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A new method allows the collection of circulating tumour cells (CTCs) despite their rarity; transcriptome sequencing of CTCs could allow identification of pathways involved in metastasis. WNT implicated in metastasis Circulating tumour cells (CTCs) shed from primary tumours are thought to initiate metastasis in distant organs. They are increasingly appreciated as potential biomarkers of disease progression and therapeutic response in patients with cancer. Their low frequency, in comparison with normal blood cells, means that it is a major technological challenge to characterize them fully. Daniel Haber and colleagues have optimized previous microfluidic approaches to capture CTCs from an endogenous mouse pancreatic cancer model. Using RNA sequencing, RNA transcripts enriched in CTCs compared with the primary tumours were identified. The proto-oncogene protein Wnt2 was also identified. The authors were also able to capture CTCs from patients with pancreatic cancer patients, which, like their mouse counterparts, show evidence of upregulation of WNT signalling. Circulating tumour cells (CTCs) shed into blood from primary cancers include putative precursors that initiate distal metastases 1 . Although these cells are extraordinarily rare, they may identify cellular pathways contributing to the blood-borne dissemination of cancer. Here, we adapted a microfluidic device 2 for efficient capture of CTCs from an endogenous mouse pancreatic cancer model 3 and subjected CTCs to single-molecule RNA sequencing 4 , identifying Wnt2 as a candidate gene enriched in CTCs. Expression of WNT2 in pancreatic cancer cells suppresses anoikis, enhances anchorage-independent sphere formation, and increases metastatic propensity in vivo . This effect is correlated with fibronectin upregulation and suppressed by inhibition of MAP3K7 (also known as TAK1) kinase. In humans, formation of non-adherent tumour spheres by pancreatic cancer cells is associated with upregulation of multiple WNT genes, and pancreatic CTCs revealed enrichment for WNT signalling in 5 out of 11 cases. Thus, molecular analysis of CTCs may identify candidate therapeutic targets to prevent the distal spread of cancer.

Nature 487, 510–513 (2012); doi:10.1038/nature11217 In this Letter, we omitted the following accession information. The digital gene expression matrix and the Helicos single-molecule sequence data from which it was derived are available at the NCBI GEO database with accession number GSE40176. A total of 44 samples were uploaded including 12 mouse and 32 human samples.

General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N  = 300,486; age 16–102) and find 148 genome-wide significant independent loci ( P  < 5 × 10 −8 ) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function. Cognitive function is associated with health and important life outcomes. Here, the authors perform a genome-wide association study for general cognitive function in 300,486 individuals and identify genetic loci that implicate neural and cell developmental pathways in this trait.

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.

Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but access to such data is often hindered by the lack of standards for sharing datasets. We brought together community experts in a workshop to develop guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are positive that the MIFA (Metadata, Incentives, Formats, and Accessibility) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high quality training data.

Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but access to such data is often hindered by the lack of standards for sharing datasets. We brought together community experts in a workshop to develop guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are positive that the MIFA (Metadata, Incentives, Formats, and Accessibility) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high quality training data.