Sex-specific cortical, hippocampal and thalamic whole genome transcriptome data from controls and a G72 schizophrenia mouse model.

OBJECTIVES: The G72 mouse model of schizophrenia represents a well-known model that was generated to meet the main translational criteria of isomorphism, homology and predictability of schizophrenia to a maximum extent. In order to get a more detailed view of the complex etiopathogenesis of schizophrenia, whole genome transcriptome studies turn out to be indispensable. Here we carried out microarray data collection based on RNA extracted from the retrosplenial cortex, hippocampus and thalamus of G72 transgenic and wild-type control mice. Experimental animals were age-matched and importantly, both sexes were considered separately. DATA DESCRIPTION: The isolated RNA from all three brain regions was purified, quantified und quality controlled before initiation of the hybridization procedure with SurePrint G3 Mouse Gene Expression v2 8  ×  60 K microarrays. Following immunofluorescent measurement und preprocessing of image data, raw transcriptome data from G72 mice and control animals were extracted and uploaded in a public database. Our data allow insight into significant alterations in gene transcript levels in G72 mice and enable the reader/user to perform further complex analyses to identify potential age-, sex- and brain-region-specific alterations in transcription profiles and related pathways. The latter could facilitate biomarker identification and drug research and development in schizophrenia research.

Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery.

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.

Effects of high-temperature stress on gene expression related to photosynthesis in two jujube (Ziziphus jujuba Mill.) varieties.

Elevated temperatures critically impact crop growth, development, and yield, with photosynthesis being the most temperature-sensitive physiological process in plants. This study focused on assessing the photosynthetic response and genetic adaptation of two different heat-resistant jujube varieties 'Junzao' (J) and 'Fucuimi' (F), to high-temperature stress (42°C Day/30°C Night). Comparative analyses of leaf photosynthetic indices, microstructural changes, and transcriptome sequencing were conducted. Results indicated superior high-temperature adaptability in F, evidenced by alterations in leaf stomatal behavior - particularly in J, where defense cells exhibited significant water loss, shrinkage, and reduced stomatal opening, alongside a marked increase in stomatal density. Through transcriptome sequencing 13,884 differentially expressed genes (DEGs) were identified, significantly enriched in pathways related to plant-pathogen interactions, amino acid biosynthesis, starch and sucrose metabolism, and carbohydrate metabolism. Key findings include the identification of photosynthetic pathway related DEGs and HSFA1s as central regulators of thermal morphogenesis and heat stress response. Revealing their upregulation in F and downregulation in J. The results indicate that these genes play a crucial role in improving heat tolerance in F. This study unveils critical photosynthetic genes involved in heat stress, providing a theoretical foundation for comprehending the molecular mechanisms underlying jujube heat tolerance.

Unravelling the biostimulant activity of a protein hydrolysate in lettuce plants under optimal and low N availability: a multi-omics approach.

The application of protein hydrolysates (PH) biostimulants is considered a promising approach to promote crop growth and resilience against abiotic stresses. Nevertheless, PHs bioactivity depends on both the raw material used for their preparation and the molecular fraction applied. The present research aimed at investigating the molecular mechanisms triggered by applying a PH and its fractions on plants subjected to nitrogen limitations. To this objective, an integrated transcriptomic-metabolomic approach was used to assess lettuce plants grown under different nitrogen levels and treated with either the commercial PH Vegamin® or its molecular fractions PH1(>10 kDa), PH2 (1-10 kDa) and PH3 (<1 kDa). Regardless of nitrogen provision, biostimulant application enhanced lettuce biomass, likely through a hormone-like activity. This was confirmed by the modulation of genes involved in auxin and cytokinin synthesis, mirrored by an increase in the metabolic levels of these hormones. Consistently, PH and PH3 upregulated genes involved in cell wall growth and plasticity. Furthermore, the accumulation of specific metabolites suggested the activation of a multifaceted antioxidant machinery. Notwithstanding, the modulation of stress-response transcription factors and genes involved in detoxification processes was observed. The coordinated action of these molecular entities might underpin the increased resilience of lettuce plants against nitrogen-limiting conditions. In conclusion, integrating omics techniques allowed the elucidation of mechanistic aspects underlying PH bioactivity in crops. Most importantly, the comparison of PH with its fraction PH3 showed that, except for a few peculiarities, the effects induced were equivalent, suggesting that the highest bioactivity was ascribable to the lightest molecular fraction.

