Title: Spectral entropy as a measure of the metaproteome complexity
Subject: Proteomics 2024.24:2300570

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^1School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
^2Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
Correspondence
Daniel Figeys, School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, K1H 8M5, Canada. Email: dfigeys@uottawa.ca
Funding information
Natural Sciences and Engineering Research Council of Canada, Grant/Award Numbers: NSERC, 210034; Government of Canada through Health Canada, Genome Canada, Grant/Award Numbers: OGI-156, OGI-149; Ontario Ministry of Economic Development and Innovation, Grant/Award Numbers: ORF-DIG-14405, 13440

Accepted: 14 May 2024

Received: 20 March 2024 Revised: 13 May 2024
DOI: 10.1002/pmic.202300570
TECHNICAL BRIEF
Spectral entropy as a measure of the metaproteome complexity
Haonan Duan^1,2	Zhibin Ning^1,2	Ailing Zhang^1,2	Daniel Figeys^1,2
Abstract
The diversity and complexity of the microbiome’s genomic landscape are not always mirrored in its proteomic profile. Despite the anticipated proteomic diversity, observed complexities of microbiome samples are often lower than expected. Two main factors contribute to this discrepancy: limitations in mass spectrometry’s detection sensitivity and bioinformatics challenges in metaproteomics identification. This study introduces a novel approach to evaluating sample complexity directly at the full mass spectrum (MS1) level rather than relying on peptide identifications. When analyzing under identical mass spectrometry conditions, microbiome samples displayed significantly higher complexity, as evidenced by the spectral entropy and peptide candidate entropy, compared to single-species samples. The research provides solid evidence for the complexity of microbiome in proteomics indicating the optimization potential of the bioinformatics workflow.
KEYWORDS
bioinformatics, metaproteomics, spectral entropy

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
© 2024 The Author(s). Proteomics published by Wiley-VCH GmbH.

