Single Cell Multi-Omics Technology: Methodology and Application

Hu, Youjin; An, Qin; Sheu, Katherine M.; Trejo, Brandon; Fan, Shuangshuang; Guo, Ying

doi:10.3389/fcell.2018.00028

Cited by 145 publications

(156 citation statements)

References 78 publications

Supporting

Mentioning

152

Contrasting

Unclassified

Order By: Relevance

“…Single-cell omics. RNA-seq, methylome-seq, and ATACseq can also be applied to thousands of single cells in parallel (Ziegenhain et al 2017;Hu et al 2018). This may be a further direction for increasing the information for AOP development to advance from the cellular to tissue and organ response.…”

Section: Which Omics Layers To Use For Toxicological Research Questionsmentioning

confidence: 99%

See 1 more Smart Citation

Prospects and challenges of multi-omics data integration in toxicology

et al. 2020

View full text Add to dashboard Cite

Exposure of cells or organisms to chemicals can trigger a series of effects at the regulatory pathway level, which involve changes of levels, interactions, and feedback loops of biomolecules of different types. A single-omics technique, e.g., transcriptomics, will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, although applying single-omics analyses can lead to the identification of biomarkers for certain exposures, they cannot provide a systemic understanding of toxicity pathways or adverse outcome pathways. Integration of multiple omics data sets promises a substantial improvement in detecting this pathway response to a toxicant, by an increase of information as such and especially by a systemic understanding. Here, we report the findings of a thorough evaluation of the prospects and challenges of multi-omics data integration in toxicological research. We review the availability of such data, discuss options for experimental design, evaluate methods for integration and analysis of multi-omics data, discuss best practices, and identify knowledge gaps. Re-analyzing published data, we demonstrate that multi-omics data integration can considerably improve the confidence in detecting a pathway response. Finally, we argue that more data need to be generated from studies with a multi-omics-focused design, to define which omics layers contribute most to the identification of a pathway response to a toxicant.Archives of Toxicology (2020) 94:371-388Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

show abstract

Section: Which Omics Layers To Use For Toxicological Research Questionsmentioning

confidence: 99%

“…Several protocols have been published that interrogate multiple omics layers from the same single cell. These methods mainly combine genome sequencing and RNA-seq, methylome-seq and RNA-seq, or all three layers-see (Hu et al 2018) for a recent review.…”

Section: Which Omics Layers To Use For Toxicological Research Questionsmentioning

confidence: 99%

Prospects and challenges of multi-omics data integration in toxicology

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Sequencing can detect rare and novel RNA transcripts, identify single-nucleotide variants in both coding and non-coding RNA of all lengths, and is not species-dependent. Furthermore, recent development of microfluidics technology with RNA sequencing allows transcripts of individual cells to be determined, which was not previously possible ( Hu et al , 2018b ). However, single-cell RNA sequencing of the syncytiotrophoblast, which forms the placental cellular barrier, remains a challenge since its large size and multi-nucleated nature does not permit its isolation with microfluidics technology.…”

Section: Transcript Profiling Methodsmentioning

confidence: 99%

Current approaches and developments in transcript profiling of the human placenta

Yong

Chan

2020

Human Reproduction Update

View full text Add to dashboard Cite

BACKGROUND The placenta is the active interface between mother and foetus, bearing the molecular marks of rapid development and exposures in utero. The placenta is routinely discarded at delivery, providing a valuable resource to explore maternal-offspring health and disease in pregnancy. Genome-wide profiling of the human placental transcriptome provides an unbiased approach to study normal maternal–placental–foetal physiology and pathologies. OBJECTIVE AND RATIONALE To date, many studies have examined the human placental transcriptome, but often within a narrow focus. This review aims to provide a comprehensive overview of human placental transcriptome studies, encompassing those from the cellular to tissue levels and contextualize current findings from a broader perspective. We have consolidated studies into overarching themes, summarized key research findings and addressed important considerations in study design, as a means to promote wider data sharing and support larger meta-analysis of already available data and greater collaboration between researchers in order to fully capitalize on the potential of transcript profiling in future studies. SEARCH METHODS The PubMed database, National Center for Biotechnology Information and European Bioinformatics Institute dataset repositories were searched, to identify all relevant human studies using ‘placenta’, ‘decidua’, ‘trophoblast’, ‘transcriptome’, ‘microarray’ and ‘RNA sequencing’ as search terms until May 2019. Additional studies were found from bibliographies of identified studies. OUTCOMES The 179 identified studies were classifiable into four broad themes: healthy placental development, pregnancy complications, exposures during pregnancy and in vitro placental cultures. The median sample size was 13 (interquartile range 8–29). Transcriptome studies prior to 2015 were predominantly performed using microarrays, while RNA sequencing became the preferred choice in more recent studies. Development of fluidics technology, combined with RNA sequencing, has enabled transcript profiles to be generated of single cells throughout pregnancy, in contrast to previous studies relying on isolated cells. There are several key study aspects, such as sample selection criteria, sample processing and data analysis methods that may represent pitfalls and limitations, which need to be carefully considered as they influence interpretation of findings and conclusions. Furthermore, several areas of growing importance, such as maternal mental health and maternal obesity are understudied and the profiling of placentas from these conditions should be prioritized. WIDER IMPLICATIONS Integrative analysis of placental transcriptomics with other ‘omics’ (methylome, proteome and metabolome) and linkage with future outcomes from longitudinal studies is crucial in enhancing knowledge of healthy placental development and function, and in enabling the underlying causal mechanisms of pregnancy complications to be identified. Such understanding could help in predicting risk of future adversity and in designing interventions that can improve the health outcomes of both mothers and their offspring. Wider collaboration and sharing of placental transcriptome data, overcoming the challenges in obtaining sufficient numbers of quality samples with well-defined clinical characteristics, and dedication of resources to understudied areas of pregnancy will undoubtedly help drive the field forward.

show abstract

“…Following PCR, gel electrophoresis, and sequencing, genomic and transcriptional signatures in the same single cell were identified (188). This advancement offers the ability to investigate stem cell fate control, nongenetic cell-to-cell variability, epigenetic regulation, and other fundamental biology questions (188,189). Strijp et al (190) developed another method to investigate the genome and transcriptome using pressure-driven microfluidics.…”

Section: Multi-omicsmentioning

confidence: 99%

Single-Cell Omics Analyses Enabled by Microchip Technologies

Deng

Finck

Fan

2019

Annu. Rev. Biomed. Eng.

View full text Add to dashboard Cite

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.

show abstract

Single Cell Multi-Omics Technology: Methodology and Application

Cited by 145 publications

References 78 publications

Prospects and challenges of multi-omics data integration in toxicology

Prospects and challenges of multi-omics data integration in toxicology

Current approaches and developments in transcript profiling of the human placenta

Single-Cell Omics Analyses Enabled by Microchip Technologies

Contact Info

Product

Resources

About