Label-free whole blood cell differentiation based on multiple frequency AC impedance and light scattering analysis in a micro flow cytometer

Lab on a Chip ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2326-2338 ◽  
Author(s):  
Peter Simon ◽  
Marcin Frankowski ◽  
Nicole Bock ◽  
Jörg Neukammer
Keyword(s):  
Cell Differentiation ◽  
Light Scattering ◽  
Blood Cell ◽  
Whole Blood ◽  
Electrical Impedance ◽  
Label Free ◽  
Multiple Frequency ◽  
Flow Cytometric ◽  
Micro Flow ◽  
Flow Cytometric Cell

We developed a microfluidic sensor for label-free flow cytometric cell differentiation by combined multiple AC electrical impedance and light scattering analysis.

Angle-resolved Light Scattering Study of NALM-6 and HL-60 Cells for White Blood Cell Differentiation

2010 ◽  
Author(s):  
Jun Q. Lu ◽  
Huafeng Ding ◽  
Carissa L. Reynolds ◽  
Yuanming Feng ◽  
Li V. Yang ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Light Scattering ◽  
Blood Cell ◽  
White Blood Cell ◽  
Scattering Study

scholarly journals AIDeveloper: deep learning image classification in life science and beyond

2020 ◽  
Author(s):  
Martin Kräter ◽  
Shada Abuhattum ◽  
Despina Soteriou ◽  
Angela Jacobi ◽  
Thomas Krüger ◽  
...  
Keyword(s):  
Blood Cell ◽  
Cell Count ◽  
Image Classification ◽  
Whole Blood ◽  
Classification Performance ◽  
Neural Nets ◽  
Neural Net ◽  
Label Free ◽  
Blood Cell Count ◽  
Free Classification

AbstractPublications on artificial intelligence (AI)-based image analysis have increased drastically in recent years. However, all applications use individual solutions highly specialized for a particular task. Here, we present an easy-to-use, adaptable, open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming. The software provides a variety of NN-architectures that can be simply selected for training. AID allows the user to apply trained models on new data, obtain metrics for classification performance, and export final models to different formats. The working principles of AID are first illustrated by training a convolutional neural net (CNN) on a large dataset consisting of images of different objects (CIFAR-10). We further explore the potential of AID by training a model to distinguish areas of differentiated and non-differentiated mesenchymal stem cells (MSCs) in culture. Additionally, we compare a conventional clinical whole blood cell count with a whole blood cell count performed by an NN-trained, using a dataset of more than 1.2 million images obtained by real-time deformability cytometry, delivering comparable results. Finally, we demonstrate how AID can be used for label-free classification of B- and T-cells derived from human blood, which currently requires costly and time-consuming sample preparation. Thus, AID can empower anyone to develop, train, and apply NNs for image classification. Moreover, models can be generated by non-programmers, exported, and used on different devices, which allows for an interdisciplinary use.

scholarly journals Telomere length varies substantially between blood cell types in a reptile

2020 ◽  
Vol 7 (6) ◽  
pp. 192136 ◽  
Author(s):  
Mats Olsson ◽  
Nicholas J. Geraghty ◽  
Erik Wapstra ◽  
Mark Wilson
Keyword(s):  
Blood Cell ◽  
Telomere Length ◽  
Whole Blood ◽  
Phylogenetic Analyses ◽  
Natural Populations ◽  
Cell Types ◽  
Tissue Type ◽  
Telomeric Dna ◽  
Blood Cell Type ◽  
Widespread Phenomenon

Telomeres are repeat sequences of non-coding DNA-protein molecules that cap or intersperse metazoan chromosomes. Interest in telomeres has increased exponentially in recent years, to now include their ongoing dynamics and evolution within natural populations where individuals vary in telomere attributes. Phylogenetic analyses show profound differences in telomere length across non-model taxa. However, telomeres may also differ in length within individuals and between tissues. The latter becomes a potential source of error when researchers use different tissues for extracting DNA for telomere analysis and scientific inference. A commonly used tissue type for assessing telomere length is blood, a tissue that itself varies in terms of nuclear content among taxa, in particular to what degree their thrombocytes and red blood cells (RBCs) contain nuclei or not. Specifically, when RBCs lack nuclei, leucocytes become the main source of telomeric DNA. RBCs and leucocytes differ in lifespan and how long they have been exposed to ‘senescence' and erosion effects. We report on a study in which cells in whole blood from individual Australian painted dragon lizards ( Ctenophorus pictus ) were identified using flow cytometry and their telomere length simultaneously measured. Lymphocyte telomeres were on average 270% longer than RBC telomeres, and in azurophils (a reptilian monocyte), telomeres were more than 388% longer than those in RBCs. If this variation in telomere length among different blood cell types is a widespread phenomenon, and DNA for comparative telomere analyses are sourced from whole blood, evolutionary inference of telomere traits among taxa may be seriously complicated by the blood cell type comprising the main source of DNA.

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