Structured Review

Abcam rabbit anti cd81
Characterization of exosomes derived from human umbilical cord mesenchymal stem cells (hucMSC-Ex). A. Electron microscopy analysis of exosomes secreted by hucMSCs (scale bar = 100 nm). B. Exsome-specific markers CD9, CD63 and <t>CD81</t> were measured by western blot analysis. Ex exosome; hucMSC human umbilical cord-derived mesenchymal stem cell.
Rabbit Anti Cd81, supplied by Abcam, used in various techniques. Bioz Stars score: 97/100, based on 9 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/rabbit anti cd81/product/Abcam
Average 97 stars, based on 9 article reviews
Price from $9.99 to $1999.99
rabbit anti cd81 - by Bioz Stars, 2022-10
97/100 stars

Images

1) Product Images from "Exosomes released by human umbilical cord mesenchymal stem cells protect against renal interstitial fibrosis through ROS-mediated P38MAPK/ERK signaling pathway"

Article Title: Exosomes released by human umbilical cord mesenchymal stem cells protect against renal interstitial fibrosis through ROS-mediated P38MAPK/ERK signaling pathway

Journal: American Journal of Translational Research

doi:

Characterization of exosomes derived from human umbilical cord mesenchymal stem cells (hucMSC-Ex). A. Electron microscopy analysis of exosomes secreted by hucMSCs (scale bar = 100 nm). B. Exsome-specific markers CD9, CD63 and CD81 were measured by western blot analysis. Ex exosome; hucMSC human umbilical cord-derived mesenchymal stem cell.
Figure Legend Snippet: Characterization of exosomes derived from human umbilical cord mesenchymal stem cells (hucMSC-Ex). A. Electron microscopy analysis of exosomes secreted by hucMSCs (scale bar = 100 nm). B. Exsome-specific markers CD9, CD63 and CD81 were measured by western blot analysis. Ex exosome; hucMSC human umbilical cord-derived mesenchymal stem cell.

Techniques Used: Derivative Assay, Electron Microscopy, Western Blot

2) Product Images from "Landscape of surfaceome and endocytome in human glioma is divergent and depends on cellular spatial organization"

Article Title: Landscape of surfaceome and endocytome in human glioma is divergent and depends on cellular spatial organization

Journal: Proceedings of the National Academy of Sciences of the United States of America

doi: 10.1073/pnas.2114456119

TS-MAP uncovers a divergent surfaceome landscape in patient gliomas. ( A ) TS-MAP was applied in a mixed cohort ( n = 10) of patient gliomas. Shown are MRI (presurgery and postsurgery within 48 h) and histology for pathological-anatomical diagnostics (PAD) according to clinical routine, here exemplified by patient #8ODG with a low-grade ODG and #1GBM representing a high-grade GBM. Freshly resected tumors were biotinylated ex vivo for downstream analyses, as indicated. ( B ) Confocal microscopy shows biotinylation (green) of intact patient tumor. Higher magnification ( Bottom ) indicates surface labeling, which is further supported by Airyscan imaging of disintegrated SCS ( Bottom Right ), showing specific plasma membrane labeling. Top Right shows example of fresh tumor dissection into pieces of 0.3 to 0.5 cm in diameter prior to biotinylation (ruler scale in centimeters). (Scale bars, 20 μm [ Top Left ], 5 μm [ Bottom Left and Right ].) ( C ) FACS quantification of streptavidin-AF488 association with nonbiotinylated (control) and biotinylated (surface) in a representative GBM patient tissue SCS. ( D ) TS-MAP was applied in a pilot cohort of 10 freshly resected patient tumors, including low-grade ODG (WHO grade II), high-grade anaplastic astrocytoma (AA, WHO grade III), primary glioblastoma (GBM, WHO grade IV), recurrent GBM (GBMr), and gliosarcoma (WHO grade IV). Heatmap of SURFME protein abundance demonstrates divergent expression profile among patient tumors. ( E ) Categories of SURFME proteins relatively up-regulated in the respective patient tumor. ( F ) Quantification of normalized, relative abundance (log 2 fold) of selected SURFME proteins expressed in different patient tumors. Normalization was done based on the sample with the lowest abundance for each SURFME protein. EGFR, BCAN, and TF are normalized to #8ODG II abundances, CD81 is normalized to #2GBM abundance, and MCT2 to #3AA III. ( G ) Validation of LC-MS/MS data by immunofluorescence of selected SURFME proteins in matched patient tumor sections. Shown are representative images from at least three independent experiments. White squares indicate zoomed areas shown in Insets . (Scale bars, 20 μm, and 2 or 5 μm for narrow and wide bars, respectively, in Insets .)
Figure Legend Snippet: TS-MAP uncovers a divergent surfaceome landscape in patient gliomas. ( A ) TS-MAP was applied in a mixed cohort ( n = 10) of patient gliomas. Shown are MRI (presurgery and postsurgery within 48 h) and histology for pathological-anatomical diagnostics (PAD) according to clinical routine, here exemplified by patient #8ODG with a low-grade ODG and #1GBM representing a high-grade GBM. Freshly resected tumors were biotinylated ex vivo for downstream analyses, as indicated. ( B ) Confocal microscopy shows biotinylation (green) of intact patient tumor. Higher magnification ( Bottom ) indicates surface labeling, which is further supported by Airyscan imaging of disintegrated SCS ( Bottom Right ), showing specific plasma membrane labeling. Top Right shows example of fresh tumor dissection into pieces of 0.3 to 0.5 cm in diameter prior to biotinylation (ruler scale in centimeters). (Scale bars, 20 μm [ Top Left ], 5 μm [ Bottom Left and Right ].) ( C ) FACS quantification of streptavidin-AF488 association with nonbiotinylated (control) and biotinylated (surface) in a representative GBM patient tissue SCS. ( D ) TS-MAP was applied in a pilot cohort of 10 freshly resected patient tumors, including low-grade ODG (WHO grade II), high-grade anaplastic astrocytoma (AA, WHO grade III), primary glioblastoma (GBM, WHO grade IV), recurrent GBM (GBMr), and gliosarcoma (WHO grade IV). Heatmap of SURFME protein abundance demonstrates divergent expression profile among patient tumors. ( E ) Categories of SURFME proteins relatively up-regulated in the respective patient tumor. ( F ) Quantification of normalized, relative abundance (log 2 fold) of selected SURFME proteins expressed in different patient tumors. Normalization was done based on the sample with the lowest abundance for each SURFME protein. EGFR, BCAN, and TF are normalized to #8ODG II abundances, CD81 is normalized to #2GBM abundance, and MCT2 to #3AA III. ( G ) Validation of LC-MS/MS data by immunofluorescence of selected SURFME proteins in matched patient tumor sections. Shown are representative images from at least three independent experiments. White squares indicate zoomed areas shown in Insets . (Scale bars, 20 μm, and 2 or 5 μm for narrow and wide bars, respectively, in Insets .)

