Holly Giles was awarded the Joachim-Herz Add-On Fellowship for Interdisciplinary Science. The fellowship enables interdisciplinary research and qualification (e.g. stays at a research lab and participation in conferences), specialized equipment and tools (laptops, software, etc.), participation in events of the Joachim Herz Foundation and fellowship meetings. Its a € 12.500 grant to be spent over a period of two years.
Dorothee Childs, Karsten Bach, Holger Franken, Simon Anders, Nils Kurzawa, Marcus Bantscheff, Mikhail Savitski and Wolfgang Huber
Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present non-parametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP datasets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. To facilitate access to a wide range of users, a freely available software implementation of NPARC is provided.
Arne H. Smits, Frederik Ziebell, Gerard Joberty, …, Lars M. Steinmetz, Gerard Drewes and Wolfgang Huber.
Gene knockouts (KOs) are efficiently engineered through CRISPR-Cas9-induced frameshift mutations. While DNA editing efficiency is readily verified by DNA sequencing, a systematic understanding of the efficiency of protein elimination has been lacking. Here, we devised an experimental strategy combining RNA-seq and triple-stage mass spectrometry to characterize 193 genetically verified deletions targeting 136 distinct genes generated by CRISPR-induced frameshifts in HAP1 cells. We observed residual protein expression for about one third of the quantified targets, at variable levels from low to original, and identified two causal mechanisms, translation reinitiation leading to N-terminally truncated target proteins, or skipping of the edited exon leading to protein isoforms with internal sequence deletions. Detailed analysis of three truncated targets, BRD4, DNMT1 and NGLY1, revealed partial preservation of protein function. Our results imply that systematic characterization of residual protein expression or function in CRISPR-Cas9 generated KO lines is necessary for phenotype interpretation.
After completing his pharmacy studies at University College Cork and the Royal College of Surgeons in Ireland, Donnacha joined the Huber group in October 2019 for a joint PhD with the Dietrich group at the National Centre for Tumour Diseases in Heidelberg. His current research focuses on using single-cell multi-omics to understand intra-tumour heterogeneity of drug response in blood cancers.
After a quite successful traineeship in the Huber group in 2018 and 2019, Emma Dann has moved to Cambridge to start her PhD studies at the Sanger Institute, located on the Wellcome Genome Campus and affiliated to Cambridge University. She is currently doing three months rotation projects before picking a lab for the PhD project. At the moment she is working on methods for alignment of scRNA-seq and scATAC-seq data in the lab of Sarah Teichmann.
Good luck and lots of success, Emma!
The Holmes and Huber labs collaborate on developing statistical tools for large multi-layer data analyses, for integrating large, heterogeneous biological data, and for finding applications in molecular medicine. They aim to deliver tools that are easy to use by domain-scientists to analyze their own data – for instance by providing the tools in the form of R / Bioconductor packages.
Together they want to help the next generation of biologists understand the “black box” of statistics by training them in quantitative statistical methods. They have written a textbook (Modern Statistics for Modern Biology) and together, they teach a summer course (Stats 366 – Bios 221) at Stanford. They keep further developing these materials, to take up new scientific developments (e.g. new data types), new methods, or new statistical or computational ideas.