dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011)  (Figure  3A ). Conversely, levels of Complex V proteins were substantially lower in mitochondria from BAT compared to all other measured tissues (Figure  3A ), which agrees with the specialized function of BAT in non-shivering thermogenesis  (Jastroch et al. , 2010; Kajimura and Saito, 2014; Oelkrug et al. , 2015). This was further supported by the high abundance of the uncoupling protein 1 (UCP1) in our proteome measurements, the key mediator of the heat-generating proton leak in the mitochondria of BAT (Figure  3B ). In contrast to the well described UCP1, several other members of the SLC25 family remain uncharacterized  (Ruprecht and Kunji, 2020). In our dataset, we identified a total of 47 members of the SLC25 family  (Kunji et al. , 2020). We found that levels of SLC25A23 and SLC25A25, two ATP-Mg 2+ /Pi carrier paralogues, (del  Arco and Satrustegui, 2004; Fiermonte et al.
, 1998; Huang et al. , 2003) , it also provided quantitative data to assess the magnitude of these differences. Together, our results imply a tailored regulation of kinase and phosphatase abundances across tissues to modulate the mitochondrial phosphoproteome. To investigate if and how kinase and phosphatase levels translate into protein phosphorylation, we analyzed the mitochondrial phosphoproteomes of the same samples collected from all seven tissues (Figure  1A ). This analysis resulted in the identification of 1263 phosphorylation sites on 626 mitochondrial proteins (Figure  5B , Figure  5  -Source Data 1). After stringent filtering of the data for more than four identifications across six biological replicates in at least one tissue and a site localization score higher than 75%, we obtained a dataset of 758 phosphorylation sites on 423 mitochondrial proteins (Figure  5C , Figure  5  -Source Data 1).
Of these high confidence sites, 16% have previously not been reported in mouse according to the PhosphoSitePlus (PSP) database  (Hornbeck et al. , 2012). Strikingly, in contrast to the mitochondrial proteomes, 37% of the phosphosites identified on mitochondrial proteins were exclusive to one tissue, and only 8% were identified in all the tissues measured (Figure  5C ). This might indicate that mitochondrial diversity is more strongly pronounced at the phosphorylation than the protein level.
Mitochondrial phosphoproteomes exhibit extensive intra-mitochondrial phosphorylation To understand the distribution of mitochondrial phosphoproteins across mitochondrial compartments we examined the sub-mitochondrial localization based on the curated MitoCarta3. 0 annotation. Mitochondria are typically divided into four main compartments, i. e. mitochondrial outer membrane (OMM), intermembrane space (IMS), inner mitochondrial membrane (IMM) and matrix, although the complex organization of the IMM possibly may define additional compartments  (Colina-Tenorio et al. , 2020). In line with the high proportion of shared mitochondrial proteomes across all tissues (Figure  1C ), the overall localization of proteins was not different between tissues and closely resembled the distribution of all annotated mitochondrial proteins in the database (Figure  6A ).
Web application makes Mouse Mitochondria Atlas data readily accessible As indicated by the above examples, this study presents a rich resource to explore the mitochondrial proteomes and phosphoproteomes across mouse tissues. Preceding examples show the potential of this resource for investigation of tissue-specific mitochondrial regulations on the proteome and phosphoproteome level, ultimately permitting the generation and analysis of new hypotheses. However, utilization of such resources largely depends on the ease of data access for exploration. To this end, we created a web application mitophos. de offering the end user an interface to easily explore datasets, including MitoCarta3. 0 networks, abundance comparisons across tissues and sequence analysis (Figure  8A ). As an example, Figure  8  illustrates the MICOS complex, which has a central role in mitochondria  (Khosravi and Harner, 2020).
In the network view one can see (I) members of the complex as well as the phosphorylation sites identified on these proteins and (II) in which tissues and how reproducibly they are identified in our dataset (Figure  8B ). Moreover, the user can inspect individual abundance distributions of all identified proteins/STY sites across all measured tissues. For instance, MINOS1, a core component of the MICOS complex  (Bohnert et al. , 2015) , displayed high abundance in mitochondria isolated from heart, SKM and BAT tissues (Figure  8C ). Moreover, in the sequence view the AlphaMap tool  (Voytik et al. , 2021)  is integrated to map all identified peptides, including phosphorylated peptides, onto their respective protein sequence along with structural information such as topological domains and transmembrane regions (Figure  8D ) and to visualize phosphorylation sites in their 3D structures (unpublished data) as predicted by AlphaFold  (Jumper et al.