Techniques Used: Magnetic Resonance Imaging, Ex Vivo, Confocal Microscopy, Labeling, Imaging, Dissection, FACS, Expressing, Liquid Chromatography with Mass Spectroscopy, Immunofluorescence

3) Product Images from "Unbiased proteomic profiling of host cell extracellular vesicle composition and dynamics upon HIV‐1 infection"

Article Title: Unbiased proteomic profiling of host cell extracellular vesicle composition and dynamics upon HIV‐1 infection

Journal: The EMBO Journal

doi: 10.15252/embj.2020105492

Biochemical analysis of the composition of EVs released by primary CD4 + T cells EVs were purified by SEC from supernatant of activated CD4 + T cells. EVs were subjected to immunoisolation with beads coupled to antibodies against CD81 or CD63. Bead‐associated (Pull‐down: PD) vesicles and those left behind (Flow‐Through: FT) were loaded on a gel for Western blot analysis with antibodies specific for CD63, CD81, ADAM10 and CD3G. A representative image and quantification (mean ± SD) of the proportion of signal in FT as compared with total (PD + FT) in samples obtained from four independent donors are shown. Multiplex bead‐based flow cytometry assay for detection of EV surface markers. Antibody‐coated capture beads were incubated with 2 × 10 9 particles. Captured EVs were detected with either APC‐labelled anti‐CD81, anti‐CD63 or anti‐CD3E. Left: Median APC fluorescence values for the different bead populations are shown as a ratio to the median APC fluorescence of control beads (log10 scale). Mean ± SD for four independent experiments is shown. Right: Heat‐map representation of the median APC fluorescence values for the different bead populations detected with anti‐CD63 or anti‐CD3E antibodies relative to the values detected with anti‐CD81 (mean value of 4 independent donors).
Figure Legend Snippet: Biochemical analysis of the composition of EVs released by primary CD4 + T cells EVs were purified by SEC from supernatant of activated CD4 + T cells. EVs were subjected to immunoisolation with beads coupled to antibodies against CD81 or CD63. Bead‐associated (Pull‐down: PD) vesicles and those left behind (Flow‐Through: FT) were loaded on a gel for Western blot analysis with antibodies specific for CD63, CD81, ADAM10 and CD3G. A representative image and quantification (mean ± SD) of the proportion of signal in FT as compared with total (PD + FT) in samples obtained from four independent donors are shown. Multiplex bead‐based flow cytometry assay for detection of EV surface markers. Antibody‐coated capture beads were incubated with 2 × 10 9 particles. Captured EVs were detected with either APC‐labelled anti‐CD81, anti‐CD63 or anti‐CD3E. Left: Median APC fluorescence values for the different bead populations are shown as a ratio to the median APC fluorescence of control beads (log10 scale). Mean ± SD for four independent experiments is shown. Right: Heat‐map representation of the median APC fluorescence values for the different bead populations detected with anti‐CD63 or anti‐CD3E antibodies relative to the values detected with anti‐CD81 (mean value of 4 independent donors).

Techniques Used: Purification, Western Blot, Multiplex Assay, Flow Cytometry, Incubation, Fluorescence

Biochemical analysis of the composition of EVs released by Jurkat cells EVs were purified by SEC from supernatants of Jurkat cells. EVs were subjected to immunoisolation with beads coupled to antibodies against CD81 or CD63. Bead‐associated (Pull‐down: PD) vesicles and those left behind (Flow‐Through: FT) were loaded on a gel for Western blot analysis with antibodies specific for CD63, Syntenin‐1, CD81, ADAM10 and CD3G. A representative blot (top panel) and quantification (mean ± SD) of the proportion of signal in FT as compared with total (PD + FT) from four independent experiments (bottom panel) are shown. Representative images of double‐immunogold labelling of EVs purified by SEC from Jurkat cells. Left panel, CD81/CD63; right panel, CD81/CD3E. Arrows show CD63 staining in CD81 + CD63 + EVs. Multiplex bead‐based flow cytometry assay for detection of EV surface markers. Antibody‐coated capture beads were incubated with 2 × 10 9 particles. Captured EVs were detected with either APC‐labelled anti‐CD81, anti‐CD63 or anti‐CD3E. (C) Median APC fluorescence values for the different bead populations is shown as the ratio to the median APC fluorescence of control beads (log10 scale). Mean ± SD for four independent experiments is shown. Heat‐map representation of the median APC fluorescence values for the different bead populations detected with anti‐CD63 or anti‐CD3E antibodies relative to the values detected with anti‐CD81. Mean of four independent experiments.
Figure Legend Snippet: Biochemical analysis of the composition of EVs released by Jurkat cells EVs were purified by SEC from supernatants of Jurkat cells. EVs were subjected to immunoisolation with beads coupled to antibodies against CD81 or CD63. Bead‐associated (Pull‐down: PD) vesicles and those left behind (Flow‐Through: FT) were loaded on a gel for Western blot analysis with antibodies specific for CD63, Syntenin‐1, CD81, ADAM10 and CD3G. A representative blot (top panel) and quantification (mean ± SD) of the proportion of signal in FT as compared with total (PD + FT) from four independent experiments (bottom panel) are shown. Representative images of double‐immunogold labelling of EVs purified by SEC from Jurkat cells. Left panel, CD81/CD63; right panel, CD81/CD3E. Arrows show CD63 staining in CD81 + CD63 + EVs. Multiplex bead‐based flow cytometry assay for detection of EV surface markers. Antibody‐coated capture beads were incubated with 2 × 10 9 particles. Captured EVs were detected with either APC‐labelled anti‐CD81, anti‐CD63 or anti‐CD3E. (C) Median APC fluorescence values for the different bead populations is shown as the ratio to the median APC fluorescence of control beads (log10 scale). Mean ± SD for four independent experiments is shown. Heat‐map representation of the median APC fluorescence values for the different bead populations detected with anti‐CD63 or anti‐CD3E antibodies relative to the values detected with anti‐CD81. Mean of four independent experiments.

