scholarly journals Comparison of Multiplexed Immunofluorescence Imaging to Chromogenic Immunohistochemistry of Skin Biomarkers in Response to Monkeypox Virus Infection

Viruses ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 787
Author(s):  
Anup Sood ◽  
Yunxia Sui ◽  
Elizabeth McDonough ◽  
Alberto Santamaría-Pang ◽  
Yousef Al-Kofahi ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Response ◽  
Skin Lesions ◽  
Protein Markers ◽  
Cell Level ◽  
Monkeypox Virus ◽  
Level Data ◽  
Additional Cell ◽  
Antibody Conjugation ◽  
Automated Image Processing

Over the last 15 years, advances in immunofluorescence-imaging based cycling methods, antibody conjugation methods, and automated image processing have facilitated the development of a high-resolution, multiplexed tissue immunofluorescence (MxIF) method with single cell-level quantitation termed Cell DIVETM. Originally developed for fixed oncology samples, here it was evaluated in highly fixed (up to 30 days), archived monkeypox virus-induced inflammatory skin lesions from a retrospective study in 11 rhesus monkeys to determine whether MxIF was comparable to manual H-scoring of chromogenic stains. Six protein markers related to immune and cellular response (CD68, CD3, Hsp70, Hsp90, ERK1/2, ERK1/2 pT202_pY204) were manually quantified (H-scores) by a pathologist from chromogenic IHC double stains on serial sections and compared to MxIF automated single cell quantification of the same markers that were multiplexed on a single tissue section. Overall, there was directional consistency between the H-score and the MxIF results for all markers except phosphorylated ERK1/2 (ERK1/2 pT202_pY204), which showed a decrease in the lesion compared to the adjacent non-lesioned skin by MxIF vs an increase via H-score. Improvements to automated segmentation using machine learning and adding additional cell markers for cell viability are future options for improvement. This method could be useful in infectious disease research as it conserves tissue, provides marker colocalization data on thousands of cells, allowing further cell level data mining as well as a reduction in user bias.

scholarly journals GENE-45. DISSECTING THE DRIVERS OF ADULT H3K27M-GLIOMAS AT THE SINGLE-CELL LEVEL

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi107-vi107
Author(s):  
Ilon Liu ◽  
Byron Avihai ◽  
Johannes Gojo ◽  
Keith Ligon ◽  
Thomas Czech ◽  
...  
Keyword(s):  
Single Cell ◽  
Time Window ◽  
Molecular Entity ◽  
Single Cell Level ◽  
Cell Level ◽  
Developmental Context ◽  
Level Data ◽  
Cellular Context ◽  
Insight Into ◽  
Shared Gene

Abstract Diffuse midline gliomas are characterized by a lysine27-to-methionine mutation in histone H3 (H3K27M-glioma) and represent a highly aggressive molecular entity of high-grade gliomas. These mutations have primarily been described in children, but are also increasingly recognized in adults. In pediatric H3K27M-gliomas, the transcriptional architecture and cellular composition have been studied at the single-cell level. Data suggest that these tumors arise through malignant transformation of a glial progenitor during a specific neurodevelopmental time window. On the other hand, little is known about the architecture and cellular context of adult H3K27M-gliomas, and it remains to be elucidated whether they are driven by the same or by distinct oncogenic programs as their pediatric counterparts. Here, we utilize single-cell transcriptomics to characterize the transcriptional landscape of five H3K27M-gliomas from adult patients aged 22 to 56 years (median 33 years). We describe the specific cellular and microenvironmental architecture of the adult tumors, which comprises distinct populations of cancer and normal cells. We contrast our findings to 12 location-matched pediatric H3K27M-tumors from patients aged 2.5 to 15 years (median 8 years) to identify features related to tumorigenesis and developmental context in light of the shared hallmark H3K27M mutation. Our preliminary data indicate shared gene expression programs between adult and pediatric tumors. However, we find significant differences in the composition of the immune compartment as well as less pronounced differentiation programs in the adult tumors. Our findings provide an unprecedented insight into the composition of adult H3K27M-gliomas and advance our understanding of this molecular tumor class.

scholarly journals Unmasking cellular response of a bloom-forming alga to viral infection by resolving expression profiles at a single-cell level

