Journal: JCI Insight

Article Title: Collectin-11 promotes cancer cell proliferation and tumor growth

doi: 10.1172/jci.insight.159452

Figure Lengend Snippet: Tumors excised from Colec11 +/+ (WT) or Colec11 –/– (KO) mice (d14) were used for analysis of tumor cell proliferation, angiogenesis, and CL-11 expression. ( A ) Representative microscopy images of immunochemical staining for Ki67 (green)/CD45 (red)/DAPI (blue) in tumor core area. The bottom panel show higher-magnification images of boxed regions in the panel above. Scale bars: 50 μm (top panel), 25 μm (bottom panel). ( B ) Quantification of Ki67 + /CD45 – cells corresponding to the 2 groups of mice in A . Data were analyzed by unpaired t test ( n = 12; 3 mice, 4 image regions from each tumor section each mouse). ( C ) Representative microscopy images of immunochemical staining for CD31 (green)/DAPI (blue) or VWF (green)/DAPI (blue) in tumor core area. Scale bar: 50 μm. ( D ) Quantification of CD31- or VWF-stained areas corresponding to the WT and KO mice in C . Data were analyzed by unpaired t test ( n = 18; 3 mice, 6 image regions from each tumor section each mouse). ** P < 0.01; **** P < 0.0001.

Article Snippet: The following antibodies were used in immunochemical staining: monoclonal rat anti–mouse CD45 (103120), CD11b (101202), and F4/80 (123102) (all from BioLegend); rat anti–mouse CD31(557355, BD Biosciences); rabbit anti-mouse CD3 (ab237721), CD8α (ab217344), VWF (ab6994), and rabbit anti–human COLEC11 (ab238585) (all from Abcam); rabbit anti–mouse H2-Ab1 (A18658, ABclonal); rat anti–mouse CD68 (FA11) (Bio-Rad); goat anti–mouse CD206(AF2535) (from R&D systems); rabbit anti–mouse Ki67 (9129, Cell Signaling Technology); Alexa Fluor 488 goat anti–rat IgG (catalog 405418), Alexa Fluor 555 goat anti–rat IgG (catalog 405420), and Alexa Fluor 647 donkey anti–rabbit IgG (catalog 406414) (all from BioLegend); and Alexa Fluor 488 goat anti–rabbit IgG (catalog 4412) and Alexa Flour 594 goat anti–rabbit IgG (catalog 8889) (both from Cell Signaling Technology).

