GARAGE improves rare cell detection in single-cell RNA sequencing analysis using AI
GARAGE uses artificial intelligence to generate realistic single-cell RNA sequencing data, improving rare cell representation, gene selection and cell clustering in complex biological datasets...
GP-DHT uses single-cell RNA sequencing to map gene regulatory networks
Genes rarely act alone. Instead, cells rely on networks of genes that turn one another on or off, helping determine how cells develop, respond to signals and change during disease. Researchers at the Jingdezhen Ceramic University developed GP-DHT, a computational framework designed to infer these gene regulatory networks from single-cell RNA sequencing data. Overview of the GP-DHT framework (A) Human and mouse scRNA-seq datasets are integrated, log-normalized, and truncated for outlier control. Continuous expression values are then discretized by uniform bucketing into ordinal expression-level relations, yielding a discretized gene-cell expression matrix for downstream heterogeneous graph construction. (B) A multi-relational gene-cell heterogeneous graph is constructed from the discretized expression matrix. For each relation type, transformed source-node messages are aggregated over the target node’s neighborhood by element-wise max pooling. The pooled message is subsequently modulated by a target-conditioned feature-wise gate. The relation-specific representations are then linearly fused to obtain the final ...
CellART extracts single-cell information from high-resolution spatial transcriptomics
Spatial transcriptomics allows researchers to measure gene activity while preserving information about where that activity occurs inside tissue. Newer technologies can reach very high spatial resolution, but they often face an important limitation, either they detect relatively few RNA molecules in each location or measure only a limited number of genes. Researchers at the Hong Kong University of Science and Technology have developed CellART, a computational framework designed to recover detailed single-cell information from high-resolution spatial transcriptomics data. The CellART framework and cell-resolved analysis of a Xenium breast cancer section a, CellART takes high-resolution ST data and an scRNA-Seq reference, and returns boundaries, expression profiles and cell-type labels per cell. Step 1: an FPN encodes each subcellular spot with its neighborhood; a probabilistic model links the aggregated representations to the reference. Step 2: spots inside nuclei and outside dilated nuclei train a network that classifies the remaining spots to ...
Cell2space restores spatial context to single-cell RNA sequencing data
Single-cell RNA sequencing can reveal which genes are active in individual cells, but the process of separating cells from tissue removes information about where those cells originally lived. That spatial information matters because cells behave differently depending on their neighbors and their position within a tissue. Researchers at Shandong University have developed Cell2space, a deep learning framework that combines single-cell RNA sequencing with spatial transcriptomics to reconstruct cellular organization. Overview of Cell2space (a) The inputs of Cell2space include scRNA-seq (SC) data and ST with domain annotations. (b) The gene expression profiles from scRNA-seq and ST are integrated into a unified feature space to mitigate technical discrepancies and align the distributional differences between the two modalities. (c) Cell2space establishes a universal spatial affinity function, parameterized by a dense MLP, by contrasting spatially adjacent positive pairs and distant negative pairs in the shared latent feature space. (d) Once trained, the MLP-based ...
scFlowReport brings multiple single-cell RNA sequencing analyses into one workflow
Single-cell RNA sequencing allows researchers to measure gene activity in individual cells, making it possible to study how different cell types respond to disease, treatment or genetic changes. But generating the sequencing data is only the beginning. Researchers often need several separate analyses to understand what the results mean biologically. These can include identifying genes that change between groups, determining which biological pathways are affected, examining gene regulatory networks and studying how different cell types communicate with one another. Researchers at the Department of Biomedical Science and Engineering at Konkuk University developed scFlowReport, a workflow designed to bring these downstream analyses together under a single comparison framework. scFlowReport accepts a processed, cell-type-annotated Seurat object (.rds) together with a YAML configuration file as input and executes a three-stage workflow comprising preprocessing, analytical module execution, and report generation. The analytical stage includes cell-level and pseudobulk differential expression analysis, transcription factor regulon ...
Single-cell TimeLapse-seq reveals MTA is an essential retrotransposon
Very early in development, an embryo must begin using its own genome instead of relying only on RNA and proteins inherited from the egg. This transition is known as zygotic genome activation, or ZGA, and it is a critical step in early embryonic development. Transposable elements, or TEs, are repetitive DNA sequences that can become active during this period. Although they were once viewed mainly as genomic passengers, growing evidence suggests that some transposable elements help regulate gene activity. Researchers at Tongji University in Shanghai and colleagues developed a new single-cell RNA sequencing method called single-cell TimeLapse sequencing, or scTL-seq, to examine newly produced RNA in individual mouse embryos. scTL-seq reveals nascent transcription in mouse early embryos a Schematic diagram of the scTimeLapse-seq (scTL-seq) procedure. 4sU, 4-thiouridine. TFEA, 2,2,2-trifluoroethylamine. C*, cytidine substitution. b Nucleotide mutation rates after TimeLapse-seq using 2 μg, 100 ng, and 10 ng 293T RNAs. Unconversion, ...
Single-cell RNA sequencing provides a closer look at the Aedes aegypti midgut
Single-cell RNA sequencing, or scRNA-seq, has given researchers a way to examine gene activity one cell at a time. Unlike traditional bulk RNA sequencing, which measures RNA from many cells together and produces an averaged signal, scRNA-seq can reveal important differences among individual cells, including rare cell populations that might otherwise be overlooked. This ability is particularly useful when studying complex tissues made up of many different cell types. Researchers can use patterns of gene expression to identify groups of cells and investigate how those cells perform different biological functions or respond to changes in their environment. Single-cell RNA sequencing is now increasingly being applied to insects, where it can help researchers understand cellular diversity and how different cells respond to infection. One important organism for this type of research is Aedes aegypti, the mosquito species responsible for transmitting viruses that cause diseases such as dengue, Zika, yellow fever, ...
RNA-Seq Products & Services
Stay Connected