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Mosquitoes transmit numerous pathogens, but large gaps remain in our understanding of their physiology. To facilitate explorations of mosquito biology, we have created Aegypti-Atlas ( http://aegyptiatlas.buchonlab.com/ ), an online resource hosting RNAseq profiles of Ae. aegypti body parts (head, thorax, abdomen, gut, Malpighian tubules, ovaries), gut regions (crop, proventriculus, anterior and posterior midgut, hindgut), and a gut time course of blood meal digestion. Using Aegypti-Atlas, we provide insights into regionalization of gut function, blood feeding response, and immune defenses. We find that the anterior and posterior midgut possess digestive specializations which are preserved in the blood-fed state. Blood feeding initiates the sequential induction and repression/depletion of multiple cohorts of peptidases. With respect to defense, immune signaling components, but not recognition or effector molecules, show enrichment in ovaries. Basal expression of antimicrobial peptides is dominated by holotricin and gambicin, which are expressed in carcass and digestive tissues, respectively, in a mutually exclusive manner. In the midgut, gambicin and other effectors are almost exclusively expressed in the anterior regions, while the posterior midgut exhibits hallmarks of immune tolerance. Finally, in a cross-species comparison between Ae. aegypti and Anopheles gambiae midguts, we observe that regional digestive and immune specializations are conserved, indicating that our dataset may be broadly relevant to multiple mosquito species. We demonstrate that the expression of orthologous genes is highly correlated, with the exception of a ‘species signature’ comprising a few highly/disparately expressed genes. With this work, we show the potential of Aegypti-Atlas to unlock a more complete understanding of mosquito biology.

Intramuscular fat (IMF) and intramuscular connective tissue (IMC) are often seen in human myopathies and are central to beef quality. The mechanisms regulating their accumulation remain poorly understood. Here, we explored the possibility of using beef cattle as a novel model for mechanistic studies of intramuscular adipogenesis and fibrogenesis. Skeletal muscle single-cell RNAseq was performed on three cattle breeds, including Wagyu (high IMF), Brahman (abundant IMC but scarce IMF), and Wagyu/Brahman cross. Sophisticated bioinformatics analyses, including clustering analysis, gene set enrichment analyses, gene regulatory network construction, RNA velocity, pseudotime analysis, and cell-cell communication analysis, were performed to elucidate heterogeneities and differentiation processes of individual cell types and differences between cattle breeds. Experiments were conducted to validate the function and specificity of identified key regulatory and marker genes. Integrated analysis with multiple published human and non-human primate datasets was performed to identify common mechanisms. A total of 32 708 cells and 21 clusters were identified, including fibro/adipogenic progenitor (FAP) and other resident and infiltrating cell types. We identified an endomysial adipogenic FAP subpopulation enriched for COL4A1 and CFD (log2FC = 3.19 and 1.92, respectively; P < 0.0001) and a perimysial fibrogenic FAP subpopulation enriched for COL1A1 and POSTN (log2FC = 1.83 and 0.87, respectively; P < 0.0001), both of which were likely derived from an unspecified subpopulation. Further analysis revealed more progressed adipogenic programming of Wagyu FAPs and more advanced fibrogenic programming of Brahman FAPs. Mechanistically, NAB2 drives CFD expression, which in turn promotes adipogenesis. CFD expression in FAPs of young cattle before the onset of intramuscular adipogenesis was predictive of IMF contents in adulthood (R  = 0.885, P < 0.01). Similar adipogenic and fibrogenic FAPs were identified in humans and monkeys. In aged humans with metabolic syndrome and progressed Duchenne muscular dystrophy (DMD) patients, increased CFD expression was observed (P < 0.05 and P < 0.0001, respectively), which was positively correlated with adipogenic marker expression, including ADIPOQ (R  = 0.303, P < 0.01; and R  = 0.348, P < 0.01, respectively). The specificity of Postn/POSTN as a fibrogenic FAP marker was validated using a lineage-tracing mouse line. POSTN expression was elevated in Brahman FAPs (P < 0.0001) and DMD patients (P < 0.01) but not in aged humans. Strong interactions between vascular cells and FAPs were also identified. Our study demonstrates the feasibility of beef cattle as a model for studying IMF and IMC. We illustrate the FAP programming during intramuscular adipogenesis and fibrogenesis and reveal the reliability of CFD as a predictor and biomarker of IMF accumulation in cattle and humans.

