Pancreatic cancer is notoriously lethal due to its late diagnosis and poor patient response to treatments, posing a significant clinical challenge. This study introduced a novel approach that combines a single-cell capturing platform, tumor-targeted silver (Ag) nanoprobes, and precisely docking tapered fiber integrated with Raman spectroscopy. This approach focuses on early detection and progression monitoring of pancreatic cancer. Utilizing tumor-targeted Ag nanoparticles and tapered multimode fibers enhances Raman signals, minimizes light loss, and reduces background noise. This advanced Raman system allows for detailed molecular spectroscopic examination of individual cells, offering more practical information and enabling earlier detection and accurate staging of pancreatic cancer compared to conventional multicellular Raman spectroscopy. Transcriptomic analysis using high-throughput gene screening and transcriptomic databases confirmed the ability and accuracy of this method to identify molecular changes in normal, early, and metastatic pancreatic cancer cells. Key findings revealed that cell adhesion, migration, and the extracellular matrix are closely related to single-cell Raman spectroscopy (SCRS) results, highlighting components such as collagen, phospholipids, and carotene. Therefore, the SCRS approach provides a comprehensive view of the molecular composition, biological function, and material changes in cells, offering a novel, accurate, reliable, rapid, and efficient method for diagnosing and monitoring pancreatic cancer.

Via retrospective isolation of clones using Rewind, Jain et al. identified primed states of cells that reprogram to induced pluripotent stem cells. Examining clones, they find that cells retain memory of over several rounds of cell division. Moreover, they show that extrinsic factors change the number of primed cells, suggesting that there exist diverse paths of reprogramming and states of priming.

Single-cell technologies have recently expanded the possibilities for researchers to gain, at an unprecedented resolution level, knowledge about tissue composition, cell complexity, and heterogeneity. Moreover, the integration of data coming from different technologies and sources also offers, for the first time, the possibility to draw a holistic portrait of how cells behave to sustain tissue physiology during the human lifespan and disease. Here, we interrogated and integrated publicly available single-cell RNAseq data to advance the understanding of how macrophages, fibro/adipogenic progenitors, and other cell types establish gene regulatory networks and communicate with each other in the muscle tissue. We identified altered gene signatures and signaling pathways associated with the dystrophic condition, including an enhanced Spp1-Cd44 signaling in dystrophic macrophages. We shed light on the differences among dystrophic muscle aging, considering wild type, mdx, and more severe conditions as in the case of the mdx-2d model. Contextually, we provided details on existing communication relations between muscle niche cell populations, highlighting increased interactions and distinct signaling events that these cells stablish in the dystrophic microenvironment. We believe our findings can help scientists to formulate and test new hypotheses by moving towards a more complete understanding of muscle regeneration and immune system biology.

BACKGROUND: Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear.
METHODS: Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed.
RESULTS: We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis.
CONCLUSIONS: By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.

The heart has been the center of numerous transcriptomic studies in the past decade. Even though our knowledge of the key organ in our cardiovascular system has significantly increased over the last years, it is still not fully understood yet. In recent years, extensive efforts were made to understand the genetic and transcriptomic contribution to cardiac function and failure in more detail. The advent of Next Generation Sequencing (NGS) technologies has brought many discoveries but it is unable to comprehend the finely orchestrated interactions between and within the various cell types of the heart. With the emergence of single-cell sequencing more than 10 years ago, researchers gained a valuable new tool to enable the exploration of new subpopulations of cells, cell-cell interactions, and integration of multi-omic approaches at a single-cell resolution. Despite this innovation, it is essential to make an informed choice regarding the appropriate technique for transcriptomic studies, especially when working with myocardial tissue. Here, we provide a primer for researchers interested in transcriptomics using NGS technologies.

