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An ultrasensitive spatial tissue proteomics workflow exceeding 100 proteomes per day

ABSTRACT: Achieving high-resolution spatial tissue proteomes requires careful balancing and integration of optimized sample processing, chromatography, and MS acquisition. Here, we present an advanced cellenONE protocol for loss-reduced tissue processing and compare all Evosep ONE Whisper Zoom gradients (20, 40, 80, and 120 samples per day), along with three common DIA acquisition schemes on a timsUltra AIP mass spectrometer. We found that tissue type was as important as gradient length and sample amount in determining proteome coverage. Moreover, the benefit of increased tissue sampling was gradient- and dynamic range-dependent. Analyzing mouse liver, a high dynamic range tissue, over tenfold more tissue sampling led to only ~30% gain in protein identification for short gradients (120 SPD and 80 SPD). However, even the lowest tested tissue amount (0.04 nL, 40,000 µm3) yielded 3,200 reproducibly quantified proteins for the 120 SPD method. Longer gradients (40 SPD and 20 SPD) instead significantly benefited from more tissue sampling, quantifying over 7,500 proteins from 0.5 nL of tonsil T-cell niches. Finally, we applied our workflow to a rare squamous cell carcinoma of the oral cavity, uncovering disease-associated pathways and region-specific protein level changes. Our study demonstrates that more than 100 high-quality spatial tissue proteomes can be prepared and acquired daily, laying a strong foundation for cohort-size spatial tissue proteomics in translational research.

INSTRUMENT(S):

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

TISSUE(S): Malignant Cell, Tonsil, Hepatocyte, T Cell, Liver, Epithelial Cell, Squamous Cell Carcinoma Cell

DISEASE(S): Oral Squamous Cell Carcinoma

SUBMITTER: Fabian Coscia

LAB HEAD: Dr. Fabian

PROVIDER: PXD069877 | Pride | 2026-01-05

REPOSITORIES: Pride

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Project description:Introduction: Cerebrospinal fluid (CSF) is the proximal body fluid to the brain and contains biomarkers for pathological brain processes. Discovery of protein biomarkers by mass spectrometry (MS) is challenging due to the high abundance range of CSF proteins, issues with sampling and processing, and the complex interplay of pathology and human variability. We previously quantified more than a thousand proteins in about 200 samples; establishing a promising biomarker panel for Alzheimer’s disease. However, due to technological constraints, studies including ours typically had to compromise on the number of quantified proteins or the sample size. Here, we developed and optimized a high-throughput label-free workflow which we validate by analyzing thousands of CSF samples to focus on study size scalability. Methods: Our high-throughput CSF proteomics workflow uses a streamlined, data independent acquisition (DIA) strategy. It is built around the Evosep chromatography system run with 21-minute gradients (60 samples per day) and the Bruker timsTOF Pro 2. MS-Fragger and DIA-NN were chosen for library and DIA data analysis, respectively. We optimized workflow aspects including sample preparation, MS acquisition, spectral library generation, raw data processing and normalization algorithms. We demonstrate scalability by measuring more than 6000 CSF samples from patients with Multiple Sclerosis, and diverse neurological conditions. Additionally, we investigated factors causing analytical variability and used differential expression and machine learning approaches to characterize how pathology, age, sex, disruption of blood brain barrier (BBB), and sampling-related biases impact the CSF proteome. Preliminary data: We found that the above protocol – combined with in-solution digestion, off-line peptide clean-up and single-run analysis, yielded optimal CSF proteome depth. It permitted sample preparation streamlining for eight 96-well plates at a time. Furthermore, we enhanced protein identification by adapting the DIA acquisition scheme to the CSF precursor distribution in the m/z dimension and focusing on enhanced ion mobility separation instead of covering the complete ion mobility range. We generated a CSF-specific DIA spectral library of initially 3000 proteins using MS-Fragger, by depletion of abundant proteins, 96 high-pH fractions, data-dependent measurement, complemented by deep-leaning peptide intensity predictions of the entire proteome. These libraries were pruned to about 2,000 proteins experimentally observed in cohort samples, which enabled processing in only three days. We achieved stable identification of about 1200 proteins per sample across the cohort, totaling 2000 proteins in the entire dataset, of which 1000 were quantified in more than 75% of all samples. Quantitative precision was high (CV 13% intraplate and 23% overall). Quality control samples revealed a drastic effect of blood contamination on CSF proteomes; we excluded affected patient samples. The impact of circadian sampling time was minor. Across the cohort, disease and the covariates sex, age, and disease-unspecific BBB impairment accounted for 11, 6, 11, and 38% of variability in the dataset, respectively. The BBB impairment had an indirect effect on the relative abundance of the other CSF proteins (those not expected to leak from blood to CSF). In the CSF from Multiple Sclerosis patients, we found an immunoglobulin signature, which was expected and provides positive control. Another set consisted of known and novel proteins, that were dominantly MS associated but partially also perturbed in other conditions including autoimmunity or infection. Novel aspect: We present a powerful CSF protein biomarker discovery workflow amenable to unprecedented study sizes at good proteome coverage and quantitation.

Project description:Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using Strap and analysed on Evosep and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searched using DIANN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhances protein identification in neat plasma samples. Globally, out optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.

			Action	DRS
	DIANN_fig1.rar	Other
	DIANN_fig3.rar	Other
	ExperimentalDesign.csv	Csv
	Fig1_liver.rar	Other
	Fig1_tonsil.tar	Other

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An ultrasensitive spatial tissue proteomics workflow exceeding 100 proteomes per day

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