scholarly journals A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer

Medicina ◽  
2021 ◽  
Vol 57 (11) ◽  
pp. 1257
Author(s):  
Andrea Angius ◽  
Antonio Mario Scanu ◽  
Caterina Arru ◽  
Maria Rosaria Muroni ◽  
Ciriaco Carru ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Single Cell ◽  
Cell Population ◽  
Drug Targets ◽  
Published Data ◽  
Specific Cell ◽  
Sequencing Analysis ◽  
Genome Features ◽  
Unique Markers ◽  
Individualized Prediction

In the study of cancer, omics technologies are supporting the transition from traditional clinical approaches to precision medicine. Intra-tumoral heterogeneity (ITH) is detectable within a single tumor in which cancer cell subpopulations with different genome features coexist in a patient in different tumor areas or may evolve/differ over time. Colorectal carcinoma (CRC) is characterized by heterogeneous features involving genomic, epigenomic, and transcriptomic alterations. The study of ITH is a promising new frontier to lay the foundation towards successful CRC diagnosis and treatment. Genome and transcriptome sequencing together with editing technologies are revolutionizing biomedical research, representing the most promising tools for overcoming unmet clinical and research challenges. Rapid advances in both bulk and single-cell next-generation sequencing (NGS) are identifying primary and metastatic intratumoral genomic and transcriptional heterogeneity. They provide critical insight in the origin and spatiotemporal evolution of genomic clones responsible for early and late therapeutic resistance and relapse. Single-cell technologies can be used to define subpopulations within a known cell type by searching for differential gene expression within the cell population of interest and/or effectively isolating signal from rare cell populations that would not be detectable by other methods. Each single-cell sequencing analysis is driven by clustering of cells based on their differentially expressed genes. Genes that drive clustering can be used as unique markers for a specific cell population. In this review we analyzed, starting from published data, the possible achievement of a transition from clinical CRC research to precision medicine with an emphasis on new single-cell based techniques; at the same time, we focused on all approaches and issues related to this promising technology. This transition might enable noninvasive screening for early diagnosis, individualized prediction of therapeutic response, and discovery of additional novel drug targets.

scholarly journals Single cell immune profiling reveals a developmentally distinct CD4+ GM-CSF+ T cell lineage that induces GI tract GVHD

2022 ◽  
Author(s):  
Clint Piper ◽  
Emma Hainstock ◽  
Cheng Yin-Yuan ◽  
Yao Chen ◽  
Achia Khatun ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Population ◽  
Cell Populations ◽  
Sequencing Analysis ◽  
Gi Tract ◽  
Gm Csf ◽  
Mediators Of Inflammation ◽  
Ifn Γ

Gastrointestinal (GI) tract involvement is a major determinant for subsequent morbidity and mortality arising during graft versus host disease (GVHD). CD4+ T cells that produce GM-CSF have emerged as central mediators of inflammation in this tissue site as GM-CSF serves as a critical cytokine link between the adaptive and innate arms of the immune system. However, cellular heterogeneity within the CD4+ GM-CSF+ T cell population due to the concurrent production of other inflammatory cytokines has raised questions as to whether these cells have a common ontology or if there exists a unique CD4+ GM-CSF+ subset that differs from other defined T helper (TH) subtypes. Using single cell RNA sequencing analysis, we identified two CD4+ GM-CSF+ T cell populations that arose during GVHD and were distinguishable by the presence or absence of IFN-γ co-expression. CD4+ GM-CSF+ IFN-γ- T cells which emerged preferentially in the colon had a distinct transcriptional profile, employed unique gene regulatory networks, and possessed a non-overlapping TCR repertoire when compared to CD4+ GM-CSF+ IFN-γ+ T cells as well as all other transcriptionally defined CD4+ T cell populations in the colon. Functionally, this CD4+ GM-CSF+ T cell population contributed to pathological damage in the GI tract which was critically dependent upon signaling through the IL-7 receptor but was independent of type 1 interferon signaling. Thus, these studies help to unravel heterogeneity within CD4+ GM-CSF+ T cells that arise during GVHD and define a developmentally distinct colitogenic TH GM-CSF+ subset that mediates immunopathology.

scholarly journals scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets

2021 ◽  
Author(s):  
Massimo Andreatta ◽  
Ariel J. Berenstein ◽  
Santiago J Carmona
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
R Package ◽  
Training Data ◽  
Cell Populations ◽  
Specific Cell ◽  
A Cell ◽  
Cell Data

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. In our benchmark for blood-derived and tumor-infiltrating immune cells, scGate outperforms SingleR, a state-of-the-art classifier for single-cell data. scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from complex scRNA-seq datasets. Availability: R package source code and reproducible tutorials are available at https://github.com/carmonalab/scGate

scholarly journals BRAin INteractive Sequencing Analysis Tool (BRAIN-SAT); facilitating interactive transcriptome analyses (http://brainsat.eu/)

2019 ◽  
Author(s):  
M.L. Dubbelaar ◽  
M.L. Brummer ◽  
M. Meijer ◽  
B.J.L. Eggen ◽  
H.W.G.M. Boddeke
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
New Technologies ◽  
Easy Access ◽  
Published Data ◽  
Data Sets ◽  
Analysis Tool ◽  
Sequencing Analysis ◽  
Quantitative Expression

