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We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow that integrates best practices and an optimized protocol to enhance efficiency, scalability, and method reproducibility. This workflow consistently produces high-quality data with high cell capture efficiency and gene detection sensitivity. In a rigorous comparison with the 10X platform using human primary CD4 + T-cells, HT Smart-seq3 demonstrated higher cell capture efficiency, greater gene detection sensitivity, and lower dropout rates. Additionally, when sufficiently scaled, HT Smart-seq3 achieved a comparable resolution of cellular heterogeneity to 10X. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified a greater number of productive alpha and beta chain pairs without the need for additional primer design to amplify full-length V(D)J segments, enabling more comprehensive TCR profiling across a broader range of species. Taken together, HT Smart-seq3 overcomes key technical challenges, offering distinct advantages that position it as a promising solution for the characterization of single-cell transcriptomes and immune repertoires, particularly well-suited for low-input, low-RNA content samples.

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state \"signature\". These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.

To elucidate cellular machinery on a global scale, we performed a multiple comparison of the recently available protein-protein interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae. This comparison integrated protein interaction and sequence information to reveal 71 network regions that were conserved across all three species and many exclusive to the metazoans. We used this conservation, and found statistically significant support for 4,645 previously undescribed protein functions and 2,609 previously undescribed protein interactions. We tested 60 interaction predictions for yeast by two-hybrid analysis, confirming approximately half of these. Significantly, many of the predicted functions and interactions would not have been identified from sequence similarity alone, demonstrating that network comparisons provide essential biological information beyond what is gleaned from the genome.

Analysis of expression quantitative trait loci (eQTLs) is an emerging technique in which individuals are genotyped across a panel of genetic markers and, simultaneously, phenotyped using DNA microarrays. Because of the spacing of markers and linkage disequilibrium, each marker may be near many genes making it difficult to finely map which of these genes are the causal factors responsible for the observed changes in the downstream expression. To address this challenge, we present an efficient method for prioritizing candidate genes at a locus. This approach, called ‘eQTL electrical diagrams’ (eQED), integrates eQTLs with protein interaction networks by modeling the two data sets as a wiring diagram of current sources and resistors. eQED achieved a 79% accuracy in recovering a reference set of regulator–target pairs in yeast, which is significantly higher than the performance of three competing methods. eQED also annotates 368 protein–protein interactions with their directionality of information flow with an accuracy of approximately 75%.

Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV) and Kaposi's sarcoma-associated herpesvirus (KSHV). In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1), murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H), and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species.

HIV affects more than 38 million people worldwide. Although HIV can be effectively treated by lifelong combination antiretroviral therapy, only a handful of patients have been cured. Therapeutic vaccines that induce robust de novo immune responses targeting HIV proteins and latent reservoirs will likely be integral for functional HIV cure. Our study shows that immunization of naïve rhesus macaques with arenavirus-derived vaccine vectors encoding simian immunodeficiency virus (SIV SME543 Gag, Env, and Pol) immunogens is safe, immunogenic, and efficacious. Immunization induced robust SIV-specific CD8 + and CD4 + T-cell responses with expanded cellular breadth, polyfunctionality, and Env-binding antibodies with antibody-dependent cellular cytotoxicity. Vaccinated animals had significant reductions in median SIV viral load (1.45-log 10 copies/mL) after SIV MAC251 challenge compared with placebo. Peak viral control correlated with the breadth of Gag-specific T cells and tier 1 neutralizing antibodies. These results support clinical investigation of arenavirus-based vectors as a central component of therapeutic vaccination for HIV cure.

Arenavirus-based vectors are being investigated as therapeutic vaccine candidates with the potential to elicit robust CD8 T-cell responses. We compared the immunogenicity of replicating (artPICV and artLCMV) and non-replicating (rPICV and rLCMV) arenavirus-based vectors expressing simian immunodeficiency virus (SIV) Gag and Envelope (Env) immunogens in treatment-naïve non-human primates. Heterologous regimens with non-replicating and replicating vectors elicited more robust SIV IFN-γ responses than a homologous regimen, and replicating vectors elicited significantly higher cellular immunogenicity than non-replicating vectors. The heterologous regimen elicited high anti-Env antibody titers when administered intravenously, with replicating vectors inducing significantly higher titers than non-replicating vectors. Intramuscular immunization resulted in more durable antibody responses than intravenous immunization for both vector platforms, with no difference between the replicating and non-replicating vectors. Overall, both replicating and non-replicating arenavirus vectors generated robust T- and B-cell-mediated immunity to SIV antigens in treatment-naïve non-human primates, supporting further evaluation of these vectors in a clinical setting for HIV therapy.