Techniques: Expressing, Microscopy, Staining

Journal: JCI Insight

Article Title: Collectin-11 promotes cancer cell proliferation and tumor growth

doi: 10.1172/jci.insight.159452

Figure Lengend Snippet: ( A – G ) WT mice that received treatment with saline or L-fucose (Fuc) daily by i.p. tumors were excised at d14 and used for analysis. ( A ) Representative images of tumors ( n = 3 independent experiments). Scale bar: 5 μm. ( B ) Tumor weights. Data were analyzed by unpaired t test ( n = 14 mice/group, pooled from 3 experiments). ( C ) Representative microscopy images of immunochemical staining for Ki67 (green)/CD45 (red)/DAPI (blue) in tumor core area. Scale bar: 25 μm. ( D ) Quantification of Ki67 + /CD45 – cells. Data are analyzed by unpaired t test ( n = 12; 4 mice, 3 fields from each tumor section). ( E ) Representative microscopy images of immunochemical staining for CD31 (red)/DAPI (blue) in tumor core area. Scale bar: 50 μm. ( F ) Quantification of CD31. Data were analyzed by unpaired t test ( n = 20; 4 mice, 5 fields from each tumor). ( G ) qPCR analysis of intratumor cytokines/chemokines. Data were analyzed by unpaired t test ( n = 5 mice/group). ( H and I ) Tumors were excised at d14 from Colec11 +/+ (WT) or Colec11 –/– (KO) mice that received treatment with saline or Fuc daily. ( H ) Images of tumors. Scale bar: 5 μm. ( I ) Tumor weights. Data were analyzed by 1-way ANOVA with Tukey’s multiple-comparison test ( n = 5 mice/group). H and I are representative of 2 independent experiments. ( J and K ) Representative microscopy images and flow cytometry histogram showing CL-11 (red) binding to B16 cells (DAPI) was blocked by Fuc ( n = 3). Scale bar: 10 μm. ( L ) EdU proliferation assay in B16 melanoma cells following the treatment with rCL-11 or rCL-11 plus Fuc for 72 hours. Cell proliferation rate and total cell numbers were shown; each dot represents the average value of percentage of EdU + cells, or cell numbers calculated from 5 image fields at 100 × for each sample. Data were analyzed by paired t test (4 independent experiments). * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Article Snippet: The following antibodies were used in immunochemical staining: monoclonal rat anti–mouse CD45 (103120), CD11b (101202), and F4/80 (123102) (all from BioLegend); rat anti–mouse CD31(557355, BD Biosciences); rabbit anti-mouse CD3 (ab237721), CD8α (ab217344), VWF (ab6994), and rabbit anti–human COLEC11 (ab238585) (all from Abcam); rabbit anti–mouse H2-Ab1 (A18658, ABclonal); rat anti–mouse CD68 (FA11) (Bio-Rad); goat anti–mouse CD206(AF2535) (from R&D systems); rabbit anti–mouse Ki67 (9129, Cell Signaling Technology); Alexa Fluor 488 goat anti–rat IgG (catalog 405418), Alexa Fluor 555 goat anti–rat IgG (catalog 405420), and Alexa Fluor 647 donkey anti–rabbit IgG (catalog 406414) (all from BioLegend); and Alexa Fluor 488 goat anti–rabbit IgG (catalog 4412) and Alexa Flour 594 goat anti–rabbit IgG (catalog 8889) (both from Cell Signaling Technology).

Techniques: Microscopy, Staining, Flow Cytometry, Binding Assay, Proliferation Assay

Journal: Brain

Article Title: Predicting progression to Alzheimer’s disease with human hippocampal progenitors exposed to serum

doi: 10.1093/brain/awac472

Figure Lengend Snippet: Outline of the experimental design and sample collection . ( A ) HN is regulated by a complex microenvironment, composed of blood vessels and various cell types such as hippocampal neural stem cells (NSCs), neural progenitor cells (NPCs), neuroblasts, immature/mature granule cells (i.e. neurons), microglia and astrocytes (i.e. neurogenic niche). Blood-derived factors, delivered to the niche by its rich vasculature, play a fundamental role in modulating HN. We aimed to model the role of systemic environment on the hippocampal neurogenic process during Alzheimer’s disease progression, by treating a human HPC line with 1% longitudinal serum at different stages of HN (i.e. proliferation and differentiation of HPCs). ( B ) Longitudinal serum samples were collected during annual follow-up visits from 56 participants diagnosed with MCI at baseline ( n = 38 converted to Alzheimer’s disease, n = 18 remained cognitively stable). A total of 338 samples were analysed. For each sample, three biological replicates (cells of three different passage numbers) were used and for each biological replicate, there were technical triplicates. ( C ) Neurogenic markers measured in the proliferation and differentiation phases of the assay are outlined ( top ) and representative images of cells positive for Ki67, CC3, Nestin, Sox2, MAP2 and DCX are shown ( bottom ). Scale bar = 100 μm. ( D ) An overview of the proliferation and differentiation phases in the assay. HPC0A07/03C cell line was treated with 1% serum samples from MCI converters and non-converters collected at sequential follow-up visits. Proliferation medium included EGF, bFGF and 4-OHT. Differentiation medium lacked these factors. To analyse serum effects on proliferation, 24 h after seeding, medium was replaced with proliferation medium supplemented with 1% serum. Cells were fixed 48 h later and subjected to ICC. To analyse the effects of serum on differentiation, at the end of proliferation phase, medium was replaced with differentiation medium supplemented with 1% serum. Cells were fixed 7 days later and subjected to ICC. c = converters; nc = non-converters. Panel A was created with BioRender.com.

