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Overview Of Cell Separation And Isolation

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

Cell Diversity

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The concept of a cell started with microscopic observations of dead cork tissue by Robert Hooke in 1665. Hooke coined the term "cell" based on the resemblance of the small subdivisions in the cork to the rooms that monks inhabited, called cells. About ten years later, Antonie van Leeuwenhoek became the first person to observe the living and moving cells under a microscope. In the century that followed, the theory that cells represented the basic unit of life developed.
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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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MARS: discovering novel cell types across heterogeneous single-cell experiments.

Maria Brbić¹, Marinka Zitnik², Sheng Wang³

¹Department of Computer Science, Stanford University, Stanford, CA, USA.

|October 20, 2020

Summary

MARS, a novel meta-learning approach, identifies and annotates both known and new cell types from single-cell RNA sequencing data. This method effectively handles data heterogeneity and discovers previously uncharacterized cell populations.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Accurate cell-type annotation is crucial for understanding biological systems.
Identifying novel cell types in complex single-cell RNA sequencing (scRNA-seq) datasets remains a significant challenge.
Existing methods struggle with data heterogeneity and the discovery of previously uncharacterized cell types.

Purpose of the Study:

To develop a robust computational method for identifying and annotating both known and novel cell types in scRNA-seq data.
To overcome the challenge of cell type heterogeneity across multiple datasets.
To enable automated annotation of unannotated scRNA-seq experiments.

Main Methods:

MARS (Meta-learning Approach for cell type Recognition and annotation) utilizes a meta-learning framework.
Deep learning is employed to learn a transferable cell embedding function and identify landmarks in the embedding space.
Latent cell representations are transferred across multiple datasets to address heterogeneity.

Main Results:

MARS successfully identifies and annotates known cell types with high accuracy.
The method demonstrates a unique capability to discover and annotate previously uncharacterized cell types.
Application to a large mouse cell atlas validated MARS's performance in identifying novel cell populations.
MARS automatically generates interpretable names for newly discovered cell types.

Conclusions:

MARS provides a powerful and versatile tool for cell type identification and annotation in scRNA-seq data.
The meta-learning approach effectively handles data heterogeneity and facilitates the discovery of novel cell types.
MARS has the potential to significantly advance cell atlas projects and biological discovery.