AbstractOver the last decade, a large number of glia transcriptome studies has been published. New technologies and platforms have been developed to allow access and interrogation of the published data. The increase in large transcriptomic data sets allows for innovative in silico analyses to address biological questions. Here we present BRAIN-SAT, the follow-up of our previous database GOAD, with several new features available on an interactive platform that enables access to recent, high quality bulk and single cell RNA-Seq data. The combination of several functions including gene searches, differential and quantitative expression analysis and a single cell expression analysis feature enables the exploration of published data sets at different levels. These different functionalities can be used for researchers and research companies in the neuroscience field to evaluate and visualize gene expression levels in a set of relevant publications. Here, we present a new platform with easy access to published gene expression studies for data exploration and gene of interest searches.

scholarly journals Identification of cell-type marker genes from plant single-cell RNA-seq data using machine learning

2020 ◽  
Author(s):  
Haidong Yan ◽  
Qi Song ◽  
Jiyoung Lee ◽  
John Schiefelbein ◽  
Song Li
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Single Cell ◽  
Correlation Method ◽  
Cell Types ◽  
Support Vector ◽  
Marker Genes ◽  
Specific Cell ◽  
Sequencing Analysis ◽  
Cell Type

AbstractAn essential step of single-cell RNA sequencing analysis is to classify specific cell types with marker genes in order to dissect the biological functions of each individual cell. In this study, we integrated five published scRNA-seq datasets from the Arabidopsis root containing over 25,000 cells and 17 cell clusters. We have compared the performance of seven machine learning methods in classifying these cell types, and determined that the random forest and support vector machine methods performed best. Using feature selection with these two methods and a correlation method, we have identified 600 new marker genes for 10 root cell types, and more than 70% of these machine learning-derived marker genes were not identified before. We found that these new markers not only can assign cell types consistently as the previously known cell markers, but also performed better than existing markers in several evaluation metrics including accuracy and sensitivity. Markers derived by the random forest method, in particular, were expressed in 89-98% of cells in endodermis, trichoblast, and cortex clusters, which is a 29-67% improvement over known markers. Finally, we have found 111 new orthologous marker genes for the trichoblast in five plant species, which expands the number of marker genes by 58-170% in non-Arabidopsis plants. Our results represent a new approach to identify cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.

scholarly journals Cell type classification and discovery across diseases, technologies and tissues reveals conserved gene signatures and enables standardized single-cell readouts

2021 ◽  
Author(s):  
Mathew Chamberlain ◽  
Richa Hanamsagar ◽  
Frank O. Nestle ◽  
Emanuele de Rinaldis ◽  
Virginia Savova
Keyword(s):  
Precision Medicine ◽  
Single Cell ◽  
Rna Sequencing ◽  
Drug Targets ◽  
Sequencing Data ◽  
Candidate Drug ◽  
Machine Learning Approach ◽  
Single Cell Rna Sequencing ◽  
Conserved Gene ◽  
Cellular Phenotypes

ABSTRACTAutoimmune diseases are a major cause of mortality1,2. Current treatments often yield severe insult to host tissue. It is hypothesized that improved “precision medicine” therapies will target pathogenic cells selectively and thus reduce or eliminate severe side effects, and potentially induce robust immune tolerance3. However, it remains challenging to systematically identify which cellular phenotypes are present in cellular ensembles. Here, we present a novel machine learning approach, Signac, which uses neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. We demonstrate that Signac accurately classified single cell RNA-sequencing data across diseases, technologies, species and tissues. Then we applied Signac to identify known and novel immune-relevant precision medicine candidate drug targets (n = 12) in rheumatoid arthritis. A full release of this workflow can be found at our GitHub repository (https://github.com/mathewchamberlain/Signac).

Cross-species integration of single-cell RNA-seq resolved alveolar-epithelial transitional states in idiopathic pulmonary fibrosis

2021 ◽  
Author(s):  
Kevin Y. Huang ◽  
Enrico Petretto
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Single Cell ◽  
Transitional Cell ◽  
Notch Pathway ◽  
Alveolar Epithelium ◽  
State Transitions ◽  
Specific Cell ◽  
Differentiation Process ◽  
Cell State

Single-cell transcriptomics analyses of the fibrotic lung uncovered two cell states critical to lung injury recovery in the alveolar epithelium- a reparative transitional cell state in the mouse and a disease-specific cell state (KRT5-/KRT17+) in human idiopathic pulmonary fibrosis (IPF). The murine transitional cell state lies between the differentiation from type 2 (AT2) to type 1 pneumocyte (AT1), and the human KRT5-/KRT17+ cell state may arise from the dysregulation of this differentiation process. We review major findings of single-cell transcriptomics analyses of the fibrotic lung and re-analyzed data from 7 single-cell RNA sequencing studies of human and murine models of IPF, focusing on the alveolar epithelium. Our comparative and cross-species single-cell transcriptomics analyses allowed us to further delineate the differentiation trajectories from AT2 to AT1 and AT2 to the KRT5-/KRT17+ cell state. We observed AT1 cells in human IPF retain the transcriptional signature of the murine transitional cell state. Using pseudotime analysis, we recapitulated the differentiation trajectories from AT2 to AT1 and from AT2 to KRT5-/KRT17+ cell state in multiple human IPF studies. We further delineated transcriptional programs underlying cell state transitions and determined the molecular phenotypes at terminal differentiation. We hypothesize that in addition to the reactivation of developmental programs (SOX4, SOX9), senescence (TP63, SOX4) and the Notch pathway (HES1) are predicted to steer intermediate progenitors to the KRT5-/KRT17+ cell state. Our analyses suggest that activation of SMAD3 later in the differentiation process may explain the fibrotic molecular phenotype typical of KRT5-/KRT17+ cells.

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