Journal: Journal of Neuroinflammation

Article Title: Disruption of Midkine gene reduces traumatic brain injury through the modulation of neuroinflammation

doi: 10.1186/s12974-020-1709-8

Figure Lengend Snippet: Effect of MK-deficiency on M1 and M2 microglia/macrophages phenotype marker after TBI. The M1 and M2 phenotype markers (CD16/32 and arginase-1, respectively) were expressed in the perilesional site of Mdk +/+ and Mdk −/− mice at 3 days ( a ). The immunohistochemical staining was performed through serial sections of mice (corresponding to * and + in a ). The ratios of the CD16/32-immunoreactive area were significantly reduced in Mdk −/− mice compared to Mdk +/+ mice at 3 days. The CD16/32- and arginase-1-immunoreactive areas were significantly decreased at 7 days ( b ). RT-qPCR analysis revealed the mRNA levels of the M1 phenotype markers (TNF-α, CD11b) to be significantly downregulated in Mdk −/− than in Mdk +/+ mice ( c ). Data are presented as mean ± SE ( n = 5 mice/group in immunohistochemistry, n = 3–4 mice/group in RT-qPCR). * p < 0.05, ** p < 0.01 (comparison with MK +/+ and Mdk −/− ). ## p < 0.01 (comparison with 3 days and 7 days). Scale bar = 50 μm (all panels)

Article Snippet: TBI, traumatic brain injury The coronal sections were immunostained with the following antibodies: rabbit anti-glial fibrillary acidic protein (GFAP; a marker of activated astrocytes) (Cosmo Bio Co., Japan; RO1003), rabbit anti-ionized calcium-binding adaptor molecule1 (Iba1; a marker of resting microglia/macrophage) (Wako, Osaka, Japan; 019-19741), rat anti-CD16/32 (a marker of M1 microglia/macrophage) (BD Bioscience, US; #553142), rabbit anti-arginase-1 (a marker of M2 microglia/macrophage) (Cell Signaling Technology, Inc., US; #93668), rabbit anti-activated caspase-3 (a marker of apoptotic activity) (Proteintech Group, Inc.; USA; 19677-1-AP), and mouse anti-neuronal nuclei (NeuN; a marker of neuron) (Abcam plc, Cambridge, UK; ab104224).

Techniques: Marker, Immunohistochemical staining, Staining, Quantitative RT-PCR, Immunohistochemistry

Journal: Physiological Genomics

Article Title: Microarray analysis of aging-associated immune system alterations in the rostral ventrolateral medulla of F344 rats

doi: 10.1152/physiolgenomics.00131.2016

Figure Lengend Snippet: List of immune system genes used for quantitative PCR validation of microarray analysis and their TaqMan assay IDs

Article Snippet: Actb, Hprt1, Ldha , and Rplp1 genes were used as endogenous controls and the geometric average of their Ct values was used to calculate each gene’s ΔCt value ( 1 ). table ft1 table-wrap mode="anchored" t5 Table 1. caption a7 Functional Category Gene Symbol TaqMan Assay ID Complement system C1qa Rn01519903_m1 C1qc(C1qg) Rn01516757_m1 Cd93(C1qr1) Rn00584525_g1 C3 Rn00584525_g1 C4a Rn00566466_m1 Cfd Rn00709527_m1 Cfh Rn01535436_g1 Vwf Rn00590326_m1 Klkb1 Rn01488161_m1 Microglial cells Cd14 Rn00572656_g1 Cd68 Rn01495634_g1 Tlr2 Rn02133647_s1 Cx3cr1 Rn02134446_s1 Trem2 Rn01512170_m1 Fcrl2 Rn01455191_m1 B2m Rn00560865_m1 Endogenous controls Actb Rn00667869_m1 Hprt1 Rn01527840_m1 Ldha Rn00820751_g1 Rplp1 Rn03467157_gH Open in a separate window List of immune system genes used for quantitative PCR validation of microarray analysis and their TaqMan assay IDs Microarray Data Analysis Hybridization signal intensities from microarrays were extracted with Agilent Feature Extraction Software, version 9.5.1.1. (Agilent Technologies) (Gene Expression Omnibus accession number {"type":"entrez-geo","attrs":{"text":"GSE90956","term_id":"90956"}} GSE90956 ).

