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PubMed2026

Identification of compounds that repress DUX4 expression in facioscapulohumeral muscular dystrophy.

Facioscapulohumeral muscular dystrophy (FSHD) is caused by epigenetic dysregulation of the disease locus, leading to pathogenic misexpression of DUX4 in skeletal muscle. Thus, most FSHD therapeutic approaches target DUX4. Our previous study identified the chromatin remodeling factor BAZ1A (bromodomain adjacent to zinc finger domain protein 1A) as a promising target for therapeutic development. Here we used an artificial intelligence-based screening pipeline to identify molecules predicted to bind the BAZ1A bromodomain, and validated hit compounds using FSHD-specific assays in FSHD myocytes. One compound, termed C06, emerged as a potent repressor of DUX4 and DUX4 target gene expression. Interestingly, while C06 exhibited binding to BAZ1A in vitro, it can also inhibit multiple kinases, including p38α, an upstream activator of DUX4. Despite this, at low doses C06 was an equally effective and more specific repressor of DUX4 than losmapimod, which is a robust and specific p38 inhibitor. At low concentrations, C06 returns the DUX4 gene expression signature to a healthier profile without major effects on the muscle transcriptome. Thus, C06 is a useful tool for potent and specific DUX4 suppression, and a viable candidate for further development. Our results highlight both the utility and limitations of AI for targeted drug discovery, and the importance of using an FSHD-specific functional screening strategy for selecting relevant candidates.

Muscular Dystrophy, Facioscapulohumeral↗
PubMed2026

Structural variant discovery and diagnostic impact in rare diseases from short-read and long-read sequencing.

Rare diseases collectively affect 1 in 10 individuals, yet current genetic testing fails to identify a causal variant for most cases. At present, cytogenetic methods and/or sequencing approaches such as exome (ES) or short-read genome sequencing (srGS) represent the state-of-the-art for comprehensive clinical discovery of sequence and structural variants (SVs), including copy number variants, balanced SVs, complex SVs, and tandem repeats (TRs). Recently, long-read genome sequencing (lrGS), coupled with multiomics data, has presented great promise to resolve variation in genomic regions recalcitrant to characterization by srGS such as highly repetitive simple repeat sequences and segmental duplications. However, there are few guidelines to enable clinical interpretation of genetic variation in these highly repetitive genomic regions, and the enthusiasm of the field in adopting lrGS has made it difficult to assess the true added diagnostic yield of this technology due to widely variable and inconsistently applied analytic pipelines and variable degrees of pre-screening by ES or srGS. Here, we investigated the contribution of SVs to rare diseases using srGS as a front-line strategy when paired with highly sensitive SV discovery and evaluate the added diagnostic yield of incorporating lrGS for a subset of cases. Our srGS analysis encompassed 1,462 families (3,450 individuals) recruited through the Broad Institute Center for Mendelian Genetics and the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) programs. Diagnostic SVs were identified in 5.4% of cases (79/1,462), of which 80% were uniquely detectable by srGS compared to standard cytogenetic techniques. For 96 families (including 10 families with a heterozygous variant observed in a known recessive gene of clinical relevance), we performed lrGS with methylation profiling, as well as long-read transcriptomic analyses in a subset of 20 trios. Analyses with lrGS yielded over 25,000 SVs per genome, 63% of which were not captured by srGS, along with an additional ~200 rare SNV/indels per genome not previously captured and 12 differentially methylated regions per genome. Among these, we identified only one diagnostic variant not interpreted by srGS, an apparently mosaic de novo SNV in CASK that was absent in the srGS callset due to allelic imbalance. No new diagnoses were supported by long-read transcriptomics or episignatures. In this well characterized rare disease cohort, the added diagnostic yield was thus 1.04% (1/96 families). Following a systematic literature review of prior lrGS studies, we find that most reported diagnoses were detectable by srGS and that our added diagnostic yield is consistent with those prior studies. These studies emphasize the significant impact of comprehensive SV discovery in rare disease cases and further demonstrate the power for increased discovery of novel genomic variation and episignatures from lrGS. Nonetheless, they also serve to temper expectations of dramatic diagnostic advances in rare disease patients until there is more extensive annotation of the functional and clinical impact of all coding and noncoding variation uniquely accessible to lrGS with extensive reference databases spanning highly repetitive genomic sequencing that could be enabled by this transformative technology.

