3.2.9 software-based partial least squares path modeling Search Results


tools plus software  (Datacolor ag)
90
Datacolor ag tools plus software
Tools Plus Software, supplied by Datacolor ag, 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/product/3%2E2%2E9+software-based+partial+least+squares+path+modeling/tools++software/pmc11013069-110-9-9
Average 90 stars, based on 1 article reviews
tools plus software - by Bioz Stars, 2026-10
90/100 stars
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pass software  (ncss llc)
90
ncss llc pass software
Pass Software, supplied by ncss llc, 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/product/3%2E2%2E9+software-based+partial+least+squares+path+modeling/pass+software/pmc08182196-121-10-12
Average 90 stars, based on 1 article reviews
pass software - by Bioz Stars, 2026-10
90/100 stars
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nkg2a fitc  (Miltenyi Biotec)
96
Miltenyi Biotec nkg2a fitc
Exhaustion markers PD-1 and LAG-3 are upregulated in GBM patients, but not TIGIT (A) Diagram depicting interactions between TIGIT, DNAM-1, CD96 and CD155, CD112, as well as intracellular cross-talk between TIGIT and other NK cell receptors (PD-1, 4-1BB, CD69, LAG-3. (B) TCGA Kaplan-Meier survival plot indicating that, together, CD155 and TIGIT are prognostic factors in GBM. High CD155 + TIGIT = 81 patients; Low CD155 + TIGIT = 81 patients. (C and D) Groups were compared by Kaplan-Meier survival analysis. Bar plots depicting (C) MFI ( left ) and (D) percentage ( right ) expression of NK cell activating (CD16, DNAM-1, NKG2D, CD69, NKp30, CD57, 4-1BB) and inhibitory (CD158e1, CD96, CD158b, TIGIT, <t>NKG2A,</t> PD-1, LAG-3, CD94) receptors on GBM patient cNK and tiNK cells, as well as NK cells from healthy donors (n = 8 patients ). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (E) Histograms depicting differences in select NK ligands between healthy donors, GBM patient cNK, and GBM patient tiNK cells. (F and G) Correlation between TIGIT expression (Fold MFI) and 4-1BB expression (Fold MFI) on (F) cNK (R 2 = 0.9181) and (G) tiNK (R 2 = 0.9649) cells harvested from GBM patients (n = 6). R 2 was calculated by simple linear regression. ∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001 .
Nkg2a Fitc, supplied by Miltenyi Biotec, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/3%2E2%2E9+software-based+partial+least+squares+path+modeling/CD159a+(NKG2A)+Antibody%2C+anti-human%2C+REAfinity/pmc10694670-371-55-56
Average 96 stars, based on 1 article reviews
nkg2a fitc - by Bioz Stars, 2026-10
96/100 stars
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Image Search Results


Exhaustion markers PD-1 and LAG-3 are upregulated in GBM patients, but not TIGIT (A) Diagram depicting interactions between TIGIT, DNAM-1, CD96 and CD155, CD112, as well as intracellular cross-talk between TIGIT and other NK cell receptors (PD-1, 4-1BB, CD69, LAG-3. (B) TCGA Kaplan-Meier survival plot indicating that, together, CD155 and TIGIT are prognostic factors in GBM. High CD155 + TIGIT = 81 patients; Low CD155 + TIGIT = 81 patients. (C and D) Groups were compared by Kaplan-Meier survival analysis. Bar plots depicting (C) MFI ( left ) and (D) percentage ( right ) expression of NK cell activating (CD16, DNAM-1, NKG2D, CD69, NKp30, CD57, 4-1BB) and inhibitory (CD158e1, CD96, CD158b, TIGIT, NKG2A, PD-1, LAG-3, CD94) receptors on GBM patient cNK and tiNK cells, as well as NK cells from healthy donors (n = 8 patients ). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (E) Histograms depicting differences in select NK ligands between healthy donors, GBM patient cNK, and GBM patient tiNK cells. (F and G) Correlation between TIGIT expression (Fold MFI) and 4-1BB expression (Fold MFI) on (F) cNK (R 2 = 0.9181) and (G) tiNK (R 2 = 0.9649) cells harvested from GBM patients (n = 6). R 2 was calculated by simple linear regression. ∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001 .

