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 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.

scholarly journals Unified single-cell analysis of testis gene regulation and pathology in 5 mouse strains

2018 ◽  
Author(s):  
Min Jung ◽  
Daniel Wells ◽  
Jannette Rusch ◽  
Suhaira Ahmed ◽  
Jonathan Marchini ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Web Application ◽  
Single Cell Analysis ◽  
De Novo ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Mouse Strains ◽  
Seminiferous Tubules ◽  
Sequence Motifs

AbstractBy removing the confounding factor of cellular heterogeneity, single cell genomics can revolutionize the study of development and disease, but methods are needed to simplify comparison among individuals. To develop such a framework, we assayed the transcriptome in 62,600 single cells from the testes of wildtype mice, and mice with gonadal defects due to disruption of the genes Mlh3, Hormad1, Cul4a or Cnp. The resulting expression atlas of distinct cell clusters revealed novel markers and new insights into testis gene regulation. By jointly analysing mutant and wildtype cells using a model-based factor analysis method, SDA, we decomposed our data into 46 components that identify novel meiotic gene regulatory programmes, mutant-specific pathological processes, and technical effects. Moreover, we identify, de novo, DNA sequence motifs associated with each component, and show that SDA can be used to impute expression values from single cell data. Analysis of SDA components also led us to identify a rare population of macrophages within the seminiferous tubules of Mlh3-/- and Hormad1-/- testes, an area typically associated with immune privilege. We provide a web application to enable interactive exploration of testis gene expression and components at http://www.stats.ox.ac.uk/~wells/testisAtlas.html

scholarly journals Challenges and emerging directions in single-cell analysis

2017 ◽  
Author(s):  
Guo-Cheng Yuan ◽  
Long Cai ◽  
Michael Elowitz ◽  
Tariq Enver ◽  
Guoping Fan ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Systematic Investigation ◽  
Cellular Heterogeneity ◽  
Cell Analysis ◽  
Cell Level ◽  
Wide Range ◽  
Molecular Information ◽  
The Individual ◽  
Genome Scale

AbstractSingle-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances, and look to future opportunities.

scholarly journals A Quantitative Single-Cell Proteomics Approach to Characterize an Acute Myeloid Leukemia Hierarchy

2019 ◽  
Author(s):  
Erwin M. Schoof ◽  
Nicolas Rapin ◽  
Simonas Savickas ◽  
Coline Gentil ◽  
Eric Lechman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Mass ◽  
Science Research ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Significant Shift ◽  
Cell Analysis ◽  
Computational Pipeline

AbstractIn recent years, cellular life science research has experienced a significant shift, moving away from conducting bulk cell interrogation towards single-cell analysis. It is only through single cell analysis that a complete understanding of cellular heterogeneity, and the interplay between various cell types that are fundamental to specific biological phenotypes, can be achieved. Single-cell assays at the protein level have been predominantly limited to targeted, antibody-based methods. However, here we present an experimental and computational pipeline, which establishes a comprehensive single-cell mass spectrometry-based proteomics workflow.By exploiting a leukemia culture system, containing functionally-defined leukemic stem cells, progenitors and terminally differentiated blasts, we demonstrate that our workflow is able to explore the cellular heterogeneity within this aberrant developmental hierarchy. We show our approach is capable to quantifying hundreds of proteins across hundreds of single cells using limited instrument time. Furthermore, we developed a computational pipeline (SCeptre), that effectively clusters the data and permits the extraction of cell-specific proteins and functional pathways. This proof-of-concept work lays the foundation for future global single-cell proteomics studies.

scholarly journals Profiling of transcribed cis-regulatory elements in single cells

2021 ◽  
Author(s):  
Jonathan Moody ◽  
Tsukasa Kouno ◽  
Akari Suzuki ◽  
Youtaro Shibayama ◽  
Chikashi Terao ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Cell Analysis ◽  
Cell Type ◽  
Simultaneous Quantification ◽  
Promoter Usage

Profiling of cis-regulatory elements (CREs, mostly promoters and enhancers) in single cells allows the interrogation of the cell-type and -state specific contexts of gene regulation and genetic predisposition to diseases. Here we demonstrate single-cell RNA-5′end-sequencing (sc-end5-seq) methods can detect transcribed CREs (tCREs), enabling simultaneous quantification of gene expression and enhancer activities in a single assay with no extra cost. We show enhancer RNAs can be effectively detected using sc-end5-seq methods with either random or oligo(dT) priming. To analyze tCREs in single cells, we developed SCAFE (Single Cell Analysis of Five-prime Ends) to identify genuine tCREs and analyze their activities (https://github.com/chung-lab/scafe). As compared to accessible CRE (aCRE, based on chromatin accessibility), tCREs are more accurate in predicting CRE interactions by co-activity, more sensitive in detecting shifts in alternative promoter usage and more enriched in diseases heritability. Our results highlight additional dimensions within sc-end5-seq data which can be used for interrogating gene regulation and disease heritability.

