Journal: Seminars in Liver Disease

Article Title: Unraveling the Complexity of Liver Disease One Cell at a Time

doi: 10.1055/s-0042-1755272

Figure Lengend Snippet: Single-cell experimental and analysis workflow. (A) Spatial transcriptomics: liver tissue samples are sectioned, and transcripts are barcoded according to their location based on a matrix of spots. These barcodes are then used to spatially resolve gene signatures across the tissue section. (B) Droplet-based experimental workflow: dissected tissues are dissociated into either single-cell or single-nucleus suspensions. CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing): cells can be tagged using oligo-labeled antibodies to link protein to RNA expression. ScATAC-seq: (single-cell assay for transposase-accessible chromatin with sequencing) is an unbiased, epigenetic regulation discovery tool that determines regions of open chromatin genomic DNA that are accessible to transcriptional machinery. Tn5 is used to sequentially cleave accessible DNA regions and to attach PCR amplification primers to generated barcoded accessible DNA fragments. RNA from single cells, DNA-oligomer labeled antibody-tagged cells, and single-nuclei or DNA from transposed nuclei are used to generate gene expression and accessible DNA libraries at a single-cell resolution through droplet-based experimental workflows such as the 10× genomics platform. Amplification of T and B cell receptor regions is used to link adaptive lymphocyte transcriptomes to their receptor sequences and determines clonal expansion. (C) Downstream analysis of these data relies on clustering to group cells together based on similarity of transcriptomic, proteomic, or epigenetic features. Trajectory inference analysis orders cells along a smooth continuous path of transcriptomic changes and can help deepen our understanding of cellular differentiation pathways and how cell states change with conditions. Differential gene expression analysis helps determine the genes directing these differences in cell type and or state and intracellular interaction analysis can be used to infer the pathways that cells use to communicate with each other in health and disease. GEX, gene expression; PCR, polymerase chain reaction; RT, reverse transcription; scRNA-seq, single-cell RNA-sequencing; snRNA-seq, single-nucleus RNA-sequencing; Tn5, Transposon Tn5.

Article Snippet: Spatial transcriptomics deconvolution , MuSiC , Giotto , Deconvolution of spots in spatial transcriptomics into constituent cell types based on reference gene signatures..

Techniques: Sequencing, Labeling, RNA Expression, Amplification, Generated, Gene Expression, Cell Differentiation, Polymerase Chain Reaction, Reverse Transcription, RNA Sequencing

Journal: Seminars in Liver Disease

Article Title: Unraveling the Complexity of Liver Disease One Cell at a Time

doi: 10.1055/s-0042-1755272

Figure Lengend Snippet: Key steps in single-cell analysis

Article Snippet: Spatial transcriptomics deconvolution , MuSiC , Giotto , Deconvolution of spots in spatial transcriptomics into constituent cell types based on reference gene signatures..

Techniques: Gene Expression, RNA Sequencing, Sequencing, Cell Differentiation, Expressing

Journal: Biophysics Reviews

Article Title: Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing

doi: 10.1063/5.0091135

Figure Lengend Snippet: An overview of deep learning (and machine learning) methods for spatial transcriptomics presented in this review. In this work, we provide a brief background on related biological concepts, such as single-cell RNA sequencing (scRNAseq) and spatial transcriptomic (ST) technologies (Sec. II), followed by an overview of common deep learning architectures in Sec. III. We then dive deeper into specific machine learning techniques for spatial reconstruction (Sec. IV A), scRNAseq and ST alignment (Sec. IV B), ST spot deconvolution (Sec. IV C), spatial clustering (Sec. IV D), and cell–cell interaction (Sec. IV E). A more comprehensive list of the state-of-the-art methods for spatial transcriptomics is provided in Table I.

Article Snippet: C. Spot deconvolution One downside of using NGS-based technologies remains to be their resolution: Despite the recent technological advancements, most ST platforms (e.g., spatial transcriptomics , Visium, DBiT-seq, 181 Nanostring GeoMx, 182 and SlideSeq) do not have a single-cell resolution.

Techniques: RNA Sequencing

Journal: Biophysics Reviews

Article Title: Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing

doi: 10.1063/5.0091135

Figure Lengend Snippet: Visualization of DestVI's computation workflow for spot deconvolution. DestVI uses information from both data modalities of the ST data (coordinates and scRNAseq). DestVI defines two latent variable models (LVMs) for each data modality: an LVM for modeling scRNAseq data (scLVM, shown at the top) and one that aims to model the ST data (stLVM, shown at the bottom). We describe each one in Sec. IV C. This figure was recreated for this manuscript based on illustrations from Lopez et al.69

Article Snippet: C. Spot deconvolution One downside of using NGS-based technologies remains to be their resolution: Despite the recent technological advancements, most ST platforms (e.g., spatial transcriptomics , Visium, DBiT-seq, 181 Nanostring GeoMx, 182 and SlideSeq) do not have a single-cell resolution.

Techniques:

Journal: Biophysics Reviews

Article Title: Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing

doi: 10.1063/5.0091135

Figure Lengend Snippet: Visualization of DestVI's computation workflow for spot deconvolution. DestVI uses information from both data modalities of the ST data (coordinates and scRNAseq). DestVI defines two latent variable models (LVMs) for each data modality: an LVM for modeling scRNAseq data (scLVM, shown at the top) and one that aims to model the ST data (stLVM, shown at the bottom). We describe each one in Sec. IV C. This figure was recreated for this manuscript based on illustrations from Lopez et al.69

Article Snippet: DestVI (deconvolution of spatial transcriptomics profiles using variation inference) is a Bayesian model for spot deconvolution.

Techniques: