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Cuticular hydrocarbons (CHCs) have two fundamental functions in insects. They protect terrestrial insects against desiccation and serve as signaling molecules in a wide variety of chemical communication systems. It has been hypothesized that these pivotal dual traits for adaptation to both desiccation and signaling have contributed to the considerable evolutionary success of insects. CHCs have been extensively studied concerning their variation, behavioral impact, physiological properties, and chemical compositions. However, our understanding of the genetic underpinnings of CHC biosynthesis has remained limited and mostly biased towards one particular model organism (Drosophila). This rather narrow focus has hampered the establishment of a comprehensive view of CHC genetics across wider phylogenetic boundaries. This review attempts to integrate new insights and recent knowledge gained in the genetics of CHC biosynthesis, which is just beginning to incorporate work on more insect taxa beyond Drosophila. It is intended to provide a stepping stone towards a wider and more general understanding of the genetic mechanisms that gave rise to the astonishing diversity of CHC compounds across different insect taxa. Further research in this field is encouraged to aim at better discriminating conserved versus taxon-specific genetic elements underlying CHC variation. This will be instrumental in greatly expanding our knowledge of the origins and variation of genes governing the biosynthesis of these crucial phenotypic traits that have greatly impacted insect behavior, physiology, and evolution.

There is a growing demand for methods that can effectively align and compare spatial data in the absence of obvious visual correspondence. To address this challenge, we developed an interpretable cell mapping strategy based on solving a Linear Assignment Problem (LAP) where the total cost is computed by considering cells and their niches. We demonstrate that our approach outperforms other methods at capturing the spatial context of cells in synthetic and real data sets. The flexibility of our implementation enhances the interpretability of mapping and allows for accurate cell mapping across samples, technologies, resolutions, developmental and regenerative time. We show spatiotemporal decoupling of cells during development and patient level sub-populations in In Situ Mass Cytometry (IMC) cancer data sets. Our interpretable mapping approach facilitates systemic comparison and analysis of heterogeneous spatial data. We provide a flexible framework for researchers to tailor their analysis to the specific biological and research context. The alignment of heterogeneous spatial samples has become a growing challenge. Here, authors present a multi-scale, multi-context, and interpretable mapping strategy to map cells across space, time, and disease.

The prevailing concept is that gestational alloimmune liver disease (GALD) is caused by maternal antibodies targeting a currently unknown antigen on the liver of the fetus. This leads to deposition of complement on the fetal hepatocytes and death of the fetal hepatocytes and extensive liver injury. In many cases, the newborn dies. In subsequent pregnancies early treatment of the woman with intravenous immunoglobulin can be instituted, and the prognosis for the fetus will be excellent. Without treatment the prognosis can be severe. Crucial improvements of diagnosis require identification of the target antigen. For this identification, this work was based on two hypotheses: 1. The GALD antigen is exclusively expressed in the fetal liver during normal fetal life in all pregnancies; 2. The GALD antigen is an alloantigen expressed in the fetal liver with the woman being homozygous for the minor allele and the father being, most frequently, homozygous for the major allele. We used three different experimental approaches to identify the liver target antigen of maternal antibodies from women who had given birth to a baby with the clinical GALD diagnosis: 1. Immunoprecipitation of antigens from either a human liver cell line or human fetal livers by immunoprecipitation with maternal antibodies followed by mass spectrometry analysis of captured antigens; 2. Construction of a cDNA expression library from human fetal liver mRNA and screening about 1.3 million recombinants in Escherichia coli using antibodies from mothers of babies diagnosed with GALD; 3. Exome/genome sequencing of DNA from 26 presumably unrelated women who had previously given birth to a child with GALD with husband controls and supplementary HLA typing. In conclusion, using the three experimental approaches we did not identify the GALD target antigen and the exome/genome sequencing results did not support the hypothesis that the GALD antigen is an alloantigen, but the results do not yield basis for excluding that the antigen is exclusively expressed during fetal life., which is the hypothesis we favor.

In clinical applications, spatial data collected under varying conditions, time points, or patients often lack discernible structural alignment. Computational tools designed to align adjacent tissue sections are unsuited for dealing with this structural heterogeneity. There is a growing demand for methods that can effectively align and compare spatial data in the absence of obvious visual correspondence. To address this challenge, we developed an interpretable cell mapping strategy by considering spatial context at various scales. Our approach outperforms existing mapping tools in dealing with heterogeneous samples and is flexible enough to map cells across samples, technologies, resolutions, developmental and regenerative time. Using our approach, we showed spatiotemporal decoupling of cells during development. We even performed alignment for a population of spatial data from cancer patients to identify sub- populations. Our interpretable mapping approach facilitates systemic comparison and analysis of heterogeneous spatial data.