scholarly journals Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeremy A. Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy H. Nguyen ◽  
Kai Kessenbrock ◽  
Jered B. Haun
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Tissue Processing ◽  
Single Cell Rna Sequencing ◽  
Culture Models ◽  
Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

scholarly journals More than efficacy revealed by single-cell analysis of antiviral therapeutics

2019 ◽  
Author(s):  
Wu Liu ◽  
Mehmet U. Caglar ◽  
Zhangming Mao ◽  
Andrew Woodman ◽  
Jamie J. Arnold ◽  
...  
Keyword(s):  
Single Cell ◽  
Drug Targets ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Principal Component ◽  
Viral Population ◽  
Cell Analysis ◽  
Toxic Dose ◽  
Time Required

SUMMARYDevelopment of antiviral therapeutics emphasizes minimization of the effective dose and maximization of the toxic dose, first in cell culture and later in animal models. Long-term success of an antiviral therapeutic is determined not only by its efficacy but also by the duration of time required for drug-resistance to evolve. We have developed a microfluidic device comprised of ~6000 wells, with each well containing a microstructure to capture single cells. We have used this device to characterize enterovirus inhibitors with distinct mechanisms of action. In contrast to population methods, single-cell analysis reveals that each class of inhibitor interferes with the viral infection cycle in a manner that can be distinguished by principal component analysis. Single-cell analysis of antiviral candidates reveals not only efficacy but also properties of the members of the viral population most sensitive to the drug, the stage of the lifecycle most affected by the drug, and perhaps even if the drug targets an interaction of the virus with its host.

scholarly journals μCB-seq: Microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells

2020 ◽  
Author(s):  
Tyler N. Chen ◽  
Anushka Gupta ◽  
Mansi Zalavadia ◽  
Aaron M. Streets
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Protein Localization ◽  
Single Cells ◽  
Optical Measurements ◽  
Detection Sensitivity ◽  
High Resolution Imaging ◽  
Cell Analysis ◽  
Resolution Imaging

AbstractSingle-cell RNA sequencing (scRNA-seq) enables the investigation of complex biological processes in multicellular organisms with high resolution. However, many phenotypic features that are critical to understanding the functional role of cells in a heterogeneous tissue or organ are not directly encoded in the genome and therefore cannot be profiled with scRNA-seq. Quantitative optical microscopy has long been a powerful approach for characterizing diverse cellular phenotypes including cell morphology, protein localization, and chemical composition. Combining scRNA-seq with optical imaging has the potential to provide comprehensive single-cell analysis, allowing for functional integration of gene expression profiling and cell-state characterization. However, it is difficult to track single cells through both measurements; therefore, coupling current scRNA-seq protocols with optical measurements remains a challenge. Here, we report Microfluidic Cell Barcoding and Sequencing (μCB-seq), a microfluidic platform that combines high-resolution imaging and sequencing of single cells. μCB-seq is enabled by a novel fabrication method that preloads primers with known barcode sequences inside addressable reaction chambers of a microfluidic device. In addition to enabling multi-modal single-cell analysis, μCB-seq improves gene detection sensitivity, providing a scalable and accurate method for information-rich characterization of single cells.

scholarly journals Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

2020 ◽  
Vol 52 (10) ◽  
pp. 468-477
Author(s):  
Alexander C. Zambon ◽  
Tom Hsu ◽  
Seunghee Erin Kim ◽  
Miranda Klinck ◽  
Jennifer Stowe ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Imaging System ◽  
Single Cells ◽  
Cell Types ◽  
Cell Analysis ◽  
Mcf7 Cells ◽  
Genomic Studies ◽  
Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

scholarly journals Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells

The Analyst ◽  
2019 ◽  
Vol 144 (10) ◽  
pp. 3226-3238 ◽  
Author(s):  
Jitraporn Vongsvivut ◽  
David Pérez-Guaita ◽  
Bayden R. Wood ◽  
Philip Heraud ◽  
Karina Khambatta ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Spatial Resolution ◽  
Environmental Science ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Chemical Mapping ◽  
Ftir Microspectroscopy ◽  
Resolution Single

Coupling synchrotron IR beam to an ATR element enhances spatial resolution suited for high-resolution single cell analysis in biology, medicine and environmental science.

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