Journal Article↗
PubMed2026

Transcriptomic signatures of mind-body transformations therapy in breast cancer: Downregulation of the interferon signaling pathway.

BACKGROUND: Growing evidence has shown that Mind-Body Transformations-Therapies (MBT-T) are able to modulate chronic inflammation, a well-known driver of cancer progression and drug resistance. In our previous work, we showed that a specific MBT-T protocol was able to reduce the release of various pro-inflammatory cytokines and chemokines in the sera of patients with breast cancer that completed adjuvant chemotherapy. Despite these clinical observations, the underlying molecular pathways through which this therapy exerts its effects remain unclear. This study aims to address this gap by characterizing genome-wide transcriptional profiles in patients undergoing a novel MBT-T protocol. METHODS: In this proof-of-concept study, patients with breast cancer were randomized into two groups: Group 1 (CTL), receiving standard follow-up care, and Group 2 (MBT-T), receiving standard follow-up plus biweekly MBT-T for 4 months. Blood samples were collected at different timepoints during the treatment. After RNA extraction from whole blood, gene expression was analyzed on twenty-one patients (CTL, n = 7; MBT-T, n = 14) using the nCounter® Human Inflammation Panel (249 genes). RESULTS: Patients undergoing MBT-T showed a significant global downregulation of inflammatory gene expression compared to the control group. The analysis revealed that the Interferon (IFN) signaling pathway was the most significantly suppressed, by downregulation of key genes such as IFIT1, IFIT3, IFI44, MX1 and OASL in the MBT-T group. CONCLUSIONS: MBT-T acts as a biological modulator capable of downregulating key inflammatory pathways at the transcriptional level. These findings provide a genomic basis for the clinical benefits of mind-body interventions in oncology.

Breast cancer↗
PubMed2025

Rhythm profiling using COFE reveals multi-omic circadian rhythms in human cancers in vivo.

The study of ubiquitous circadian rhythms in human physiology requires regular measurements across time. Repeated sampling of the different internal tissues that house circadian clocks is both practically and ethically infeasible. Here, we present a novel unsupervised machine learning approach (COFE) that can use single high-throughput omics samples (without time labels) from individuals to reconstruct circadian rhythms across cohorts. COFE can simultaneously assign time labels to samples and identify rhythmic data features used for temporal reconstruction, while also detecting invalid orderings. With COFE, we discovered widespread de novo circadian gene expression rhythms in 11 different human adenocarcinomas using data from The Cancer Genome Atlas (TCGA) database. The arrangement of peak times of core clock gene expression was conserved across cancers and resembled a healthy functional clock except for the mistiming of a few key genes. Moreover, rhythms in the transcriptome were strongly associated with the cancer-relevant proteome. The rhythmic genes and proteins common to all cancers were involved in metabolism and the cell cycle. Although these rhythms were synchronized with the cell cycle in many cancers, they were uncoupled with clocks in healthy matched tissue. The targets of most of FDA-approved and potential anti-cancer drugs were rhythmic in tumor tissue with different amplitudes and peak times. These findings emphasize the utility of considering "time" in cancer therapy, and suggest a focus on clocks in healthy tissue rather than free-running clocks in cancer tissue. Our approach thus creates new opportunities to repurpose data without time labels to study circadian rhythms.

Humans↗
PubMed2026

Decoding regional keratinization in human oral mucosa through high-resolution spatial transcriptomics.

Oral mucosa exhibits region-specific keratinization, essential for periodontal health, yet the spatial and molecular mechanisms driving these differences remain poorly understood. This study aimed to generate a high-resolution spatial transcriptomic atlas of the human oral mucosa around the mucogingival junction, to reveal stromal-epithelial interactions, that distinguish keratinized from non-keratinized programs. Formalin-fixed paraffin-embedded specimens from the mucogingival junction area of two healthy donors were analyzed with the 10 × Genomics Visium HD platform, yielding two keratinized and two non-keratinized regions. Spatial clustering, pseudotime trajectory inference, cell-type integration with a single-cell reference, and ligand-receptor network analysis were applied to delineate epithelial and stromal compartments. Sixteen reproducible clusters, recapitulating tissue architecture, were identified and revealed distinct transcriptional signatures, distinguishing gingiva from lining mucosa. Pseudotime analysis revealed bifurcating epithelial lineages, originating from a shared basal progenitor layer into keratinized and non-keratinized programs. Gingival keratinization was driven by stromal collagen ligands (COL1A1, COL1A2, COL6A1, COL6A2) engaging epithelial receptors (CD44, SDC1), further reinforced within the epithelium by desmosomal adhesion via DSG1-DSC2/3. Gingival keratinization emerges from integrated stromal collagen signaling and epithelial adhesion. This spatially resolved framework advances understanding of oral mucosal specialization and provides a foundation for biologically guided regenerative therapies.

