- Количество слайдов: 6
Bioscience Innovation for Health and Security LA-UR: 07 -3608 Metabolic Analysis of Tumor Progression Norma Pawley Los Alamos National Laboratory Stable Isotope Cancer Cells: Chemistry & Biochemistry: Spectroscopy: Analysis Tools: Metabolism: Mass Clifford J. Freyer James. Unkefer Steven Brumby Pat J. P. Unkefer Munehiro Teshima Susan Carpenter Jason D. Gans
In vitro Tumor Model: Understanding the Components Exponential SEM Image of Tumor Spheroid ~250, 000 cells/ spheroid Plateau Normal Rat Fibroblasts c-myc transfection Fraction 1 Fraction 2 Fraction 3 Fraction 4 Necrosis 600 0 Distance from Surface (µm) Necrotic Unique Mass Values “Normal” (Immortalized) Tumorigenic E P Rat 1 Immortalized h-ras E transfection P Rat 1 -T 1 Cell Type (modeline): Small. Tumorigenic μm) Spheroids (142 Large Spheroids (1318 μm)
Understanding the Components: Methods – Analysis For each data collection: 5 x In-house software for automated, LT high-throughput analysis of FT accurate mass data Þ 240 spectra (not including blanks, 3 replicate internal standards, QC, etc. ) Positive Mode 1) Filter and Identify Peaks “Normal” Tumorigenic -- hundreds of compounds E per spectrum. E injections LT FT 2) Match Peaks across Samples LT (retention time alignment) FT 3) Fill in Missing Peak Data LT 100, 000 – 1, 000 P P FT 4) Extract Peak Statistics compounds per data (intensity, reproducibility, etc. ) LT collection exceeds 3 replicate Negative Mode reasonable manual analysis injections FT 5) Identify Data Trends and Patterns LT FT
Understanding the Components: Initial Results – Sanity Check Hundreds of compounds per spectrum Assess Glycolytic Phenotype: Energy Charge: – in process of assigning. In the meantime, we can look for known compounds 6 -phosphate Glucose/Glucose and assess our results with respect to current body of knowledge. ratios show shift of glycolytic phenotype (Warburg effect) for tumorigenic cells. – Energy charge consistent between normal and tumorigenic cell lines From: Gatenby and Gillies, – Energy charge is lower in exponential cells than in plateau (2004), Nature Reviews Cancer Vol 4, p. 891 -899
Understanding the Spheroid Tumor Model Initial Results – presence (blue) vs. absence (white) Plateau Exponential Spheroid Lactate Pyruvate RAT 1 Alanine (Normal) Glutamine Glutamate Glucose 6 -phosphate 82 μm 112 μm Lactate Pyruvate Alanine RAT 1 T 1 (Tumorigenic) Glutamine Glutamate Glucose 6 -phosphate
Conclusions – We can observe differences in metabolic phenotype between normal and tumorigenic cells. – ‘Classic’ differences between metabolic phenotype of normal and tumorigenic cells (use of glucose and glutamine, glycolytic phenotype) are consistent with literature. – Exponential growth states look similar between normal and tumorigenic cells, but final states (plateau) differ significantly. – Effects of microenvironment have greater impact on energetic fingerprint than does cell type.