Journal: iScience

Article Title: TIGIT contributes to the regulation of 4-1BB and does not define NK cell dysfunction in glioblastoma

doi: 10.1016/j.isci.2023.108353

Figure Lengend Snippet: Exhaustion markers PD-1 and LAG-3 are upregulated in GBM patients, but not TIGIT (A) Diagram depicting interactions between TIGIT, DNAM-1, CD96 and CD155, CD112, as well as intracellular cross-talk between TIGIT and other NK cell receptors (PD-1, 4-1BB, CD69, LAG-3. (B) TCGA Kaplan-Meier survival plot indicating that, together, CD155 and TIGIT are prognostic factors in GBM. High CD155 + TIGIT = 81 patients; Low CD155 + TIGIT = 81 patients. (C and D) Groups were compared by Kaplan-Meier survival analysis. Bar plots depicting (C) MFI ( left ) and (D) percentage ( right ) expression of NK cell activating (CD16, DNAM-1, NKG2D, CD69, NKp30, CD57, 4-1BB) and inhibitory (CD158e1, CD96, CD158b, TIGIT, NKG2A, PD-1, LAG-3, CD94) receptors on GBM patient cNK and tiNK cells, as well as NK cells from healthy donors (n = 8 patients ). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (E) Histograms depicting differences in select NK ligands between healthy donors, GBM patient cNK, and GBM patient tiNK cells. (F and G) Correlation between TIGIT expression (Fold MFI) and 4-1BB expression (Fold MFI) on (F) cNK (R 2 = 0.9181) and (G) tiNK (R 2 = 0.9649) cells harvested from GBM patients (n = 6). R 2 was calculated by simple linear regression. ∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001 .

Article Snippet: Antibodies used for flow cytometry and cell sorting were CD3-PeCy7 (BD, UCHT1, Franklin Lakes, NJ), CD56-PeCy5.5 (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-APC (Biolegend, A15153G, San Diego, CA), DNAM-1-BV510 (Biolegend, 11A8, San Diego, CA), Sytox Green (Thermo Fisher Scientific, Franklin Lakes, NJ), Sytox Blue (Thermo Fisher Scientific, Waltham, MA), CD16-BUV395 (BD, 3G8, Franklin Lakes, NJ), NKG2A-FITC (Miltenyi Biotec, REA110, Gaithersburg, MD), CD158e1-BV421 (BD Biosciences, DX9, Franklin Lakes, NJ), PD-1-BV510 (Biolegend, NAT105, San Diego, CA), NKG2D-BV605 (Biolegend, 1D11, San Diego, CA), CD57-BV605 (Biolegend, QA17A04, San Diego, CA), CD69-BV650 (Biolegend, FN50, San Diego, CA), LAG-3-BV650 (Biolegend, 11C3C65, San Diego, CA), NKp30-BV711 (Biolegend, P30-15, San Diego, CA), TIM-3-BV711 (Biolegend, F38-2E2, San Diego, CA), A2AR-PE (R&D Systems, 599717, Minneapolis, MN), CD94-PE (Biolegend, DX22, San Diego, CA), CD158b-APC/Fire750 (Biolegend, DX27, San Diego, CA), 41BB-APC/Fire750 (Biolegend, 4B4-1, San Diego, CA), and KLRG1-APC (Biolegend, SA231A2, San Diego, CA), CD96-PE (Biolegend, NK92.39, San Diego, CA), NKp46-BV785 (Biolegend, 9E2, San Diego, CA), CD56-APC (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-BV421 (Biolegend, A15153G), IFN-γ-PerCP-Cy5.5 (Biolegend, 4S.B3, San Diego, CA), CD107a-PE (Biolegend, H4A3, San Diego, CA), m4-1BB-APC (Thermo Fisher Scientific, 17B5, Waltham, MA), mCD16/32-BV421 (Biolegend, 93, San Diego, CA), mNK1.1-BV510 (Biolegend, PK136, San Diego, CA), mCD11b-BV605 (Biolegend, M1/70, San Diego, CA), mCD27-BV650 (Biolegend, LG.3A10, San Diego, CA), mCD3-BV711 (Biolegend, 145-2C11, San Diego, CA), mLAG-3-BV785 (Thermo Fisher Scientific, C9B7W, Waltham, MA), mTIGIT-PE-Cy7 (Biolegend, 1G9, San Diego, CA), mDNAM-1-APC-Fire 750 (Biolegend, 10E5, San Diego, CA), and mCD96-PE (Biolegend, 3.3, San Diego, CA).