Techniques Used: Purification, Western Blot, Staining, Multiplex Assay, Flow Cytometry, Incubation, Fluorescence

Identification by unbiased proteomic analysis of groups of proteins likely released in the same EV subtypes by Jurkat cells Overlay of proteomic profiles of CD63 and CD81 (left) versus CD81 and ITGB4 (right) showing the relative abundance distribution across the 3 × 3 subfractions obtained from untreated Jurkat cells. Although all three proteins are strongly enriched in F3 fractions, the profiles of CD63 and CD81 are reproducibly different, whereas profiles of CD81 and ITGB4 are extremely similar. Neighbourhood Network plot of CD3G as a single query (replicate tolerance = 50, **network members, cut‐off for replicates = 2, 25% distance percentile for edges). Nodes: red = query, orange = close neighbour in all three replicates, grey = close neighbour in two out of three replicates. Edges: percentile within the local distance distribution (thicker edge and darker shade = smaller distance, i.e. closer neighbour); see Materials and Methods for details. Multiple query Neighbourhood Network plot for CD63, CD81 and CD3G. The top 30 close neighbours of each query were jointly used for the network layout (B ranking network members, 50% distance percentile of edges). Nodes: red = query, light red = close neighbours in 2 or 3 replicates, blue = neighbours validated by Immunoisolation, Immuno‐EM or MacsPlex Exo in figure 3 . Edges: percentile within the local distance distribution (thicker edge and darker shade = smaller distance, i.e. closer neighbour); see Materials and Methods for details. The three networks are remotely connected, but clearly separate.
Figure Legend Snippet: Identification by unbiased proteomic analysis of groups of proteins likely released in the same EV subtypes by Jurkat cells Overlay of proteomic profiles of CD63 and CD81 (left) versus CD81 and ITGB4 (right) showing the relative abundance distribution across the 3 × 3 subfractions obtained from untreated Jurkat cells. Although all three proteins are strongly enriched in F3 fractions, the profiles of CD63 and CD81 are reproducibly different, whereas profiles of CD81 and ITGB4 are extremely similar. Neighbourhood Network plot of CD3G as a single query (replicate tolerance = 50, **network members, cut‐off for replicates = 2, 25% distance percentile for edges). Nodes: red = query, orange = close neighbour in all three replicates, grey = close neighbour in two out of three replicates. Edges: percentile within the local distance distribution (thicker edge and darker shade = smaller distance, i.e. closer neighbour); see Materials and Methods for details. Multiple query Neighbourhood Network plot for CD63, CD81 and CD3G. The top 30 close neighbours of each query were jointly used for the network layout (B ranking network members, 50% distance percentile of edges). Nodes: red = query, light red = close neighbours in 2 or 3 replicates, blue = neighbours validated by Immunoisolation, Immuno‐EM or MacsPlex Exo in figure 3 . Edges: percentile within the local distance distribution (thicker edge and darker shade = smaller distance, i.e. closer neighbour); see Materials and Methods for details. The three networks are remotely connected, but clearly separate.

Techniques Used:

Unbiased proteomic profiling analysis of EV subtypes released by Jurkat cells Proteomic profiles of different proteins, showing the relative abundance distribution across the 3 × 3 subfractions obtained from Jurkat cells. Proteins with very similar profiles (represented by the same colours) are likely part of the same EV subtypes. Each profile consists of three independent data triplets (F1‐F2‐F3A, F1‐F2‐F3B and F1‐F2‐F3C). Abundance profiles of over 3,000 proteins in EVs recovered after 10K (F1), 30K (F2) and 100K (F3) centrifugations (see Appendix Fig S1 ), from Jurkat cells were subjected to principal component analysis (PCA). Each scatter point represents one protein; proximity indicates similar profiles and hence similar distributions across EVs. The PCA scores plot was annotated with known marker proteins of intracellular organelles, and with markers identified previously (Kowal et al, 2016 ) as specific for CD9‐ or CD63‐ or CD81‐bearing sEVs in human dendritic cells (Appendix Table S1 ), as indicated in the legend. Non‐marker proteins are shown as grey dots. EVs of different subcellular origin are clearly separated by the profiling analysis. PC1 and PC2 account for 44.5 and 19.1% of the variability in the data, respectively. Violin plots showing enrichment of the protein markers of intracellular organelles across the F1‐F2‐F3 subfractions as compared to expression in the total cell proteome. Solid horizontal lines indicate medians, and dashed line indicates quartiles ( n = 3). Mitochondria and ER markers are de‐enriched, and plasma membrane markers are enriched in EV fractions, with progressively pronounced effects from F1 to F3. Neighbourhood Network plots (top 25% quantile edges) for single queries CD63 (E, **network members, cut‐off for replicates = 3) and CD81 (D, *network members, cut‐off for replicates = 2), show several ESCRT components in the CD63 network, and ARRDC1 and integrins ITGA4/B1 in the CD81 network. A multi‐query network for these two proteins and CD3G, shown in Fig EV1 , shows that all three networks are separated, indicating presence of the markers in different EV subtypes. Nodes: red = query, orange = close neighbour in all three replicates, grey = close neighbour in two out of three replicates. Edges: percentile within the local distance distribution (thicker edge and darker shade = smaller distance, i.e. closer neighbour); see Materials and Methods for details.
Figure Legend Snippet: Unbiased proteomic profiling analysis of EV subtypes released by Jurkat cells Proteomic profiles of different proteins, showing the relative abundance distribution across the 3 × 3 subfractions obtained from Jurkat cells. Proteins with very similar profiles (represented by the same colours) are likely part of the same EV subtypes. Each profile consists of three independent data triplets (F1‐F2‐F3A, F1‐F2‐F3B and F1‐F2‐F3C). Abundance profiles of over 3,000 proteins in EVs recovered after 10K (F1), 30K (F2) and 100K (F3) centrifugations (see Appendix Fig S1 ), from Jurkat cells were subjected to principal component analysis (PCA). Each scatter point represents one protein; proximity indicates similar profiles and hence similar distributions across EVs. The PCA scores plot was annotated with known marker proteins of intracellular organelles, and with markers identified previously (Kowal et al, 2016 ) as specific for CD9‐ or CD63‐ or CD81‐bearing sEVs in human dendritic cells (Appendix Table S1 ), as indicated in the legend. Non‐marker proteins are shown as grey dots. EVs of different subcellular origin are clearly separated by the profiling analysis. PC1 and PC2 account for 44.5 and 19.1% of the variability in the data, respectively. Violin plots showing enrichment of the protein markers of intracellular organelles across the F1‐F2‐F3 subfractions as compared to expression in the total cell proteome. Solid horizontal lines indicate medians, and dashed line indicates quartiles ( n = 3). Mitochondria and ER markers are de‐enriched, and plasma membrane markers are enriched in EV fractions, with progressively pronounced effects from F1 to F3. Neighbourhood Network plots (top 25% quantile edges) for single queries CD63 (E, **network members, cut‐off for replicates = 3) and CD81 (D, *network members, cut‐off for replicates = 2), show several ESCRT components in the CD63 network, and ARRDC1 and integrins ITGA4/B1 in the CD81 network. A multi‐query network for these two proteins and CD3G, shown in Fig EV1 , shows that all three networks are separated, indicating presence of the markers in different EV subtypes. Nodes: red = query, orange = close neighbour in all three replicates, grey = close neighbour in two out of three replicates. Edges: percentile within the local distance distribution (thicker edge and darker shade = smaller distance, i.e. closer neighbour); see Materials and Methods for details.