Article Snippet: Mouse monoclonal anti-Ki67 (Cell Signaling, #9449, 1:800) was used to assess proliferation (i.e. HPCs in active phases of the cell cycle such as G1, S, G2 and mitosis); rabbit monoclonal anti-CC3 (Cell Signaling, #9664, 1:500) to assess apoptotic cell death; mouse monoclonal anti-Nestin clone 10C2 (Sigma Aldrich, #MAB5326, 1:1000) and rabbit polyclonal anti-Sox2 (SRY-Box Transcription Factor 2) (Sigma Aldrich, #AB5603, 1:1000) to assess neural stemcellness; rabbit polyclonal anti-DCX (Abcam, #ab18723, 1:500) for neuroblasts and mouse monoclonal anti-MAP2 (Abcam, #ab11267, 1:500) for mature neurons.

Techniques: Derivative Assay

Journal: Brain

Article Title: Predicting progression to Alzheimer’s disease with human hippocampal progenitors exposed to serum

doi: 10.1093/brain/awac472

Figure Lengend Snippet: Exposure to 1% serum from MCI converters leads to decreased proliferation, increased cell death and increased neurogenesis . ( A ) Representative images of proliferation phase cells treated with serum from the same individual. Left (MCI panel): serum sample from 1 year before conversion. Right (AD panel): serum sample taken at the time of conversion to Alzheimer’s disease (AD). Nuclei are stained with DAPI. Ki67 and CC3 were used to label proliferating and apoptotic cells, respectively. ( B – D ) Modelled trajectories (with 95% CIs) of linear mixed-effects regression models fitted to the proliferation phase data. Time of conversion to Alzheimer’s disease was assigned 0, and the number of years before conversion were assigned negative values (i.e. 1 year before conversion is −1). Longitudinal serum samples from MCI converters increased average cell number ( B ), decreased proliferation (% Ki67 + ) ( C ) and increased apoptotic cell death (% CC3 + ) ( D ). Slopes (β coefficient estimates) are indicated within the plots. ( E ) Representative images of differentiation phase cells treated with serum from the same individual. Left (MCI panel): serum sample from 1 year before conversion. Right (AD panel): serum sample taken at the time of conversion to AD. Nuclei are stained with DAPI. DCX and MAP2 were used to label neuroblasts and mature neurons, respectively. ( F – H ) Modelled trajectories (with 95% CIs) of linear mixed-effects regression models fitted to the differentiation phase data. Time of conversion to Alzheimer’s disease was assigned 0, and the number of years before conversion were assigned negative values (i.e. 1 year before conversion is −1). Longitudinal serum samples from MCI converters increased average cell number ( F ), neuroblasts (% DCX + ) ( G ) and mature neurons (% MAP2 + ) ( H ). Slopes (β coefficient estimates) are indicated within the plots. Scale bar = 100 μm.

Article Snippet: Mouse monoclonal anti-Ki67 (Cell Signaling, #9449, 1:800) was used to assess proliferation (i.e. HPCs in active phases of the cell cycle such as G1, S, G2 and mitosis); rabbit monoclonal anti-CC3 (Cell Signaling, #9664, 1:500) to assess apoptotic cell death; mouse monoclonal anti-Nestin clone 10C2 (Sigma Aldrich, #MAB5326, 1:1000) and rabbit polyclonal anti-Sox2 (SRY-Box Transcription Factor 2) (Sigma Aldrich, #AB5603, 1:1000) to assess neural stemcellness; rabbit polyclonal anti-DCX (Abcam, #ab18723, 1:500) for neuroblasts and mouse monoclonal anti-MAP2 (Abcam, #ab11267, 1:500) for mature neurons.

Techniques: Staining

Journal: Brain

Article Title: Predicting progression to Alzheimer’s disease with human hippocampal progenitors exposed to serum

