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Deep single-cell proteomics quantifies low-abundant embryonic transcription factors and lineage markers during induced pluripotent stem cell differentiation

ABSTRACT: Single-cell proteomics (SCP) promises to revolutionize biomedicine by providing an unparalleled view of the proteome in individual cells. Here, we present a high-sensitivity SCP workflow identifying >5000 proteins in individual HeLa cells. It also facilitated direct detection of post-translational modifications (PTMs) in single-cells, negating the need for specific PTM-enrichment. Our study demonstrates the feasibility of processing up to 80 label-free SCP samples per day. An optimized tissue dissociation buffer enabled effective single cell disaggregation of drug-treated cancer cell spheroids, refining overall SCP analysis. Analyzing non-directed induced pluripotent stem cell (iPSC) differentiation, we consistently quantified stem cell markers OCT4 and SOX2 in hiPSCs and lineage markers like GATA4 (mesoderm), HAND1 (endoderm) and MAP2 (ectoderm) in different embryoid body cells. Our workflow sets a new benchmark in SCP for sensitivity and throughput, with broad applications in basic biology and biomedicine for identification of cell type-specific markers and therapeutic targets.

INSTRUMENT(S): Orbitrap Astral

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Pierre Sabatier

LAB HEAD: Jesper V. Olsen

PROVIDER: PXD049211 | Pride | 2024-08-17

REPOSITORIES: Pride

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Project description:The analysis of single cell proteomes has recently become a viable complement to transcript and genomics studies. Proteins are the main driver of cellular functionality and mRNA levels are often an unreliable proxy of such. Therefore, the global analysis of the proteome is essential to study cellular identities. Both multiplexed and label-free mass spectrometry-based approaches with single cell resolution have lately attributed surprising heterogeneity to believed homogenous cell populations. Even though specialized experimental designs and instrumentation have demonstrated remarkable advances, the efficient sample preparation of single cells still lacks behind. Here, we introduce the proteoCHIP, a universal option for single cell proteomics sample preparation at surprising sensitivity and throughput. The automated processing using a commercial system combining single cell isolation and picoliter dispensing, the cellenONE®, allows to reduce final sample volumes to low nanoliters submerged in a hexadecane layer simultaneously eliminating error prone manual sample handling and overcoming evaporation. With this specialized workflow we achieved around 1,000 protein groups per analytical run at remarkable reporter ion signal to noise while reducing or eliminating the carrier proteome. We identified close to 2,000 protein groups across 158 multiplexed single cells from two highly similar human cell types and clustered them based on their proteome. In-depth investigation of regulated proteins readily identified one of the main drivers for tumorigenicity in this cell type. Our workflow is compatible with all labeling reagents, can be easily adapted to custom workflows and is a viable option for label-free sample preparation. The specialized proteoCHIP design allows for the direct injection of label-free single cells via a standard autosampler resulting in the recovery of 30% more protein groups compared to samples transferred to PEG coated vials. We therefore are confident that our versatile, sensitive, and automated sample preparation workflow will be easily adoptable by non-specialized groups and will drive biological applications of single cell proteomics.

Project description:In colorectal cancer (CRC), chromosomal instability (CIN) is typically studied using comparative-genomic hybridization (CGH) arrays. We studied paired (tumor and surrounding healthy) fresh-frozen tissue from 86 CRC patients using Illumina’s Infinium-based SNP array. This method allowed us to study CIN in CRC, with simultaneous analysis of copy number (CN) and B-allele frequency (BAF), which is a representation of allelic composition. This data helped us to detect mono-allelic and bi-allelic amplifications/deletion, copy neutral loss of heterozygosity, and levels of mosaicism for mixed cell populations, some of which can not be assessed with other methods that do not measure BAF. We identified associations between CN abnormalities and different CRC phenotypes (MSI, histological diagnosis, location, tumor grade, stage, MSI and presence of lymph node metastasis). We showed commonalities between regions of CN change observed in CRC and the regions reported in previous studies of other solid cancers (e.g., amplifications of 20q, 13q, 8q, 5p and deletions of 18q, 17p and 8p). From the Therapeutic Target Database we found relevant drugs, targeted to the genes located in these regions with CN changes, approved or in trials for other cancers and common diseases. These drugs may be considered for future therapeutic trials in CRC, based on personalized cytogenetic diagnosis. We also found many regions harboring genes which are not currently targeted by any relevant drugs that may be considered for future drug discovery studies. Our study shows the application of high-density SNP arrays for cytogenetic study in CRC and its importance for personalized treatment. DNA was extracted from colon tissue samples provided by 86 CRC patients. Each patient provided paired CRC tumor tissue and adjacent normal colon mucosa samples, which were fresh-frozen after resection. Samples were genotyped using Illumina's Infinium-based 610 Quad and CytoSNP 12 microarray chips. The genotype data from paired tumor and normal samples was used to detect tumor-specific chromosomal instabilities in copy number, including copy-neutral LOH, which cannot be assessed by many other cytogenetic methods. Supplementary file "GSE34678_raw_GPL8887.txt" includes the raw data for Samples using GPL8887 (GSM853162-GSM853239); supplementary file "GSE34678_raw_GPL13829.txt" includes the raw data for Samples using GPL13829 (GSM853240-GSM853363).

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Deep single-cell proteomics quantifies low-abundant embryonic transcription factors and lineage markers during induced pluripotent stem cell differentiation

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