Humans↗
PubMed2026

Longitudinal multiorgan transcriptomic atlas of salt-induced hypertension.

High dietary salt intake elevates blood pressure and drives multiorgan damage. However, the molecular programs underlying progressive organ injury remain poorly defined. Here, we present a longitudinal multiorgan transcriptomic atlas of salt-induced hypertensive injury. We profiled kidney cortex, kidney medulla, heart, and liver across 4 stages, spanning early hypertension to advanced pathology in Dahl salt-sensitive rats. We identified dynamic and tissue-specific molecular trajectories, including a shared early proliferative response that converges on proinflammatory and fibrotic remodeling. Notably, we uncovered compartment-specific renal responses, showing that the cortex and medulla, despite their proximity, follow distinct molecular trajectories during disease progression. We further identified 79 stage- and tissue-specific transcription factors that drive gene expression dynamics in salt-induced hypertensive injury. Integration with human genome-wide association studies revealed conserved pathways in endocrine signaling, ion transport, lipid metabolism, and detoxification, establishing cross-species relevance and highlighting mechanistic targets of clinical importance. Compound-transcriptome analysis revealed stage- and organ-specific therapeutic opportunities, prioritizing kinase and epigenetic modulators as candidates to rebalance maladaptive gene programs. Overall, this study provides a resource for understanding molecular mechanisms from early salt-induced hypertension to tissue-specific injury and underscores the need for precision interventions.

Animals↗
PubMed2026

Enhancing pan-cancer spatial transcriptomics at single-cell resolution with stPainter.

Subcellular spatial transcriptomics can resolve tissue architecture at cellular scale, but sparse gene panels and limited detection sensitivity constrain downstream analysis. Existing enhancement methods often require tissue-matched single-cell RNA sequencing (scRNA-seq) references and dataset-specific retraining. Here we show that stPainter, a conditional generative model pretrained on a pan-cancer scRNA-seq atlas, can enhance spatial transcriptomics data without matched references or retraining. Using a latent diffusion architecture guided by Stochastic Differential Equations (SDE), stPainter reconstructs expanded expression profiles from sparse measurements and produces latent representations for clustering and cell-state analysis. When we apply stPainter upon 6 spatial transcriptomics datasets of different cancer types, we demonstrate that our model empowers downstream biological analyses, including fine-grained subpopulation clustering and pathway enrichment. Comparison with spatially resolved proteomics (CODEX) provided independent support for regional agreement between imputed cellular compositions and protein-level tissue organization. These results establish stPainter as a scalable approach for analyzing tumor microenvironments without auxiliary sequencing data.

Spatial Transcriptomics↗
PubMed2026

Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.

We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.

Humans↗
PubMed2026

Comprehensive Viral Detection and Profiling of Plasma Cell-Free RNA in Patients With Suspected Hemophagocytic Lymphohistiocytosis.

