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.

Predictions about the cellular efficacy of drugs tested in vitro 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 in vivo cellular response to drugs. This might explain some large discrepancies between in vitro drug studies and drug responses observed in patients. We present a novel in vitro 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 in vitro. The Raman studies detect large panitumumab-induced differences in vitro 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, in vitro 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.

Spectral histopathology (SHP) is an emerging tool for label free annotation of tissue. While FTIR based SHP provides fast annotation of larger tissue sections, Raman based SHP is slower but achieves a 10 times higher spatial resolution as compared to FTIR. Usually NIR excitation is used for Raman measurements on biological samples. Here, for the first time 532 nm excitation is used to annotate colon tissue by Raman SHP. Excellent data quality is obtained, which resolves for example erythrocytes and lymphocytes. In addition to Raman scattering auto-fluorescence is observed. We found that this auto-fluorescence overlaps spatially with the fluorescence of antibodies against p53 used in routine immunohistochemistry in surgical pathology. This fluorescence indicates nuclei of cancer cells with mutated p53 and allows new label free assignment of cancer cells. These results open new avenues for optical diagnosis by Raman spectroscopy and autofluorescence.

The great capability of the label-free classification of tissue via vibrational spectroscopy, like Raman or infrared imaging, is shown in numerous publications (review: Diem et al., J. Biophotonics, 2013, 6, 855-886). Herein, we present a new approach, virtual staining, that improves the Raman spectral histopathology (SHP) images of colorectal cancer tissue by combining the integrated Raman intensity image in the C-H stretching region (2800-3050 cm) with the pseudo-colour Raman image. This allows the display of fine structures such as the filamentous composition of muscle tissue. The morphology of the virtually stained images is in agreement with the gold standard in medical diagnosis, the haematoxylin-eosin staining. The virtual staining image also represents the whole biochemical fingerprint, and several tissue components including carcinoma were identified automatically with high sensitivity and specificity. For fast tissue classifications, a similar approach was applied on coherent anti-Stokes Raman scattering (CARS) spectral data that is faster and therefore potentially more suitable for clinical applications.

Mutational acquired resistance is a major challenge in cancer therapy. Somatic tumours harbouring some oncogenic mutations are characterised by a high mortality rate. Surprisingly, preclinical evaluation methods do not show clearly resistance of mutated cancers to some drugs. Here, we implemented Raman spectral imaging to investigate the oncogenic mutation resistance to epidermal growth factor receptor targeting therapy. Colon cancer cells with and without oncogenic mutations such as KRAS and BRAF mutations were treated with erlotinib, an inhibitor of epidermal growth factor receptor, in order to detect the impact of these mutations on Raman spectra of the cells. Clinical studies suggested that oncogenic KRAS and BRAF mutations inhibit the response to erlotinib therapy in patients, but this effect is not observed in vitro. The Raman results indicate that erlotinib induces large spectral changes in SW-48 cells that harbour wild-type KRAS and BRAF. These spectral changes can be used as a marker of response to therapy. HT-29 cells (BRAF mutated) and SW-480 cells (KRAS mutated) display a smaller and no significant response, respectively. However, the erlotinib effect on these cells is not observed when phosphorylation of extracellular-signal-regulated kinase and AKT is monitored by Western blot, where this phosphorylation is the conventional in vitro test. Lipid droplets show a large response to erlotinib only in the case of cells harbouring wild-type KRAS and BRAF, as indicated by Raman difference spectra. This study shows the great potential of Raman spectral imaging as an in vitro tool for detecting mutational drug resistance.Electronic supplementary materialThe online version of this article (doi:10.1007/s00216-015-8875-z) contains supplementary material, which is available to authorized users.