Techniques: Expressing

TIGIT blockade correlates with low CD16, high 4-1BB, and higher inflammatory cytokines (A) Heatmap depicting normalized MFI expression of NK cell activating (CD16, DNAM-1, NKG2D, CD69, NKp30, CD57, 4-1BB) and inhibitory (CD158e1, CD158b, NKG2A, PD-1, LAG-3, CD94, A2AR, TIM-3, KLRG1) receptors on NK cells cocultured with GBM43 cells, with or without CD155 and/or TIGIT mAb blockade (n = 3). (B) CD16, (C) 4-1BB, (D) and LAG-3 percentage ( left ) and MFI ( right ) expression on NK cells co-cultured with GBM43-WT cells, with or without CD155 and/or TIGIT mAb blockade (n = 3). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (E) Density plots depicting CD16, 4-1BB and LAG-3 expression on NK cells co-cultured with GBM43 cells, with or without CD155 and/or TIGIT mAb blockade. Levels of proinflammatory cytokines (F) IFN-γ, TNF-α, IL-8, IL-1β (G) IL-10, IL-5, GM-CSF, and IL-4 in cell supernatant of NK cells co-cultured with GBM43-WT cells, with or without CD155 and/or TIGIT mAb blockade (n = 3). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (H) Change in expression (measured as MFI fold over isotype control) of 4-1BB on TIGIT KO and WT NK cells generated via CRISPR/Cas9 editing in the absence and presence of GBM43 cells (incubated as co-culture at an E:T 2.5:1). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. Data are represented as mean ± SEM. ∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001 .

Journal: iScience

Article Title: TIGIT contributes to the regulation of 4-1BB and does not define NK cell dysfunction in glioblastoma

doi: 10.1016/j.isci.2023.108353

Figure Lengend Snippet: TIGIT blockade correlates with low CD16, high 4-1BB, and higher inflammatory cytokines (A) Heatmap depicting normalized MFI expression of NK cell activating (CD16, DNAM-1, NKG2D, CD69, NKp30, CD57, 4-1BB) and inhibitory (CD158e1, CD158b, NKG2A, PD-1, LAG-3, CD94, A2AR, TIM-3, KLRG1) receptors on NK cells cocultured with GBM43 cells, with or without CD155 and/or TIGIT mAb blockade (n = 3). (B) CD16, (C) 4-1BB, (D) and LAG-3 percentage ( left ) and MFI ( right ) expression on NK cells co-cultured with GBM43-WT cells, with or without CD155 and/or TIGIT mAb blockade (n = 3). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (E) Density plots depicting CD16, 4-1BB and LAG-3 expression on NK cells co-cultured with GBM43 cells, with or without CD155 and/or TIGIT mAb blockade. Levels of proinflammatory cytokines (F) IFN-γ, TNF-α, IL-8, IL-1β (G) IL-10, IL-5, GM-CSF, and IL-4 in cell supernatant of NK cells co-cultured with GBM43-WT cells, with or without CD155 and/or TIGIT mAb blockade (n = 3). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (H) Change in expression (measured as MFI fold over isotype control) of 4-1BB on TIGIT KO and WT NK cells generated via CRISPR/Cas9 editing in the absence and presence of GBM43 cells (incubated as co-culture at an E:T 2.5:1). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. Data are represented as mean ± SEM. ∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001 .

Article Snippet: Antibodies used for flow cytometry and cell sorting were CD3-PeCy7 (BD, UCHT1, Franklin Lakes, NJ), CD56-PeCy5.5 (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-APC (Biolegend, A15153G, San Diego, CA), DNAM-1-BV510 (Biolegend, 11A8, San Diego, CA), Sytox Green (Thermo Fisher Scientific, Franklin Lakes, NJ), Sytox Blue (Thermo Fisher Scientific, Waltham, MA), CD16-BUV395 (BD, 3G8, Franklin Lakes, NJ), NKG2A-FITC (Miltenyi Biotec, REA110, Gaithersburg, MD), CD158e1-BV421 (BD Biosciences, DX9, Franklin Lakes, NJ), PD-1-BV510 (Biolegend, NAT105, San Diego, CA), NKG2D-BV605 (Biolegend, 1D11, San Diego, CA), CD57-BV605 (Biolegend, QA17A04, San Diego, CA), CD69-BV650 (Biolegend, FN50, San Diego, CA), LAG-3-BV650 (Biolegend, 11C3C65, San Diego, CA), NKp30-BV711 (Biolegend, P30-15, San Diego, CA), TIM-3-BV711 (Biolegend, F38-2E2, San Diego, CA), A2AR-PE (R&D Systems, 599717, Minneapolis, MN), CD94-PE (Biolegend, DX22, San Diego, CA), CD158b-APC/Fire750 (Biolegend, DX27, San Diego, CA), 41BB-APC/Fire750 (Biolegend, 4B4-1, San Diego, CA), and KLRG1-APC (Biolegend, SA231A2, San Diego, CA), CD96-PE (Biolegend, NK92.39, San Diego, CA), NKp46-BV785 (Biolegend, 9E2, San Diego, CA), CD56-APC (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-BV421 (Biolegend, A15153G), IFN-γ-PerCP-Cy5.5 (Biolegend, 4S.B3, San Diego, CA), CD107a-PE (Biolegend, H4A3, San Diego, CA), m4-1BB-APC (Thermo Fisher Scientific, 17B5, Waltham, MA), mCD16/32-BV421 (Biolegend, 93, San Diego, CA), mNK1.1-BV510 (Biolegend, PK136, San Diego, CA), mCD11b-BV605 (Biolegend, M1/70, San Diego, CA), mCD27-BV650 (Biolegend, LG.3A10, San Diego, CA), mCD3-BV711 (Biolegend, 145-2C11, San Diego, CA), mLAG-3-BV785 (Thermo Fisher Scientific, C9B7W, Waltham, MA), mTIGIT-PE-Cy7 (Biolegend, 1G9, San Diego, CA), mDNAM-1-APC-Fire 750 (Biolegend, 10E5, San Diego, CA), and mCD96-PE (Biolegend, 3.3, San Diego, CA).