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction.

Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.

A new dawn for the study of cell type evolution.

Animal evolution is influenced by the emergence of new cell types, yet our understanding of this process remains elusive. This prompts the need for a broader exploration across diverse research organisms, facilitated by recent breakthroughs, such as gene editing tools and single-cell genomics. Essential to our understanding of cell type evolution is the accurate identification of homologous cells. We delve into the significance of considering developmental ontogeny and potential pitfalls when drawing conclusions about cell type homology. Additionally, we highlight recent discoveries in the study of cell type evolution through the application of single-cell transcriptomics and pinpoint areas ripe for further exploration.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality.

BACKGROUND: Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality. METHODS: CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis. RESULTS: A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]). CONCLUSION: We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.

BACKGROUND: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins. RESULTS: We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer. CONCLUSION: By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.

Topology and Dynamics of Transcriptome (Dys)Regulation.

RNA transcripts play a crucial role as witnesses of gene expression health. Identifying disruptive short sequences in RNA transcription and regulation is essential for potentially treating diseases. Let us delve into the mathematical intricacies of these sequences. We have previously devised a mathematical approach for defining a "healthy" sequence. This sequence is characterized by having at most four distinct nucleotides (denoted as nt≤4). It serves as the generator of a group denoted as fp. The desired properties of this sequence are as follows: fp should be close to a free group of rank nt-1, it must be aperiodic, and fp should not have isolated singularities within its SL2(C) character variety (specifically within the corresponding Groebner basis). Now, let us explore the concept of singularities. There are cubic surfaces associated with the character variety of a four-punctured sphere denoted as S24. When we encounter these singularities, we find ourselves dealing with some algebraic solutions of a dynamical second-order differential (and transcendental) equation known as the Painlevé VI Equation. In certain cases, S24 degenerates, in the sense that two punctures collapse, resulting in a "wild" dynamics governed by the Painlevé equations of an index lower than VI. In our paper, we provide examples of these fascinating mathematical structures within the context of miRNAs. Specifically, we find a clear relationship between decorated character varieties of Painlevé equations and the character variety calculated from the seed of oncomirs. These findings should find many applications including cancer research and the investigation of neurodegenative diseases.

Transcriptome and functional analyses of phenotypic plasticity in sea grape Caulerpa okamurae.

Caulerpa is a marine green macroalga distinguished by a large single cell with multiple nuclei. It also exhibits remarkable morphological intraspecies variations, in response to diverse environmental types. However, the molecular mechanisms underlying this phenotypic plasticity remain poorly understood. In this work, we compare the transcriptomes of Caulerpa okamurae Weber Bosse, 1897 displaying altered phenotypes of cultivation and natural phenotypes and investigate significantly regulated genes and their biological functions using differential expression analyses. We observe light-harvesting complex upregulation and cellular framework stability downregulation in altered phenotypes compared to the natural phenotypes. Intertidal macrophytes reduce light capture to avoid photodamage and regulate their morphology to protect against wave damage. In contrast, the lower light conditions and the cultivation environment augment light capture and increase a morphology prioritizing light trapping. Moreover, the addition of simulated wave-sweeping stimuli induces a return to the natural morphology under high-light conditions, showing how mechanical stress affects morphological organization in C. okamurae. We provide detailed gene expression patterns in C. okamurae under varying light intensities and water conditions, suggesting a distinct influence on its morphological traits.

Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer.

