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Navolić J, Moritz M, Voß H, Schlumbohm S, Schumann Y, Schlüter H, Neumann JE, Hahn J. Direct 3D Sampling of the Embryonic Mouse Head: Layer-wise Nanosecond Infrared Laser (NIRL) Ablation from Scalp to Cortex for Spatially Resolved Proteomics. Anal Chem 2023; 95:17220-17227. [PMID: 37956982 PMCID: PMC10688223 DOI: 10.1021/acs.analchem.3c02637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/06/2023] [Accepted: 10/12/2023] [Indexed: 11/21/2023]
Abstract
Common workflows in bottom-up proteomics require homogenization of tissue samples to gain access to the biomolecules within the cells. The homogenized tissue samples often contain many different cell types, thereby representing an average of the natural proteome composition, and rare cell types are not sufficiently represented. To overcome this problem, small-volume sampling and spatial resolution are needed to maintain a better representation of the sample composition and their proteome signatures. Using nanosecond infrared laser ablation, the region of interest can be targeted in a three-dimensional (3D) fashion, whereby the spatial information is maintained during the simultaneous process of sampling and homogenization. In this study, we ablated 40 μm thick consecutive layers directly from the scalp through the cortex of embryonic mouse heads and analyzed them by subsequent bottom-up proteomics. Extra- and intracranial ablated layers showed distinct proteome profiles comprising expected cell-specific proteins. Additionally, known cortex markers like SOX2, KI67, NESTIN, and MAP2 showed a layer-specific spatial protein abundance distribution. We propose potential new marker proteins for cortex layers, such as MTA1 and NMRAL1. The obtained data confirm that the new 3D tissue sampling and homogenization method is well suited for investigating the spatial proteome signature of tissue samples in a layerwise manner. Characterization of the proteome composition of embryonic skin and bone structures, meninges, and cortex lamination in situ enables a better understanding of molecular mechanisms of development during embryogenesis and disease pathogenesis.
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Affiliation(s)
- Jelena Navolić
- Research
Group Molecular Pathology in Neurooncology, Center for Molecular Neurobiology
(ZMNH), University Medical Center Hamburg−Eppendorf, Falkenried 94, 20251 Hamburg, Germany
| | - Manuela Moritz
- Section/Core
Facility Mass Spectrometry and Proteomics, Center for Diagnostics, University Medical Center Hamburg−Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Hannah Voß
- Section/Core
Facility Mass Spectrometry and Proteomics, Center for Diagnostics, University Medical Center Hamburg−Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Simon Schlumbohm
- High
Performance Computing, Helmut Schmidt University, Holstenhofweg 85, 22043 Hamburg, Germany
| | - Yannis Schumann
- High
Performance Computing, Helmut Schmidt University, Holstenhofweg 85, 22043 Hamburg, Germany
| | - Hartmut Schlüter
- Section/Core
Facility Mass Spectrometry and Proteomics, Center for Diagnostics, University Medical Center Hamburg−Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Julia E. Neumann
- Research
Group Molecular Pathology in Neurooncology, Center for Molecular Neurobiology
(ZMNH), University Medical Center Hamburg−Eppendorf, Falkenried 94, 20251 Hamburg, Germany
- Institute
of Neuropathology, University Medical Center
Hamburg−Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Jan Hahn
- Section/Core
Facility Mass Spectrometry and Proteomics, Center for Diagnostics, University Medical Center Hamburg−Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
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Voß H, Schlumbohm S, Wurlitzer M, Dottermusch M, Neumann P, Barwikowski P, Schlüter H, Krisp C, Neumann J. OTHR-07. A new framework for missing value tolerant data integration. Neuro Oncol 2022. [PMCID: PMC9164682 DOI: 10.1093/neuonc/noac079.546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Dataset integration is common practice to overcome limitations, e.g., in statistically underpowered omics datasets. This is of particular importance when analyzing rare tumor entities. However, combining datasets leads to the introduction of biases, so called 'batch effects', which are due to differences in quantification techniques, laboratory equipment or used tissue type. A common problem is the missing quantification for features like gene transcripts or proteins within a dataset. These missing values can appear at random in a given dataset and also get introduced by combination of multiple datasets. Currently, strategies beyond common normalization for batch effect reduction are either missing entirely or are unable to handle absence of data points and therefore rely on error-prone data imputation. We introduce a framework that enables batch effect adjustments for combined datasets while avoiding data loss by appropriately handling missing values without imputation. The underlying idea is based on a matrix dissection approach, adjusting common information from the integrated dataset under guarantee of sufficient data presence. The strategy is implemented within the R environment and linked with popular software stacks that are built on top of R. Successful data adjustment is exemplarily shown for proteomic data generated by different quantification approaches and LC-MS/MS instrumentation setups.
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Affiliation(s)
- Hannah Voß
- University Medical Center Hamburg-Eppendorf , Hamburg , Germany
| | - Simon Schlumbohm
- University Medical Center Hamburg-Eppendorf , Hamburg , Germany
- Helmut-Schmidt-University , Hamburg , Germany
| | | | | | | | | | | | - Christoph Krisp
- University Medical Center Hamburg-Eppendorf , Hamburg , Germany
| | - Julia Neumann
- University Medical Center Hamburg-Eppendorf , Hamburg , Germany
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Voss H, Godbole S, Schlumbohm S, Dottermusch M, Schuhmann Y, Neumann P, Schlüter H, Schüller U, Peng B, Barwikowski P, Krisp C, Neumann JE. OTHR-42. Missing data tolerant integration of proteomic datasets enables the identification and characterization of brain cancer subtypes. Neuro Oncol 2022. [PMCID: PMC9164826 DOI: 10.1093/neuonc/noac079.580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Investigating the proteome can add a significant layer of information to manifold existing methylation, mutation, and transcriptome data on brain tumors as proteins represent the pharmacologically addressable phenotype of a disease. Small cohorts limit the usability and validity of statistical methods, and variable technical setups and high numbers of missing values make data integration from public sources challenging. Using a newly developed framework being able to reduce batch effects without the need for data reduction or missing value imputation, we show –based on in-house and publicly available datasets- successful integration of proteomic data across different tissue types, quantification platforms, and technical setups. Exemplarily, data of a Sonic hedgehog (Shh) medulloblastoma mouse model were analyzed, showing efficient data integration independent of tissue preservation strategy or batch. We further integrated batches of publicly available data of human brain tumors, confirming proposed proteomic cancer subtypes correlating with clinical features. We show that, missing value tolerant reduction of technical variances may be helpful to identify biomarkers, proteomic signatures, and altered pathways characteristic for molecular brain cancer subtypes.
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