Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

Shengbo Wang; David García-Seisdedos; Ananth Prakash; Deepti Jaiswal Kundu; Andrew Collins; Nancy George; Silvie Fexova; Pablo Moreno; Irene Papatheodorou; Andrew R Jones; Juan Antonio Vizcaíno

doi:10.1371/journal.pcbi.1010174

. 2022 Jun 17;18(6):e1010174. doi: 10.1371/journal.pcbi.1010174

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

Shengbo Wang ^1,^‡, David García-Seisdedos ^1,^2,^‡, Ananth Prakash ^1,^2,^‡,^*, Deepti Jaiswal Kundu ¹, Andrew Collins ³, Nancy George ¹, Silvie Fexova ¹, Pablo Moreno ¹, Irene Papatheodorou ^1,², Andrew R Jones ³, Juan Antonio Vizcaíno ^1,^2,^*

Editor: Zhaolei Zhang⁴

PMCID: PMC9246241 PMID: 35714157

Abstract

The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively.

In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination.

Author summary

We have reanalysed 23 baseline mass spectrometry-based public proteomics datasets stored in the PRIDE database. Overall, the aggregated dataset contained 211 samples, coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. We analysed the distribution of protein expression across organs in both species. We also studied how protein abundances compared across different datasets and organs for both species. Then we performed gene ontology and pathway enrichment analyses to identify enriched biological processes and pathways across organs. We also carried out a comparative analysis of baseline protein expression across mouse, rat and human tissues, observing a high level of expression correlation among orthologs in all three species, in brain, kidney, heart and liver samples. To disseminate these findings, we have integrated the protein expression results into the resource Expression Atlas.

1. Introduction

High-throughput mass spectrometry (MS)-based proteomics approaches have matured significantly in recent years, becoming an essential tool in biological research [1]. This has been the consequence of very significant technical improvements in MS instrumentation, chromatography, automation in sample preparation and computational analyses, among other areas. The most used MS-based experimental approach is Data Dependent Acquisition (DDA) bottom-up proteomics. Among the main quantitative proteomics DDA techniques, label-free intensity-based approaches remain very popular, although labelled-approaches, especially those techniques based on the isotopic labelling of peptides (MS² labelling), such as iTRAQ (Isobaric tag for relative and absolute quantitation) and TMT (Tandem Mass Tagging), are becoming increasingly used as well.

Following the steps initiated by genomics and transcriptomics, open data practices in the field have become embedded and commonplace in proteomics in recent years. In this context, datasets are now commonly available in the public domain to support the claims published in the corresponding manuscripts. The PRIDE database [2], located at the European Bioinformatics Institute (EBI), is currently the largest resource worldwide for public proteomics data deposition. PRIDE is also one of the founding members of the global ProteomeXchange consortium [3], involving five other resources, namely PeptideAtlas, MassIVE, iProX, jPOST and PanoramaPublic. ProteomeXchange has standardised data submission and dissemination of public proteomics data worldwide.

As a consequence, there is an unprecedented availability of data in the public domain, which is triggering multiple applications [4], including the joint reanalysis of datasets (so-called meta-analysis studies) [5–7]. Indeed, public proteomics datasets can be systematically reanalysed and integrated e.g., to confirm the results reported in the original publications, potentially in a more robust manner since evidence can be strengthened if it is found consistently across different datasets. Potentially, new insights different to the aims of the original studies can also be obtained by reanalysing the datasets using different strategies, this includes repurposing of public datasets [8], including for instance approaches such as proteogenomics studies for genome annotation purposes [9–12].

In this context of reuse of public proteomics data, PRIDE has started to work on developing data dissemination and integration pipelines into popular added-value resources at the EBI. This is perceived as a more sustainable approach in the medium-long term than setting up new independent bioinformatics resources. One of them is Expression Atlas [13], a resource that has enabled over the years easy access to gene expression data across species, tissues, cells, experimental conditions and diseases. Only recently, protein expression information coming from reanalysed datasets has been integrated in the ‘bulk’ section of Expression Atlas. As a result, proteomics expression data can be integrated with transcriptomics information, mostly coming from RNA-Seq experiments. So far, we have performed two meta-analysis studies involving the reanalysis and integration of: (i) 11 public quantitative datasets coming from cell lines and human tumour samples [13]; and (ii) 24 human baseline datasets coming from 31 different organs [14].

The next logical step is to perform an analogous study of baseline protein expression in two of the main model organisms: Mus musculus and Rattus norvegicus. To date, there are only a small number of bioinformatics resources providing access to reanalysed MS-based quantitative proteomics datasets, and even fewer if one considers only mouse and rat data. In this context, at the end of 2020, ProteomicsDB [15] released a first version of the mouse proteome, based on the reanalysis of five label-free datasets. To the best of our knowledge, there is no such public resource storing accurate MS-derived data for rat data yet. PaxDB is a resource [16] that provides protein expression information coming from many species (including mouse and rat) but the reported data relies on spectral counting, a technique that generally does not provide the same level of accuracy than intensity-based label-free approaches. Additionally, although antibody-based human protein expression information is provided via the Human Protein Atlas [17], their efforts are focused on human protein expression.

Here, we report the reanalysis and integration of 23 public mouse (14 datasets) and rat (9 datasets) label-free datasets, and the incorporation of the results into the resource Expression Atlas as baseline studies. Additionally, we report a comparative analysis of protein expression across mouse, rat and human (in this case using the results reported at [14] using the same methodology).

2. Results

2.1. Baseline proteomics datasets

Overall, we quantified protein expression from 34 healthy tissues in 14 organs coming from 23 datasets. The analyses covered a total of 1,173 MS runs from 211 samples that were annotated as healthy/control/non-treated samples, thus representing baseline protein expression. Non-control/disease samples associated with these datasets were also reanalysed but are not discussed here. Normalised protein abundances values (as ppb, parts per billion, see Methods for calculation) from both control/healthy/non-treated and disease/treated tissue samples are available to view as heatmaps in Expression Atlas. The protein abundances along with sample annotations, sample quality assessment summary and experimental parameter inputs for MaxQuant can be downloaded from Expression Atlas as text files. A summary of the data selection and reanalysis protocols is shown in Fig 1. The total number of peptides and proteins identified in these datasets are shown in Table 1.

Table 1. List of mouse and rat proteomics datasets that were reanalysed.

