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Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

Updated: Jul 19, 2025

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Published on: August 14, 2019

Deep Learning-Enabled, Detection of Rare Circulating Tumor Cell Clusters in Whole Blood Using Label-free, Flow

Nilay Vora¹, Prashant Shekar², Michael Esmail^3,4

¹Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA.

Biorxiv : the Preprint Server for Biology

|August 14, 2023

Summary

Deep learning enhances circulating tumor cell cluster (CTCC) detection in blood using confocal backscatter and fluorescence flow cytometry (BSFC). This advanced method offers improved accuracy for identifying these metastasis biomarkers, paving the way for in vivo monitoring.

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Last Updated: Jul 19, 2025

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

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Published on: August 14, 2019

Capture and Release of Viable Circulating Tumor Cells from Blood

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Published on: October 28, 2016

Adaptation of Semiautomated Circulating Tumor Cell CTC Assays for Clinical and Preclinical Research Applications

Adaptation of Semiautomated Circulating Tumor Cell CTC Assays for Clinical and Preclinical Research Applications

Published on: February 28, 2014

Area of Science:

Biomedical Engineering
Cancer Research
Medical Diagnostics

Background:

Metastatic tumors significantly worsen patient prognosis, with circulating tumor cell clusters (CTCCs) being critical indicators of metastatic progression and increased risk.
Existing methods for detecting circulating tumor cells (CTCs) and CTCCs are primarily ex vivo, lacking in vivo utility and real-time monitoring capabilities.

Approach:

A deep learning (DL) model was developed to enhance the detection and classification of CTCCs in whole blood using confocal backscatter and fluorescence flow cytometry (BSFC).
This approach leverages machine learning for label-free detection, building upon previous ML-enabled peak classification methods.

Key Points:

The DL-based BSFC model achieved a low false alarm rate (0.78 events/min) and a high correlation (0.943) between detected and expected events.
Detection purity was 72% with 35.3% sensitivity for both homotypic and heterotypic CTCCs (minimum size of two cells).
Artificial spiking studies confirmed the model's sensitivity to CTCC quantity variations without introducing significant extra variability.

Conclusions:

Deep learning-based BSFC demonstrates robust performance for detecting CTCCs in whole blood, showing promise for in vivo applications.
Further advancements in label-free BSFC could enable clinical detection and ex vivo isolation of CTCCs with minimal processing.