Techniques: Real-time Polymerase Chain Reaction, Biomarker Discovery, Microarray, TaqMan Assay

Journal: bioRxiv

Article Title: Minimizing the ex vivo confounds of cell-isolation techniques on transcriptomic -profiles of purified microglia

doi: 10.1101/2021.07.15.452509

Figure Lengend Snippet: Cartidge-based FACS gating strategy for microglial sorting on Miltenyi Biotec MACSQuant Tyto.

Article Snippet: To test the effect of transcription and translation inhibitors on the relative abundance of cell types following cell preparation, aliquots of cells were stained with: 1) Microglial (Cd11b-APC (M1/70, #130-113-793, Miltenyi Biotec) / Cd45-Vioblue (REA737, #130-110-802, Miltenyi Biotec)), 2) Neuronal (Cd24-Vioblue (REA743, #130-110-831, Miltenyi Biotec)), 3) Astrocytic (ACSA2-APC (REA969, #130-116-245, Miltenyi Biotec)), or 4) Oligodendrocytic (O4-APC (REA576, #130-119-982, Miltenyi Biotec)) fluorophore-conjugated antibodies, according to manufacturer’s instructions.

Techniques:

Journal: bioRxiv

Article Title: Minimizing the ex vivo confounds of cell-isolation techniques on transcriptomic -profiles of purified microglia

doi: 10.1101/2021.07.15.452509

Figure Lengend Snippet: Cytometer-based FACS gating strategy for microglial sorting on FACSAria.

Article Snippet: To test the effect of transcription and translation inhibitors on the relative abundance of cell types following cell preparation, aliquots of cells were stained with: 1) Microglial (Cd11b-APC (M1/70, #130-113-793, Miltenyi Biotec) / Cd45-Vioblue (REA737, #130-110-802, Miltenyi Biotec)), 2) Neuronal (Cd24-Vioblue (REA743, #130-110-831, Miltenyi Biotec)), 3) Astrocytic (ACSA2-APC (REA969, #130-116-245, Miltenyi Biotec)), or 4) Oligodendrocytic (O4-APC (REA576, #130-119-982, Miltenyi Biotec)) fluorophore-conjugated antibodies, according to manufacturer’s instructions.

Techniques: Cytometry

Journal: bioRxiv

Article Title: Minimizing the ex vivo confounds of cell-isolation techniques on transcriptomic -profiles of purified microglia

doi: 10.1101/2021.07.15.452509

Figure Lengend Snippet: Gating strategy for assessment of microglial purity by different sort methods.

Article Snippet: To test the effect of transcription and translation inhibitors on the relative abundance of cell types following cell preparation, aliquots of cells were stained with: 1) Microglial (Cd11b-APC (M1/70, #130-113-793, Miltenyi Biotec) / Cd45-Vioblue (REA737, #130-110-802, Miltenyi Biotec)), 2) Neuronal (Cd24-Vioblue (REA743, #130-110-831, Miltenyi Biotec)), 3) Astrocytic (ACSA2-APC (REA969, #130-116-245, Miltenyi Biotec)), or 4) Oligodendrocytic (O4-APC (REA576, #130-119-982, Miltenyi Biotec)) fluorophore-conjugated antibodies, according to manufacturer’s instructions.