Techniques: Expressing, Cell Culture, Control, Generated, CRISPR, Incubation, Co-Culture Assay

Journal: iScience

Article Title: TIGIT contributes to the regulation of 4-1BB and does not define NK cell dysfunction in glioblastoma

doi: 10.1016/j.isci.2023.108353

Figure Lengend Snippet:

Article Snippet: Antibodies used for flow cytometry and cell sorting were CD3-PeCy7 (BD, UCHT1, Franklin Lakes, NJ), CD56-PeCy5.5 (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-APC (Biolegend, A15153G, San Diego, CA), DNAM-1-BV510 (Biolegend, 11A8, San Diego, CA), Sytox Green (Thermo Fisher Scientific, Franklin Lakes, NJ), Sytox Blue (Thermo Fisher Scientific, Waltham, MA), CD16-BUV395 (BD, 3G8, Franklin Lakes, NJ), NKG2A-FITC (Miltenyi Biotec, REA110, Gaithersburg, MD), CD158e1-BV421 (BD Biosciences, DX9, Franklin Lakes, NJ), PD-1-BV510 (Biolegend, NAT105, San Diego, CA), NKG2D-BV605 (Biolegend, 1D11, San Diego, CA), CD57-BV605 (Biolegend, QA17A04, San Diego, CA), CD69-BV650 (Biolegend, FN50, San Diego, CA), LAG-3-BV650 (Biolegend, 11C3C65, San Diego, CA), NKp30-BV711 (Biolegend, P30-15, San Diego, CA), TIM-3-BV711 (Biolegend, F38-2E2, San Diego, CA), A2AR-PE (R&D Systems, 599717, Minneapolis, MN), CD94-PE (Biolegend, DX22, San Diego, CA), CD158b-APC/Fire750 (Biolegend, DX27, San Diego, CA), 41BB-APC/Fire750 (Biolegend, 4B4-1, San Diego, CA), and KLRG1-APC (Biolegend, SA231A2, San Diego, CA), CD96-PE (Biolegend, NK92.39, San Diego, CA), NKp46-BV785 (Biolegend, 9E2, San Diego, CA), CD56-APC (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-BV421 (Biolegend, A15153G), IFN-γ-PerCP-Cy5.5 (Biolegend, 4S.B3, San Diego, CA), CD107a-PE (Biolegend, H4A3, San Diego, CA), m4-1BB-APC (Thermo Fisher Scientific, 17B5, Waltham, MA), mCD16/32-BV421 (Biolegend, 93, San Diego, CA), mNK1.1-BV510 (Biolegend, PK136, San Diego, CA), mCD11b-BV605 (Biolegend, M1/70, San Diego, CA), mCD27-BV650 (Biolegend, LG.3A10, San Diego, CA), mCD3-BV711 (Biolegend, 145-2C11, San Diego, CA), mLAG-3-BV785 (Thermo Fisher Scientific, C9B7W, Waltham, MA), mTIGIT-PE-Cy7 (Biolegend, 1G9, San Diego, CA), mDNAM-1-APC-Fire 750 (Biolegend, 10E5, San Diego, CA), and mCD96-PE (Biolegend, 3.3, San Diego, CA).

Techniques: Recombinant, Lactate Dehydrogenase Assay, Cell Based Assay, Gene Knockout, Enzyme-linked Immunosorbent Assay, shRNA, Sequencing, Software, Gene Expression