Expression Atlas accession numbers	PRIDE dataset identifiers	Tissues	Organs	Species	Strains	Fractionation	Number of MS runs	Number of samples	Number of protein groups^†	Number of peptides^†	Number of unique peptides^†	Number of unique genes mapped^†
E-PROT-7^§	PXD000867 [18]	Liver	Liver	Mus musculus	C57BL/6J	Yes	24	4	12,792	246,738	167,725	9,715
E-PROT-10^§	PXD000288 [19]	Triceps muscles	Triceps Muscles	Mus musculus	C57BL/6	Yes	36	3	10,870	189,553	126,670	6,421
E-PROT-16	PXD003155 [20]	Cerebellum, Liver	Brain, Liver	Mus musculus	C57BL/6	No	24	12	4,508	59,696	45,728	3,797
E-PROT-74	PXD004612 [21]	Achilles and Plantaris tendon	Tendon	Mus musculus	C57BL/6	No	8	8	457	6,643	3,271	273
E-PROT-75	PXD005230 [22]	Hippocampus, Cerebellum, Cortex	Brain	Mus musculus	C57BL/10J	Yes	72	36	7,663	63,479	41,683	6,037
E-PROT-76	PXD009909 [23]	Retina	Eye	Mus musculus	ND4 Swiss Webster	Yes	12	1	5,002	29,454	24,961	3,686
E-PROT-77	PXD012307 [24]	Lung	Lung	Mus musculus	C57BL/6	No	32	2	6,809	106,391	73,950	5,795
E-PROT-78	PXD009639 [25]	Lens	Eye	Mus musculus	CD1	Yes	10	1	4,519	20,779	18,006	3,064
E-PROT-79	PXD019394 [26]	Heart, Kidney, Liver, Lung, Brain, Spleen, Testis, Pancreas	Heart, Kidney, Liver, Lung, Brain, Spleen, Testis, Pancreas	Mus musculus	Swiss-Webster	Yes	96	8	9,853	141,506	105,701	8,185
E-PROT-81	PXD012636 [27]	Left atrium, Left ventricle, Right atrium, Right ventricle	Heart	Mus musculus	C57BL/6	Yes	120	4	7,772	146,966	99,577	6,435
E-PROT-82	PXD019431 [28]	Articular cartilage	Articular cartilage	Mus musculus	BALB\_c	No	72	6	1,815	17,695	15,191	1,518
E-PROT-83	PXD022614 [29]	Brain	Brain	Mus musculus	C57BL/6J:Rj C57BL/6JRccHsd	Yes	120	6	6,645	97,443	69,884	5,673
E-PROT-84	PXD004496 [30]	Hippocampus	Brain	Mus musculus	C57BL/6J	Yes	204	17	4,192	37,363	30,100	3,424
E-PROT-85	PXD008736 [31]	Right atrium, Sinus node	Heart	Mus musculus	C57BL/6J	Yes	143	6	7,906	144,926	94,379	6,554
E-PROT-86^§	PXD012677 [32]	Amygdala	Brain	Rattus norvegicus	Sprague Dawley	No	3	3	1,872	15,326	12,367	1,382
E-PROT-87^§	PXD006692 [33]	Lung	Lung	Rattus norvegicus	Sprague Dawley	No	10	10	2,079	14,440	11,696	1,398
E-PROT-88^§	PXD016793 [34]	Liver	Liver	Rattus norvegicus	Sprague Dawley	No	8	8	4,787	57,998	46,411	3,743
E-PROT-89^§	PXD004364 [35]	Testis	Testis	Rattus norvegicus	Sprague Dawley	No	3	3	2,351	15,880	13,674	1,601
E-PROT-91	PXD001839 [36]	Left ventricle	Heart	Rattus norvegicus	F344/BN	No	12	12	1,345	10,310	8,804	925
E-PROT-92^§	PXD013543 [37]	Left ventricle	Heart	Rattus norvegicus	Wistar	No	8	8	1,858	17,303	13,622	1,340
E-PROT-93	PXD016958 [38]	First segment of proximal tubule, second segment of proximal tubule, third segment of proximal tubule, medullary thick ascending limb, cortical thick ascending limb, distal convoluted tubule, connecting tubule, cortical collecting duct, outer medullary collecting duct, inner medullary collecting duct	Kidney	Rattus norvegicus	Sprague Dawley	Yes	132	32	7,846	103,886	83,662	6,130
E-PROT-94	PXD003375 [39]	Caudal and rostral segments of spinal cord	Spinal cord	Rattus norvegicus	Wistar	Yes	21	18	2,477	29,213	22,025	1,926
E-PROT-95^§	PXD015928 [40]	Tendon	Tendon	Rattus norvegicus	Wistar	No	3	3	199	1,253	1,063	101
TOTAL	23 datasets (Mouse: 14, Rat: 9)	34 tissues (Mouse: 21, Rat: 18)	14 organs (Mouse: 12, Rat: 8)				1,173 MS runs (Mouse: 973, Rat: 200)	211 samples (Mouse: 114, Rat: 97)

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^§Only normal/untreated samples within this dataset are reported in this study. However, results from both normal and disease samples are available in Expression Atlas

^† Numbers after post-processing.

2.2. Protein coverage across organs and datasets

One of our main aims was to study protein expression across various organs. To enable a simpler comparison [14] we first grouped 34 different tissues into 14 distinct organs, as discussed in ‘Methods’. We defined ‘tissue’ as a distinct functional or structural region within an ‘organ’. We estimated the number of ‘canonical proteins’ identified across organs by first mapping all members of each protein group to their respective parent genes. We defined the parent gene as equivalent to the UniProt ‘canonical protein’ and we will denote the term ‘protein abundance’ to mean ‘canonical protein abundance’ from here on in the manuscript.

2.2.1. Mouse proteome

A total of 21,274 protein groups were identified from mouse datasets, among which 8,176 protein groups (38.4%) were uniquely present in only one organ and 70 protein groups (0.3%) were ubiquitously observed (see the full list in S2 File). This does not imply that these proteins are unique to these organs. Merely, this is the outcome considering the selected datasets. Mouse protein groups were mapped to 12,570 genes (canonical proteins) (S3 File). We detected the largest number of canonical proteins in samples coming from liver (9,920, 78.9% of the total) and the lowest numbers in samples from tendon (273, 2.2%) and articular cartilage (1,519, 12.1%) (Fig 2A). In the case of tendon and articular cartilage, both experiments did not include sample fractionation in their sample preparation methodology, which can also explain the lower number of detected proteins. The comparatively even lower number of proteins identified in tendon could be attributed to the smallest sample size (only one sample out of 114, 0.9%). Also, tendon is a relatively hypocellular tissue, which has a low protein turnover rate. Dataset PXD000867, containing mouse liver samples, had the highest number of canonical proteins detected (9,715, 77.3%), while the smallest number of proteins was detected in dataset PXD004612 (tendon, 273, 2.2%), as highlighted above (Fig 2C).

We studied the normalised protein abundance distribution in organs (Fig 2B) and found that all organs, except tendon, had similar median abundances. However, one cannot attribute further biological meaning to these observations, since by definition the method of normalisation fixes each sample to have the same “total abundance”, which then gets shared out amongst all proteins. The normalised protein abundance distribution in datasets indicated a higher than median abundances detected in datasets PXD004612 (tendon) and PXD003164 (testis) (Fig 2D). A linear relationship was observed between the number of canonical proteins detected in datasets and organs, when compared to the relative amount of their spectral data (Fig 2E). We found a significant number of proteins uniquely detected in one organ (Fig 2F). However, the list of concrete canonical proteins that were detected in just one organ should be taken with caution since the list is subjected to inflated False Discovery Rate (FDR), due to the accumulation of false positives when analysing the datasets separately.

Some of the organs (liver, heart and brain) were represented across multiple mouse studies in the aggregated dataset. A pairwise comparison of protein abundances in these organs generally showed a good correlation in expression (heart: R² values ranged from 0.54 to 0.83; brain: R² from 0.28 to 0.72; and liver: R² from 0.59 to 0.74) (Figs A-C in S4 File).

2.2.2. Rat proteome

A total of 7,769 protein groups were identified across 8 different rat organs among which 3,649 (46.9%) protein groups were unique to one specific organ while 13 (0.16%) protein groups were present among all organs (see full list in S2 File). The protein groups were mapped to 7,116 genes (canonical proteins) (S3 File). The highest number of canonical proteins (6,106, 85.1%) was found in rat kidney samples. The lowest number of canonical proteins (101, 1.4%) was found in samples from tendon, as shown in Fig 3A. The largest number of canonical proteins identified in kidney is likely because of the relatively large number of samples (32 samples), when compared to other organs. However, it is interesting to note that large numbers of canonical proteins were detected in liver samples, which relatively had fewer number of samples, when compared to the total number of samples in heart and spinal cord.

Fig 3 — (A) Number of canonical proteins identified across different rat organs. The number within the parenthesis indicates the number of samples. (B) Range of normalised iBAQ protein abundances across different organs. The number within the parenthesis indicates the number of samples. (C) Canonical proteins identified across different datasets. The number within the parenthesis indicate the number of unique tissues in the dataset. (D) Range of normalised iBAQ protein abundances across different datasets. The number within parenthesis indicate the number of unique tissues in the dataset. (E) Comparison of total spectral data with the number of canonical proteins identified in each dataset and organ. (F) Distribution of canonical proteins identified across organs.