doi: 10.1093/brain/awac472

Figure Lengend Snippet: Exposure to 1% serum from MCI converters leads to differential changes in average cell number, proliferation and neuronal differentiation compared to non-converters . ( A and B ) Modelled trajectories (with 95% CIs) of linear mixed-effects regression models fitted to the proliferation phase data. Time to last visit (for non-converters) and time of conversion to Alzheimer’s disease (for converters) was assigned 0, and the number of years before that were assigned negative values (i.e. 1 year before conversion is −1). Longitudinal serum samples from MCI converters (turquoise) predicted overall higher average cell number ( A ) and proliferation (% Ki67 + ) ( B ) compared to non-converters (red). Slopes (β coefficient estimates) are indicated within the plots. ( C and D ) Modelled trajectories (with 95% CIs) of linear mixed-effects regression models fitted to the differentiation phase data. Time to last visit (for non-converters) and time of conversion to Alzheimer’s disease (for converters) was assigned 0, and the number of years before that were assigned negative values (i.e. 1 year before conversion is −1). Longitudinal serum samples from MCI converters (turquoise) predicted overall lower average cell number ( C ) and higher neuronal differentiation (% MAP2 + ) ( D ) compared to non-converters (red). Slopes (β coefficient estimates) are indicated within the plots.

Article Snippet: Mouse monoclonal anti-Ki67 (Cell Signaling, #9449, 1:800) was used to assess proliferation (i.e. HPCs in active phases of the cell cycle such as G1, S, G2 and mitosis); rabbit monoclonal anti-CC3 (Cell Signaling, #9664, 1:500) to assess apoptotic cell death; mouse monoclonal anti-Nestin clone 10C2 (Sigma Aldrich, #MAB5326, 1:1000) and rabbit polyclonal anti-Sox2 (SRY-Box Transcription Factor 2) (Sigma Aldrich, #AB5603, 1:1000) to assess neural stemcellness; rabbit polyclonal anti-DCX (Abcam, #ab18723, 1:500) for neuroblasts and mouse monoclonal anti-MAP2 (Abcam, #ab11267, 1:500) for mature neurons.

Techniques:

Journal: Brain

Article Title: Predicting progression to Alzheimer’s disease with human hippocampal progenitors exposed to serum

doi: 10.1093/brain/awac472

Figure Lengend Snippet: Predictors of progression to Alzheimer’s disease from stepwise logistic regression analysis

Article Snippet: Mouse monoclonal anti-Ki67 (Cell Signaling, #9449, 1:800) was used to assess proliferation (i.e. HPCs in active phases of the cell cycle such as G1, S, G2 and mitosis); rabbit monoclonal anti-CC3 (Cell Signaling, #9664, 1:500) to assess apoptotic cell death; mouse monoclonal anti-Nestin clone 10C2 (Sigma Aldrich, #MAB5326, 1:1000) and rabbit polyclonal anti-Sox2 (SRY-Box Transcription Factor 2) (Sigma Aldrich, #AB5603, 1:1000) to assess neural stemcellness; rabbit polyclonal anti-DCX (Abcam, #ab18723, 1:500) for neuroblasts and mouse monoclonal anti-MAP2 (Abcam, #ab11267, 1:500) for mature neurons.

Techniques:

Journal: Brain

Article Title: Predicting progression to Alzheimer’s disease with human hippocampal progenitors exposed to serum

doi: 10.1093/brain/awac472

Figure Lengend Snippet: Average cell number and Ki67 during proliferation, and CC3 during differentiation, combined with education in years can predict progression from MCI to Alzheimer’s disease . ( A ) ROC curve for the logistic regression model predicting progression from MCI to Alzheimer’s disease. AUC for the model, an indicator of the discriminative performance, is 0.967. Sensitivity = 92.1%, specificity = 94.1%, positive predictive value = 97.2% and negative predictive value = 84.2%. ( B ) ROC curves for each four individual predictors included in the full logistic regression model. ( C ) Odds ratios for the four predictors. Blue and red indicate >1 and <1, respectively. * P < 0.05. *** P < 0.001. ( D ) ROC curve for the cross-validated logistic regression model predicting progression to Alzheimer’s disease. Internal validation of the model was done with repeated k -fold cross-validation ( k = 5, 1000 repeats) using SMVs (radial basis function kernel). AUC = 0.93, sensitivity 90.3% and specificity 79.0%.