Discussion Here we present a tissue-specific atlas of mouse mitochondrial proteomes and phosphoproteomes, an indepth resource towards a better understanding of the composition and function of this vital organelle in a tissue-specific manner. Previous MS-based studies combining mitochondrial phosphoproteome and proteome measurements typically focused on a single or few tissues and were generally shallower than our study. In addition, differences in study designs such as utilization of various organisms or mitochondria/phosphopeptide enrichment protocols, analysis pipelines, and mitochondrial protein annotation databases, complicate the integration of such datasets to understand tissue specificity. We now globally and precisely quantified different protein expression and phosphorylation patterns at subcellular level across seven mouse tissues, providing a detailed view on mitochondrial diversity.
It was recently reported that 91% of mitochondrial proteins on MitoCarta3. 0 have at least one phosphorylation site reported on the PSP database  (Niemi and Pagliarini, 2021). However, this analysis also includes proteins that do not always localize to mitochondria  (Ben-Menachem et al. , 2011)  and phosphorylation sites that can specifically be captured upon perturbations and stimuli. Our study revealed that around half of the mitochondria-localized proteins were phosphorylated in tissues at steady state -39% in brain, 15% in spleen, 29% in liver, 28% in kidney, 32% in SKM, 31% in heart and 22% in BAT. This suggests that the mitochondrial proteome and phosphoproteome compositions are dynamically modulated in response to environmental changes. Furthermore, we identified over 60 kinases and 10 phosphatases that either are localized to mitochondria or associate with mitochondria, providing a global view on important modulators of mitochondrial protein phosphorylation.
Raw data analysis DDA raw data were analyzed with MaxQuant (1. 6. 14. 0) against the mouse fasta file (downloaded 19. October 2020) using default settings. PSM and protein dales discovery rate were controlled at 1% FDR. The match between runs (MBR) functionality was enabled and set in a way that only biological replicates belonging to the same tissue type were allowed to match each other. This eliminates the possibility of potentially false MBR identification transfer between tissues. Carbamidomethyl (C) was selected as fixed modification and Acetyl (Protein N-term) and oxidation (M) were defined as variable modifications. For mitochondrial phosphoproteome analysis, STY site phosphorylation was additionally selected as variable modification.
Bioinformatics analysis Data analysis was performed using the python programing language using python (3. 8. 12) and the following packages: alphamap, matplotlib (3. 5. 0), mygene (3. 2. 2), numpy (1. 19. 2), pandas (1. 1. 3), pyteomics (4. 3. 3), requests (2. 26. 0), scipy (1. 7. 2), seaborn (0. 11. 2), sklearn (0. 0), upsetplot (0. 6. 0). All notebooks used for data analysis are available at GitHub (https://github. com/MannLabs). Identified proteins were filtered for at least 3 valid values in at least one tissue. Similarly, phosphorylation sites were filtered for at least 5 valid values in at least one tissue and a localization probability >75%. The UniProt API was used to map 'ACC+ID' protein group identifiers provided by the MQ analysis to 'ENSEMBL_ID', 'P_ENTREZGENEID' and 'STRING_ID' for further analysis.
'ENSEMBL_ID' and 'P_ENTREZGENEID' identifier were used for mitochondrial protein annotation based on the IMPI (IMPI_2020_Q3, downloaded 27. October 2020) and MitoCarta3. 0 (downloaded 1. January 2021) databases, respectively. Network analysis and visualization were performed with the StringApp (1. 6. 0) in Cytoscape (3. 8. 2). Kinase annotations are based on manual annotations (Figure  6  -Source Data 1) and 'pkinfam' (downloaded 9. Arpil 2021). Networkin3. 0 was used for kinase substrate association (KSA) predictions, while the Networkin score was set to 1. Gene Ontology (GO) annotations for the GOBP term enrichment analysis were retrieved from UniProt (accessed 8. March 2021). Enrichment analysis was performed in Perseus (1. 6. 7. 0) against the set of identified proteins in the corresponding tissue and the results were filtered for an intersection size >10.
