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 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 Mitochondrial isolation: when size matters

2020 ◽  
Vol 5 ◽  
pp. 226
Author(s):  
Alexander G. Bury ◽  
Amy E. Vincent ◽  
Doug M. Turnbull ◽  
Paolo Actis ◽  
Gavin Hudson
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Cellular Heterogeneity ◽  
Cell Analysis ◽  
Cell Level ◽  
Isolation Techniques ◽  
Cellular Environment ◽  
Dynamics And Function ◽  
And Function ◽  
Subcellular Resolution

Mitochondrial vitality is critical to cellular function, with mitochondrial dysfunction linked to a growing number of human diseases. Tissue and cellular heterogeneity, in terms of genetics, dynamics and function means that increasingly mitochondrial research is conducted at the single cell level. Whilst there are several technologies that are currently available for single-cell analysis, each with their advantages, they cannot be easily adapted to study mitochondria with subcellular resolution. Here we review the current techniques and strategies for mitochondrial isolation, critically discussing each technology’s limitations for future mitochondrial research. Finally, we highlight and discuss the recent breakthroughs in sub-cellular isolation techniques, with a particular focus on nanotechnologies that enable the isolation of mitochondria from subcellular compartments. This allows isolation of mitochondria with unprecedented spatial precision with minimal disruption to mitochondria and their immediate cellular environment.

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 Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Human Cell ◽  
Transcriptome Analysis ◽  
Single Cell Analysis ◽  
Embryonic Stem ◽  
Single Cell Level ◽  
Cell Analysis ◽  
Cell Level

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

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

2020 ◽  
Author(s):  
Jeremy Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy Nguyen ◽  
Kai Kessenbrock ◽  
Jered Haun
Keyword(s):  
Single Cell ◽  
Stress Responses ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Types ◽  
Tissue Preparation ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Wide Range

Abstract Tissues are composed of highly heterogeneous mixtures of cell subtypes, and this diversity is increasingly being characterized using high-throughput single cell analysis methods. However, these efforts are hindered by the fact that tissues must first be dissociated into single cell suspensions that are viable and still accurately represent phenotypes from the original tissue. Current methods for breaking down tissues are inefficient, labor-intensive, subject to high variability, and potentially biased towards cell subtypes that are easier to release. Here, we present a microfluidic platform consisting of three different tissue processing technologies that can perform the complete tissue to single cell workflow, including digestion, disaggregation, and filtration. First, we developed a new microfluidic digestion device that can be loaded with minced tissue specimens quickly and easily, and then use the combination of proteolytic enzyme activity and fluid shear forces to accelerate tissue breakdown. Next, we integrated dissociation and filter technologies into a single device, which enhanced single cell numbers and fully prepared the sample for single cell analysis. The final multi-device platform was then evaluated using a diverse array of tissue types that exhibited a wide range of properties. For murine kidney and mammary tumor, we found that microfluidic processing produced 2.5-fold more single, viable cells. Single cell RNA sequencing (scRNA-seq) further revealed that device processing enriched for endothelial cells, fibroblasts, and basal epithelium, and did not increase stress responses. For murine liver and heart, which are softer tissues containing fragile cell types, processing time could be reduced to 15 min, and even as short as 1 min. We also demonstrated that periodic recovery at defined time intervals produced substantially more hepatocytes and cardiomyocytes than continuous operation, most likely by preventing damage to fragile cell types. In future work, we will seek to integrate additional operations such as upstream tissue preparation and downstream microfluidic cell sorting and detection to create powerful point-of-care single cell diagnostic platforms.

scholarly journals Single-Cell Analysis of Antigen-Specific CD8+ T-Cell Transcripts Reveals Profiles Specific to mRNA or Adjuvanted Protein Vaccines

2021 ◽  
Vol 12 ◽  
Author(s):  
Trine Sundebo Meldgaard ◽  
Fabiola Blengio ◽  
Denise Maffione ◽  
Chiara Sammicheli ◽  
Simona Tavarini ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cd8 T Cells ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Cd8 T Cell ◽  
Cellular Heterogeneity ◽  
Cell Analysis

