Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning-Reference-Cited by-同舟云学术

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Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning

Published:2023-05-04 Issue:19 Volume:95 Page:7779-7787
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Yang Liwei¹^ORCID,Dutta Pratik²,Davuluri Ramana V.²,Wang Jun¹^ORCID

Affiliation:

1. Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, New York 11794, United States

2. Department of Biomedical Informatics, State University of New York at Stony Brook, Stony Brook, New York 11794, United States

Funder

National Institute on Aging

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.3c01157

Reference43 articles.

1. Single-cell analysis tools for drug discovery and development

2. Single-cell profiling of tumour evolution in multiple myeloma — opportunities for precision medicine

3. Highly Multiplexed Single-Cell Protein Analysis

4. Design and Analysis of Single-Cell Sequencing Experiments

5. Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum

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