BACKGROUND: Schizophrenia is a mental disorder that causes considerable morbidity, whose risk largely results from genetic factors. Setd1a is a gene implicated in schizophrenia.
OBJECTIVE: To study the gene expression changes found in heterozygous Setd1a± knockout mice in order to gain useful insight into schizophrenia pathogenesis.
METHODS: We mined a single-cell RNA sequencing (scRNAseq) dataset from the prefrontal cortex (PFC) and striatum of Setd1a± mice and identified cell type-specific differentially expressed genes (DEGs) and differential transcript usage (DTU). DEGs and genes containing DTU found in each cell type were used to identify affected biological pathways using Ingenuity Pathway Analysis (IPA).
RESULTS: We identified 273 unique DEGs across all cell types in PFC and 675 unique gene peaks containing DTU. In striatum, we identified 327 unique DEGs across all cell types and 8 unique gene peaks containing DTU. Key IPA findings from the analysis of DEGs found in PFC and striatum implicate processes involved in protein synthesis, mitochondrial function, cell metabolism, and inflammation. IPA analysis of genes containing DTU in PFC points to protein synthesis, as well as cellular activities involving intracellular signaling and neurotransmission. One canonical pathway, \'EIF2 Signaling\', which is involved in the regulation of protein synthesis, was detected in PFC DEGs, striatum DEGs, and PFC genes containing DTU, drawing attention to its importance in schizophrenia pathophysiology.
CONCLUSIONS: Processes involving protein synthesis in general and the \'EIF2 Signaling\' pathway in particular could be targets for the development of new research strategies and biomarkers in schizophrenia.

UNASSIGNED: To analyze the expression of mitochondrial translational initiation factor 2 (MTIF2) and the biological functions of the gene in hepatocellular carcinoma (HCC).
UNASSIGNED: The treatment of HCC treatment and its prognostic prediction are limited by a lack of comprehensive understanding of the molecular mechanisms in HCC. OBJECTIVE: To determine the cells expressing MTIF2 in HCC and the function of the MTIF2+ cell subpopulation.
UNASSIGNED: Gene expression analysis on TIMER 2.0, UALCAN, and GEPIA databases was performed to measure the expression of MTIF2 in HCC tissues. Cell clustering subgroups and annotation were conducted based on the single-cell sequencing data of HCC and paracancerous tissues in the Gene Expression Omnibus (GEO) database. MTIF2 expression in different cell types was analyzed. Further, biological pathways potentially regulated by MTIF2 in each cell type were identified. In addition, protein-protein interaction (PPI) networks of MTIF2 with genes in its regulated biological pathways were developed. The cell function assay was performed to verify the effects of superoxide dismutase-2 (SOD2) and MTIF2 on HCC cells. Finally, we screened virtual drugs targeting MTIF2 and SOD2 employing database screening, molecular docking and molecular dynamics.
UNASSIGNED: MTIF2 showed a remarkably high expression in HCC tissues. We identified a total of 10 cell types between HCC tissues and paracancerous tissues. MTIF2 expression was upregulated in epithelial cells, macrophages, and hepatocytes. More importantly, high-expressed MTIF2 in HCC tissues was mainly derived from epithelial cells and hepatocytes, in which the reactive oxygen species (ROS) pathway was significantly positively correlated with MTIF2. In the PPI network, there was a unique interaction pair between SOD2 and MTIF2 in the ROS pathway. Cell function experiments showed that overexpression of MTIF2 enhanced the proliferative and invasive capacities of HCC, which could synergize with SOD2 to co-promote the development of HCC. Finally, molecular dynamics simulations showed that DB00183 maintained a high structural stability with MTIF2 and SOD2 proteins during the simulation process.
UNASSIGNED: Our study confirmed that the high-expressed MTIF2 in HCC tissues was derived from epithelial cells and hepatocytes. MTIF2 might act on SOD2 to regulate the ROS pathway, thereby affective the progression of HCC.

