A5046889345- Mathematical Biology Tumor Growth
- Cancer Research and Treatment
- Gene Regulatory Network Analysis
- Radiomics and Machine Learning in Medical Imaging
- Cancer Cells and Metastasis
- CRISPR and Genetic Engineering
- Cancer Genomics and Diagnostics
- Vitamin D Research Studies
- Molecular Biology Techniques and Applications
- Data Analysis with R
- Cell Image Analysis Techniques
- Molecular Communication and Nanonetworks
- Computational Physics and Python Applications
- Cancer Research and Treatments
- Telomeres, Telomerase, and Senescence
- Science, Research, and Medicine
- 3D Printing in Biomedical Research
- MicroRNA in disease regulation
- Circadian rhythm and melatonin
- RNA Interference and Gene Delivery
- Single-cell and spatial transcriptomics
- Genetics, Bioinformatics, and Biomedical Research
- Cell death mechanisms and regulation
Universidade Federal do Rio Grande do Sul
2015-2023
Although cancer is a chronic disease, most of the in vitro experiments to assess effectiveness intervention are performed hours or few days. Moreover, none available methodologies measure cell proliferation adapted provide information about growth kinetic during and after treatment. Thus, objective this work guide long-term changes population size be used mainly research. Cumulative doubling (CPD) graphs based on counting for tumor volume vivo assays were calculate four parameters: relative...
Several phenotypes that impact the capacity of cancer cells to survive and proliferate are dynamic. Here we used number in colonies as an assessment fitness devised a novel method called Dynamic Fitness Analysis (DynaFit) measure dynamics over course colony formation. DynaFit is based on variance growth rate population founder compared with different sizes. revealed cell lines, primary cells, fibroblasts under unhindered conditions Key cellular mechanisms such ERK signaling cell-cycle...
<b><i>Objective:</i></b> Glioblastomas are a kind of cancer with high resistance to treatments, requiring more efficient alternatives treatment. X-linked inhibitor apoptosis (XIAP) is highly expressed in gliomas and, due its inhibition caspases, can participate therapy. Here we test the sensitization glioma cells XIAP gene knockdown (KD) drugs used chemotherapy. <b><i>Methods:</i></b> We silenced expression U87MG glioblastoma using stable...
Senescence is a cellular state in which the cell loses its proliferative capacity, often irreversibly. Physiologically, it occurs due to limited capacity of division associated with telomere shortening, so-called replicative senescence. It can also be induced early DNA damage, oncogenic activation, oxidative stress, or damage other components (collectively named senescence). Tumor cells acquire ability bypass senescence, thus ensuring immortality, hallmark cancer. Many anti-cancer therapies,...
Abstract Background: Cancer cell culture has contributed with pivotal discoveries for cancer biology and pharmacology. However, cells in have not provided a good test method patient´s response to chemotherapy similar extent as antibiograms been useful predict antibiotics. Although the reason this is multifactorial, some very common mistakes pharmacology are made most studies that, if corrected, could improve overall results. Methods: We systematically tested key conditions such...
<div>Abstract<p>Several phenotypes that impact the capacity of cancer cells to survive and proliferate are dynamic. Here we used number in colonies as an assessment fitness devised a novel method called Dynamic Fitness Analysis (DynaFit) measure dynamics over course colony formation. DynaFit is based on variance growth rate population founder compared with different sizes. revealed cell lines, primary cells, fibroblasts under unhindered conditions Key cellular mechanisms such ERK...
<p>Video explaining the use of DynaFit Desktop app and R some applications.</p>
<p>Video describing the basic concepts of DynaFit</p>
<p>Supplementary Fig. S5  Growth characteristics of individual treated colonies. a, GR all colonies used in 1e. Mean {plus minus} SD red. b. Pairwise correlations among the three GRs. c. variance GR2 U251 glioma according to number cells CS1 or rate with 1 and 2 were different from growth rates (ANOVA, Tukey's multiple comparison post-hoc test). d, Distribution each bin for e, indicated cell lines at high low density. f, CVP g, hypothesis plot line A172wt (WT), GFP-tagged alone...
<p>Supplementary Fig. S2 DynaFit implementations. a, data collected from Supplementary S1c is given as input to the two DynaFit apps. The Python bootstrap app and R predictive modeling are based on different analytical strategies. b, Colony Variance Plot formed by c, its hypothesis plot. d, e, plot.</p>
<p>Supplementary Fig. S3Analysis of the desynchronization colonies cells expressing FastFUCCI. a, FUCCI simulation for a single, synchronized colony with 4 cells. At given time point, all in will either be green or not. b, simulating multiple gives rise to multimodal distributions cell percentage colonies. c, adding phase shift parameter allows each out sync, producing percentages other than 0% 100% observation point. d, by modifying parameters, various frequency can obtained...
