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Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier

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Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives.

Felipe Segato Dezem^1,2, Wani Arjumand^1,2, Hannah DuBose^1,2

¹Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.

Annual Review of Biomedical Data Science

|May 20, 2024

Summary

This summary is machine-generated.

Spatial omics technologies map molecules within tissues, revealing cellular details. Analyzing these complex datasets requires advanced computational strategies like artificial intelligence and machine learning for deeper biological insights.

Keywords:

ISH bioinformatic analysis computational tools in situ hybridization multiomics proteomics single cell spatial

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Area of Science:

Biotechnology
Genomics
Proteomics

Background:

Spatial omics integrates molecular data with tissue architecture for high-resolution biological insights.
Understanding cellular heterogeneity and molecular microenvironments is crucial in normal and disease states.

Purpose of the Study:

To review current spatial omics techniques and computational strategies.
To highlight the advantages and limitations of various spatial omics modalities.
To discuss future prospects of spatial omics in scientific research.

Main Methods:

Categorization of spatial omics into sequencing-based and imaging-based (transcriptomics and proteomics) modalities.
Discussion of computational challenges in analyzing large, non-uniform spatial omics datasets.
Exploration of artificial intelligence and machine learning applications for data analysis and visualization.

Main Results:

Spatial omics provides detailed insights into tissue organization and cellular interactions.
Diverse techniques exist, including next-generation sequencing, in situ hybridization/sequencing, and spatial proteomics.
Significant computational challenges necessitate advanced analytical approaches.

Conclusions:

Spatial omics is a powerful tool for dissecting biological complexity at a molecular level.
Standardized computational pipelines and AI/ML are essential for unlocking the full potential of spatial omics data.
Future research will likely focus on refining techniques and analytical methods for broader applications.