scholarly journals Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Robrecht Cannoodt ◽  
Wouter Saelens ◽  
Louise Deconinck ◽  
Yvan Saeys
Keyword(s):  
Single Cell ◽  
Computational Methods ◽  
Network Inference ◽  
Method Development ◽  
Developmental Trajectories ◽  
Single Cells ◽  
Cellular Processes ◽  
Simulation Engine ◽  
Cell Simulation ◽  
Simulation Engines

AbstractWe present dyngen, a multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current single-cell simulation engines, and allows better method development and benchmarking, thereby stimulating development and testing of computational methods. We demonstrate its potential for spearheading computational methods on three applications: aligning cell developmental trajectories, cell-specific regulatory network inference and estimation of RNA velocity.

scholarly journals dyngen: a multi-modal simulator for spearheading new single-cell omics analyses

2020 ◽  
Author(s):  
Robrecht Cannoodt ◽  
Wouter Saelens ◽  
Louise Deconinck ◽  
Yvan Saeys
Keyword(s):  
Single Cell ◽  
Computational Methods ◽  
Network Inference ◽  
Method Development ◽  
Developmental Trajectories ◽  
Cellular Processes ◽  
Simulation Engine ◽  
Cell Simulation ◽  
Novel Applications ◽  
Simulation Engines

AbstractWe present dyngen, a novel, multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current single-cell simulation engines, and allows better method development and benchmarking, thereby stimulating development and testing of novel computational methods. We demonstrate its potential for spearheading novel computational methods on three novel applications: aligning cell developmental trajectories, single-cell regulatory network inference and estimation of RNA velocity.

The 3D Genome Structure of Single Cells

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tianming Zhou ◽  
Ruochi Zhang ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Data Science ◽  
Spatial Organization ◽  
Method Development ◽  
Single Cells ◽  
Genome Structure ◽  
Computational Method ◽  
Publication Date ◽  
3D Genome ◽  
Genome Features

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Machine learning for single cell genomics data analysis

2021 ◽  
Author(s):  
Félix Raimundo ◽  
Laetitia Papaxanthos ◽  
Céline Vallot ◽  
Jean-Philippe Vert
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Network Inference ◽  
Method Development ◽  
Biological Knowledge ◽  
Omics Data ◽  
Gene Regulatory Network Inference ◽  
Multimodal Data ◽  
Low Dimensional ◽  
Type Classification

AbstractSingle-cell omics technologies produce large quantities of data describing the genomic, transcriptomic or epigenomic profiles of many individual cells in parallel. In order to infer biological knowledge and develop predictive models from these data, machine learning (ML)-based model are increasingly used due to their flexibility, scalability, and impressive success in other fields. In recent years, we have seen a surge of new ML-based method development for low-dimensional representations of single-cell omics data, batch normalization, cell type classification, trajectory inference, gene regulatory network inference or multimodal data integration. To help readers navigate this fast-moving literature, we survey in this review recent advances in ML approaches developed to analyze single-cell omics data, focusing mainly on peer-reviewed publications published in the last two years (2019-2020).

scholarly journals An acoustic platform for single-cell, high-throughput measurements of the viscoelastic properties of cells

2020 ◽  
Author(s):  
Valentin Romanov ◽  
Giulia Silvani ◽  
Huiyu Zhu ◽  
Charles D Cox ◽  
Boris Martinac
Keyword(s):  
Mechanical Properties ◽  
Single Cell ◽  
High Throughput ◽  
Single Cells ◽  
Adherent Cells ◽  
Dependent Manner ◽  
Cellular Processes ◽  
Membrane Cytoskeleton ◽  
Change Temperature

ABSTRACTCellular processes including adhesion, migration and differentiation are governed by the distinct mechanical properties of each cell. Importantly, the mechanical properties of individual cells can vary depending on local physical and biochemical cues in a time-dependent manner resulting in significant inter-cell heterogeneity. While several different methods have been developed to interrogate the mechanical properties of single cells, throughput to capture this heterogeneity remains an issue. While new high-throughput techniques are slowly emerging, they are primarily aimed at characterizing cells in suspension, whereas high-throughput measurements of adherent cells have proven to be more challenging. Here, we demonstrate single-cell, high-throughput characterization of adherent cells using acoustic force spectroscopy. We demonstrate that cells undergo marked changes in viscoelasticity as a function of temperature, the measurements of which are facilitated by a closed microfluidic culturing environment that can rapidly change temperature between 21 °C and 37 °C. In addition, we show quantitative differences in cells exposed to different pharmacological treatments specifically targeting the membrane-cytoskeleton interface. Further, we utilize the high-throughput format of the AFS to rapidly probe, in excess of 1000 cells, three different cell-lines expressing different levels of a mechanosensitive protein, Piezo1, demonstrating the ability to differentiate between cells based on protein expression levels.

scholarly journals Multifactorial chromatin regulatory landscapes at single cell resolution

2021 ◽  
Author(s):  
Michael P. Meers ◽  
Derek H. Janssens ◽  
Steven Henikoff
Keyword(s):  
Single Cell ◽  
Target Identification ◽  
Developmental Trajectories ◽  
Single Cells ◽  
High Sensitivity ◽  
Cell Types ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Chromatin Profiling ◽  
Regulatory Landscapes