Article Snippet: Mouse monoclonal anti-Ki67 (Cell Signaling, #9449, 1:800) was used to assess proliferation (i.e. HPCs in active phases of the cell cycle such as G1, S, G2 and mitosis); rabbit monoclonal anti-CC3 (Cell Signaling, #9664, 1:500) to assess apoptotic cell death; mouse monoclonal anti-Nestin clone 10C2 (Sigma Aldrich, #MAB5326, 1:1000) and rabbit polyclonal anti-Sox2 (SRY-Box Transcription Factor 2) (Sigma Aldrich, #AB5603, 1:1000) to assess neural stemcellness; rabbit polyclonal anti-DCX (Abcam, #ab18723, 1:500) for neuroblasts and mouse monoclonal anti-MAP2 (Abcam, #ab11267, 1:500) for mature neurons.

Techniques:

Journal: iScience

Article Title: Tumor-suppressive role of the musculoaponeurotic fibrosarcoma gene in colorectal cancer

doi: 10.1016/j.isci.2023.106478

Figure Lengend Snippet: Key resources table

Article Snippet: They were incubated overnight at 4°C with anti-mouse monoclonal rabbit antibody against Ki-67 (#12202, Cell Signaling Technology) at dilutions of 1:200.

Techniques: Recombinant, Transfection, DNA Extraction, CCK-8 Assay, SYBR Green Assay, Plasmid Preparation, Amplification, Bradford Protein Assay, Microarray, RNA Sequencing Assay, Negative Control, Expressing, Sequencing, Software, CRISPR

Journal: bioRxiv

Article Title: Machine-learning and mechanistic modeling of primary and metastatic breast cancer growth after neoadjuvant targeted therapy

doi: 10.1101/2023.02.22.529613

Figure Lengend Snippet: (A-C) Examples of molecular and cellular biomarker analysis. (A) Proliferating endothelial cell identification by immunofluorescence. Tissue sections from resected tumors were stained with antibodies against mouse CD31 (red) and mouse Ki67 (green) and counterstained with DAPI (blue). Single channel and merged images are shown. Yellow arrows show proliferating endothelial cells which were counted manually. (B) Myeloid-Derived Suppressor Cells (MDSC) quantification by flow cytometry. Whole blood was stained with anti-mouse antibodies for CD45, CD11b, and Gr1. After selection of CD45 positive cells MDSCs were analyzed based on CD11b and Gr1 levels. Monocytic-MDSC (M-MDSC) are CD11b+/Gr1high and granulocytic-MDSC (G-MDSC) are CD1 1 b+/Gr1 Medium. Examples of MDSC in untreated and treated animals are shown. ( C) CTC quantification by flow cytometry. CTCs for xenografts were identified using anti-human HLA. Blood was stained with anti-mouse CD45 and anti-human HLA. Blood and LM2-4 cell samples were overlaid in a dot plot to identify and create the gates for CTCs. Once the gates were created CTC were identified in blood of tumor-bearing mice. (D) Pearson correlation coefficients between biomarkers. Blue (resp. red) color indicates positive (resp. negative) correlation, with size of the circle proportional to the R 2 correlation coefficient. *p<0.05, **p<0.01, ***p<0.001 . (E) Univariate correlations between the biomarkers and the mathematical parameters. DT = doubling time. (F) Cross-validated Root Mean Square Error (RMSE) across different machine learning regression models (see ) utilizing the values of the biomarkers for predicting log( μ ). To assess the significance of the covariate in the models, RMSE were compared against the value of this metric obtained using a only-intercept model. Bars are 95% confidence intervals. Shown in red is the model with lowest RMSE. PLS = Partial Least Squares. SVM = Support Vector Machines (G) Cross validated R2 with 95% confidence intervals. (H) Predictions versus observations for the conditional random forest algorithm.

Article Snippet: Non-specific binding was blocked with 2% BSA in PBS, followed by staining with antibody mix containing rabbit anti-mouse Ki67 antibody (Cell Signaling Technologies; 12202) and rat anti-mouse CD31 antibody (Dianova; DIA-310).

