Sample preparation protocols for spatial proteomics in lung tissues

Beatriz Pereira de Morais1, Geovana Stival Borro1, Ana Paula Masson1, Pedro de Sordi Rezende1, Virginia Campos Silvestrini1,2, Saulo Brito Silva3, Vitor Marcel Faça 1

1. FMRP - USP, Departamento de Bioquímica e Imunologia da Faculdade de Medicina de Ribeirão Preto; Av. Bandeirantes, 3900 - Campus da Usp, Ribeirão Preto - SP, 14049-900
2. Hemocentro - RP, Fundação Hemocentro de Ribeirão Preto; R. Ten. Catão Roxo, 2501 - Vila Monte Alegre, Ribeirão Preto - SP, 14051-140
3. HC FMRP USP, Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto; R. Ten. Catão Roxo, 3900 - Vila Monte Alegre, Ribeirão Preto - SP, 14015-010

SAMPLE PREPARATION PROTOCOLS FOR SPATIAL PROTEOMICS IN LUNG TISSUES

 

Morais, B.P.1; Borro, G.S1; Masson, A.P1; Rezende, P. S.1; Silvestrini, V. C.1,2;

Silva, S.B.1 Faça, V.M.1

 

1Department of Biochemistry and Immunology, University of São Paulo, Ribeirão Preto Medical School, Brazil

2Laboratory of Translational Oncology, Regional Blood Center of Ribeirão Preto, Ribeirão Preto, Brazil

Introduction: Spatial evaluation of tumors enables the identification of heterogeneous features within tumor microenvironment, allowing for the assessment of tumor, normal, and adjacent tissues from the same patient and the integration of data into relevant sample sets. Among tissues used for spatial proteomics, lung tissue presents a particular challenge due to its alveolar structure, fibrosis, low cellularity, and high content of surfactant and matrix proteins. Objectives: To perform a comparative evaluation of different extraction, sample preparation, and purification methods for lung tissue. Additionally, to determine the minimum microdissected region size that preserves sample complexity for high-resolution proteomic analysis. Methods: We evaluated commonly used sample buffers for proteomic workflows, including RIPA, HEPES, and 8M Urea. We also compared sample preparation protocols involving reduction, alkylation, and digestion, either performed in solution or with magnetic bead-based SP3 method (single-pot, solid-phase-enhanced sample preparation), as well as a bead-free comparative protocol referred to as SP4. These protocols were applied to both A549 lung cell line models and kidney tissue samples. Using these preparations, we tested high-resolution identification on Orbitrap Exploris 240 using 20-minute and 131-minute runs with Data-Dependent Acquisition (DDA) method. Results: Protein recovery from lung tissue was significantly lower than from A549 cells, likely due to tissue heterogeneity. Despite starting with the same quantified amount of protein (3 µg), the number of proteins identified in lung tissue was three times lower than in the cell line. A comparison using microdissected kidney tissue of the same area resulted in the identification of 1,000 more proteins than in lung tissue. By evaluating section size and the use of spectral libraries, we identified the most effective methods for FFPE lung tissue and strategies to improve data processing. Conclusion: We showed a great difference between optimal sample preparation procedures for different sources of tumor samples and optimized these procedures for lung tissue. Additional gain of proteome depth can be obtained with long LC-MS/MS runs, potentially combined with extended chromatography columns. Furthermore, constructing a spectral library from total lung tumor proteomes can substantially improve protein identification in spatial samples.

Keywords: Lung Cancer, Proteomics, Spatial Proteomics.

Sponsorship: FAPESP, CAPES, CNPq and FAEPA.

 

Agradecimentos: Agradecimento a agência de fomento FAPESP (processo nº 2024/06240-8), e a estrutura e aporte da USP e da FMRP. Agradecimento a toda a equipe da plataforma de microscopia do departamento de Biologia Celular e Molecular da FMRP por toda a ajuda com preparo de lâminas e identificação histológica de tecidos, em especial a Vani e Bete.