Proteomics 2024, 24:2300570
https://doi.org/10.1002/pmic.202300570

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1		INTRODUCTION
The microbiome is a complex system, its complex genomic landscape highlights its inherent diversity [1, 2]. Intuitively, it might be expected that the proteomic profile of the microbiome would mirror this complexity and diversity. However, the observed proteomic complexity often does not meet this expectation. In some instances, the number of peptides identified from the microbiome is even fewer than that from a single species (Figure S1A). This discrepancy can be attributed to two primary factors:
1.1		Limitations in mass spectrometry’s detection sensitivity
The observed complexity by mass spectrometry of the metaproteome is often not greater than that of single species, due to functional
Abbreviations: MS1, Full mass spectrum; DDA, Data dependent acquisition; DIA, Data independent acquisition; NCBI, National Center for Biotechnology Information.
redundancy [3] and the prevalence of shared peptides. These shared peptides, collectively expressed across species, dominate the mass spectrometry detection in data-dependent experiments. In MetaPep [4], a collection of identified peptides, only 25.2% peptides are speciesunique peptides. However, 84.5% peptides in the database used for searching are species-unique peptide. Consequently, without enrichment procedures, detection of less abundant, species-specific peptides becomes challenging, potentially reducing the apparent complexity of microbiome samples in mass spectrometry analysis due to the limit on sensitivity of the mass spectrometer.
1.2		Bioinformatics challenges in metaproteomics identification
Bioinformatics is one of the most challenging aspects of metaproteomics [5]. The huge size of the database used in metaproteomics increases the probability of random matches. Given that proteomics
employs strategies like false discovery rate and decoy databases to filter false positives [6], the vast number of false positives can overshadow true matches, resulting in a low identification rate. Although methods that reduce the database size have significantly alleviated this issue [7–11], the size of the trimmed microbiome database remains hundreds of times larger than that of a single species database, indicating room for further optimization in bioinformatics. Besides, the accuracy of metagenomic sequencing will directly influence peptide identification.
Comparing the complexity of samples directly based on the number of peptide identifications is biased because of the differences in the bioinformatics workflows. The choice of database for searching profoundly influences the results of peptide identification [12]. Consequently, it is inappropriate to assert that a higher number of identified peptides corresponds to a greater diversity of peptide species within the sample. Therefore, we propose a method for comparing the complexity of single organism samples and microbiome samples that do not rely on peptide identification and utilizes instead the information available in MS1. In this approach, we evaluate the sample complexity directly at the mass spectrum level. The MS1 mass spectrum provides a deeper profile of the peptides present in the sample, giving valuable information about peptide ions, which is an ideal material for our comparative analysis.
In the present study, we conducted a comparative analysis of the human gut microbiomes and single strains at the MS1 level. To evaluate the complexity of the samples, we quantified various parameters, including the number of peaks in the spectra, spectral entropy, and the count of peptide features. Furthermore, we developed a computational tool to facilitate the calculation of spectral entropy. Our findings illustrate that spectral entropy is an effective and straightforward metric for assessing the complexity of microbiome samples.
2		MATERIALS AND METHODS
2.1		Sample preparation
The human stool was collected from a healthy adult volunteer at the University of Ottawa, Ottawa, ON, CAN. The protocol (# 2016058501H) was approved by Ottawa Health Science Network Research Ethics. The human microbiome was cultured in an optimized medium [13]. The strain samples were cultured in a broth medium. Both cultures were under 37^◦C for 24 h. Protein extraction and digestion were based on previously published methods [14]. Briefly, cell pellets were harvested by centrifugation. Then, bacterial cells were subjected to lysis in a specified buffer (6 M urea, 50 mM Tris-HCl, 4% SDS, Protease Inhibitor 1x), followed by ultrasonication at 8^◦C. The lysate was then centrifuged at 16,000 g to discard debris, and proteins were precipitated overnight using a cold acetone/ethanol/acetic acid mixture. The proteins were pelleted and washed with acetone before dissolving in urea and ammonium bicarbonate, with concen- tration determined by the DC assay. A sample of 50 µg protein underwent reduction, alkylation, and digestion by trypsin at 37^◦ C for 24 h. The resulting peptides were then desalted using a C18 column and, after freeze-drying, redissolved in 0.1% formic acid. For the experiment investigating the influence of loading amount on spectral complexity, the peptide concentration was determined using a quantitative peptide assay (Thermo Fisher Scientific, USA, catalog number 23275).
2.2		LC-MS/MS analysis
An UltiMate 3000 RSLCnano system was used with an Orbitrap Exploris 480 mass spectrometer for sample analysis (Thermo Fisher Scientific, USA). Peptides were loaded onto a column with reverse phase beads, followed by a 60-min gradient from 5% to 35% buffer at a rate of 300 µL/min. For the data-dependent acquisition (DDA) experiment, the mass spectrometer was set to a top 15 method, dynamic exclusion, and a full mass scan range of 350–1200 (m/z). For the data-independent acquisition (DIA) experiment, the m/z range is 380–980 with window widths set at 10 m/z.
2.3		Data processing
Raw files from single-species samples were searched against corresponding assembled genomes using Maxquant version 1.5.2.8 [15]. The theoretical protein databases for five bacterial strains were retrieved from the National Center for Biotechnology Information (NCBI). The respective genome assembly identifiers for these bacteria are ASM15797v1, ASM16903v1, ASM1282v1, ASM2514748v1, and ASM15637v1. Raw files from microbiome samples were searched against the human gut microbial integrated gene catalogue database using Maxquant workflow in Metalab version 2.2 [8]. We achieved satisfactory identification rates from all the samples, ranging between 23% and 34% (Figure S1A). For the consecutive injections experiment, Blautia hydrogenotrophica was searched against the corresponding strain database. The microbiome sample was searched against MetaPep, a peptide database specific to human gut microbiome samples [16]. The peptide identification for DDA and DIA samples was performed on Maxquant (Version 1.5.2.8) [15] and DIA-NN 1.8 [17] under library-free mode, respectively.

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For spectral entropy analysis, the raw data generated in DDA mode was converted from profile data into centroid data in the MS1 and mzML formats using MSconvert Version 3.0. An absolute intensity of 5000, which represents the lowest intensity to trigger an MS/MS scan, was employed as the threshold to filter out noise. The “feature finder centroided” algorithm in TOPPAS version 3.0 was used to search for peptide candidates from mzML files using default parameters [18, 19]. The MS1 files were used to calculate spectral entropy using “Spectral Entropy Calculator.” The calculation method for spectral entropy

FIGURE 1 Spectra complexity comparison between microbiome and single species. (A) Peaks number on MS1. (B) Spectral entropy of each MS1. (C) Normalized spectral entropy of each MS1. (D) The total ion current of each MS1. Each sample was injected three times under identical conditions. Three replicates were shown in different colors. The black line represents the median values of the parameters within each sample. The gray line represents the boundary between microbiome and strain samples. The equations to calculate spectral entropy and normalized spectral entropy were shown in the method. CM, cultured microbiome; SM, stool microbiome; BU, Bacteroides uniformis; BH, Blautia hydrogenotrophica; CA, Collinsella aerofaciens; DP, Desulfovibrio piger; PV, Phocaeicola vulgatus.