Blau and colleagues show that non-dividing heterokaryons between mouse embryonic stem cells and human fibroblasts, in which human nuclei reprogram to a more embryonic state, can be used to identify signalling pathways involved in reprogramming. They delineate that IL-6 signalling and JAK/STAT target kinase Pim1 signalling are induced during this reprogramming, and that they increase the efficiency of factor-mediated reprogramming to induced pluripotent stem cell status. Molecular insights into somatic cell reprogramming to induced pluripotent stem cells (iPS) would aid regenerative medicine, but are difficult to elucidate in iPS because of their heterogeneity, as relatively few cells undergo reprogramming (0.1–1%; refs  1 , 2 ). To identify early acting regulators, we capitalized on non-dividing heterokaryons (mouse embryonic stem cells fused to human fibroblasts), in which reprogramming towards pluripotency is efficient and rapid 3 , enabling the identification of transient regulators required at the onset. We used bi-species transcriptome-wide RNA-seq to quantify transcriptional changes in the human somatic nucleus during reprogramming towards pluripotency in heterokaryons. During heterokaryon reprogramming, the cytokine interleukin 6 ( IL6 ), which is not detectable at significant levels in embryonic stem cells, was induced 50-fold. A 4-day culture with IL6 at the onset of iPS reprogramming replaced stably transduced oncogenic c-Myc such that transduction of only Oct4, Klf4 and Sox2 was required. IL6 also activated another Jak/Stat target, the serine/threonine kinase gene Pim1 , which accounted for the IL6-mediated twofold increase in iPS frequency. In contrast, LIF, another induced GP130 ligand, failed to increase iPS frequency or activate c-Myc or Pim1 , thereby revealing a differential role for the two Jak/Stat inducers in iPS generation. These findings demonstrate the power of heterokaryon bi-species global RNA-seq to identify early acting regulators of reprogramming, for example, extrinsic replacements for stably transduced transcription factors such as the potent oncogene c-Myc.

What makes a parasite tick A powerful approach for understanding protein function is to identify which proteins bind to each other, as protein complexes are at the heart of most biological processes. Protein–protein interactions have now been mapped for one quarter of the malaria parasite's proteins. This large data set sheds new light on how parasites infect red blood cells and will be a vital tool for the development of new antimalarial drugs and vaccines. The primary data are freely available on the PlasmoDB database. Suthram et al . have used this new resource and find that the Plasmodium network has significantly less cross-species similarity than other eukaryotes. Its novel life style is reflected in a novel protein network, which therefore has a good chance of providing drug targets unique to the malaria parasite. Plasmodium falciparum is the pathogen responsible for over 90% of human deaths from malaria 1 . Therefore, it has been the focus of a considerable research initiative, involving the complete DNA sequencing of the genome 2 , large-scale expression analyses 3 , 4 , and protein characterization of its life-cycle stages 5 . The Plasmodium genome sequence is relatively distant from those of most other eukaryotes, with more than 60% of the 5,334 encoded proteins lacking any notable sequence similarity to other organisms 2 . To systematically elucidate functional relationships among these proteins, a large two-hybrid study has recently mapped a network of 2,846 interactions involving 1,312 proteins within Plasmodium 6 . This network adds to a growing collection of available interaction maps for a number of different organisms, and raises questions about whether the divergence of Plasmodium at the sequence level is reflected in the configuration of its protein network. Here we examine the degree of conservation between the Plasmodium protein network and those of model organisms. Although we find 29 highly connected protein complexes specific to the network of the pathogen, we find very little conservation with complexes observed in other organisms (three in yeast, none in the others). Overall, the patterns of protein interaction in Plasmodium , like its genome sequence, set it apart from other species.