Techniques Used: Marker, Expressing

4) Product Images from "Exosomal transfer of obesity adipose tissue for decreased miR-141-3p mediate insulin resistance of hepatocytes"

Article Title: Exosomal transfer of obesity adipose tissue for decreased miR-141-3p mediate insulin resistance of hepatocytes

Journal: International Journal of Biological Sciences

doi: 10.7150/ijbs.28522

Characterization of exosomes released from WT-, Ob-, and HFD-adipose tissue. ( A ) WT- (WT-Exo), Ob- (Ob-Exo) and HFD-exosomes (HFD-Exo) were examined under electron microscopy. ( B ) WT-, Ob- and HFD-exosomes were analyzed by immunoblotting for the presence of the exosomal protein markers CD81, CD63 and CD9, and for the absence of the cellular protein Calnexin, adipose tissue lysate was used as control (20 μg of exosomal/ adipose tissue lysate protein was loaded for Western-blotting). ( C ) The exosome size was measured using A Nanoparticle Tracking Analysis. n=3 independent experiments. All values were expressed as means ± SD.
Figure Legend Snippet: Characterization of exosomes released from WT-, Ob-, and HFD-adipose tissue. ( A ) WT- (WT-Exo), Ob- (Ob-Exo) and HFD-exosomes (HFD-Exo) were examined under electron microscopy. ( B ) WT-, Ob- and HFD-exosomes were analyzed by immunoblotting for the presence of the exosomal protein markers CD81, CD63 and CD9, and for the absence of the cellular protein Calnexin, adipose tissue lysate was used as control (20 μg of exosomal/ adipose tissue lysate protein was loaded for Western-blotting). ( C ) The exosome size was measured using A Nanoparticle Tracking Analysis. n=3 independent experiments. All values were expressed as means ± SD.

Techniques Used: Electron Microscopy, Western Blot

5) Product Images from "Lung CSC‐derived exosomal miR‐210‐3p contributes to a pro‐metastatic phenotype in lung cancer by targeting FGFRL1, et al. Lung CSC‐derived exosomal miR‐210‐3p contributes to a pro‐metastatic phenotype in lung cancer by targeting FGFRL1"

Article Title: Lung CSC‐derived exosomal miR‐210‐3p contributes to a pro‐metastatic phenotype in lung cancer by targeting FGFRL1, et al. Lung CSC‐derived exosomal miR‐210‐3p contributes to a pro‐metastatic phenotype in lung cancer by targeting FGFRL1

Journal: Journal of Cellular and Molecular Medicine

doi: 10.1111/jcmm.15274

Characterization of exosomes purified from lung CSCs. A, Representative transmission electron micrographic image of exosomes. Scale bar: 500 nm. B, Nanoparticle tracking showing exosome diameter. C, Western blot analysis of CD63, CD81 and TSG101. D, Representative images of PKH26‐labelled exosomes co‐cultured with A549 and NCI‐H1703 cells. Scale bars: 100 μm. E, MTT assay analysing cell viability of A549 and NCI‐H1703 cells after treatment with lung CSC‐derived exosomes. Control vs. 10, 20, 40 or 80 μg/mL exosomes, respectively. * P
Figure Legend Snippet: Characterization of exosomes purified from lung CSCs. A, Representative transmission electron micrographic image of exosomes. Scale bar: 500 nm. B, Nanoparticle tracking showing exosome diameter. C, Western blot analysis of CD63, CD81 and TSG101. D, Representative images of PKH26‐labelled exosomes co‐cultured with A549 and NCI‐H1703 cells. Scale bars: 100 μm. E, MTT assay analysing cell viability of A549 and NCI‐H1703 cells after treatment with lung CSC‐derived exosomes. Control vs. 10, 20, 40 or 80 μg/mL exosomes, respectively. * P

Techniques Used: Purification, Transmission Assay, Western Blot, Cell Culture, MTT Assay, Derivative Assay

6) Product Images from "Exosomal transfer of obesity adipose tissue for decreased miR-141-3p mediate insulin resistance of hepatocytes"

Article Title: Exosomal transfer of obesity adipose tissue for decreased miR-141-3p mediate insulin resistance of hepatocytes

Journal: International Journal of Biological Sciences

doi: 10.7150/ijbs.28522

Characterization of exosomes released from WT-, Ob-, and HFD-adipose tissue. ( A ) WT- (WT-Exo), Ob- (Ob-Exo) and HFD-exosomes (HFD-Exo) were examined under electron microscopy. ( B ) WT-, Ob- and HFD-exosomes were analyzed by immunoblotting for the presence of the exosomal protein markers CD81, CD63 and CD9, and for the absence of the cellular protein Calnexin, adipose tissue lysate was used as control (20 μg of exosomal/ adipose tissue lysate protein was loaded for Western-blotting). ( C ) The exosome size was measured using A Nanoparticle Tracking Analysis. n=3 independent experiments. All values were expressed as means ± SD.
Figure Legend Snippet: Characterization of exosomes released from WT-, Ob-, and HFD-adipose tissue. ( A ) WT- (WT-Exo), Ob- (Ob-Exo) and HFD-exosomes (HFD-Exo) were examined under electron microscopy. ( B ) WT-, Ob- and HFD-exosomes were analyzed by immunoblotting for the presence of the exosomal protein markers CD81, CD63 and CD9, and for the absence of the cellular protein Calnexin, adipose tissue lysate was used as control (20 μg of exosomal/ adipose tissue lysate protein was loaded for Western-blotting). ( C ) The exosome size was measured using A Nanoparticle Tracking Analysis. n=3 independent experiments. All values were expressed as means ± SD.

Techniques Used: Electron Microscopy, Western Blot

7) Product Images from "Exosomes Derived From miR-133b-Modified Mesenchymal Stem Cells Promote Recovery After Spinal Cord Injury"

Article Title: Exosomes Derived From miR-133b-Modified Mesenchymal Stem Cells Promote Recovery After Spinal Cord Injury

Journal: Frontiers in Neuroscience

doi: 10.3389/fnins.2018.00845

MSCs package miR-133b into secreted exosomes. (A) Expression levels of miR-133b in injured spinal cords were measured by qRT-PCR at 12 h, 24 h, and at 2, 3, 4, 5, and 7 days after acute SCI. (B) Expressions of CD63, CD81, and CD9 in exosomes derived from miR-133b- or miR-con-transfected MSCs were evaluated by western blot. (C) Expression of miR-133b in exosomes derived from miR-133b- or miR-con-transfected MSCs was assessed by qRT-PCR. Values are presented as means ± SD. NS: p > 0.05, ∗ p
Figure Legend Snippet: MSCs package miR-133b into secreted exosomes. (A) Expression levels of miR-133b in injured spinal cords were measured by qRT-PCR at 12 h, 24 h, and at 2, 3, 4, 5, and 7 days after acute SCI. (B) Expressions of CD63, CD81, and CD9 in exosomes derived from miR-133b- or miR-con-transfected MSCs were evaluated by western blot. (C) Expression of miR-133b in exosomes derived from miR-133b- or miR-con-transfected MSCs was assessed by qRT-PCR. Values are presented as means ± SD. NS: p > 0.05, ∗ p