BACKGROUND: Recurrent spontaneous abortion (RSA) is a challenging condition that affects the health of women both physically and mentally, but its pathogenesis and treatment have yet to be studied in detail. In recent years, Wharton\'s jelly-derived mesenchymal stem cells (WJ-MSCs) have been shown to be effective in treating various diseases. Current understanding of RSA treatment using WJ-MSCs is limited, and the exact mechanisms of WJ-MSCs action in RSA remains largely unclear. In this study, we explored the decidual deficiencies in RSA and the therapeutic potential of WJ-MSCs at single-cell resolution.
METHODS: Three mouse models were established: a normal pregnancy group, an RSA group, and a WJ-MSC treatment group. Decidual tissue samples were collected for single-cell RNA sequencing (scRNA-seq) and functional verification, including single-cell resolution in situ hybridization on tissues (SCRINSHOT) and immunofluorescence.
RESULTS: We generated a single-cell atlas of decidual tissues from normal pregnant, RSA, and WJ-MSC-treated mice and identified 14 cell clusters in the decidua on day 14. Among these cell populations, stromal cells were the most abundant cell clusters in the decidua, and we further identified three novel subclusters (Str_0, Str_1, and Str_2). We also demonstrated that the IL17 and TNF signaling pathways were enriched for upregulated DEGs of stromal cells in RSA mice. Intriguingly, cell-cell communication analysis revealed that Str_1 cell-related gene expression was greatly reduced in the RSA group and rescued in the WJ-MSC treatment group. Notably, the interaction between NK cells and other cells in the RSA group was attenuated, and the expression of Spp1 (identified as an endometrial toleration-related marker) was significantly reduced in the NK cells of the RSA group but could be restored by WJ-MSC treatment.
CONCLUSIONS: Herein, we implemented scRNA-seq to systematically evaluate the cellular heterogeneity and transcriptional regulatory networks associated with RSA and its treatment with WJ-MSCs. These data revealed potential therapeutic targets of WJ-MSCs to remodel the decidual subpopulations in RSA and provided new insights into decidua-derived developmental defects at the maternal-foetal interface.

Biochemical analysis of human brain tissue is typically done by homogenizing whole pieces of brain and separately characterizing the proteins, RNA, DNA, and other macromolecules within. While this has been sufficient to identify substantial changes, there is little ability to identify small changes or alterations that may occur in subsets of cells. To effectively investigate the biochemistry of disease in the brain, with its different cell types, we must first separate the cells and study them as phenotypically defined populations or even as individuals. In this project, we developed a new method for the generation of Whole Cell Dissociated Suspensions (WCDS) in fresh human brain tissue that could be shared as a resource with scientists to study single human cells or populations. Characterization of WCDS was done in paraffin-embedded sections stained with H&E, and by phenotyping with antibodies using immunohistochemistry and Fluorescence Activated Cell Sorting (FACS). Additionally, we compared extracted RNA from WCDS with RNA from adjacent intact cortical tissue, using RT-qPCR for cell-type-specific RNA for the same markers as well as whole transcriptome sequencing. More than 11,626 gene transcripts were successfully sequenced and classified using an external database either as being mainly expressed in neurons, astrocytes, microglia, oligodendrocytes, endothelial cells, or mixed (in two or more cell types). This demonstrates that we are currently capable of producing WCDS with a full representation of different brain cell types combined with RNA quality suitable for use in biochemical analysis.

BACKGROUND: Plant meristems are structured organs consisting of distinct layers of stem cells, which differentiate into new plant tissue. Mutations in meristematic layers can propagate into large sectors of the plant. However, the characteristics of meristematic mutations remain unclear, limiting our understanding of the genetic basis of somaclonal phenotypic variation.
RESULTS: Here, we analyse the frequency and distribution of somatic mutations in an apricot tree. We separately sequence the epidermis (developing from meristem layer 1) and the flesh (developing from meristem layer 2) of several fruits sampled across the entire tree. We find that most somatic mutations (> 90%) are specific to individual layers. Interestingly, layer 1 shows a higher mutation load than layer 2, implying different mutational dynamics between the layers. The distribution of somatic mutations follows the branching of the tree. This suggests that somatic mutations are propagated to developing branches through axillary meristems. In turn, this leads us to the unexpected observation that the genomes of layer 1 of distant branches are more similar to each other than to the genomes of layer 2 of the same branches. Finally, using single-cell RNA sequencing, we demonstrate that layer-specific mutations were only transcribed in the cells of the respective layers and can form the genetic basis of somaclonal phenotypic variation.
CONCLUSIONS: Here, we analyse the frequency and distribution of somatic mutations with meristematic origin. Our observations on the layer specificity of somatic mutations outline how they are distributed, how they propagate, and how they can impact clonally propagated crops.