Literature DB >> 30741527

Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning.

Sukrut Hemant Karandikar1, Chi Zhang2, Akilan Meiyappan2, Ishan Barman2,3, Christine Finck4,5, Pramod Kumar Srivastava1,6, Rishikesh Pandey5.   

Abstract

CD8+ T cells constitute an essential compartment of the adaptive immune system. During immune responses, naı̈ve T cells become functional, as they are primed with their cognate determinants by the antigen presenting cells. Current methods of identifying activated CD8+ T cells are laborious, time-consuming and expensive due to the extensive list of required reagents. Here, we demonstrate an optical imaging approach featuring quantitative phase imaging to distinguish activated CD8+ T cells from naı̈ve CD8+ T cells in a rapid and reagent-free manner. We measured the dry mass of live cells and employed transport-based morphometry to better understand their differential morphological attributes. Our results reveal that, upon activation, the dry cell mass of T cells increases significantly in comparison to that of unstimulated cells. By employing deep learning formalism, we are able to accurately predict the population ratios of unknown mixed population based on the acquired quantitative phase images. We envision that, with further refinement, this label-free method of T cell phenotyping will lead to a rapid and cost-effective platform for assaying T cell responses to candidate antigens in the near future.

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Mesh:

Year:  2019        PMID: 30741527      PMCID: PMC6423970          DOI: 10.1021/acs.analchem.8b04895

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  33 in total

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Authors:  R BARER
Journal:  Nature       Date:  1953-12-12       Impact factor: 49.962

2.  Diffraction phase microscopy for quantifying cell structure and dynamics.

Authors:  Gabriel Popescu; Takahiro Ikeda; Ramachandra R Dasari; Michael S Feld
Journal:  Opt Lett       Date:  2006-03-15       Impact factor: 3.776

3.  Quantitative phase imaging of nanoscale cell structure and dynamics.

Authors:  Gabriel Popescu
Journal:  Methods Cell Biol       Date:  2008       Impact factor: 1.441

Review 4.  New technologies for measuring single cell mass.

Authors:  Gabriel Popescu; Kidong Park; Mustafa Mir; Rashid Bashir
Journal:  Lab Chip       Date:  2014-02-21       Impact factor: 6.799

5.  Optical imaging of cell mass and growth dynamics.

Authors:  Gabriel Popescu; Youngkeun Park; Niyom Lue; Catherine Best-Popescu; Lauren Deflores; Ramachandra R Dasari; Michael S Feld; Kamran Badizadegan
Journal:  Am J Physiol Cell Physiol       Date:  2008-06-18       Impact factor: 4.249

6.  Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry.

Authors:  Saurav Basu; Soheil Kolouri; Gustavo K Rohde
Journal:  Proc Natl Acad Sci U S A       Date:  2014-02-18       Impact factor: 11.205

7.  Machine Learning in Medical Imaging.

Authors:  Miles N Wernick; Yongyi Yang; Jovan G Brankov; Grigori Yourganov; Stephen C Strother
Journal:  IEEE Signal Process Mag       Date:  2010-07       Impact factor: 12.551

Review 8.  Fluorescence live cell imaging.

Authors:  Andreas Ettinger; Torsten Wittmann
Journal:  Methods Cell Biol       Date:  2014       Impact factor: 1.441

9.  Quantifying biomass changes of single CD8+ T cells during antigen specific cytotoxicity.

Authors:  Thomas A Zangle; Daina Burnes; Colleen Mathis; Owen N Witte; Michael A Teitell
Journal:  PLoS One       Date:  2013-07-23       Impact factor: 3.240

10.  Highly sensitive quantitative imaging for monitoring single cancer cell growth kinetics and drug response.

Authors:  Mustafa Mir; Anna Bergamaschi; Benita S Katzenellenbogen; Gabriel Popescu
Journal:  PLoS One       Date:  2014-02-18       Impact factor: 3.240

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  6 in total

Review 1.  High-speed laser-scanning biological microscopy using FACED.

Authors:  Queenie T K Lai; Gwinky G K Yip; Jianglai Wu; Justin S J Wong; Michelle C K Lo; Kelvin C M Lee; Tony T H D Le; Hayden K H So; Na Ji; Kevin K Tsia
Journal:  Nat Protoc       Date:  2021-08-02       Impact factor: 13.491

2.  Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry.

Authors:  Minh Doan; Claire Barnes; Claire McQuin; Juan C Caicedo; Allen Goodman; Anne E Carpenter; Paul Rees
Journal:  Nat Protoc       Date:  2021-06-18       Impact factor: 13.491

3.  Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential.

Authors:  Santosh Kumar Paidi; Vaani Shah; Piyush Raj; Kristine Glunde; Rishikesh Pandey; Ishan Barman
Journal:  Biosens Bioelectron       Date:  2020-11-27       Impact factor: 10.618

4.  Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.

Authors:  Diane N H Kim; Alexander A Lim; Michael A Teitell
Journal:  Sci Rep       Date:  2021-09-30       Impact factor: 4.996

5.  Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia.

Authors:  Santosh Kumar Paidi; Piyush Raj; Rosalie Bordett; Chi Zhang; Sukrut H Karandikar; Rishikesh Pandey; Ishan Barman
Journal:  Biosens Bioelectron       Date:  2021-06-12       Impact factor: 12.545

6.  Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.

Authors:  Zijie J Wang; Alex J Walsh; Melissa C Skala; Anthony Gitter
Journal:  J Biophotonics       Date:  2019-12-15       Impact factor: 3.207

  6 in total

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