Datasets PXD016958 and PXD016793 consisted entirely of kidney (where fractionation was performed) and liver (no fractionation) samples, respectively, and as mentioned above had the largest number of canonical proteins identified (Fig 3C). The normalised protein abundances were similar among the various organs and datasets (Fig 3B and 3D). We also observed a linear relation between the number of canonical proteins identified and the MS spectra identified (Fig 3E). As seen in the mouse datasets, we also observed a large number of proteins uniquely detected in one organ (Fig 3F). As highlighted above, the list of concrete canonical proteins that were detected in just one organ should be taken with caution since the list is subjected to inflated False Discovery Rate (FDR).

In the case of rat datasets, left ventricle heart samples were the only ones represented in more than one study (PXD001839 and PXD013543) in the aggregated dataset. A pairwise comparison of protein abundances of heart between these two datasets was performed, showing a strong correlation in protein expression (R² = 0.9) (Fig D in S4 File).

2.3. Protein abundance comparison across organs

Next, we studied how protein abundances compared across different datasets and organs. The presence of batch effects between datasets makes this type of comparisons challenging. To aid comparison of protein abundances between datasets we transformed the normalised iBAQ intensities into ranked bins as explained in ‘Methods’, i.e., proteins included in bin 5 are highly abundant whereas proteins in bin 1 are expressed in the lowest abundances (among the detected proteins).

2.3.1. Mouse proteome

We found that 1,086 (8.6%) proteins were found with their highest level of expression in at least 3 organs, with a median bin value greater than 4 (S3 File). On the other end of the scale, 138 (1.1%) canonical proteins were found with their lowest expression in at least 3 organs, with a median bin value of less than 2. The bin transformed abundances in all organs are provided in S3 File.

To compare protein expression across all organs, we calculated pairwise Pearson correlation coefficients across 117 samples (Fig 4A). We observed some correlation in protein expression within brain (median R² = 0.31) and a higher one in heart (median R² = 0.67) samples. We performed Principal Component Analysis (PCA) on all samples from mouse datasets for testing the effectiveness of the bin transformation method in reducing batch effects. Fig 4B shows the clustering of samples from various organs of mouse. We observed samples from the same organ generally clustered together. For example, we observed that brain samples all clustered together in one group, even though they come from different datasets, indicating decent removal of batch effects (Fig 4C). However, we also observed that samples from other organs such as liver did not cluster according to their organ types but clustered together within the dataset they were part of, indicating some residual batch effects, which are hard to remove completely.

In addition, we compared the protein abundances generated in this study with the data available in the resource PaxDB generated using spectral counting across different mouse organs. We observed generally a strong correlation of protein abundances calculated using iBAQ from this study (fraction of total (FOT) normalised ppb) and spectral counting methods (Fig E in S4 File). However, the expression of low abundant proteins seemed to be underestimated in PaxDB when compared with our results, as shown by a S-shaped curve in the scatterplot in organs such as brain, heart, liver and lung. The ‘dynamic exclusion’ [41] setting used by modern mass spectrometers prevents the instrument from fragmenting abundant peptides multiple times when they are repeatedly observed in scans nearby in time. This has the effect that spectral counting approaches will limit the dynamic range observed, as high abundant proteins will be under sampled. This is a limitation when using spectral counting methods, and these days spectral counting is not commonly used as a truly quantitative data type in proteomics.

2.3.2. Rat proteome

Next, we studied the distribution of protein abundances across organs in rat. On one hand, 311 (4.3%) proteins were found with their highest expression in at least 3 organs with a median bin value greater than 4. On the other hand, 27 (0.37%) canonical proteins were found with their lowest expression in at least 3 organs, with a median bin value of less than 2. The bin transformed abundances in all organs are provided in S3 File.

Overall, the samples from rat datasets showed a better correlation in protein expression (Fig 5A) than in the case of mouse. We observed generally a strong correlation of protein expression within samples from liver (median Pearson’s correlation R² = 0.85), lung (median R² = 0.71), spinal cord (median R² = 0.65), heart (median R² = 0.71) and brain (median R² = 0.86). We also observed the clustering in the PCA of samples coming from the same organ (Fig 5B). Kidney, lung, spinal cord and heart samples all clustered together according to their organ type. Fig 5C shows the samples based on the dataset they were part of. However, most organ samples were part of individual datasets except in the case of samples from heart, which came from two datasets (PXD001839 and PXD013543). Fig 5C shows that the heart samples clustered into two nearby groups (bottom left two clusters on Fig 5B and 5C), wherein each cluster included samples from a different dataset, indicating the presence of small batch effects.

2.4. The organ elevated proteome and the over-representative biological processes

Based on their expression, canonical proteins were classified into three different groups based on their organ specificity: “mixed”, “group-enriched” and “organ-enriched” (see S5 File). We considered over-expressed canonical proteins in each organ as those which were in “group-enriched” and “organ-enriched”. The analysis showed that on average, 20.8% and 26.0% of the total elevated canonical proteins were organ group-specific in mouse and rat, respectively (Fig 6). In addition, 4.3% and 14.2% were unique organ-enriched in mouse and rat, respectively. The highest ratio of organ-enriched in mouse was found in liver (13.6%), whereas in rat, it was found in kidney (39.8%).

We then performed a gene ontology (GO) enrichment analysis of those proteins that were ‘organ-enriched’ and ‘group-enriched’ using GO terms associated with biological processes. We found 1,036 GO terms to be statistically significant in all organs, as seen in S6 File. The most significant GO terms for each organ are shown in Table 2.

Table 2. Analysis of the top three GO terms for each organ in mouse and rat using the elevated organ-specific and group-specific canonical proteins as described in the ‘Methods’ section.