2019 ◽  
Vol 15 (4) ◽  
pp. e1007708 ◽  
Author(s):  
Shilo Rosenwasser ◽  
Uri Sheyn ◽  
Miguel J. Frada ◽  
David Pilzer ◽  
Ron Rotkopf ◽  
...  
Keyword(s):  
Viral Infection ◽  
Single Cell ◽  
Cellular Response ◽  
Expression Profiles ◽  
Single Cell Level ◽  
Cell Level

scholarly journals A synchrotron-based infrared microspectroscopy study on the cellular response induced by gold nanoparticles combined with X-ray irradiations on F98 and U87-MG glioma cell lines

The Analyst ◽  
2019 ◽  
Vol 144 (21) ◽  
pp. 6352-6364 ◽  
Author(s):  
I. Martínez-Rovira ◽  
O. Seksek ◽  
I. Yousef
Keyword(s):  
Gold Nanoparticles ◽  
Single Cell ◽  
Treatment Response ◽  
Cellular Response ◽  
Glioma Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
X Ray ◽  
Infrared Microspectroscopy ◽  
Glioma Cell Lines

Synchrotron-based infrared microspectroscopy is a powerful tool for nanoparticle-based treatment response at single cell-level.

An optical detection method for analyzing the cellular response to paclitaxel at the single cell level

2016 ◽  
Vol 8 (18) ◽  
pp. 3698-3703 ◽  
Author(s):  
Yijia Wang ◽  
Yuquan Zhang ◽  
Shiwu Zhang ◽  
Zhenying Zhao ◽  
Changjun Min ◽  
...  
Keyword(s):  
Single Cell ◽  
Biomedical Research ◽  
Cellular Response ◽  
Detection Method ◽  
Optical Detection ◽  
Cellular Responses ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level

The detection of cellular responses to drugs is important for biomedical research, but there is a lack of convenient label-free methods to analyze responses at the single cell level.

scholarly journals Individual Level Differential Expression Analysis for Single Cell RNA-seq data

2021 ◽  
Author(s):  
Mengqi Zhang ◽  
Si Liu ◽  
Zhen Miao ◽  
Fang Han ◽  
Raphael Gottardo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Bulk Solution ◽  
Rna Seq ◽  
Cell Level ◽  
Individual Level ◽  
Level Data

Bulk RNA-seq data quantify the expression of a gene in an individual by one number (e.g., fragment count). In contrast, single cell RNA-seq (scRNA-seq) data provide much richer information: the distribution of gene expression across many cells. To assess differential expression across individuals using scRNA-seq data, a straightforward solution is to create ''pseudo'' bulk RNA-seq data by adding up the fragment counts of a gene across cells for each individual, and then apply methods designed for differential expression using bulk RNA-seq data. This pseudo-bulk solution reduces the distribution of gene expression across cells to a single number and thus loses a good amount of information. We propose to assess differential expression using the gene expression distribution measured by cell level data. We find denoising cell level data can substantially improve the power of this approach. We apply our method, named IDEAS (Individual level Differential Expression Analysis for scRNA-seq), to study the gene expression difference between autism subjects and controls. We find neurogranin-expressing neurons harbor a high proportion of differentially expressed genes, and ERBB signals in microglia are associated with autism.

Single Cell Based Microelectrode Array Biosensors

2003 ◽  
Vol 773 ◽  
Author(s):  
Mo Yang ◽  
Shalini Prasad ◽  
Xuan Zhang ◽  
Mihrimah Ozkan ◽  
Cengiz S. Ozkan
Keyword(s):  
Electrical Activity ◽  
Single Cell ◽  
Cellular Response ◽  
Microelectrode Array ◽  
Fast Fourier Transformation ◽  
Chemical Agent ◽  
Extracellular Potential ◽  
Membrane Excitability ◽  
Specific Chemical ◽  
Chemical Agents

AbstractExtracellular potential is an important parameter which indicates the electrical activity of live cells. Membrane excitability in osteoblasts plays a key role in modulating the electrical activity in the presence of chemical agents. The complexity of cell signal makes interpretation of the cellular response to a chemical agent very difficult. By analyzing shifts in the signal power spectrum, it is possible to determine a frequency spectrum also known as Signature Pattern Vectors (SPV) specific to a chemical. It is also essential to characterize single cell sensitivity and response time for specific chemical agents for developing detect-to-warn biosensors. We used a 4x4 multiple Pt microelectrode array to spatially position single osteoblast cells, by using a gradient AC field. Fast Fourier Transformation (FFT) and Wavelet Transformation (WT) analyses were used to extract information pertaining to the frequency of firing from the extracellular potential.

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