Organogenetic transcriptomes of the Drosophila embryo at single cell resolution

To gain insight into the transcription programs activated during the formation of Drosophila larval structures, we carried out single cell RNA sequencing during two periods of Drosophila embryogenesis: stages 10-12, when most organs are first specified and initiate morphological and physiological specialization; and stages 13-16, when organs achieve their final mature architectures and begin to function. Our data confirm previous findings with regards to functional specialization of some organs - the salivary gland and trachea - and clarify the embryonic functions of another - the plasmatocytes. We also identify two early developmental trajectories in germ cells and uncover a potential role for proteolysis during germline stem cell specialization. We identify the likely cell type of origin for key components of the Drosophila matrisome and several commonly used Drosophila embryonic cell culture lines. Finally, we compare our findings with other recent related studies and with other modalities for identifying tissue-specific gene expression patterns. These data provide a useful community resource for identifying many new players in tissue-specific morphogenesis and functional specialization of developing organs.

A systematic comparison of computational methods for expression forecasting

Due to the abundance of single cell RNA-seq data, a number of methods for predicting expression after perturbation have recently been published. Expression prediction methods are enticing because they promise to answer pressing questions in fields ranging from developmental genetics to cell fate engineering and because they are faster, cheaper, and higher-throughput than their experimental counterparts. However, the absolute and relative accuracy of these methods is poorly characterized, limiting their informed use, their improvement, and the interpretation of their predictions. To address these issues, we created a benchmarking platform that combines a panel of large-scale perturbation datasets with an expression forecasting software engine that encompasses or interfaces to current methods. We used our platform to systematically assess methods, parameters, and sources of auxiliary data. We found that uninformed baseline predictions, which were not always included in prior evaluations, yielded the same or better mean absolute error than benchmarked methods in all test cases. These results cast doubt on the ability of current expression forecasting methods to provide mechanistic insights or to rank hypotheses for experimental follow-up. However, given the rapid pace of innovation in the field, new approaches may yield more accurate expression predictions. Our platform will serve as a neutral benchmark to improve methods and to identify contexts in which expression prediction can succeed.

Variants regulating ZBTB4 are associated with age-at-onset of Alzheimer's disease

The identification of novel genetic modifiers of age-at-onset (AAO) of Alzheimer's disease (AD) could advance our understanding of AD and provide novel therapeutic targets. A previous genome scan for modifiers of AAO among families affected by early-onset AD caused by the PSEN2 N141I variant identified 2 loci with significant evidence for linkage: 1q23.3 and 17p13.2. Here, we describe the fine-mapping of these 2 linkage regions, and test for replication in 6 independent datasets. By fine-mapping these linkage signals in a single large family, we reduced the linkage regions to 11% their original size and nominated 54 candidate variants. Among the 11 variants associated with AAO of AD in a larger sample of Germans from Russia, the strongest evidence implicated promoter variants influencing NCSTN on 1q23.3 and ZBTB4 on 17p13.2. The association between ZBTB4 and AAO of AD was replicated by multiple variants in independent, trans-ethnic datasets. Our results show association between AAO of AD and both ZBTB4 and NCSTN. ZBTB4 is a transcriptional repressor that regulates the cell cycle, including the apoptotic response to amyloid beta, while NCSTN is part of the gamma secretase complex, known to influence amyloid beta production. These genes therefore suggest important roles for amyloid beta and cell cycle pathways in AAO of AD.