scholarly journals Understanding angiodiversity: insights from single cell biology

Development ◽  
2020 ◽  
Vol 147 (15) ◽  
pp. dev146621 ◽  
Author(s):  
Moritz Jakab ◽  
Hellmut G. Augustin

ABSTRACTBlood vessels have long been considered as passive conduits for delivering blood. However, in recent years, cells of the vessel wall (endothelial cells, smooth muscle cells and pericytes) have emerged as active, highly dynamic components that orchestrate crosstalk between the circulation and organs. Encompassing the whole body and being specialized to the needs of distinct organs, it is not surprising that vessel lining cells come in different flavours. There is calibre-specific specialization (arteries, arterioles, capillaries, venules, veins), but also organ-specific heterogeneity in different microvascular beds (continuous, discontinuous, sinusoidal). Recent technical advances in the field of single cell biology have enabled the profiling of thousands of single cells and, hence, have allowed for the molecular dissection of such angiodiversity, yielding a hitherto unparalleled level of spatial and functional resolution. Here, we review how these approaches have contributed to our understanding of angiodiversity.

2019 ◽  
Author(s):  
Wenfa Ng

While many microbes could be cultivated on common nutrient medium from environmental samples, there is perhaps a larger consortium of microbes that could not be brought under cultivation. Known as viable but non-culturable (VBNC) microbes, many facets of cell biology, biochemistry and physiology remain hidden from view given the inability to culture them in the laboratory. Without the ability to culture VBNC, many modern genetic tools could not be used to interrogate intrinsic metabolic capabilities and regulatory mechanisms of the cells. A more important question is perhaps what defines the VBNC state. Specifically, what is the level of metabolic activity in such cells and which branch of metabolism remains active in helping cells maintain cellular sensory system essential to understanding extracellular nutrition and environmental conditions crucial for activating vegetative growth under the right conditions? To answer the questions, we first need to develop methods for identifying cells in the VBNC state. One possibility involves screening environmental microbes for their ability to grow in rich medium under standard laboratory incubation conditions using 96 well plate assay where single cells are inoculated into each well. Cells that fail to grow would subsequently be selected for single cell RNA sequencing to understand the transcriptome that could be correlated to the VBNC state. In parallel, single cell whole genome sequencing could also be conducted to obtain the reference genome on which expression of different genes in the transcriptome could be assessed. Specifically, automated gene annotation pipelines could be used for gene detection; thereby, yielding an ensemble of genes useful for understanding the transcriptome. But, detection of mRNA transcripts does not mean the successful translation of mRNA into proteins. More importantly, while single cell proteomics might be achievable on a routine basis in future, conventional methods lack the sensitivity for profiling cellular proteome at the global level in single cell given the inability to massively amplify proteins unlike the case for DNA or RNA. Similarly, single cell metabolomics, which is essential to obtaining a complete picture of cellular metabolism in VBNC state faces challenges associated with sensitivity and detection of a broad range of intermediates and compounds. Thus, at present, efforts to access the metabolic state associated with VBNC would most likely stop at probing the global transcriptome at the single cell level. But, future developments in single cell proteomics and metabolomics would hopefully provide new tools for biologists to revisit the important question on what is the metabolic status of cells in VBNC, and more importantly, which metabolic branch remain active in maintaining sensory awareness of the cell’s immediate environment.


2019 ◽  
Author(s):  
Zucha Daniel ◽  
Androvic Peter ◽  
Kubista Mikael ◽  
Valihrach Lukas

ABSTRACTBackgroundRecent technical advances allowing quantification of RNA from single cells are revolutionizing biology and medicine. Currently, almost all single-cell transcriptomic protocols rely on conversion of RNA to cDNA by reverse transcription (RT). However, RT is recognized as highly limiting step due to its inherent variability and suboptimal sensitivity, especially at minute amounts of RNA. Primary factor influencing RT outcome is reverse transcriptase (RTase). Recently, several new RTases with potential to decrease the loss of information during RT have been developed, but the thorough assessment of their performance is missing.MethodsWe have compared the performance of 11 RTases in RT-qPCR on single-cell and 100-cell bulk templates using two priming strategies: conventional mixture of random hexamers with oligo(dT)s and reduced concentration of oligo(dT)s mimicking common single-cell RNA-Seq library preparation protocols. Based on the performance, two RTases were further tested in high-throughput single-cell experiment.ResultsAll RTases tested reverse transcribed low-concentration templates with high accuracy (R2 > 0.9445) but variable reproducibility (median CVRT = 40.1 %). The most pronounced differences were found in the ability to capture rare transcripts (0 - 90% reaction positivity rate) as well as in the rate of RNA conversion to cDNA (7.3 - 124.5 % absolute yield). Finally, RTase performance and reproducibility across all tested parameters were compared using Z-scores and validity of obtained results was confirmed in a single-cell model experiment. The better performing RTase provided higher positive reaction rate and expression levels and improved resolution in clustering analysis.ConclusionsWe performed a comprehensive comparison of 11 RTases in low RNA concentration range and identified two best-performing enzymes (Maxima H-; SuperScript IV). We found that using better-performing enzyme (Maxima H-) over commonly-used below-average performer (SuperScript II) increases the sensitivity of single-cell experiment. Our results provide a reference for the improvement of current single-cell quantification protocols.


Micromachines ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 131 ◽  
Author(s):  
Yong-Jiang Li ◽  
Yu-Nong Yang ◽  
Hai-Jun Zhang ◽  
Chun-Dong Xue ◽  
De-Pei Zeng ◽  
...  