Single cell RNA sequencing (scRNA-seq), a powerful tool for studying the tumor microenvironment (TME), does not preserve/provide spatial information on tissue morphology and cellular interactions. To understand the crosstalk between diverse cellular components in proximity in the TME, we performed scRNA-seq coupled with spatial transcriptomic (ST) assay to profile 41,700 cells from three colorectal cancer (CRC) tumor-normal-blood pairs. Standalone scRNA-seq analyses revealed eight major cell populations, including B cells, T cells, Monocytes, NK cells, Epithelial cells, Fibroblasts, Mast cells, Endothelial cells. After the identification of malignant cells from epithelial cells, we observed seven subtypes of malignant cells that reflect heterogeneous status in tumor, including tumor_CAV1, tumor_ATF3_JUN | FOS, tumor_ZEB2, tumor_VIM, tumor_WSB1, tumor_LXN, and tumor_PGM1. By transferring the cellular annotations obtained by scRNA-seq to ST spots, we annotated four regions in a cryosection from CRC patients, including tumor, stroma, immune infiltration, and colon epithelium regions. Furthermore, we observed intensive intercellular interactions between stroma and tumor regions which were extremely proximal in the cryosection. In particular, one pair of ligands and receptors (C5AR1 and RPS19) was inferred to play key roles in the crosstalk of stroma and tumor regions. For the tumor region, a typical feature of TMSB4X-high expression was identified, which could be a potential marker of CRC. The stroma region was found to be characterized by VIM-high expression, suggesting it fostered a stromal niche in the TME. Collectively, single cell and spatial analysis in our study reveal the tumor heterogeneity and molecular interactions in CRC TME, which provides insights into the mechanisms underlying CRC progression and may contribute to the development of anticancer therapies targeting on non-tumor components, such as the extracellular matrix (ECM) in CRC. The typical genes we identified may facilitate to new molecular subtypes of CRC.

A Principal Components Analysis and Functional Annotation of Differentially Expressed Genes in Brain Regions of Gray Rats Selected for Tame or Aggressive Behavior.

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms underlying these changes remain poorly understood. The present study deals with an analysis of the transcriptomes from four brain regions of gray rats (Rattus norvegicus), serving as an experimental model object of domestication. We compared gene expression profiles in the hypothalamus, hippocampus, periaqueductal gray matter, and the midbrain tegmental region between tame domesticated and aggressive gray rats and revealed subdivisions of differentially expressed genes by principal components analysis that explain the main part of differentially gene expression variance. Functional analysis (in the DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources database) of the differentially expressed genes allowed us to identify and describe the key biological processes that can participate in the formation of the different behavioral patterns seen in the two groups of gray rats. Using the STRING- DB (search tool for recurring instances of neighboring genes) web service, we built a gene association network. The genes engaged in broad network interactions have been identified. Our study offers data on the genes whose expression levels change in response to artificial selection for behavior during animal domestication.

Trophic Position of the White Worm (Enchytraeus albidus) in the Context of Digestive Enzyme Genes Revealed by Transcriptomics Analysis.

To assess the impact of Enchytraeidae (potworms) on the functioning of the decomposer system, knowledge of the feeding preferences of enchytraeid species is required. Different food preferences can be explained by variations in enzymatic activities among different enchytraeid species, as there are no significant differences in the morphology or anatomy of their alimentary tracts. However, it is crucial to distinguish between the contribution of microbial enzymes and the animal's digestive capacity. Here, we computationally analyzed the endogenous digestive enzyme genes in Enchytraeus albidus. The analysis was based on RNA-Seq of COI-monohaplotype culture (PL-A strain) specimens, utilizing transcriptome profiling to determine the trophic position of the species. We also corroborated the results obtained using transcriptomics data from genetically heterogeneous freeze-tolerant strains. Our results revealed that E. albidus expresses a wide range of glycosidases, including GH9 cellulases and a specific digestive SH3b-domain-containing i-type lysozyme, previously described in the earthworm Eisenia andrei. Therefore, E. albidus combines traits of both primary decomposers (primary saprophytophages) and secondary decomposers (sapro-microphytophages/microbivores) and can be defined as an intermediate decomposer. Based on assemblies of publicly available RNA-Seq reads, we found close homologs for these cellulases and i-type lysozymes in various clitellate taxa, including Crassiclitellata and Enchytraeidae.

Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data.

Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.

Transcriptome analysis reveals important regulatory factors for condensed tannins synthesis in acorn.

