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Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics.

Zhixing Zhong^1,2, Junchen Hou³, Zhixian Yao²

¹Institute of Artificial Intelligence, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361102, China.

Nature Communications

|March 2, 2024

Summary

Cancer-Finder, a novel deep-learning algorithm, accurately identifies malignant cells in single-cell and spatial transcriptomic data. This tool enhances cancer research by improving malignant cell annotation for better disease understanding and patient prognosis.

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Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Area of Science:

Genomics
Computational Biology
Oncology

Background:

Single-cell and spatial transcriptomic sequencing are vital for cancer research.
Accurate malignant cell annotation is critical but challenging with current methods.
Existing algorithms struggle with accuracy and generalization across diverse cancer types.

Purpose of the Study:

To develop a robust algorithm for rapid and accurate malignant cell identification.
To address limitations in current cell annotation tools for pan-cancer analysis.
To enable precise malignant cell and spot detection in transcriptomic data.

Main Methods:

Developed Cancer-Finder, a domain generalization-based deep-learning algorithm.
Trained and validated the algorithm on single-cell transcriptomic data.
Adapted Cancer-Finder for spatial transcriptomic data by replacing training datasets.

Main Results:

Cancer-Finder achieved an average accuracy of 95.16% in identifying malignant cells from single-cell data.
The algorithm accurately identified malignant spots in spatial transcriptomic slides.
Applied to clear cell renal cell carcinoma, it identified a 10-gene signature at the tumor-normal interface linked to patient prognosis.

Conclusions:

Cancer-Finder is an efficient and extensible tool for malignant cell annotation.
The algorithm demonstrates high accuracy and generalization capabilities in transcriptomic data analysis.
Identified gene signature provides insights into tumor-normal interactions and patient outcomes.