Articles tagged with Single Cell Sequencing
Scientists have mapped neuroblastoma tumors at the cell level and discovered a brake on the immune system that can be blocked with existing immunotherapy. A new combination treatment targeting TIGIT and PD-L1 is being developed, showing promising results in lab experiments.
A new tool, CellHint, has been developed to unify different single-cell data, creating harmonized datasets. Researchers applied CellHint to reveal underexplored connections between healthy and diseased lung cell states and identified potential interests for future research in adult human hippocampus.
A new genetic atlas details the gene expression programs that drive zebrafish development, revealing insights into human embryonic growth and the origins of diseases such as gastrointestinal disorders. The atlas provides a comprehensive resource for studying cell development and differentiation in vertebrates.
Studies at single-cell resolution reveal significant tumor cell heterogeneity and an immune-evasive environment that contributes to treatment resistance in T follicular helper cell lymphomas. A novel marker, PLS3, is also identified as a key player in this process.
Scientists have identified 77 main cell types and around 650 cell subtypes in a single experiment, enabling research on embryonic malformations. The new approach reduces the number of animals used for analysis and allows for faster and more accurate study of genetic disorders.
Researchers found that tumour cells escape immunotherapy by losing or changing BCMA and GPRC5D targets on their surface. This understanding has led to the suggestion of periodically profiling myeloma cells throughout a patient's treatment course to adapt treatment strategies.
A new DNA sequencing test revealed a novel species with an unusual genetic code, where TAA and TAG specify different amino acids. This finding breaks some long-held rules about gene translation, highlighting the complexity and diversity of life on Earth.
Researchers at Memorial Sloan Kettering Cancer Center developed an open-source computational method, Spectra, to analyze single-cell transcriptomic data. The algorithm identifies functionally relevant gene expression programs and is well-suited for studying large patient cohorts.
Researchers identified new cell types in the developing fly's visual system using a tool that combines single-cell sequencing data with a novel algorithm. This discovery could provide exceptional tools for neuroscience to investigate developmental questions with high precision.
A new process developed by researchers at the Chinese Academy of Sciences Headquarters greatly reduces bias in gene amplification, enabling high-coverage genome sequencing from single bacterial cells. The improved method uses an engineered phi29 DNA polymerase that is more efficient and robust than traditional versions.
A team led by Carmen Birchmeier has investigated the process of swallowing in more detail, revealing that sensory cells in the vagus nerve play a key role in detecting mechanical stimuli in the esophagus. This understanding could lead to better treatments for swallowing disorders, including malnutrition and weight loss.
Researchers developed a computational platform called IRIS to discover tumor antigens from alternative RNA splicing, expanding cancer immunotherapy targets. Hundreds of predicted TCR targets were found to be presented by human leukocyte antigen molecules.
A new approach, STING-seq, combines genetic association studies, gene editing, and single-cell sequencing to identify causal variants and genetic mechanisms for blood cell traits. This method can help scientists identify drug targets for diseases with a genetic basis.
Researchers have developed a new technology to sequence individual mitochondria in single cells, allowing for unbiased analysis of full-length mtDNA. This has revealed complex patterns of pathogenic mtDNA mutations and the potential risks of off-target mutations in genetic editing strategies.
Researchers have developed a new microfluidic chip that enables the visualization of rare cell types and disease patterns in tissue. This method combines imaging and sequencing techniques, providing spatially resolved information about individual cells and their environment.
Researchers developed a computational analysis method to detect and identify somatic SVs in leukemia patients, gaining insights into molecular consequences and potential therapies. The approach enables understanding of individual somatic mutations and may lead to targeted treatments.
Leif Ludwig's analytical method allows for easier disentanglement of blood cell trajectories, enabling identification of leukemia cell development or degenerative changes. This breakthrough opens up possibility for human medicine to conduct studies in clinical practice and derive therapeutic interventions.
The network aims to develop a comprehensive children's cell 'atlas' to examine the earliest origins of disease. Researchers will work with young patients and their families to identify disease triggers and intervene early, potentially preventing chronic diseases.
Langerhans' Cell Histiocytosis (LCH) is a serious disease affecting children that can be fatal in severe cases. Researchers have identified the origin of LCH cells, which are derived from both dendritic and monocyte cells.
A spatial cell atlas of the developing human lung describes 144 cell types and their interactions, uncovering new links between developmental cells and lung cancer. The atlas provides a unique resource for understanding healthy lung development and investigating disease origins.
Researchers studied the lung immune responses of cave nectar bats to Malacca virus, identifying unique white blood cell behaviors and gene signatures that help limit inflammation. The findings could lead to better ways of combatting human viral infections.
