Quantitative Models for the Kinetics of Cell-Mediated Cytotoxicity at the Single Cell Level

1985 ◽  
pp. 551-561 ◽  
Author(s):  
Alan S. Perelson ◽  
Catherine A. Macken
Keyword(s):  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Quantitative Models ◽  
Kinetics Of

scholarly journals Kinetics of HIV-1 Latency Reversal Quantified on the Single-Cell Level Using a Novel Flow-Based Technique

2016 ◽  
Vol 90 (20) ◽  
pp. 9018-9028 ◽  
Author(s):  
G. Martrus ◽  
A. Niehrs ◽  
R. Cornelis ◽  
A. Rechtien ◽  
W. García-Beltran ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Gag Protein ◽  
Novel Approach ◽  
Latently Infected ◽  
Infected Cells ◽  
First Time ◽  
Kinetics Of ◽  
Hiv 1

ABSTRACTHIV-1 establishes a pool of latently infected cells early following infection. New therapeutic approaches aiming at diminishing this persisting reservoir by reactivation of latently infected cells are currently being developed and tested. However, the reactivation kinetics of viral mRNA and viral protein production, and their respective consequences for phenotypical changes in infected cells that might enable immune recognition, remain poorly understood. We adapted a novel approach to assess the dynamics of HIV-1 mRNA and protein expression in latently and newly infected cells on the single-cell level by flow cytometry. This technique allowed the simultaneous detection ofgagpolmRNA, intracellular p24 Gag protein, and cell surface markers. Following stimulation of latently HIV-1-infected J89 cells with human tumor necrosis factor alpha (hTNF-α)/romidepsin (RMD) or HIV-1 infection of primary CD4+T cells, four cell populations were detected according to their expression levels of viral mRNA and protein.gagpolmRNA in J89 cells was quantifiable for the first time 3 h after stimulation with hTNF-α and 12 h after stimulation with RMD, while p24 Gag protein was detected for the first time after 18 h poststimulation. HIV-1-infected primary CD4+T cells downregulated CD4, BST-2, and HLA class I expression at early stages of infection, proceeding Gag protein detection. In conclusion, here we describe a novel approach allowing quantification of the kinetics of HIV-1 mRNA and protein synthesis on the single-cell level and phenotypic characterization of HIV-1-infected cells at different stages of the viral life cycle.IMPORTANCEEarly after infection, HIV-1 establishes a pool of latently infected cells, which hide from the immune system. Latency reversal and immune-mediated elimination of these latently infected cells are some of the goals of current HIV-1 cure approaches; however, little is known about the HIV-1 reactivation kinetics following stimulation with latency-reversing agents. Here we describe a novel approach allowing for the first time quantification of the kinetics of HIV-1 mRNA and protein synthesis after latency reactivation orde novoinfection on the single-cell level using flow cytometry. This new technique furthermore enabled the phenotypic characterization of latently infected andde novo-infected cells dependent on the presence of viral RNA or protein.

scholarly journals Single-cell analysis of macrophage chemotactic protein-1-regulated cytosolic Ca2+ increase in human adherent monocytes

Blood ◽  
1995 ◽  
Vol 86 (6) ◽  
pp. 2388-2394 ◽  
Author(s):  
C Bizzarri ◽  
R Bertini ◽  
P Bossu ◽  
S Sozzani ◽  
A Mantovani ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cell Level ◽  
Blood Monocytes ◽  
Cell Level ◽  
Slow Increase ◽  
Chemotactic Protein ◽  
Kinetics Of ◽  
Subsequent Opening ◽  
Ca2 Channels

The increase in intracellular free Ca2+ ([Ca2+]i) associated with interaction of monocyte chemotactic protein-1 (MCP-1) and related chemokines beta with adherent human blood monocytes was investigated at the single-cell level. We used f-MLP as reference chemotactic agent. MCP-1 caused an increase in [Ca2+]i in individual adherent monocytes, with 95% of cells responding to the chemokine at 20 ng/mL. Response to MCP-1 was already detectable at 1 pg/mL, whereas at least 5 ng/mL were required for significant chemotactic response. The kinetics of the increase in [Ca2+]i were considerably different for MCP-1 compared with f-MLP. MCP-1 produced a slow increase of [Ca2+]i that reached a plateau in 5 to 7 minutes. On the other hand, the increase of [Ca2+]i induced by f-MLP appeared to be biphasic, with a fast phase peaking after 5 to 40 seconds followed by a slower wave. Blocking of Ca2+ channels by Ni2+ or Cd2+ and/or chelation of extracellular free Ca2+ considerably reduced but did not abolish response to MCP-1, had no effect on the first wave of [Ca2+]i induced by f-MLP, and completely abrogated the second, slower wave. Thapsigargin, which empties intracellular Ca2+ stores, inhibited f-MLP-induced [Ca2+]i increase but fully blocked the action of MCP-1 only when combined with Ni2+. Thus, increase of [Ca2+]i induced by MCP-1 is apparently due to independent opening of a channel and mobilization from intracellular stores, whereas f-MLP-induced mobilization of Ca2+ from stores causes subsequent opening of a channel. At variance with MCP-1, the related chemokine MCP-2 induced only a low increase of [Ca2+]i in about 40% of adherent monocytes. Inhibition of chemokine-induced increase of [Ca2+]i by cholera or pertussis toxin indicated that MCP-1 and MCP-2 activate monocytes through different intracellular pathways. These results demonstrate at the single-cell level that the mechanisms and dynamics of increased [Ca2+]i are considerably different for f-MLP and chemokines beta. In addition, the [Ca2+]i increase induced by the two related chemokines beta MCP-1 and MCP-2 appears to be differently regulated, suggesting interaction with distinct receptors.

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

scholarly journals Quality control of direct cell–mineral adhesion measurements in air and liquid using inverse AFM imaging

RSC Advances ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 5384-5392
Author(s):  
Abd Alaziz Abu Quba ◽  
Gabriele E. Schaumann ◽  
Mariam Karagulyan ◽  
Doerte Diehl
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
C Cell ◽  
Afm Imaging ◽  
Direct Cell ◽  
Mineral Interaction

Setup for a reliable cell-mineral interaction at the single-cell level, (a) study of the mineral by a sharp tip, (b) study of the bacterial modified probe by a characterizer, (c) cell-mineral interaction, (d) subsequent check of the modified probe.

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.

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