Condensed tannins are widely present in the fruits and seeds of plants and effectively prevent them from being eaten by animals before maturity due to their astringent taste. In addition, condensed tannins are a natural compound with strong antioxidant properties and significant antibacterial effects. Four samples of mature and near-mature Quercus fabri acorns, with the highest and lowest condensed tannin content, were used for genome-based transcriptome sequencing. The KEGG enrichment analysis revealed that the differentially expressed genes (DEGs) were highly enriched in phenylpropanoid biosynthesis and starch and sucrose metabolism. Given that the phenylpropanoid biosynthesis pathway is a crucial step in the synthesis of condensed tannins, we screened for significantly differentially expressed transcription factors and structural genes from the transcriptome data of this pathway and found that the expression levels of four MADS-box, PAL, and 4CL genes were significantly increased in acorns with high condensed tannin content. The quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) experiment further validated this result. In addition, yeast one-hybrid assay confirmed that three MADS-box transcription factors could bind the promoter of the 4CL gene, thereby regulating gene expression levels. This study utilized transcriptome sequencing to discover new important regulatory factors that can regulate the synthesis of acorn condensed tannins, providing new evidence for MADS-box transcription factors to regulate the synthesis of secondary metabolites in fruits.

Identification and analysis of MAPK cascade gene families of Camellia oleifera and their roles in response to cold stress.

BACKGROUND: Low-temperature severely limits the growth and development of Camellia oleifera (C. oleifera). The mitogen-activated protein kinase (MAPK) cascade plays a key role in the response to cold stress. METHODS AND RESULTS: Our study aims to identify MAPK cascade genes in C. oleifera and reveal their roles in response to cold stress. In our study, we systematically identified and analyzed the MAPK cascade gene families of C. oleifera, including their physical and chemical properties, conserved motifs, and multiple sequence alignments. In addition, we characterized the interacting networks of MAPKK kinase (MAPKKK)-MAPK kinase (MAPKK)-MAPK in C. oleifera. The molecular mechanism of cold stress resistance of MAPK cascade genes in wild C. oleifera was analyzed by differential gene expression and real-time quantitative reverse transcription-PCR (qRT-PCR). CONCLUSION: In this study, 21 MAPKs, 4 MAPKKs and 55 MAPKKKs genes were identified in the leaf transcriptome of C. oleifera. According to the phylogenetic results, MAPKs were divided into 4 groups (A, B, C and D), MAPKKs were divided into 3 groups (A, B and D), and MAPKKKs were divided into 2 groups (MEKK and Raf). Motif analysis showed that the motifs in each subfamily were conserved, and most of the motifs in the same subfamily were basically the same. The protein interaction network based on Arabidopsis thaliana (A. thaliana) homologs revealed that MAPK, MAPKK, and MAPKKK genes were widely involved in C. oleifera growth and development and in responses to biotic and abiotic stresses. Gene expression analysis revealed that the CoMAPKKK5/CoMAPKKK43/CoMAPKKK49-CoMAPKK4-CoMAPK8 module may play a key role in the cold stress resistance of wild C. oleifera at a high-elevation site in Lu Mountain (LSG). This study can facilitate the mining and utilization of genetic resources of C. oleifera with low-temperature tolerance.

Transcriptome analysis of apical meristem enriched bud samples for size dependent flowering commitment in Crocus sativus reveal role of sugar and auxin signalling.

