Searching journal content for articles similar to Pokhilko et al. 31 (6): 1069.

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  1. ...that direct cell state transitions at the single-cell level.ResultsOverview of the TFcomb frameworkWe developed TFcomb as a computational method for identifying reprogramming TF combinations directing cell state transitions from the source state to the target state (Fig. 1A). TFcomb takes both scRNA-seq data...
  2. ...-Iseppi et al. 2021). In our study, we developed a workflow designed to streamline the analysis aspect of high-throughput long-read RNA sequencing data, enabling the identification of transcript isoforms at the single-cell level. Our workflow modified the 10x Genomics scRNA-seq protocol (Gupta et al. 2018...
  3. ...-mutated subclones exhibit distinct transcriptomic behavior when compared to other cancer subclones. To achieve these goals, we use scBayes, which integrates bulk DNA sequencing and single-cell RNA sequencing (scRNA-seq) data to genotype individual cells for subclone-defining mutations. Although the most common...
  4. ..., facilitating the development of future targeted therapeutics.In this study, we aim to address the knowledge gap by performing single-cell RNA sequencing to profile m6A RNA methylation patterns in individual cells of the hippocampus. A myriad of different m6A sequencing detection methods exist, such as m6A RNA...
  5. ..., resting control participants were sampled and analyzed at the same two times of day as the exercised participants (Fig. 1A; Supplemental Table S1). Nuclei isolated from the 12 skeletal muscle samples underwent single-nuclei (sn) multiome library construction and sequencing to generate same-nucleus RNA...
  6. .... This makes them insensitive in complex environments where the detailed dynamics of cell interactions matter. We introduce CellAgentChat, an agent-based model (ABM) designed to decipher CCIs from single-cell RNA sequencing and spatial transcriptomics data. This approach models biological systems...
  7. ...is applicable across multiple independent single-cell RNA sequencing (scRNA-seq) data sets. Using the pretrained model of the PD scRNA-seq data set (midbrains of young and aged healthy donors and PD patients) (as used in Fig. 1; Adams et al. 2024), we inferred disease progression levels of individual cells...
  8. ...of transcriptome and chromatin accessibility at the single-cell level, thus allowing for an in-depth exploration of the cellular dynamics of tissue regeneration.Several studies have utilized single-cell/nucleus RNA sequencing (sc/snRNA-seq) to elucidate cell-type contributions, identify cell...
  9. ...cells from scRNA-seq experiments (both full-transcript and end-capture protocols) is the primary goal of our method, we showed that it is generally applicable to a wide range of single-cell sequencing technologies that are capable of assessing the presence of somatic mutations. We provided proof...
  10. ...of misidentifying the correct cell groups. These inaccuracies introduce biases into the depiction of the complexity of biological processes, impeding an accurate understanding of the molecular mechanisms involved (Kester and van Oudenaarden 2018).The recent breakthrough in single-cell RNA sequencing (scRNA-seq) has...
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