Techniques Used: Expressing, Quantitative RT-PCR, Derivative Assay, Transfection, Western Blot

Similar Products

  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
  • 88
    Abcam anti cd8 antibody
    CD4 + TILs colocalize with PD-L1 + cells and contribute to poor survival. (a) Representative multi-IF images showing a sample with more CD4 + TILs than <t>CD8</t> + TILs in the stroma. Scale bar, 100 μ m. (b) Representative multi-IF images showing a sample with more CD8 + TILs than CD4 + TILs in the stroma. Scale bar, 100 μ m. (c) A pie chart was plotted according to IHC staining results showing 84% of patients have more CD4 + TILs than CD8 + TILs in the stroma. (d) Representative multi-IF images showing a sample with PD-L1 + cells colocalizing more with CD4 + TILs than with CD8 + TILs in the stroma. Scale bar, 100 μ m. (e) The Kaplan–Meier survival analysis showing patients with elevated levels of CD4 + TILs (red line, n = 26) have poor OS, compared to patients with low levels of CD4 + TILs (blue line, n = 51). (f) The Kaplan–Meier survival analysis showing patients with a low CD8/CD4 ratio (blue line, n = 46) have poor OS, compared to patients with a high CD8/CD4 ratio (red line, n = 31).
    Anti Cd8 Antibody, supplied by Abcam, used in various techniques. Bioz Stars score: 88/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/anti cd8 antibody/product/Abcam
    Average 88 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    anti cd8 antibody - by Bioz Stars, 2022-10
    88/100 stars
      Buy from Supplier

    90
    Abcam rabbit anti mouse cd8 monoclonal antibody
    Immunofluorescence staining of secondary tumors indicate infiltration of CD4 and <t>CD8</t> cells. Treated primary tumors were collected from animals treated with PBS, OX40 agonist, inCPMV, and inCPMV + OX40 12 days after the initial treatment. Tumor sectioned from animals treated with PBS or OX40 agonist showed no staining for CD4, CD8, or FoxP3 (not shown). Significant staining with CD4 and CD8 marker is apparent in both the peripheral and deep tumors from animals receiving inCPMV and inCPMV + OX40 agonist. Only a small number of Tregs (FoxP3) were detected. Representative images are shown; blue = DAPI staining of the cell nuclei; red and green = <t>antibody-specific</t> staining as indicated in the legends.
    Rabbit Anti Mouse Cd8 Monoclonal Antibody, supplied by Abcam, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/rabbit anti mouse cd8 monoclonal antibody/product/Abcam
    Average 90 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    rabbit anti mouse cd8 monoclonal antibody - by Bioz Stars, 2022-10
    90/100 stars
      Buy from Supplier

    cd8  (Abcam)
    99
    Abcam cd8
    Expression of cell surface receptors, CD69 and CD28, on splenic CD4 + T and <t>CD8</t> + T cells in the two sepsis mouse models. (A) Flow cytometry analysis of the mean fluorescence intensity (MFI) of CD69 and CD28 expression on gated CD3 + CD4 + T and CD3 + CD8 + T cells (gated on single cells) in the two sepsis models. Mean fluorescence intensity (MFI) of CD69 (B) and CD28 (C) on CD4 + T cell subsets in the acute sepsis (AS) (left) and recurrent sepsis (RS) (right) mouse models. MFI of CD69 (D) and CD28 (E) on CD8 + T cell subsets in the AS (left) and RS (right) models. Data are from at least three independent experiments with more than three mice per group in each experiment. Data points indicate means ± SD. *p
    Cd8, supplied by Abcam, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/cd8/product/Abcam
    Average 99 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    cd8 - by Bioz Stars, 2022-10
    99/100 stars
      Buy from Supplier

    Image Search Results


    CD4 + TILs colocalize with PD-L1 + cells and contribute to poor survival. (a) Representative multi-IF images showing a sample with more CD4 + TILs than CD8 + TILs in the stroma. Scale bar, 100 μ m. (b) Representative multi-IF images showing a sample with more CD8 + TILs than CD4 + TILs in the stroma. Scale bar, 100 μ m. (c) A pie chart was plotted according to IHC staining results showing 84% of patients have more CD4 + TILs than CD8 + TILs in the stroma. (d) Representative multi-IF images showing a sample with PD-L1 + cells colocalizing more with CD4 + TILs than with CD8 + TILs in the stroma. Scale bar, 100 μ m. (e) The Kaplan–Meier survival analysis showing patients with elevated levels of CD4 + TILs (red line, n = 26) have poor OS, compared to patients with low levels of CD4 + TILs (blue line, n = 51). (f) The Kaplan–Meier survival analysis showing patients with a low CD8/CD4 ratio (blue line, n = 46) have poor OS, compared to patients with a high CD8/CD4 ratio (red line, n = 31).

    Journal: Journal of Immunology Research

    Article Title: Tumor-Infiltrating Immune Cells and PD-L1 as Prognostic Biomarkers in Primary Esophageal Small Cell Carcinoma

    doi: 10.1155/2020/8884683

    Figure Lengend Snippet: CD4 + TILs colocalize with PD-L1 + cells and contribute to poor survival. (a) Representative multi-IF images showing a sample with more CD4 + TILs than CD8 + TILs in the stroma. Scale bar, 100 μ m. (b) Representative multi-IF images showing a sample with more CD8 + TILs than CD4 + TILs in the stroma. Scale bar, 100 μ m. (c) A pie chart was plotted according to IHC staining results showing 84% of patients have more CD4 + TILs than CD8 + TILs in the stroma. (d) Representative multi-IF images showing a sample with PD-L1 + cells colocalizing more with CD4 + TILs than with CD8 + TILs in the stroma. Scale bar, 100 μ m. (e) The Kaplan–Meier survival analysis showing patients with elevated levels of CD4 + TILs (red line, n = 26) have poor OS, compared to patients with low levels of CD4 + TILs (blue line, n = 51). (f) The Kaplan–Meier survival analysis showing patients with a low CD8/CD4 ratio (blue line, n = 46) have poor OS, compared to patients with a high CD8/CD4 ratio (red line, n = 31).

