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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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Updated: Sep 3, 2025

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data.

Adriano Luca Martinelli¹, Johanna Wagner², Bernd Bodenmiller³

¹IBM Research Europe, Saeumerstrasse 4, CH-8803 Rueschlikon, Switzerland.

|July 26, 2022

Summary

scQUEST is a new Python library for analyzing tumor ecosystems using single-cell data. It helps identify cell types and quantify heterogeneity in patient samples, aiding cancer research.

Keywords:

Bioinformatics Cancer Flow Cytometry/Mass Cytometry Single Cell

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Last Updated: Sep 3, 2025

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

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Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

Published on: February 27, 2015

Area of Science:

Immunology
Computational Biology
Oncology

Background:

Mass and flow cytometry enable high-dimensional single-cell profiling.
Tumor ecosystems are complex and require advanced analytical tools.
Understanding tumor heterogeneity is crucial for effective cancer treatment.

Purpose of the Study:

Introduce scQUEST, an open-source Python library for single-cell data analysis.
Enable cell type identification and quantification of tumor ecosystem heterogeneity.
Provide a protocol for applying scQUEST to patient cohort data.

Main Methods:

Utilized mass cytometry for single-cell profiling of human breast cancer.
Developed and applied scQUEST for cell type identification.
Quantified tumor ecosystem heterogeneity in patient samples.

Main Results:

Demonstrated the utility of scQUEST on a human breast cancer single-cell atlas.
Successfully identified cell types and characterized tumor heterogeneity.
Provided a reproducible protocol for scQUEST application.

Conclusions:

scQUEST is a valuable tool for analyzing complex tumor microenvironments.
The library facilitates the study of tumor ecosystem heterogeneity in patient cohorts.
scQUEST can be adapted for diverse single-cell datasets and analytical needs.