Development of a Micro-Scale Differential Scanning Calorimeter for Single Cell Measurements

2007 ◽  
Author(s):  
William G. Zhao ◽  
Gary L. Solbrekken ◽  
Steven F. Mullen ◽  
James D. Benson ◽  
Xu Han ◽  
...  
Keyword(s):  
Phase Change ◽  
Single Cell ◽  
Extended Period ◽  
Cell Types ◽  
Biological Materials ◽  
Micro Scale ◽  
Heating And Cooling ◽  
Macro Scale ◽  
In Series ◽  
Change Temperature

Cryopreservation is an effective way to store biological materials for an extended period of time. The process of freezing biological materials is rather traumatic, however, due to the water phase change that takes place. Successful cooling procedures have been developed for a limited number of cell types primarily through a large amount of experimentation. Fundamental cryobiological studies of cellular heat and mass transfer are currently under way in an attempt to develop an accurate model that will reduce the need for many costly experiments. The purpose of this paper is to describe the development of a tool that will allow more accurate measurements of the phase change temperature for a single cell (∼100 μm diameter) as it freezes. The resulting data will be used to improve the fundamental cryopreservation model. A proof-of-concept micro-scale DSC (μDSC) is designed and built using 250 μm × 250 μm × 500 μm thermoelectric elements. The elements are connected electrically in series to one another with metal electrodes that double as the sample and reference pans. The advantage of this configuration is that Peltier heating and cooling of the sample each have the same time constant, in contrast with macro-scale DSC machines. Thermocouples with 25 μm beads are attached to the pans providing the local temperature of the sample used for feedback control. The feedback temperature is used as the control signal for the power supply which supplies electric current to the thermoelectric structure described above. Initial experiments on the prototype μDSC indicate that the phase change of a single swine oocyte with a diameter of about 100 μm can be detected.

Enhancement in Thermal Property of Phase Change Microcapsule with Modified Carbon Nanotube

2013 ◽  
Vol 856 ◽  
pp. 314-317 ◽  
Author(s):  
Min Li ◽  
Zhi Shen Wu
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Thermal Stability ◽  
Phase Change ◽  
Stearyl Alcohol ◽  
Analysis Method ◽  
Phase Change Temperature ◽  
Heating And Cooling ◽  
Change Temperature ◽  
Thermal Stability Of

Carbon nanotubes grafted with stearyl alcohol (CNTs-SA) was used to enhance the thermal conductivities of the microcapsules. Differential Scanning Calorimeter (DSC) and thermogravimetry (TG) analysis method are employed to measure thermal properties of the prepared MicroPCM containing the grafted CNTs (MicroPCM/CNTs-SA). The results indicated the phase change temperature and latent heat of MicroPCM/CNTs-SA was 26.2°C and 47.7J/g. An increase in thermal conductivity, thermal stability of MicroPCM/CNTs-SA was observed. After 100 heating and cooling cycles, MicroPCM/CNTs-SA still had better durability and thermal stability.

scholarly journals Macro-scale phenomena of arterial coupled cells: a massively parallel simulation

2011 ◽  
Vol 9 (70) ◽  
pp. 972-987 ◽  
Author(s):  
Mohsin Ahmed Shaikh ◽  
David J. N. Wall ◽  
Tim David
Keyword(s):  
Cell Types ◽  
Inhibitory Effect ◽  
Massively Parallel ◽  
Arterial Segment ◽  
Micro Scale ◽  
Spatial Gradients ◽  
Macro Scale ◽  
Large Populations ◽  
Intracellular Ca ◽  
Blue Gene

Impaired mass transfer characteristics of blood-borne vasoactive species such as adenosine triphosphate in regions such as an arterial bifurcation have been hypothesized as a prospective mechanism in the aetiology of atherosclerotic lesions. Arterial endothelial cells (ECs) and smooth muscle cells (SMCs) respond differentially to altered local haemodynamics and produce coordinated macro-scale responses via intercellular communication. Using a computationally designed arterial segment comprising large populations of mathematically modelled coupled ECs and SMCs, we investigate their response to spatial gradients of blood-borne agonist concentrations and the effect of micro-scale-driven perturbation on the macro-scale. Altering homocellular (between same cell type) and heterocellular (between different cell types) intercellular coupling, we simulated four cases of normal and pathological arterial segments experiencing an identical gradient in the concentration of the agonist. Results show that the heterocellular calcium (Ca 2+ ) coupling between ECs and SMCs is important in eliciting a rapid response when the vessel segment is stimulated by the agonist gradient. In the absence of heterocellular coupling, homocellular Ca 2+  coupling between SMCs is necessary for propagation of Ca 2+  waves from downstream to upstream cells axially. Desynchronized intracellular Ca 2+  oscillations in coupled SMCs are mandatory for this propagation. Upon decoupling the heterocellular membrane potential, the arterial segment looses the inhibitory effect of ECs on the Ca 2+  dynamics of the underlying SMCs. The full system comprises hundreds of thousands of coupled nonlinear ordinary differential equations simulated on the massively parallel Blue Gene architecture. The use of massively parallel computational architectures shows the capability of this approach to address macro-scale phenomena driven by elementary micro-scale components of the system.