Organ	Species	GO ID	Description	adjusted p-value
Articular cartilage	Mus musculus	GO:0030198	Extracellular matrix organization	8.94*10⁻³⁸
		GO:0043062	Extracellular structure organization	8.94*10⁻³⁸
		GO:0045229	External encapsulating structure organization	8.94*10⁻³⁸
Brain	Mus musculus	GO:0050804	Modulation of chemical synaptic transmission	7.03*10⁻⁶⁵
		GO:0099177	Regulation of trans-synaptic signalling	7.03*10⁻⁶⁵
		GO:0050808	Synapse organization	1.41*10⁻⁴⁸
Heart	Mus musculus	GO:0060047	Heart contraction	7.10*10⁻¹¹
		GO:0008016	Regulation of heart contraction	4.43*10⁻¹⁰
		GO:0060537	Muscle tissue development	6.16*10⁻¹⁰
Kidney	Mus musculus	GO:0015711	Organic anion transport	4.59*10⁻¹⁹
		GO:0044282	Small molecule catabolic process	4.91*10⁻¹⁵
		GO:0016054	Organic acid catabolic process	6.25*10⁻¹⁵
Eye	Mus musculus	GO:0007601	Visual perception	7.54*10⁻⁵⁰
		GO:0001654	Eye development	5.31*10⁻³¹
		GO:0099504	Synaptic vesicle cycle	8.36*10⁻¹⁸
Liver	Mus musculus	GO:0016569	Covalent chromatin modification	6.26*10⁻¹⁰
		GO:0016570	Histone modification	1.71*10⁻⁰⁸
		GO:0019369	Arachidonic acid metabolic process	1.71*10⁻⁰⁸
Lung	Mus musculus	GO:0120031	Plasma membrane bounded cell projection assembly	3.61*10⁻¹⁴
		GO:0030031	Cell projection assembly	3.61*10⁻¹⁴
		GO:0044782	Cilium organization	9.83*10⁻¹⁴
Pancreas	Mus musculus	GO:0007586	Digestion	0.005
Pancreas	Mus musculus	GO:0032328	Alanine transport	0.018
Spleen	Mus musculus	GO:0046649	Lymphocyte activation	4.12*10⁻²²
		GO:0050776	Regulation of immune response	2.00*10⁻²⁰
		GO:0045087	Innate immune response	2.23*10⁻²⁰
Tendon	Mus musculus	GO:0003012	Muscle system process	1.46*10⁻²⁵
		GO:0050879	Multicellular organismal movement	3.14*10⁻¹⁹
		GO:0050881	Musculoskeletal movement	1.46*10⁻²⁵
Testis	Mus musculus	GO:0048232	Male gamete generation	8.75*10⁻⁴⁹
		GO:0003341	Cilium movement	3.04*10⁻³⁸
		GO:0044782	Cilium organization	6.78*10⁻³⁷
Triceps muscles	Mus musculus	GO:0061061	Muscle structure development	1.56*10⁻¹⁴
		GO:0055002	Striated muscle cell development	2.41*10⁻¹⁴
		GO:0003009	Skeletal muscle contraction	3.53*10⁻¹⁴
Brain	Rattus norvegicus	GO:0099537	Trans-synaptic signalling	1.79*10⁻⁶⁰
		GO:0007268	Chemical synaptic transmission	1.79*10⁻⁶⁰
		GO:0098916	Anterograde trans-synaptic signalling	1.79*10⁻⁶⁰
Heart	Rattus norvegicus	GO:0061061	Muscle structure development	2.94*10⁻¹⁷
		GO:0003012	Muscle system process	6.30*10⁻¹⁶
		GO:0055001	Muscle cell development	4.00*10⁻¹⁵
Kidney	Rattus norvegicus	GO:0006396	RNA processing	6.19*10⁻¹³
		GO:0045944	positive regulation of transcription by RNA polymerase II	7.29*10⁻⁰⁶
		GO:0006260	DNA replication	1.74*10⁻⁰⁵
Liver	Rattus norvegicus	GO:0008202	Steroid metabolic process	2.74*10⁻¹⁰
		GO:0016054	Organic acid catabolic process	1.61*10⁻⁰⁹
		GO:0032787	Monocarboxylic acid metabolic process	1.64*10⁻⁰⁹
Lung	Rattus norvegicus	GO:0031589	Cell-substrate adhesion	7.62*10⁻⁰⁸
		GO:0009617	Response to bacterium	7.62*10⁻⁰⁸
		GO:0030036	Actin cytoskeleton organization	1.40*10⁻⁰⁷
Spinal cord	Rattus norvegicus	GO:0061564	Axon development	4.26*10⁻¹⁸
		GO:0099537	Trans-synaptic signalling	5.93*10⁻¹⁶
		GO:0007268	Chemical synaptic transmission	5.93*10⁻¹⁶
Tendon	Rattus norvegicus	GO:0030199	Collagen fibril organization	1.23*10⁻¹³
		GO:0061448	Connective tissue development	2.31*10⁻⁰⁹
		GO:0001501	Skeletal system development	3.39*10⁻⁰⁹
Testis	Rattus norvegicus	GO:0019953	Sexual reproduction	3.98*10⁻²⁴
		GO:0051704	Multi-organism process	1.61*10⁻¹⁸
		GO:0007018	Microtubule-based movement	4.00*10⁻¹²

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2.5. Protein abundances across orthologs in three species

In a previous study, we analysed 25 label-free proteomics datasets from healthy human samples to assess baseline protein abundances in 14 organs following the same analytical methodology [14]. We compared the expression of canonical proteins identified in all three species (rat, mouse and human). Overall, 13,248 detected human genes (corresponding to the canonical proteins) were compared with 12,570 genes detected in mouse and 7,116 genes detected in rat. The number of orthologous mappings (i.e., “one-to-one” mappings, see ‘Methods’) between rat, mouse and human genes are listed in Table 3. We only considered one-to-one mapped orthologues for the comparison of protein abundances.

Table 3. Homologs identified in mouse and rat datasets when compared with the background list of genes (corresponding to canonical proteins) identified in human datasets (Supplementary File 2 in [14]).

Species	Identified genes	Orthologs of human genes identified in [14]	Percentage of genes with different mapping against identified human genes
Species	Identified genes	Orthologs of human genes identified in [14]	one-to-one	one-to-many	many-to-many	many-to-one	not mapped
Mus musculus	12,570	10,601	80.4%	1.9%	0.56%	1.46%	15.7%
Rattus norvegicus	7,116	6,058	82.0%	2.2%	0.70%	0.25%	14.9%

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Among human and mouse orthologues we observed relatively high levels of correlation of protein abundances in brain (R² = 0.61), heart (R² = 0.65) and liver (R² = 0.56) (Fig 7A). Human and rat orthologs showed also relatively high levels of correlation in brain (R² = 0.62), kidney (R² = 0.53) and liver (R² = 0.56), but almost no correlation in lung (R² = 0.12) and testis (R² = 0.18) (Fig 7B). Between mouse and rat orthologs, the correlation of protein abundances was higher in liver (R² = 0.65), kidney (R² = 0.54) and brain (R² = 0.57) samples, when compared to the samples coming from the rest of the organs (Fig 7C). Fig 7D shows an illustration of some example comparisons of individual orthologs using binned protein abundances.

Fig 7 — (A) Pairwise correlation using normalised protein abundances of human and mouse orthologues. (B) Human and rat orthologs. (C) Mouse and rat orthologs. (D) As an example, the comparisons of binned protein expression of ten randomly sampled orthologs are shown. Data corresponding to all cases (as reported in panel D) are available in S7 File and the corresponding illustration of binned values is available in S8 File. Orthologs in (D) are shown using their human gene symbol.

For the same corresponding subsets, we also investigated the correlation of protein expression between various organs within each organism. We observed that in general the correlation of protein expression was slightly lower between organs within the same species, when compared to a higher correlation, which was observed among orthologs (Figs F-H in S4 File). The found lower correlation of protein expression between different organs was more apparent in mouse and rat.

Among the orthologs expressed in all organs in all three species, 747 (12.3%) orthologs were detected with a median bin expression value of more than 4, i.e., proteins that appear to have conserved high expression in all organs and all tissues. Additionally, 13 (0.2%) orthologs were found with a median bin expression value less than 2 in all organs, although, it is harder to detect consistently proteins with low abundances across all organs. A full list of the binned protein abundances of orthologs is available in S7 File. The illustration of all binned protein abundances across the three species is shown in S8 File.

Since each sample contains potentially thousands of protein values this creates a high level of dimensionality within the data. To reduce this, we used the non-linear dimension reduction algorithm, Uniform Manifold Approximation and Projection (UMAP) (see Section 4.7 in the ‘Methods’ section). The UMAP algorithm enables the reduction of multidimensional data to a two-dimensional space upon which the relationship between each sample can be visualised. Specifically, it enables the visualisation of the relationships of proteins across individual samples and organs. Should multiple samples be positioned near to each other, it allows for us to predict that these samples shared similar properties (in this case, similar protein abundance values). Consequently, by overlaying samples from various species UMAP representations can be used to visualise the relationship of various orthologs across similar organs.

Using the UMAP algorithm, we were able to visualise the relationships between individual organs regardless of the involved species (human, mouse, rat) and to identify similar genes (corresponding to canonical proteins) within those organs. The overall view of all samples labelled by their respective organ is shown as Fig 8A. We chose to use the biological system as the basis for the colouring scheme for each sample to reduce the overall complexity of the visualisation, due the high number of organs included. By using this labelling scheme, we could see that the clustering of each sample was deterministic. Each sample was positioned within a clear region for the corresponding organs, despite the original layout being unaware of this information. This indicates that not only do the samples within those organs share common protein abundance values, but furthermore, that samples that come from the same organs share similar protein expression (as three species are present).

Furthermore, in Fig 8B we show the representation of binned protein abundance values for three example genes (SH3GL2, MYOZ2 and PYROXD2), providing information on the abundance of them across different biological systems. These visualisations use the same layout than within Fig 8A. In the example of SH3GL2, it can be seen that Fig 8B shows multiple values that have been scored as bin 5. By referring to Fig 8A, we can see that those points corresponding to highly abundant proteins, come from samples from the nervous system (in all three species). Furthermore, using the same method, it can be seen that MYOZ2 is highly abundant in the circulatory system, and that PYROXD2 is highly abundant in the urinary system. The UMAP coordinates and our binned protein abundance data that is used in these plots to allow for the generation of similar visualisations are provided in S9 File.