A research study led by Mabel Vidal identified a common genetic signature among infiltrating T cells of different cancer types. The study used AI to analyze data from public repositories and confirmed the results through proteomics experiments.
Researchers at Osaka University have developed a computational tool called CAPITAL that can carry out accurate comparative analysis of complex single-cell sequencing datasets. The tool uses a pseudotime trajectory approach to align and compare cells along hypothetical paths reflecting their progress through transitional processes.
Researchers have developed a new DNA nanotechnology-driven method called Light-Seq that enables the analysis of gene expression patterns in hard-to-access cells within intact tissues. This approach overcomes limitations of existing spatial transcriptomics methods, allowing for deeper understanding of disease mechanisms and biology.
Researchers have used single-cell sequencing to uncover novel gene expression patterns in injured kidney cells, providing new avenues for biomarker discovery and treatment. The studies reveal that epithelial cells of all tubule segments are involved in the injury processes, with distinct molecular patterns across patients.
Researchers at Brown University have developed a new method to isolate single cells from complex tissues using electric fields. This approach results in high-quality, intact single cells that are superior to standard isolation methods in terms of labor, cost, and efficiency.
Scientists discovered a new species of giant filamentous bacterium, Thiomargarita magnifica, with DNA clustered within membrane-bound compartments. This unique organism challenges traditional understanding of bacterial morphology and genomic complexity.
Researchers studied ant brains using single cell technology, revealing functional specialization and complementary brain cell composition differences among individuals. The study found specialized neurons for learning and memory in worker ants and optic lobe cells in male ants.
Researchers developed a mathematical technique to measure total tumor-specific mRNA levels from bulk tumor sequencing data, associating higher mRNA levels with reduced patient survival. The study suggests this approach could serve as a prognostic biomarker for various cancers, guiding treatment selection.
The Tabula Sapiens Consortium has unveiled a massive digital atlas that maps gene expression in nearly 500,000 cells from 24 human tissues and organs. The atlas provides unprecedented insights into human health and disease, offering new avenues for therapies and treatments.
Researchers applied scRNA-seq to study hypertrophic cardiomyopathy, identifying novel regulatory interactions and genes driving disease-related swelling. This knowledge can be used to develop new drugs that target underlying causes, reducing disease progression.
Researchers developed a new software tool, called 'meta-transcriptome detector,' that integrates genetic sequencing analysis of hosts and their microbiomes. The tool enables analysis of gene expression activity in both microbes and hosts simultaneously, allowing researchers to spot relationships between them.
An international team led by BGI-Research has produced the first spatiotemporal maps of cellular dynamics in mice, Drosophila, zebrafish, and Arabidopsis using Stereo-seq technology. This breakthrough enables scientists to analyze the distribution and placement of molecules and cells in situ and over time.
The study found significant heterogeneity among non-hematopoietic cells (NHCs) in human lymph nodes, with subgroups exhibiting distinct transcriptional changes and interaction patterns with malignant cells. This discovery may lead to the identification of potential biomarkers for therapeutic approaches.
The study provides a single-cell transcriptome map of 45 tissues and organs from long-tailed macaque monkeys, identifying 113 major cell types. This will improve the ability to pinpoint how to develop potential treatments for human diseases with greater precision.
Scientists have constructed a detailed map of lung development after birth, providing insights into the genetic and epigenetic factors that affect lung health. The study used next-generation single-cell sequencing technologies to analyze over 80,000 human and mouse lung cells, revealing clues on how cell types communicate and develop.
The UNC Charlotte team developed a universal AI algorithm called AutoClass to clean noisy single-cell RNA sequencing (scRNA-Seq) data. The algorithm effectively removes noise and enhances downstream analysis in multiple aspects, demonstrating its robustness and scalability.
Researchers developed a transgene-free method to convert human pluripotent stem cells into 8-cell totipotent embryo-like cells, paving the way for advances in organ regeneration and synthetic biology. These cells can be used to regenerate human organs, study human embryonic development, and prevent pregnancy loss.
Recent studies found that intestinal cells can change specializations in response to BMP signaling. This process, called zonation, is crucial for the proper functioning of the gut. Researchers used organoids and mouse models to confirm this discovery, which may lead to new treatments for metabolic diseases.
A new method called DisCo enhances the efficiency of single-cell RNA sequencing by actively detecting and capturing cells using machine-vision. This approach allows for continuous operation and high capture efficiency, making it suitable for processing small cell samples such as tissues or patient biopsies.