    Article Snippet: Immunohistochemistry StainingIHC staining was performed using anti-PD-L1 antibody (1 : 500) (clone 28-8, Abcam, Cambridge, UK) [ – ], anti-CD4 antibody (1 : 500) (clone EPR6855, Abcam, Cambridge, UK), anti-CD8 antibody (1 : 200) (rabbit polyclonal anti-CD8, Abcam, Cambridge, UK), anti-CD163 antibody (1 : 500) (clone EPR19518, Abcam, Cambridge, UK), and anti-FoxP3 antibody (1 : 100) (clone 236A/E7, Abcam, Cambridge, UK) as the primary antibodies.

    Techniques: Immunohistochemistry, Staining

    Expression and prognostic value of FoxP3 in PESCC. (a) Representative multi-IF images of two samples showing FoxP3/CD4 ratios in the stroma. Scale bar, 100 μ m. (b) Representative multi-IF images showing two samples with high (left) and low (right) FoxP3/CD8 ratios in stroma. Scale bar, 100 μ m. (c) Rich FoxP3 (red line, n = 26) was associated with significantly shorter OS than poor FoxP3 (blue line, n = 51). (d) A high FoxP3/CD8 ratio (red line, n = 28) was associated with shorter OS than a low FoxP3/CD8 ratio (blue line, n = 49).

    Journal: Journal of Immunology Research

    Article Title: Tumor-Infiltrating Immune Cells and PD-L1 as Prognostic Biomarkers in Primary Esophageal Small Cell Carcinoma

    doi: 10.1155/2020/8884683

    Figure Lengend Snippet: Expression and prognostic value of FoxP3 in PESCC. (a) Representative multi-IF images of two samples showing FoxP3/CD4 ratios in the stroma. Scale bar, 100 μ m. (b) Representative multi-IF images showing two samples with high (left) and low (right) FoxP3/CD8 ratios in stroma. Scale bar, 100 μ m. (c) Rich FoxP3 (red line, n = 26) was associated with significantly shorter OS than poor FoxP3 (blue line, n = 51). (d) A high FoxP3/CD8 ratio (red line, n = 28) was associated with shorter OS than a low FoxP3/CD8 ratio (blue line, n = 49).

    Article Snippet: Immunohistochemistry StainingIHC staining was performed using anti-PD-L1 antibody (1 : 500) (clone 28-8, Abcam, Cambridge, UK) [ – ], anti-CD4 antibody (1 : 500) (clone EPR6855, Abcam, Cambridge, UK), anti-CD8 antibody (1 : 200) (rabbit polyclonal anti-CD8, Abcam, Cambridge, UK), anti-CD163 antibody (1 : 500) (clone EPR19518, Abcam, Cambridge, UK), and anti-FoxP3 antibody (1 : 100) (clone 236A/E7, Abcam, Cambridge, UK) as the primary antibodies.

    Techniques: Expressing

    The expression of PD-L1 in the stroma correlates with tumor-infiltrating immune cells in PESCC. (a) Representative immunohistochemistry (IHC) images showing different expression patterns of PD-L1 in PESCC tissues. Scale bar, 100 μ m. S: stroma; T: tumor. (b) Distribution of different expression patterns of PD-L1 are plotted in the pie chart. (c) The Kaplan–Meier survival analysis of overall survival (OS) in a cohort of 77 PESCC patients according to positive (red line, n = 26) and negative (blue line, n = 51) PD-L1 expression. (d) Representative IHC images showing PD-L1, CD4, CD8, and CD163 expression in the stroma using consecutive sections of PD-L1-positive and PD-L1-negative specimens. Scale bar, 100 μ m. S: stroma; T: tumor. (e) The presence of PD-L1 is positively associated with the number of CD4 + TILs, CD8 + TILs, and CD163 + TAMs ( ∗∗ p

    Journal: Journal of Immunology Research

    Article Title: Tumor-Infiltrating Immune Cells and PD-L1 as Prognostic Biomarkers in Primary Esophageal Small Cell Carcinoma

    doi: 10.1155/2020/8884683

    Figure Lengend Snippet: The expression of PD-L1 in the stroma correlates with tumor-infiltrating immune cells in PESCC. (a) Representative immunohistochemistry (IHC) images showing different expression patterns of PD-L1 in PESCC tissues. Scale bar, 100 μ m. S: stroma; T: tumor. (b) Distribution of different expression patterns of PD-L1 are plotted in the pie chart. (c) The Kaplan–Meier survival analysis of overall survival (OS) in a cohort of 77 PESCC patients according to positive (red line, n = 26) and negative (blue line, n = 51) PD-L1 expression. (d) Representative IHC images showing PD-L1, CD4, CD8, and CD163 expression in the stroma using consecutive sections of PD-L1-positive and PD-L1-negative specimens. Scale bar, 100 μ m. S: stroma; T: tumor. (e) The presence of PD-L1 is positively associated with the number of CD4 + TILs, CD8 + TILs, and CD163 + TAMs ( ∗∗ p

    Article Snippet: Immunohistochemistry StainingIHC staining was performed using anti-PD-L1 antibody (1 : 500) (clone 28-8, Abcam, Cambridge, UK) [ – ], anti-CD4 antibody (1 : 500) (clone EPR6855, Abcam, Cambridge, UK), anti-CD8 antibody (1 : 200) (rabbit polyclonal anti-CD8, Abcam, Cambridge, UK), anti-CD163 antibody (1 : 500) (clone EPR19518, Abcam, Cambridge, UK), and anti-FoxP3 antibody (1 : 100) (clone 236A/E7, Abcam, Cambridge, UK) as the primary antibodies.

    Techniques: Expressing, Immunohistochemistry

    CD4 + TILs colocalize with PD-L1 + CD163 + TAMs. Representative multicolor immunofluorescence (multi-IF) images showing (a) high and (b) low colocalization of PD-L1 + cells with CD163 + TAMs. Scale bar, 100 μ m. Representative multi-IF images showing costaining of (c) CD4 + PD-L1 + CD163 + TILs and (d) CD8 + PD-L1 + CD163 + TILs in serial sections of the same specimen. Cells were counterstained with DAPI (blue, nucleus). Scale bar, 100 μ m.

    Journal: Journal of Immunology Research

    Article Title: Tumor-Infiltrating Immune Cells and PD-L1 as Prognostic Biomarkers in Primary Esophageal Small Cell Carcinoma

    doi: 10.1155/2020/8884683

    Figure Lengend Snippet: CD4 + TILs colocalize with PD-L1 + CD163 + TAMs. Representative multicolor immunofluorescence (multi-IF) images showing (a) high and (b) low colocalization of PD-L1 + cells with CD163 + TAMs. Scale bar, 100 μ m. Representative multi-IF images showing costaining of (c) CD4 + PD-L1 + CD163 + TILs and (d) CD8 + PD-L1 + CD163 + TILs in serial sections of the same specimen. Cells were counterstained with DAPI (blue, nucleus). Scale bar, 100 μ m.