Techniques:

Journal: bioRxiv

Article Title: Minimizing the ex vivo confounds of cell-isolation techniques on transcriptomic -profiles of purified microglia

doi: 10.1101/2021.07.15.452509

Figure Lengend Snippet: A) Schematic of the experimental design. Cx3cr1-NuTRAP brains were enzymatically and mechanically dissociated to create a single cell suspension. Different microglial sorting techniques were compared to cell input for purity, yield, and transcriptomic signatures. B) Representative flow cytometry plots of immunostained single-cell suspensions from input and after each of the sorting strategies shows a distinct population of: eGFP+ cells (identified as Cx3cr1+ microglia) and Cd11b+Cd45+ cells (identified as microglia per traditional cell surface markers). All sort positive fractions were enriched for: (C) eGFP+ singlets and (D) Cd11b+Cd45+ singlets in the positive fractions (as compared to input). (Two-Way ANOVA, Main effect of TRAP Fraction, p<0.001). When comparing positive fractions, the AutoMACS positive fraction had lower %eGFP+ singlets as compared to all other sort methods. FACSAria had higher percentage of eGFP+ singlets than all other sort methods. FACSAria had higher percentage of Cd11b+Cd45+ singlets as compared to all other sort methods positive fractions (Two-way ANOVA, Tukey’s post-hoc, *p<0.05). E) Microglial yield was significantly higher in the MACSQuant Tyto positive fraction as compared to the AutoMACS to MACSQuant Tyto and FACSAria positive fractions (One-Way ANOVA, Tukey’s posthoc, #p<0.01).

Article Snippet: To test the effect of transcription and translation inhibitors on the relative abundance of cell types following cell preparation, aliquots of cells were stained with: 1) Microglial (Cd11b-APC (M1/70, #130-113-793, Miltenyi Biotec) / Cd45-Vioblue (REA737, #130-110-802, Miltenyi Biotec)), 2) Neuronal (Cd24-Vioblue (REA743, #130-110-831, Miltenyi Biotec)), 3) Astrocytic (ACSA2-APC (REA969, #130-116-245, Miltenyi Biotec)), or 4) Oligodendrocytic (O4-APC (REA576, #130-119-982, Miltenyi Biotec)) fluorophore-conjugated antibodies, according to manufacturer’s instructions.

Techniques: Suspension, Flow Cytometry

Journal: bioRxiv

Article Title: Minimizing the ex vivo confounds of cell-isolation techniques on transcriptomic -profiles of purified microglia

doi: 10.1101/2021.07.15.452509

Figure Lengend Snippet: RNA-Seq libraries were made from each of the groups represented in Figure 1A to compare the transcriptomic profiles of microglia isolated via four different cell sorting strategies. Each of the strategies had similar levels of (A) enrichment of microglial transcripts and depletion of: (B) astrocytic, (C) oligodendrocytic, (D) neuronal, and (E) endothelial transcripts when compared to cell input. F) Principal component analysis of all expressed genes shows clear separation of cell input from all other sort methods in the first component with 81% of explained variance. G) Hierarchical clustering of differentially expressed genes (DEGs) (One-Way ANOVA, BHMTC, SNK FDR<0.1, |FC|>2) shows separation of cell input and sort methods. Each of the sort methods show very similar patterning of expression across DEGs. H) Comparison of SNK post-hocs from each of the sort methods v. cell input, showed the majority of enrichments/depletions (ie.,DEGs) (7084/7378 = 96%) were in common between all sort methods. I) There were 5322 DEGs (72%) that were depleted and 1759 DEGs (24%) that were enriched in all sort methods compared to cell input. There were only 297 discordant DEGs (4%) between the different sort methods as compared to cell input. J) Top 10 biological processes over-represented in the 1759 genes upregulated in all sort methods compared to cell input (Gene Ontology Over-Representation Analysis, BHMTC FDR <0.05). K) Top 10 biological processes over-represented in the 5322 genes downregulated in all sort methods compared to cell input (Gene Ontology Over-Representation Analysis, BHMTC FDR <0.05). L) Top 5 transcription factor targets over-represented in the 1759 genes upregulated in all sort methods compared to cell input (Transcription factor target network over-representation analysis, BHMTC FDR<0.05). M) Top 5 transcription factor targets over-represented in the 5322 genes downregulated in all sort methods compared to cell input (Transcription factor target network over-representation analysis, BHMTC FDR<0.05).

