Virtual-freezing fluorescence imaging flow cytometry with 5-aminolevulinic acid stimulation and antibody labeling for detecting all forms of circulating tumor cells.
Hiroki MatsumuraLarina Tzu-Wei ShenAkihiro IsozakiHideharu MikamiDan YuanTaichi MiuraYuto KondoTomoko MoriYoshika KusumotoMasako NishikawaAtsushi YasumotoAya UedaHiroko BandoHisato HaraYuhong LiuYunjie DengMasahiro SonoshitaYutaka YatomiKeisuke GodaSatoshi MatsusakaPublished in: Lab on a chip (2023)
Circulating tumor cells (CTCs) are precursors to cancer metastasis. In blood circulation, they take various forms such as single CTCs, CTC clusters, and CTC-leukocyte clusters, all of which have unique characteristics in terms of physiological function and have been a subject of extensive research in the last several years. Unfortunately, conventional methods are limited in accurately analysing the highly heterogeneous nature of CTCs. Here we present an effective strategy for simultaneously analysing all forms of CTCs in blood by virtual-freezing fluorescence imaging (VIFFI) flow cytometry with 5-aminolevulinic acid (5-ALA) stimulation and antibody labeling. VIFFI is an optomechanical imaging method that virtually freezes the motion of fast-flowing cells on an image sensor to enable high-throughput yet sensitive imaging of every single event. 5-ALA stimulates cancer cells to induce the accumulation of protoporphyrin (PpIX), a red fluorescent substance, making it possible to detect all cancer cells even if they show no expression of the epithelial cell adhesion molecule, a typical CTC biomarker. Although PpIX signals are generally weak, VIFFI flow cytometry can detect them by virtue of its high sensitivity. As a proof-of-principle demonstration of the strategy, we applied cancer cells spiked in blood to the strategy to demonstrate image-based detection and accurate classification of single cancer cells, clusters of cancer cells, and clusters of a cancer cell(s) and a leukocyte(s). To show the clinical utility of our method, we used it to evaluate blood samples of four breast cancer patients and four healthy donors and identified EpCAM-positive PpIX-positive cells in one of the patient samples. Our work paves the way toward the determination of cancer prognosis, the guidance and monitoring of treatment, and the design of antitumor strategies for cancer patients.
Keyphrases
- circulating tumor cells
- flow cytometry
- fluorescence imaging
- photodynamic therapy
- induced apoptosis
- circulating tumor
- deep learning
- cell adhesion
- high throughput
- high resolution
- papillary thyroid
- cell cycle arrest
- squamous cell
- poor prognosis
- machine learning
- oxidative stress
- label free
- peripheral blood
- quantum dots
- childhood cancer
- real time pcr