Digital cytometry: extraction of forward and side scattering signals from holotomography

TL;DR

This paper presents a novel method to extract and analyze forward and side scattering signals from holotomography data, linking microscopic imaging with flow cytometry for label-free cell analysis.

Contribution

It introduces a new approach to derive FSC and SSC signals from 3D refractive index distributions, enabling accurate, label-free cell characterization and segmentation.

Findings

Successfully extracted FSC and SSC signals from holotomography data.

Enabled effective cell segmentation and classification without labels.

Bridged flow cytometry signals with microscopic imaging techniques.

Abstract

Flow cytometry is a cornerstone technique in medical and biological research, providing crucial information about cell size and granularity through forward scatter (FSC) and side scatter (SSC) signals. Despite its widespread use, the precise relationship between these scatter signals and corresponding microscopic images remains underexplored. Here, we investigate this intrinsic relationship by utilizing scattering theory and holotomography, a three-dimensional quantitative phase imaging (QPI) technique. We demonstrate the extraction of FSC and SSC signals from individual, unlabeled cells by analyzing their three-dimensional refractive index distributions obtained through holotomography. Additionally, we introduce a method for digitally windowing SSC signals to facilitate effective segmentation and morphology-based cell type classification. Our approach bridges the gap between flow cytometry and microscopic imaging, offering a new perspective on analyzing cellular characteristics with high accuracy and without the need for labeling.