is illustrated by the following equation (p: peaks): S=-^∑_pIplnIp	(1)
^∑_pIplnIp
Normalized S =-	(2) lnp
_I _= Intensity of peak	_(3)
^P Total intensity
3		RESULTS
Briefly, we performed LC-MS/MS analysis of five individual gut microbiome samples and five single strains. Around 4209–4882 MS1 spectra were collected per sample (Figure S1B). The number of spectra collected per sample did not vary between single species and microbiome samples. We then counted the number of ion peaks with intensity exceeding 5000 (the lowest intensity to trigger MS2 scan) in each MS1 spectrum. The average peak number in all microbiome samples was significantly higher than that in single-specie samples (Figure 1A), which indicated a higher complexity of the microbiome sample. To further evaluate the amount of information contained in each sample, we employed spectral entropy. In information theory, entropy is used to quantify the amount of uncertainty or randomness associated with a set of data. The spectral entropy has been successfully used for library matching in metabolomics [20, 21]. We developed a tool with GUI called “Spectral Entropy Calculator” to compute the spec- tral entropy of mass spectra (https://github.com/northomics/spectra_ entropy). As expected, the MS1 spectra from the microbiome had significantly higher spectral entropy (Figure 1B). The spectral entropy achieves its maximum at ln(number of peak) when all ions exhibit identical intensity. Given that spectral entropy could increase with the number of peaks, we also compared the normalized spectral entropy by dividing the spectral entropy by ln(number of peak) which ranges from 0 to 1 (Equation 2). Once again, the microbiome samples displayed a higher normalized spectral entropy (Figure 1C). This indicates a more uniform distribution of ion intensities and a greater diversity of ion species in microbiome samples. Consequently, this suggests that more information could be translated from the spectra of the microbiome.

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Increasing the loading amount might enable the detection of low- abundance peptides that were previously undetectable, potentially increasing the spectral complexity. We investigated the impact of the loading amount on peak numbers, spectral entropy, and normalized spectral entropy. One microbiome sample and a Blautia hydrogenotrophica sample were loaded with quantities ranging from 500 to 2000 ng in three replicates. Interestingly, the result shows that the spectral entropy and peak number remain relatively stable as the sample loading amount increases (Figure S2). This indicates that within a certain range, spectral entropy is not affected by the sample loading amount, thereby accurately reflecting the complexity of the sample. In proteomics, feature detection algorithms play a crucial role in identifying potential peptide candidates [22]. These algorithms do not rely on theoretical protein databases. Instead, they directly search for peptide candidates in the MS1 spectra based on retention time,

FIGURE 2 Candidates’ complexity comparison between microbiome and single species. (A) The candidate entropy of each sample. (B) The normalized candidate entropy of each sample. (C) The intensity was normalized to the total intensity in each sample and log-transformed (base 10). The intensity was arranged in descending order. Each sample had three replicates which were in the same color. CM, cultured microbiome; SM, stool microbiome; BU, Bacteroides uniformis; BH, Blautia hydrogenotrophica; CA, Collinsella aerofaciens; DP, Desulfovibrio piger; PV, Phocaeicola vulgatus.

chromatographic peak shape, and isotopic patterns [19]. Using the same criteria, we employed feature detection algorithms to search for peptide candidates in both microbiome and single-species samples. Interestingly, microbiome samples contained a larger number of peptide candidates, ranging from 45,000 to 61,000 (Figure 2C). However, in single-species samples, the number of peptide candidates never exceeds 41,000 in the five species. The feature detection algorithms also quantified the intensity of candidates, enabling the application of a similar strategy to calculate candidate entropy in which the peptide candidate is similar to the peak in spectral entropy. Our findings reveal that both candidate entropy and normalized candidate entropy are markedly higher in microbiome samples when compared to singlespecies samples, suggesting a greater complexity and diversity in microbial communities (Figure 2A,B). We normalized the candidate intensity to the total intensity in each sample and plotted candidates in descending order of intensity. Both sets of data follow a normal distribution, displaying a higher number of moderate values and fewer extreme values. Microbiome samples displayed a higher count of candidates with moderate intensities (Figure 2C, Area II). Interestingly, we found that the intensity drops more quickly in the microbiome for the highly abundant areas (Figure 2C, Area I). This may suggest that the accumulation of shared peptides results in a much higher abundance of these peptides compared to others. Additionally, despite the stability of spectral complexity across a range of loading amounts from 500 to 2000 ng (Figure S2), our analysis indicated a positive correlation between the number of peptide candidates and the loading amount (Figure S3). This suggests that although the signals generated by peptides are recorded, these peptides must still achieve a certain criterion (chromatographic peak shape, intensity) to be recognized by the feature detection algorithm. The microbiome samples yielded a greater number of peptide candidates compared to Blautia hydrogenotrophica samples when loading the same amount. Moreover, the difference in the number of candidates between these samples expanded with an increase in the loading amount. In our experiment, we observed that the loading amount of microbiome samples was either comparable to or less than that of the single-species samples (Figure 1D), which implies that ifan equal amount of peptides were loaded, the differences between the two sample types would be even more pronounced.
We conclude that the metaproteome complexity is reflected in the information in the MS1, entropy calculations provide a good assessment of the sample complexity, and that it can detect differences in sample complexity.
4		DISCUSSION