Scientists sequenced the gene expression profiles of more than 170,000 individual cells to shed light on a key mystery: the role of Type I Interferons (IFN) during viral infections. The study reveals interferon plays a crucial role in clearing the virus by alerting immune cells, such as macrophages, to search and destroy infected cells.
Researchers at the Buck Institute discovered a naturally occurring metabolite, 25-hydroxycholesteral, that significantly reduces senescent cells in multiple cell types and improves muscle mass in aged mice. The molecule targets CRYAB, a small heat shock protein associated with age-related diseases like myopathies.
Researchers have developed a machine learning framework called dynamo that can predict a cell's path over time, including its fate and genetic changes. The tool uses data from individual cells to create mathematical equations describing the cell's trajectory.
Researchers developed a new tool called cell2location to visualize cell function and spatial information. The tool combines single-cell sequencing data with spatial transcriptomic data, enabling the identification of rare cell subtypes and their precise locations within tissues.
Scientists used single-cell sequencing to analyze over 150,000 cells from 11 tumor samples, discovering novel mechanisms of tumor development and identifying potential targets for therapy. The findings may lead to personalized medicine and improved treatment options for gynecologic cancers.
Researchers at Memorial Sloan Kettering Cancer Center have developed a molecular atlas of small cell lung cancer, revealing a rare population of stem-like cells that correlate with patient outcomes. These cells have metastatic properties and are found across many SCLC tumors, suggesting they may be driving the disease's aggressiveness.
Researchers developed an integrated framework combining single-cell and metagenomics to characterize microbes. The approach showed higher accuracy and precise binning, revealing more bacterial genera and intra-species diversity.
Scientists have developed a technique to sequence individual malaria parasites' genomes, allowing for the detection of new mutations. These mutations are often targeting a gene family controlling transcription in malaria, suggesting potential avenues for developing more effective treatments and vaccines.
A team of scientists has identified molecular biomarkers associated with sepsis, which could be used to predict patient outcomes and guide treatment. The study found that changes in CD52 expression were linked to good outcomes, while S100A9 acted as a driver of fatal sepsis.
Researchers have established an organoid biobank to search for genes essential for SARS-CoV-2 replication and spread. The study identified TMPRSS2 as a potential therapeutic target for the coronavirus, with specific inhibitors recently developed.
Researchers at Tampere University identified a specific population of treatment-resistant cells that persists in prostate cancer tissue. This finding suggests that the presence of these cells can predict patient responses to treatment and may help tailor treatment for different subgroups of patients.
Researchers at the University of Toronto have created a new tool called DISCO that enables the analysis of individual cells in their natural environment. The method combines cell microscopy with single-cell omics platforms, allowing scientists to study stem cells and other rare cell types in greater detail.
A new method, knowledge-primed neural networks (KPNNs), combines deep learning with biological interpretability to understand complex biological systems. KPNNs have been applied to large single-cell datasets, revealing unexpected diversity in cell-type-defining regulatory networks.
Scientists have improved a technique called RACE-Seq to better understand the functions of individual cells in environmental samples. The new protocol increases genome coverage from around 20% to over 50%, allowing researchers to analyze the genetic and functional links between cells and their environments.
Researchers used single cell genome sequencing to analyze malaria parasite cells, finding that nearly all infections were caused by a single mosquito bite. This discovery could lead to more effective interventions and models for predicting antimalarial drug resistance.
Researchers have created a spatial map of gene expression for individual cells in various tissues, including the liver and intestinal epithelium. The new algorithm, called 'novoSpaRc', uses machine learning to track gene activity and reveals new insights into tissue organization and regulation.
Researchers have developed a novel method to precisely detect and characterize genes in individual cells, enabling selective enrichment of selected molecules. This approach, called BART-Seq, addresses the challenge of detecting low-abundance gene transcripts and has potential applications in disease diagnosis and precision gene-editing.
Researchers at Harvard University have developed a new platform for rapid single-cell sequencing, combining microfluidics and novel software to scale up single-cell ATAC-seq. This approach enables the analysis of gene control in individual cells, revealing new insights into cell development and disease processes.
BRB-seq, a novel approach to RNA sequencing, preserves strand-specificity and detects the same number of genes as gold standard methods. The technique is 25 times less expensive than commercial RNA sequencing technologies, enabling bulk RNA sequencing of large sets of samples.
A new study compared traditional Illumina platforms to an alternative BGISEQ-500 short-read sequencing platform for single-cell transcriptomics. The authors found that BGISEQ-500 was highly comparable in sensitivity, accuracy, and reproducibility of detected RNA molecules.