Significance The brain comprises an immense number of cells and cellular connections. We describe the first, to our knowledge, single cell whole transcriptome analysis of human adult cortical samples. We have established an experimental and analytical framework with which the complexity of the human brain can be dissected on the single cell level. Using this approach, we were able to identify all major cell types of the brain and characterize subtypes of neuronal cells. We observed changes in neurons from early developmental to late differentiated stages in the adult. We found a subset of adult neurons which express major histocompatibility complex class I genes and thus are not immune privileged. The human brain is a tissue of vast complexity in terms of the cell types it comprises. Conventional approaches to classifying cell types in the human brain at single cell resolution have been limited to exploring relatively few markers and therefore have provided a limited molecular characterization of any given cell type. We used single cell RNA sequencing on 466 cells to capture the cellular complexity of the adult and fetal human brain at a whole transcriptome level. Healthy adult temporal lobe tissue was obtained during surgical procedures where otherwise normal tissue was removed to gain access to deeper hippocampal pathology in patients with medical refractory seizures. We were able to classify individual cells into all of the major neuronal, glial, and vascular cell types in the brain. We were able to divide neurons into individual communities and show that these communities preserve the categorization of interneuron subtypes that is typically observed with the use of classic interneuron markers. We then used single cell RNA sequencing on fetal human cortical neurons to identify genes that are differentially expressed between fetal and adult neurons and those genes that display an expression gradient that reflects the transition between replicating and quiescent fetal neuronal populations. Finally, we observed the expression of major histocompatibility complex type I genes in a subset of adult neurons, but not fetal neurons. The work presented here demonstrates the applicability of single cell RNA sequencing on the study of the adult human brain and constitutes a first step toward a comprehensive cellular atlas of the human brain.

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

Viral infection can dramatically alter a cell’s transcriptome. However, these changes have mostly been studied by bulk measurements on many cells. Here we use single-cell mRNA sequencing to examine the transcriptional consequences of influenza virus infection. We find extremely wide cell-to-cell variation in the productivity of viral transcription – viral transcripts comprise less than a percent of total mRNA in many infected cells, but a few cells derive over half their mRNA from virus. Some infected cells fail to express at least one viral gene, but this gene absence only partially explains variation in viral transcriptional load. Despite variation in viral load, the relative abundances of viral mRNAs are fairly consistent across infected cells. Activation of innate immune pathways is rare, but some cellular genes co-vary in abundance with the amount of viral mRNA. Overall, our results highlight the complexity of viral infection at the level of single cells. When viruses infect cells, they take over the cell’s machinery and use it to express their own genes. This process has mostly been studied by looking at the average outcome of infection when many viruses infect many cells. However, it is less clear what happens in individual cells. For example, does the virus take over every cell to make lots of viral gene products, or do some cells produce far more viral gene products than others? Russell et al. have now used a new technique called single-cell RNA sequencing to look at how well influenza virus genes were expressed in hundreds of individual mammalian cells. The goal was to work out how the outcome of infection varied between different cells. One way to quantify variability – also known as heterogeneity – is by using a statistical measure called the Gini coefficient. This statistic is often used to assess the inequality in incomes across a nation.In the hypothetical situation where everyone earned the same income, the Gini coefficient would equal zero; while if only one person had all the income and all others had none, the value would be very close to one. In reality, countries fall somewhere in between these two extremes. In the United States for instance, the Gini coefficient for income is 0.47. When Russell et al. worked out the Gini coefficient for the amount of viral genes expressed in different cells, the value was at least 0.64. This indicates that there is more unevenness in viral gene expression for influenza than there is income inequality in the United States. So, what characterizes the “Bill Gates” cells and viruses that have the highest viral gene expression? Influenza viruses sometimes fail to express some of their genes. Russell et al. found that this failure often led to “poor” viruses that were less productive than “rich” viruses that expressed all the critical genes. However, the results suggest that there are also other factors that contribute a lot to the heterogeneity. Real influenza virus infections are usually started by very few viruses, so this new understanding of the variability that occurs when individual viruses infect individual cells might prove important for understanding the properties of infections at larger scales too.