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In this study, we found that both the spectral entropy and peptide features that were directly derived from MS1 can reflect the complexity of samples. However, the spectral entropy is relatively unaffected by the loading amount. We observed excellent reproducibility of spectral entropy in each of the three replicate samples, indicating the potential use of spectral entropy as an assessment tool for sample quality. Without peptide identification, our research demonstrates that microbiome samples exhibit significantly higher complexity than single-species samples on MS1 under the current mass spectrometry sensitivity. In mass spectrometry, the DDA mode inherently possesses a degree of selection bias towards higher abundance. This characteristic means that although the number of identified peptides per injection
remains relatively consistent, successive injections of a sample tend to yield an increased number of unique peptides [23, 24] (Figure S4). On the other hand, DIA operates without such bias. In an experiment involving 21 consecutive injections of the Blautia hydrogenotrophica samples (Figure S4B), the DDA mode continuously revealed new peptides. In contrast, the peptide counts in DIA mode quickly reached a plateau. Interestingly, when this process was replicated with microbiome samples, both DDA and DIA modes consistently showed a rise in peptide identification as the number of injections increased (Figure S4B). High reproducibility is one of the major advantages of the DIA mode [25]. However, it is not reflected in the microbiome sample. This phenomenon may be attributed to the enhanced complexity of the microbiome samples, which potentially magnifies the bias in signal detection and peptide identification. In highly complex samples, numerous peptides may produce signals in the same or close m/z ranges. In DIA mode, although signals are not selectively collected based on ion intensity, these overlapping signals are still acquired simultaneously within the same mass window, forming a composite spectrum. The higher degree of signal overlap in complex samples makes the deconvolution process more complex and challenging, potentially leading to decreased identification accuracy and reproducibility.
Overall, our study underscores the high complexity of microbiome samples observed in mass spectrometry profiles. Furthermore, both DDA and DIA modes are challenged by this complexity. Methods to address the complexity of the spectrum are required for deep metaproteomics analysis. The most straightforward and simple approach is to employ longer chromatographic gradients. However, this method increases the analysis time and is not suitable for projects with a large number of samples. An alternative approach involves utilizing a higher acquisition rate [26]. For DDA, an increased acquisition rate allows the mass spectrometer to select more ions when employing a top-N strategy. In the case of DIA, a higher acquisition rate permits the use of a smaller isolation window, which in turn reduces the complexity of MS2 spectra. Additionally, the introduction of ion mobility separation technology adds a new dimension to peptide separation, which could also contribute to alleviating the issue of sample complexity [27].
ACKNOWLEDGMENTS
This work was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) discovery grant to Daniel Figeys. Haonan Duan was funded by a stipend from the NSERC CREATE in Technologies for Microbiome Science and Engineering (TECHNOMISE) Program.
CONFLICT OF INTEREST STATEMENT
Daniel Figeys is co-founder of MedBiome Inc. a microbiome nutrition and therapeutic company.
DATA AVAILABILITY STATEMENT
The code and Spectral Entropy Calculator are available at: https:// github.com/northomics/spectra_entropy. The raw data and search- ing results can be found at ProteomeXchange Consortium (dataset identifier: PXD050781).
ORCID
Haonan Duan https://orcid.org/0000-0002-2753-8594
Daniel Figeys https://orcid.org/0000-0002-5373-7546
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SUPPORTING INFORMATION
Additional supporting information may be found online https://doi.org/10.1002/pmic.202300570 in the Supporting Information section at the end of the article.
How to cite this article: Duan, H., Ning, Z., Zhang, A., & Figeys, D. (2024). Spectral entropy as a measure of the metaproteome complexity. Proteomics, 24, e2300570.

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