gene expression. We performed Principal Component Analysis (PCA) to linearly reduce the dimensionality of the highly variable gene set. We defined distance metrics based on K-nearest neighbor analysis, grouped cells with Louvian algorithm modality optimization, and visualized cell embeddings in low-dimensional space with Uniform Manifold Approximation and Projection (UMAP) nonlinear dimensionality reduction. To focus our analysis on neurons, we subsetted neuronal clusters based on their enriched expression of neuronal marker genes (Syt1, Syn1, Tubb3). To correct for batch effects, we integrated across sample batches using Seurat’s function for reciprocal principal component analysis (RPCA). Next, we subsetted region-specific clusters based on expression of positive and negative marker genes for each target brain region (SCN, DMH), as described in the next section. We reclustered the identified DMH and SCN neurons using the following parameters: DMH, 2,000 most variable genes, first 15 PCs, resolution setting of 0.8; SCN, 2,000 most variable genes, first 13 PCs, resolution setting of 0.5 Finally, we assessed cluster markers with the Wilcoxon Rank Sum test using Seurat default settings. Cluster markers were selected based on top p-values (adjusted to correct for multiple comparisons), high percent expression within the cluster and low percent expression outside of the cluster, and validated based on Allen Brain Atlas mouse in situ hybridization data and previous literature.
Tang Q., Godschall E., Brennan C.D., Zhang Q., Abraham-Fan R.J., Williams S.P., Güngül T.B., Onoharigho R., Buyukaksakal A., Salinas R., Olivieri J.J., Deppmann C.D., Campbell J.N., Podyma B, & Güler A.D. (2023). A leptin-responsive hypothalamic circuit inputs to the circadian feeding network. bioRxiv.
Publication Preprint 2023
Brain Cells Gene Expression Genes Genetic Markers In Situ Hybridization Mice, Laboratory Mitochondrial Inheritance Neurons Suprachiasmatic Nucleus Neurons SYT1 protein, human TCL1B protein, human TUBB3 protein, human
5
Quantification of Neurite Length
After 5 days of maturation, neurons were fixed in 4% PFA and ICC was performed with Beta-III-Tubulin (Tuj1, Millipore, MAB1637). Neurite length was measured and analyzed using Neurite Tracer in Fiji ImageJ (Pool et al., 2008 (link)), from three independent experiments.
Haukedal H., Corsi G.I., Gadekar V.P., Doncheva N.T., Kedia S., de Haan N., Chandrasekaran A., Jensen P., Schiønning P., Vallin S., Marlet F.R., Poon A., Pires C., Agha F.K., Wandall H.H., Cirera S., Simonsen A.H., Nielsen T.T., Nielsen J.E., Hyttel P., Muddashetty R., Aldana B.I., Gorodkin J., Nair D., Meyer M., Larsen M.R, & Freude K. (2023). Golgi fragmentation – One of the earliest organelle phenotypes in Alzheimer’s disease neurons. Frontiers in Neuroscience, 17, 1120086.
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Publication 2023
Neurites Neurons TUBB3 protein, human

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Bovine serum albumin (bsa) by Merck Group
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Bovine serum albumin (BSA) is a common laboratory reagent derived from bovine blood plasma. It is a protein that serves as a stabilizer and blocking agent in various biochemical and immunological applications. BSA is widely used to maintain the activity and solubility of enzymes, proteins, and other biomolecules in experimental settings.
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Ab18207 by Abcam
Sourced in United Kingdom, United States, China
Ab18207 is a primary antibody that recognizes the CD68 antigen. CD68 is a glycoprotein that is highly expressed by monocytes and macrophages, making it a useful marker for these cell types.
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Trizol by Thermo Fisher Scientific
Sourced in United States, Germany, China, Japan, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Belgium, Denmark, Netherlands, India, Ireland, Lithuania, Singapore, Sweden, Norway, Austria, Brazil, Argentina, Hungary, Sao Tome and Principe, New Zealand, Hong Kong, Cameroon, Philippines
TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.

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