2.6. Pathway enrichment analysis

Based on the ortholog protein expression analysis described above, we mapped canonical proteins from mouse and rat to the corresponding ortholog human proteins, which were subsequently subjected to pathway-enrichment analysis using Reactome (Fig 9). After filtering out the disease and statistically insignificant pathways, there were 2,990 pathways found in all the organs of mouse and 2,162 pathways in all the organs of rat. In mouse samples, the largest number of pathways (367) were found in articular cartilage, and the lowest number of pathways was found in liver (44). We also observed that Neuronal System-related pathways were predominantly present in the brain and eye, which is consistent with expectations. In rat samples, brain included the largest number of pathways (387), while the lowest number of pathways was found in tendon, with 117.

3. Discussion

We have previously reported two meta-analysis studies involving the reanalysis and integration in Expression Atlas of public quantitative datasets coming from cell lines and human tumour samples [13], and from human baseline tissues [14], respectively. In this study, we reanalysed mouse and rat baseline proteomics datasets representing protein expression across 34 healthy tissues and 14 organs. We have used the same methodology as in the study involving baseline human tissues, which enabled a comparison of protein expression levels across the three species. Our main overall aim was to provide a system-wide baseline protein expression catalogue across various tissues and organs of mouse and rat and to offer a reference for future related studies.

We analysed each dataset separately using the same software (MaxQuant) and the same search protein sequence database. The disadvantage of this approach is that the FDR statistical thresholds are applied at a dataset level and not to all datasets together as a whole. However, as reported before [14], using a dataset per dataset analysis approach is in our view the only sustainable manner to reanalyse and integrate quantitative proteomics datasets, at least at present. The disadvantage of this approach is that the FDR statistical threshold are applied at a dataset level and not to all datasets together as a whole, with the potential accumulation of false positives across datasets. However, it is important to highlight that the number of commonly detected false positives is reduced in parallel with the increase in the number of common datasets where a given protein is detected. As also reported in previous studies, one of the major bottlenecks was the curation of dataset metadata, consisting of mapping files to samples and biological conditions. Very recently, the MAGE-TAB-Proteomics format has been developed and formalised to enable the reporting of the experimental design in proteomics experience, including the relationship between samples and raw files, which is recorded in the SDRF-Proteomics section of the file [42]. Submission of the SDRF-Proteomics files to PRIDE is now supported. The more well-annotated datasets in the public domain, the easier these data reuse activities will become.

The generated baseline protein expression data can be used with different purposes such as the generation of protein co-expression networks and/or the inference of protein complexes. For the latter application, expression data can be alone or for potentially refining predictions obtained using different methods such as the recently developed AlphaFold-based protein complexes predictions [43]. Mouse and rat are widely used species in the context of drug discovery, the latter especially, to undertake regulatory pre-clinical safety studies. Therefore, it is important to know quantitative protein expression distribution in these species in different tissues [44] to assist in the selection of species for such studies and also for the interpretation of the final results.

In addition to the analyses reported, it would have also been possible to perform correlation studies between gene and protein expression information. However, we did not find any relevant public datasets in the context of this manuscript where the same samples were analysed by both techniques, which is the optimal way to perform these studies. Future directions in analogous studies will involve: (i) additional baseline protein expression studies of other species, including other model organisms or other species of economic importance; (ii) the inclusion of differential proteomics datasets (e.g. using TMT and/or iTRAQ); and (iii) include relevant proteomics expression data coming from the reanalysis of Data Independent Acquisition (DIA) datasets [45].

As mentioned above, we performed a comparative analysis of baseline protein expression across human, mouse and rat. It was possible to perform this analysis for six common organs (brain, heart, kidney, liver, lung and testis). Ortholog expression across species is useful to infer protein function across experimentally studied proteins. This is particularly useful as evolutionarily closely related species are likely to conserve protein function. We could not find in the literature an analogous comparative study performed at the protein level. However, expression from closely related orthologs across tissues or organs has been compared at the transcriptomics level, providing a complete picture of gene expression. In this context, many studies have compared gene-expression in mouse, rat and human orthologues and found that orthologues had generally a highly correlated expression tissue distribution profile in baseline conditions [46–50]. Gene expression levels among orthologs were found to be highly similar in muscle and heart tissues, liver and nervous system and less similar in epithelial cells, reproductive systems, bone and endocrine organs [48]. Studies have also shown that variability of gene expression between homologous tissues/organs in closely related species can be lower than the variability between unrelated tissues within the same organism [46,47], in agreement with the results reported here at the protein level. Additionally, we showed an initial analysis of protein expression of orthologs across the three species using UMAP.

In conclusion we here present a meta-analysis study of public mouse and rat baseline proteomics datasets from PRIDE. We demonstrate its feasibility, perform a comparative analysis across the three species and show the main current challenges. Finally, the data is made available via Expression Atlas. Whereas there are several analogous studies performed at the gene expression level for mouse and rat tissues, to the best of our knowledge this is the first of this kind at protein expression level.

4. Materials and methods

4.1. Datasets

As of May 2021, there were 2,060 mouse (Mus musculus) and 339 rat (Rattus norvegicus) MS proteomics datasets publicly available in the PRIDE database (https://www.ebi.ac.uk/pride/). Datasets were manually selected based on the selection criteria described previously [14]. Briefly, we selected datasets where baseline expression experiments were performed on (i) label-free samples from tissues not enriched for post-translational modifications; (ii) Thermo Fisher Scientific instruments such as LTQ Orbitrap, LTQ Orbitrap Elite, LTQ Orbitrap Velos, LTQ Orbitrap XL ETD, LTQ-Orbitrap XL ETD, Orbitrap Fusion and Q-Exactive, since they represent a large proportion of datasets in PRIDE and to avoid heterogeneity introduced by data from other vendor instruments; (iii) had suitable sample metadata available in the original publication or it was possible to obtain it by contacting the authors; and (iv) our previous experience in the team of some datasets deposited in PRIDE, which were discarded because they were not considered to be useful. Overall, 14 mouse and 9 rat datasets were selected from all mouse and rat datasets for further analysis. Table 1 lists the selected datasets. The 23 datasets contained a total of 211 samples from 34 different tissues across 14 organs (meaning groups of related tissues, more details below), comprising 9 different mouse and 3 rat strains, respectively.

The sample and experimental metadata were manually curated using the information provided in the respective publications or by contacting the original authors/submitters. Annotare [51] was used for annotating the metadata and stored using the Investigation Description Format (IDF) and Sample-Data Relationship Format (SDRF) file formats [42], which are required for integration in Expression Atlas. An overview of the experimental design including experimental factors, protocols, publication information and contact information are present in the IDF file, and the SDRF includes sample metadata describing the relationship between the various sample characteristics and the data files contained in the dataset.

4.2. Proteomics raw data processing

All datasets were analysed with MaxQuant (version 1.6.3.4) [52,53] on a Linux high-performance computing cluster for peptide/protein identification and protein quantification. Input parameters for each dataset, such as MS¹ and MS² tolerances, digesting enzymes, fixed and variable modifications, were set as described in their respective publications, with two missed cleavage sites. The FDR at the PSM (peptide spectrum match) and protein levels were set to 1%. The MaxQuant parameters were otherwise set to default values: the maximum number of modifications per peptide was 5, the minimum peptide length was 7, the maximum peptide mass was set to 4,600 Da, and for the matches between runs the minimum match time window was set to 0.7 seconds and the minimum retention time alignment window was set to 20 seconds. The MaxQuant parameter files are available for downloading from Expression Atlas. The Mus musculus UniProt Reference proteome release-2021_04 (including isoforms, 63,656 sequences) and Rattus norvegicus UniProt Reference proteome release-2021_04 (including isoforms, 31,562 sequences) were used as the target sequence databases for mouse and rat datasets, respectively. The built-in contaminant database within MaxQuant was used and a decoy database was generated by MaxQuant by reversing the input database sequences after the respective enzymatic digestion. The datasets were run separately in multi-threaded mode.