Article Snippet: To test the effect of transcription and translation inhibitors on the relative abundance of cell types following cell preparation, aliquots of cells were stained with: 1) Microglial (Cd11b-APC (M1/70, #130-113-793, Miltenyi Biotec) / Cd45-Vioblue (REA737, #130-110-802, Miltenyi Biotec)), 2) Neuronal (Cd24-Vioblue (REA743, #130-110-831, Miltenyi Biotec)), 3) Astrocytic (ACSA2-APC (REA969, #130-116-245, Miltenyi Biotec)), or 4) Oligodendrocytic (O4-APC (REA576, #130-119-982, Miltenyi Biotec)) fluorophore-conjugated antibodies, according to manufacturer’s instructions.

Techniques: RNA Sequencing, Isolation, FACS, Expressing, Comparison

Journal: bioRxiv

Article Title: Minimizing the ex vivo confounds of cell-isolation techniques on transcriptomic -profiles of purified microglia

doi: 10.1101/2021.07.15.452509

Figure Lengend Snippet: A) Schematic of the experimental design. Cx3cr1-NuTRAP brains were hemisected and processed in halves for whole-tissue homogenization or enzymatic and mechanical dissociation to create a single cell suspension. Single cell suspensions were then sorted using MACS-and/or FACS-based isolation of microglia. Tissue homogenate, mixed-cell suspension, and microglia sorted by each of the four depicted methods were subjected to TRAP to isolate microglial-specific ribosomally-bound RNA for creation of RNA-Seq libraries. B) PCA of all expressed genes (>5 read counts in all samples from at least one group) separates Tissue TRAP from all other groups in the first component (79% explained variance) and Cell Suspension TRAP from all other groups in the second component (10.9% explained variance). C) Third component of PCA on all expressed genes separated AutoMACS TRAP from all other groups (7.2% explained variance). Each of the sort strategies had similar levels of (D) enrichment of microglial transcripts and depletion of: (E) astrocytic, (F) oligodendrocytic, (G) neuronal, and (G) endothelial transcripts when compared to Tissue TRAP. All of the sort methods showed stronger depletion of: (E) astrocytic, (F) oligodendrocytic, (G) neuronal, and (G) endothelial transcripts when compared to Cell TRAP (One-way ANOVA, Tukey’s post-hoc, ***p<0.001).

Article Snippet: To test the effect of transcription and translation inhibitors on the relative abundance of cell types following cell preparation, aliquots of cells were stained with: 1) Microglial (Cd11b-APC (M1/70, #130-113-793, Miltenyi Biotec) / Cd45-Vioblue (REA737, #130-110-802, Miltenyi Biotec)), 2) Neuronal (Cd24-Vioblue (REA743, #130-110-831, Miltenyi Biotec)), 3) Astrocytic (ACSA2-APC (REA969, #130-116-245, Miltenyi Biotec)), or 4) Oligodendrocytic (O4-APC (REA576, #130-119-982, Miltenyi Biotec)) fluorophore-conjugated antibodies, according to manufacturer’s instructions.

Techniques: Tissue Homogenization, Suspension, Isolation, RNA Sequencing

Journal: bioRxiv

Article Title: Minimizing the ex vivo confounds of cell-isolation techniques on transcriptomic -profiles of purified microglia

