scholarly journals High throughput hemogram of T cells using digital holographic microscopy and deep learning

2021 ◽  
Author(s):  
Roopam K Gupta ◽  
Nils Hempler ◽  
Graeme Malcolm ◽  
Kishan Dholakia ◽  
Simon J Powis
Keyword(s):  
T Cells ◽  
Deep Learning ◽  
Adaptive Immune System ◽  
Cost Effective ◽  
Magnetic Bead ◽  
T Cell Subsets ◽  
Digital Holographic Microscopy ◽  
Label Free ◽  
Throughput Rate ◽  
Holographic Microscopy

T cells of the adaptive immune system provide effective protection to the human body against numerous pathogenic challenges. Current labelling methods of detecting these cells, such as flow cytometry or magnetic bead labelling, are time consuming and expensive. To overcome these limitations, the label-free method of digital holographic microscopy (DHM) combined with deep learning has recently been introduced which is both time and cost effective. In this study, we demonstrate the application of digital holographic microscopy with deep learning to classify the key CD4+ and CD8+ T cell subsets. We show that combining DHM of varying fields of view, with deep learning, can potentially achieve a classification throughput rate of 78,000 cells per second with an accuracy of 76.2% for these morphologically similar cells. This throughput rate is 100 times faster than the previous studies and proves to be an effective replacement for labelling methods.

scholarly journals Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Joost Verduijn ◽  
Louis Van der Meeren ◽  
Dmitri V. Krysko ◽  
André G. Skirtach
Keyword(s):  
Cell Death ◽  
Deep Learning ◽  
Cancer Cells ◽  
Cell Biology ◽  
Learning Algorithm ◽  
Digital Holographic Microscopy ◽  
Label Free ◽  
Deep Learning Algorithm ◽  
Regulated Cell Death ◽  
Holographic Microscopy

AbstractRegulated cell death modalities such as apoptosis and necroptosis play an important role in regulating different cellular processes. Currently, regulated cell death is identified using the golden standard techniques such as fluorescence microscopy and flow cytometry. However, they require fluorescent labels, which are potentially phototoxic. Therefore, there is a need for the development of new label-free methods. In this work, we apply Digital Holographic Microscopy (DHM) coupled with a deep learning algorithm to distinguish between alive, apoptotic and necroptotic cells in murine cancer cells. This method is solely based on label-free quantitative phase images, where the phase delay of light by cells is quantified and is used to calculate their topography. We show that a combination of label-free DHM in a high-throughput set-up (~10,000 cells per condition) can discriminate between apoptosis, necroptosis and alive cells in the L929sAhFas cell line with a precision of over 85%. To the best of our knowledge, this is the first time deep learning in the form of convolutional neural networks is applied to distinguish—with a high accuracy—apoptosis and necroptosis and alive cancer cells from each other in a label-free manner. It is expected that the approach described here will have a profound impact on research in regulated cell death, biomedicine and the field of (cancer) cell biology in general.

scholarly journals Label-free fingerprinting of tumor cells in bulk flow using inline digital holographic microscopy

2017 ◽  
Vol 8 (2) ◽  
pp. 536 ◽  
Author(s):  
Dhananjay Kumar Singh ◽  
Caroline C. Ahrens ◽  
Wei Li ◽  
Siva A. Vanapalli
Keyword(s):  
Tumor Cells ◽  
Bulk Flow ◽  
Digital Holographic Microscopy ◽  
Label Free ◽  
Holographic Microscopy

Label-free second-harmonic phase imaging of biological specimen by digital holographic microscopy

2010 ◽  
Vol 35 (24) ◽  
pp. 4102 ◽  
Author(s):  
Etienne Shaffer ◽  
Corinne Moratal ◽  
Pierre Magistretti ◽  
Pierre Marquet ◽  
Christian Depeursinge
Keyword(s):  
Second Harmonic ◽  
Digital Holographic Microscopy ◽  
Phase Imaging ◽  
Label Free ◽  
Biological Specimen ◽  
Harmonic Phase ◽  
Holographic Microscopy ◽  
Harmonic Phase Imaging

scholarly journals Functionally specialized human CD4+ T-cell subsets express physicochemically distinct TCRs