Hemophagocytic lymphohistiocytosis (HLH) is a severe, rapidly progressive disease. While viral infection is considered a common etiology of pediatric HLH, specific causative viruses other than the Epstein-Barr virus (EBV) have been rarely identified. This study utilized metagenomic next-generation sequencing (NGS) to identify potential causative pathogens in plasma samples from 17 pediatric patients with suspected HLH. Additionally, one case each of confirmed EBV- and cytomegalovirus (CMV)-associated HLH was analyzed for methodological validation. Plasma cell-free RNA (cfRNA) profiling was performed using NGS data to assess the host transcriptome response. Significant viral reads of human herpesvirus-6B, human herpesvirus-7, and Hubei reo-like virus (HRLV) 14 were detected using metagenomic NGS in one patient each. Plasma cfRNA profiles from five patients with viral infection (including EBV and CMV) were compared to those of 14 patients without viral infection. By comparing the two patient groups, 1053 differentially expressed genes were identified. The gene ontology (GO) term of "adaptive immune response" (GO: 0002250) was significantly enriched among upregulated genes in the virus-positive group. Furthermore, an isolated cluster consisting specifically of mitochondrial RNAs, was identified in the upregulated genes of the virus-positive group. Using metagenomic NGS, several candidate viral pathogens were identified in patients with suspected infection-related HLH. The viral genome of HRLV 14, previously undetected in human clinical samples, was identified in one patient. The results from plasma cfRNA profiling suggest that mitochondrial RNAs may reflect the underlying pathogenesis of virus-associated HLH and have potential utility as disease biomarkers.

Humans↗
PubMed2026

Advancing insect research through cell line transcriptomics.

This review emphasizes the significance of insect cell lines in transcriptomic research, highlighting their role as vital tools for uncovering cellular and molecular mechanisms of insect physiology, immune responses, and adaptation to environmental stressors. Cell lines derived from tissues such as the midgut, fat body, nervous system, and reproductive organs enable researchers to examine gene expression changes in a controlled setting, making discoveries that are difficult to achieve through whole-organism studies. High-throughput sequencing and single-cell RNA sequencing (scRNA-seq) have identified genes linked to detoxification, stress response, development, and immune defense, offering valuable insights for future applications in agriculture, pest control, and biotechnology. To organize this information clearly, we have summarized key findings in a table, providing an accessible overview of each cell line's important roles in transcriptomic research. This method not only highlights the adaptability of insect cell lines in functional genomics but also underscores their usefulness as model systems in pest management, virology, and bioengineering. Through utilizing transcriptomics, insect cell lines continue to advance our understanding of insect biology and foster the development of innovative strategies for sustainable crop protection and biotechnological use.

Animals↗
PubMed2024

Acute MeCP2 loss in adult mice reveals transcriptional and chromatin changes that precede neurological dysfunction and inform pathogenesis.

Mutations in the X-linked methyl-CpG-binding protein 2 (MECP2) gene cause Rett syndrome, a severe childhood neurological disorder. MeCP2 is a well-established transcriptional repressor, yet upon its loss, hundreds of genes are dysregulated in both directions. To understand what drives such dysregulation, we deleted Mecp2 in adult mice, circumventing developmental contributions and secondary pathogenesis. We performed time series transcriptional, chromatin, and phenotypic analyses of the hippocampus to determine the immediate consequences of MeCP2 loss and the cascade of pathogenesis. We find that loss of MeCP2 causes immediate and bidirectional progressive dysregulation of the transcriptome. To understand what drives gene downregulation, we profiled genome-wide histone modifications and found that a decrease in histone H3 acetylation (ac) at downregulated genes is among the earliest molecular changes occurring well before any measurable deficiencies in electrophysiology and neurological function. These data reveal a molecular cascade that drives disease independent of any developmental contributions or secondary pathogenesis.

Animals↗
PubMed2026

Human brown fat metabolism associates with systemic branched-chain amino acids homeostasis.

Circulating branched-chain amino acids (BCAAs) are linked with insulin resistance, but the human tissues contributing to systemic BCAA homeostasis remain incompletely defined. Brown adipose tissue (BAT) is a metabolically active adipose depot associated with favourable insulin sensitivity, yet its role in BCAA metabolism in humans remains unclear. We tested whether human BAT metabolism is associated with circulating BCAA levels, BAT-resident BCAA-catabolic signatures, and longitudinal changes in systemic BCAA homeostasis. We studied 83 adults who underwent metabolic phenotyping, PET-CT assessment of cold-stimulated BAT metabolism, and serum metabolomic profiling at room temperature and during acute mild cold exposure. Supraclavicular BAT biopsies from 25 participants were analysed by transcriptomics and metabolomics, and 40 participants were re-examined for circulating BCAA profiles after approximately five years. Participants with high BAT metabolism had lower circulating BCAA levels than those with low BAT metabolism. Within BAT, metabolically active individuals exhibited lower relative BCAA abundance together with higher expression of genes involved in BCAA catabolism. These BAT BCAA-catabolic signatures aligned with thermogenic capacity and indices of systemic insulin sensitivity. In contrast, individuals with low BAT metabolism showed increases in circulating BCAAs over five years. Integrative analyses further linked circulating lipopolysaccharide, a marker of metabolic endotoxemia, with higher BAT BCAA and aminomalonate abundance, together with transcriptional patterns involving inflammatory and mitochondrial pathways. Together, these findings identify human BAT metabolism as a tissue phenotype linked to systemic BCAA homeostasis and extend the role of human BAT beyond thermogenesis, suggesting that BAT-associated BCAA handling may contribute to systemic metabolic health.

