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期刊文献

Makhmut, Anuar et al.

An automated and fast sample preparation workflow for laser microdissection guided ultrasensitive proteomics

Molecular & Cellular Proteomics, Volume 23, Issue 5, 100750
Rune Daucke, Charlotte V. Rift, et al.

Refining Spatial Proteomics by Mass Spectrometry: An Efficient Workflow Tailored for Archival Tissue

bioRxiv 2024.01.25.577263
Karl K. Krull, Arlene Kühn, et al.

Deep learning-based proteomics enables accurate classification of bulk and single-cell samples

Pre – Published in BioRxiv, February 07, 2024.
Khan S, Conover R, Asthagiri AR, Slavov N.

Dynamics of single-cell protein covariation during epithelial–mesenchymal transition

J Proteome Res. 2024
Leduc, A., Khoury, L., Cantlon, J. et al.

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP

Nat Protoc 19, 3750–3776 (2024)
Wahle, Maria et al.

IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma

Molecular & Cellular Proteomics, Volume 23, Issue 1, 100689
Gurung, H.R., Heidersbach, A.J., Darwish, M. et al

Systematic discovery of neoepitope–HLA pairs for neoantigens shared among patients and tumor types

Nat Biotechnol 42, 1107–1117 (2024)
Antunes, A.V., Shahinas, M., Swale, C. et al.

In vitro production of cat-restricted Toxoplasma pre-sexual stages

Nature 625, 366–376 (2024)
J. A. Bubis; T. N. Arrey; et al.

Challenging the Astral mass analyzer – going beyond 5200 proteins per single-cell at unseen quantitative accuracy to study cellular heterogeneity

BioRxiv – Feb 1, 2024
C. Ctortecka; N. M. Clark; B. Boyle; A. Seth; D. R

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

BioRxiv – January 22, 2024

期刊文献

Makhmut, Anuar et al.

An automated and fast sample preparation workflow for laser microdissection guided ultrasensitive proteomics

Molecular & Cellular Proteomics, Volume 23, Issue 5, 100750

Rune Daucke, Charlotte V. Rift, et al.

Refining Spatial Proteomics by Mass Spectrometry: An Efficient Workflow Tailored for Archival Tissue

bioRxiv 2024.01.25.577263

Karl K. Krull, Arlene Kühn, et al.

Deep learning-based proteomics enables accurate classification of bulk and single-cell samples

Pre – Published in BioRxiv, February 07, 2024.

Khan S, Conover R, Asthagiri AR, Slavov N.

Dynamics of single-cell protein covariation during epithelial–mesenchymal transition

J Proteome Res. 2024

Leduc, A., Khoury, L., Cantlon, J. et al.

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP

Nat Protoc 19, 3750–3776 (2024)

Wahle, Maria et al.

IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma

Molecular & Cellular Proteomics, Volume 23, Issue 1, 100689

Gurung, H.R., Heidersbach, A.J., Darwish, M. et al

Systematic discovery of neoepitope–HLA pairs for neoantigens shared among patients and tumor types

Nat Biotechnol 42, 1107–1117 (2024)

Antunes, A.V., Shahinas, M., Swale, C. et al.

In vitro production of cat-restricted Toxoplasma pre-sexual stages

Nature 625, 366–376 (2024)

J. A. Bubis; T. N. Arrey; et al.

Challenging the Astral mass analyzer – going beyond 5200 proteins per single-cell at unseen quantitative accuracy to study cellular heterogeneity

BioRxiv – Feb 1, 2024

C. Ctortecka; N. M. Clark; B. Boyle; A. Seth; D. R

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

BioRxiv – January 22, 2024

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