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Label-Free Single Cell Proteomics Utilizing Ultrafast LC and MS Instrumentation: A Valuable Complementary Technique to Multiplexing.

ABSTRACT: In recent years, single cell proteomics became one of the hottest topics within the field of proteomics. The potential ability to map the proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotypes and cellular functions. In contrast to nucleic acid sequencing, in vitro amplification of proteins is impossible to date and no single cell proteomic workflow has been established as gold standard yet. Lots of advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies and intelligent data analysis algorithms already brought major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, its commonly accepted, that highest possible sensitivity, robustness and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free single cell proteomics is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances of label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or method combinations for their specific application. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell

DISEASE(S): Cervix Carcinoma

SUBMITTER: Manuel Matzinger

LAB HEAD: Karl Mechtler

PROVIDER: PXD039208 | Pride | 2023-02-09

REPOSITORIES: Pride

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			Action	DRS
	20220927_E0_Neo0_FAIMS_uPAC_5cm_250pg_HeLa_DIA___10_.raw	Raw
	20220927_E0_Neo0_FAIMS_uPAC_5cm_250pg_HeLa_DIA___11_.raw	Raw
	20220927_E0_Neo0_FAIMS_uPAC_5cm_250pg_HeLa_DIA___12_.raw	Raw
	20220927_E0_Neo0_FAIMS_uPAC_5cm_250pg_HeLa_DIA___13_.raw	Raw
	20220927_E0_Neo0_FAIMS_uPAC_5cm_250pg_HeLa_DIA___14_.raw	Raw

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Project description:A comprehensive proteome map is essential to elucidate molecular pathways and protein functions. Although great improvements in sample preparation, instrumentation and data analysis already yield-ed impressive results, current studies suffer from a limited proteomic depth and dynamic range there-fore lacking low abundant or highly hydrophobic proteins. Here, we combine and benchmark advanced micro pillar array columns (µPAC) operated at nanoflow with wide window acquisition (WWA) and the AI-based CHIMERYS search engine for data analysis to maximize chromatographic separation power, sensitivity and proteome coverage. The wide window acquisition method merges the strengths of DDA and DIA. WWA uses a broad isolation window (≥4 Th) for data-dependent precursor selection. Similar to DIA, precursor ions close to those selected are co-fragmented, producing chimeric spectra that boost IDs and improve coverage of low-abundance peptides, which would otherwise be missed. WWA is particularly powerful when combined with the AI-driven CHIMERYS search algorithm, which allows confident identification of up to eleven peptides from a single chimeric spectrum. Developed by MSAID, the CHIMERYS algorithm makes use of accurate fragment spectrum predictions trained on millions of spectra, which allows to utilize additional spectral properties such as relative signal intensities for drastically improved identification rates. Entrapment experiments were conducted searching mouse immunoprecipitation samples with MS Amanda 2.0 and CHIMERYS by applying an additional custom-made decoy database, which demonstrated excellent protein, peptide and PSM FDR control from 1% upwards for both search engines. Combining wide precursor isolation widths of 4-12 Th with the CHIMERYS search engine identified 51-74% more proteins and 59-150% more peptides for single cell, immunoprecipitation and multi-species samples in comparison to a standard proteomics workflow employing MS Amanda 2.0 and an isolation width of 1 Th. Application of different isolation widths revealed that the optimal isolation window size varies according to sample amount and complexity with 12 Th resulting in the highest number of identifications for single cell-equivalent injection amounts, while 4 Th provided best results for classical bulk inputs of 400ng. Evaluation of coefficients of variation also yielded improved results for WWA over classical DDA when assessing single cell, 40 cell and 250pg bulk HeLa lysates. Overall, WWA and CHIMERYS resulted in substantially improved proteomic depth and quantification for all sample types tested and will provide a highly valuable toolset for the proteomics community.

Project description:A data set of normal epithelium, serous ovarian surface epithelial-stromal tumors (benign and type II malignancies), stroma distal to tumor, and stroma adjacent to tumor (50 samples total). Additional cel files are included which represent replicate sampling from patients, and cel files that failed quality control but may be bioinformatically interesting. Additional replicate or failed cel files were not included in the final analysis (and so these samples were not included in the matrix). Background: Ovarian cancer is the most lethal gynecologic cancer in the United States. If caught in early stages, patient survival rate can reach 94%, when diagnosed at late stages survival rates drop to 28%. Correct diagnosis depends on the presence of definite symptoms: while ~90% of diagnosed ovarian cancers have symptoms, they tend to be unfocused and subacute. A definitive and early molecular signature of disease is thus desired. To further progress towards this goal, we present an Affymetrix™ human exon array data set measuring ovarian tumor expression, assembled using best practices. Method: Samples were collected from patients with benign and malignant (type II) serous ovarian surface epithelial-stromal tumors. Normal epithelium, tumor, stroma adjacent to tumor, and distal stroma were selected based on histopathology, and isolated using laser capture microdissection. Nugen products were used to perform random-primed mRNA amplification procedures (for full transcript capture) before hybridization to Affymetrix exon chips. Tumor expression and paracrine signaling was assessed using GC-RMA and a two-way Model I ANOVA. Single enrichment ontological analysis and gene network construction were performed to guide inferences about biological context. Results: In total, across 50 microarrays, ~270 million measurements were obtained. Based on comparisons to known ovarian cancer properties as established in molecular genetics literature, the initial analysis presented emphasizes data quality. Major trends between sample classes included: apical surface and tight junction activity, mitotic activity, benign tumor suppression, epithelial-mesenchymal transitioning, tumor oncogene activity, and paracrine signaling. A list of differentially expressed transcripts has been produced to enable rapid comparisons with published biomarker lists, but it is expected that detailed alternative transcript analysis will refine these predictions. Conclusions: A data set of 50 arrays, from carefully dissected serous ovarian surface epithelial-stromal tumors, has been produced, from which high quality measurements were obtained. While relatively small in number, this represents an important addition to the community pool of ovarian tumor samples, and the chosen platform enables bridging between 3' expression and exome sequencing data sets. This represents a significant contribution to the ovarian cancer genetics community. A total of 50 human ovary samples were used in analysis: 23 tissue samples laser capture microdissected from an ovary with a benign serous tumor (specifically 4 normal epithelium samples, 5 tumor samples, 6 stroma samples adjacent to the tumor, and 8 stroma samples distal of the tumor), and 27 tissue samples laser capture microdissected from an ovary with a malignant serous tumor (specifically 5 normal epithelium samples, 8 tumor samples, 7 stroma samples adjacent to the tumor, and 7 stroma samples distal of the tumor). Additional cel files were provided which, although were used in the quality control of the data set, were not used in the final experimental analysis. 8 replicates are included as cel files (1 from each cohort previously listed). 14 cel files were also included which failed quality control. Although the replicate and failed cel files were not used in the final analysis, they may still be interesting in other research.

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Label-Free Single Cell Proteomics Utilizing Ultrafast LC and MS Instrumentation: A Valuable Complementary Technique to Multiplexing.

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