Humans↗
PubMed2026

Gene expression profiling identifies potential biomarkers for vaso-occlusive episodes in sickle cell disease.

Vaso-occlusive episodes (VOEs) or acute pain events, involving complex interactions between sickle erythrocytes and other blood cells, are a hallmark of sickle cell disease (SCD). In this study, we analyzed changes in peripheral blood transcriptomes between steady state and VOEs in individuals with SCD. We followed a cohort of 174 individuals with SCD with or without chronic pain and collected peripheral blood at clinic visits (steady state) and during hospitalizations (VOEs). We performed RNA-Seq profiling of CD45+ leukocytes and CD71+ erythroid cells. Pathways linked to complement activation, coagulation, and IL-6/JAK/STAT3 signaling were enriched during VOEs in the CD45+ cells. Contrastingly, the CD71+ cells showed an enrichment of pathways related to the cell cycle, such as mTORC1 signaling and the G2M checkpoint during VOEs. We then analyzed the expression changes of genes in patients with longitudinal data to determine potential biomarkers for VOEs. Expression of 4 genes - FAM20A, IL1B, MS4A4A, and SERPINB2 - was elevated during VOEs compared with steady state in the majority of patients. Furthermore, our results indicate that patients experiencing chronic pain exhibited 44% increased enrichment of significant pathways during VOEs when compared with patients without chronic pain.

Humans↗
PubMed2025

MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.

In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvironment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and reference-to-target difference. Here, we present MOADE, a multimodal autoencoder pipeline linking multi-dimensional features to jointly predict personalized multi-omic profiles and cellular compositions, using pseudo-bulk data constructed by internal non-transcriptomic reference and external scRNA-seq data. MOADE is evaluated through rigorous simulation experiments and real multi-omic data from multiple tissue types, outperforming nine deconvolution pipelines with superior generalizability and fidelity.

Humans↗
PubMed2026

Integrative genomic and transcriptomic analyses identify key regulators of skin pigmentation in Larimichthys crocea.

The yellow body coloration of large yellow croaker (Larimichthys crocea) constitutes a crucial economic trait, yet its underlying genetic regulatory mechanisms remain poorly understood. This study systematically elucidated the molecular basis of body color variation by integrating genome resequencing and skin transcriptome analyses, combined with the contextual analysis of key pigmentation-related genes and phenotypic histological validation. 200 phenotyped individuals (including yellow-selected lines, F1 progeny, and normal control groups, all derived from a well-characterized aquaculture stock) identified 39 significantly associated SNPs (-log₁₀(P) ≥ 6), mapping to multiple candidate genes. These genes were significantly enriched in pathways related to pigment deposition (GO:0033059), melanosome organization (GO:0032438), melanogenesis, and tyrosine metabolism. Cross-developmental stage transcriptome analysis revealed 2395 differentially expressed genes (DEGs). Multi-omics integration identified eight overlapping candidate genes, including tyrp1, slc45a2, oca2, and dgat2, among which tyrp1 was prioritized for in-depth validation based on its core regulatory role in eumelanin synthesis, significant SNP association signal, and consistent downregulation in transcriptomic data. Experimental validation demonstrated that the g.895C > T mutation in exon 2 of tyrp1b was strongly significantly associated with the yellow phenotype: the frequency of mutant genotypes (TT/CT) reached 92.86%in the yellow-selected group, whereas the control group exclusively exhibited the wild-type genotype (CC). qPCR confirmed significantly downregulated tyrp1b expression in the skin of yellow individuals, consistent with the transcriptome trend. Histological and stereomicroscopic observations of skin tissues further validated the physiological basis of the yellow phenotype, revealing a significant reduction in melanophore number and abnormal melanosome morphology in yellow-phenotype individuals, accompanied by increased xanthophore density. These results suggest that tyrp1b mutation is strongly associated with the yellow phenotype. However, the presence of a wild-type CC individual in the yellow group indicates that this mutation is not strictly required for yellow coloration, suggesting that other genetic or environmental factors may also contribute to the phenotype, Additionally, downregulation of the carotenoid metabolism gene bco2 coupled with upregulation of xdh, together with the functional changes of slc45a2 and oca2, may synergistically promote xanthophore pigment deposition, contributing to the yellow phenotype. As melanin synthesis in large yellow croaker relies on the conserved tyrosinase pathway and transporter proteins, mutations in associated genes (tyrp1b, slc45a2, oca2) represent a primary underlying cause for the loss of melanin-based coloration and transition to a yellow phenotype in L. crocea. These findings provide key molecular targets and a theoretical foundation for molecular breeding of body color in this species, and also enrich the understanding of xanthism regulatory mechanisms in teleosts.