    Article Snippet: Immunohistochemistry StainingIHC staining was performed using anti-PD-L1 antibody (1 : 500) (clone 28-8, Abcam, Cambridge, UK) [ – ], anti-CD4 antibody (1 : 500) (clone EPR6855, Abcam, Cambridge, UK), anti-CD8 antibody (1 : 200) (rabbit polyclonal anti-CD8, Abcam, Cambridge, UK), anti-CD163 antibody (1 : 500) (clone EPR19518, Abcam, Cambridge, UK), and anti-FoxP3 antibody (1 : 100) (clone 236A/E7, Abcam, Cambridge, UK) as the primary antibodies.

    Techniques: Immunofluorescence

    Immunofluorescence staining of secondary tumors indicate infiltration of CD4 and CD8 cells. Treated primary tumors were collected from animals treated with PBS, OX40 agonist, inCPMV, and inCPMV + OX40 12 days after the initial treatment. Tumor sectioned from animals treated with PBS or OX40 agonist showed no staining for CD4, CD8, or FoxP3 (not shown). Significant staining with CD4 and CD8 marker is apparent in both the peripheral and deep tumors from animals receiving inCPMV and inCPMV + OX40 agonist. Only a small number of Tregs (FoxP3) were detected. Representative images are shown; blue = DAPI staining of the cell nuclei; red and green = antibody-specific staining as indicated in the legends.

    Journal: Molecular pharmaceutics

    Article Title: Inactivated Cowpea Mosaic Virus in Combination with OX40 Agonist Primes Potent Antitumor Immunity in a Bilateral Melanoma Mouse Model

    doi: 10.1021/acs.molpharmaceut.1c00681

    Figure Lengend Snippet: Immunofluorescence staining of secondary tumors indicate infiltration of CD4 and CD8 cells. Treated primary tumors were collected from animals treated with PBS, OX40 agonist, inCPMV, and inCPMV + OX40 12 days after the initial treatment. Tumor sectioned from animals treated with PBS or OX40 agonist showed no staining for CD4, CD8, or FoxP3 (not shown). Significant staining with CD4 and CD8 marker is apparent in both the peripheral and deep tumors from animals receiving inCPMV and inCPMV + OX40 agonist. Only a small number of Tregs (FoxP3) were detected. Representative images are shown; blue = DAPI staining of the cell nuclei; red and green = antibody-specific staining as indicated in the legends.

    Article Snippet: For the CD8+ T cell panel, primary antibody was rabbit anti-mouse CD8 monoclonal antibody (Abcam, ab209775, 1:500 dilution) and secondary antibody was goat anti-rabbit Alexa Fluor 488 (Abcam, ab150077, 1:500 dilution).

    Techniques: Immunofluorescence, Staining, Marker

    Expression of cell surface receptors, CD69 and CD28, on splenic CD4 + T and CD8 + T cells in the two sepsis mouse models. (A) Flow cytometry analysis of the mean fluorescence intensity (MFI) of CD69 and CD28 expression on gated CD3 + CD4 + T and CD3 + CD8 + T cells (gated on single cells) in the two sepsis models. Mean fluorescence intensity (MFI) of CD69 (B) and CD28 (C) on CD4 + T cell subsets in the acute sepsis (AS) (left) and recurrent sepsis (RS) (right) mouse models. MFI of CD69 (D) and CD28 (E) on CD8 + T cell subsets in the AS (left) and RS (right) models. Data are from at least three independent experiments with more than three mice per group in each experiment. Data points indicate means ± SD. *p

    Journal: Frontiers in Immunology

    Article Title: Recurrent Sepsis Exacerbates CD4+ T Cell Exhaustion and Decreases Antiviral Immune Responses

    doi: 10.3389/fimmu.2021.627435

    Figure Lengend Snippet: Expression of cell surface receptors, CD69 and CD28, on splenic CD4 + T and CD8 + T cells in the two sepsis mouse models. (A) Flow cytometry analysis of the mean fluorescence intensity (MFI) of CD69 and CD28 expression on gated CD3 + CD4 + T and CD3 + CD8 + T cells (gated on single cells) in the two sepsis models. Mean fluorescence intensity (MFI) of CD69 (B) and CD28 (C) on CD4 + T cell subsets in the acute sepsis (AS) (left) and recurrent sepsis (RS) (right) mouse models. MFI of CD69 (D) and CD28 (E) on CD8 + T cell subsets in the AS (left) and RS (right) models. Data are from at least three independent experiments with more than three mice per group in each experiment. Data points indicate means ± SD. *p

    Article Snippet: Then followed by the staining with primary antibodies [the following primary antibodies were used: CD4 (rabbit monoclonal, ab16667, 1:200, Abcam), CD8 (rabbit monoclonal, ab217344, 1:300, Abcam)] overnight at 4°C, and then incubated in secondary antibody [enzyme-labeled goat anti-rabbit IgG polymer (PV-6002, ZSGB-BIO)] for 2 h. The tissue was then reacted with 3,3’-diaminobenzidine (DAB, Sigma) to visualize antibody location.

    Techniques: Expressing, Flow Cytometry, Fluorescence, Mouse Assay

    CD4 + T cell and CD4 + /CD8 + T cell ratio decrease in the acute and the recurrent sepsis mouse models, which are more prominent in recurrent septic mice. (A) Schematic figure of the induction of acute sepsis (AS) and recurrent sepsis (RS) in mice using different frequencies of lipopolysaccharide (LPS) injection. The number of total splenocytes in the AS (B) and RS mice (C) . Flow cytometry analysis of CD3 + CD19 - T cells (gated on single cells) (D) and CD4 + T and CD8 + T cells (gated on CD3 + CD19 - T cells cells) (E) . The percentage (left) and number (right) of total T cells (F) , CD4 + T cells (H) , and CD8 + T cells (J) in the AS model at the indicated time points. The percentage (left) and the number (right) of total T cells (G) , CD4 + T cells (I) , and CD8 + T cells (K) in the RS model. The CD4 + /CD8 + T cell ratio in the AS (L) and RS model (M) at the indicated time points. Data are from at least three independent experiments with more than three mice per group in each experiment. Data points indicate means ± SD. *p

