The Lage team has barely slowed down during the pandemic as evidenced by the mass production of 1.2 billion human excitatory neurons by @gretapinta and @JacquiMMartn1 to study the biological mechanisms that are affected in neuropsychiatric disease.
Lage Lab presents at the Stanley Center Program Meeting
During the Stanley Center Program Meeting this week, Yu-Han and Frederik presented an overview of Genoppi and gave a live demo to illustrate how it can facilitate QC and analysis of quantitative proteomic data. Greta gave an update on the BINe-ASD project, in which we generated protein-protein interaction networks of autism risk genes in human induced neurons to uncover biological mechanisms and pathways underlying these genes.
Yu-Han completes her T32 post-doctoral fellowship
Yu-Han completed her two-year post-doctoral fellowship in the Harvard T32 Training Program in Bioinformatics Applied to Diabetes, Obesity and Metabolism. For the first part of her fellowship, she worked on a project that combined human genetics and untargeted metaoblomics to infer causal relationships in the obesity metabolome in the Hirschhorn Lab. After joining Lage Lab, she contributed to our efforts in using cell-type-specific protein-protein interaction data to study complex diseases such as schizophrenia, autism, type 2 diabetes, and myocardial infarction. Yu-Han is looking forward to continuing these exciting projects in our group after the end of her fellowship!
Fatma’s paper on auditing machine learning applications published in Communications Biology
Fatma’s paper titled ‘Systematic auditing is essential to debiasing machine learning in biology’ has been published in Communications Biology. In this work, we developed a systematic auditing framework to uncover biases in three machine learning applications of therapeutic interest, and provided guidelines for tailoring this framework to other biomedical applications. Great work by Fatma, Nadine, and colleagues!
A new award from the Lundbeck Foundation
The Lage Lab is awarded a Collaborative Grant by the Lundbeck Foundation together with @thomas_werge, at the Institute for Biological Psychiatry, and @Kirkeby_Lab, at the University of Copenhagen. As a consortium, we will tackle the discovery of cellular mechanisms that are compromised in patients with schizophrenia. At a time when mental illness is raising growing awareness, our research will inform the much-needed discovery of new therapies to treat mental disorders.
Kasper lends his voice to Cell Systems
Kasper contributes to Cell Systems’ Voices, a piece on Leadership in cross-disciplinary research environments, where he explains that a key component of a lab’s success and positive mentorship is achieved through maintaining and encouraging communication.
Kasper to co-chair the Data Workgroup of the Convergent Neuroscience Consortium
Kasper was invited by NIMH to co-chair the Data Workgroup of the Convergent Neuroscience Consortium with Gene Yeo. The group will coordinate efforts to collect, harmonize, and share key data types (ranging from multi-omics to imaging, electrophysiology, etc.) generated by members of the consortium.
The Lage group attends the annual multi-consortium NIMH meeting
The Lage group attends the virtual NIMH Convergent Neuroscience and National Cooperative Reprogrammed Cell Research Groups (NCRCRG) meeting with presentations by @gretapinta and @yuhanhsu and a session on psychiatric data analysis and modeling moderated by Kasper and others.
Kasper interviewed by Nature News about SARS-CoV-2 genomics
Kasper was interviewed in a Nature News article that discussed the cluster of SARS-CoV-2 mutations found in farmed mink and people in Denmark. Current data suggest that the mink mutations do not allow the virus to spread more easily or make symptoms more severe in people. However, culling the animals is probably necessary as uncontrolled spread in mink could eventually lead to dangerous mutations.
A short-cut into the identification of cellular cancer dependencies
The Lage team posted its preprint of an algorithm to identify cancer dependencies directly from tumor genome data with validation using targeted CRISPR screens. Great work by @hornheiko @CFagre @anikagupta18 and a wonderful collaboration with @broadinstitute @jamesTneal: https://www.biorxiv.org/content/10.1101/2020.08.27.270520v1