Animals↗
PubMed2025

LungGENIE: the lung gene-expression and network imputation engine.

BACKGROUND: Few cohorts have study populations large enough to conduct molecular analysis of ex vivo lung tissue for genomic analyses. Transcriptome imputation is a non-invasive alternative with many potential applications. We present a novel transcriptome-imputation method called the Lung Gene Expression and Network Imputation Engine (LungGENIE) that uses principal components from blood gene-expression levels in a linear regression model to predict lung tissue-specific gene-expression. METHODS: We use paired blood and lung RNA sequencing data from the Genotype-Tissue Expression (GTEx) project to train LungGENIE models. We replicate model performance in a unique dataset, where we generated RNA sequencing data from paired lung and blood samples available through the SUNY Upstate Biorepository (SUBR). We further demonstrate proof-of-concept application of LungGENIE models in an independent blood RNA sequencing data from the Genetic Epidemiology of COPD (COPDGene) study. RESULTS: We show that LungGENIE prediction accuracies have higher correlation to measured lung tissue expression compared to existing cis-expression quantitative trait loci-based methods (median Pearson's r = 0.25, IQR 0.19-0.32), with close to half of the reliably predicted transcripts being replicated in the testing dataset. Finally, we demonstrate significant correlation of differential expression results in chronic obstructive pulmonary disease (COPD) from imputed lung tissue gene-expression and differential expression results experimentally determined from lung tissue. CONCLUSION: Our results demonstrate that LungGENIE provides complementary results to existing expression quantitative trait loci-based methods and outperforms direct blood to lung results across internal cross-validation, external replication, and proof-of-concept in an independent dataset. Taken together, we establish LungGENIE as a tool with many potential applications in the study of lung diseases.

Humans↗
PubMed2026

Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.

Multiomics↗
PubMed2025

HERV Modulation in Colorectal Carcinoma Patients: A Snapshot of Endogenous Retroviral Transcriptome.

Human endogenous retroviruses (HERVs) are proviral relics of infections that affected primates' germ line. Many HERV elements retain a residual capacity to encode transcripts and proteins that have been occasionally domesticated for the host physiology. In addition, HERV transcriptional modulation is of great interest to clarify the etiology of complex disorders such as cancer, even if a few studies assessed the specific HERV loci modulated in tumor tissues. In the present work, we used a transcriptomic approach to investigate the specific expression of ~3300 HERV loci in paired tumor and normal tissues of 7 colorectal cancer (CRC) patients. A total of 102 HERVs were significantly modulated in CRC, with a general tendency towards downregulation. Of note, among the 42 upregulated HERVs 23 belonged to the HERV-H group, that is the most investigated in CRC. De novo transcriptome reconstruction and qPCR validation allowed to identify a transcript from a HERV-H locus on chromosome Xp22.3 with high specific expression in CRC samples, potentially encoding for a partial Pol protein. These results provide a detailed description of HERV transcriptional variations in CRC and its interindividual variability, identifying a HERV-H transcript that deserves further investigation for its possible impact on tumor progression.

Endogenous Retroviruses↗