In brain, signaling mediated by cell adhesion molecules defines the identity and functional properties of synapses. The specificity of presynaptic and postsynaptic interactions that is presumably mediated by cell adhesion molecules suggests that there exists a logic that could explain neuronal connectivity at the molecular level. Despite its importance, however, the nature of such logic is poorly understood, and even basic parameters, such as the number, identity, and single-cell expression profiles of candidate synaptic cell adhesion molecules, are not known. Here, we devised a comprehensive list of genes involved in cell adhesion, and used single-cell RNA sequencing (RNAseq) to analyze their expression in electrophysiologically defined interneurons and projection neurons. We compared the cell type-specific expression of these genes with that of genes involved in transmembrane ion conductances (i.e., channels), exocytosis, and rho/rac signaling, which regulates the actin cytoskeleton. Using these data, we identified two independent, developmentally regulated networks of interacting genes encoding molecules involved in cell adhesion, exocytosis, and signal transduction. Our approach provides a framework for a presumed cell adhesion and signaling code in neurons, enables correlating electrophysiological with molecular properties of neurons, and suggests avenues toward understanding synaptic specificity.

The specific function of microglia, the tissue resident macrophages of the brain and spinal cord, has been difficult to ascertain because of a lack of tools to distinguish microglia from other immune cells, thereby limiting specific immunostaining, purification, and manipulation. Because of their unique developmental origins and predicted functions, the distinction of microglia from other myeloid cells is critically important for understanding brain development and disease; better toolswould greatly facilitate studies of microglia function in the developing, adult, and injured CNS. Here, we identify transmembrane protein 119 (Tmem119), a cell-surface protein of unknown function, as a highly expressed microglia-specific marker in both mouse and human. We developed monoclonal antibodies to its intracellular and extracellular domains that enable the immunostaining of microglia in histological sections in healthy and diseased brains, as well as isolation of pure nonactivated microglia by FACS. Using our antibodies, we provide, to our knowledge, the first RNAseq profiles of highly pure mouse microglia during development and after an immune challenge. We used these to demonstrate that mouse microglia mature by the second postnatal week and to predict novel microglial functions. Together, we anticipate these resources will be valuable for the future study and understanding of microglia in health and disease.

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High ambient temperature has multiple potential effects on the organism such as hyperthermia, endotoxemia, and/or systemic inflammation. However, it is often difficult to discriminate between cause and consequence of phenotypic effects, such as the indirect influence of heat stress via reduced food intake. Lactating dairy cows are a particularly sensitive model to examine the effects of heat stress due to their intensive metabolic heat production and small surface:volume ratio. Results from this model show heat stress directly induced a so-far unknown infiltration of yet uncategorized cells into the mucosa and submucosa of the jejunum. Due to a pair-feeding design, we can exclude this effect being a consequence of the concurrent heat-induced reduction in feed intake. Isolation and characterization of the infiltrating cells using laser capture microdissection and RNA sequencing indicated a myeloic origin and macrophage-like phenotype. Furthermore, targeted transcriptome analyses provided evidence of activated immune- and phagocytosis-related pathways with LPS and cytokines as upstream regulators directly associated with heat stress. Finally, we obtained indication that heat stress may directly alter jejunal tight junction proteins suggesting an impaired intestinal barrier. The penetration of toxic and bacterial compounds during heat stress may have triggered a modulated immune repertoire and induced an antioxidative defense mechanism to maintain homeostasis between commensal bacteria and the jejunal immune system. Our bovine model indicates direct effects of heat stress on the jejunum of mammals already at moderately elevated ambient temperature. These results need to be considered when developing concepts to combat the negative consequences of heat stress.

Background The analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data. Results In this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes. Conclusions In general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.