4.3. Post-processing

The post-processing of results from MaxQuant is explained in detail in [14]. In brief, the protein groups labelled as potential contaminants, decoys and those with fewer than 2 PSMs were removed. Protein intensities in each sample were normalised by scaling the iBAQ intensity values to the total amount of signal in each MS run and converted to parts per billion (ppb).

p p b_i B A Q_{i} = (\frac{i B A Q_{i}}{\sum_{i = 1}^{n} i B A Q_{i}}) x 1, 000, 000, 000

The ‘majority protein identifiers’ within each protein group were mapped to their Ensembl gene identifiers/annotations using the Bioconductor package ‘mygene’. For downstream analysis only protein groups whose isoforms mapped to a single unique Ensembl gene ID were considered. Protein groups that mapped to more than one Ensembl gene ID are provided in S1 File. The protein intensity values from different protein groups with the same Ensembl gene ID were aggregated as median values. The parent genes to which the different protein groups were mapped to are equivalent to ‘canonical proteins’ in UniProt (https://www.uniprot.org/help/canonical_and_isoforms) and therefore the term protein abundance is used to describe the protein abundance of the canonical protein throughout the manuscript.

4.4. Integration into expression atlas

The calculated canonical protein abundances (mapped to genes), together with the validated SDRF files, summary files detailing the quality of post-processing and the input MaxQuant parameter files (mqpar.xml) were integrated into Expression Atlas (https://www.ebi.ac.uk/gxa/home) as proteomics baseline experiments (E-PROT identifiers are available in Table 1).

4.5. Protein abundance comparison across datasets

To compare protein abundances, the normalised protein abundances (in ppb) from each group of tissues in a dataset were converted into ranked bins. In this study, ‘tissue’ is defined as a distinct functional or structural region within an ‘organ’. For example, hippocampus, cerebellum and cortex are defined as ‘tissues’ that are part of the brain (organ) and similarly sinus node, left atria, left ventricle, right atria, right ventricle are defined as ‘tissues’ in heart (organ). Protein abundances were transformed into bins by first grouping MS runs from each tissue within a dataset as a batch. The normalised protein abundances (ppb) for each MS run within a batch were sorted from lowest to highest abundance and ranked into 5 bins. Proteins whose ppb abundances are ranked in the lowest bin (bin 1) represent lowest abundance and correspondingly proteins within bin 5 are of highest abundance in their respective tissue. When merging tissues into organs, median bin values were used.

Proteins that were detected in at least 50% of the samples were selected for PCA (Principal Component Analysis) and was performed using R (The R Stats package) [54] using binned abundance values. For generating heatmaps, a Pearson correlation coefficient for all samples was calculated on pairwise complete observations of bin transformed values. Missing values were marked as NA (not available). For each organ a median R² was calculated from all pairwise R² values of their respective samples. Samples were hierarchically clustered on columns and rows using Euclidean distances. To compare the correlation in protein expression of shared organs between datasets, the FOT normalised protein abundances (ppb) were aggregated by calculating the median over samples. The regression line was computed using the ‘linear model’ (lm) method in R.

4.6. Comparison of protein abundances using iBAQ and spectral counting data available in PaxDB

To compare protein abundances generated from iBAQ in this study and spectral counting methods, protein abundance data from different mouse organs was obtained from PaxDB (https://www.pax-db.org/) [16]. FOT normalised iBAQ abundances, as described above, were compared with the spectral counting abundances for the matching mouse organs. Organs from mouse labelled as ‘integrated’ in PaxDB were selected. It was not possible to perform this comparison for rat organs since data in PaxDB for rat are available for either the ‘whole organism’ or for “cell types” only. Abundances were compared across mouse adipose tissue, brain, heart, kidney, liver, lung, pancreas and spleen. The Ensembl ENSG gene ids were mapped to ENSP protein ids in PaxDB using the ‘mygene’ bioconductor package in R.

4.7. UMAP analysis

To generate the UMAP visualisations we used the binned protein abundance values generated in this study from rat and mouse, as well as the binned human protein abundance values from [14]. First, we reduced this data to only contain the orthologs found in all three species. For the purpose of only the initial visualisation layout, we filtered the data to include those proteins present in 90% of samples. Once the initial layout was generated, we then used the full protein abundance values to generate protein-specific visualisations. We use R v4.1.0 with the package ‘umap’ (Uniform Manifold Approximation and Projection in R) [55] v0.2.7.0 to generate the UMAP visualisations.

4.8. Organ-specific expression profile analysis

For comparison across organs, the tissues were aggregated into organs and their median bin values were considered. As described previously [14] the classification scheme done by Uhlén et al. [17] was modified to classify the proteins into one of the three categories: (1) “Organ-enriched”: present in one unique organ with bin values 2-fold higher than the mean bin value across all organs; (2) “Group enriched”: present in at least 7 organs in mouse or in at least 4 organs in rat, with bin values 2-fold higher than the mean bin value across all organs; and (3) “Mixed”: the remaining canonical proteins that are not part of the above two categories.

Enriched gene ontology (GO) terms analysis was carried out through over-representation test described previously [14], it was combined with “Organ-enriched” and “Group enriched” mapped gene lists for each organ. In addition, Reactome [56] pathway analysis was performed using mapped gene lists and running pathway-topology and over-representation analysis, as reported previously [14].

4.9. Comparison of protein expression across species

The g:Orth Orthology search function in the g:Profiler suite of programs [57] was used for translating gene identifiers between organisms. Since a custom list of gene identifiers could not be used as the background search set, the mouse and rat genes were first mapped against the background Ensembl database. The resulting list of mouse and rat genes mapped to human orthologs were then filtered so that they only included parent gene identifiers of the protein groups from mouse and rat organs identified in this study and the parent genes of human organs described in our previous study (Supplementary File 2 in [14]), respectively.

The orthologs were grouped into various categories denoting the resulting mapping between identifiers: “one-to-one”, “one-to-many”, “many-to-one”, “many-to-many”, and “no mappings” between gene identifiers. Only “one-to-one” mapped ortholog identifiers were used to compare protein intensities between mouse, rat and human organs. The normalised ppb protein abundances of the one-to-one mapped orthologues in 6 organs (brain, heart, kidney, liver, lung and testis), that were studied across all three organisms were used to assess the pairwise correlation of protein abundances. The linear regression was calculated using the linear fit ‘lm’ method in R.

Supporting information

S1 File. Protein groups from all datasets that are mapped to more than one Ensembl Gene ID.

(XLSX)

Click here for additional data file.^{(224.8KB, xlsx)}

S2 File. Median protein abundances (in ppb) for each protein group across various tissue samples in each organ.

(XLSX)

Click here for additional data file.^{(2.5MB, xlsx)}

S3 File. Median binned protein abundances across various tissue samples in each organ of mouse and rat.

(XLSX)

Click here for additional data file.^{(1.1MB, xlsx)}

S4 File

Supplementary figures (A) illustrating correlation of protein abundances in organs represented in different datasets. (B) Correlation of protein abundances generated using iBAQ and spectral counting methods in various mouse organs. (C) Correlation of protein expression between organs within human, mouse and rat.

(PDF)

Click here for additional data file.^{(5.5MB, pdf)}

S5 File. Organ distribution of canonical proteins in mouse and rat.

(XLSX)

Click here for additional data file.^{(603.5KB, xlsx)}

S6 File. Gene Ontology enrichment analysis of ‘organ-enriched’ and ‘group-enriched’ proteins.

(XLSX)

Click here for additional data file.^{(315.5KB, xlsx)}

S7 File. Binned protein abundances of one-to-one mapped orthologs across all organs studied.