    Journal: Frontiers in Immunology

    Article Title: Recurrent Sepsis Exacerbates CD4+ T Cell Exhaustion and Decreases Antiviral Immune Responses

    doi: 10.3389/fimmu.2021.627435

    Figure Lengend Snippet: CD4 + T cell and CD4 + /CD8 + T cell ratio decrease in the acute and the recurrent sepsis mouse models, which are more prominent in recurrent septic mice. (A) Schematic figure of the induction of acute sepsis (AS) and recurrent sepsis (RS) in mice using different frequencies of lipopolysaccharide (LPS) injection. The number of total splenocytes in the AS (B) and RS mice (C) . Flow cytometry analysis of CD3 + CD19 - T cells (gated on single cells) (D) and CD4 + T and CD8 + T cells (gated on CD3 + CD19 - T cells cells) (E) . The percentage (left) and number (right) of total T cells (F) , CD4 + T cells (H) , and CD8 + T cells (J) in the AS model at the indicated time points. The percentage (left) and the number (right) of total T cells (G) , CD4 + T cells (I) , and CD8 + T cells (K) in the RS model. The CD4 + /CD8 + T cell ratio in the AS (L) and RS model (M) at the indicated time points. Data are from at least three independent experiments with more than three mice per group in each experiment. Data points indicate means ± SD. *p

    Article Snippet: Then followed by the staining with primary antibodies [the following primary antibodies were used: CD4 (rabbit monoclonal, ab16667, 1:200, Abcam), CD8 (rabbit monoclonal, ab217344, 1:300, Abcam)] overnight at 4°C, and then incubated in secondary antibody [enzyme-labeled goat anti-rabbit IgG polymer (PV-6002, ZSGB-BIO)] for 2 h. The tissue was then reacted with 3,3’-diaminobenzidine (DAB, Sigma) to visualize antibody location.

    Techniques: Mouse Assay, Injection, Flow Cytometry

    CD4 + T cells presented an exhausted phenotype in recurrent sepsis. Mean fluorescence intensity (MFI) of PD-1 on CD4 + T cells (A) and CD8 + T cells (B) in the acute sepsis (AS) (left) and recurrent sepsis (RS) (right) mouse models. MFI of Tim-3 on CD4 + T cells (C) and CD8 + T cells (D) in the AS (left) and RS (right) mouse models. (E) Gating strategy used for the identification of CD3 + CD4 + CD8 - CD25 + FOXP3 + Tregs. (F) Flow cytometric analysis of Tregs in the RS mice (down) and the control mice (up). The percentage (left) and the number (right) of Tregs in AS (G) and RS (H) model at the indicated time points. Data are from at least three independent experiments with more than three mice per group in each experiment. Data points indicate means ± SD. *p

    Journal: Frontiers in Immunology

    Article Title: Recurrent Sepsis Exacerbates CD4+ T Cell Exhaustion and Decreases Antiviral Immune Responses

    doi: 10.3389/fimmu.2021.627435

    Figure Lengend Snippet: CD4 + T cells presented an exhausted phenotype in recurrent sepsis. Mean fluorescence intensity (MFI) of PD-1 on CD4 + T cells (A) and CD8 + T cells (B) in the acute sepsis (AS) (left) and recurrent sepsis (RS) (right) mouse models. MFI of Tim-3 on CD4 + T cells (C) and CD8 + T cells (D) in the AS (left) and RS (right) mouse models. (E) Gating strategy used for the identification of CD3 + CD4 + CD8 - CD25 + FOXP3 + Tregs. (F) Flow cytometric analysis of Tregs in the RS mice (down) and the control mice (up). The percentage (left) and the number (right) of Tregs in AS (G) and RS (H) model at the indicated time points. Data are from at least three independent experiments with more than three mice per group in each experiment. Data points indicate means ± SD. *p

    Article Snippet: Then followed by the staining with primary antibodies [the following primary antibodies were used: CD4 (rabbit monoclonal, ab16667, 1:200, Abcam), CD8 (rabbit monoclonal, ab217344, 1:300, Abcam)] overnight at 4°C, and then incubated in secondary antibody [enzyme-labeled goat anti-rabbit IgG polymer (PV-6002, ZSGB-BIO)] for 2 h. The tissue was then reacted with 3,3’-diaminobenzidine (DAB, Sigma) to visualize antibody location.

    Techniques: Fluorescence, Mouse Assay

    Recurrent sepsis decreased antiviral immune responses. (A) Experimental design. Mice received acute sepsis (AS) and recurrent sepsis (RS) stimulation followed by i.n. infection with 10 3 pfu of PR8 virus. (B) Viral titers of infected mice were monitored. (C) Hematoxylin-eosin staining (H E) of lung tissue sections in the control, AS, RS, PR8-infected control (control+PR8), PR8-infected AS (AS+PR8), and PR8-infected RS (RS+PR8) mice (original magnification×10, as labeled; scale bars, 100um). Statistical analysis of relative alveoli area (D) and histological scores (E) in lung tissue sections from control, AS, RS, control+PR8, AS+PR8, and RS+PR8 mice. (F) Representative pictures of CD4 (up) and CD8 (down) immunohistochemical staining in lung tissue sections from control, AS, RS, control+PR8, AS+PR8, and RS+PR8 mice. (G) Typical inflammation and anti-inflammation cytokine levels in control, AS, RS, control+PR8, AS+PR8, and RS+PR8 mice. Results are represented as means ± SD. *p

    Journal: Frontiers in Immunology

    Article Title: Recurrent Sepsis Exacerbates CD4+ T Cell Exhaustion and Decreases Antiviral Immune Responses

    doi: 10.3389/fimmu.2021.627435

    Figure Lengend Snippet: Recurrent sepsis decreased antiviral immune responses. (A) Experimental design. Mice received acute sepsis (AS) and recurrent sepsis (RS) stimulation followed by i.n. infection with 10 3 pfu of PR8 virus. (B) Viral titers of infected mice were monitored. (C) Hematoxylin-eosin staining (H E) of lung tissue sections in the control, AS, RS, PR8-infected control (control+PR8), PR8-infected AS (AS+PR8), and PR8-infected RS (RS+PR8) mice (original magnification×10, as labeled; scale bars, 100um). Statistical analysis of relative alveoli area (D) and histological scores (E) in lung tissue sections from control, AS, RS, control+PR8, AS+PR8, and RS+PR8 mice. (F) Representative pictures of CD4 (up) and CD8 (down) immunohistochemical staining in lung tissue sections from control, AS, RS, control+PR8, AS+PR8, and RS+PR8 mice. (G) Typical inflammation and anti-inflammation cytokine levels in control, AS, RS, control+PR8, AS+PR8, and RS+PR8 mice. Results are represented as means ± SD. *p

    Article Snippet: Then followed by the staining with primary antibodies [the following primary antibodies were used: CD4 (rabbit monoclonal, ab16667, 1:200, Abcam), CD8 (rabbit monoclonal, ab217344, 1:300, Abcam)] overnight at 4°C, and then incubated in secondary antibody [enzyme-labeled goat anti-rabbit IgG polymer (PV-6002, ZSGB-BIO)] for 2 h. The tissue was then reacted with 3,3’-diaminobenzidine (DAB, Sigma) to visualize antibody location.

    Techniques: Mouse Assay, Infection, Staining, Labeling, Immunohistochemistry