Raman-mikrospektroskopische Untersuchungen an Darmkrebsgewebe und Darmkrebszellen

Mit weltweit mehr als 1 Millionen Neuerkrankungen pro Jahr ist das kolorektale Karzinom eine der häufigsten Krebserkrankung des Menschen und somit ein wichtiges Krankheitsbild für medizinische Studien. Um die Früherkennung dieser Krebserkrankungen und damit die Überlebensrate der Patienten zu erhöhen, wurden im Rahmen dieser Arbeit mittels Raman Mikrospektroskopie die Veränderungen des Proteoms in Zellen und in Gewebe untersucht. Am Beispiel des kolorektalen Karzinoms wurde die Raman basierte Spektrale Histopathologie (SHP) gewählt, mittels welcher man zuvor definierte Gewebetypen aufgrund ihrer spektralen Ähnlichkeiten automatisiert zuordnen und somit den Mediziner bei der Diagnose unterstützen kann. Die Messungen an Darmkrebsgewebe zeigen dabei, dass die hohe Auflösung in der Raman basierten SHP von Nutzen ist, um zelluläre Strukturen, wie mobile Zellen im Gewebe aufzulösen und sogar Zellkompartimente einzelner Zellen einer Darmkrebszelllinie zu visualisiere

NearSense – Advances Towards a Silicon-Based Terahertz Near-Field Imaging Sensor for Ex Vivo Breast Tumour Identification

International audienc

Label-Free Raman Spectroscopic Imaging Monitors the Integral Physiologically Relevant Drug Responses in Cancer Cells

Predictions about the cellular efficacy of drugs tested <i>in vitro</i> are usually based on the measured responses of a few proteins or signal transduction pathways. However, cellular proteins are highly coupled in networks, and observations of single proteins may not adequately reflect the <i>in vivo</i> cellular response to drugs. This might explain some large discrepancies between <i>in vitro</i> drug studies and drug responses observed in patients. We present a novel <i>in vitro</i> marker-free approach that enables detection of cellular responses to a drug. We use Raman spectral imaging to measure the effect of the epidermal growth factor receptor (EGFR) inhibitor panitumumab on cell lines expressing wild-type Kirsten-Ras (K-Ras) and oncogenic K-Ras mutations. Oncogenic K-Ras mutation blocks the response to anti-EGFR therapy in patients, but this effect is not readily observed <i>in vitro</i>. The Raman studies detect large panitumumab-induced differences <i>in vitro</i> in cells harboring wild-type K-Ras as seen in A in red but not in cells with K-Ras mutations as seen in B; these studies reflect the observed patient outcomes. However, the effect is not observed when extracellular-signal-regulated kinase phosphorylation is monitored. The Raman spectra show for cells with wild-type K-Ras alterations based on the responses to panitumumab. The subcellular component with the largest spectral response to panitumumab was lipid droplets, but this effect was not observed when cells harbored K-Ras mutations. This study develops a noninvasive, label-free, <i>in vitro</i> vibrational spectroscopic test to determine the integral physiologically relevant drug response in cell lines. This approach opens a new field of patient-centered drug testing that could deliver superior patient therapies

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.</p

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries