Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity

Despite significant advancements in sample preparation, instrumentation and data analysis, single-cell proteomics is currently limited by proteomic depth and quantitative performance. Here we demonstrate highly improved depth of proteome coverage as well as accuracy and precision for quantification of ultra-low input amounts. Using a tailored library, we identify up to 7,400 protein groups from as little as 250 pg of HeLa cell peptides at a throughput of 50 samples per day. Using a two-proteome mix, we check for optimal parameters of quantification and show that fold change differences of 2 can still be successfully determined at single-cell-level inputs. Eventually, we apply our workflow to A549 cells, yielding a proteome coverage ranging from 1,801 to a maximum of >5,300 protein groups from a single cell depending on cell size and search strategy used, which allows for the study of dependencies between cell size and cell cycle phase. Additionally, our workflow enables us to distinguish between in vitro analogs of two human blastocyst lineages: naive human pluripotent stem cells (epiblast) and trophectoderm-like cells. Our data harmoniously align with transcriptomic data, indicating that single-cell proteomics possesses the capability to identify biologically relevant differences within the blastocyst.

Authors

Institutions

Publication Details

Journal
Nature Methods
Published
2025-01-16
DOI
https://doi.org/10.1038/s41592-024-02559-1
Citations
71
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
112.74

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity

Peter Pichler, Karl Mechtler, Jana Slováková, Manuel Matzinger et al.
71 citations
Nature Methods
Cell Image Analysis Techniques
112.74
article

Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity

Peter Pichler, Karl Mechtler, Jana Slováková, Manuel Matzinger, Julia A. Bubis, Theresa Maria Sommer, Tabiwang N. Arrey, Eugen Damoc, Harunobu Kagawa, Bernard Delanghe, Nicolas Rivron
article en
71 citations

Abstract

Despite significant advancements in sample preparation, instrumentation and data analysis, single-cell proteomics is currently limited by proteomic depth and quantitative performance. Here we demonstrate highly improved depth of proteome coverage as well as accuracy and precision for quantification of ultra-low input amounts. Using a tailored library, we identify up to 7,400 protein groups from as little as 250 pg of HeLa cell peptides at a throughput of 50 samples per day. Using a two-proteome mix, we check for optimal parameters of quantification and show that fold change differences of 2 can still be successfully determined at single-cell-level inputs. Eventually, we apply our workflow to A549 cells, yielding a proteome coverage ranging from 1,801 to a maximum of >5,300 protein groups from a single cell depending on cell size and search strategy used, which allows for the study of dependencies between cell size and cell cycle phase. Additionally, our workflow enables us to distinguish between in vitro analogs of two human blastocyst lineages: naive human pluripotent stem cells (epiblast) and trophectoderm-like cells. Our data harmoniously align with transcriptomic data, indicating that single-cell proteomics possesses the capability to identify biologically relevant differences within the blastocyst.

Nature MethodsVol. 22(3)
Institute of Molecular Biotechnology (AT), Gregor Mendel Institute of Molecular Plant Biology (AT), Austrian Academy of Sciences (AT), Research Institute of Molecular Pathology (AT), Vienna Biocenter (AT), Thermo Fisher Scientific (Germany) (DE), Medical University of Vienna (AT)
International Society for Stem Cell Research, Vienna Science and Technology Fund, Österreichischen Akademie der Wissenschaften, Austrian Science Fund, Österreichische Forschungsförderungsgesellschaft
Openalex Percentile: Top 0%
Cell Image Analysis Techniques
112.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.