CD8+ T cells play a key role in mediating protective immunity after immune challenges such as infection or vaccination. Several subsets of differentiated CD8+ T cells have been identified, however, a deeper understanding of the molecular mechanism that underlies T-cell differentiation is lacking. Conventional approaches to the study of immune responses are typically limited to the analysis of bulk groups of cells that mask the cells’ heterogeneity (RNA-seq, microarray) and to the assessment of a relatively limited number of biomarkers that can be evaluated simultaneously at the population level (flow and mass cytometry). Single-cell analysis, on the other hand, represents a possible alternative that enables a deeper characterization of the underlying cellular heterogeneity. In this study, a murine model was used to characterize immunodominant hemagglutinin (HA533-541)-specific CD8+ T-cell responses to nucleic- and protein-based influenza vaccine candidates, using single-cell sorting followed by transcriptomic analysis. Investigation of single-cell gene expression profiles enabled the discovery of unique subsets of CD8+ T cells that co-expressed cytotoxic genes after vaccination. Moreover, this method enabled the characterization of antigen specific CD8+ T cells that were previously undetected. Single-cell transcriptome profiling has the potential to allow for qualitative discrimination of cells, which could lead to novel insights on biological pathways involved in cellular responses. This approach could be further validated and allow for more informed decision making in preclinical and clinical settings.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
Tung-Shing Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan D. Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Abstract Background: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Methods: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. Results: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Conclusions: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Single-cell analysis in situ in a Bacillus subtilis swarming community identifies distinct spatially separated subpopulations differentially expressing hag (flagellin), including specialized swarmers

Microbiology ◽  
2011 ◽  
Vol 157 (9) ◽  
pp. 2456-2469 ◽  
Author(s):  
Kassem Hamze ◽  
Sabine Autret ◽  
Krzysztof Hinc ◽  
Soumaya Laalami ◽  
Daria Julkowska ◽  
...  
Keyword(s):  
Bacillus Subtilis ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Laboratory Strain ◽  
Cell Types ◽  
Spatiotemporal Pattern ◽  
Cell Analysis ◽  
Wide Range ◽  
Low Humidity

The non-domesticated Bacillus subtilis strain 3610 displays, over a wide range of humidity, hyper-branched, dendritic, swarming-like migration on a minimal agar medium. At high (70 %) humidity, the laboratory strain 168 sfp + (producing surfactin) behaves very similarly, although this strain carries a frameshift mutation in swrA, which another group has shown under their conditions (which include low humidity) is essential for swarming. We reconcile these different results by demonstrating that, while swrA is essential for dendritic migration at low humidity (30–40 %), it is dispensable at high humidity. Dendritic migration (flagella- and surfactin-dependent) of strains 168 sfp + swrA and 3610 involves elongation of dendrites for several hours as a monolayer of cells in a thin fluid film. This enabled us to determine in situ the spatiotemporal pattern of expression of some key players in migration as dendrites develop, using gfp transcriptional fusions for hag (encoding flagellin), comA (regulation of surfactin synthesis) as well as eps (exopolysaccharide synthesis). Quantitative (single-cell) analysis of hag expression in situ revealed three spatially separated subpopulations or cell types: (i) networks of chains arising early in the mother colony (MC), expressing eps but not hag; (ii) largely immobile cells in dendrite stems expressing intermediate levels of hag; and (iii) a subpopulation of cells with several distinctive features, including very low comA expression but hyper-expression of hag (and flagella). These specialized cells emerge from the MC to spearhead the terminal 1 mm of dendrite tips as swirling and streaming packs, a major characteristic of swarming migration. We discuss a model for this swarming process, emphasizing the importance of population density and of the complementary roles of packs of swarmers driving dendrite extension, while non-mobile cells in the stems extend dendrites by multiplication.

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.

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