BACKGROUND: Cultivation of Crocus sativus (saffron) faces challenges due to inconsistent flowering patterns and variations in yield. Flowering takes place in a graded way with smaller corms unable to produce flowers. Enhancing the productivity requires a comprehensive understanding of the underlying genetic mechanisms that govern this size-based flowering initiation and commitment. Therefore, samples enriched with non-flowering and flowering apical buds from small (< 6 g) and large (> 14 g) corms were sequenced. METHODS AND RESULTS: Apical bud enriched samples from small and large corms were collected immediately after dormancy break in July. RNA sequencing was performed using Illumina Novaseq 6000 to access the gene expression profiles associated with size dependent flowering. De novo transcriptome assembly and analysis using flowering committed buds from large corms at post-dormancy and their comparison with vegetative shoot primordia from small corms pointed out the major role of starch and sucrose metabolism, Auxin and ABA hormonal regulation. Many genes with known dual responses in flowering development and circadian rhythm like Flowering locus T and Cryptochrome 1 along with a transcript showing homology with small auxin upregulated RNA (SAUR) exhibited induced expression in flowering buds. Thorough prediction of Crocus sativus non-coding RNA repertoire has been carried out for the first time. Enolase was found to be acting as a major hub with protein-protein interaction analysis using Arabidopsis counterparts. CONCLUSION: Transcripts belong to key pathways including phenylpropanoid biosynthesis, hormone signaling and carbon metabolism were found significantly modulated. KEGG assessment and protein-protein interaction analysis confirm the expression data. Findings unravel the genetic determinants driving the size dependent flowering in Crocus sativus.

Alzheimer's disease rewires gene coexpression networks coupling different brain regions.

Connectome studies have shown how Alzheimer's disease (AD) disrupts functional and structural connectivity among brain regions. But the molecular basis of such disruptions is less studied, with most genomic/transcriptomic studies performing within-brain-region analyses. To inspect how AD rewires the correlation structure among genes in different brain regions, we performed an Inter-brain-region Differential Correlation (Inter-DC) analysis of RNA-seq data from Mount Sinai Brain Bank on four brain regions (frontal pole, superior temporal gyrus, parahippocampal gyrus and inferior frontal gyrus, comprising 264 AD and 372 control human post-mortem samples). An Inter-DC network was assembled from all pairs of genes across two brain regions that gained (or lost) correlation strength in the AD group relative to controls at FDR 1%. The differentially correlated (DC) genes in this network complemented known differentially expressed genes in AD, and likely reflects cell-intrinsic changes since we adjusted for cell compositional effects. Each brain region used a distinctive set of DC genes when coupling with other regions, with parahippocampal gyrus showing the most rewiring, consistent with its known vulnerability to AD. The Inter-DC network revealed master dysregulation hubs in AD (at genes ZKSCAN1, SLC5A3, RCC1, IL17RB, PLK4, etc.), inter-region gene modules enriched for known AD pathways (synaptic signaling, endocytosis, etc.), and candidate signaling molecules that could mediate region-region communication. The Inter-DC network generated in this study is a valuable resource of gene pairs, pathways and signaling molecules whose inter-brain-region functional coupling is disrupted in AD, thereby offering a new perspective of AD etiology.

Patient-Derived Organoids Recapitulate Pathological Intrinsic and Phenotypic Features of Fibrous Dysplasia.

Fibrous dysplasia (FD) is a rare bone disorder characterized by the replacement of normal bone with benign fibro-osseous tissue. Developments in our understanding of the pathophysiology and treatment options are impeded by the lack of suitable research models. In this study, we developed an in vitro organotypic model capable of recapitulating key intrinsic and phenotypic properties of FD. Initially, transcriptomic profiling of individual cells isolated from patient lesional tissues unveiled intralesional molecular and cellular heterogeneity. Leveraging these insights, we established patient-derived organoids (PDOs) using primary cells obtained from patient FD lesions. Evaluation of PDOs demonstrated preservation of fibrosis-associated constituent cell types and transcriptional signatures observed in FD lesions. Additionally, PDOs retained distinct constellations of genomic and metabolic alterations characteristic of FD. Histological evaluation further corroborated the fidelity of PDOs in recapitulating important phenotypic features of FD that underscore their pathophysiological relevance. Our findings represent meaningful progress in the field, as they open up the possibility for in vitro modeling of rare bone lesions in a three-dimensional context and may signify the first step towards creating a personalized platform for research and therapeutic studies.

Adult Human, but Not Rodent, Spermatogonial Stem Cells Retain States with a Foetal-like Signature.

Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling, it has been proposed that undifferentiated human spermatogonia can be subclassified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc) RNA-sequencing libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat, and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate 'state 0B' with a suppressive transcriptomic programme that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells-which are mitotically arrested-can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mice (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive lifespan while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.