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Nature Papers Describe Map of Human Cells, Genomic and Machine Learning Approach to Screen for Lung Cancer, More

Using single-cell mRNA sequencing, a team led by scientists from Zhejiang University have built a comprehensive map of the human cell landscape. As reported in Nature, the researchers generated single-cell transcriptomic data for 60 human tissue types, including both adult and fetal tissue, enabling a comparative analysis of gene expression between cell types during development. They also compared their results to similar datasets from mouse organs to uncover conserved genetic networks. The findings, the study's authors note, will be contributed to the international Human Cell Atlas initiative. GenomeWeb has more on this study, here.

A review of how genomics, human induced pluripotent stem cells (hiPSCs), and CRISPR genome editing are being used to reveal the genetic architectures of brain disease is presented in Nature Genetics this week. Investigators from the Icahn School of Medicine at Mount Sinai discuss how hiPSCs can now be used to generate all the major cell types in the brain and examine how combining hiPSC models with CRISPR systems for genomic, epigenomic, and transcriptomic engineering enables the study of combinatorial disease-relevant perturbations in cell-type-specific isogenic system. They also consider how genotype-based diagnosis and treatment could allow patients to be identified and treated before their symptoms begin, paving the way toward precision medicine.

A method that combines genomics and machine learning for non-invasive early lung cancer screening is reported in this week's Nature. A group led by Stanford University scientists optimized a sequencing-based technique for circulating tumor DNA (ctDNA) analysis to improve its ability to detect rare variants, and use it to show that ctDNA is present in most early-stage lung cancer patients prior to treatment, albeit at very low levels, and is strongly prognostic. They then use these and other findings to develop and validate a machine learning method — dubbed lung cancer likelihood in plasma, or Lung-CLiP — that can robustly discriminate early-stage lung cancer patients from risk-matched controls.