Adapting the Smart-seq2 Protocol for Robust Single Worm RNA-seq.
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ABSTRACT:
SUBMITTER: Serra L
PROVIDER: S-EPMC5857950 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This protocol presents a plate-based workflow to perform RNA sequencing analysis of single cells/nuclei using Smart-seq2. We describe (1) the dissociation procedures for cell/nucleus isolation from the mouse brain and human organoids, (2) the flow sorting of single cells/nuclei into 384-well plates, and (3) the preparation of libraries following miniaturization of the Smart-seq2 protocol using a liquid-handling robot. This pipeline allows for the reliable, high-throughput, and cost-effective preparation of mouse and human samples for full-length deep single-cell/nucleus RNA sequencing. For complete details on the use and execution of this protocol, please refer to Bowers et al. (2020).
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Project description:The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.
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Project description:Single-cell RNA-seq of mitotically enriched cells in the medial ganglionic eminence (Dlx6 negative), as well as labeled cells using FlashTag technology.
2018-03-05 | GSE104157 | GEO
Project description:We optimized Voltage-Seq which combines, all-optical physiology, spatial mapping, on-site classification, and RNA-transcriptomics to robustly increase the throughput of synaptic connectivity testing and targeted molecular classification of postsynaptic neurons. Single-cell RNA-sequencing was performed on spatial- and voltage-recorded neurons in the mouse PAG. Uniform Manifold Approximation and Projection (UMAP) clustered cells as excitatory or inhibitory, and further differential expression analyses highlighted putative marker genes of GABAergic neurons. These sequencing results were in agreement with in-situ hybridization (ISH) and neuronal activity recordings.
2023-06-22 | E-MTAB-13104 | biostudies-arrayexpress
Project description:MotivationCell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable.ResultsWe herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL.AvailabilityAn implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch.Contactdengmh@pku.edu.cn.Supplementary informationSupplementary data are available at Journal Name online.
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Project description:BackgroundSingle-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand cell function and constitutes the basis of other advanced analysis. Nonnegative Matrix Factorization (NMF) has been widely used in clustering analysis of transcriptome data and achieved good performance. However, the existing NMF model is unsupervised and ignores known gene functions in the process of clustering. Knowledges of cell markers genes (genes that only express in specific cells) in human and model organisms have been accumulated a lot, such as the Molecular Signatures Database (MSigDB), which can be used as prior information in the clustering analysis of scRNA-seq data. Because the same kind of cells is likely to have similar biological functions and specific gene expression patterns, the marker genes of cells can be utilized as prior knowledge in the clustering analysis.MethodsWe propose a robust and semi-supervised NMF (rssNMF) model, which introduces a new variable to absorb noises of data and incorporates marker genes as prior information into a graph regularization term. We use rssNMF to solve the clustering problem of scRNA-seq data.ResultsTwelve scRNA-seq datasets with true labels are used to test the model performance and the results illustrate that our model outperforms original NMF and other common methods such as KMeans and Hierarchical Clustering. Biological significance analysis shows that rssNMF can identify key subclasses and latent biological processes. To our knowledge, this study is the first method that incorporates prior knowledge into the clustering analysis of scRNA-seq data.
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