(XLSX)

Click here for additional data file.^{(1.1MB, xlsx)}

S8 File. Figure illustrating binned protein abundances of all one-to-one mapped orthologs across six common organs in mouse, rat and human.

(PDF)

Click here for additional data file.^{(4.5MB, pdf)}

S9 File. UMAP co-ordinates and source data of UMAP analysis.

(XLSX)

Click here for additional data file.^{(13.9MB, xlsx)}

Acknowledgments

First of all, we would like to thank all data submitters who made their datasets available via PRIDE and ProteomeXchange. We would also like to thank Andrew Leach and the rest of the team involved in the Open Targets “Target Safety” project, for helpful discussions.

Data Availability

The R code used for downstream data analyses and for plotting of results presented in this manuscript are available from https://github.com/Ananth-Prakash/Mouse_Rat_DDA_proteomics.

Funding Statement

AP and DGS were funded by Open Targets (project OTAR-02-068), https://www.opentargets.org/ AP, IP, NG, PM, SF and JAV were funded by BBSRC BB/T019670/1, https://bbsrc.ukri.org/ AC and ARJ were funded by BBSRC BB/T019557/1, https://bbsrc.ukri.org/ AP, DGS, SW and JAV were funded by Wellcome Trust [grant number 208391/Z/17/Z], https://wellcome.org/ DJK, IP and JAV were funded by EMBL core funding, https://www.embl.org/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010174.r001

Decision Letter 0

Zhaolei Zhang, Ilya Ioshikhes

27 Jan 2022

Dear Dr. Prakash,

Thank you very much for submitting your manuscript "Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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PLOS Computational Biology

Ilya Ioshikhes

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PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: This manuscript integrated mouse and rat proteome datasets from published works and performed some correlation-based analysis across organs and species. I have the following questions regarding this manuscript, and hopes the authors could address.

1. The paper seems to be a following up work from the group’s previous paper (Jarnuczak, A.F., Najgebauer, H., Barzine, M. et al. An integrated landscape of protein expression in human cancer. Sci Data 8, 115 (2021). https://doi.org/10.1038/s41597-021-00890-2), but applies to mouse/rat proteomics data. The original work focused on human cell lines. The concepts and methodologies of the two papers are extremely similar. It would be nice if the authors could highlight the computational innovations introduced in the current paper not in the previous one.

2. To my knowledge, PaxDB (https://pax-db.org/) is a very popular database for checking protein abundance. PaxDB contains many widely studied species including mouse and rat. It also has tissue information, protein interaction information and is regularly updated for years. To me, PaxDB seems to cover all values this paper could provide to me. It would nice that the authors could highlight the advantages of this paper that PaxDB doesn’t have.

3. One issue related to DDA based bottom-up proteomics is the reproducibility. One comparison that is missing in this paper is how well the proteins correlate with each other from different datasets for the same organ. This is critical. If the common proteins sampled from the same organ from two individual studies do not well correlate with each other, then to establish a global baseline for proteome across organs and species would be not very useful. For example, is TMT labelling different from iTRAQ labelling? Is deep fractionation proteome different from non-fractionation proteome? How do the authors adjust the difference using any kind of statistical modelling?

4. The title of this paper is about baseline expression of proteins. How did the baseline is set? By just the mean of expression or with any statistical justification?

5. How was the pathway analysis performed? If it was performed like gene set enrichment analysis, of course you will identify so many pathways with significant p-values. This will not provide much values. The interesting thing to see would be if particular pathways are detected in specific organs/tissue, but not in the others.

6. I am not super on top of the current mouse/rat proteomics literature. I am not sure if any targeted/DIA proteomics work has been done in mouse or rat. It would be nice to benchmark the DDA proteome to targeted/DIA proteome, since it was argued that targeted/DIA proteome measure is more accurate than DDA proteome.

Reviewer #2: Wang et al. described the results from a comparative analysis of publicly available rat and mouse proteomics data sets generated for fourteen tissues in the baseline (healthy) state. They verified that nearly half the detected proteins were significantly over-expressed in one or two types of tissue/organ system, and certain tissues such as tendon and testis have different sets of proteins dominating the abundance distribution given the uniqueness of their physiological and biological functions. They also noted that the protein expression levels are highly correlated between orthologs across species in line with general expectation. They are aware of the potential batch effects as individual data sets profile specific target organs only, and not all tissues and organs were analyzed in one single experiment. All in all, re-analyzing >20 proteomics datasets and standardizing the identification and quantification results (e.g. binning abundance levels) is a giant undertaking, and technical aspects of the work look solid in my opinion. Having said that, I believe the manuscript could have added more informative and exciting data analysis, rather than ending with the casual analysis of ortholog correlations (Figure 7) and generic functional enrichment-based clustering of organs (Figure 8).

Major recommendations

• Expression Atlas has a large number of human proteomics data sets as well. One way to utilize the resource in the context of this paper would be to map unambiguous orthologs across the three species (as much as one can) in similar organs and tissues, and perform a projection analysis of proteins (e.g. t-SNE or UMAP) all at once. In such a visualization, for instance, serum amyloid A1 proteins should be almost uniquely synthesized in hepatocytes of the liver, and this protein from the three species should co-localize to the same proximal neighborhood in the projection plot (they can be labeled as SAA1_rat, SAA1_mouse, SAA1_human). It will be an interesting exercise to catalogue what proteins are quantitatively enriched in particular organ systems across the three species, and what proteins are not – the latter of which is no doubt the more intriguing part of the results. Describing the consistent and inconsistent findings across the species for endocrine (liver, pancreas, kidney, adrenal glands) and immune systems (spleen, if you have it) will be very useful for many investigators working on the molecular pathophysiology of a disease in specific organ system.

• I wonder if it is possible to acquire or assemble similar baseline mRNA expression data sets for matching sample types (MGI for mouse, RGD for rat, GTEx for human). This will allow you to evaluate tissue specific mRNA-protein ratio comparisons between species. While the lack of absolute quantification precludes the calculation of protein translation rates, comparison of pseudo-ratios of protein/mRNA across organ systems may turn out to be divergent across the species (or not).

Minor comments

• Unlike tissue specific mRNA expression data sets (e.g. RNA-seq), MS/MS-based proteomics analyses report identifiable, mostly soluble fraction of the proteome, which may differ by tissue types. For this reason, the current proportion-based normalization (ppb-iBAQ) may underestimate the missing fraction of the proteome in the denominator, i.e. the sum of all quantified proteins in that analysis of the tissue sample. In my humble opinion, the denominator should add a tissue-specific fudge factor to the sum, if one can estimate it. For instance, if you can find matching RNA-seq data sets, you can look at the overlap between identified proteins and the number of genes whose mRNA is expressed >1 in TPM in each tissue/organ type. This will reveal the fraction of identified and unidentified proteins, and you can add the estimate of the missing proteome abundance to the denominator. Of course this will require huge assumptions such as mRNA and protein levels are generally linearly correlated. I wonder whether this is a worthy investigation, or of interest to the authors. If you believe that the current normalization approach is robust enough and my suggestion is beyond the scope of the work, I will accept that.

Reviewer #3: The manuscript by Wang et al. describes a well-conducted and very relevant example of reuse of proteomics datasets available in public repositories. Twenty-three datasets from Pride corresponding to 211 samples originating from 34 tissues across 14 organs and including mouse and rat strains were used. First, they have elegantly extracted comparative protein expression maps between different tissues/organs of a given species to propose baseline protein expression profiles before deducing organ-specific enriched biological processes and pathways. The authors have previously applied an equivalent strategy on human baseline datasets coming from 32 different organs (Prakash A, et al. 2021, bioRxiv) and to compare human cell lines and tumour samples (Jarnuczak AF, et al. 2021, Sci Data). The originality of the present work relies in the cross-species comparisons that were further added. Indeed, the authors also conducted orthologs analyses to compare protein expression profiles of different tissue/organ types across mouse, rat and human samples. Finally, the output of the study has been integrated into the Expression Atlas, which is a nice way to make the work widely available.

