Dataset Information

High throughput single-cell proteomics of in-vivo cells

ABSTRACT: Major advancements in single-cell proteomics (SCP) methodologies have been introduced in recent years, providig highly sensitive sample preparation methods and mass spectrometric technologies. However, most studies present limited throughput and mainly focus on the analysis of cultured cells. To enhance the depth, accuracy, and throughput of SCP for tumor analysis, we developed an automated, high-throughput pipeline that enables the analysis of 1,536 single cells in a single experiment. This approach integrates low-volume sample preparation, automated sample purification, and LC-MS analysis with the Slice-PASEF method. Integration of these methodologies into a streamlined pipeline led to a robust and reproducible identification of more than 3000 proteins per cell. We applied this pipeline to analyze tumor macrophages in a murine lung metastasis model. We identified over 1,800 proteins per cell, including key macrophage markers and ~500 differentially expressed proteins between tumor and control macrophages. PCA analysis successfully separated these populations, revealing the utility of SCP in capturing biologically relevant signals in the tumor microenvironment. Our results demonstrate a robust and scalable pipeline poised to advance single-cell proteomics in cancer research.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human) Mus Musculus (mouse)

TISSUE(S): Lung, Cell Culture, Macrophage

DISEASE(S): Colon Cancer

SUBMITTER: Tamar Geiger

LAB HEAD: Tamar Geiger

PROVIDER: PXD058457 | Pride | 2025-07-09

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	Annotated_Spectra.zip	Other
	Karagach_Smollich_Resubmision.sdrf.tsv	Tabular
	S1_Rehyd_50percent.zip	Other
	S1_Rehyd_Covered.zip	Other
	S1_rehyd_cover_384.pg_matrix.tsv	Tabular

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Project description:Background: It is a challenge to identify those patients who, after undergoing potentially curative treatments for hepatocellular carcinoma, are at greatest risk of recurrence. Such high-risk patients could receive novel interventional measures. An obstacle to the development of genome-based predictors of outcome in patients with hepatocellular carcinoma has been the lack of a means to carry out genomewide expression profiling of fixed, as opposed to frozen, tissues. Methods: We aimed to demonstrate the feasibility of gene-expression profiling of more than 6000 human genes in formalin-fixed paraffin-embedded tissues. We applied the method to tissues from 307 patients with hepatocellular carcinoma, from four series of patients, to discover and validate a gene-expression signature associated with survival. Results: The expression-profiling method for formalin-fixed, paraffin-embedded tissue was highly effective: samples from 90% of the patients yielded data of high quality, including samples that had been archived for more than 24 years. Gene-expression profiles of tumor tissue failed to yield a significant association with survival. In contrast, profiles of the surrounding nontumoral liver tissue were highly correlated with survival in a training set of 82 Japanese patients, and the signature was validated in tissues from an independent group of 225 patients from the United States and Europe (p = 0.04). Conclusions: We have demonstrated the feasibility of genomewide expression profiling of formalin-fixed, paraffin-embedded tissues and have shown that a reproducible gene-expression signature correlating with survival is present in liver tissue adjacent to the tumor in patients with hepatocellular carcinoma. Keywords: Hepatocellular carcinoma, Expression array, Illumina, Signatures, Outcome prediction Training cohort: 80 tumor and 82 non-tumor liver tissues surgically resected from patients with hepatocellular carcinoma (HCC); Validation cohort: 225 non-tumor liver tissues surgically resected from patients with HCC. Clinical data has been withheld from GEO due to privacy concerns.

Project description:Background: It is a challenge to identify those patients who, after undergoing potentially curative treatments for hepatocellular carcinoma, are at greatest risk of recurrence. Such high-risk patients could receive novel interventional measures. An obstacle to the development of genome-based predictors of outcome in patients with hepatocellular carcinoma has been the lack of a means to carry out genomewide expression profiling of fixed, as opposed to frozen, tissues. Methods: We aimed to demonstrate the feasibility of gene-expression profiling of more than 6000 human genes in formalin-fixed paraffin-embedded tissues. We applied the method to tissues from 307 patients with hepatocellular carcinoma, from four series of patients, to discover and validate a gene-expression signature associated with survival. Results: The expression-profiling method for formalin-fixed, paraffin-embedded tissue was highly effective: samples from 90% of the patients yielded data of high quality, including samples that had been archived for more than 24 years. Gene-expression profiles of tumor tissue failed to yield a significant association with survival. In contrast, profiles of the surrounding nontumoral liver tissue were highly correlated with survival in a training set of 82 Japanese patients, and the signature was validated in tissues from an independent group of 225 patients from the United States and Europe (p = 0.04). Conclusions: We have demonstrated the feasibility of genomewide expression profiling of formalin-fixed, paraffin-embedded tissues and have shown that a reproducible gene-expression signature correlating with survival is present in liver tissue adjacent to the tumor in patients with hepatocellular carcinoma. This SuperSeries is composed of the following subset Series: GSE10140: Gene Expression in Fixed Tissues and Outcome in Hepatocellular Carcinoma (Training Set, Liver) GSE10141: Gene Expression in Fixed Tissues and Outcome in Hepatocellular Carcinoma (Training Set, HCC) GSE10142: Gene Expression in Fixed Tissues and Outcome in Hepatocellular Carcinoma (Validation Set) Keywords: Hepatocellular carcinoma, Expression array, Illumina, Signatures, Outcome prediction Training cohort: 80 tumor and 82 non-tumor liver tissues surgically resected from patients with hepatocellular carcinoma (HCC); Validation cohort: 225 non-tumor liver tissues surgically resected from patients with HCC. Clinical data has been withheld from GEO due to privacy concerns.

Project description:The sensitivity of single-cell proteomics (SCP) has increased dramatically in recent years due to advances in experimental design, sample preparation, separations and mass spectrometry instrumentation. However, further increasing the sensitivity of SCP methods and instrumentation will enable the study of proteins within single cells that are expressed at copy numbers too small to be measured by current methods. Here we further increase SCP sensitivity by combining efficient nanoPOTS sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to extend the achievable proteome coverage for single cells to >3,000 for single-cell data analyses. WWA is based on data-dependent acquisition (DDA) but employs larger precursor isolation windows to intentionally co-isolate and co-fragment additional precursors along with the selected precursor. The resulting chimeric MS2 spectra are then resolved using the CHIMERYS search engine within Proteome Discoverer 3.0. Compared to standard DDA workflows, WWA employing isolation windows of 8-12 Th increases peptide and proteome coverage by ~28% and ~39%, respectively. For a 40 min LC gradient operated at ~15 nL/min, we identified an average of 2,150 proteins per single-cell-sized aliquots of protein digest directly from MS2 spectra, which increased to an average 3,524 proteins including proteins identified with MS1-level feature matching. Reducing the active gradient to 20 min resulted in only a 10% decrease in proteome coverage. We also compared performance of WWA with DIA underperformed relative to WWA in terms of proteome coverage, especially with faster separations. Average proteome coverage for single HeLa and K562 cells was respectively 1,758 and 1,642 based on MS2 identifications with 1% false discovery rate and 3042 and 2891 with MS1 feature matching. As such, WWA combined with efficient sample preparation and rapid separations extends the depths of the proteome that can be studied at the single-cell level.

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High throughput single-cell proteomics of in-vivo cells

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