0 and protein interactions are based on STRING interaction scores. These STRING interaction scores were retrieved from STRING (17. November 2021) using the 'STRING_ID' and the STRING API. For the 'Sequence view', the 'evidence. txt' of the MaxQuant output files was directly used as input to annotated sequences. The python programming language was used for data processing and visualization for the Dashboard. The following libraries were used for data processing: numpy (1. 19. 2), pandas (1. 19. 2), re, sys, os, and pyteomics (4. 3. 3). Several libraries from the HoloViz family of tools were used for data visualization and creation of the dashboard, including panel (1. 14. 6), holoviews (1. 14. 6), bokeh (2. 2. 2), plotly (4. 12. 0), and param (1. 10. 0). Network visualization was achieved with the NetworkX package  (Hagberg et al.
Data and code availability Datasets generated in this study have been deposited at ProteomeXchange and are publicly available as of the date of publication. The accession number is listed in the key resource table. (Identifier: PXD030062). All original code has been deposited on GitHub (https://github. com/MannLabs) and is available as of the date of publication. 47 extensive phosphorylation of inner membrane protein complexes and enzymes. Mol Cell Proteomics 10, M110 000299. 10. 1074/mcp. M110. 000299.
TiO2 enrichment or subjected to LC-MS/MS. (B) Distinct protein identification across biological replicates (n=6). Annotation of proteins as mitochondrial is based on MitoCarta3. 0 and the IMPI database. (C) Mitochondrial protein numbers after filtering for mitochondrial proteins identified in at least 50% of biological replicates (n=6) in at least one tissue. Skeletal muscle (SKM), brown adipose tissue (BAT). See also Figure 1 -Source Data 1.
Acknowledgments This work was supported by grants to N. G. L from the  Swedish Research Council  ( 2015-00418 ),  Swedish Cancer Foundation  ( 2021. 1409 ), the  Knut and Alice Wallenberg foundation, European Research Council  ( ERC Advanced Grant   2016-741366 ), grants from the  Swedish state  under the agreement between the Swedish government and the county councils ( SLL2018. 0471 ). L. S. K was supported by an  EMBO  long-term fellowship ( ALTF 570-2019 ). I. K. was supported by the  French Muscular Dystrophy Association (AFM-Téléthon  # 23294 ) and  Agence nationale de la recherche  ( ANR-20-CE12-0011 ). We thank all members of the  Department of Signal Transduction and Proteomics , in particular,  Isabel Bludau  for helpful discussions regarding the website tool,  Igor Paron  for MS technical assistance,  Mario Oroshi  for technical assistance during website setup,  Marta Murgia  for helpful discussions on mitochondrial biology and  Johannes Müller-Reif ,  Patricia Skowronek  and  Sophia Steigerwald  for columns.
All six algorithms allowed reliable identification of target cells, as well as their quantification in a given ROI (DLAP-3 & 4), and/or quantification of mRNAderived (DLAP-1 &-2) or protein-derived (DLAP-5 & 6) fluorescence signals of distinct genes of interest in these target cellsall common tasks in life science. Deep learning networks for image segmentation of complicated scenes, such as the Pascal Visual Object Classes (VOC) dataset  51  , that we used in the DeepLab3 based algorithms, are usually very large, and their filters are particularly trained to learn abstract concepts such as "tree" or "car". Particularly, for the analysis of simple, RNAscope derived fluorescence signals (i. e. , individual small dots, diameter Ø ~0. 3 μm), we found that such a complicated pre-trained neural network was not of benefit.
118  for a recent discussion of these loss functions in the context of medical image segmentation). DLAP dramatically reduces the respective analysis-time, and it is straightforward to use (utilised by under-graduate students in our lab). Our approach directly combines deep learning-based segmentation with a variety of more conventional post-processing methods (such as the watershed algorithm), leading to high quality segmentation results. Moreover, as training and imageanalyses are carried out via an easy-to-use web-based interface (https://wolution. ai/), and Wolution provides in-depth support for specific adjustments, our approach does not require any sophisticated hardware, software, or programming skills. Importantly, DLAP details are not proprietary, but all steps and strategies are provided here for free and in full detail, to facilitate distribution and use within the scientific community.