As a specific remark, it is a shame that the current status of annotation/metadata availability of public datasets still requires, prior and fastidious, thorough manual cleaning/reannotation of the datasets before they can be reused for such a study. The authors should even more clearly highlight this shortcoming which constitutes a real brake to this type of studies and more generally to the re-use of public datasets.

This work is worth being published in a journal like PLOS Computational Biology as this strategy can, and should be, increasingly applied to a wide range of available –omics, and in particular, proteomics datasets.

I have only two major comments that should be adressed in a revised version of the manuscript :

- Instead of using finely extracted quantitative data from MaxQuant and then proceeding to a "coarse" binning, the authors should conduct the same analysis directly on spectral counting data (ex. length-normalised unique peptide counts). It would be very interesting to show whether/or not this has an impact on the results.

- The authors should correlate their results achieved with proteomics data with antibody-based data extracted from Protein Atlas. This later inclusion would provide an added-value to the work.

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes: Hyungwon Choi

Reviewer #3: No

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PLoS Comput Biol. 2022 Jun 17;18(6):e1010174. doi: 10.1371/journal.pcbi.1010174.r002

Author response to Decision Letter 0

29 Mar 2022

Attachment

Submitted filename: Response to reviewers for mouse_rat manuscript_JAV_28March.pdf

Click here for additional data file.^{(5.3MB, pdf)}

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010174.r003

Decision Letter 1

Zhaolei Zhang, Ilya Ioshikhes

7 Apr 2022

Dear Dr. Prakash,

Thank you very much for submitting your manuscript "Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Zhaolei Zhang

Associate Editor

PLOS Computational Biology

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Thank authors for addressing my questions. I still have questions regarding Question 2 and Question 5.

For Question 2, from my personal experience, spectral counts and ion intensity are highly correlated (also documented in many papers), there is not much difference in practice. Regarding PaxDb paper, I think they applied more interesting integration method to weigh each dataset and peptides and then benchmark using protein-protein interaction information. I think their work is quite innovative and interesting. It is hard to convince me this dataset has better quality or more useful than PaxDb.

For Question 5, I would suggest the authors to remove the pathway analysis section from the paper. The result is not very useful as detecting many enriched pathways is expected by the authors’ setting, unless the organ/tissue specific pathways can be reported, which would be very interesting results.

I have no further questions.

Reviewer #2: Authors have addressed my major concerns in the first review, and the manuscript has more substance than the initial submission. However, I strongly recommend the authors to consider rewriting the abstract, focusing on the key results rather than stating what was performed, e.g. comparison of orthologues and pathway enrichment analysis, etc.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

Reviewer #1: None

Reviewer #2: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes: Hyungwon Choi

Figure Files:

Data Requirements:

Reproducibility:

References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

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PLoS Comput Biol. 2022 Jun 17;18(6):e1010174. doi: 10.1371/journal.pcbi.1010174.r004

Author response to Decision Letter 1

30 Apr 2022

Attachment

Submitted filename: Response to reviewers for mouse_rat manuscript_JAV_30April.pdf

Click here for additional data file.^{(51.5KB, pdf)}

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010174.r005

Decision Letter 2

Zhaolei Zhang, Ilya Ioshikhes

5 May 2022

Dear Dr. Prakash,

We are pleased to inform you that your manuscript 'Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Zhaolei Zhang

Associate Editor

PLOS Computational Biology

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: For Q2, the easiest way is to do the same benchmark analysis as PaxDB paper does and show the performance is better than PaxDB.

For Q5, I don't agree with the authors.

I have no further comments.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010174.r006

Acceptance letter

Zhaolei Zhang, Ilya Ioshikhes

8 Jun 2022

PCOMPBIOL-D-21-02288R2

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

Dear Dr Prakash,

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Protein groups from all datasets that are mapped to more than one Ensembl Gene ID.

(XLSX)

Click here for additional data file.^{(224.8KB, xlsx)}

S2 File. Median protein abundances (in ppb) for each protein group across various tissue samples in each organ.

(XLSX)

Click here for additional data file.^{(2.5MB, xlsx)}

S3 File. Median binned protein abundances across various tissue samples in each organ of mouse and rat.

(XLSX)

Click here for additional data file.^{(1.1MB, xlsx)}

S4 File

(PDF)

Click here for additional data file.^{(5.5MB, pdf)}

S5 File. Organ distribution of canonical proteins in mouse and rat.

(XLSX)

Click here for additional data file.^{(603.5KB, xlsx)}

S6 File. Gene Ontology enrichment analysis of ‘organ-enriched’ and ‘group-enriched’ proteins.

(XLSX)

Click here for additional data file.^{(315.5KB, xlsx)}

S7 File. Binned protein abundances of one-to-one mapped orthologs across all organs studied.

(XLSX)

Click here for additional data file.^{(1.1MB, xlsx)}

S8 File. Figure illustrating binned protein abundances of all one-to-one mapped orthologs across six common organs in mouse, rat and human.

(PDF)

Click here for additional data file.^{(4.5MB, pdf)}

S9 File. UMAP co-ordinates and source data of UMAP analysis.

(XLSX)

Click here for additional data file.^{(13.9MB, xlsx)}

Attachment

Submitted filename: Response to reviewers for mouse_rat manuscript_JAV_28March.pdf

Click here for additional data file.^{(5.3MB, pdf)}

Attachment

Submitted filename: Response to reviewers for mouse_rat manuscript_JAV_30April.pdf

Click here for additional data file.^{(51.5KB, pdf)}

Data Availability Statement

The R code used for downstream data analyses and for plotting of results presented in this manuscript are available from https://github.com/Ananth-Prakash/Mouse_Rat_DDA_proteomics.

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PERMALINK

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

Shengbo Wang

David García-Seisdedos

Ananth Prakash

Deepti Jaiswal Kundu

Andrew Collins

Nancy George

Silvie Fexova

Pablo Moreno

Irene Papatheodorou

Andrew R Jones

Juan Antonio Vizcaíno

Roles

Abstract

Author summary

1. Introduction

2. Results

2.1. Baseline proteomics datasets

Fig 1. An overview of the study design and reanalysis pipeline.

Table 1. List of mouse and rat proteomics datasets that were reanalysed.

2.2. Protein coverage across organs and datasets

2.2.1. Mouse proteome

Fig 2.

2.2.2. Rat proteome

Fig 3.

2.3. Protein abundance comparison across organs

2.3.1. Mouse proteome

Fig 4.

2.3.2. Rat proteome

Fig 5.

2.4. The organ elevated proteome and the over-representative biological processes

Fig 6. Organ specificity of canonical proteins in (A) mouse and (B) rat.

Table 2. Analysis of the top three GO terms for each organ in mouse and rat using the elevated organ-specific and group-specific canonical proteins as described in the ‘Methods’ section.

2.5. Protein abundances across orthologs in three species

Table 3. Homologs identified in mouse and rat datasets when compared with the background list of genes (corresponding to canonical proteins) identified in human datasets (Supplementary File 2 in [14]).

Fig 7. Comparison of protein abundances (in ppb) between one-to-one mapped orthologs of mouse, rat and human in various organs.

Fig 8. Visualisations generated using the UMAP algorithm to show the relationships between human, mouse, and rat samples.

2.6. Pathway enrichment analysis

Fig 9. Pathway analysis performed using the canonical proteins, showing the statistically significant representative pathways (p-value < 0.05) in (A) mouse and (B) rat organs.

3. Discussion

4. Materials and methods

4.1. Datasets

4.2. Proteomics raw data processing

4.3. Post-processing

4.4. Integration into expression atlas

4.5. Protein abundance comparison across datasets

4.6. Comparison of protein abundances using iBAQ and spectral counting data available in PaxDB

4.7. UMAP analysis

4.8. Organ-specific expression profile analysis

4.9. Comparison of protein expression across species

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

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Associated Data

Supplementary Materials

Data Availability Statement

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