Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Multimodal characterization of variation in neuronal types in the mouse basal ganglia.

bioRxiv : the preprint server for biology·2026

Same author

[Modern therapy of pulmonary embolism with PERT and EKOS: an advantage also for in-hospital time?]

Medizinische Klinik, Intensivmedizin und Notfallmedizin·2026

Same author

Connecting single-cell transcriptomes to projectomes in the mouse visual cortex.

Nature·2026

Same author

Conserved Cell Type Signatures Across the Brainstem and Spinal Cord in the Mouse Central Nervous System.

bioRxiv : the preprint server for biology·2026

Same author

Brain-wide topographic coordination of rotating waves.

Science (New York, N.Y.)·2026

Same author

Invasive ventilation and association with outcomes in heart failure-related cardiogenic shock.

Clinical research in cardiology : official journal of the German Cardiac Society·2026

Search research articles

Related Experiment Video

Updated: Nov 2, 2025

Isolation and Transcriptome Analysis of Plant Cell Types

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

Cell segmentation-free inference of cell types from in situ transcriptomics data.

Jeongbin Park^1,2,3, Wonyl Choi⁴, Sebastian Tiesmeyer¹

¹Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany.

Nature Communications

|June 11, 2021

Summary

This study introduces SSAM, a novel computational framework for cell-type identification in spatial transcriptomics. SSAM overcomes segmentation limitations, enhancing tissue characterization and discovering new cell types.

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Related Experiment Videos

Last Updated: Nov 2, 2025

Isolation and Transcriptome Analysis of Plant Cell Types

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Area of Science:

Spatial transcriptomics
Computational biology
Neuroscience

Background:

Multiplexed in situ hybridization enables linking transcriptional and spatial cell heterogeneity.
Inaccurate cell segmentation hinders cell-type identification and tissue characterization in spatial transcriptomics.

Purpose of the Study:

To present SSAM (Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation), a robust, segmentation-free computational framework.
To enable accurate cell-type identification and tissue domain characterization in 2D and 3D spatial transcriptomics data.
To demonstrate SSAM's applicability across various in situ transcriptomics techniques and its ability to integrate prior cell-type knowledge.

Main Methods:

SSAM utilizes a spot-based, multidimensional mRNA density estimation approach.
The framework is designed to be cell segmentation-free, addressing a key limitation in current methods.
SSAM can integrate prior knowledge of cell types to improve identification accuracy.

Main Results:

SSAM was applied to mouse brain datasets from osmFISH, MERFISH, and multiplexed smFISH.
The method successfully identified regions occupied by known cell types that were previously missed.
SSAM demonstrated the capability to discover novel cell types within the analyzed tissues.

Conclusions:

SSAM provides a robust and versatile computational framework for cell-type identification in spatial transcriptomics.
By eliminating the need for cell segmentation, SSAM enhances the accuracy and efficacy of tissue characterization.
The framework's ability to detect missed cell types and discover new ones advances the field of single-cell spatial biology.