scholarly journals Semi‐automated background removal limits data loss and normalises imaging mass cytometry data

2021 ◽  
Author(s):  
Marieke E. Ijsselsteijn ◽  
Antonios Somarakis ◽  
Boudewijn P. F. Lelieveldt ◽  
Thomas Höllt ◽  
Noel F. C. C. de Miranda
Keyword(s):  
Data Loss ◽  
Mass Cytometry ◽  
Background Removal

scholarly journals Semi-automated background removal limits loss of data and normalises the images for downstream analysis of imaging mass cytometry data

2020 ◽  
Author(s):  
Marieke E. Ijsselsteijn ◽  
Antonios Somarakis ◽  
Boudewijn P.F. Lelieveldt ◽  
Thomas Hollt ◽  
Noel F.C.C. de Miranda
Keyword(s):  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Mass Cytometry ◽  
Background Removal ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Ffpe Tissues ◽  
Downstream Analysis ◽  
Cancer Tissues ◽  
Multiple Samples

AbstractImaging mass cytometry (IMC) allows the detection of multiple antigens (approximately 40 markers) combined with spatial information, making it a unique tool for the evaluation of complex biological systems. Due to its widespread availability and retained tissue morphology, formalin-fixed, paraffin-embedded (FFPE) tissues are often a material of choice for IMC studies. However, antibody performance and signal-to-noise ratio can differ considerably between FFPE tissues as a consequence of variations in tissue processing, including fixation. We investigated the effect of immunodetection-related signal intensity fluctuations on IMC analysis and phenotype identification in a cohort of twelve colorectal cancer tissues. Furthermore, we explored different normalisation strategies and propose a workflow to normalise IMC data by semi-automated background removal, using publicly available tools. This workflow can be directly applied to previously obtained datasets and considerably improves the quality of IMC data, thereby supporting the analysis and comparison of multiple samples.

Author response for "Mass cytometry analysis of blood immune cells from psoriasis patients on biological therapy"

2020 ◽  
Author(s):  
SM Solberg ◽  
AK Aarebrot ◽  
I Sarkar ◽  
A Petrovic ◽  
LF Sandvik ◽  
...  
Keyword(s):  
Immune Cells ◽  
Biological Therapy ◽  
Author Response ◽  
Mass Cytometry

Effectiveness of Nanoprober in Detecting Single/Multiple Bit Flash Data Gain & Data Loss Failures

2009 ◽  
Author(s):  
Rajesh Medikonduri
Keyword(s):  
Flash Memory ◽  
Data Loss ◽  
Voltage Contrast

Abstract This paper discusses the physics, definitions, and nanoprobing flow of a flash bit memory. In addition, a case study showing the effectiveness of nanoprobing in detecting the Single Bit Fail Data Gain and Data Loss in Flash Memory is also discussed. The paper also includes cases where no passive voltage contrast was observed at the SEM and no leakage was observed at AFM, yet the units failing SBF DG, SBF DL and depletion, were detected by nanoprobing of the single bit. The major finding of this paper is a way to resolve data gain, data loss, and depletion failures of flash memory by nanoprobing procedure, despite no PVC seen at the SEM and no leakage seen at the AFM.

Liability for Data Loss

2018 ◽  
Author(s):  
Vincenzo Zeno-Zencovich
Keyword(s):  
Data Loss
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