Techniques: Biomarker Assay, Immunofluorescence, Staining, Derivative Assay, Flow Cytometry, Selection, Plasmid Preparation

Journal: bioRxiv

Article Title: Cooperative super-enhancer inactivation caused by heterozygous loss of CREBBP and KMT2D skews B cell fate decisions and yields T cell-depleted lymphomas

doi: 10.1101/2023.02.13.528351

Figure Lengend Snippet: A , Odds ratio (OR) analysis of CREBBP and KMT2D mutations among EZB/Cluster3 DLBCL (n=319) and FL_all (merged FL datasets, n=478) cohorts of patients. The p values were calculated by Fisher’s exact test. B , Representative H&E and B220 IHC images of formalin-fixed paraffin-embedded kidney and liver sections prepared from mice euthanized at day 116 post-BMT. The scale bars represent 380 pixels. C , Representative H&E, B220 and Ki67 IHC images of formalin-fixed paraffin-embedded spleen sections from mice euthanized at day 235 post-BMT. The scale bars represent 200 pixels. D , Representative FACS plots show the gating strategy and frequency of B220 + CD38 - FAS + splenic GC B cells in mice at day 235 post-BMT. E , FACS analysis showing the relative abundance of splenic total B cells (B220 + ) normalized to total single cells at day 116 and 235 post-BMT (mean ± SD). Each dot represents a mouse (n=4 mice per genotype).

Article Snippet: For IF, the sections were stained with primary antibodies Rat Anti-Mouse B220 (BD 550286, RRID:AB_393581, 1:100 dilution) and Rabbit Anti-Mouse Ki67 (CST 12202, RRID:AB_2687446, 1:250 dilution) overnight at 4°C, followed by incubation with secondary antibodies Donkey anti-Rat IgG-AF488 (Invitrogen A21208, RRID:AB_141709, 1:500 dilution) and Donkey anti-Rabbit IgG-AF594 (Invitrogen A21207, RRID:AB_141637, 1:500 dilution) at room temperature for 1 hour.

Techniques: Formalin-fixed Paraffin-Embedded

Journal: bioRxiv

Article Title: Cooperative super-enhancer inactivation caused by heterozygous loss of CREBBP and KMT2D skews B cell fate decisions and yields T cell-depleted lymphomas

doi: 10.1101/2023.02.13.528351

Figure Lengend Snippet: A , Experimental scheme for GC characterization (results shown in B-H ). IF: immunofluorescence. B,D,H, FACS analysis showing the relative abundance (mean ± SD) of splenic ( B ) total B cells (%CD45 + with B220 + ), ( D ) GC B cells (%B220 + with CD38 - FAS + ), and ( H ) CB (%GC B with CXCR4 hi CD86 lo ) divided by CC (%GC B with CXCR4 lo CD86 hi ). Each dot represents a mouse (n=2-5 per genotype). Statistical significance was determined using ordinary one-way ANOVA followed by Tukey-Kramer’s post-test (**p < 0.01, ****p < 0.0001). C , Representative FACS plots show the gating strategy and relative frequency of splenic CD38 - FAS + GC B cells, CXCR4 hi CD86 lo CB and CXCR4 lo CD86 hi CC. E , B220 (AF488, green), Ki67 (AF594, red), and DAPI (blue) IF staining images of spleen sections. Bottom images show the zoom-in of outlined areas in top images. Scale, 1000 um (top), 200 um (bottom). F-G , Quantification of ( F ) splenic GC area (left to right: n=119, 91, 122, 95 GCs) and ( G ) relative splenic GC number normalized to spleen section area (each dot represents a mouse, mean ± SD) based on IF images as shown in ( E ). Statistical significance was determined using Kruskal-Wallis test followed by Dunn’s multiple comparisons test ( F ) or ordinary one-way ANOVA followed by Tukey-Kramer’s post-test ( G ) (ns p > 0.05, **p < 0.01, ****p < 0.0001). I , Experimental design for fitness study (results shown in J-M ). J , Representative FACS plots show gating strategy and relative frequency of the indicated splenic cell types (left to right: total B, GC B, EdU+ GC B cells). WT and CK-derived cells were separated as CD45.1/2 and CD45.2/2, respectively. K , FACS data showing the proportion of WT (CD45.1/2, black dots) and CK (CD45.2/2, red dots)-derived splenic total B cells (B220 + ) in recipients. Each pair of connected dots represents a mouse (n=7). P value was calculated by two-tailed paired t test (ns p > 0.05). L-M , FACS data showing the ratio of WT (CD45.1/2, black dots) or CK (CD45.2/2, red dots)-derived GC B cell percentage to their respective parental total B cell percentage ( L ) or EdU+ GC B cell percentage to their respective parental total GC B cell percentage ( M ). Each pair of connected dots represents a mouse (n=7). P value was calculated by two-tailed paired t test (ns p > 0.05, ****p < 0.0001).