Prioritization of cell types responsive to biological perturbations in single-cell data with Augur

2021 ◽  
Author(s):  
Jordan W. Squair ◽  
Michael A. Skinnider ◽  
Matthieu Gautier ◽  
Leonard J. Foster ◽  
Grégoire Courtine
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Data

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals 2D-wavelet based micro and macro texture analysis for asphalt pavement under snow or ice condition

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Feng Li ◽  
Gulnigar Ablat ◽  
Siqi Zhou ◽  
Yixin Liu ◽  
Yufeng Bi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Asphalt Pavement ◽  
Asphalt Mixture ◽  
Micro Scale ◽  
Braking System ◽  
Macro Scale ◽  
Texture Characteristics ◽  
The Mean ◽  
Resistance Performance

AbstractIn ice and snow weather, the surface texture characteristics of asphalt pavement change, which will significantly affect the skid resistance performance of asphalt pavement. In this study, five asphalt mixture types of AC-5, AC-13, AC-16, SMA-13, SMA-16 were prepared under three conditions of the original state, ice and snow. In this paper, a 2D-wavelet transform approach is proposed to characterize the micro and macro texture of pavement. The Normalized Energy (NE) is proposed to describe the pavement texture quantitatively. Compared with the mean texture depth (MTD), NE has the advantages of full coverage, full automation and wide analytical scale. The results show that snow increases the micro-scale texture because of its fluffiness, while the formation of the ice sheets on the surface reduces the micro-scale texture. The filling effect of snow and ice reduces the macro-scale texture of the pavement surface. In a follow-up study, the 2D-wavelet transform approach can be applied to improve the intelligent driving braking system, which can provide pavement texture information for the safe braking strategy of driverless vehicles.

scholarly journals Adult tissue-resident stem cells—fact or fiction?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Deepa Bhartiya
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Adult Stem Cells ◽  
Cell Types ◽  
Specific Cell ◽  
Tissue Specific ◽  
Cardiac Tissues ◽  
Adult Tissues ◽  
Resident Stem Cells ◽  
Abundant Cytoplasm

AbstractLife-long tissue homeostasis of adult tissues is supposedly maintained by the resident stem cells. These stem cells are quiescent in nature and rarely divide to self-renew and give rise to tissue-specific “progenitors” (lineage-restricted and tissue-committed) which divide rapidly and differentiate into tissue-specific cell types. However, it has proved difficult to isolate these quiescent stem cells as a physical entity. Recent single-cell RNAseq studies on several adult tissues including ovary, prostate, and cardiac tissues have not been able to detect stem cells. Thus, it has been postulated that adult cells dedifferentiate to stem-like state to ensure regeneration and can be defined as cells capable to replace lost cells through mitosis. This idea challenges basic paradigm of development biology regarding plasticity that a cell enters point of no return once it initiates differentiation. The underlying reason for this dilemma is that we are putting stem cells and somatic cells together while processing for various studies. Stem cells and adult mature cell types are distinct entities; stem cells are quiescent, small in size, and with minimal organelles whereas the mature cells are metabolically active and have multiple organelles lying in abundant cytoplasm. As a result, they do not pellet down together when centrifuged at 100–350g. At this speed, mature cells get collected but stem cells remain buoyant and can be pelleted by centrifuging at 1000g. Thus, inability to detect stem cells in recently published single-cell RNAseq studies is because the stem cells were unknowingly discarded while processing and were never subjected to RNAseq. This needs to be kept in mind before proposing to redefine adult stem cells.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Single-cell transcriptomic analysis of mIHC images via antigen mapping

2021 ◽  
Vol 7 (10) ◽  
pp. eabc5464
Author(s):  
Kiya W. Govek ◽  
Emma C. Troisi ◽  
Zhen Miao ◽  
Rachael G. Aubin ◽  
Steven Woodhouse ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Patterns ◽  
Cell Types ◽  
Level Of Detail ◽  
Cell Populations ◽  
Sequencing Data ◽  
Spatially Resolved ◽  
Murine Spleen ◽  
Single Cell Rna Sequencing ◽  
Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

scholarly journals treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruizhu Huang ◽  
Charlotte Soneson ◽  
Pierre-Luc Germain ◽  
Thomas S.B. Schmidt ◽  
Christian Von Mering ◽  
...  
Keyword(s):  
Single Cell ◽  
Synthetic Data ◽  
Cell Types ◽  
Data Driven ◽  
Rna Seq ◽  
Hierarchical Trees

AbstracttreeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

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