With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.

The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filters on peptide and sample levels followed by an evaluation of several popular normalization methods and visualization of the missing value. The user then selects an adequate normalization method and one of the several imputation methods used for the subsequent comparison of different differential expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results are demonstrated on two tandem mass tag multiplex (TMT6plex and TMT10plex) and one label-free spike-in mass spectrometry example data set. The three data sets reveal how the normalization methods perform differently on different experimental designs and the need for evaluation of normalization methods for each mass spectrometry experiment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for differential expression analysis.

Trimethylation of histone H3 lysine 9 (H3K9me3) is crucial for the regulation of gene repression and heterochromatin formation, cell-fate determination and organismal development¹. H3K9me3 also provides an essential mechanism for silencing transposable elements^1,2,3,4. However, previous studies have shown that canonical H3K9me3 readers (for example, HP1 (refs. ^5,6,7,8,9) and MPP8 (refs. ^10,11,12)) have limited roles in silencing endogenous retroviruses (ERVs), one of the main transposable element classes in the mammalian genome¹³. Here we report that trinucleotide-repeat-containing 18 (TNRC18), a poorly understood chromatin regulator, recognizes H3K9me3 to mediate the silencing of ERV class I (ERV1) elements such as LTR12 (ref. ¹⁴). Biochemical, biophysical and structural studies identified the carboxy-terminal bromo-adjacent homology (BAH) domain of TNRC18 (TNRC18(BAH)) as an H3K9me3-specific reader. Moreover, the amino-terminal segment of TNRC18 is a platform for the direct recruitment of co-repressors such as HDAC–Sin3–NCoR complexes, thus enforcing optimal repression of the H3K9me3-demarcated ERVs. Point mutagenesis that disrupts the TNRC18(BAH)-mediated H3K9me3 engagement caused neonatal death in mice and, in multiple mammalian cell models, led to derepressed expression of ERVs, which affected the landscape of cis-regulatory elements and, therefore, gene-expression programmes. Collectively, we describe a new H3K9me3-sensing and regulatory pathway that operates to epigenetically silence evolutionarily young ERVs and exert substantial effects on host genome integrity, transcriptomic regulation, immunity and development.

Astronauts are reported to have experienced some impairment in visual acuity during their mission on the International Space Station (ISS) and after they returned to Earth. There is emerging evidence that changes in vision may involve alterations in ocular structure and function. To investigate possible mechanisms, changes in protein expression profiles and oxidative stress-associated apoptosis were examined in mouse ocular tissue after spaceflight. Nine-week-old male C57BL/6 mice (n = 12) were launched from the Kennedy Space Center on a SpaceX rocket to the ISS for a 35-day mission. The animals were housed in the mouse Habitat Cage Unit (HCU) in the Japan Aerospace Exploration Agency (JAXA) “Kibo” facility on the ISS. The flight mice lived either under an ambient microgravity condition (µg) or in a centrifugal habitat unit that produced 1 g artificial gravity (µg + 1 g). Habitat control (HC) and vivarium control mice lived on Earth in HCUs or normal vivarium cages, respectively. Quantitative assessment of ocular tissue demonstrated that the µg group induced significant apoptosis in the retina vascular endothelial cells compared to all other groups (p < 0.05) that was 64% greater than that in the HC group. Proteomic analysis showed that many key pathways responsible for cell death, cell repair, inflammation, and metabolic stress were significantly altered in µg mice compared to HC animals. Additionally, there were more significant changes in regulated protein expression in the µg group relative to that in the µg + 1 g group. These data provide evidence that spaceflight induces retinal apoptosis of vascular endothelial cells and changes in retinal protein expression related to cellular structure, immune response and metabolic function, and that artificial gravity (AG) provides some protection against these changes. These retinal cellular responses may affect blood–retinal barrier (BRB) integrity, visual acuity, and impact the potential risk of developing late retinal degeneration.

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biomolecular changes induced by anti–CTLA4 and anti–PD-L1 immune checkpoint inhibitors (ICI) in the tumor microenvironment (TME) of colorectal tumor xenografts. Multivariate curve resolution–alternating least squares (MCR-ALS) decomposition of Raman spectral datasets revealed early changes in lipid, nucleic acid, and collagen content following therapy. Support vector machine classifiers and random forests analysis provided excellent prediction accuracies for response to both ICIs and delineated spectral markers specific to each therapy, consistent with their differential mechanisms of action. Corroborated by proteomics analysis, our observation of biomolecular changes in the TME should catalyze detailed investigations for translating such markers and label-free Raman spectroscopy for clinical monitoring of immunotherapy response in cancer patients.Significance: This study provides first-in-class evidence that optical spectroscopy allows sensitive detection of early changes in the biomolecular composition of tumors that predict response to immunotherapy with immune checkpoint inhibitors.