These features represent important advantages in comparison to other cloud-based analysis pipelines, for example, Aiforia  54, 74  , CDeep3M 119 , or Visiopharm 120 that do not disclose insights into the underlying algorithms, do not allow user-based modifications, or require advanced programming experience (e. g. , DeepCell Kiosk or Cellpose 109,121 ). For example, the recent freely distributed well-suited algorithm for cell-segmentation, Cellpose2. 0 did enable equally proper segmentation of TH-positive neurons from our IF-images, as our DLAP algorithms. However, in contrast to DLAP, it does not enable any automated post-processing or post-analysis of segmented ROIs for further downstream analysis (like whatershed algorithms, threshold based background exclusion, or quantification of fluorescent dot numbers or intensities) in one single package, but the user would require to write additional scripts for such downstream-application.
Hence, as Cellpose2. 0 relies on Python packages, despite its Graphical User Interface, basic programming knowledge is required, e. g. , for troubleshooting package related issues or run-time errors. One particular additional advantage of our DLAP-6 is that it automatically segments the cellular membrane compartment, in the absence of any membrane marker, and thus enables a more detailed sub-cellular analysis. The here detailed DLAP approach allows the systematic quantitative analysis and anatomical mapping of several thousands of neurons in reasonable time, and thus facilitates the identification and characterization of small cellular subpopulations. Accordingly, by DLAP-analysis of about 40,000 THimmuno-positive SN neurons in PFA-fixed brain-sections from adult mice, and by generation of expression-maps according to anatomical coordinates, we defined a small subpopulation of neurons (~5% of all TH-positive SN neurons).
Moreover, they assign to each pixel in the image a class, such as cellular nucleus or background. This type of analysis is called semantic segmentation. The performance of deep learning based semantic segmentation depends strongly on the neural network architecture, training parameters and in particular the quality of the training dataset. By specific training and further optimizing these two network-types for distinct tasks, we generated six distinct algorithms, as specified in Tables  1, 2. For cell counting and immunofluorescence signal quantification (DLAP-3 to 6), the large pre-trained neural network DeepLab3  40  was further trained and optimized post training for the respective specific tasks. DeepLab3 is one of the best performing image segmentation algorithms on the Pascal Visual Object Classes (VOC) dataset  161.
The Pascal VOC is a standardized image dataset for building and evaluating algorithms, often used as a benchmark for segmentation quality. In general, the key innovation of DeepLab algorithms is the use of so called dilated or atrous convolutions. This type of convolutions has a dilated filter size, increasing the area covered by each filter, and the context that can be incorporated. Therefore, they have a higher resolution and can integrate information over larger areas, while keeping the number of parameters of the convolutions the same 159. Specifically, for our algorithms 3 to 6, we used xception65 as the CNN backend, provided by Google [https:// github. com/tensorflow/models/tree/master/research/deeplab] 162 , which was pre-trained on the Pascal VOC 2012 segmentation dataset (Everingham et al.
Such a convolution is used between feature map 2 and feature map 3 to reduce the number of kernels by a factor of 4, thus reducing the parameter size of the following convolution by a factor of 16. To conserve image dimension, we extend the outer frame at the boundary of each image, which increases the space for the filter/kernel to scan the image, a process also called mirror padding. This architecture provided a good compromise between simplicity (and thus computational speed) and segmentation quality. Algorithm training and post processing parameters. Training datasets need to have sufficient quality and size, depending on data variability  112, 165. However, we observed that using too large training datasets leads to overfitting, which resulted in decreased generalization. We ensured the quality (e.
g. , resolution, frame size) of our training datasets, and performed a quality evaluation during training (see below), in addition to data-validation by comparison with manual analyses. Images showing different neuronal shapes/forms in different brightness intensities were used for training to ensure the identification and recognition over a vast array of brightness and cellular structure. As our FCN is not based on a pre-trained neural network (no "transfer learning"), it was trained by us from scratch, by only using our own training data. This is possible because of its smaller size compared to the Deeplab neural network, which also means that less training data is required. The FCN-based algorithms 1 and 2 analyse image patches of size 63 × 63 pixels. To train the network, we randomly cropped the images from our manually labelled training dataset (ground truth data) into such patches.