<p>Supplementary Fig. S1 DynaFit principles. a, numerical representation of the rationale presented in 1a. Cells with static growth rates will produce colonies same variance rate, regardless colony size (upper scenario). Colonies containing cells dynamic present a lower as they grow number (lower b, schematic fitness experiment. Single are seeded, and is quantified between two distinct time points. c, Example experimental data obtained from b. For DynaFit, grouped by their initial...
<p>Supplementary Fig. S4 Tagging of the endogenous locus Ki67 with EYFP. Sequence coding mVenus-P2A-Neomycin was PCR amplified from eFlut-mVenus-Neomycin plasmid generously donated by Galit Lahav (Harvard University) and used to insert flanking regions about 500 nt, indicated primers. This for homologous recombination in CRISPR-Cas9 targeted C-terminal region gene.</p>
<p>Supplementary Fig. S7  Comparison of Dynafit results with growth rate. a, GR and DynaFit Cumulative Hypothesis plot b. GR2 versus CS1-GR2 c. GR3 d. CS1-GR3 untreated (green), treated only cytotoxic drugs (red) the combination epigenetic modulators (blue).</p>
<p>Supplementary Fig. S6  Growth characteristics of individual colonies. a, GR (mean {plus minus} variance) U251 glioma colonies according to the colony size at beginning analyzed during presence TMZ and b. after withdrawal TMZ. In orange, mean variance all combined c. d. untreated or TMZ-treated colonies, either including excluding that had least one event cell death, as evaluated by single-cell tracking in e. cells treated with indicated concentration cisplatin, f. CVP line 1...
<p>File describing in more detail the methods.</p>
<p>Supplementary Fig. S4 Tagging of the endogenous locus Ki67 with EYFP. Sequence coding mVenus-P2A-Neomycin was PCR amplified from eFlut-mVenus-Neomycin plasmid generously donated by Galit Lahav (Harvard University) and used to insert flanking regions about 500 nt, indicated primers. This for homologous recombination in CRISPR-Cas9 targeted C-terminal region gene.</p>
<p>Video describing the basic concepts of DynaFit</p>
<p>Supplementary Fig. S3Analysis of the desynchronization colonies cells expressing FastFUCCI. a, FUCCI simulation for a single, synchronized colony with 4 cells. At given time point, all in will either be green or not. b, simulating multiple gives rise to multimodal distributions cell percentage colonies. c, adding phase shift parameter allows each out sync, producing percentages other than 0% 100% observation point. d, by modifying parameters, various frequency can obtained...
<p>Supplementary Fig. S1 DynaFit principles. a, numerical representation of the rationale presented in 1a. Cells with static growth rates will produce colonies same variance rate, regardless colony size (upper scenario). Colonies containing cells dynamic present a lower as they grow number (lower b, schematic fitness experiment. Single are seeded, and is quantified between two distinct time points. c, Example experimental data obtained from b. For DynaFit, grouped by their initial...
<p>Supplementary Fig. S5  Growth characteristics of individual treated colonies. a, GR all colonies used in 1e. Mean {plus minus} SD red. b. Pairwise correlations among the three GRs. c. variance GR2 U251 glioma according to number cells CS1 or rate with 1 and 2 were different from growth rates (ANOVA, Tukey's multiple comparison post-hoc test). d, Distribution each bin for e, indicated cell lines at high low density. f, CVP g, hypothesis plot line A172wt (WT), GFP-tagged alone...
<p>Supplementary Fig. S6  Growth characteristics of individual colonies. a, GR (mean {plus minus} variance) U251 glioma colonies according to the colony size at beginning analyzed during presence TMZ and b. after withdrawal TMZ. In orange, mean variance all combined c. d. untreated or TMZ-treated colonies, either including excluding that had least one event cell death, as evaluated by single-cell tracking in e. cells treated with indicated concentration cisplatin, f. CVP line 1...
<p>File describing in more detail the methods.</p>
<p>Supplementary Fig. S2 DynaFit implementations. a, data collected from Supplementary S1c is given as input to the two DynaFit apps. The Python bootstrap app and R predictive modeling are based on different analytical strategies. b, Colony Variance Plot formed by c, its hypothesis plot. d, e, plot.</p>
<p>Supplementary Fig. S7  Comparison of Dynafit results with growth rate. a, GR and DynaFit Cumulative Hypothesis plot b. GR2 versus CS1-GR2 c. GR3 d. CS1-GR3 untreated (green), treated only cytotoxic drugs (red) the combination epigenetic modulators (blue).</p>