The biomechanical properties of single cells show great potential for early disease diagnosis and effective treatments. In this study, a microfluidic device was developed for quantifying the mechanical properties of a single cell. Micropipette aspiration was integrated into a microfluidic device that mimics a classical Wheatstone bridge circuit. This technique allows us not only to effectively alter the flow direction for single-cell trapping, but also to precisely control the pressure exerted on the aspirated cells, analogous to the feature of the Wheatstone bridge that can precisely control bridge voltage and current. By combining the micropipette aspiration technique into the microfluidic device, we can effectively trap the microparticles and Hela cells as well as measure the deformability of cells. The Young’s modulus of Hela cells was evaluated to be 387 ± 77 Pa, which is consistent with previous micropipette aspiration studies. The simplicity, precision, and usability of our device show good potential for biomechanical trials in clinical diagnosis and cell biology research.


2021 ◽  
Author(s):  
Rachelle N. Palchesko ◽  
Yiqin Du ◽  
Moira L. Geary ◽  
Santiago Carrasquilla ◽  
Daniel J. Shiwarski ◽  
...  

AbstractCell injection has emerged as a widespread approach for therapeutic delivery of healthy cells into diseased and damaged tissues to achieve regeneration. However, cell retention, viability and integration at the injection site has generally been poor, driving the need for improved approaches. Additionally, it is unknown how efficiently single cells can integrate and repair tissue level function. Here we have developed a technique to address these issues by engineering islands of interconnected cells on ECM nanoscaffolds that can be non-destructively released from the surface via thermal dissolution of the underlying thermo-responsive polymer. Upon dissolution of the polymer, the ECM nanoscaffold shrink-wraps around the small island of cells, creating a small patch of cells that maintain their cell-cell junctions and cytoskeletal structure throughout collection, centrifugation and injection that we have termed μMonolayers. These μMonolayers were made with corneal endothelial cells, as a model system, as single cell injections of corneal endothelial cells have been used with some success clinically to treat corneal blindness. In vitro our μMonolayers exhibited increased integration compared to single cells into low density corneal endothelial monolayers and in vivo into the high-density healthy rabbit corneal endothelium. These results indicate that this technique could be used to increase the integration of healthy cells into existing tissues to treat not only corneal blindness, but also other conditions such as cystic fibrosis, myocardial infarction, diabetes, etc.One Sentence SummarySmall monolayers of interconnected endothelial cells are shrinkwrapped in a thin layer of ECM and exhibit enhanced adhesion and integration in vivo compared to single cell suspensions.


2015 ◽  
Vol 7 (20) ◽  
pp. 8524-8533 ◽  
Author(s):  
Alireza Valizadeh ◽  
Ahmad Yari Khosroushahi

The combination of nano/microfabrication-based technologies with cell biology has laid the foundation for facilitating the spatiotemporal analysis of single cells under well-defined physiologically relevant conditions.


Science ◽  
2021 ◽  
Vol 371 (6526) ◽  
pp. eaah6266
Author(s):  
T. Stadler ◽  
O. G. Pybus ◽  
M. P. H. Stumpf

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how “tree thinking” is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.


2019 ◽  
Vol 125 (Suppl_1) ◽  
Author(s):  
David T Paik ◽  
Lei Tian ◽  
Ian M Williams ◽  
Chun Liu ◽  
Hao Zhang ◽  
...  

Author(s):  
David S. Tourigny ◽  
Arthur P. Goldberg ◽  
Jonathan R. Karr

AbstractStochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the population-level, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Experimental methods for probing metabolism in single cells currently lag far behind advancements in single-cell genomics, transcriptomics, and proteomics, which motivates the development of computational techniques to bridge this gap in the systems approach to single-cell biology. In this paper, we present SSA-FBA (stochastic simulation algorithm with flux-balance analysis embedded) as a modelling framework for simulating the stochastic dynamics of metabolism in individual cells. SSA-FBA extends the constraint-based formalism of metabolic network modelling to the single-cell regime, providing a suitable approach to simulation when kinetic information is lacking from models. We also describe an advanced algorithm that significantly improves the efficiency of exact SSA-FBA simulations, which is necessary because of the computational costs associated with stochastic simulation and the observation that approximations can be inaccurate and numerically unstable. As a preliminary case study we apply SSA-FBA to a single-cell model of Mycoplasma pneumoniae, and explore the use of simulation to understand the role of stochasticity in metabolism at the single-cell level.


Author(s):  
Roger Volden ◽  
Christopher Vollmers

AbstractSingle cell transcriptome analysis elucidates facets of cell biology that have been previously out of reach. However, the high-throughput analysis of thousands of single cell transcriptomes has been limited by sample preparation and sequencing technology. High-throughput single cell analysis today is facilitated by protocols like the 10X Genomics platform or Drop-Seq which generate cDNA pools in which the origin of a transcript is encoded at its 5’ or 3’ end. These cDNA pools are currently analyzed by short read Illumina sequencing which can identify the cellular origin of a transcript and what gene it was transcribed from. However, these methods fail to retrieve isoform information. In principle, cDNA pools prepared using these approaches can be analyzed with Pacific Biosciences and Oxford Nanopore long-read sequencers to retrieve isoform information but all current implementations rely heavily on Illumina short-reads for the analysis in addition to long reads. Here, we used R2C2 to sequence and demultiplex 9 million full-length cDNA molecules generated by the 10X Chromium platform from ∼3000 peripheral blood mononuclear cells (PBMCs). We used these reads to – independent from Illumina data – cluster cells into B cells, T cells, and Monocytes and generate isoform-level transcriptomes for these cell-types. We also generated isoform-level transcriptomes for all single cells and used this information to identify a wide range of isoform diversity between genes. Finally, we also designed a computational workflow to extract paired adaptive immune receptor – T cell receptor and B cell receptor (TCR and BCR) –sequences unique to each T and B cell. This work represents a new, simple, and powerful approach that –using a single sequencing method – can extract an unprecedented amount of information from thousands of single cells.


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