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Sofya A Kasatskaya ◽  
Kristin Ladell ◽  
Evgeniy S Egorov ◽  
Kelly L Miners ◽  
Alexey N Davydov ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptors ◽  
Adaptive Immune System ◽  
T Cell Subsets ◽  
Effector Memory ◽  
Intrinsic Properties ◽  
Antigen Specificity ◽  
Adaptive Immune ◽  
Transition Pathways

The organizational integrity of the adaptive immune system is determined by functionally discrete subsets of CD4+ T cells, but it has remained unclear to what extent lineage choice is influenced by clonotypically expressed T-cell receptors (TCRs). To address this issue, we used a high-throughput approach to profile the αβ TCR repertoires of human naive and effector/memory CD4+ T-cell subsets, irrespective of antigen specificity. Highly conserved physicochemical and recombinatorial features were encoded on a subset-specific basis in the effector/memory compartment. Clonal tracking further identified forbidden and permitted transition pathways, mapping effector/memory subsets related by interconversion or ontogeny. Public sequences were largely confined to particular effector/memory subsets, including regulatory T cells (Tregs), which also displayed hardwired repertoire features in the naive compartment. Accordingly, these cumulative repertoire portraits establish a link between clonotype fate decisions in the complex world of CD4+ T cells and the intrinsic properties of somatically rearranged TCRs.

scholarly journals Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection

2017 ◽  
Vol 25 (13) ◽  
pp. 15043 ◽  
Author(s):  
Thanh Nguyen ◽  
Vy Bui ◽  
Van Lam ◽  
Christopher B. Raub ◽  
Lin-Ching Chang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Holographic Microscopy ◽  
Phase Aberration ◽  
Automatic Phase ◽  
Holographic Microscopy

scholarly journals Magnetic Control of an Electrochemical Microfluidic Device with an Arrayed Immunosensor for Simultaneous Multiple Immunoassays

2007 ◽  
Vol 53 (7) ◽  
pp. 1323-1329 ◽  
Author(s):  
Dianping Tang ◽  
Ruo Yuan ◽  
Yaqin Chai
Keyword(s):  
Tumor Markers ◽  
Microfluidic Device ◽  
Simultaneous Detection ◽  
Reference Electrode ◽  
Cost Effective ◽  
Sio2 Nanoparticles ◽  
Magnetic Bead ◽  
Label Free ◽  
Specific Tumor ◽  
Protein Diagnostics

Abstract Background: Methods based on magnetic bead probes have been developed for immunoassay, but most involve complicated labeling or stripping procedures and are unsuitable for routine use. Methods: We synthesized magnet core/shell NiFe2O4/SiO2 nanoparticles and fabricated an electrochemical magnetic controlled microfluidic device for the detection of 4 tumor markers. The immunoassay system consisted of 5 working electrodes and an Ag/AgCl reference electrode integrated on a glass substrate. Each working electrode contained a different antibody immobilized on the NiFe2O4/SiO2 nanoparticle surface and was capable of measuring a specific tumor marker using noncompetitive electrochemical immunoassay. Results: Under optimal conditions, the multiplex immunoassay enabled the simultaneous detection of 4 tumor markers. The sensor detection limit was <0.5 μg/L (or <0.5 kunits/L) for most analytes. Intra- and interassay imprecisions (CVs) were <4.5% and 8.7% for analyte concentrations >5 μg/L (or >5 kunits/L), respectively. No nonspecific adsorption was observed during a series of procedures to detect target proteins, and electrochemical cross-talk (CV) between neighboring sites was <10%. Conclusion: This immunoassay system offers promise for label-free, rapid, simple, cost-effective analysis of biological samples. Importantly, the chip-based immunosensor could be suitable for use in the mass production of miniaturized lab-on-a-chip devices and open new opportunities for protein diagnostics and biosecurity.

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