A robust experimental and computational framework analysis at multiple resolutions, modalities and coverages
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ABSTRACT:
PROVIDER: PRJEB54964 | ENA |
REPOSITORIES: ENA
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Project description:We assessed the utilities of integrating four advanced sequencing and multiplex imaging technologies to study cancer-immune cell interactions. These four technologies include single-cell RNA sequencing and Spatial Transcriptomic (both measuring over >20,000 genes), RNA In Situ Hybridization (multiplex 4-12 genes) and Opal multiplex protein staining (4-9 proteins). We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), which account for over 70% of skin cancer cases.
2022-07-26 | E-MTAB-11932 | biostudies-arrayexpress
Project description:The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed, especially for cancer immunotherapy development, which requires understanding of molecular mechanisms and discovery of more druggable targets. In this work, we assembled and evaluated an integrated experimental framework and analytical process to enable genome-wide scale discovery of ligand-receptors potentially used for cellular crosstalks, followed by targeted validation. We assessed the complementarity of four different technologies: single-cell RNA sequencing and Spatial transcriptomic (measuring over >20,000 genes), RNA In Situ Hybridization (RNAscope, measuring 4-12 genes) and Opal Polaris multiplex protein staining (4-9 proteins). To utilize the multimodal data, we implemented existing methods and also developed STRISH (Spatial TRanscriptomic In Situ Hybridization), a computational method that can automatically scan across the whole tissue section for local expression of gene (e.g. RNAscope data) and/or protein markers (e.g. Polaris data) to recapitulate an interaction landscape across the whole tissue. We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We showed that inference of cell-cell interactions using scRNA-seq data can misdetect or detect false positive interactions. Spatial transcriptomics still suffers from misdetecting lowly expressed ligand-receptor interactions, but reduces false discovery. RNAscope and Polaris are sensitive methods for defining the location of potential ligand receptor interactions, and the STRISH program can determine the probability that local gene co-expression reflects true cell-cell interaction. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.
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Project description:This SuperSeries is composed of the following subset Series: GSE14694: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (reproducibility) GSE14695: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (standards) Refer to individual Series
2009-06-02 | E-GEOD-14696 | biostudies-arrayexpress
Project description:SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators
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Project description:Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high-dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. Here, we developed a quantitative computational framework, pseudo-genome divergence quantification (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from scRNA-seq data allowed for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagonostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies demonstrated that applying pgDQ to single cell and bulk tissue transcriptome data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution.
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Project description:Genome-wide transcription factor binding profiles provide valuable insights into hematopoietic development and malignancies. Today, advanced computational methods exploit cis-regulatory information to predict complex gene regulatory networks controlled by transcription factors. However, these prediction methods still require large amounts of reference data or experimental expertise. Here, we present a low-requirement network-based computational framework that exploits bidirectional transcription marked regulatory elements to identify important transcription factors driving hematopoietic decision making. Using CAGE-seq we predicted TF binding and confirmed experimentally validated TF important in cell conversion strategies by exploiting bidirectional regions. Next, we applied our framework to predict driving transcription factors in induced normal erythropoiesis and early myelopoiesis, as well as in acute myeloid leukemia associated MLL-AF9-driven immortalisation. Our approach allowed the identification of experimentally validated as well as thus far unexplored transcription factors in these processes.
2022-11-09 | GSE204710 | GEO
Project description:Genome-wide transcription factor binding profiles provide valuable insights into hematopoietic development and malignancies. Today, advanced computational methods exploit cis-regulatory information to predict complex gene regulatory networks controlled by transcription factors. However, these prediction methods still require large amounts of reference data or experimental expertise. Here, we present a low-requirement network-based computational framework that exploits bidirectional transcription marked regulatory elements to identify important transcription factors driving hematopoietic decision making. Using CAGE-seq we predicted TF binding and confirmed experimentally validated TF important in cell conversion strategies by exploiting bidirectional regions. Next, we applied our framework to predict driving transcription factors in induced normal erythropoiesis and early myelopoiesis, as well as in acute myeloid leukemia associated MLL-AF9-driven immortalisation. Our approach allowed the identification of experimentally validated as well as thus far unexplored transcription factors in these processes.
2022-11-09 | GSE204707 | GEO
Project description:Genome-wide transcription factor binding profiles provide valuable insights into hematopoietic development and malignancies. Today, advanced computational methods exploit cis-regulatory information to predict complex gene regulatory networks controlled by transcription factors. However, these prediction methods still require large amounts of reference data or experimental expertise. Here, we present a low-requirement network-based computational framework that exploits bidirectional transcription marked regulatory elements to identify important transcription factors driving hematopoietic decision making. Using CAGE-seq we predicted TF binding and confirmed experimentally validated TF important in cell conversion strategies by exploiting bidirectional regions. Next, we applied our framework to predict driving transcription factors in induced normal erythropoiesis and early myelopoiesis, as well as in acute myeloid leukemia associated MLL-AF9-driven immortalisation. Our approach allowed the identification of experimentally validated as well as thus far unexplored transcription factors in these processes.
2022-11-09 | GSE204708 | GEO
Project description:We developed a computational framework to discover short DNA sequences that confer strong expression in non-model organisms. The framework relies solely on whole genome and RNA sequencing data types, which are easily accessible to a variety of research groups. The framework proceeds in three main stages: 1) identification of a group of highly expressed loci that maintain high transcript counts across a broad range of experimental conditions, 2) extraction of the corresponding upstream candidate promoter regions of these highly expressed loci while minding nearby annotations and avoiding those that may potentially reside in operons, and 3) application of the motif finding algorithm in BioProspector to these upstream regions to predict the location and sequence of the -35 and -10 hexamers that drive the strong expression of these loci. Ultimately, we report sequences of 27-30 bases in length as candidate -35, -10 signals for each of the top loci and create a consensus motif from these predictions. We apply our framework to 80 RNA-seq datasets collected for the methanotroph Methylotuvimicrobium buryatense 5GB1 and validate our predictions computationally and experimentally. The data deposited here represent all RNA-seq data that, until this study, has not previously been published.
2021-05-24 | GSE162089 | GEO
Project description:RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants
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