Article Snippet: For IF, the sections were stained with primary antibodies Rat Anti-Mouse B220 (BD 550286, RRID:AB_393581, 1:100 dilution) and Rabbit Anti-Mouse Ki67 (CST 12202, RRID:AB_2687446, 1:250 dilution) overnight at 4°C, followed by incubation with secondary antibodies Donkey anti-Rat IgG-AF488 (Invitrogen A21208, RRID:AB_141709, 1:500 dilution) and Donkey anti-Rabbit IgG-AF594 (Invitrogen A21207, RRID:AB_141637, 1:500 dilution) at room temperature for 1 hour.

Techniques: Immunofluorescence, Staining, Derivative Assay, Two Tailed Test

Journal: Cell Reports Medicine

Article Title: Single-cell sequencing identifies differentiation-related markers for molecular classification and recurrence prediction of PitNET

doi: 10.1016/j.xcrm.2023.100934

Figure Lengend Snippet: The recurrence prediction capability of Ki-67 index for PitNET was stratified by the new differentiation classification (A) Cox regression survival analyses reveal the recurrence predictive values of multiple factors in three lineages. HR, hazard ratios; N, number of patients; ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. (B–D) Kaplan-Meier PFS curves for PIT1 lineage tumors (B), silent TPIT tumors (C), and SF1 lineage tumors (D) stratified by Ki-67 index. The p value was calculated by the log rank test. (E–G) Kaplan-Meier PFS curves for patients with PIT1 lineage tumors (E), silent TPIT tumors (F), and SF1 lineage tumors (G) stratified by differentiation status and Ki-67 index. The p value was calculated by the log rank test. (H) Recurrence prediction value of differentiation status and Ki-67 index in each lineage. n.s., not significant.

Article Snippet: Mouse monoclonal anti-Ki-67 (clonal 8D5) , Cell Signaling Technology , Cat#9449; RRID: AB_2797703.

Techniques:

Journal: Cell Reports Medicine

Article Title: Single-cell sequencing identifies differentiation-related markers for molecular classification and recurrence prediction of PitNET

doi: 10.1016/j.xcrm.2023.100934

Figure Lengend Snippet:

Article Snippet: Mouse monoclonal anti-Ki-67 (clonal 8D5) , Cell Signaling Technology , Cat#9449; RRID: AB_2797703.

Techniques: Multiplex Assay, Software

Journal: Cancers

Article Title: Genome-Wide Analysis of lncRNA-mRNA Co-Expression Networks in CD133+/CD44+ Stem-like PDAC Cells

doi: 10.3390/cancers15041053

Figure Lengend Snippet: CD133+/CD44+ PDAC cells are highly oncogenic in vivo. 8×105 CD133+/CD44+, CD133−/CD44− or total (unsorted) cells derived from ( A ) MIA PaCa-2 and ( B ) PANC-1 cell lines were inoculated heterotopically into the flanks of NGS mice, and the tumor growth was monitored. CD133+/CD44+ cells show similar growth curves with total cells, whilst the sorted cells fail to grow. Tumor volumes were plotted as mean +/− SEM for each data point retrieved by two independent experiments (N = 8–10 mice/per group/experiment). ( C ) Representative immunohistochemical staining for Ki67 expression in tumors formed by MIA PaCa-2- and PANC-1-derived CD133+/CD44+ and total cells. Scale Bar = 200 μm. ( D ) The corresponding percentages of Ki67-positive cells indicate no statistically significant differences in proliferation rate between CD133+/CD44+ and total cells from both cell lines. Data were collected through three independent experiments.

Article Snippet: Cell proliferation was calculated by IHC, using a mouse anti-human mAb against Ki67 (Cell Signaling Technology, Inc., Danvers, MA, USA) (1:1000 dilution), followed by incubation with an HRP-conjugated anti-mouse secondary Ab (Sigma-Aldrich).

Techniques: In Vivo, Derivative Assay, Immunohistochemical staining, Staining, Expressing