Understanding the molecular pathogenesis of MLL fusion oncoprotein (MLL-FP) leukaemia has spawned epigenetic therapies that have improved clinical outcomes in this often-incurable disease. Using genetic and pharmacological approaches, we define the individual and combined contribution of KAT6A, KAT6B and KAT7, in MLL-FP leukaemia. Whilst inhibition of KAT6A/B is efficacious in some pre-clinical models, simultaneous targeting of KAT7, with the novel inhibitor PF-9363, increases the therapeutic efficacy. KAT7 interacts with Menin and the MLL complex and is co-localised at chromatin to co-regulate the MLL-FP transcriptional program. Inhibition of KAT6/KAT7 provides an orthogonal route to targeting Menin to disable the transcriptional activity of MLL-FP. Consequently, combined inhibition rapidly evicts the MLL-FP from chromatin, potently represses oncogenic transcription and overcomes primary resistance to Menin inhibitors. Moreover, PF-9363 or genetic depletion of KAT7 can also overcome acquired genetic/non-genetic resistance to Menin inhibition. These data provide the molecular rationale for rapid clinical translation of combination therapy in MLL-FP leukaemia.

Cellular barcoding using heritable synthetic barcodes coupled to high throughput sequencing is a powerful technique for the accurate tracing of clonal lineages in a wide variety of biological contexts. Recent studies have integrated cellular barcoding with a single-cell transcriptomics readout, extending the capabilities of these lineage tracing methods to the single-cell level. However there remains a lack of scalable and standardised open-source tools to pre-process and visualise both population-level and single-cell level cellular barcoding datasets. To address these limitations, we developed BARtab, a portable and scalable Nextflow pipeline that automates upstream barcode extraction, quality control, filtering and enumeration from high throughput sequencing data; and bartools, an open-source R package that streamlines the analysis and visualisation of population and single-cell level cellular barcoding datasets. BARtab contains additional methods for the extraction and annotation of transcribed barcodes from single-cell RNA-seq and spatial transcriptomics experiments, thus extending this analytical toolbox to also support novel expressed cellular barcoding methodologies. We showcase the integrated BARtab and bartools workflow through comparison with previously published toolsets and via the analysis of exemplar bulk, single-cell, and spatial transcriptomics cellular barcoding datasets.Competing Interest StatementM.A.D. has been a member of advisory boards for GSK, CTX CRC, Storm Therapeutics, Celgene and Cambridge Epigenetix. The Dawson Laboratory is a recipient of grant funding through the emerging science fund administered through Pfizer. All other authors declare no competing interests.

Cuticular hydrocarbons (CHCs) serve two fundamental functions in insects: protection against desiccation and chemical signaling. CHC profiles can consist of dozens of different compounds and are considered a prime example for a complex trait. How the interaction of genes shapes CHC profiles, which are essential for insect survival, adaptation, and reproductive success, is still poorly understood. Here we investigate the genetic and genomic basis of CHC biosynthesis and variation in parasitoid wasps of the genus Nasonia. Taking advantage of the wasps haplo-diploid sex determination and cross-species fertility, we mapped 91 quantitative trait loci (QTL) explaining variation of a total of 43 CHCs in F2 hybrid males from interspecific crosses between three Nasonia species. To identify candidate genes, we localized orthologs of CHC biosynthesis-related genes in the Nasonia genomes. By doing so, we discovered multiple genomic regions where the location of QTL coincides with the location of CHC biosynthesis-related candidate genes. Most conspicuously, on a region on chromosome 1 close to the centromere, multiple CHC biosynthesis-related candidate genes co-localize with several QTL explaining variation in methyl-branched alkanes. The genetic underpinnings behind this compound class are not well understood so far, despite their high potential for encoding chemical information as well as their prevalence in both Nasonia CHC profiles and many other Hymenoptera. Our study considerably extends our knowledge on the so far little-known genetic and genomic architecture governing biosynthesis and variation of this fundamental compound class, establishing a model for methyl-branched alkane genetics in the Hymenoptera in general. Competing Interest Statement The authors have declared no competing interest.

Single-cell transcriptomics (scRNA-seq) has enabled the characterization of cell state heterogeneity and recapitulation of differentiation trajectories. However, since proteins are the main functional entities in cells, the exclusive use of mRNA measurements comes at the risk of missing important biological information. Here we leverage recent technological advances in single-cell proteomics by Mass Spectrometry (scp-MS) to generate the first scp-MS dataset of an in vivo differentiation hierarchy encompassing over 2,500 human CD34+ hematopoietic stem and progenitor cells. Through integration with scRNA-seq, we identify proteins that are important for stem cell quiescence, which were not indicated by their mRNA transcripts, and demonstrate functional expression covariance during differentiation that is only detectable on protein level. Finally, we show that modeling translation dynamics can infer cell progression during differentiation and explain 45% more protein variation from mRNA than linear correlation. Our work serves as a framework for future single-cell multi-omics studies across biological systems.