Chromatin profiling at locus resolution uncovers gene regulatory features that define cell types and developmental trajectories, but it remains challenging to map and compare distinct chromatin-associated proteins within the same sample. Here we describe a scalable antibody barcoding approach for profiling multiple chromatin features simultaneously in the same individual cells, Multiple Target Identification by Tagmentation (MulTI-Tag). MulTI-Tag is optimized to retain high sensitivity and specificity of enrichment for multiple chromatin targets in the same assay. We use MulTI-Tag to resolve distinct cell types using multiple chromatin features on a commercial single-cell platform, and to distinguish unique, coordinated patterns of active and repressive element regulatory usage in the same individual cells. Multifactorial profiling allows us to detect novel associations between histone marks in single cells and holds promise for comprehensively characterizing cell-specific gene regulatory landscapes in development and disease.

scholarly journals Forecasting when cells die during antibiotic exposure using stochastic gene expression

2018 ◽  
Author(s):  
Nicholas A. Rossi ◽  
Imane El Meouche ◽  
Mary J. Dunlop
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cells ◽  
Strong Relationship ◽  
Antibiotic Exposure ◽  
Survival Times ◽  
Time To Death ◽  
Cellular Processes ◽  
Expression Of Genes

AbstractAntibiotic killing does not occur at a single, precise time for all cells within a population. Variability in time to death can be caused by stochastic expression of genes, resulting in differences in endogenous stress-resistance levels between individual cells in a population. This variability can be part of a bet-hedging strategy where cells leverage noise to ensure a subset of the population can tolerate the drug, while decreasing the overall cost of expressing resistance genes. We asked whether single-cell differences in gene expression prior to antibiotic addition were related to cell survival times after antibiotic exposure for a range of genes of diverse function. We quantified the time to death of single cells under antibiotic exposure in combination with expression of reporters. For some reporters, the time to cell death had a strong relationship with the initial expression level of the genes. Reporters that could forecast cell fate included stress response genes, but also genes involved in a variety of other cellular processes like metabolism. Our results highlight the single-cell level non-uniformity of antibiotic killing and also provide examples of key genes where cell-to-cell variation in expression prior to antibiotic exposure is strongly linked to extended durations of antibiotic survival.

scholarly journals Cyclic Microchip Assay for Measurement of Hundreds of Functional Proteins in Single Neurons

2021 ◽  
Author(s):  
Liwei Yang ◽  
Avery Ball ◽  
Jesse Liu ◽  
Tanya Jain ◽  
Yueming Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Protein Detection ◽  
Cellular Processes ◽  
Cell Functions ◽  
Cellular Life ◽  
5Xfad Mouse ◽  
A Cell ◽  
Mouse Cells ◽  
Neuron Function

Proteins are responsible for nearly all cell functions throughout cellular life. To date, the molecular functions of hundreds of proteins have been studied as they are critical to cellular processes. Those proteins are varied dramatically at different statuses and differential stages of the cells even in the same tissue. The existing single-cell tools can only analyze dozens of proteins and thus have not been able to fully characterize a cell yet. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology that affords the comprehensive functional proteome profiling of single cells. It permits multiple, separate rounds of multiplex assays of the same single cells on a microchip where each round detects 40-50 proteins. A decoding process is followed to assign protein identities and quantify protein detection signals. We demonstrate the technology on a neuron cell line by detecting 182 proteins that includes surface makers, neuron function proteins, neurodegeneration markers, signaling pathway proteins and transcription factors. Further study on 5XFAD mouse, an Alzheimer s Disease (AD) model, cells validate the utility of our technology which reveals the deep heterogeneity of brain cells. Through comparison with control mouse cells, the differentially expressed proteins in the AD mouse model have been detected. The single-cell CycMIST technology can potentially analyze the entire functional proteome spectrum, and thus it may offer new insights into cell machinery and advance many fields including systems biology, drug discovery, molecular diagnostics, and clinical studies.

scholarly journals DTFLOW: Inference and Visualization of Single-cell Pseudo-temporal Trajectories Using Diffusion Propagation

2020 ◽  
Author(s):  
Jiangyong Wei ◽  
Tianshou Zhou ◽  
Xinan Zhang ◽  
Tianhai Tian
Keyword(s):  
Single Cell ◽  
Reduction Method ◽  
Developmental Trajectories ◽  
Branching Processes ◽  
Single Cells ◽  
New Method ◽  
Developmental States ◽  
Novel Approach ◽  
Cell Data

ABSTRACTOne of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. In this work we devise a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. This method consists of two major steps: namely a new dimension reduction method (i.e. Bhattacharyya kernel feature decomposition (BKFD)) and a novel approach, named Reverse Searching on kNN Graph (RSKG), to identify the underlying multi-branching processes of cellular differentiations. In BKFD we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm and then propose a new distance metric for calculating pseudo-times of single-cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of the new method with two state-of-the-art methods. Simulation results suggest that our proposed method has superior accuracy and strong robustness properties for constructing pseudo-time trajectories. Availability: DTFLOW is implemented in Python and available at https://github.com/statway/DTFLOW.

scholarly journals SCENIC: Single-cell regulatory network inference and clustering

2017 ◽  
Author(s):  
Sara Aibar ◽  
Carmen Bravo González-Blas ◽  
Thomas Moerman ◽  
Jasper Wouters ◽  
Vân Anh Huynh-Thu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
Target Genes ◽  
Single Cells ◽  
State Transitions ◽  
Rna Seq ◽  
Cell State ◽  
Gene Regulatory

AbstractSingle-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

scholarly journals Reversed graph embedding resolves complex single-cell developmental trajectories

2017 ◽  
Author(s):  
Xiaojie Qiu ◽  
Qi Mao ◽  
Ying Tang ◽  
Li Wang ◽  
Raghav Chawla ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Developmental Trajectories ◽  
Single Cells ◽  
Graph Embedding ◽  
Developmental Trajectory ◽  
Loss Of Function ◽  
Cell Fate Decisions ◽  
Principal Graph ◽  
Cell Trajectories

AbstractOrganizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.

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