Training was performed using the stochastic first-order gradientbased Adam optimizer with default settings (learning rate α = 0. 001, estimates decay rates β 1 = 0. 9, β 2 = 0. 999, to prevent division by zero ε = 10 -8 ) 166. For DeepLab3 based algorithms DLAP-3 to 6, we adjusted the neural network weights by further training with our own datasets using the training script provided by Google Research 162. This was performed on randomly sampled patches of size 512 2 pixels, much larger than the 63 2 pixels we use in the FCN algorithm, as Deeplab3 is designed to detect large, complex objects within an image. For both network-types, the training time on a modern GPU was several hours for each of the datasets. The number of images and neurons each that were used to train each of the individual algorithms is given in Table  2.
We used 80% of manually labelled images for the training dataset, and the remaining 20% of the images for the "test data" set. Train-test-split on the level of images was chosen to avoid data leakage. For this performanceevaluation of the individual algorithms, the "test data" was analysed and the results were compared to the ground truth provided by the labels. For all algorithms, we assessed typical quality measures (Table  1 , S1): The pixel error rate, i. e. , the global percentage of pixels that are wrongly classified. This global error rate was calculated by defining for each segmentation class the rates of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN), each given in % of the total pixels of the whole training dataset. The specificity [defined as TN/ (TN + FP)] and the sensitivity [defined as TP/(TP + FN)], for each segmentation class, were also calculated, as these values are more suitable measures in cases of large class imbalance.
We trained each dataset until the training error/loss function (difference between output and ground truth) converged to a minimum. We also use "Early Stopping" in the training process 167 to avoid overtraining (see above). We verified that we did not overtrain, by tracking the pixel error in the "test data". If overtraining is happening, only the training error is still getting lower, while the test error increases. Training was stopped as soon as the pixel error was getting worse not better. Performances of all algorithms were evaluated on the respective "test data" (compare Table  2 ). For post training image analysis optimization, the quality of the CNN outputs for each task (summarized in Table  1 ) was further improved by, implementing additional classic image analysis procedures (a) to (e), specified below, into the individual CNN procedures.
c. The Binary fill-holes-function was applied to the DeepLab3-based CNN outputs for DLAP-4 to 6, where pixel-wise semantic segmentation resulted in holes within the detected compartments, as such holes are of no biological meaning. Therefore, all holes in the segmentation masks of each region were filled using the "binary_fill_holes" function 147 of the SciPy library http://scipy. github. io/devdocs/reference/generated/scipy. ndimage. binary_fill_ holes. html. d. A procedure to remove target gene colour channel was applied to the DeepLab3 based algorithms 5 and 6 to facilitate identification of the target areas within the ROI (i. e. , neuronal cell bodies or neuronal compartments), in which the relative target gene derived fluorescence-signal was quantified. This procedure is removing the target gene channel for the algorithm-based detection of the target areas, to ensure its recognition, independently of the target gene fluorescence-signal.
Fluorescent images containing the ROI (i. e. , the SN), of murine and human midbrain sections were acquired at 63x magnification using a Leica DM6 B epifluorescence microscope. All images were acquired as Z-stacks, covering the full depth of cells, and reduced to maximum intensity Z-projections, by using Fiji [http://imagej. net/Fiji]. TH-derived RNAscope fluorescence signals were visualized at 480/40 nm excitation, 527/30 nm emission and 505 nm dichroic mirror, target gene-derived fluorescence signals were visualized at 546/10 nm excitation, 585/40 nm emission and 560 nm dichroic mirror, DAPI-signals were visualized at 350/ 50 nm excitation, 460/50 nm emission and 400 nm dichroic mirror. Files were aquired using the LASX software (Leica), and stored as PNG files. For manual image analysis, the images were further processed using the Fiji software [http://imagej.