doi: 10.1101/2021.07.15.452509

Figure Lengend Snippet: RNA-Seq libraries were made from each of the groups represented in Figure 3A to compare the TRAP-isolated microglial translatomes between whole-tissue-TRAP and each of the cell isolation and sorting methods. A) Upset plot of DEGs for all groups v. Tissue TRAP (One-Way ANOVA, BHMTC, SNK FDR<0.1, |FC|>2). B) Hierarchical clustering of DEGs shows separation of tissue TRAP from all other groups. Cell TRAP also clusters separately from all Sort-TRAP groups. C) Comparison of DEGs from each group (Cell-and Sort-TRAP) v. Tissue-TRAP, revealed 5337 common DEGs (67%) that were depleted and 2329 common DEGs (29%) that were enriched in all groups (Cell-and Sort-TRAP) compared to Tissue-TRAP. There were only 352 discordant DEGs (4%) between the different sort methods as compared to cell input. D) Top 10 biological processes over-represented in the 2329 genes upregulated in Cell TRAP and Sort-TRAP compared to Tissue TRAP (Gene Ontology Over-Representation Analysis, Hypergeometric test, BHMTC FDR <0.05). E) Comparison of upregulated transcriptomic pathways (Figure 2J; Supplemental Table 3) and upregulated translatome pathways (Figure 4D, Supplemntal Table 5) reveal 55 biological processes that are upregulated in the translatome only. F) Selection of 10 biological processes that are uniquely upregulated in the translatome (from the 55 identified in Figure 4E). G) Heatmap of genes involved in “Response to Interleukin-6 (GO:0070741)” biological process. H) Cytokines ( Il1a, Il1b, Il6, Il10, Il16, Il27 ) that are upregulated in the Cell-and Sort-TRAP groups compared to Tissue-TRAP (One-Way ANOVA, BHMTC, SNK FDR<0.1, |FC|>2). I) Chemokines ( Cxcl1, Cxcl2, Cxcl10, Ccl2, Ccl3, Ccl4, Ccl7, Ccl12 ) that are upregulated in the Cell-and Sort-TRAP groups compared to Tissue-TRAP (One-Way ANOVA, BHMTC, SNK FDR<0.1, |FC|>2). J) Intersection of activational genes identified in three previous studies ( ; ; ) identified 21 ex vivo activational transcripts represented in at least two of the studies. I) PCA of 21 ex vivo activational genes shows clear separation of tissue TRAP from all other groups in the first component (92.8% explained variance). J) Heatmap of 21 activational genes shows high levels of ex vivo activational transcripts across Cell-and Sort-TRAP methods compared to Tissue-TRAP. K) Zfp36 is enriched in Cell TRAP and Sort-TRAP compared to Tissue TRAP (One-Way ANOVA, Tukey’s posthoc, ***p<0.001).

Article Snippet: To test the effect of transcription and translation inhibitors on the relative abundance of cell types following cell preparation, aliquots of cells were stained with: 1) Microglial (Cd11b-APC (M1/70, #130-113-793, Miltenyi Biotec) / Cd45-Vioblue (REA737, #130-110-802, Miltenyi Biotec)), 2) Neuronal (Cd24-Vioblue (REA743, #130-110-831, Miltenyi Biotec)), 3) Astrocytic (ACSA2-APC (REA969, #130-116-245, Miltenyi Biotec)), or 4) Oligodendrocytic (O4-APC (REA576, #130-119-982, Miltenyi Biotec)) fluorophore-conjugated antibodies, according to manufacturer’s instructions.

Techniques: RNA Sequencing, Isolation, Cell Isolation, Comparison, Selection, Ex Vivo

Journal: Frontiers in Molecular Neuroscience

Article Title: Long Non-coding RNA TUG1 Sponges Mir-145a-5p to Regulate Microglial Polarization After Oxygen-Glucose Deprivation

doi: 10.3389/fnmol.2019.00215

Figure Lengend Snippet: Microglial polarization by immunofluorescence (A) and western blot (B) . TUG1 knockdown increased the number of ARG1 and CD206 positive cells (M2-like phenotype, green) as well as the protein levels, whereas decreased that of CD16 and CD68 positive cells (M1-like phenotype, red) as well as the protein levels after OGD. The effect of TUG1 knockdown on microglial polarization was reversed by miR-145a-5p inhibitor. * P < 0.05 vs. the corresponding control. 400×. Scale bar = 50 μm. n = 4.

Article Snippet: The number of microglial cells labeled with positive M1-like or M2-like signal as well as the total number of cells was respectively counted in five randomly selected fields under 40× magnification by using a laser confocal microscope (Nikon DS-Ri2, Japan).

Techniques: Immunofluorescence, Western Blot, Knockdown, Control