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Related Experiment Video

Updated: Aug 11, 2025

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence
06:56

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

653

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype.

Rebecca L Schmitz1, Kelsey E Tweed1,2, Peter Rehani1

  • 1Morgridge Institute for Research, Madison, WI, USA.

Biorxiv : the Preprint Server for Biology
|February 7, 2023
PubMed
Summary
This summary is machine-generated.

Optical metabolic imaging (OMI) offers a non-destructive way to assess lymphocyte activation and subtype. This label-free technique accurately distinguishes between quiescent and activated B and NK cells, paving the way for improved immune profiling.

Keywords:
B cellsNK cellsactivationautofluorescence imaginglymphocytes

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Area of Science:

  • Immunology
  • Biophotonics
  • Cellular Metabolism

Background:

  • Non-destructive methods are crucial for assessing lymphocyte function in immune profiling and cell therapy.
  • Optical metabolic imaging (OMI) is a label-free technique that quantifies cellular metabolism by measuring autofluorescence of NAD(P)H and FAD.
  • Current methods for assessing lymphocyte activation and subtype can be destructive or lack precision.

Purpose of the Study:

  • To investigate the utility of OMI in distinguishing metabolic differences between quiescent and activated human B and NK cells.
  • To determine if OMI can accurately classify lymphocytes based on activation state and cell subtype using machine learning.
  • To compare metabolic profiles of B, NK, and T cells using OMI.

Main Methods:

  • Human B and NK cells were activated using specific cytokine cocktails (IL4/CD40 for B cells, IL12/IL15/IL18 for NK cells).
  • OMI was employed to measure NAD(P)H and FAD autofluorescence intensity and lifetime in quiescent versus activated cells.
  • Machine learning algorithms (random forest) were utilized to classify cells based on OMI parameters.

Main Results:

  • Quiescent B and NK cells exhibited a more oxidized metabolic state compared to activated cells.
  • Activated B and NK cells showed decreased NAD(P)H mean fluorescence lifetime and increased unbound NAD(P)H fraction.
  • Machine learning models achieved high accuracy (up to 93.4% for B cells, 92.6% for NK cells) in classifying activation states.
  • OMI successfully differentiated between B, NK, and T cell subtypes (97.8% accuracy) and their activation states (90.0% accuracy).

Conclusions:

  • Autofluorescence lifetime imaging provides an accurate, label-free, and non-destructive method for assessing lymphocyte activation.
  • OMI can reliably differentiate between lymphocyte subtypes (B, NK, T cells) and their activation status.
  • This technique holds promise for advancing immune profiling and adoptive cell therapy applications.