Single-Cell Omics Analyses Enabled by Microchip Technologies

2019 ◽  
Vol 21 (1) ◽  
pp. 365-393 ◽  
Author(s):  
Yanxiang Deng ◽  
Amanda Finck ◽  
Rong Fan
Keyword(s):  
Single Cell ◽  
Life Science ◽  
State Of The Art ◽  
Molecular Layer ◽  
Cellular Heterogeneity ◽  
Cell Behavior ◽  
Omics Data ◽  
Behavior Development ◽  
Multiple Levels ◽  
Review State

Single-cell omics studies provide unique information regarding cellular heterogeneity at various levels of the molecular biology central dogma. This knowledge facilitates a deeper understanding of how underlying molecular and architectural changes alter cell behavior, development, and disease processes. The emerging microchip-based tools for single-cell omics analysis are enabling the evaluation of cellular omics with high throughput, improved sensitivity, and reduced cost. We review state-of-the-art microchip platforms for profiling genomics, epigenomics, transcriptomics, proteomics, metabolomics, and multi-omics at single-cell resolution. We also discuss the background of and challenges in the analysis of each molecular layer and integration of multiple levels of omics data, as well as how microchip-based methodologies benefit these fields. Additionally, we examine the advantages and limitations of these approaches. Looking forward, we describe additional challenges and future opportunities that will facilitate the improvement and broad adoption of single-cell omics in life science and medicine.

scholarly journals Integrated analysis of Xist upregulation and X-chromosome inactivation with single-cell and single-allele resolution

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Guido Pacini ◽  
Ilona Dunkel ◽  
Norbert Mages ◽  
Verena Mutzel ◽  
Bernd Timmermann ◽  
...  
Keyword(s):  
Gene Silencing ◽  
Single Cell ◽  
X Chromosome ◽  
Embryonic Stem ◽  
X Chromosome Inactivation ◽  
Chromosome Inactivation ◽  
Cellular Heterogeneity ◽  
Integrated Analysis ◽  
X Chromosomes ◽  
Multiple Levels

AbstractTo ensure dosage compensation between the sexes, one randomly chosen X chromosome is silenced in each female cell in the process of X-chromosome inactivation (XCI). XCI is initiated during early development through upregulation of the long non-coding RNA Xist, which mediates chromosome-wide gene silencing. Cell differentiation, Xist upregulation and gene silencing are thought to be coupled at multiple levels to ensure inactivation of exactly one out of two X chromosomes. Here we perform an integrated analysis of all three processes through allele-specific single-cell RNA-sequencing. Specifically, we assess the onset of random XCI in differentiating mouse embryonic stem cells, and develop dedicated analysis approaches. By exploiting the inter-cellular heterogeneity of XCI onset, we identify putative Xist regulators. Moreover, we show that transient Xist upregulation from both X chromosomes results in biallelic gene silencing right before transitioning to the monoallelic state, confirming a prediction of the stochastic model of XCI. Finally, we show that genetic variation modulates the XCI process at multiple levels, providing a potential explanation for the long-known X-controlling element (Xce) effect, which leads to preferential inactivation of a specific X chromosome in inter-strain crosses. We thus draw a detailed picture of the different levels of regulation that govern the initiation of XCI. The experimental and computational strategies we have developed here will allow us to profile random XCI in more physiological contexts, including primary human cells in vivo.

scholarly journals SIMBA: SIngle-cell eMBedding Along with features

2021 ◽  
Author(s):  
Huidong Chen ◽  
Jayoung Ryu ◽  
Michael Edward Vinyard ◽  
Adam Lerer ◽  
Luca Pinello
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Dna Sequences ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Omics Data ◽  
Cell Analysis ◽  
Genomic Features ◽  
The Individual

Recent advances in single cell omics technologies enable the individual or joint profiling of cellular measurements including gene expression, epigenetic features, chromatin structure and DNA sequences. Currently, most single-cell analysis pipelines are cluster-centric, i.e., they first cluster cells into non-overlapping cellular states and then extract their defining genomic features. These approaches assume that discrete clusters correspond to biologically relevant subpopulations and do not explicitly model the interactions between different feature types. However, cellular processes are defined in individual cells and inherently involve multiple genomic features that interact with each other and together provide complementary views on principles of gene regulation. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a single-cell embedding method that embeds single cells along with their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for cellular heterogeneity study, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a common way and thus simplifies the development of new analyses and integration of other single-cell modalities.

scholarly journals STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data

2018 ◽  
Author(s):  
Huidong Chen ◽  
Luca Albergante ◽  
Jonathan Y Hsu ◽  
Caleb A Lareau ◽  
Giosue` Lo Bosco ◽  
...  
Keyword(s):  
Single Cell ◽  
De Novo ◽  
Developmental Trajectories ◽  
Cellular Heterogeneity ◽  
State Transitions ◽  
Omics Data ◽  
Transcriptomic Data ◽  
Lineage Differentiation ◽  
Cell Trajectories

AbstractSingle-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.

scholarly journals Optimal Transport improves cell-cell similarity inference in single-cell omics data

2021 ◽  
Author(s):  
Geert-Jan Huizing ◽  
Gabriel Peyré ◽  
Laura Cantini
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Optimal Transport ◽  
State Of The Art ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Methylation Data ◽  
Omics Data ◽  
Similarity Metric ◽  
Cell Cell

AbstractThe recent advent of high-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity is typically achieved through unsupervised clustering, which crucially relies on a similarity metric.We here propose the use of Optimal Transport (OT) as a cell-cell similarity metric for single-cell omics data. OT defines distances to compare, in a geometrically faithful way, high-dimensional data represented as probability distributions. It is thus expected to better capture complex relationships between features and produce a performance improvement over state-of-the-art metrics. To speed up computations and cope with the high-dimensionality of single-cell data, we consider the entropic regularization of the classical OT distance. We then extensively benchmark OT against state-of-the-art metrics over thirteen independent datasets, including simulated, scRNA-seq, scATAC-seq and single-cell DNA methylation data. First, we test the ability of the metrics to detect the similarity between cells belonging to the same groups (e.g. cell types, cell lines of origin). Then, we apply unsupervised clustering and test the quality of the resulting clusters.In our in-depth evaluation, OT is found to improve cell-cell similarity inference and cell clustering in all simulated and real scRNA-seq data, while its performances are comparable with Pearson correlation in scATAC-seq and single-cell DNA methylation data. All our analyses are reproducible through the OT-scOmics Jupyter notebook available at https://github.com/ComputationalSystemsBiology/OT-scOmics.

scholarly journals Deep learning applications in single-cell omics data analysis

2021 ◽  
Author(s):  
Nafiseh Erfanian ◽  
A. Ali Heydari ◽  
Pablo Ianez ◽  
Afshin Derakhshani ◽  
Mohammad Ghasemigol ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
State Of The Art ◽  
High Dimensional ◽  
Omics Data ◽  
Fast Tracking ◽  
Omics Integration ◽  
Previous State ◽  
High Level ◽  
Multiple Stages

Deep learning (DL) is a branch of machine learning (ML) capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across a range of domains and applications. Single-cell (SC) omics are often high-dimensional, sparse, and complex, making DL techniques ideal for analyzing and processing such data. We examine DL applications in a variety of single-cell omics (genomics, transcriptomics, proteomics, metabolomics and multi-omics integration) and address whether DL techniques will prove to be advantageous or if the SC omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized or addressed the most pressing challenges of the SC omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) but lacking the needed biological interpretability in many cases. Although such developments have generally been gradual, recent advances reveal that DL methods can offer valuable resources in fast-tracking and advancing research in SC.

scholarly journals Machine Translation between paired Single Cell Multi Omics Data

2021 ◽  
Author(s):  
Xabier Martinez-de-Morentin ◽  
Sumeer A. Khan ◽  
Robert Lehmann ◽  
Jesper Tegner ◽  
David Gomez-Cabrero
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
State Of The Art ◽  
Data Sets ◽  
Omics Data ◽  
Paired Data ◽  
Omics Technologies ◽  
Cell Numbers ◽  
Latent Space ◽  
Single Data

AbstractSingle-cell multi-omics technologies enable profiling of several data-modalities from the same cell. We designed LIBRA, a Neural Network based framework, for learning translations between paired multi-omics profiles into a shared latent space. We demonstrate LIBRA to be state-of-the-art for multi-omics clustering. In addition, LIBRA is more robust with decreasing cell-numbers compared with existing tools. Training LIBRA on paired data-sets, LIBRA predicts multi-omic profiles using only a single data-modality from the same biological system.

scholarly journals SIMBA: SIngle-cell eMBedding Along with features

2022 ◽  
Author(s):  
Huidong Chen ◽  
Jayoung Ryu ◽  
Michael Vinyard ◽  
Adam Lerer ◽  
Luca Pinello
Keyword(s):  
Single Cell ◽  
Dna Sequences ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Omics Data ◽  
Cell Analysis ◽  
Biologically Relevant ◽  
Latent Space ◽  
The Individual

Abstract Recent advances in single-cell omics technologies enable the individual and joint profiling of cellular measurements including gene expression, epigenetic features, chromatin structure and DNA sequences. Currently, most single-cell analysis pipelines are cluster-centric, i.e., they first cluster cells into non-overlapping cellular states and then extract their defining genomic features. These approaches assume that discrete clusters correspond to biologically relevant subpopulations and do not explicitly model the interactions between different feature types. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a unified way and thus simplifies the development of new analyses and integration of other single-cell modalities.

Single-Cell (Multi)omics Technologies

2018 ◽  
Vol 19 (1) ◽  
pp. 15-41 ◽  
Author(s):  
Lia Chappell ◽  
Andrew J.C. Russell ◽  
Thierry Voet
Keyword(s):  
Single Cell ◽  
Normal Development ◽  
Molecular Layer ◽  
Cellular Heterogeneity ◽  
Data Sets ◽  
Biological Processes ◽  
Disease Etiology ◽  
Biological Insight ◽  
Development And Aging ◽  
Molecular Layers

Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.

scholarly journals scBasset: Sequence-based modeling of single cell ATAC-seq using convolutional neural networks

2021 ◽  
Author(s):  
Han Yuan ◽  
David R Kelley
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
State Of The Art ◽  
Cellular Heterogeneity ◽  
High Dimensionality ◽  
Great Promise ◽  
Sequence Information ◽  
Transcription Factor Activity ◽  
Cell Type ◽  
Network Method

1AbstractSingle cell ATAC-seq (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain significant challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single cell multiome datasets, including cell type identification, scATAC profile denoising, data integration across assays, and transcription factor activity inference.

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